DOI: 10.5061/dryad.vdncjsz1s
title: Alignment-based protein mutational landscape prediction: doing more with less

description: The wealth of genomic data has boosted the development of computational
 methods predicting the phenotypic outcomes of missense variants. The most
 accurate ones exploit multiple sequence alignments, which can be costly to
 generate. Recent efforts for democratizing protein structure prediction
 have overcome this bottleneck by leveraging the fast homology
 search of MMseqs2. Here, we show the usefulness of this strategy
 for mutational outcome prediction through a large-scale assessment of 1.5M
 missense variants across 72 protein families. Our study demonstrates the
 feasibility of producing alignment-based mutational landscape predictions
 that are both high-quality and compute-efficient for entire proteomes. We
 provide the community with the whole human proteome mutational landscape
 and simplified access to our predictive pipeline.
description: # Alignment-based protein mutational landscape prediction: doing more with
 less. [Access this dataset on
 Dryad](https://doi.org/10.5061/dryad.vdncjsz1s) This dataset contains the
 data and tools associated with *Alignment-based protein mutational
 landscape prediction: doing more with less*, Abakarova *et al.*, *Genome
 Biology and Evolution*, 2023. **doi:**
 ## Description of the data
 and file structure We provide the community with data associated with our
 assessment of four different multiple sequence alignment (MSA) resources
 and protocols, as well as the complete single-mutational landscape of the
 human proteome predicted by combining the MSA protocol implemented in
 ColabFold and the variant effect predictor GEMME. 1.
 **ProteinGym_assessment.tgz** contains the data and scripts associated
 with our assessment of the four different MSA generation protocols
 (ColabFold, ProteinGym, ProteinNet, Pfam) against the ProteinGym
 substitution benchmark. This archive is organised as follows: * *ColabFold
 (CF)*: GEMME predictions computed from ColabFold MSAs. This folder
 contains 75 subdirectories with *protein* names. Each of these
 subdirectories contains the predicted single-point mutational landscape
 for the corresponding protein in 1-3 versions, **_normPred_evolCombi.txt*,
 **_normPred_evolCombi_Pfam.txt, *_normPred_evolCombi_PrNet.txt.* The first
 one corresponds to the original output of GEMME. In the two others, the
 positions not covered by Pfam or ProteinNet are replaced by NAs. These
 versions are useful for comparing the performance. Additionally, the *CF*
 folder contains 3 other subdirectories, namely *MultipleMutants*,
 including predictions of multiple mutants for 11 proteins
 (**_normPred_evolCombi.txt,* with or without
 **_normPred_evolCombi_PfamVersion.txt)*, *CF_noFilter,* including GEMME
 predictions based on the *CF* alignements without the filter ( 4
 **_normPred_evolCombi.txt* files) and *align_CF_merged*, containing 75
 *CF* MSAs in FASTA format. * *ProteinGym (PG)*: GEMME predictions computed
 from ProteinGym MSAs. This folder contains 75 subdirectories with
 *protein* names. Each of these subdirectories contains the predicted
 single-point mutational landscape for the corresponding protein in 1-3
 versions, **_normPred_evolCombi.txt*, **_normPred_evolCombi_Pfam.txt,
 *_normPred_evolCombi_PrNet.txt.* The first one corresponds to the original
 output of GEMME. In the two others, the positions not covered by Pfam or
 ProteinNet are replaced by NAs. These versions are useful for comparing
 the performance. Additionally, the folder contains a subdirectory, namely
 *MultipleMutants*, including predictions of multiple mutants for 11
 proteins (**_normPred_evolCombi.txt,* with or without
 **_normPred_evolCombi_PfamVersion.txt)*. * *ProteinNet(PN)*: GEMME
 predictions computed from ProteinNet MSAs. This folder contains 41
 subdirectories with *protein* names corresponding to the proteins covered
 by Pfam. Each of these subdirectories contains the predicted single-point
 mutational landscape for the corresponding protein, namely
 *PDBid_normPred_evolCombi_PrNet.txt (PDBid* is the identifier from the
 Protein Data Bank of the proteins covered by ProteinNet). To match the
 original length of the query sequence in the ProteinGym benchmark, we
 added extra columns filled with NAs. This version is useful for
 evaluation. Additionally, *MultipleMutants* subdirectory is provided. *
 *Pfam*: GEMME predictions computed from Pfam MSAs. This folder contains 39
 subdirectories with *protein* names corresponding to the proteins covered
 by Pfam. Each of these subdirectories contains the predicted single-point
 mutational landscape for the corresponding protein, namely
 **_normPred_evolCombi_fullPfam.txt*. To match the original length of the
 query sequence in the ProteinGym benchmark, we added extra columns filled
 with NAs. Additionally, *MultipleMutants* subdirectory and a *readme* file
 describing the protocol are provided. * *Mutations_of_interest*: input
 files for GEMME specifying the multiple mutants * *DMS_experiments*: 87
 Deep Mutational Scanning assays retrieved from the ProteinGym substitution
 benchmark, *
 *script*: useful scripts for analysing the data (.py, .ipynb, .R), see
 more details below in the section Code/Software. 2. **CSV_ProteinGym.tgz**
 contains CSV files sumarizing the data listed above and includes: *
 *ID_UniProt_PDB.csv* provides a UniProt_ID (second column) to PDB_ID
 (third column) mapping for the proteins (names in first column) from the
 ProteinGym substitution benchmark, * *Neff_ProteinGym_ColabFold.csv*
 compares the MSA depth (as measured by the Neff metric, see Methods
 section in the manuscript) between CF and PG protocols, where *UniProt_ID*
