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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Improving Predictions of Protein-Protein Interfaces by Combining Amino Acid-Specific Classifiers Based on Structural and Physicochemical Descriptors with Their Weighted Neighbor Averages

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Author(s):
de Moraes, Fabio R. [1, 2] ; Neshich, Izabella A. P. [1, 2] ; Mazoni, Ivan [1, 2] ; Yano, Inacio H. [2] ; Pereira, Jose G. C. [1, 2] ; Salim, Jose A. [3] ; Jardine, Jose G. [2] ; Neshich, Goran [2]
Total Authors: 8
Affiliation:
[1] Univ Estadual Campinas, Inst Biol, Sao Paulo - Brazil
[2] Natl Ctr Agr Informat, Brazilian Agr Res Corp EMBRAPA, Sao Paulo - Brazil
[3] Univ Estadual Campinas, Sch Elect & Comp Engn, Sao Paulo - Brazil
Total Affiliations: 3
Document type: Journal article
Source: PLoS One; v. 9, n. 1 JAN 28 2014.
Web of Science Citations: 4
Abstract

Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR). We formulated a linear discriminative analysis (LDA) classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/) are suitable for such a task. Receiver operating characteristic (ROC) analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication) or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study is now integrated into the BlueStar STING suite of programs. Consequently, the prediction of protein-protein interfaces for all proteins available in the PDB is possible through STING\_interfaces module, accessible at the following website: (http://www.cbi.cnptia.embrapa.br/SMS/predictions/index.html). (AU)

FAPESP's process: 09/03108-1 - Study of macromolecular communication in homo and hetero complexes through their interfaces
Grantee:Fábio Rogério de Moraes
Support type: Scholarships in Brazil - Doctorate (Direct)
FAPESP's process: 09/16376-4 - Prediction of catalytic site residues (CSR) for enzymes by applying pattern recognition on protein structural descriptors found in STING database
Grantee:Goran Nesic
Support type: Regular Research Grants