Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure

Abstract

Protein structure and protein function should be related, yet the nature of this relationship remains unsolved. Mapping the critical residues for protein function with protein structure features represents an opportunity to explore this relationship, yet two important limitations have precluded a proper analysis of the structure-function relationship of proteins: (i) the lack of a formal definition of what critical residues are and (ii) the lack of a systematic evaluation of methods and protein structure features. To address this problem, here we introduce an index to quantify the protein-function criticality of a residue based on experimental data and a strategy aimed to optimize both, descriptors of protein structure (physicochemical and centrality descriptors) and machine learning algorithms, to minimize the error in the classification of critical residues. We observed that both physicochemical and centrality descriptors of residues effectively relate protein structure and protein function, and that physicochemical descriptors better describe critical residues. We also show that critical residues are better classified when residue criticality is considered as a binary attribute (i.e., residues are considered critical or not critical). Using this binary annotation for critical residues 8 models rendered accurate and non-overlapping classification of critical residues, confirming the multi-factorial character of the structure-function relationship of proteins.

ProtDCal‐Suite: A web server for the numerical codification and functional analysis of proteins

“…The capability of ProtDCal to generate useful features was assessed in several studies developing novel machine learning-based tools. [9][10][11][12][13][14][15][16][17][18][19] Here, we present web interfaces for predicting the interaction likelihood of protein-protein and protein-peptide pairs (PPI-Detect), for identifying enzymes from amino acid sequences or 3D structures (Enzyme Identifier), and for predicting N-glycosylation sites in peptides and proteins (Pred-NGlyco).…”

“…ProtDCal's features have been used to develop predictors for protein analysis. 9,[14][15][16][17]19,20 In ProtDCal-Suite we provide, for the first time, web access to some of these tools.…”

“…In this context, ProtDCal is a software package that transforms protein sequences or 3D‐structures into general‐purpose numerical descriptors, accounting for both global and local information . Due to its complementary performance with respect to other well‐established tools in the field like PROFEAT and PseAcc (later extended to Pse‐in‐one), ProtDCal has been used in a number of studies . Notable among them are the modeling of posttranslational modifications, the prediction of protein enzymatic function, the prediction of antimicrobial activity in peptides, the determination of residues critical for protein function, and the prediction of stability changes upon mutations .…”

“…5 Due to its complementary performance with respect to other well-established tools in the field like PROF-EAT 6 and PseAcc 7 (later extended to Pse-in-one 8 ), ProtDCal has been used in a number of studies. [9][10][11][12][13][14][15][16][17][18][19] Notable among them are the modeling of posttranslational modifications, 14 the prediction of protein enzymatic function, 15 the prediction of antimicrobial activity in peptides, 16 the determination of residues critical for protein function, 17 and the prediction of stability changes upon mutations. 18 Very recently, ProtDCal was enhanced with a procedure for encoding protein pairs, which allows targeting the protein-protein interaction identification problem.…”

PPI‐Detect: A support vector machine model for sequence‐based prediction of protein–protein interactions

“…In this context, Ruiz‐Blanco et al also presented numerical descriptors for individual proteins (ProtDCal) . ProtDCal found applications in several studies involving posttranslational modifications, antibacterial peptides, and protein function . Notably, these descriptors showed low correlation with those in PROFEAT, highlighting the need for further codification approaches …”