Priyadarshini P. Pai

Chou׳s pseudo amino acid composition improves sequence-based antifreeze protein prediction

Antifreeze proteins (AFP) in living organisms play a key role in their tolerance to extremely cold temperatures and have a wide range of biotechnological applications. But on account of diversity, their identification has been challenging to biologists. Earlier work explored in this area has yet to cover introduction of sequence order information which is known to represent important properties of various proteins and protein systems for prediction purposes. In this study, the effect of Chous pseudo amino acid composition that presents sequence order of proteins was systematically explored using support vector machines for AFP prediction. Our findings suggest that introduction of sequence order information helps identify AFPs with an accuracy of 84.75% on independent test dataset, outperforming approaches such as AFP-Pred and iAFP. The relative performance calculated using Youdens Index (Sensitivity+Specificity-1) was found to be 0.71 for our predictor (AFP-PseAAC), 0.48 for AFP-Pred and 0.05 for iAFP. We hope this novel prediction approach will aid in AFP based research for biotechnological applications.

Sequence-based prediction of protein-protein interaction sites with L1-logreg classifier.

Protein-protein interactions are of central importance for virtually every process in a living cell. Information about the interaction sites in proteins improves our understanding of disease mechanisms and can provide the basis for new therapeutic approaches. Since a multitude of unique residue-residue contacts facilitate the interactions, protein-protein interaction sites prediction has become one of the most important and challenging problems of computational biology. Although much progress in this field has been reported, this problem is yet to be satisfactorily solved. Here, a novel method (LORIS: L1-regularized LOgistic Regression based protein-protein Interaction Sites predictor) is proposed, that identifies interaction residues, using sequence features and is implemented via the L1-logreg classifier. Results show that LORIS is not only quite effective, but also, performs better than existing state-of-the art methods. LORIS, available as standalone package, can be useful for facilitating drug-design and targeted mutation related studies, which require a deeper knowledge of protein interactions sites.

MOWGLI: prediction of protein-MannOse interacting residues With ensemble classifiers usinG evoLutionary Information.

Proteins interact with carbohydrates to perform various cellular interactions. Of the many carbohydrate ligands that proteins bind with, mannose constitute an important class, playing important roles in host defense mechanisms. Accurate identification of mannose-interacting residues (MIR) may provide important clues to decipher the underlying mechanisms of protein–mannose interactions during infections. This study proposes an approach using an ensemble of base classifiers for prediction of MIR using their evolutionary information in the form of position-specific scoring matrix. The base classifiers are random forests trained by different subsets of training data set Dset128 using 10-fold cross-validation. The optimized ensemble of base classifiers, MOWGLI, is then used to predict MIR on protein chains of the test data set Dtestset29 which showed a promising performance with 92.0% accurate prediction. An overall improvement of 26.6% in precision was observed upon comparison with the state-of-art. It is hoped that this approach, yielding enhanced predictions, could be eventually used for applications in drug design and vaccine development.

PINGU: PredIction of eNzyme catalytic residues usinG seqUence information

Identification of catalytic residues can help unveil interesting attributes of enzyme function for various therapeutic and industrial applications. Based on their biochemical roles, the number of catalytic residues and sequence lengths of enzymes vary. This article describes a prediction approach (PINGU) for such a scenario. It uses models trained using physicochemical properties and evolutionary information of 650 non-redundant enzymes (2136 catalytic residues) in a support vector machines architecture. Independent testing on 200 non-redundant enzymes (683 catalytic residues) in predefined prediction settings, i.e., with non-catalytic per catalytic residue ranging from 1 to 30, suggested that the prediction approach was highly sensitive and specific, i.e., 80% or above, over the incremental challenges. To learn more about the discriminatory power of PINGU in real scenarios, where the prediction challenge is variable and susceptible to high false positives, the best model from independent testing was used on 60 diverse enzymes. Results suggested that PINGU was able to identify most catalytic residues and non-catalytic residues properly with 80% or above accuracy, sensitivity and specificity. The effect of false positives on precision was addressed in this study by application of predicted ligand-binding residue information as a post-processing filter. An overall improvement of 20% in F-measure and 0.138 in Correlation Coefficient with 16% enhanced precision could be achieved. On account of its encouraging performance, PINGU is hoped to have eventual applications in boosting enzyme engineering and novel drug discovery.

