Brijesh Sriwastava - Academia.edu (original) (raw)

Papers by Brijesh Sriwastava

Lecture Notes in Computer Science, 2013

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2015

Identifying protein-protein interaction sites is crucial for understanding of the principles of b... more Identifying protein-protein interaction sites is crucial for understanding of the principles of biological systems and processes, as well as mutant design. This paper describes a novel method that can predict protein interaction sites in heterocomplexes using information of evolutionary conservation and spatial sequence profile. A predictor was generated to distinguish the interface residues from protein surface region by radial basis neural networks, which is trained by expectation maximization algorithm. Based on a non-redundant data set of heterodimers consisting of 75 protein chains, the efficiency and the effectiveness of our proposed approach can be validated by a better performance such as the accuracy of 0.60, the sensitivity of 58.3% and the specificity of 59.9%.

Lecture Notes in Computer Science, 2013

Journal of Molecular Modeling, 2013

The physico-chemical properties of interaction interfaces have a crucial role in characterization... more The physico-chemical properties of interaction interfaces have a crucial role in characterization of protein-protein interactions (PPI). In silico prediction of participating amino acids helps to identify interface residues for further experimental verification using mutational analysis, or inhibition studies by screening library of ligands against given protein. Given the unbound structure of a protein and the fact that it forms a complex with another known protein, the objective of this work is to identify the residues that are involved in the interaction. We attempt to predict interaction sites in protein complexes using local composition of amino acids together with their physico-chemical characteristics. The local sequence segments (LSS) are dissected from the protein sequences using a sliding window of 21 amino acids. The list of LSSs is passed to the support vector machine (SVM) predictor, which identifies interacting residue pairs considering their inter-atom distances. We have analyzed three different model organisms of Escherichia coli, Saccharomyces Cerevisiae and Homo sapiens, where the numbers of considered hetero-complexes are equal to 40, 123 and 33 respectively. Moreover, the unified multi-organism PPI meta-predictor is also developed under the current work by combining the training databases of above organisms. The PPIcons interface residues prediction method is measured by the area under ROC curve (AUC) equal to 0.82, 0.75, 0.72 and 0.76 for the aforementioned organisms and the meta-predictor respectively.

Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012, 2012

We propose computational method for identification of protein-protein interaction sites using seq... more We propose computational method for identification of protein-protein interaction sites using sequence and structure information. The method is trained on database of interacting proteins (DIP) for E. coli. Proteins that are known to interact are first collected from experimental results. All interacting partners are mapped onto corresponding three-dimensional structures. The training dataset for support vector machine algorithm is trained using both sequence composition and structural conformations of selected structures, if ...

Lecture Notes in Computer Science, 2013

IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2015

Identifying protein-protein interaction sites is crucial for understanding of the principles of b... more Identifying protein-protein interaction sites is crucial for understanding of the principles of biological systems and processes, as well as mutant design. This paper describes a novel method that can predict protein interaction sites in heterocomplexes using information of evolutionary conservation and spatial sequence profile. A predictor was generated to distinguish the interface residues from protein surface region by radial basis neural networks, which is trained by expectation maximization algorithm. Based on a non-redundant data set of heterodimers consisting of 75 protein chains, the efficiency and the effectiveness of our proposed approach can be validated by a better performance such as the accuracy of 0.60, the sensitivity of 58.3% and the specificity of 59.9%.

Lecture Notes in Computer Science, 2013

Journal of Molecular Modeling, 2013

The physico-chemical properties of interaction interfaces have a crucial role in characterization... more The physico-chemical properties of interaction interfaces have a crucial role in characterization of protein-protein interactions (PPI). In silico prediction of participating amino acids helps to identify interface residues for further experimental verification using mutational analysis, or inhibition studies by screening library of ligands against given protein. Given the unbound structure of a protein and the fact that it forms a complex with another known protein, the objective of this work is to identify the residues that are involved in the interaction. We attempt to predict interaction sites in protein complexes using local composition of amino acids together with their physico-chemical characteristics. The local sequence segments (LSS) are dissected from the protein sequences using a sliding window of 21 amino acids. The list of LSSs is passed to the support vector machine (SVM) predictor, which identifies interacting residue pairs considering their inter-atom distances. We have analyzed three different model organisms of Escherichia coli, Saccharomyces Cerevisiae and Homo sapiens, where the numbers of considered hetero-complexes are equal to 40, 123 and 33 respectively. Moreover, the unified multi-organism PPI meta-predictor is also developed under the current work by combining the training databases of above organisms. The PPIcons interface residues prediction method is measured by the area under ROC curve (AUC) equal to 0.82, 0.75, 0.72 and 0.76 for the aforementioned organisms and the meta-predictor respectively.

Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012, 2012

We propose computational method for identification of protein-protein interaction sites using seq... more We propose computational method for identification of protein-protein interaction sites using sequence and structure information. The method is trained on database of interacting proteins (DIP) for E. coli. Proteins that are known to interact are first collected from experimental results. All interacting partners are mapped onto corresponding three-dimensional structures. The training dataset for support vector machine algorithm is trained using both sequence composition and structural conformations of selected structures, if ...