Trelis/protein_stability_single_mutation

Protein Data Stability - Single Mutation

This repository contains data on the change in protein stability with a single mutation.

Attribution of Data Sources

• Primary Source: Tsuboyama, K., Dauparas, J., Chen, J. et al. Mega-scale experimental analysis of protein folding stability in biology and design. Nature 620, 434–444 (2023). Link to the paper
• Dataset Link: Zenodo Record

As to where the dataset comes from in this broader work, the relevant dataset (#3) is shown in dataset_table.jpeg of this repository's files.

Sample Protein Stability Data [subset of 4 columns]

Base Protein Sequence	Mutation	ΔΔG_ML	Classification
FDIYVVTADYLPLGAEQDAITLREGQYVEVLDAAHPLRWLVRTKPTKSSPSRQGWVSPAYLDRRL	R63W	-0.2010871345320799	neutral
FDIYVVTADYLPLGAEQDAITLREGQYVEVLDAAHPLRWLVRTKPTKSSPSRQGWVSPAYLDRRL	R63Y	0.0194756159891467	neutral
FDIYVVTADYLPLGAEQDAITLREGQYVEVLDAAHPLRWLVRTKPTKSSPSRQGWVSPAYLDRRL	R63F	0.7231614929744659	stabilising
FDIYVVTADYLPLGAEQDAITLREGQYVEVLDAAHPLRWLVRTKPTKSSPSRQGWVSPAYLDRRL	R63P	-0.3668887752897785	neutral
FDIYVVTADYLPLGAEQDAITLREGQYVEVLDAAHPLRWLVRTKPTKSSPSRQGWVSPAYLDRRL	R63C	-0.5317304030261774	destabilising

Dataset Structure

This dataset focuses on the differential deltaG of unfolding (mutation minus base) of various protein mutations and is derived from stability measurements (free energy of unfolding) measured by two proteases, trypsin and chymotrypsin.

Columns (Trypsin):

• name: The name of the protein variant.
• dna_seq: The DNA sequence encoding the protein variant.
• log10_K50_t: The log10 of the K50 value measured with trypsin (a measure of stability).
• log10_K50_t_95CI_high: The upper bound of the 95% confidence interval for log10_K50_t.
• log10_K50_t_95CI_low: The lower bound of the 95% confidence interval for log10_K50_t.
• log10_K50_t_95CI: The width of the 95% confidence interval for log10_K50_t.
• fitting_error_t: A measure of error between the model and data for trypsin.
• log10_K50unfolded_t: The predicted log10 K50 value for the unfolded state with trypsin.
• deltaG_t: The ΔG stability calculated from the trypsin data.
• deltaG_t_95CI_high: The upper bound of the ΔG confidence interval from trypsin.
• deltaG_t_95CI_low: The lower bound of the ΔG confidence interval from trypsin.
• deltaG_t_95CI: The width of the ΔG confidence interval from trypsin.

Columns (Chymotrypsin):

• log10_K50_c: Analogous to log10_K50_t, but for chymotrypsin.
• log10_K50_c_95CI_high: Upper bound of the 95% CI for log10_K50_c.
• log10_K50_c_95CI_low: Lower bound of the 95% CI for log10_K50_c.
• log10_K50_c_95CI: Width of the 95% CI for log10_K50_c.
• fitting_error_c: A measure of error between the model and data for chymotrypsin.
• log10_K50unfolded_c: Predicted log10 K50 value for the unfolded state with chymotrypsin.
• deltaG_c: ΔG stability calculated from the chymotrypsin data.
• deltaG_c_95CI_high: Upper bound of the ΔG CI from chymotrypsin.
• deltaG_c_95CI_low: Lower bound of the ΔG CI from chymotrypsin.
• deltaG_c_95CI: Width of the ΔG CI from chymotrypsin.

Combined Data:

• deltaG: The combined ΔG estimate from both trypsin and chymotrypsin.
• deltaG_95CI_high: Upper bound of the combined ΔG confidence interval.
• deltaG_95CI_low: Lower bound of the combined ΔG confidence interval.
• deltaG_95CI: Width of the combined ΔG confidence interval.

Protein Sequencing Data:

• aa_seq_full: The full amino acid sequence.
• aa_seq: A (sometimes shortened) amino acid sequence representing the protein.
• mut_type: The type of mutation introduced to the protein.
• WT_name: Name of the wild type variant.
• WT_cluster: Cluster classification for the wild type variant.
• mutation: Represented as a combination of amino acid and its position (e.g., F10N indicates changing the 10th amino acid (F) in a sequence for N).
• base_aa_seq: The base sequence of the protein before the mutation.

Derived Data:

• log10_K50_trypsin_ML: Log10 value of K50 derived from a machine learning model using trypsin data.
• log10_K50_chymotrypsin_ML: Log10 value of K50 derived from a machine learning model using chymotrypsin data.
• dG_ML: ΔG derived from a machine learning model that makes use of stability measurements from both proteases.
• ddG_ML: Differential ΔG (mutation minus base) derived from a machine learning model.

Classification:

• Stabilizing_mut: Indicates whether the mutation is stabilizing or not.
• pair_name: Name representation combining the wild type and mutation.
• classification: Classification based on ddG_ML:
  • Rows below -0.5 standard deviations are classified as 'destabilising'.
  • Rows above +0.5 standard deviations are classified as 'stabilising'.
  • Rows between -0.5 and 0.5 standard deviations are classified as 'neutral'.

This dataset offers a comprehensive view of protein mutations, their effects, and how they relate to the stability measurements made with trypsin and chymotrypsin.

Understanding ΔG (delta G)

ΔG is the Gibbs free energy change of a process, dictating whether a process is thermodynamically favorable:

• Negative ΔG: Indicates the process is energetically favorable. For protein unfolding, it implies the protein is more stable in its unfolded form.
• Positive ΔG: Indicates the process is not energetically favorable. In protein unfolding, it means the protein requires energy to maintain its unfolded state, i.e. it is stable in folded form.

The delta delta G (ΔΔG) represents the deltaG of the mutation compared to the base protein:

• Positive ΔΔG: Suggests the mutation enhances protein stability.
• Negative ΔΔG: Suggests the mutation decreases protein stability.

Data Cleanup and Validation:

1. Filtering: The dataset has been curated to only include examples of single mutations.
2. Sequence mutations were extracted from the row names. Base mutations are labelled as 'base'.
3. Consistency Check: Only rows with a consistent 'mutation', aligned with both the base and mutated sequences from the raw data, have been retained.