Predicting Protein Interaction Sites Based on a New Integrated Radial Basis Functional Neural Network

Shen, Xiao Li; Chen, Yue Hui

doi:10.4028/www.scientific.net/amr.183-185.387

Cited by 1 publication

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The challenge here is to find a suitable compromise between the biological relevance of the results and a comprehensive coverage of the analyzed networks. Zhang et al [34] have used the graph kernel to compute dependency graphs representing the sentence structure for PPI extraction task, which can efficiently make use of full graph structural information, and particularly capture the contiguous topological and label information, ignored before. PPI networks can be grouped in two categories, one allowing a protein to participate in different clusters and the other generating only non overlapping clusters.…”

Section: Introductionmentioning

confidence: 99%

PPIcons: identification of protein-protein interaction sites in selected organisms

Sriwastava¹,

Basu

Maulik

et al. 2013

J Mol Model

View full text Add to dashboard Cite

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.Electronic supplementary materialThe online version of this article (doi:10.1007/s00894-013-1886-9) contains supplementary material, which is available to authorized users.

show abstract

Section: Introductionmentioning

confidence: 99%