Ensemble Architecture for Prediction of Enzyme–Ligand Binding Residues using Evolutionary Information

Enzyme interactions with ligands are crucial for various biochemical reactions governing life. Over many years attempts to identify these residues for biotechnological manipulations have been made using experimental and computational techniques. The computational approaches have gathered impetus with the accruing availability of sequence and structure information, broadly classified into template‐based and de novo methods. One of the predominant de novo methods using sequence information involves application of biological properties for supervised machine learning. Here, we propose a support vector machines‐based ensemble for prediction of protein‐ligand interacting residues using one of the most important discriminative contributing properties in the interacting residue neighbourhood, i. e., evolutionary information in the form of position‐specific‐ scoring matrix (PSSM). The study has been performed on a non‐redundant dataset comprising of 9269 interacting and 91773 non‐interacting residues for prediction model generation and further evaluation. Of the various PSSM‐based models explored, the proposed method named ROBBY (pRediction Of Biologically relevant small molecule Binding residues on enzYmes) shows an accuracy of 84.0 %, Matthews Correlation Coefficient of 0.343 and F‐measure of 39.0 % on 78 test enzymes. Further, scope of adding domain knowledge such as pocket information has also been investigated; results showed significant enhancement in method precision. Findings are hoped to boost the reliability of small‐molecule ligand interaction prediction for enzyme applications and drug design.

Applying Knowledge of Enzyme Biochemistry to the Prediction of Functional Sites for Aiding Drug Discovery

Enzymes are biological catalysts that play an important role in determining the patterns of chemical transformations pertaining to life. Many milestones have been achieved in unraveling the mechanisms in which the enzymes orchestrate various cellular processes using experimental and computational approaches. Experimental studies generating nearly all possible mutations of target enzymes have been aided by rapid computational approaches aiming at enzyme functional classification, understanding domain organization, functional site identification. The functional architecture, essentially, is involved in binding or interaction with ligands including substrates, products, cofactors, inhibitors, providing for their function, such as in catalysis, ligand mediated cell signaling, allosteric regulation and post-translational modifications. With the increasing availability of enzyme information and advances in algorithm development, computational approaches have now become more capable of providing precise inputs for enzyme engineering, and in the process also making it more efficient. This has led to interesting findings, especially in aberrant enzyme interactions, such as hostpathogen interactions in infection, neurodegenerative diseases, cancer and diabetes. This review aims to summarize in retrospection - the mined knowledge, vivid perspectives and challenging strides in using available experimentally validated enzyme information for characterization. An analytical outlook is presented on the scope of exploring future directions.

Sequence-based discrimination of protein-RNA interacting residues using a probabilistic approach

Protein interactions with ribonucleic acids (RNA) are well-known to be crucial for a wide range of cellular processes such as transcriptional regulation, protein synthesis or translation, and post-translational modifications. Identification of the RNA-interacting residues can provide insights into these processes and aid in relevant biotechnological manipulations. Owing to their eventual potential in combating diseases and industrial production, several computational attempts have been made over years using sequence- and structure-based information. Recent comparative studies suggest that despite these developments, many problems are faced with respect to the usability, prerequisites, and accessibility of various tools, thereby calling for an alternative approach and perspective supplementation in the prediction scenario. With this motivation, in this paper, we propose the use of a simple-yet-efficient conditional probabilistic approach based on the application of local occurrence of amino acids in the interacting region in a non-numeric sequence feature space, for discriminating between RNA interacting and non-interacting residues. The proposed method has been meticulously tested for robustness using a cross-estimation method showing MCC of 0.341 and F- measure of 66.84%. Upon exploring large scale applications using benchmark datasets available to date, this approach showed an encouraging performance comparable with the state-of-art. The software is available at https://github.com/ABCgrp/DORAEMON.