From 87cffb1f8f3bf902ed2cbf5f7f45907f9810c247 Mon Sep 17 00:00:00 2001 From: Humood Alanzi Date: Sun, 11 Aug 2024 18:47:53 -0700 Subject: [PATCH 01/19] quick script --- scripts/quick_script.py | 240 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 240 insertions(+) create mode 100644 scripts/quick_script.py diff --git a/scripts/quick_script.py b/scripts/quick_script.py new file mode 100644 index 0000000..8ba8bd2 --- /dev/null +++ b/scripts/quick_script.py @@ -0,0 +1,240 @@ +""" +Script from Evan's Claude +""" +import io +import os +import sys +import json +import re +import logging +import itertools +import tqdm +from yaml import safe_load + + + +import duckdb as ddb +import numpy as np +import pandas as pd +from scipy import stats +import matplotlib.pyplot as plt +from scipy.stats import pearsonr, spearmanr +import seaborn as sns +import codecarbon + +from nomelt.model import NOMELTModel + + +# Seaborn settings +sns.set_context("talk") +sns.set_style("whitegrid") + + +# Environment variables and logger setup +logger = logging.getLogger(__name__) +LOGLEVEL = os.getenv('LOGLEVEL', 'DEBUG') +LOGFILE = f'./logs/{os.path.basename(__file__)}.log' +os.environ['SLURM_JOBID'] = os.environ.get('SLURM_JOB_ID', '') + + +# Constants +ANALYSIS_DIR = "./data/gym/analysis/" + + +# Functions definitions +def generate_mutated_sequences(wt_sequence: str, mutation_df: pd.DataFrame) -> list: + """ + Generate a list of mutated sequences from a mutation table DataFrame, each with a single mutation applied to the wild-type. + Parameters: + - wt_sequence: The wild-type protein sequence. + - mutation_df: DataFrame containing the mutation table. + + Returns: + - List of mutated protein sequences. + """ + # Extract mutations + mutations = mutation_df["mutation_shifted"] + + # List to store all mutated sequences + all_mutated_sequences = [] + + # Apply each mutation + for mutation in mutations: + # Create a fresh copy of wt for each mutation + mutated_sequence = list(wt_sequence) + + match = re.match(r'([A-Z]+)(\d+)([A-Z]+)', mutation) + if not match: + continue # Skip any mutations that don't match the expected format + original_aa, position, new_aa = match.groups() # Return a part of the string where there was a match + position = int(position) - 1 # Convert to 0-based index + + # Check if the current amino acid matches the expected amino acid from the mutation table + if mutated_sequence[position] != original_aa: + print(f"Discrepancy at position {position+1}: Expected {original_aa}, Found {mutated_sequence[position]}, Mutation Table suggests changing to {new_aa}.") + continue + + # Apply the mutation + mutated_sequence[position] = new_aa + + # Append to the list of all mutated sequences + all_mutated_sequences.append(''.join(mutated_sequence)) + + return all_mutated_sequences + + +def pairwise_t_test(df, alpha=0.05): + """ + Perform pairwise t-tests on a dataframe of measurements. + + Parameters: + - df: Dataframe of measurements. + - alpha: Significance level. + + Returns: + - List of tuples of significant pairs and their differences. + """ + # Extract measurements + measurements = df[['dms_mean', 'dms_std']] + + # Create combinations of all pairs + pairs = list(itertools.combinations(measurements.index, 2)) + + # Store significant pairs + significant_pairs = [] + + for i, j in tqdm.tqdm(pairs, desc="Performing pairwise t-tests"): + mean_i, std_i = measurements.loc[i].values + mean_j, std_j = measurements.loc[j].values + + # Calculate t-statistic and p-value + t_stat, p_val = stats.ttest_ind_from_stats(mean1=mean_i, std1=std_i, nobs1=3, + mean2=mean_j, std2=std_j, nobs2=3) + + # Check if the difference is significant + if p_val < alpha: + + significant_pairs.append((i, j, mean_i - mean_j)) + return significant_pairs + + + +# Adjusting the 'Variants of BsLipA' to match the 'mutant' format by adding 31 to the numeric part +# Function to adjust the numeric part of the variant names +# this is because the authors reported the mutations starting from a specific region +# of LipA instead of the start of the uniprot seq +def adjust_variant(variant, offset=31): + parts = re.search(r'([A-Za-z]+)(\d+)([A-Za-z]+)', variant) + if parts: + return f"{parts.group(1)}{int(parts.group(2)) + offset}{parts.group(3)}" + else: + return variant + + + +def load_and_process_data(file_path, sheet_name=0): + """Load and process data from an Excel file.""" + data = pd.read_excel(file_path, sheet_name=sheet_name, usecols='A:C') + data.columns = ['mutation', 'dms_mean', 'dms_std'] + data['mutation_shifted'] = data['mutation'].apply(adjust_variant) + return data + + + +def evaluate_model(model, wt, data, benchmark_name): + """Evaluate the model on a given dataset and return metrics.""" + mutated_sequences_list = generate_mutated_sequences(wt, data) + data['mutated_sequence'] = mutated_sequences_list + + wt_score, variant_scores = model.score_variants(wt, mutated_sequences_list, batch_size=5, indels=False) + data['nomelt_score'] = variant_scores + + sig_pairs = pairwise_t_test(data) + mask = [] + for i, j, i_sub_j_dms in sig_pairs: + i_sub_j_nomelt = data.loc[i, 'nomelt_score'] - data.loc[j, 'nomelt_score'] + mask.append(np.sign(i_sub_j_nomelt) == np.sign(i_sub_j_dms)) + + x_data = data['dms_mean'] + y_data = data['nomelt_score'] + pearson_coef, pearson_p = pearsonr(x_data, y_data) + spearman_coef, spearman_p = spearmanr(x_data, y_data) + + plot_results(x_data, y_data, pearson_coef, pearson_p, spearman_coef, spearman_p, f'./data/plots/{benchmark_name}.png') + + return { + 'pearson_coef': pearson_coef, + 'pearson_p': pearson_p, + 'spearman_coef': spearman_coef, + 'spearman_p': spearman_p, + 'frac_qualitative_sig': sum(mask)/len(sig_pairs) + } + + + +def plot_results(x_data, y_data, pearson_coef, pearson_p, spearman_coef, spearman_p, filename): + """Plot and save the results.""" + fig, ax = plt.subplots(figsize=(5, 5)) + sns.kdeplot(x=x_data, y=y_data, fill=True, bw_adjust=0.5, cmap="Blues", ax=ax) + ax.set_xlabel('DMS score [C]') + ax.set_ylabel('NOMELT score') + plt.text(0.05, 0.95, f'Pearson: {pearson_coef:.2f} (p={pearson_p:.2e})\nSpearman: {spearman_coef:.2f} (p={spearman_p:.2e})', + fontsize=12, verticalalignment='top', bbox=dict(boxstyle='round,pad=0.5', alpha=0.5), transform=plt.gca().transAxes) + plt.savefig(filename, dpi=300, bbox_inches='tight') + + + +if __name__ == "__main__": + # Logger initialization + logger.setLevel(getattr(logging, LOGLEVEL)) + fh = logging.FileHandler(LOGFILE, mode='w') + formatter = logging.Formatter('%(filename)-12s %(asctime)s;%(funcName)-12s: %(levelname)-8s %(message)s') + fh.setFormatter(formatter) + logger.addHandler(fh) + utils_logger = logging.getLogger('nomelt') + utils_logger.setLevel(getattr(logging, LOGLEVEL)) + utils_logger.addHandler(fh) + + + + # initialize codecarbon + tracker = codecarbon.OfflineEmissionsTracker( + project_name="estimate_gym", + output_dir='./data', + country_iso_code="USA", + region="washington") + + tracker.start() + + # Data reading and processing + # Load data for both benchmarks + data_lipa = load_and_process_data("./data/gym/ci9b00954_si_002.xlsx") + # data_second = load_and_process_data("./path/to/second_benchmark.xlsx") # Replace with actual path + + # Designating WT sequence (assuming it's the same for both benchmarks, adjust if needed) + wt = "MKFVKRRIIALVTILMLSVTSLFALQPSAKAAEHNPVVMVHGIGGASFNFAGIKSYLVSQGWSRDKLYAVDFWDKTGTNYNNGPVLSRFVQKVLDETGAKKVDIVAHSMGGANTLYYIKNLDGGNKVANVVTLGGANRLTTGKALPGTDPNQKILYTSIYSSADMIVMNYLSRLDGARNVQIHGVGHIGLLYSSQVNSLIKEGLNGGGQNTN" + + + + # Model initialization for new and original weights + with open('./params.yaml', 'r') as f: + params = safe_load(f) + + hyperparams = params['model']['model_hyperparams'] + new_model = NOMELTModel('./data/nomelt-model-full/model', **hyperparams) + original_model = NOMELTModel(params['model']['pretrained_model'], **hyperparams) + + # Evaluate both models on both datasets + results = {} + + for model_name, model in [("new", new_model), ("original", original_model)]: + results[model_name] = {} + for benchmark_name, data in [("lipa", data_lipa)]: # , ("second_benchmark", data_second) + results[model_name][benchmark_name] = evaluate_model(model, wt, data, f"{model_name}_{benchmark_name}") + + # Save results + with open('./data/nomelt-model-full/benchmark_results.json', 'w') as f: + json.dump(results, f, indent=2) + + # CodeCarbon tracker stop + tracker.stop() \ No newline at end of file From 30ce94a4c724a3b52330ef824b0faaa8195eaf61 Mon Sep 17 00:00:00 2001 From: Evan Komp Date: Sat, 17 Aug 2024 09:16:54 -0600 Subject: [PATCH 02/19] protein gym compared to base model weights --- scripts/protein_gym_benchmark.py | 162 +++++++++++---------- scripts/quick_script.py | 240 ------------------------------- 2 files changed, 90 insertions(+), 312 deletions(-) delete mode 100644 scripts/quick_script.py diff --git a/scripts/protein_gym_benchmark.py b/scripts/protein_gym_benchmark.py index 93a57b2..8ba8bd2 100644 --- a/scripts/protein_gym_benchmark.py +++ b/scripts/protein_gym_benchmark.py @@ -1,9 +1,6 @@ """ -Protein Gym original data source for LipA T50: -https://pubs.acs.org/doi/10.1021/acs.jcim.9b00954 - +Script from Evan's Claude """ -# System dependencies import io import os import sys @@ -14,7 +11,8 @@ import tqdm from yaml import safe_load -# Library dependencies + + import duckdb as