ASCARIS: Positional Feature Annotation and Protein Structure-Based Representation of Single Amino Acid Variations

Abstract

Genomic variations may cause deleterious effects on protein functionality and perturb biological processes. Elucidating the effects of variations is critical for developing novel treatment strategies for diseases of genetic origin. Computational approaches have been aiding the work in this field by modeling and analyzing the mutational landscape. However, new approaches are required, especially for accurate and comprehensive representation and data-centric analysis of sequence variations.

In this study, we propose ASCARIS (Annotation and StruCture-bAsed RepresentatIon of Single amino acid variations - SAVs), a method for the featurization (i.e., quantitative representation) of SAVs, which could be used for a variety of purposes, such as predicting their functional effects or building multi-omics-based integrative models. In ASCARIS representations, we incorporated the correspondence between the location of the SAV on the sequence and 30 different types of positional feature annotations (e.g., active/lipidation/glycosylation sites; calcium/metal/DNA binding, inter/transmembrane regions, etc.) from UniProt, along with structural features such as protein domains, the location of variation (e.g., core/interface/surface), and the change in physico-chemical properties using models from PDB and AlphaFold-DB. We also mapped the mutated and annotated residues to the 3-D plane and calculated the spatial distances between them in order to account for the functional changes caused by variations in positions close to the functionally essential ones. Finally, we constructed a 74-dimensional feature set to represent each SAV in a dataset composed of ~100,000 data points.

We statistically analyzed the relationship between each of these features and the consequences of variations, and found that each of them carries information in this regard. To investigate potential applications of ASCARIS, we trained variant effect predictor models that utilize our SAV representations as input. We carried out both an ablation study and a comparison against the state-of-the-art methods over well-known benchmark datasets. We observed that our method displays a competing performance against widely-used predictors. Also, our predictions were complementary to these methods which is probably due to fact that ASCARIS has a rather unique focus in modeling variations. ASCARIS can be used either alone or in combination with other approaches, to universally represent SAVs from a functional perspective.

Development and Dependencies

• Python 3.7.3
• Pandas 1.1.4
• Numpy 1.19.5
• Ssbio 0.9.9.8.post1
• Freesasa 2.0.3.post7
• Requests 2.22.0
• Biopython 1.78

Descriptions of folders and files in the ASCARIS repository

Input Files

Inside input_files folder, users can find files that are necessary to run the code. Below, explanations of the files can be found.

• swissmodel_structures.txt.zip : Includes summary file for Swiss-Model structures. Swiss-Model summary (INDEX-metadata) files are downloaded separately for each organism from https://swissmodel.expasy.org/repository, and merged into a single file by running create_swissmodelSummary.py code file. Generated file is uploaded to GitHub as a zip file, thus it must be unzipped to input_files folder prior to usage. Alternatively it can be downloaded from here. If needed, the user can create an updated file by running script create_swissmodelSummary.py in the folder where downloaded newly meta-data is found. Relevant file will be created under /input_files.

cd ASCARIS
python3 code/create_swissmodelSummary.py -folder_name folder_to_meta_data

• domains.txt : Includes InterPro domains simplified as in the following order (tab separated) --> [uniprotID domainID domainStartPosition domainEndPosition]
• significant_domains : Selected domains from domains.txt file according to Fisher's Exact Test result. Fisher's Exact Test applied to all domains in the training test to assess their significance with respect to the the deleteriousness outcome. p_values is chosen as 0.01.
• H_sapiens_interfacesHQ.txt : High confidence interfaces downloaded from Interactome Insider for Homo sapiens
• alphafold_structures : This folder contains AlphaFold Human proteome predictions. Please download the '.tar' file in input_files folder and run get_alphafoldStructures.py to untar the structures and create alphafold_summary file. The folder in the repository contains 100 structure files for demo purposes.

cd ASCARIS
python3 code/get_alphafoldStructures.py -file_name UP000005640_9606_HUMAN.tar

• alphafold_summary: Processed data for AlphaFold structures. Includes protein identifier, chain id, sequence, model count for each entry.

Datasets

Datasets that are used to create machine learning models are provided in datasets folder. Each file has a _raw and _imputed version, in which in the latter one the empty values are imputed with the mean values of each property obtained from the training set. Feature vectors that are created using Alphafold proteins and PDB proteins can be found under related folders.

