Coupling dynamics and evolutionary information with structure to identify protein regulatory and functional binding sites

Abstract

Binding sites in proteins can be either specifically functional binding sites (active sites) that bind specific substrates with high affinity or regulatory binding sites (allosteric sites), that modulate the activity of functional binding sites through effector molecules. Owing to their significance in determining protein function, the identification of protein functional and regulatory binding sites is widely acknowledged as an important biological problem. In this work, we present a novel binding site prediction method, Active and Regulatory site Prediction (AR‐Pred), which supplements protein geometry, evolutionary, and physicochemical features with information about protein dynamics to predict putative active and allosteric site residues. As the intrinsic dynamics of globular proteins plays an essential role in controlling binding events, we find it to be an important feature for the identification of protein binding sites. We train and validate our predictive models on multiple balanced training and validation sets with random forest machine learning and obtain an ensemble of discrete models for each prediction type. Our models for active site prediction yield a median area under the curve (AUC) of 91% and Matthews correlation coefficient (MCC) of 0.68, whereas the less well‐defined allosteric sites are predicted at a lower level with a median AUC of 80% and MCC of 0.48. When tested on an independent set of proteins, our models for active site prediction show comparable performance to two existing methods and gains compared to two others, while the allosteric site models show gains when tested against three existing prediction methods. AR‐Pred is available as a free downloadable package at https://github.com/sambitmishra0628/AR-PRED_source.

Dissecting Mutational Allosteric Effects in Alkaline Phosphatases Associated with Different Hypophosphatasia Phenotypes: An Integrative Computational Investigation

“…Third, for the classification of mild mutations and differentiation from the control group, co-evolutionary conservation has been shown to be the most important predictor, thereby suggesting that mild pathogenicity may be related to amino acid changes with small evolutionary substitution probability. However, to reach the elusive goal of establishing the precise relationship between ALPL mutation genotypes and HPP phenotypes and a more reliable prediction model or score, like as protein regulatory and functional binding sites prediction done, [80] more clinical data on mutations [81] and data on enzyme activity [13] are needed.…”

Rational design of allosteric modulators: Challenges and successes

“…143 A plethora of computational methodologies and tools have recently utilized the wealth of available structural data and the predictive power of machine learning, often in combination with the aforementioned techniques, for predicting putative allosteric sites, allosteric signaling pathways, allosteric hotspots, and cryptic sites. [144][145][146][147][148][149][150][151][152][153][154][155][156] The allosteric database ASD v3.0 28 and the allosteric benchmark ASBench 157 have been extensively used for the training and testing of many developed tools for allosteric pocket detection. Tools such as Allosite 145 or Cryptosite 148 utilize pocket detection methods in conjunction with ASD to train machine learning algorithms e.g.…”

Computational Methods for Predicting Functions at the mRNA Isoform Level

“…Previously, machine learning methods have been used to address a multitude of problems, some of which include, drug target discovery, gene function prediction, protein–protein interaction (PPI) prediction, protein structure and functional site prediction, and subcellular localization protein prediction [ 21 , 22 , 23 , 24 , 25 , 26 , 27 ]. More recently, several machine learning and recommendation system methods have also been developed to predict the biological functions of mRNA isoforms [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ].…”