ddb import numpy as np import pandas as pd @@ -24,12 +22,13 @@ import seaborn as sns import codecarbon -# Local dependencies from nomelt.model import NOMELTModel + # Seaborn settings sns.set_context("talk") sns.set_style("whitegrid") + # Environment variables and logger setup logger = logging.getLogger(__name__) @@ -37,14 +36,15 @@ LOGFILE = f'./logs/{os.path.basename(__file__)}.log' os.environ['SLURM_JOBID'] = os.environ.get('SLURM_JOB_ID', '') + # Constants ANALYSIS_DIR = "./data/gym/analysis/" + # Functions definitions def generate_mutated_sequences(wt_sequence: str, mutation_df: pd.DataFrame) -> list: """ Generate a list of mutated sequences from a mutation table DataFrame, each with a single mutation applied to the wild-type. - Parameters: - wt_sequence: The wild-type protein sequence. - mutation_df: DataFrame containing the mutation table. @@ -62,7 +62,7 @@ def generate_mutated_sequences(wt_sequence: str, mutation_df: pd.DataFrame) -> l for mutation in mutations: # Create a fresh copy of wt for each mutation mutated_sequence = list(wt_sequence) - + match = re.match(r'([A-Z]+)(\d+)([A-Z]+)', mutation) if not match: continue # Skip any mutations that don't match the expected format @@ -82,6 +82,7 @@ def generate_mutated_sequences(wt_sequence: str, mutation_df: pd.DataFrame) -> l return all_mutated_sequences + def pairwise_t_test(df, alpha=0.05): """ Perform pairwise t-tests on a dataframe of measurements. @@ -95,6 +96,7 @@ def pairwise_t_test(df, alpha=0.05): """ # Extract measurements measurements = df[['dms_mean', 'dms_std']] + # Create combinations of all pairs pairs = list(itertools.combinations(measurements.index, 2)) @@ -104,18 +106,20 @@ def pairwise_t_test(df, alpha=0.05): for i, j in tqdm.tqdm(pairs, desc="Performing pairwise t-tests"): mean_i, std_i = measurements.loc[i].values mean_j, std_j = measurements.loc[j].values + # Calculate t-statistic and p-value t_stat, p_val = stats.ttest_ind_from_stats(mean1=mean_i, std1=std_i, nobs1=3, mean2=mean_j, std2=std_j, nobs2=3) # Check if the difference is significant if p_val < alpha: - significant_pairs.append((i, j, mean_i - mean_j)) + significant_pairs.append((i, j, mean_i - mean_j)) return significant_pairs -# Adjusting the 'Variants of BsLipA' to match the 'mutant' format by adding 31 to the numeric part + +# Adjusting the 'Variants of BsLipA' to match the 'mutant' format by adding 31 to the numeric part # Function to adjust the numeric part of the variant names # this is because the authors reported the mutations starting from a specific region # of LipA instead of the start of the uniprot seq @@ -126,6 +130,59 @@ def adjust_variant(variant, offset=31): else: return variant + + +def load_and_process_data(file_path, sheet_name=0): + """Load and process data from an Excel file.""" + data = pd.read_excel(file_path, sheet_name=sheet_name, usecols='A:C') + data.columns = ['mutation', 'dms_mean', 'dms_std'] + data['mutation_shifted'] = data['mutation'].apply(adjust_variant) + return data + + + +def evaluate_model(model, wt, data, benchmark_name): + """Evaluate the model on a given dataset and return metrics.""" + mutated_sequences_list = generate_mutated_sequences(wt, data) + data['mutated_sequence'] = mutated_sequences_list + + wt_score, variant_scores = model.score_variants(wt, mutated_sequences_list, batch_size=5, indels=False) + data['nomelt_score'] = variant_scores + + sig_pairs = pairwise_t_test(data) + mask = [] + for i, j, i_sub_j_dms in sig_pairs: + i_sub_j_nomelt = data.loc[i, 'nomelt_score'] - data.loc[j, 'nomelt_score'] + mask.append(np.sign(i_sub_j_nomelt) == np.sign(i_sub_j_dms)) + + x_data = data['dms_mean'] + y_data = data['nomelt_score'] + pearson_coef, pearson_p = pearsonr(x_data, y_data) + spearman_coef, spearman_p = spearmanr(x_data, y_data) + + plot_results(x_data, y_data, pearson_coef, pearson_p, spearman_coef, spearman_p, f'./data/plots/{benchmark_name}.png') + + return { + 'pearson_coef': pearson_coef, + 'pearson_p': pearson_p, + 'spearman_coef': spearman_coef, + 'spearman_p': spearman_p, + 'frac_qualitative_sig': sum(mask)/len(sig_pairs) + } + + + +def plot_results(x_data, y_data, pearson_coef, pearson_p, spearman_coef, spearman_p, filename): + """Plot and save the results.""" + fig, ax = plt.subplots(figsize=(5, 5)) + sns.kdeplot(x=x_data, y=y_data, fill=True, bw_adjust=0.5, cmap="Blues", ax=ax) + ax.set_xlabel('DMS score [C]') + ax.set_ylabel('NOMELT score') + plt.text(0.05, 0.95, f'Pearson: {pearson_coef:.2f} (p={pearson_p:.2e})\nSpearman: {spearman_coef:.2f} (p={spearman_p:.2e})', + fontsize=12, verticalalignment='top', bbox=dict(boxstyle='round,pad=0.5', alpha=0.5), transform=plt.gca().transAxes) + plt.savefig(filename, dpi=300, bbox_inches='tight') + + if __name__ == "__main__": # Logger initialization @@ -138,6 +195,8 @@ def adjust_variant(variant, offset=31): utils_logger.setLevel(getattr(logging, LOGLEVEL)) utils_logger.addHandler(fh) + + # initialize codecarbon tracker = codecarbon.OfflineEmissionsTracker( project_name="estimate_gym", @@ -147,76 +206,35 @@ def adjust_variant(variant, offset=31): tracker.start() - # Data reading and processing - logger.info("Reading data...") - data_stds = pd.read_excel("./data/gym/ci9b00954_si_002.xlsx", usecols='A:C') - data_stds.columns = ['mutation', 'dms_mean', 'dms_std'] - data_stds['mutation_shifted'] = data_stds['mutation'].apply(adjust_variant) + # Data reading and processing + # Load data for both benchmarks + data_lipa = load_and_process_data("./data/gym/ci9b00954_si_002.xlsx") + # data_second = load_and_process_data("./path/to/second_benchmark.xlsx") # Replace with actual path - # Designating WT sequence + # Designating WT sequence (assuming it's the same for both benchmarks, adjust if needed) wt = "MKFVKRRIIALVTILMLSVTSLFALQPSAKAAEHNPVVMVHGIGGASFNFAGIKSYLVSQGWSRDKLYAVDFWDKTGTNYNNGPVLSRFVQKVLDETGAKKVDIVAHSMGGANTLYYIKNLDGGNKVANVVTLGGANRLTTGKALPGTDPNQKILYTSIYSSADMIVMNYLSRLDGARNVQIHGVGHIGLLYSSQVNSLIKEGLNGGGQNTN" - # Generate mutated sequences - logger.info("Generating mutated sequences") - mutated_sequences_list = generate_mutated_sequences(wt, data_stds) - data_stds['mutated_sequence'] = mutated_sequences_list - logger.info(f"Generated {len(mutated_sequences_list)} mutated sequences") + - # Model initialization - logger.info("Initializing model") + # Model initialization for new and original weights with open('./params.yaml', 'r') as f: params = safe_load(f) - hyperparams = params['model']['model_hyperparams'] - model = NOMELTModel('./data/nomelt-model-full/model', **hyperparams) - - # Score mutated sequences - logger.info("Scoring mutated sequences") - wt_score, variant_scores = model.score_variants(wt, mutated_sequences_list, batch_size=5, indels=False) - data_stds['nomelt_score'] = variant_scores - logger.info("model scores computed!") - - # Analysis - logger.info("Analyzing scores...") - sig_pairs = pairwise_t_test(data_stds) - logger.info(f"Found {len(sig_pairs)} significant pairs out of {len(data_stds)**2}") - - # get boolean mask for whenever nomelt properly predicts the effect of a mutation wualitatively - # only for the significant pairs - mask = [] - for i, j, i_sub_j_dms in sig_pairs: - i_sub_j_nomelt = data_stds.loc[i, 'nomelt_score'] - data_stds.loc[j, 'nomelt_score'] - mask.append(np.sign(i_sub_j_nomelt) == np.sign(i_sub_j_dms)) - logger.info(f"Found {sum(mask)} out of {len(sig_pairs)} significant pairs where NOMELT qualitatively predicts the effect of the mutation") - # plotting - x_data = data_stds['dms_mean'] - y_data = data_stds['nomelt_score'] - # Calculate Pearson and Spearman coefficients and p-values - pearson_coef, pearson_p = pearsonr(x_data, y_data) - spearman_coef, spearman_p = spearmanr(x_data, y_data) + hyperparams = params['model']['model_hyperparams'] + new_model = NOMELTModel('./data/nomelt-model-full/model', **hyperparams) + original_model = NOMELTModel(params['model']['pretrained_model'], **hyperparams) - logger.info(f"Pearson correlation coefficient: {pearson_coef}, p-value: {pearson_p}") - logger.info(f"Spearman correlation coefficient: {spearman_coef}, p-value: {spearman_p}") + # Evaluate both models on both datasets + results = {} - fig, ax= plt.subplots(figsize=(5, 5)) - # create a density plot - sns.kdeplot(x=x_data, y=y_data, fill=True, bw_adjust=0.5, cmap="Blues", ax=ax) - # adding a regression line - m, b = np.polyfit(x_data, y_data, 1) - x_grid = np.linspace(x_data.min(), x_data.max(), 10) - # ax.plot(x_grid, m*x_grid + b, color='red', linestyle='--', label='Regression line') - ax.set_xlabel('DMS score [C]') - ax.set_ylabel('NOMELT score') - - # annotate with Pearson and Spearman coefficients and p-values - plt.text(0.05, 0.95, f'Pearson: {pearson_coef:.2f} (p={pearson_p:.2e})\nSpearman: {spearman_coef:.2f} (p={spearman_p:.2e})', - fontsize=12, verticalalignment='top', bbox=dict(boxstyle='round,pad=0.5', alpha=0.5), transform=plt.gca().transAxes) - plt.savefig('./data/plots/lipa_gym.png', dpi=300, bbox_inches='tight') + for model_name, model in [("new", new_model), ("original", original_model)]: + results[model_name] = {} + for benchmark_name, data in [("lipa", data_lipa)]: # , ("second_benchmark", data_second) + results[model_name][benchmark_name] = evaluate_model(model, wt, data, f"{model_name}_{benchmark_name}") - with open('./data/nomelt-model-full/lipa_gym_zero_shot.json', 'w') as f: - json.dump({'pearson_coef': pearson_coef, 'pearson_p': pearson_p, 'spearman_coef': spearman_coef, 'spearman_p': spearman_p, 'frac_qualitative_sig': sum(mask)/len(sig_pairs) - }, f) + # Save results + with open('./data/nomelt-model-full/benchmark_results.json', 'w') as f: + json.dump(results, f, indent=2) + # CodeCarbon tracker stop - tracker.stop() - - + tracker.stop() \ No newline at end of file diff --git a/scripts/quick_script.py b/scripts/quick_script.py deleted file mode 100644 index 8ba8bd2..0000000 --- a/scripts/quick_script.py +++ /dev/null @@ -1,240 +0,0 @@ -""" -Script from Evan's Claude -""" -import io -import os -import sys -import json -import re -import logging -import itertools -import tqdm -from yaml import safe_load - - - -import duckdb as ddb -import numpy as np -import pandas as pd -from scipy import stats -import matplotlib.pyplot as plt -from scipy.stats import pearsonr, spearmanr -import seaborn as sns -import codecarbon - -from nomelt.model import NOMELTModel - - -# Seaborn settings -sns.set_context("talk") -sns.set_style("whitegrid") - - -# Environment variables and logger setup -logger = logging.getLogger(__name__) -LOGLEVEL = os.getenv('LOGLEVEL', 'DEBUG') -LOGFILE = f'./logs/{os.path.basename(__file__)}.log' -os.environ['SLURM_JOBID'] = os.environ.get('SLURM_JOB_ID', '') - - -# Constants -ANALYSIS_DIR = "./data/gym/analysis/" - - -# Functions definitions -def generate_mutated_sequences(wt_sequence: str, mutation_df: pd.DataFrame) -> list: - """ - Generate a list of mutated sequences from a mutation table DataFrame, each with a single mutation applied to the wild-type. - Parameters: - - wt_sequence: The wild-type protein sequence. - - mutation_df: DataFrame containing the mutation table. - - Returns: - - List of mutated protein sequences. - """ - # Extract mutations - mutations = mutation_df["mutation_shifted"] - - # List to store all mutated sequences - all_mutated_sequences = [] - - # Apply each mutation - for mutation in mutations: - # Create a fresh copy of wt for each mutation - mutated_sequence = list(wt_sequence) - - match = re.match(r'([A-Z]+)(\d+)([A-Z]+)', mutation) - if not match: - continue # Skip any mutations that don't match the expected format - original_aa, position, new_aa = match.groups() # Return a part of the string where there was a match - position = int(position) - 1 # Convert to 0-based index - - # Check if the current amino acid matches the expected amino acid from the mutation table - if mutated_sequence[position] != original_aa: - print(f"Discrepancy at position {position+1}: Expected {original_aa}, Found {mutated_sequence[position]}, Mutation Table suggests changing to {new_aa}.") - continue - - # Apply the mutation - mutated_sequence[position] = new_aa - - # Append to the list of all mutated sequences - all_mutated_sequences.append(''.join(mutated_sequence)) - - return all_mutated_sequences - - -def pairwise_t_test(df, alpha=0.05): - """ - Perform pairwise t-tests on a dataframe of measurements. - - Parameters: - - df: Dataframe of measurements. - - alpha: Significance level. - - Returns: - - List of tuples of significant pairs and their differences. - """ - # Extract measurements - measurements = df[['dms_mean', 'dms_std']] - - # Create combinations of all pairs - pairs = list(itertools.combinations(measurements.index, 2)) - - # Store significant pairs - significant_pairs = [] - - for i, j in tqdm.tqdm(pairs, desc="Performing pairwise t-tests"): - mean_i, std_i = measurements.loc[i].values - mean_j, std_j = measurements.loc[j].values - - # Calculate t-statistic and p-value - t_stat, p_val = stats.ttest_ind_from_stats(mean1=mean_i, std1=std_i, nobs1=3, - mean2=mean_j, std2=std_j, nobs2=3) - - # Check if the difference is significant - if p_val < alpha: - - significant_pairs.append((i, j, mean_i - mean_j)) - return significant_pairs - - - -# Adjusting the 'Variants of BsLipA' to match the 'mutant' format by adding 31 to the numeric part -# Function to adjust the numeric part of the variant names -# this is because the authors reported the mutations starting from a specific region -# of LipA instead of the start of the uniprot seq -def adjust_variant(variant, offset=31): - parts = re.search(r'([A-Za-z]+)(\d+)([A-Za-z]+)', variant) - if parts: - return f"{parts.group(1)}{int(parts.group(2)) + offset}{parts.group(3)}" - else: - return variant - - - -def load_and_process_data(file_path, sheet_name=0): - """Load and process data from an Excel file.""" - data = pd.read_excel(file_path, sheet_name=sheet_name, usecols='A:C') - data.columns = ['mutation', 'dms_mean', 'dms_std'] - data['mutation_shifted'] = data['mutation'].apply(adjust_variant) - return data - - - -def evaluate_model(model, wt, data, benchmark_name): - """Evaluate the model on a given dataset and return metrics.""" - mutated_sequences_list = generate_mutated_sequences(wt, data) - data['mutated_sequence'] = mutated_sequences_list - - wt_score, variant_scores = model.score_variants(wt, mutated_sequences_list, batch_size=5, indels=False) - data['nomelt_score'] = variant_scores - - sig_pairs = pairwise_t_test(data) - mask = [] - for i, j, i_sub_j_dms in sig_pairs: - i_sub_j_nomelt = data.loc[i, 'nomelt_score'] - data.loc[j, 'nomelt_score'] - mask.append(np.sign(i_sub_j_nomelt) == np.sign(i_sub_j_dms)) - - x_data = data['dms_mean'] - y_data = data['nomelt_score'] - pearson_coef, pearson_p = pearsonr(x_data, y_data) - spearman_coef, spearman_p = spearmanr(x_data, y_data) - - plot_results(x_data, y_data, pearson_coef, pearson_p, spearman_coef, spearman_p, f'./data/plots/{benchmark_name}.png') - - return { - 'pearson_coef': pearson_coef, - 'pearson_p': pearson_p, - 'spearman_coef': spearman_coef, - 'spearman_p': spearman_p, - 'frac_qualitative_sig': sum(mask)/len(sig_pairs) - } - - - -def plot_results(x_data, y_data, pearson_coef, pearson_p, spearman_coef, spearman_p, filename): - """Plot and save the results.""" - fig, ax = plt.subplots(figsize=(5, 5)) - sns.kdeplot(x=x_data, y=y_data, fill=True, bw_adjust=0.5, cmap="Blues", ax=ax) - ax.set_xlabel('DMS score [C]') - ax.set_ylabel('NOMELT score') - plt.text(0.05, 0.95, f'Pearson: {pearson_coef:.2f} (p={pearson_p:.2e})\nSpearman: {spearman_coef:.2f} (p={spearman_p:.2e})', - fontsize=12, verticalalignment='top', bbox=dict(boxstyle='round,pad=0.5', alpha=0.5), transform=plt.gca().transAxes) - plt.savefig(filename, dpi=300, bbox_inches='tight') - - - -if __name__ == "__main__": - # Logger initialization - logger.setLevel(getattr(logging, LOGLEVEL)) - fh = logging.FileHandler(LOGFILE, mode='w') - formatter = logging.Formatter('%(filename)-12s %(asctime)s;%(funcName)-12s: %(levelname)-8s %(message)s') - fh.setFormatter(formatter) - logger.addHandler(fh) - utils_logger = logging.getLogger('nomelt') - utils_logger.setLevel(getattr(logging, LOGLEVEL)) - utils_logger.addHandler(fh) - - - - # initialize codecarbon - tracker = codecarbon.OfflineEmissionsTracker( - project_name="estimate_gym", - output_dir='./data', - country_iso_code="USA", - region="washington") - - tracker.start() - - # Data reading and processing - # Load data for both benchmarks - data_lipa = load_and_process_data("./data/gym/ci9b00954_si_002.xlsx") - # data_second = load_and_process_data("./path/to/second_benchmark.xlsx") # Replace with actual path - - # Designating WT sequence (assuming it's the same for both benchmarks, adjust if needed) - wt = "MKFVKRRIIALVTILMLSVTSLFALQPSAKAAEHNPVVMVHGIGGASFNFAGIKSYLVSQGWSRDKLYAVDFWDKTGTNYNNGPVLSRFVQKVLDETGAKKVDIVAHSMGGANTLYYIKNLDGGNKVANVVTLGGANRLTTGKALPGTDPNQKILYTSIYSSADMIVMNYLSRLDGARNVQIHGVGHIGLLYSSQVNSLIKEGLNGGGQNTN" - - - - # Model initialization for new and original weights - with open('./params.yaml', 'r') as f: - params = safe_load(f) - - hyperparams = params['model']['model_hyperparams'] - new_model = NOMELTModel('./data/nomelt-model-full/model', **hyperparams) - original_model = NOMELTModel(params['model']['pretrained_model'], **hyperparams) - - # Evaluate both models on both datasets - results = {} - - for model_name, model in [("new", new_model), ("original", original_model)]: - results[model_name] = {} - for benchmark_name, data in [("lipa", data_lipa)]: # , ("second_benchmark", data_second) - results[model_name][benchmark_name] = evaluate_model(model, wt, data, f"{model_name}_{benchmark_name}") - - # Save results - with open('./data/nomelt-model-full/benchmark_results.json', 'w') as f: - json.dump(results, f, indent=2) - - # CodeCarbon tracker stop - tracker.stop() \ No newline at end of file From 7005fc82b52a8166a4b0b46cae6a2c7180bfee75 Mon Sep 17 00:00:00 2001 From: Evan Komp Date: Sat, 17 Aug 2024 09:17:24 -0600 Subject: [PATCH 03/19] protein gym compared to base model weights --- scripts/protein_gym_benchmark.