• training_uptodate_full.txt : Full training dataset obtained from UniProt, PMD and ClinVar.
• training_uptodate_full_2014selected.txt : 2014 subset of the training set.
• swiss_test.txt : Benchmark set obtained from SwissVar database.
• varibench_test.txt : Benchmark set obtained from VariBench database.
• psnp_test.txt : Benchmark set obtained from PredictSNP database.
• mutationtaster_test : Benchmark set obtained from MutationTaster data.
• training_uptodate_full_2014selected_wo_swiss : 2014 subset of the training set, test datapoints from SwissVar removed.
• training_uptodate_full_2014selected_wo_varibench : 2014 subset of the training set, test datapoints from Varibench removed.
• training_uptodate_full_2014selected_wo_psnp : 2014 subset of the training set, test datapoints from PredictSNP removed.
• training_uptodate_full_2014selected_wo_mt : 2014 subset of the training set, test datapoints from MutationTaster removed.
• featurevector_brca1_xx : Feature vector created for benchmarking BRCA1 variations. AF and PDB versions, as well as imputed and not imputed versions are found in the relevant folders.
• featurevector_p53_xx : Feature vector created for benchmarking P53 variations. AF and PDB versions, as well as imputed and not imputed versions are found in the relevant folders.
• featurevector_calm1_xx : Feature vector created for benchmarking CALM1 variations. AF and PDB versions, as well as imputed and not imputed versions are found in the relevant folders.
• training_uptodate_full_pdb_imputed_wo3genes : Imputed PDB training feature vector without datapoints from BRCA1, P53 and CALM1.File is zipped due to size limitations.
• training_uptodate_full_pdb_raw_wo3genes : Non-imputed PDB training feature vector without datapoints from BRCA1, P53 and CALM1.
• training_uptodate_full_alphafold_imputed_wo3genes : Imputed AlphaFold training feature vector without datapoints from BRCA1, P53 and CALM1. File is zipped due to size limitations.
• training_uptodate_full_alphafold_raw_wo3genes : Non-imputed AlphaFold training feature vector without datapoints from BRCA1, P53 and CALM1.

Usage

Please unzip required files prior to running the code.

python3 code/main.py -o 1 -i P13637-T-613-M -impute True
python3 code/main.py -o 2 -i 'P13637-T-613-M, Q9Y4W6-N-432-T, Q9Y4W6-N-432-T' impute False
python3 code/main.py -o 2 -i input_files/sample_input.txt

Input Arguments

-o : selection for input structure data. (1: Use PDB-ModBase-SwissModel structures, 2: Use AlphaFold Structures)

-i : input variation. Enter datapoint to predict or input file name in the following form:

• Option 1: Comma-separated list of idenfiers (UniProt ID-wt residue-position-mutated residue (e.g. Q9Y4W6-N-432-T or Q9Y4W6-N-432-T, Q9Y4W6-N-432-T))
• Option 2: Enter tab-separated file path

-impute : imputation of NaN values. Imputation values are median values of corresponding columns. Default True

Sample Run

Contains results of a sample run from sample_input.txt input file. Example input file format is shown below. Columns represent UniProt ID of the protein, wild type amino acid, position of the amino acid change and mutated amino acid, respectively. Input file must be given without a header.

P12694	C	264	W
P13637	T	613	M
P05067	I	716	V
P41180	E	604	K
P08123	G	646	C
P06731	I	80	V
P29474	D	298	E
Q16363	Y	498	H
P23560	V	66	M
Q00889	H	85	D

Files in out_files folder are created by running the script main.py on sample_input.txt file. Depending on the input selection, two type of folders are created.

If PDB-ModBase-SwissModel structures are selected:

python3 code/main.py -o 1 -i input_files/sample_input.txt

• pdb/pdb_structures : Contains downloaded structure files from PDB for input proteins when applicable. If the user has a folder wherein PDB structures are stored, this folder might be used to decrease run time. In this case, please change the extension of files in the folder to '.txt' and rename the folder as pdb/pdb_structures.
• pdb/wissmodel_structures : Contains downloaded model files from SwissModel for input proteins when applicable.
• pdb/modbase_structures : Contains downloaded model files from ModBase for input proteins when applicable. Each file contains all models related to one protein.
• pdb/modbase_structures_individual : Contains downloaded model files from ModBase for input proteins when applicable. Each file contains individual models related to one protein.
• pdb/alignment_files : Contains alignment files of protein sequences.
• pdb/3D_alignment : Contains alignment files of structure files. This step is performed in order to avoid missing residues in the PDB files.
• pdb/sasa_files : Contains calculated solvent accessible surface area values for each data point.
• pdb/feature_vector.txt : Final feature vector file.
• pdb/log.txt : Log file

