A Machine Learning Approach for Hot-Spot Detection at Protein-Protein Interfaces

Abstract

Understanding protein-protein interactions is a key challenge in biochemistry. In this work, we describe a more accurate methodology to predict Hot-Spots (HS) in protein-protein interfaces from their native complex structure compared to previous published Machine Learning (ML) techniques. Our model is trained on a large number of complexes and on a significantly larger number of different structural- and evolutionary sequence-based features. In particular, we added interface size, type of interaction between residues at the interface of the complex, number of different types of residues at the interface and the Position-Specific Scoring Matrix (PSSM), for a total of 79 features. We used twenty-seven algorithms from a simple linear-based function to support-vector machine models with different cost functions. The best model was achieved by the use of the conditional inference random forest (c-forest) algorithm with a dataset pre-processed by the normalization of features and with up-sampling of the minor class. The method has an overall accuracy of 0.80, an F1-score of 0.73, a sensitivity of 0.76 and a specificity of 0.82 for the independent test set.

Prediction of quaternary structure by analysis of hot spot residues in protein‐protein interfaces: the case of anthranilate phosphoribosyltransferases

“…By definition, a residue in a protein‐protein interface is termed hot spot if its mutation to alanine changes the free binding energy of the interaction substantially (ΔΔ G binding ≥ 2.0 kcal/mol). Since experimental alanine scanning is an expensive and time‐consuming procedure that is not applicable on a large scale, highly efficient in silico methods, often based on machine learning approaches, have been developed in order to predict hot spots from the native complex structure or even from the sequence of one of the binding partners . Feature‐based prediction approaches use a variety of different chemical and physical characteristics of interface residues, such as solvent accessible surface area, protrusion index, residue conservation, or B factors.…”

“…Since experimental alanine scanning is an expensive and time-consuming procedure that is not applicable on a large scale, highly efficient in silico methods, often based on machine learning approaches, have been developed in order to predict hot spots from the native complex structure or even from the sequence of one of the binding partners. [23][24][25][26] Featurebased prediction approaches use a variety of different chemical and physical characteristics of interface residues, such as solvent accessible surface area, protrusion index, residue conservation, or B factors. However, predictive performances of different methods vary with data sets, and experimental validations of those computational predictions are scarce.…”

Machine Learning as an Effective Method for Identifying True Single Nucleotide Polymorphisms in Polyploid Plants

“…Unsupervised algorithms cluster objects depending on their features without providing predefined classes (Tarca et al, 2007). Both types of algorithms are used widely in different biological fields: coding region recognition, signal peptide prediction, biomarker identification, disease gene recognition, metabolic network detection, and protein–protein interaction (Bostan et al, 2009; Lingner et al, 2011; Swan et al, 2013; Jowkar and Mansoori 2016; Roche‐Lima 2016; Melo et al, 2016). Neural networks were recognized for usefulness in biological applications when it was used for recognizing the transcriptional start sites in Escherichia coli (Tarca et al, 2007).…”

Data Analysis in Chemistry and Bio-Medical Sciences

“…The issue focuses on the development and application of different theoretical algorithms combining chemoinformatics, computational chemistry, bioinformatics, and data analysis methods. In the issue, we present a total of 18 papers with full versions of the communications presented at the conference as well as papers of other authors worldwide (direct submissions) [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]. …”

“…In another work, Melo et al [2], from the University of Porto, Portugal, stated that understanding protein-protein interactions is a key challenge in biochemistry. In this work, the authors describe a more accurate methodology to predict hot spots in protein-protein interfaces from their native complex structure compared to previous published machine learning (ML) techniques.…”