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/protein_gym_benchmark.py b/scripts/protein_gym_benchmark.py index 8ba8bd2..aebe095 100644 --- a/scripts/protein_gym_benchmark.py +++ b/scripts/protein_gym_benchmark.py @@ -1,5 +1,5 @@ """ -Script from Evan's Claude +Compare original to finteuned model weights on thermal stability ProteinGym benchmark """ import io 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""" -import pandas as pd +from nomelt.model import NOMELTModel +from yaml import safe_load +import json +import os import numpy as np +from scipy.stats import spearmanr import matplotlib.pyplot as plt import seaborn as sns -import os -import json -import MDAnalysis as mda -import io -import copy -from scipy.stats import spearmanr -from nomelt.model import NOMELTModel -from yaml import safe_load - -sns.set_style('ticks') -sns.set_context('talk') - -# variant sets from https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/cctc.202101349 - -LovD_seqs = { - 'LovD': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNARLRERRGKITLRHLLTHTSGLSYVFLHPLLREYMAQGHLQSAEKFGIQSRLAPPAVNDPGAEWIYGANLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSADGRLRYDDSVYFRADGEECFGGQGVFSGPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSLTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG", - 'LovD1': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNARLRERRGKITLRHLLTHTSGLSYVFLHPLLREYMAQGHLQSAEKFGIQSRLAPPAVNDPGAEWIYGANLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSADGRLRYDDSVYFRADGEECFGGQGVFSGPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG", - 'LovD2': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYMAQGHLQSAEKFGIQSRFAPPLVNDPGAEWIYGASLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSSDGRLRYDDSVYFRADGEECFGGQGVFSGPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG", - 'LovD3': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYMAQGHLQSAEKFGIQSRFAPPLVNDPGAEWIYGASLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSSDGRLRYDDSVYFRADGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG", - 'LovD4': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQSAEKFGIQNRFAPPLVNDPGAEWIYGASLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSSDGRLRYDDSVYFRADGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG", - 'LovD5': "MGSIIDAAVAADPVVLMETAFRKAVESRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQSAEKFGIQNRFAPPLVNDPGAEWIYGASIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSSDGKLRYDDSVYFRADGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPETVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLVFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG", - 'LovD6': "MGSIIDAAVAADPVVLMETAFRKAVESRQIPGAVIMARDASGRLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQGAEKFGIQNRFAPPLVNDPGAEWIYGAGIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADMTHRNSSDGKLRYDDSVYFRADGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPETVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIIWQIDPKAGLCTLVFFQLEPWNDPVCRDLTRTFEKAIYAQYQQG", - 'LovD7': "MGSIIDAAVAADPVVLMETAFRKAVESRQIPGAVIMARDASGRLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQGAEKFGIQNRFAPPLVNDPGAEWIYGAGIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADMTHRNSSDGKLRYDDTVYFRVDGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPGTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIIWQIDPKAGLCTLVFFQLEPWSDPVCRDLTRTFEKAIYAQYQQG", - 'LovD8': "MGSNIDAAVAADPVVLMETAFRKAVESSQIPGAVIMARDASGRLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQGAEKFGIQNRFAPPLVNDPGAEWIYGAGIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADMTHRNSSDGKLRYDDTVYFRHDGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPGTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIIWQIDPKAGLCTLVFFQLEPWSDPVCRDLTRTFEKAIYAQYQQG", - 'LovD9': "MGSNIDAAVAADPVVLMETAFRKAVESSQIPGAVLMARDASGRLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVRLDETVDRLLPDLCAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQGAEKFGIQNRFAPPLVNDPGAEWIYGAGIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADMTHRNSSDGKLRYDDTVYFRHDGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPGTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVMRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIIWQIDPKAGLCTLVFFQLEPWSDPVCRDLTRTFEKAIYAQYQQG" -} -LovD_temperatures = [38.5, 39.5, 44.0, 44.3, 45.0, 46.0, 52.0, 49.0, 49.3, 50.8] - -if __name__ == "__main__": +def main(sequences, temperatures, system_name): + # Load parameters from params.yaml with open('./params.yaml', 'r') as f: params = safe_load(f) hyperparams = params['model']['model_hyperparams'] - model = NOMELTModel('./data/nomelt-model-full/model', **hyperparams) - # start with LovD - lovd_variants = list(LovD_seqs.values())[1:] - lovd_wt = LovD_seqs['LovD'] + # Load both models + original_model = NOMELTModel(params['model']['pretrained_model'], **hyperparams) + new_model = NOMELTModel('./data/nomelt-model-full/model', **hyperparams) - wt_score, variant_scores = model.score_variants(lovd_wt, lovd_variants) - lovd_scores = [0.0] + variant_scores + results = {} - fig, ax = plt.subplots(figsize=(4, 4)) - sns.regplot(x=LovD_temperatures, y=lovd_scores, ax=ax) - ax.set_xlabel('Melting Temperature (°C)') - ax.set_ylabel('NOMELT Logit Score') + for model, model_name in [(original_model, "original"), (new_model, "new")]: + wt_score, variant_scores = model.score_variants(sequences[0], sequences[1:]) + scores = [wt_score] + variant_scores - plt.savefig('./data/plots/lovd_scores.png', dpi=300, bbox_inches='tight') - # get spearmans - lovd_spear_r, lovd_spear_p = spearmanr(LovD_temperatures, lovd_scores) + # Calculate Spearman correlation + spear_r, spear_p = spearmanr(temperatures, scores) - # now do lipa - LipA = "AEHNPVVMVHGIGGASFNFAGIKSYLVSQGWSRDKLYAVDFWDKTGTNYNNGPVLSRFVQKVLDETGAKKVDIVAHSMGGANTLYYIKNLDGGNKVANVVTLGGANRLTTGKALPGTDPNQKILYTSIYSSADMIVMNYLSRLDGARNVQIHGVGHIGLLYSSQVNSLIKEGLNGGGQNTN" - LipA_mutation_table = pd.read_csv(io.StringIO(""" - Name,Mutations,TM (°C) - WT,,56.0 - TM,L114P A132D N166Y,61.2 - 2D9,F17S N89Y L114P A132D I157M N166Y,67.4 - 4D3,A15S F17S A20E N89Y G111D L114P A132D I157M N166Y,71.2 - 6B,A15S F17S A20E N89Y G111D L114P A132D M134E M137P I157M S163P N166Y,78.2 + results[model_name] = { + 'temperatures': temperatures, + 'scores': scores, + 'spearman': float(spear_r), + 'p': float(spear_p) + } - """)) + # Create plot + fig, ax = plt.subplots(figsize=(4, 4)) + sns.regplot(x=temperatures, y=scores, ax=ax) + ax.set_xlabel('Melting Temperature (°C)') + ax.set_ylabel(f'{model_name.upper()} NOMELT Logit Score') + plt.savefig(f'./data/plots/{model_name}_{system_name}_scores.png', dpi=300, bbox_inches='tight') + plt.close() - def parse_mutation(mutation_str): - """ - Parse a mutation string to get the original amino acid, position, and new amino acid. + return results - Parameters: - mutation_str (str): The mutation string (e.g., "A132D") +# variant sets from https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/cctc.202101349 - Returns: - tuple: original amino acid (str), position (int), new amino acid (str) - """ - original_aa = mutation_str[0] - new_aa = mutation_str[-1] - position = int(mutation_str[1:-1]) - 1 # Convert to Python 0-based index +if __name__ == "__main__": - return original_aa, position, new_aa + LovD_seqs = { + 'LovD': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNARLRERRGKITLRHLLTHTSGLSYVFLHPLLREYMAQGHLQSAEKFGIQSRLAPPAVNDPGAEWIYGANLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSADGRLRYDDSVYFRADGEECFGGQGVFSGPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSLTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG", + 'LovD1': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNARLRERRGKITLRHLLTHTSGLSYVFLHPLLREYMAQGHLQSAEKFGIQSRLAPPAVNDPGAEWIYGANLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSADGRLRYDDSVYFRADGEECFGGQGVFSGPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG", + 'LovD2': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYMAQGHLQSAEKFGIQSRFAPPLVNDPGAEWIYGASLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSSDGRLRYDDSVYFRADGEECFGGQGVFSGPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG", + 'LovD3': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYMAQGHLQSAEKFGIQSRFAPPLVNDPGAEWIYGASLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSSDGRLRYDDSVYFRADGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG", + 'LovD4': "MGSIIDAAAAADPVVLMETAFRKAVKSRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQSAEKFGIQNRFAPPLVNDPGAEWIYGASLDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSSDGRLRYDDSVYFRADGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPQTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLAFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG", + 'LovD5': "MGSIIDAAVAADPVVLMETAFRKAVESRQIPGAVIMARDASGNLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQSAEKFGIQNRFAPPLVNDPGAEWIYGASIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADQTHRNSSDGKLRYDDSVYFRADGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPETVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIVWQIDPKAGLCTLVFFQLEPWNDPVCRDLTRTFEHAIYAQYQQG", + 'LovD6': "MGSIIDAAVAADPVVLMETAFRKAVESRQIPGAVIMARDASGRLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQGAEKFGIQNRFAPPLVNDPGAEWIYGAGIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADMTHRNSSDGKLRYDDSVYFRADGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPETVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIIWQIDPKAGLCTLVFFQLEPWNDPVCRDLTRTFEKAIYAQYQQG", + 'LovD7': "MGSIIDAAVAADPVVLMETAFRKAVESRQIPGAVIMARDASGRLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQGAEKFGIQNRFAPPLVNDPGAEWIYGAGIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADMTHRNSSDGKLRYDDTVYFRVDGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPGTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIIWQIDPKAGLCTLVFFQLEPWSDPVCRDLTRTFEKAIYAQYQQG", + 