Virtual screening for compounds that mimic protein–protein interface epitopes

Geppert, Tim; Reisen, Felix; Pillong, Max; Hähnke, Volker; Tanrıkulu, Yusuf; Koch, Christian P.; Perna, Anna Maria; Perez, Tatiana Batista; Schneider, Petra; Schneider, Gisbert

doi:10.1002/jcc.22894

Cited by 14 publications

(11 citation statements)

References 36 publications

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“…The identification of interacting residues on protein surfaces holds potential for use in diverse fields across biology and medicine. One of the most related problems is the elucidation of the residues inside PPISs that account for the major change in free binding energy upon complex formation, known as hotspots (HSs) [ 271 , 272 ]. ML-based PPIS predictors have already been successfully used for HS prediction by simply altering the training set [ 161 ].…”

Section: Applicationsmentioning

confidence: 99%

Algorithmic approaches to protein-protein interaction site prediction

Aumentado-Armstrong

Istrate

Murgita

2015

Algorithms Mol Biol

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Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.

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Section: Applicationsmentioning

confidence: 99%

Algorithmic approaches to protein-protein interaction site prediction

Aumentado-Armstrong

Istrate

Murgita

2015

Algorithms Mol Biol

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“…The cost-effective and highthroughput feature of virtual screening has made it appealing for initial hit identification of druggable targets. 140 In this method, PPI hot spots are computationally predicted and then used to generate a pharmacophore model, which subsequently served as a query for virtual screening. Recent examples of PPI inhibitors identified by virtual screening are summarized in Table 4 and Fig.…”

Section: Virtual Screeningmentioning

confidence: 99%

“…However, the application of virtual screening to target PPIs is more challenging compared with the enzymatic cavities. This approach has been successfully used to identify interface-derived peptide mimetics of trypsin-trypsin interaction 140 (Table 4) and the small-molecule inhibitor of the IFN-a/IFNAR interface. 11.…”

Section: Virtual Screeningmentioning

confidence: 99%

State-of-the-art strategies for targeting protein–protein interactions by small-molecule inhibitors

et al. 2015

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Targeting protein-protein interactions (PPIs) has emerged as a viable approach in modern drug discovery. However, the identification of small molecules enabling us to effectively interrupt their interactions presents significant challenges. In the recent past, significant advances have been made in the development of new biological and chemical strategies to facilitate the discovery process of small-molecule PPI inhibitors. This review aims to highlight the state-of-the-art technologies and the achievements made recently in this field. The "hot spots" of PPIs have been proved to be critical for small molecules to bind. Three strategies including screening, designing, and synthetic approaches have been explored for discovering PPI inhibitors by targeting the "hot spots". Although the classic high throughput screening approach can be used, fragment screening, fragment-based drug design and newly improved virtual screening are demonstrated to be more effective in the discovery of PPI inhibitors. In addition to screening approaches, design strategies including anchor-based and small molecule mimetics of secondary structures involved in PPIs have become powerful tools as well. Finally, constructing new chemically spaced libraries with high diversity and complexity is becoming an important area of interest for PPI inhibitors. The successful cases from the recent five year studies are used to illustrate how these approaches are implemented to uncover and optimize small molecule PPI inhibitors and notably some of them have become promising therapeutics.

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“…Currently, the main approach for designing inhibitors and modulators of protein-protein interactions aims at designing peptidomimetics that compete with the natural partner protein for the same interface [4]. As some of these binding interfaces can also bind small molecule ligands, modulating the activities of protein-protein complexes by competitive or allosteric small molecule protein-protein inhibitors (SMPPIs) has become an area of very active interest in current pharmaceutical research [5], [6]. Although rational design of SMPPIs still presents a considerable challenge [5], promising progress has been made in several cases towards finding small molecules that efficiently inhibit protein-protein interactions.…”

Section: Introductionmentioning

confidence: 99%

Predicting where Small Molecules Bind at Protein-Protein Interfaces

et al. 2013

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Small molecules that bind at protein-protein interfaces may either block or stabilize protein-protein interactions in cells. Thus, some of these binding interfaces may turn into prospective targets for drug design. Here, we collected 175 pairs of protein-protein (PP) complexes and protein-ligand (PL) complexes with known three-dimensional structures for which (1) one protein from the PP complex shares at least 40% sequence identity with the protein from the PL complex, and (2) the interface regions of these proteins overlap at least partially with each other. We found that those residues of the interfaces that may bind the other protein as well as the small molecule are evolutionary more conserved on average, have a higher tendency of being located in pockets and expose a smaller fraction of their surface area to the solvent than the remaining protein-protein interface region. Based on these findings we derived a statistical classifier that predicts patches at binding interfaces that have a higher tendency to bind small molecules. We applied this new prediction method to more than 10 000 interfaces from the protein data bank. For several complexes related to apoptosis the predicted binding patches were in direct contact to co-crystallized small molecules.

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Virtual screening for compounds that mimic protein–protein interface epitopes

Cited by 14 publications

References 36 publications

Algorithmic approaches to protein-protein interaction site prediction

Algorithmic approaches to protein-protein interaction site prediction

State-of-the-art strategies for targeting protein–protein interactions by small-molecule inhibitors

Predicting where Small Molecules Bind at Protein-Protein Interfaces

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