Progress and challenges in predicting protein-protein interaction sites

Ezkurdia, Iakes; Bartoli, Lisa; Fariselli, Piero; Casadio, Rita; Valencia, Alfonso; Tress, Michael L.

doi:10.1093/bib/bbp021

Cited by 154 publications

(156 citation statements)

References 112 publications

Supporting

Mentioning

153

Contrasting

Order By: Relevance

“…In the past two decades, two major classes of computational methods for protein−protein interface prediction have emerged (7): (i) data-driven methods (8)(9)(10)(11)(12)(13) and (ii) molecular docking methods (14). Data-driven methods include homology modeling (8,9), machine learning (10), and coevolution-based statistical models (11)(12)(13).…”

mentioning

confidence: 99%

Elucidating the druggable interface of protein−protein interactions using fragment docking and coevolutionary analysis

Bai

Morcos

Cheng

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

Protein−protein interactions play a central role in cellular function. Improving the understanding of complex formation has many practical applications, including the rational design of new therapeutic agents and the mechanisms governing signal transduction networks. The generally large, flat, and relatively featureless binding sites of protein complexes pose many challenges for drug design. Fragment docking and direct coupling analysis are used in an integrated computational method to estimate druggable protein−protein interfaces. (i) This method explores the binding of fragment-sized molecular probes on the protein surface using a molecular dockingbased screen. (ii) The energetically favorable binding sites of the probes, called hot spots, are spatially clustered to map out candidate binding sites on the protein surface. (iii) A coevolution-based interface interaction score is used to discriminate between different candidate binding sites, yielding potential interfacial targets for therapeutic drug design. This approach is validated for important, well-studied disease-related proteins with known pharmaceutical targets, and also identifies targets that have yet to be studied. Moreover, therapeutic agents are proposed by chemically connecting the fragments that are strongly bound to the hot spots.protein−protein interface | druggable surface | hot spots | direct coupling analysis | drug design P rotein−protein interactions (PPIs) mediate a wide range of important cellular functions, including signal transduction and enzymatic processes. The regulation of these interactions has drawn intense focus in physiology and pathology, where PPI interfaces have emerged as a new class of molecular targets for pharmacological intervention (1, 2). The design of drugs to target PPIs, however, faces numerous challenges (3). Although over 41,000 unique human protein interactions have been experimentally discovered and reported on Human Protein Reference Database (4), only ∼2,500 nonredundant multiprotein complexes have experimentally determined structures available in the Protein Data Bank (5). Therefore, the current experimental methods cannot accommodate the high demand for structural details of these interactions. Even when the structural complexes are known, targeting PPIs with small molecules poses a significant challenge. Many protein interfaces have large, featureless surfaces that lack obvious small-molecule binding pockets, making it difficult to design drugs with the ability to modulate (inhibit or stabilize) the PPIs with the necessary selectivity and potency (6).In the past two decades, two major classes of computational methods for protein−protein interface prediction have emerged (7): (i) data-driven methods (8-13) and (ii) molecular docking methods (14). Data-driven methods include homology modeling (8, 9), machine learning (10), and coevolution-based statistical models (11-13). These approaches make predictions using homologous data as templates or by extracting interaction patterns from data using statistical mo...

show abstract

mentioning

confidence: 99%

Elucidating the druggable interface of protein−protein interactions using fragment docking and coevolutionary analysis

Bai

Morcos

Cheng

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

show abstract

“…Aside from the sequence-level data, structural information is also a valuable resource to predict PPIs, especially a protein 3D structure. (Aloy & Russell, 2003;Ezkurdia et al, 2009;Hosur et al, 2011;Shoemaker et al, 2010;Singh et al, 2010;Zhang et al, 2010). A huge amount of genome-wide gene expression profiles are another useful data to predict PPIs and they are investigated to define gene co-expression patterns of any pairs and consider higher correlation degree as higher probability of PPIs (Grigoriev, 2001;Lukk et al, 2010;Stuart et al, 2003).…”

Section: Computational Prediction Methods For Ppismentioning

confidence: 99%

Protein Interactome and Its Application to Protein Function Prediction

Jung¹,

Jeong²,

Lee³

2012

Protein-Protein Interactions - Computational and Experimental Tools

View full text Add to dashboard Cite

“…Surface residues were taken to be those with an ASA more than 20% of the surface area. The dataset chosen is summarised in Table 4. A set of parameters indicated by previous work [16,26] was then derived for each surface residue in each chain. The parameters represent orthogonal characteristics of nodes within the network as shown in Table 3.…”

Section: Aggregating Parameter Valuesmentioning

confidence: 99%

Local Pre-processing for Node Classification in Networks

Foley

Azwari

Dufton

et al. 2013

Information Technology in Bio- And Medical Informatics

View full text Add to dashboard Cite

Abstract. Network modelling provides an increasingly popular conceptualisation in a wide range of domains, including the analysis of protein structure. Typical approaches to analysis model parameter values at nodes within the network. The spherical locality around a node provides a microenvironment that can be used to characterise an area of a network rather than a particular point within it. Microenvironments that centre on the nodes in a protein chain can be used to quantify parameters that are related to protein functionality. They also permit particular patterns of such parameters in node-centred microenvironments to be used to locate sites of particular interest. This paper evaluates an approach to index generation that seeks to rapidly construct microenvironment data. The results show that index generation performs best when the radius of microenvironments matches the granularity of the index. Results are presented to show that such microenvironments improve the utility of protein chain parameters in classifying the structural characteristics of nodes using both support vector machines and neural networks.

show abstract

Progress and challenges in predicting protein-protein interaction sites

Cited by 154 publications

References 112 publications

Elucidating the druggable interface of protein−protein interactions using fragment docking and coevolutionary analysis

Elucidating the druggable interface of protein−protein interactions using fragment docking and coevolutionary analysis

Protein Interactome and Its Application to Protein Function Prediction

Local Pre-processing for Node Classification in Networks

Contact Info

Product

Resources

About