'LovD8': "MGSNIDAAVAADPVVLMETAFRKAVESSQIPGAVIMARDASGRLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVDLDETVDRLLPDLSAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQGAEKFGIQNRFAPPLVNDPGAEWIYGAGIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADMTHRNSSDGKLRYDDTVYFRHDGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPGTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVLRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIIWQIDPKAGLCTLVFFQLEPWSDPVCRDLTRTFEKAIYAQYQQG", + 'LovD9': "MGSNIDAAVAADPVVLMETAFRKAVESSQIPGAVLMARDASGRLNYTRCFGARTVRRDENNQLPPLQVDTPCRLASATKLLTTIMALQCMERGLVRLDETVDRLLPDLCAMPVLEGFDDAGNPRLRERRGKITLRHLLTHTSGLSYVFLHPLLREYVAQGHLQGAEKFGIQNRFAPPLVNDPGAEWIYGAGIDWAGKLVERATGLDLEQYLQENICAPLGITDMTFKLQQRPDMLARRADMTHRNSSDGKLRYDDTVYFRHDGEECFGGQGVFSSPGSYMKVLHSLLKRDGLLLQPGTVDLMFQPALEPRLEEQMNQHMDASPHINYGGPMPMVMRRSFGLGGIIALEDLDGENWRRKGSMTFGGGPNIIWQIDPKAGLCTLVFFQLEPWSDPVCRDLTRTFEKAIYAQYQQG" + } + LovD_temperatures = [38.5, 39.5, 44.0, 44.3, 45.0, 46.0, 52.0, 49.0, 49.3, 50.8] - def check_mutation_table(df, wt): - df = df.copy() - df['Mutations'] = df['Mutations'].fillna('') - df['Mutations'] = df['Mutations'].apply(lambda s: s.split()) - - all_mutations = np.unique(np.hstack(df['Mutations'].values)) - print(all_mutations) - for m in all_mutations: - a, pos, b = parse_mutation(m) - assert wt[pos] == a, m - return df - LipA_mutation_table = check_mutation_table(LipA_mutation_table, LipA) - def convert_seq(mutations): - seq = list(copy.copy(LipA)) - for m in mutations: - _, pos, new = parse_mutation(m) - seq[pos] = new - return ''.join(seq) - LipA_mutation_table['seq'] = LipA_mutation_table['Mutations'].apply(convert_seq) - LipA_temps = LipA_mutation_table['TM (°C)'].values - LipA_seqs = LipA_mutation_table['seq'].values - LipA_wt = LipA_seqs[0] - LipA_variants = LipA_seqs[1:] - lipa_wt_score, lipa_v_scores = model.score_variants(LipA_wt, LipA_variants, indels=False) - lipa_scores = [lipa_wt_score] + lipa_v_scores - # get spearmans - lipa_spear_r, lipa_spear_p = spearmanr(LipA_temps, lipa_scores) + if not os.path.exists('./data/plots'): + os.makedirs('./data/plots', exist_ok=True) - # make plot - fig, ax = plt.subplots(2,1, figsize=(4,8), sharex=True) - sns.regplot(x=LovD_temperatures, y=lovd_scores, ax=ax[0]) - sns.regplot(x=LipA_temps, y=lipa_scores, ax=ax[1]) - ax[0].hlines(y=0.0, xmin=ax[0].get_xlim()[0], xmax=ax[0].get_xlim()[1], color='k') - ax[1].hlines(y=0.0, xmin=ax[0].get_xlim()[0], xmax=ax[0].get_xlim()[1], color='k') - ax[1].set_xlabel('Experimental TM [C]') - ax[1].set_ylabel('Model logP score') - ax[0].set_ylabel('Model logP score') - ax[0].set_title('LovD Variants') - ax[1].set_title('LipA Variants') - plt.savefig('./data/plots/exp_tm_scores.png', dpi=400, bbox_inches='tight') + results = main(list(LovD_seqs.values()), LovD_temperatures, 'lovd') - # save the data too with open('./data/nomelt-model-full/zero_shot_estimated.json', 'w') as f: - json.dump( - {'lovd': {'temps': list(LovD_temperatures), 'scores': list(lovd_scores), 'spearman': float(lovd_spear_r), 'p': float(lovd_spear_p)}, - 'lipa': {'temps': list(LipA_temps), 'scores': list(lipa_scores), 'spearman': float(lipa_spear_r), 'p': float(lipa_spear_p)}}, - f - ) \ No newline at end of file + json.dump(results, f, indent=2) \ No newline at end of file From 33b80fc6ca155271f253d41ceeb403f2e72fde38 Mon Sep 17 00:00:00 2001 From: Evan Komp Date: Sat, 17 Aug 2024 09:39:05 -0600 Subject: [PATCH 05/19] meltome benchmark script added --- nomelt/model.py | 34 ++++++++++++++++++ scripts/meltome_benchmark.py | 69 ++++++++++++++++++++++++++++++++++++ 2 files changed, 103 insertions(+) create mode 100644 scripts/meltome_benchmark.py diff --git a/nomelt/model.py b/nomelt/model.py index 61e608c..ec12e55 100644 --- a/nomelt/model.py +++ b/nomelt/model.py @@ -204,6 +204,40 @@ def score_variants(self, wt: str, variants: Iterable[str], batch_size: int = 5, wt_probs = [float(wt_probs_all[i][vocab['▁' + wt[i]]]) for i in range(len(wt))] wt_score = self.compute_metric(wt_probs, wt_probs, normalize=indels) return wt_score, variant_scores + + def score_wts(self, sequences: Iterable[str], batch_size: int = 5) -> list: + """ + Score a list of sequences by computing the mean softmax log logits. + + Args: + sequences (Iterable[str]): List of protein sequences to score. + batch_size (int): Batch size for processing. + + Returns: + list: List of mean softmax log logits scores for each input sequence. + """ + dataset = datasets.Dataset.from_dict({'sequence': sequences}) + dataset = dataset.map(self._preprocess_dataset_to_model_inputs, batched=True, remove_columns=dataset.column_names) + dataset.set_format(type='torch') + + dataloader = DataLoader(dataset, batch_size=batch_size) + + scores = [] + for batch in tqdm(dataloader, desc="Scoring sequences"): + logits = self._predict(batch) + + # Compute softmax + softmax_logits = torch.nn.functional.softmax(logits, dim=-1) + + # Compute log of softmax + log_softmax_logits = torch.log(softmax_logits + 1e-10) # adding small epsilon to avoid log(0) + + # Compute mean across sequence length and vocab size + mean_log_softmax = log_softmax_logits.mean(dim=(1, 2)) + + scores.extend(mean_log_softmax.cpu().numpy()) + + return scores diff --git a/scripts/meltome_benchmark.py b/scripts/meltome_benchmark.py new file mode 100644 index 0000000..f3d68c1 --- /dev/null +++ b/scripts/meltome_benchmark.py @@ -0,0 +1,69 @@ +import os +import io +import requests +import zipfile +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +from scipy.stats import spearmanr, pearsonr +from yaml import safe_load +from nomelt.model import NOMELTModel + +def download_and_read_csv(url, csv_name): + response = requests.get(url) + z = zipfile.ZipFile(io.BytesIO(response.content)) + return pd.read_csv(z.open(csv_name)) + +def evaluate_model(model, sequences, temperatures): + scores = model.score_wts(sequences) + spearman_corr, spearman_p = spearmanr(temperatures, scores) + pearson_corr, pearson_p = pearsonr(temperatures, scores) + return scores, spearman_corr, spearman_p, pearson_corr, pearson_p + +def plot_results(temperatures, scores, spearman_corr, pearson_corr, model_name): + plt.figure(figsize=(10, 6)) + sns.scatterplot(x=temperatures, y=scores) + plt.xlabel('Melting Temperature (°C)') + plt.ylabel(f'{model_name} NOMELT Score') + plt.title(f'{model_name} Model: NOMELT Score vs Melting Temperature\n' + f'Spearman R: {spearman_corr:.3f}, Pearson R: {pearson_corr:.3f}') + plt.savefig(f'./data/plots/{model_name.lower()}_meltome_scores.png', dpi=300, bbox_inches='tight') + plt.close() + +def main(): + # Load parameters + with open('./params.yaml', 'r') as f: + params = safe_load(f) + + # Download and read data + url = "https://github.com/J-SNACKKB/FLIP/raw/main/splits/meltome/splits.zip" + df = download_and_read_csv(url, "mixed_splits.csv") + + # Initialize models + hyperparams = params['model']['model_hyperparams'] + original_model = NOMELTModel(params['model']['pretrained_model'], **hyperparams) + new_model = NOMELTModel('./data/nomelt-model-full/model', **hyperparams) + + sequences = df['sequence'].tolist() + temperatures = df['target'].tolist() + + results = {} + for model, model_name in [(original_model, "Original"), (new_model, "New")]: + scores, spearman_corr, spearman_p, pearson_corr, pearson_p = evaluate_model(model, sequences, temperatures) + + results[model_name] = { + 'spearman_correlation': float(spearman_corr), + 'spearman_p_value': float(spearman_p), + 'pearson_correlation': float(pearson_corr), + 'pearson_p_value': float(pearson_p) + } + + plot_results(temperatures, scores, spearman_corr, pearson_corr, model_name) + + # Save results + os.makedirs('./data/nomelt-model-full', exist_ok=True) + with open('./data/nomelt-model-full/meltome_benchmark_results.json', 'w') as f: + json.dump(results, f, indent=2) + +if __name__ == "__main__": + main() \ No newline at end of file From 9dae332a915060aae1de83ecf6c469d216df4b18 Mon Sep 17 00:00:00 2001 From: Evan Komp Date: Sat, 17 Aug 2024 10:04:23 -0600 Subject: [PATCH 06/19] meltome benchmark now loads from file if present --- scripts/meltome_benchmark.py | 24 ++++++++++++++++++++---- 1 file changed, 20 insertions(+), 4 deletions(-) diff --git a/scripts/meltome_benchmark.py b/scripts/meltome_benchmark.py index f3d68c1..a68d7fd 100644 --- a/scripts/meltome_benchmark.py +++ b/scripts/meltome_benchmark.py @@ -5,14 +5,30 @@ import pandas as pd import matplotlib.pyplot as plt import seaborn as sns +import json from scipy.stats import spearmanr, pearsonr from yaml import safe_load from nomelt.model import NOMELTModel -def download_and_read_csv(url, csv_name): - response = requests.get(url) - z = zipfile.ZipFile(io.BytesIO(response.content)) - return pd.read_csv(z.open(csv_name)) +def download_and_read_csv(url, csv_name, cache_dir='./data/cache'): + # Create cache directory if it doesn't exist + os.makedirs(cache_dir, exist_ok=True) + cache_file = os.path.join(cache_dir, csv_name) + + if os.path.exists(cache_file): + print(f"Loading data from cache: {cache_file}") + return pd.read_csv(cache_file) + else: + print(f"Downloading data from {url}") + response = requests.get(url) + z = zipfile.ZipFile(io.BytesIO(response.content)) + df = pd.read_csv(z.open(csv_name)) + + # Save to cache + df.to_csv(cache_file, index=False) + print(f"Data cached to {cache_file}") + + return df def evaluate_model(model, sequences, temperatures): scores = model.score_wts(sequences) From 5fa599ae35a36fd57309f7dc18abbf1fe2ba9665 Mon Sep 17 00:00:00 2001 From: Evan Komp Date: Sat, 17 Aug 2024 10:09:54 -0600 Subject: [PATCH 07/19] add meltome to dvc pipe --- dvc.yaml | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/dvc.yaml b/dvc.yaml index b48ff1f..f3db482 100644 --- a/dvc.yaml +++ b/dvc.yaml @@ -278,6 +278,24 @@ stages: plots: - ./data/plots/lipa_gym.png: # density plot of exp vs model scores cache: false + + meltome_benchmark: + wdir: ./ + cmd: python scripts/meltome_benchmark.py + deps: + - ./scripts/meltome_benchmark.py + - ./data/nomelt-model-full/model/ + params: + - model.generation_max_length + - model.model_hyperparams + outs: + - ./data/nomelt-model-full/meltome_benchmark_results.json: + cache: false + plots: + - ./data/plots/original_meltome_scores.png: + cache: false + - ./data/plots/new_meltome_scores.png: + cache: false # proof of principle scripts ################################# From 1edb8d4f01b2b5469d453df35d138c9bb83145fc Mon Sep 17 00:00:00 2001 From: Evan Komp Date: Sat, 17 Aug 2024 10:34:21 -0600 Subject: [PATCH 08/19] debugging meltome --- scripts/meltome_benchmark.