 gives the protein UniProt identifier, *MSA_filename* the MSA filename in
 ProteinGym, *shortName* the short name of the protein, *theta* the
 threshold used for computing the MSA depth, *CF_Neff* the ColabFold MSA
 depth, *CF_Nseqs* the ColabFold MSA number of sequences, *MSA_len* the
 length of the query sequence in the MSA, *PG_num_seqs* the ProteinGym MSA
 number of sequences, *PG_perc_cov* the ProteinGym MSA percentage of
 positions covered, *PG_Neff* the ProteinGym MSA depth, *PG_Neff_cat* the
 ProteinGym MSA category as defined in ProteinGym benchmark, *
 *Pfam_potocol.csv* provides the Pfam domain composition of each query
 protein, where *UniProt_ID* gives the protein UniProt identifier,
 *shortName* the short name of the protein, *pfamID* the Pfam identifier(s)
 of the Pfam domains contained in the protein, *pfamStart* the residue
 index(ices) where the Pfam domain(s) start(s), *pfamEnd* the residue
 index(ices) where the Pfam domain(s) end(s), *avail* whether the MSA was
 available from the Pfam website, * *Spearman_Pfam.csv,
 Spearman_ProteinGymMSA.csv, Spearman_ProteinNet.csv* contains the
 performances of the three protocols (Pfam, PG and PN) along with CF
 performances measured in Spearman rank correlation coefficient, where
 *DMS_filename* gives the filename of the experimental data, *UniProt_ID*
 the protein UniProt identifier, *GEMME_Tranception_spearman* the Spearman
 rank correlation for GEMME combined with the ProteinGym MSA,
 *GEMME_CF_spearman* the Spearman rank correlation for GEMME combined with
 the ColabFold MSA, *Tranception_spearman* the Spearman rank correlation
 for Tranception, *Tranception_no_retrieval_score* the Spearman rank
 correlation for Tranception without retrieval, *MSA_Neff_l_Tranception*
 the depth of the proteinGym MSA, *MSA_Neff_L_category* the depth category
 of the proteinGym MSA, *Multiple_mutants* whether there are multiple
 mutants, *GEMME_CF_Uniref_Spearman* the Spearman rank correlation for
 GEMME combined with the ColabFold MSA generated with Uniref only,
 *GEMME_CF_speamanNoFilter* the Spearman rank correlation for GEMME
 combined with the ColabFold MSA when the filter is removed, *CF_noFilter*
 whether the filter was removed. * *Summary4Protocols.csv* indicates
 whether the predictions for each of 72 proteins have been successfully
 calculated by the four protocols and whether the experimental measurements
 contain multiple mutants, where *ProtName* gives the protein name,
 *UniProt_ID* the protein UniProt identifier, *containsMultiple* whether it
 has multiple mutants, *isTreatedbyProteinGym* whether it has a ProteinGym
 MSA, *isTreatedbyColabFold* whether it has a ColabFold MSA,
 *isTreatedbyProteinNet* whether it has a ProteinNet MSA, *PDB_id* the PDB
 identifier (if applicable), *isTreatedbyPfam* whether it has a Pfam MSA,
 *nbPfam* the number of Pfam domains, * *Tranception_data.csv* is a table
 provided by giving
 detailed information about the benchmark. A detailed description of it can
 be found at this URL link. 3. **HumanProteome_GEMME.tgz** contains
 predictions for the entire human proteome, obtained by running GEMME on
 MSAs generated by ColabFold. The archive contains 20 586 folders with a
 *UniProt_ID* name containing the predictions. The detailed description of
 a folder can be found in *CSV_Human/ReadmePredictions.* 4.
 **CSV_HumanProteome.tgz** is an archive containing annotations for the
 predictions over the human proteome. It contains: * *ReadmePredictions*:
 file describing the content of each protein prediction folder, *
 *HumanProteome_analyse.csv*: summary table of the human proteome
 predictions, whose detailed description is provided in *ReadmeCSV,* *
 *ReadmeCSV*: description of the file *HumanProteome_analyse.csv,* *
 *UP000005640_9606.fasta* human proteome in fasta file (20 586 sequences).
 ## Sharing/Access information * All predictions were computed using the
 alignment-based variant effect predictor GEMME, available at:
 . * The experimental deep mutational
 scan measurements and the ProteinGym MSAs come from the ProteinGym
 substitution benchmark available at:
 . * The ColabFold MSAs
 were generated locally following the instructions and downloading data
 from . * The Pfam MSAs were retrieved
 from the Pfam website, now accessible at:
 . * The ProteinNet MSAs were
 downloaded upon request to the authors, see
 for more information. * The MSAs associated with the predictions over the whole human proteome are available upon request. Please [email us](elodie.laine@sorbonne-universite.fr) to request access. ## Code/Software We provide a set of scripts used to analyse the data and produce the figures. They are located in the *script* subfolder of **ProteinGym_assessment.tar**: * *analInfluenceMSA.R* is written in R and contains a set of functions for computing summary statistics and plotting the results, * *Calculate_Spearman_correlation_DMS.py* is written in Python and contains a set of functions for computing the Spearman rank correlations, * *make_plots.ipynb* is a Jupyter Notebook written in Python and contains a set of functions for plotting the results, * *code4Neff.tgz* is an archive containing R and bash scripts for computing the MSA depths : * *runAll.sh* is a bash script launching the script *computeMat.R* on a set of MSAs, * *computeMat.R* is an R script computing all pairwise hamming distances between the sequences contained in the input MSA, * *runAllNeff.sh* is a bash script launching the script *computeNeff.R* on a set of MSAs, * *computeNeff.R* is an R script computing the depth of an MSA, given its distance matrix and a threshold as input. The R scripts were run using R version 4.2.2. The Python script were run using Python 2.7.16.