If AlphaFold structures are selected:

python3 code/main.py -o 2 -i input_files/sample_input.txt impute False

• alphafold/alignment_files : Contains alignment of UniProt sequence files.
• alphafold/3D_alignment : Contains alignment of UniProt sequence files to PDB sequence files.
• alphafold/sasa_files : Contains calculated solvent accessible surface area values for each data point.
• alphafold/featurevector_alphafold.txt : Final feature vector file.
• alphafold/log.txt : Log file

Description of the Dimensions of the Output Representations

In ASCARIS representations, dimensions 1-5 correspond to datapoint identifier, 6-9 correspond to physicochemical property values, 10-12 correspond to domain-related information, 13-14 correspond to information regarding variation's position on the protein (both the sasa value and the categorization), 15-44 correspond to binary correspondence between variations and different types of positional annotations (1 dimension for each annotation type), 45-74 correspond to spatial (Euclidian) distances between variations and different types of positional annotations (1 dimension for each annotation type).

Order of dimension	Column name in the output file	Description	Source
1	prot_uniprotAcc	UniProt accession	UniProtKB/Swiss-Prot (Humsavar), ClinVar, PMD
2	wt_residue	Wild type residue	UniProtKB/Swiss-Prot (Humsavar), ClinVar, PMD
3	mut_residue	Mutated residue	UniProtKB/Swiss-Prot (Humsavar), ClinVar, PMD
4	position	Variation position	UniProtKB/Swiss-Prot (Humsavar), ClinVar, PMD
5	meta_merged	Datapoint identifier (UniProt accession-WT Residue-VariationPosition-Mutated Residue)	-
6	composition	Change in composition values upon the occurrence of variation. Composition is defined as the atomic weight ratio of hetero (non-carbon) elements in end groups or rings to carbons in the side chain.	Aboderin, 1971, Goldsack and Chalifoux, 1973, Grantham, 1974
7	polarity	Change in polarity values upon variation.	Aboderin, 1971, Goldsack and Chalifoux, 1973, Grantham, 1974
8	volume	Change in volume values upon variation.	Aboderin, 1971, Goldsack and Chalifoux, 1973, Grantham, 1974
9	granthamScore	Change in Grantham scores (the combination of composition, polarity and volume) values upon variation.	Grantham, 1974
10	domains_all	InterPro Domain IDs of all domains found in the dataset	InterPro
11	domains_sig	InterPro Domain IDs of significant domains in the dataset. Domains that are not found to be significant in Fisher's Exact Test are labelled as "NULL".	Newly engineered
12	domains_3Ddist	Shortest Euclidian distance between the domain and the variation site.	Newly engineered
13	sasa	Solvent accessible surface area values.	FreeSASA
14	location_3state	Caterozied location of the variation in the structure: surface, core or interface.	FreeSASA, InteractomeInsider
15-44	disulfide_bin, intMet_bin,intramembrane_bin, naturalVariant_bin, dnaBinding_bin, activeSite_bin, nucleotideBinding_bin, lipidation_bin, site_bin, transmembrane_bin, crosslink_bin, mutagenesis_bin, strand_bin, helix_bin, turn_bin, metalBinding_bin, repeat_bin, caBinding_bin, topologicalDomain_bin, bindingSite_bin, region_bin, signalPeptide_bin, modifiedResidue_bin, zincFinger_bin, motif_bin, coiledCoil_bin, peptide_bin, transitPeptide_bin, glycosylation_bin, propeptide_bin	Positional sequence annotations, binary correspondence-based (30 different types of annotations, each one on a different dimension). Categories: 0: annotatation does not exist on the protein, 1: annotation is presented, but the variation is not on the annotated site, 2: variation is on the annotated site.	Newly engineered
45-74	disulfide_dist, intMet_dist, intramembrane_dist, naturalVariant_dist, dnaBinding_dist, activeSite_dist, nucleotideBinding_dist, lipidation_dist, site_dist, transmembrane_dist, crosslink_dist, mutagenesis_dist, strand_dist, helix_dist, turn_dist, metalBinding_dist, repeat_dist, caBinding_dist, topologicalDomain_dist, bindingSite_dist, region_dist, signalPeptide_dist, modifiedResidue_dist, zincFinger_dist, motif_dist, coiledCoil_dist, peptide_dist, transitPeptide_dist, glycosylation_dist, propeptide_dist	Positional sequence annotations, distance-based (the spatial distance between the annotated residue and the mutated residue, in the protein structure, for 30 different types of annotations, each one on a different dimension), in terms of Angstroms.	Newly engineered

Please refer for more information:

Cankara, F., & Dogan, T. (2022). ASCARIS: Positional Feature Annotation and Protein Structure-Based Representation of Single Amino Acid Variations. bioRxiv, 514934v1. Link

License

Copyright (C) 2022 HUBioDataLab

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.