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scripts/meltome_benchmark.py b/scripts/meltome_benchmark.py index a68d7fd..e2f2fd3 100644 --- a/scripts/meltome_benchmark.py +++ b/scripts/meltome_benchmark.py @@ -10,7 +10,7 @@ from yaml import safe_load from nomelt.model import NOMELTModel -def download_and_read_csv(url, csv_name, cache_dir='./data/cache'): +def download_and_read_csv(url, csv_name, cache_dir='./tmp/cache'): # Create cache directory if it doesn't exist os.makedirs(cache_dir, exist_ok=True) cache_file = os.path.join(cache_dir, csv_name) @@ -53,7 +53,7 @@ def main(): # Download and read data url = "https://github.com/J-SNACKKB/FLIP/raw/main/splits/meltome/splits.zip" - df = download_and_read_csv(url, "mixed_splits.csv") + df = download_and_read_csv(url, "splits/mixed_splits.csv") # Initialize models hyperparams = params['model']['model_hyperparams'] From 3e87885c5fa418b1a63434610ff8bf4e7e41021b Mon Sep 17 00:00:00 2001 From: Evan Komp Date: Sat, 17 Aug 2024 10:35:48 -0600 Subject: [PATCH 09/19] debugging meltome --- scripts/meltome_benchmark.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/meltome_benchmark.py b/scripts/meltome_benchmark.py index e2f2fd3..bec01da 100644 --- a/scripts/meltome_benchmark.py +++ b/scripts/meltome_benchmark.py @@ -53,7 +53,7 @@ def main(): # Download and read data url = "https://github.com/J-SNACKKB/FLIP/raw/main/splits/meltome/splits.zip" - df = download_and_read_csv(url, "splits/mixed_splits.csv") + df = download_and_read_csv(url, "splits/mixed_split.csv") # Initialize models hyperparams = params['model']['model_hyperparams'] From 98cd39873cd14251f62381437b9161f9e461c875 Mon Sep 17 00:00:00 2001 From: Evan Komp Date: Sat, 17 Aug 2024 10:37:32 -0600 Subject: [PATCH 10/19] debugging meltome --- scripts/meltome_benchmark.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/meltome_benchmark.py b/scripts/meltome_benchmark.py index bec01da..f73224c 100644 --- a/scripts/meltome_benchmark.py +++ b/scripts/meltome_benchmark.py @@ -10,7 +10,7 @@ from yaml import safe_load from nomelt.model import NOMELTModel -def download_and_read_csv(url, csv_name, cache_dir='./tmp/cache'): +def download_and_read_csv(url, csv_name, cache_dir='./tmp/'): # Create cache directory if it doesn't exist os.makedirs(cache_dir, exist_ok=True) cache_file = os.path.join(cache_dir, csv_name) From 6c9700269755d75ffdfca6955610d12996738eaf Mon Sep 17 00:00:00 2001 From: Evan Komp Date: Sat, 17 Aug 2024 10:40:30 -0600 Subject: [PATCH 11/19] debugging meltome --- scripts/meltome_benchmark.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/meltome_benchmark.py b/scripts/meltome_benchmark.py index f73224c..dadf3d4 100644 --- a/scripts/meltome_benchmark.py +++ b/scripts/meltome_benchmark.py @@ -12,8 +12,8 @@ def download_and_read_csv(url, csv_name, cache_dir='./tmp/'): # Create cache directory if it doesn't exist - os.makedirs(cache_dir, exist_ok=True) cache_file = os.path.join(cache_dir, csv_name) + os.makedirs(os.path.dirname(cache_file), exist_ok=True) if os.path.exists(cache_file): print(f"Loading data from cache: {cache_file}") From 17522222b86756c84682e74f5ee8d325be9964d1 Mon Sep 17 00:00:00 2001 From: Evan Komp Date: Sat, 17 Aug 2024 10:48:34 -0600 Subject: [PATCH 12/19] drafting score wt method --- nomelt/model.py | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/nomelt/model.py b/nomelt/model.py index ec12e55..775a516 100644 --- a/nomelt/model.py +++ b/nomelt/model.py @@ -216,15 +216,12 @@ def score_wts(self, sequences: Iterable[str], batch_size: int = 5) -> list: Returns: list: List of mean softmax log logits scores for each input sequence. """ - dataset = datasets.Dataset.from_dict({'sequence': sequences}) - dataset = dataset.map(self._preprocess_dataset_to_model_inputs, batched=True, remove_columns=dataset.column_names) - dataset.set_format(type='torch') - - dataloader = DataLoader(dataset, batch_size=batch_size) + # Use the same sequence for both input and output + logit_data = self.predict(sequences, sequences, batch_size=batch_size) scores = [] - for batch in tqdm(dataloader, desc="Scoring sequences"): - logits = self._predict(batch) + for i in range(len(sequences)): + logits = torch.tensor(logit_data[i]['logits']) # Compute softmax softmax_logits = torch.nn.functional.softmax(logits, dim=-1) @@ -233,9 +230,9 @@ def score_wts(self, sequences: Iterable[str], batch_size: int = 5) -> list: log_softmax_logits = torch.log(softmax_logits + 1e-10) # adding small epsilon to avoid log(0) # Compute mean across sequence length and vocab size - mean_log_softmax = log_softmax_logits.mean(dim=(1, 2)) + mean_log_softmax = log_softmax_logits.mean().item() - scores.extend(mean_log_softmax.cpu().numpy()) + scores.append(mean_log_softmax) return scores From 5f39ee96b05b928bfb2ae892810babbc08ab409c Mon Sep 17 00:00:00 2001 From: Evan Komp Date: Sun, 25 Aug 2024 10:07:34 -0700 Subject: [PATCH 13/19] ran as a zs predictor of wt sequences in meltome, does not work. --- data/nomelt-model-full/benchmark_results.json | 20 +++++++++++++ .../meltome_benchmark_results.json | 14 +++++++++ nomelt/model.py | 29 +++++++++++++------ scripts/meltome_benchmark.py | 9 ++++-- 4 files changed, 61 insertions(+), 11 deletions(-) create mode 100644 data/nomelt-model-full/benchmark_results.json create mode 100644 data/nomelt-model-full/meltome_benchmark_results.json diff --git a/data/nomelt-model-full/benchmark_results.json b/data/nomelt-model-full/benchmark_results.json new file mode 100644 index 0000000..f534efb --- /dev/null +++ b/data/nomelt-model-full/benchmark_results.json @@ -0,0 +1,20 @@ +{ + "new": { + "lipa": { + "pearson_coef": 0.2890140057583261, + "pearson_p": 4.670293896734481e-43, + "spearman_coef": 0.3205967878373402, + "spearman_p": 4.1698546948049733e-53, + "frac_qualitative_sig": 0.6286221472483685 + } + }, + "original": { + "lipa": { + "pearson_coef": 0.0070886669820527425, + "pearson_p": 0.7412644748916395, + "spearman_coef": 0.001625281365076926, + "spearman_p": 0.9396560014153784, + "frac_qualitative_sig": 0.49945479059432124 + } + } +} \ No newline at end of file diff --git a/data/nomelt-model-full/meltome_benchmark_results.json b/data/nomelt-model-full/meltome_benchmark_results.json new file mode 100644 index 0000000..bf713b5 --- /dev/null +++ b/data/nomelt-model-full/meltome_benchmark_results.json @@ -0,0 +1,14 @@ +{ + "Original": { + "spearman_correlation": -0.1340010992676487, + "spearman_p_value": 1.0461819369876712e-37, + "pearson_correlation": -0.00571109525792792, + "pearson_p_value": 0.586060502266146 + }, + "New": { + "spearman_correlation": 0.0643856191766336, + "spearman_p_value": 7.970986911625336e-10, + "pearson_correlation": -0.037172837582440874, + "pearson_p_value": 0.00039167343648485395 + } +} \ No newline at end of file diff --git a/nomelt/model.py b/nomelt/model.py index 775a516..dac7a9a 100644 --- a/nomelt/model.py +++ b/nomelt/model.py @@ -218,26 +218,37 @@ def score_wts(self, sequences: Iterable[str], batch_size: int = 5) -> list: """ # Use the same sequence for both input and output logit_data = self.predict(sequences, sequences, batch_size=batch_size) - + scores = [] - for i in range(len(sequences)): + vocab = self.tokenizer.get_vocab() + + for i, seq in enumerate(sequences): logits = torch.tensor(logit_data[i]['logits']) # Compute softmax - softmax_logits = torch.nn.functional.softmax(logits, dim=-1) + softmax_logits = torch.nn.functional.log_softmax(logits, dim=-1) + + # Get the actual sequence length (without padding) + seq_length = len(seq) - # Compute log of softmax - log_softmax_logits = torch.log(softmax_logits + 1e-10) # adding small epsilon to avoid log(0) + # Extract probabilities for observed tokens + observed_probs = [] + for j, char in enumerate(seq): + token = '▁' + char # Adjust this if your tokenizer uses a different prefix + if token in vocab: + observed_probs.append(softmax_logits[j, vocab[token]].item()) + else: + # Handle the case where the token is not in the vocabulary + observed_probs.append(1e-10) # Small probability for unknown tokens - # Compute mean across sequence length and vocab size - mean_log_softmax = log_softmax_logits.mean().item() + # Compute mean across the