Recent Advances in Computational Models for the Study of Protein–Peptide Interactions

Kilburg, Denise; Gallicchio, Emilio

doi:10.1016/bs.apcsb.2016.06.002

Cited by 26 publications

(23 citation statements)

References 102 publications

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“…Since then, a range of other original and very successful tools for modeling peptide-protein complexes have been proposed, [18][19][20][21][22][23][24][25][26] and a book about modeling these interactions is about to appear (Modeling Peptide-Protein Interactions, to appear in the Methods in Molecular Biology Series, Ed. Springer).…”

Section: Introductionmentioning

confidence: 99%

FlexPepDock lessons from CAPRI peptide–protein rounds and suggested new criteria for assessment of model quality and utility

et al. 2017

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CAPRI rounds 28 and 29 included, for the first time, peptide-receptor targets of three different systems, reflecting increased appreciation of the importance of peptide-protein interactions. The CAPRI rounds allowed us to objectively assess the performance of Rosetta FlexPepDock, one of the first protocols to explicitly include peptide flexibility in docking, accounting for peptide conformational changes upon binding. We discuss here successes and challenges in modeling these targets: we obtain top-performing, high-resolution models of the peptide motif for cases with known binding sites but there is a need for better modeling of flanking regions, as well as better selection criteria, in particular for unknown binding sites. These rounds have also provided us the opportunity to reassess the success criteria, to better reflect the quality of a peptide-protein complex model. Using all models submitted to CAPRI, we analyze the correlation between current classification criteria and the ability to retrieve critical interface features, such as hydrogen bonds and hotspots. We find that loosening the backbone (and ligand) RMSD threshold, together with a restriction on the side chain RMSD measure, allows us to improve the selection of high-accuracy models. We also suggest a new measure to assess interface hydrogen bond recovery, which is not assessed by the current CAPRI criteria. Finally, we find that surprisingly much can be learned from rather inaccurate models about binding hotspots, suggesting that the current status of peptide-protein docking methods, as reflected by the submitted CAPRI models, can already have a significant impact on our understanding of protein interactions. Proteins 2017; 85:445-462. © 2016 Wiley Periodicals, Inc.

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Section: Introductionmentioning

confidence: 99%

FlexPepDock lessons from CAPRI peptide–protein rounds and suggested new criteria for assessment of model quality and utility

et al. 2017

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“…We used the default settings of BLASTP2.2.28+ algorithm [ 43 ] with E-value ≤ 10 to screen out peptides that show sequence similarity with a protein chain, as entire or a part of a SIP may mimic a specific binding motif on the protein, or resemble a loop from a large structured protein, a disordered region in protein termini or interfaces between defined domains [ 18 , 20 ].…”

Section: Methodsmentioning

confidence: 99%

“…Peptide-mediated interactions constitute 15–40% of all protein-protein interactions [ 21 , 22 ]. Most of the studies performed so far in order to understand protein-peptide interactions focused on small peptides that may be short linear recognition motifs originating from disordered protein regions [ 18 , 20 ]. Investigating those interactions is experimentally challenging, and this has led to limited progress in the field of protein-peptide interactions validation.…”

Section: Introductionmentioning

confidence: 99%

Large-scale docking predicts that sORF-encoded peptides may function through protein-peptide interactions in Arabidopsis thaliana

et al. 2018

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Several recent studies indicate that small Open Reading Frames (sORFs) embedded within multiple eukaryotic non-coding RNAs can be translated into bioactive peptides of up to 100 amino acids in size. However, the functional roles of the 607 Stress Induced Peptides (SIPs) previously identified from 189 Transcriptionally Active Regions (TARs) in Arabidopsis thaliana remain unclear. To provide a starting point for functional annotation of these plant-derived peptides, we performed a large-scale prediction of peptide binding sites on protein surfaces using coarse-grained peptide docking. The docked models were subjected to further atomistic refinement and binding energy calculations. A total of 530 peptide-protein pairs were successfully docked. In cases where a peptide encoded by a TAR is predicted to bind at a known ligand or cofactor-binding site within the protein, it can be assumed that the peptide modulates the ligand or cofactor-binding. Moreover, we predict that several peptides bind at protein-protein interfaces, which could therefore regulate the formation of the respective complexes. Protein-peptide binding analysis further revealed that peptides employ both their backbone and side chain atoms when binding to the protein, forming predominantly hydrophobic interactions and hydrogen bonds. In this study, we have generated novel predictions on the potential protein-peptide interactions in A. thaliana, which will help in further experimental validation.

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“…These methods are known to be generally accurate for small drug-like ligands with up to 10 flexible bonds, or degrees of freedom (DoFs) [5]. In recent years, there has been a growing interest in the use of peptides as protein inhibitors, given their natural role as binders and regulators in a variety of pathways [6,7]. However, even small peptides and peptidomimetics (i.e., synthetic peptide-based inhibitors [8]) are too large and too flexible for most available molecular docking methods [9].…”

Section: Introductionmentioning

confidence: 99%

“…However, even small peptides and peptidomimetics (i.e., synthetic peptide-based inhibitors [8]) are too large and too flexible for most available molecular docking methods [9]. Note that peptide-docking is a growing field of research and that other tools have been recently proposed [7]. However, a more detailed review of these methods goes beyond the scope of this paper.…”

Section: Introductionmentioning

confidence: 99%

DINC 2.0: A New Protein–Peptide Docking Webserver Using an Incremental Approach

et al. 2017

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Molecular docking is a standard computational approach to predict binding modes of protein-ligand complexes, by exploring alternative orientations and conformations of the ligand (i.e., by exploring ligand flexibility). Docking tools are largely used for virtual screening of small drug-like molecules, but their accuracy and efficiency greatly decays for ligands with more than 10 flexible bonds. This prevents a broader use of these tools to dock larger ligands such as peptides, which are molecules of growing interest in cancer research. To overcome this limitation, our group has previously proposed a meta-docking strategy, called DINC, to predict binding modes of large ligands. By incrementally docking overlapping fragments of a ligand, DINC allowed predicting binding modes of peptide-based inhibitors of transcription factors involved in cancer. Here we describe DINC 2.0, a revamped version of the DINC webserver with enhanced capabilities and a more user-friendly interface. DINC 2.0 allows docking ligands that were previously too challenging for DINC, such as peptides with more than 25 flexible bonds. The webserver is freely accessible at http://dinc.kavrakilab.org, together with additional documentation and video tutorials. Our team will provide continuous support for this tool and is working on extending its applicability to other challenging fields, such as personalized immunotherapy against cancer.

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Recent Advances in Computational Models for the Study of Protein–Peptide Interactions

Cited by 26 publications

References 102 publications

FlexPepDock lessons from CAPRI peptide–protein rounds and suggested new criteria for assessment of model quality and utility

FlexPepDock lessons from CAPRI peptide–protein rounds and suggested new criteria for assessment of model quality and utility

Large-scale docking predicts that sORF-encoded peptides may function through protein-peptide interactions in Arabidopsis thaliana

DINC 2.0: A New Protein–Peptide Docking Webserver Using an Incremental Approach

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