VoroMQA: Assessment of protein structure quality using interatomic contact areas

卡斯普 沃罗诺图 计算机科学 软件 蛋白质结构预测 蛋白质结构 蛋白质数据库 质量(理念) Atom(片上系统) 生物系统 数据挖掘 算法 化学 数学 几何学 物理 生物 量子力学 生物化学 嵌入式系统 程序设计语言
作者
Kliment Olechnovič,Česlovas Venclovas
出处
期刊:Proteins [Wiley]
卷期号:85 (6): 1131-1145 被引量:149
标识
DOI:10.1002/prot.25278
摘要

ABSTRACT In the absence of experimentally determined protein structure many biological questions can be addressed using computational structural models. However, the utility of protein structural models depends on their quality. Therefore, the estimation of the quality of predicted structures is an important problem. One of the approaches to this problem is the use of knowledge‐based statistical potentials. Such methods typically rely on the statistics of distances and angles of residue‐residue or atom‐atom interactions collected from experimentally determined structures. Here, we present VoroMQA (Voronoi tessellation‐based Model Quality Assessment), a new method for the estimation of protein structure quality. Our method combines the idea of statistical potentials with the use of interatomic contact areas instead of distances. Contact areas, derived using Voronoi tessellation of protein structure, are used to describe and seamlessly integrate both explicit interactions between protein atoms and implicit interactions of protein atoms with solvent. VoroMQA produces scores at atomic, residue, and global levels, all in the fixed range from 0 to 1. The method was tested on the CASP data and compared to several other single‐model quality assessment methods. VoroMQA showed strong performance in the recognition of the native structure and in the structural model selection tests, thus demonstrating the efficacy of interatomic contact areas in estimating protein structure quality. The software implementation of VoroMQA is freely available as a standalone application and as a web server at http://bioinformatics.lt/software/voromqa . Proteins 2017; 85:1131–1145. © 2017 Wiley Periodicals, Inc.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
李还好完成签到,获得积分10
刚刚
满意的柏柳完成签到,获得积分10
1秒前
2秒前
3秒前
3秒前
buno应助88采纳,获得10
3秒前
4秒前
三千世界完成签到,获得积分10
4秒前
4秒前
愉快的访旋完成签到,获得积分10
5秒前
Alpha完成签到,获得积分10
6秒前
大大发布了新的文献求助30
6秒前
翠翠发布了新的文献求助10
7秒前
半山发布了新的文献求助10
8秒前
8秒前
天天快乐应助CO2采纳,获得10
8秒前
隐形曼青应助junzilan采纳,获得10
9秒前
Dksido发布了新的文献求助10
9秒前
10秒前
思源应助卓哥采纳,获得10
10秒前
mysci完成签到,获得积分10
13秒前
14秒前
Quzhengkai发布了新的文献求助10
15秒前
15秒前
16秒前
落寞晓灵完成签到,获得积分10
16秒前
ORAzzz应助翠翠采纳,获得20
17秒前
zoe完成签到,获得积分10
17秒前
习习应助学术小白采纳,获得10
17秒前
18秒前
19秒前
tianny关注了科研通微信公众号
20秒前
20秒前
CO2发布了新的文献求助10
20秒前
桐桐应助zhangscience采纳,获得10
21秒前
求助发布了新的文献求助10
22秒前
buno应助zoe采纳,获得10
23秒前
junzilan发布了新的文献求助10
23秒前
23秒前
细品岁月完成签到 ,获得积分10
23秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527961
求助须知:如何正确求助?哪些是违规求助? 3108159
关于积分的说明 9287825
捐赠科研通 2805882
什么是DOI,文献DOI怎么找? 1540070
邀请新用户注册赠送积分活动 716926
科研通“疑难数据库(出版商)”最低求助积分说明 709808