actual sequence length + mean_log_prob = np.mean(observed_probs) - scores.append(mean_log_softmax) + scores.append(mean_log_prob) return scores - diff --git a/scripts/meltome_benchmark.py b/scripts/meltome_benchmark.py index dadf3d4..721c2e5 100644 --- a/scripts/meltome_benchmark.py +++ b/scripts/meltome_benchmark.py @@ -31,7 +31,10 @@ def download_and_read_csv(url, csv_name, cache_dir='./tmp/'): return df def evaluate_model(model, sequences, temperatures): - scores = model.score_wts(sequences) + scores = [] + for i in range(0, len(sequences), 50): + seq_batch = sequences[i:i+50] + scores.extend(model.score_wts(seq_batch, batch_size=2)) spearman_corr, spearman_p = spearmanr(temperatures, scores) pearson_corr, pearson_p = pearsonr(temperatures, scores) return scores, spearman_corr, spearman_p, pearson_corr, pearson_p @@ -54,6 +57,8 @@ def main(): # Download and read data url = "https://github.com/J-SNACKKB/FLIP/raw/main/splits/meltome/splits.zip" df = download_and_read_csv(url, "splits/mixed_split.csv") + # remove real long sequences + df = df[df['sequence'].str.len()<=300] # Initialize models hyperparams = params['model']['model_hyperparams'] @@ -82,4 +87,4 @@ def main(): json.dump(results, f, indent=2) if __name__ == "__main__": - main() \ No newline at end of file + main() From fb96a8238a24eabff1eba61064deee39efc8e587 Mon Sep 17 00:00:00 2001 From: Evan Komp Date: Sun, 25 Aug 2024 10:15:54 -0700 Subject: [PATCH 14/19] draft of fireprot evaluation script --- scripts/fireprot_zero.py | 188 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 188 insertions(+) create mode 100644 scripts/fireprot_zero.py diff --git a/scripts/fireprot_zero.py b/scripts/fireprot_zero.py new file mode 100644 index 0000000..e41b70f --- /dev/null +++ b/scripts/fireprot_zero.py @@ -0,0 +1,188 @@ +import os +import requests +import json +import logging +import io +import re +from typing import List, Tuple + +import pandas as pd +import numpy as np +from scipy.stats import spearmanr +from sklearn.metrics import roc_auc_score +import matplotlib.pyplot as plt +import seaborn as sns +from tqdm import tqdm +from yaml import safe_load +import codecarbon + +from nomelt.model import NOMELTModel + +# Seaborn settings +sns.set_context("talk") +sns.set_style("whitegrid") + +# Logger setup +logger = logging.getLogger(__name__) +LOGLEVEL = os.getenv('LOGLEVEL', 'INFO') +LOGFILE = f'./logs/fireprotdb_benchmark.log' + +def setup_logging(): + logger.setLevel(getattr(logging, LOGLEVEL)) + fh = logging.FileHandler(LOGFILE, mode='w') + formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') + fh.setFormatter(formatter) + logger.addHandler(fh) + +def download_fireprotdb_data(url: str, output_file: str) -> pd.DataFrame: + if os.path.exists(output_file): + logger.info(f"Loading FireProtDB data from file: {output_file}") + return pd.read_csv(output_file) + + logger.info(f"Downloading FireProtDB data from {url}") + payload = {"searchData": {"type": "expr", "key": "all", "value": " ", "checkOptions": []}, "filter": {"filterKey": "ddG", "order": "asc"}} + response = requests.post(url, json=payload) + content = response.content.decode('utf-8') + df = pd.read_csv(io.StringIO(content)) + df.to_csv(output_file, index=False) + return df + +def process_fireprotdb_data(df: pd.DataFrame) -> Tuple[pd.DataFrame, pd.DataFrame]: + wt_data = {} + variant_data = [] + + for _, row in df.iterrows(): + uniprot_id = row['uniprot_id'] + if uniprot_id not in wt_data: + wt_data[uniprot_id] = { + 'sequence': row['sequence'], + 'tm': row['tm'] - row['dTm'] # Calculate wildtype Tm + } + + if row['sequence'][row['position']-1] == row['wild_type']: + variant_data.append({ + 'uniprot_id': uniprot_id, + 'wildtype_sequence': row['sequence'], + 'mutation': f"{row['wild_type']}{row['position']}{row['mutation']}", + 'delta_tm': row['dTm'] + }) + + wt_df = pd.DataFrame.from_dict(wt_data, orient='index') + wt_df.reset_index(inplace=True) + wt_df.rename(columns={'index': 'uniprot_id'}, inplace=True) + + variant_df = pd.DataFrame(variant_data) + + return wt_df, variant_df + +def apply_mutation(sequence: str, mutation: str) -> str: + match = re.match(r'([A-Z])(\d+)([A-Z])', mutation) + if not match: + raise ValueError(f"Invalid mutation format: {mutation}") + wt, pos, mut = match.groups() + pos = int(pos) - 1 + if sequence[pos] != wt: + raise ValueError(f"Mutation {mutation} does not match sequence at position {pos+1}") + return sequence[:pos] + mut + sequence[pos+1:] + +def evaluate_model(model: NOMELTModel, wt_df: pd.DataFrame, variant_df: pd.DataFrame) -> dict: + results = [] + for _, wt_row in tqdm(wt_df.iterrows(), total=len(wt_df), desc="Evaluating proteins"): + wt_sequence = wt_row['sequence'] + variants = variant_df[variant_df['uniprot_id'] == wt_row['uniprot_id']] + + if len(variants) == 0: + continue + + mutated_sequences = [apply_mutation(wt_sequence, mut) for mut in variants['mutation']] + wt_score, variant_scores = model.score_variants(wt_sequence, mutated_sequences, batch_size=5, indels=False) + + for _, variant, score in zip(variants.itertuples(), variants['delta_tm'], variant_scores): + results.append({ + 'uniprot_id': variant.uniprot_id, + 'mutation': variant.mutation, + 'delta_tm': variant.delta_tm, + 'nomelt_score': score + }) + + results_df = pd.DataFrame(results) + + x_data = results_df['delta_tm'] + y_data = results_df['nomelt_score'] + + spearman_coef, spearman_p = spearmanr(x_data, y_data) + auroc = roc_auc_score((x_data > 0).astype(int), y_data) + + return { + 'spearman_coef': spearman_coef, + 'spearman_p': spearman_p, + 'auroc': auroc, + 'results_df': results_df + } + +def plot_results(results: dict, model_name: str, output_dir: str): + x_data = results['results_df']['delta_tm'] + y_data = results['results_df']['nomelt_score'] + + fig, ax = plt.subplots(figsize=(8, 8)) + sns.kdeplot(x=x_data, y=y_data, fill=True, cmap="YlGnBu", ax=ax) + ax.set_xlabel('ΔTm [°C]') + ax.set_ylabel('NOMELT score') + ax.set_title(f'{model_name} Model Performance on FireProtDB') + + textstr = f"Spearman: {results['spearman_coef']:.3f} (p={results['spearman_p']:.2e})\nAUROC: {results['auroc']:.3f}" + props = dict(boxstyle='round', facecolor='wheat', alpha=0.5) + ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=12, + verticalalignment='top', bbox=props) + + plt.tight_layout() + plt.savefig(os.path.join(output_dir, f'fireprotdb_{model_name.lower()}_performance.png'), dpi=300) + plt.close() + +def main(): + setup_logging() + + # Initialize codecarbon + tracker = codecarbon.OfflineEmissionsTracker( + project_name="fireprotdb_benchmark", + output_dir='./data', + country_iso_code="USA", + region="washington") + tracker.start() + + # Load parameters + with open('./params.yaml', 'r') as f: + params = safe_load(f) + + hyperparams = params['model']['model_hyperparams'] + + # Download and process data + url = "https://loschmidt.chemi.muni.cz/fireprotdb/v1/export?searchType=advanced&type=csv" + output_file = "./data/fireprotdb_data.csv" + df = download_fireprotdb_data(url, output_file) + wt_df, variant_df = process_fireprotdb_data(df) + + # Initialize models + new_model = NOMELTModel('./data/nomelt-model-full/model', **hyperparams) + original_model = NOMELTModel(params['model']['pretrained_model'], **hyperparams) + + # Evaluate models + results = {} + for model_name, model in [("New", new_model), ("Original", original_model)]: + logger.info(f"Evaluating {model_name} model") + results[model_name] = evaluate_model(model, wt_df, variant_df) + plot_results(results[model_name], model_name, './data/plots') + + # Save results + with open('./data/nomelt-model-full/fireprotdb_benchmark_results.json', 'w') as f: + json.dump({k: {kk: vv for kk, vv in v.items() if kk != 'results_df'} for k, v in results.items()}, f, indent=2) + + # Save detailed results + for model_name, result in results.items(): + result['results_df'].to_csv(f'./data/nomelt-model-full/fireprotdb_{model_name.lower()}_detailed_results.csv', index=False) + + logger.info("Benchmark completed. Results saved.") + tracker.stop() + +if __name__ == "__main__": + main() From 3e8c1eea65908fcedcf67f27c70d5740fe1abe1a Mon Sep 17 00:00:00 2001 From: Evan Komp Date: Sun, 25 Aug 2024 10:26:58 -0700 Subject: [PATCH 15/19] cleaninf fireprot data --- scripts/fireprot_zero.py | 31 ++++++++++++++++++++++++------- 1 file changed, 24 insertions(+), 7 deletions(-) diff --git a/scripts/fireprot_zero.py b/scripts/fireprot_zero.py index e41b70f..4932503 100644 --- a/scripts/fireprot_zero.py +++ b/scripts/fireprot_zero.py @@ -59,13 +59,25 @@ def process_fireprotdb_data(df: pd.DataFrame) -> Tuple[pd.DataFrame, pd.DataFram 'tm': row['tm'] - row['dTm'] # Calculate wildtype Tm } - if row['sequence'][row['position']-1] == row['wild_type']: - variant_data.append({ - 'uniprot_id': uniprot_id, - 'wildtype_sequence': row['sequence'], - 'mutation': f"{row['wild_type']}{row['position']}{row['mutation']}", - 'delta_tm': row['dTm'] - }) + # Check if delta Tm is available + if pd.isna(row['dTm']): + continue + + # Verify that the mutation matches the sequence + wt_aa = row['wild_type'] + mut_aa = row['mutation'] + pos = row['position'] - 1 # Convert to 0-based index + + if pos >= len(row['sequence']) or row['sequence'][pos] != wt_aa: + logger.warning(f"Mutation {wt_aa}{row['position']}{mut_aa} does not match sequence for {uniprot_id}") + continue + + variant_data.append({ + 'uniprot_id': uniprot_id, + 'wildtype_sequence': row['sequence'], + 'mutation': f"{wt_aa}{row['position']}{mut_aa}", + 'delta_tm': row['dTm'] + }) wt_df = pd.DataFrame.from_dict(wt_data, orient='index') wt_df.reset_index(inplace=True) @@ -73,6 +85,11 @@ def process_fireprotdb_data(df: pd.DataFrame) -> Tuple[pd.DataFrame, pd.DataFram variant_df = pd.DataFrame(variant_data) + # Drop any remaining rows with NaN values + variant_df = variant_df.dropna() + + logger.info(f"Processed {len(wt_df)} wildtype sequences and {len(variant_df)} variants") + return wt_df, variant_df def apply_mutation(sequence: str, mutation: str) -> str: From d2a2367d1a52269f875644b28a0e70c6349d4b88 Mon Sep 17 00:00:00 2001 From: Evan Komp Date: Sun, 25 Aug 2024 10:36:31 -0700 Subject: [PATCH 16/19] cleaninf fireprot data --- scripts/fireprot_zero.