文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期,新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

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

作者信息

Marcu Orly, Dodson Emma-Joy, Alam Nawsad, Sperber Michal, Kozakov Dima, Lensink Marc F, Schueler-Furman Ora

机构信息

Department of Microbiology and Molecular Genetics, Institute for Medical Research Israel-Canada, Faculty of Medicine, the Hebrew University of Jerusalem, Israel.

Department of Applied Mathematics and Statistics, Stony Brooks University, Stony Brook, New York, 11794.

出版信息

Proteins. 2017 Mar;85(3):445-462. doi: 10.1002/prot.25230.


DOI:10.1002/prot.25230
PMID:28002624
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6618814/
Abstract

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.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a7/6618814/1fc119f1daed/PROT-85-445-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a7/6618814/73770eeee910/PROT-85-445-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a7/6618814/9f58aed5a243/PROT-85-445-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a7/6618814/86d20abaf426/PROT-85-445-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a7/6618814/3b55d9c53a8c/PROT-85-445-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a7/6618814/ae8820823d02/PROT-85-445-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a7/6618814/1fc119f1daed/PROT-85-445-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a7/6618814/73770eeee910/PROT-85-445-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a7/6618814/9f58aed5a243/PROT-85-445-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a7/6618814/86d20abaf426/PROT-85-445-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a7/6618814/3b55d9c53a8c/PROT-85-445-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a7/6618814/ae8820823d02/PROT-85-445-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85a7/6618814/1fc119f1daed/PROT-85-445-g006.jpg

相似文献

[1]
FlexPepDock lessons from CAPRI peptide-protein rounds and suggested new criteria for assessment of model quality and utility.

Proteins. 2017-3

[2]
pyDock scoring for the new modeling challenges in docking: Protein-peptide, homo-multimers, and domain-domain interactions.

Proteins. 2017-3

[3]
Modeling beta-sheet peptide-protein interactions: Rosetta FlexPepDock in CAPRI rounds 38-45.

Proteins. 2020-8

[4]
Modeling oblong proteins and water-mediated interfaces with RosettaDock in CAPRI rounds 28-35.

Proteins. 2017-3

[5]
Addressing recent docking challenges: A hybrid strategy to integrate template-based and free protein-protein docking.

Proteins. 2017-3

[6]
Protein-protein and peptide-protein docking and refinement using ATTRACT in CAPRI.

Proteins. 2017-3

[7]
New additions to the ClusPro server motivated by CAPRI.

Proteins. 2017-3

[8]
Performance of MDockPP in CAPRI rounds 28-29 and 31-35 including the prediction of water-mediated interactions.

Proteins. 2017-3

[9]
Modeling protein-protein and protein-peptide complexes: CAPRI 6th edition.

Proteins. 2017-3

[10]
Modeling and minimizing CAPRI round 30 symmetrical protein complexes from CASP-11 structural models.

Proteins. 2017-3

引用本文的文献

[1]
Learning with Privileged Knowledge Distillation for Improved Peptide-Protein Docking.

ACS Omega. 2025-6-16

[2]
Use of phosphotyrosine-containing peptides to target SH2 domains: Antagonist peptides of the Crk/CrkL-p130Cas axis.

Methods Enzymol. 2024

[3]
From In Silico to a Cellular Model: Molecular Docking Approach to Evaluate Antioxidant Bioactive Peptides.

Antioxidants (Basel). 2023-3-8

[4]
Opportunistic Challenges of Computer-aided Drug Discovery of Lipopeptides: New Insights for Large Molecule Therapeutics.

Avicenna J Med Biotechnol. 2023

[5]
PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design.

Biophysicist (Rockv). 2021-4

[6]
Insights into protein-DNA interactions from hydrogen bond energy-based comparative protein-ligand analyses.

Proteins. 2022-6

[7]
Harnessing protein folding neural networks for peptide-protein docking.

Nat Commun. 2022-1-10

[8]
Predicting Protein-Peptide Complex Structures by Accounting for Peptide Flexibility and the Physicochemical Environment.

J Chem Inf Model. 2022-1-10

[9]
Approaches for the Design and Optimization of Interfering Peptides Against Protein-Protein Interactions.

Front Mol Biosci. 2021-4-28

[10]
Modeling beta-sheet peptide-protein interactions: Rosetta FlexPepDock in CAPRI rounds 38-45.

Proteins. 2020-8

本文引用的文献

[1]
Modeling protein-protein and protein-peptide complexes: CAPRI 6th edition.

Proteins. 2017-3

[2]
Fully Blind Docking at the Atomic Level for Protein-Peptide Complex Structure Prediction.

Structure. 2016-10-4

[3]
Recent Advances in Computational Models for the Study of Protein-Peptide Interactions.

Adv Protein Chem Struct Biol. 2016

[4]
DockQ: A Quality Measure for Protein-Protein Docking Models.

PLoS One. 2016-8-25

[5]
Novel function discovery through sequence and structural data mining.

Curr Opin Struct Biol. 2016-6-10

[6]
Exploring the interplay between experimental methods and the performance of predictors of binding affinity change upon mutations in protein complexes.

Protein Eng Des Sel. 2016-8

[7]
Critical assessment of methods of protein structure prediction: Progress and new directions in round XI.

Proteins. 2016-9

[8]
PinaColada: peptide-inhibitor ant colony ad-hoc design algorithm.

Bioinformatics. 2016-3-11

[9]
De novo design of protein homo-oligomers with modular hydrogen-bond network-mediated specificity.

Science. 2016-5-6

[10]
Structure-Based Identification of HDAC8 Non-histone Substrates.

Structure. 2016-3-1

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索