Prediction of protein-protein binding site by using core interface residue and support vector machine

Li, Nan; Sun, Zhonghua; Jiang, Fan

doi:10.1186/1471-2105-9-553

Cited by 62 publications

(49 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, native interfaces are generally found to contain residues of high evolutionary conservation [12,35]. Docking methods have exploited knowledge of interaction interfaces in guiding the search towards native-like configurations [18,26,33,34,36,62]. Recent work by us has extended geometrybased methods to directly sample rigid-body transformations that match geometrically-complementary and evolutionary-conserved surface regions [19,20,22].…”

Section: Introductionmentioning

confidence: 99%

Informatics-driven Protein-protein Docking

Hashmi

Shehu

2013

Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

View full text Add to dashboard Cite

Predicting the structure of protein assemblies is fundamental to our ability to understand the molecular basis of biological function. The basic protein-protein docking problem involving two protein units docking onto each-other remains challenging. One direction of research is exploring probabilistic search algorithms with high exploration capability, but these algorithms are limited by errors in current energy functions. A complementary direction is choosing to understand what constitutes true interaction interfaces. In this paper we present a method that combines the two directions and advances research into computationally-efficient yet highaccuracy docking. We present an informatics-driven probabilistic search algorithm for rigid protein-protein docking. The algorithm builds upon the powerful basin hopping framework, which we have shown in many settings in molecular modeling to have high exploration capability. Rather than operate de novo, the algorithm employs information on what constitutes a native interaction interface. A predictive machine learning model is built and trained a priori on known dimeric structures to learn features correlated with a true interface. The model is fast, accurate, and replaces expensive physics-based energy functions in scoring sampled configurations. A sophisticated energy function is used to refine only high-scoring configurations. The result is an ensemble of high-quality decoy configurations that we show here to approach the known native dimeric structure better than other state-of-the-art docking methods. We believe the proposed method advances computationally-efficient highaccuracy docking.

show abstract

Section: Introductionmentioning

confidence: 99%

Informatics-driven Protein-protein Docking

Hashmi

Shehu

2013

Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics

View full text Add to dashboard Cite

show abstract

“…22,23,24,25,26,27,28 Some existing approaches are based on analyzing differences between interface residues and noninterface residues, through machine learning methods or statistical methods. 29,30,31,32,33,34 In addition, several structural algorithms have also been used to identify binding sites, through analyzing surface structures. 35,36,37 Meta-servers have also been constructed to combine strengths of some existing approaches.…”

Section: Introductionmentioning

confidence: 99%

Identification of Protein–Protein Interactions by Detecting Correlated Mutation at the Interface

Guo

Zhao

Tang

2015

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Protein-protein interactions play key roles in a multitude of biological processes, such as de novo drug design, immune response, and enzymatic activity. It is of great interest to understand how proteins in a complex interact with each other. Here, we present a novel method for identifying protein-protein interactions, based on typical co-evolutionary information. Correlated mutation analysis can be used to predict interface residues. In this paper, we propose a non-redundant database to detect correlated mutation at the interface. First, we construct structure alignments for one input protein, based on all aligned proteins in the database. Evolutionary distance matrices, one for each input protein, can be calculated through geometric similarity and evolutionary information. Then, we use evolutionary distance matrices to estimate correlation coefficient between each pair of fragments from two input proteins. Finally, we extract interacting residues with high values of correlation coefficient, which can be grouped as interacting patches. Experiments illustrate that our method achieves better results than some existing co-evolution-based methods. Applied to SK/RR interaction between sensor kinase and response regulator proteins, our method has accuracy and coverage values of 53% and 45%, which improves upon accuracy and coverage values of 50% and 30% for DCA method. We evaluate interface prediction on four protein families, and our method has overall accuracy and coverage values of 34% and 30%, which improves upon overall accuracy and coverage values of 27% and 21% for PIFPAM. Our method has overall accuracy and coverage values of 59% and 63% on Benchmark v4.0, and 50% and 49% on CAPRI targets. Comparing to existing methods, our method improves overall accuracy value by at least 2%.

show abstract

“…Protein-protein interface prediction algorithms can be classified into three categories: (i) sequence-based methods, which use only the primary amino acid sequence of the query protein as input [3,22-28]; (ii) structure-based methods, which make use of information derived from the structure of the query protein [5,18,29-31]; and (iii) methods that use both sequence and structure derived information in making predictions [32,33]. …”

Section: Introductionmentioning

confidence: 99%

HomPPI: a class of sequence homology based protein-protein interface prediction methods

2011

View full text Add to dashboard Cite

BackgroundAlthough homology-based methods are among the most widely used methods for predicting the structure and function of proteins, the question as to whether interface sequence conservation can be effectively exploited in predicting protein-protein interfaces has been a subject of debate.ResultsWe studied more than 300,000 pair-wise alignments of protein sequences from structurally characterized protein complexes, including both obligate and transient complexes. We identified sequence similarity criteria required for accurate homology-based inference of interface residues in a query protein sequence.Based on these analyses, we developed HomPPI, a class of sequence homology-based methods for predicting protein-protein interface residues. We present two variants of HomPPI: (i) NPS-HomPPI (Non partner-specific HomPPI), which can be used to predict interface residues of a query protein in the absence of knowledge of the interaction partner; and (ii) PS-HomPPI (Partner-specific HomPPI), which can be used to predict the interface residues of a query protein with a specific target protein.Our experiments on a benchmark dataset of obligate homodimeric complexes show that NPS-HomPPI can reliably predict protein-protein interface residues in a given protein, with an average correlation coefficient (CC) of 0.76, sensitivity of 0.83, and specificity of 0.78, when sequence homologs of the query protein can be reliably identified. NPS-HomPPI also reliably predicts the interface residues of intrinsically disordered proteins. Our experiments suggest that NPS-HomPPI is competitive with several state-of-the-art interface prediction servers including those that exploit the structure of the query proteins. The partner-specific classifier, PS-HomPPI can, on a large dataset of transient complexes, predict the interface residues of a query protein with a specific target, with a CC of 0.65, sensitivity of 0.69, and specificity of 0.70, when homologs of both the query and the target can be reliably identified. The HomPPI web server is available at http://homppi.cs.iastate.edu/.ConclusionsSequence homology-based methods offer a class of computationally efficient and reliable approaches for predicting the protein-protein interface residues that participate in either obligate or transient interactions. For query proteins involved in transient interactions, the reliability of interface residue prediction can be improved by exploiting knowledge of putative interaction partners.

show abstract

Prediction of protein-protein binding site by using core interface residue and support vector machine

Cited by 62 publications

References 49 publications

Informatics-driven Protein-protein Docking

Informatics-driven Protein-protein Docking

Identification of Protein–Protein Interactions by Detecting Correlated Mutation at the Interface

HomPPI: a class of sequence homology based protein-protein interface prediction methods

Contact Info

Product

Resources

About