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/scripts/fireprot_zero.py b/scripts/fireprot_zero.py index 4932503..b5d7a1d 100644 --- a/scripts/fireprot_zero.py +++ b/scripts/fireprot_zero.py @@ -53,6 +53,8 @@ def process_fireprotdb_data(df: pd.DataFrame) -> Tuple[pd.DataFrame, pd.DataFram for _, row in df.iterrows(): uniprot_id = row['uniprot_id'] + if len(row['sequence']) > 400: + continue if uniprot_id not in wt_data: wt_data[uniprot_id] = { 'sequence': row['sequence'], @@ -114,11 +116,9 @@ def evaluate_model(model: NOMELTModel, wt_df: pd.DataFrame, variant_df: pd.DataF mutated_sequences = [apply_mutation(wt_sequence, mut) for mut in variants['mutation']] wt_score, variant_scores = model.score_variants(wt_sequence, mutated_sequences, batch_size=5, indels=False) - for _, variant, score in zip(variants.itertuples(), variants['delta_tm'], variant_scores): + for _, label, score in zip(variants.itertuples(), variants['delta_tm'], variant_scores): results.append({ - 'uniprot_id': variant.uniprot_id, - 'mutation': variant.mutation, - 'delta_tm': variant.delta_tm, + 'delta_tm': label, 'nomelt_score': score }) From 953e47b01a130224cac56911ac4405637fc815ad Mon Sep 17 00:00:00 2001 From: Evan Komp Date: Sun, 25 Aug 2024 10:43:38 -0700 Subject: [PATCH 17/19] sampling to test script --- scripts/fireprot_zero.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/scripts/fireprot_zero.py b/scripts/fireprot_zero.py index b5d7a1d..c42fc15 100644 --- a/scripts/fireprot_zero.py +++ b/scripts/fireprot_zero.py @@ -106,13 +106,16 @@ def apply_mutation(sequence: str, mutation: str) -> str: def evaluate_model(model: NOMELTModel, wt_df: pd.DataFrame, variant_df: pd.DataFrame) -> dict: results = [] + counter = 0 + for _, wt_row in tqdm(wt_df.iterrows(), total=len(wt_df), desc="Evaluating proteins"): wt_sequence = wt_row['sequence'] variants = variant_df[variant_df['uniprot_id'] == wt_row['uniprot_id']] - + if counter > 3: + break if len(variants) == 0: continue - + counter +=1 mutated_sequences = [apply_mutation(wt_sequence, mut) for mut in variants['mutation']] wt_score, variant_scores = model.score_variants(wt_sequence, mutated_sequences, batch_size=5, indels=False) From c08a64733d0db18b4b5afdced0ab1e252ef87b8f Mon Sep 17 00:00:00 2001 From: Evan Komp Date: Sun, 25 Aug 2024 11:15:55 -0700 Subject: [PATCH 18/19] small sample fireport plots --- data/plots/fireprotdb_new_performance.png | Bin 0 -> 147979 bytes data/plots/fireprotdb_original_performance.png | Bin 0 -> 173529 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 data/plots/fireprotdb_new_performance.png create mode 100644 data/plots/fireprotdb_original_performance.png diff --git a/data/plots/fireprotdb_new_performance.png 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zd|CSyd4j;=!v0Gy?Us5bL*S;E;lda7OAj}-1D+uOkxG^cGNRL$boGG01d2QfH6JFH zC^V?#3V;{l-przy5@scyY9j)p^M628I;qrfS2pZH4nG^B2fpo5iU{~J#s?J^qWZU& zy5>}&AXdW8UL;h8`qc$5cU*gOE5=;m;gLPol}3VqWsCm!M4l|coMH-gvJjt@BX5fD4fo8Gtw4#|Zq zOD{asiD(x4%Q&@5RVT{F#evT^`>jqj&zdtZ7oeeKY9ASI2gJ#>cW4fMZxZ6G00V|Z z)U39l7ywA^7cjdN*j_3c-wJ0;-~Sr;9^m+yQ?cmcLFd|Fhe!}Epn936jpA>J19ESzVXl8 zogr#Wf>zctBp$PC$FxP+0n*~!Il91w{Zulap5JGX_~73aoh<{{Ec_1ta6Mq~G{T=1 zg>O)}0Ka@qs-n9WM+_6Gx!Ks;zJfj6rouiBi~;+4xQ3!t(imA0>X`aYum Date: Sun, 3 Nov 2024 05:48:47 -0800 Subject: [PATCH 19/19] added fireprot db benchmark and compared to original model weights --- README.md | 3 +- .../fireprotdb_benchmark_results.json | 12 + .../fireprotdb_new_detailed_results.csv | 9104 +++++++++++++++++ .../fireprotdb_original_detailed_results.csv | 9104 +++++++++++++++++ .../zero_shot_estimated.json | 85 +- data/plots/fireprotdb_new_performance.png | Bin 147979 -> 150003 bytes .../plots/fireprotdb_original_performance.png | Bin 173529 -> 160936 bytes data/plots/new_lipa.png | Bin 0 -> 122422 bytes data/plots/new_lovd_scores.png | Bin 0 -> 82564 bytes data/plots/new_meltome_scores.png | Bin 0 -> 705290 bytes data/plots/original_lipa.png | Bin 0 -> 108341 bytes data/plots/original_lovd_scores.png | Bin 0 -> 111186 bytes data/plots/original_meltome_scores.png | Bin 0 -> 139887 bytes scripts/fireprot_zero.py | 2 - scripts/zero_shot_experiment.py | 19 +- 15 files changed, 18323 insertions(+), 6 deletions(-) create mode 100644 data/nomelt-model-full/fireprotdb_benchmark_results.json create mode 100644 data/nomelt-model-full/fireprotdb_new_detailed_results.csv create mode 100644 data/nomelt-model-full/fireprotdb_original_detailed_results.csv create mode 100644 data/plots/new_lipa.png create mode 100644 data/plots/new_lovd_scores.png create mode 100644 data/plots/new_meltome_scores.png create mode 100644 data/plots/original_lipa.png create mode 100644 data/plots/original_lovd_scores.png create mode 100644 data/plots/original_meltome_scores.png diff --git a/README.md b/README.md index 499f31d..5354cbf 100644 --- a/README.md +++ b/README.md @@ -106,8 +106,7 @@ Some of the figures in the manuscript were created during the main pipeline step - Figure 2: located at `./analysis/figures/AA_propensities.png` created in notebook `./analysis/probe_model.ipynb` - Figure 3: located at `./analysis/figures/disulfide_logits.png` created in notebook `./analysis/probe_model.ipynb` - Figure 4: located at `./analysis/figures/estimated_shift_thermo_gen.png` created in notebook `./analysis/dataset_stats.ipynb` -- Figure 5: located at `./analysis/figures/mAF_scores.png` created in notebook `./analysis/compare_estimated_stability.ipynb` -- Figure 6: see repo https://zenodo.org/records/10625583 +- Figure 5: see repo https://zenodo.org/records/10625583 - Figure 7: located at './data/plots/exp_tm_scores.png' created in script `./scripts/zero_shot_experiment.py` ## License diff --git a/data/nomelt-model-full/fireprotdb_benchmark_results.json b/data/nomelt-model-full/fireprotdb_benchmark_results.json new file mode 100644 index 0000000..099e394 --- /dev/null +++ b/data/nomelt-model-full/fireprotdb_benchmark_results.json @@ -0,0 +1,12 @@ +{ + "New": { + "spearman_coef": 0.32183325388483064, + "spearman_p": 2.11588628138216e-218, + "auroc": 0.6122423981479029 + }, + "Original": { + "spearman_coef": -0.016768034527282, + "spearman_p": 0.10965961456982194, + "auroc": 0.5013025291347248 + } +} \ No newline at end of file diff --git a/data/nomelt-model-full/fireprotdb_new_detailed_results.csv b/data/nomelt-model-full/fireprotdb_new_detailed_results.csv new file mode 100644 index 0000000..97557a1 --- /dev/null +++ b/data/nomelt-model-full/fireprotdb_new_detailed_results.csv @@ -0,0 +1,9104 @@ +delta_tm,nomelt_score +2.1,0.7807564890528838 +-0.4,-3.045412105963819 +5.2,1.415734741757757 +3.1,0.7196423520282167 +1.1,-0.349120640999391 +0.5,-0.4455361032223957 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break if len(variants) == 0: continue counter +=1 diff --git a/scripts/zero_shot_experiment.py b/scripts/zero_shot_experiment.py index 4b5b9ff..a765c19 100644 --- a/scripts/zero_shot_experiment.py +++ b/scripts/zero_shot_experiment.py @@ -36,14 +36,31 @@ def main(sequences, temperatures, system_name): 'p': float(spear_p) } + # generate random mutations and check scores + wt_seq = sequences[0] + variants = [] + for i in range(10): + positions = np.random.choice(list(range(len(wt_seq))), size=10, replace=False) + var_ = list(wt_seq) + for p in positions: + var_[p] = str(np.random.choice(list('ACDEFGHIKLMNPQRSTVWY'))) + variants.append(''.join(var_)) + + _, random_scores = model.score_variants(sequences[0], variants) + + results[model_name]['random'] = random_scores + results[model_name]['random_mean'] = np.mean(random_scores) + # Create plot fig, ax = plt.subplots(figsize=(4, 4)) sns.regplot(x=temperatures, y=scores, ax=ax) ax.set_xlabel('Melting Temperature (°C)') ax.set_ylabel(f'{model_name.upper()} NOMELT Logit Score') + ax.hlines(np.mean(random_scores), xmin=min(temperatures), xmax=max(temperatures), label='10 random mutations', color='r') plt.savefig(f'./data/plots/{model_name}_{system_name}_scores.png', dpi=300, bbox_inches='tight') plt.close() + return results # variant sets from https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/cctc.202101349 @@ -70,4 +87,4 @@ def main(sequences, temperatures, system_name): results = main(list(LovD_seqs.values()), LovD_temperatures, 'lovd') with open('./data/nomelt-model-full/zero_shot_estimated.json', 'w') as f: - json.dump(results, f, indent=2) \ No newline at end of file + json.dump(results, f, indent=2)

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