亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Fragment-based modeling of membrane protein loops: Successes, failures, and prospects for the future

片段(逻辑) 计算生物学 化学 计算机科学 生物 算法
作者
Sebastian Kelm,Anna Vangone,Yoonjoo Choi,Jean-Paul Ebejer,Jiye Shi,Charlotte M. Deane
出处
期刊:Proteins [Wiley]
卷期号:82 (2): 175-186 被引量:8
标识
DOI:10.1002/prot.24299
摘要

Membrane proteins (MPs) have become a major focus in structure prediction, due to their medical importance. There is, however, a lack of fast and reliable methods that specialize in the modeling of MP loops. Often methods designed for soluble proteins (SPs) are applied directly to MPs. In this article, we investigate the validity of such an approach in the realm of fragment-based methods. We also examined the differences in membrane and soluble protein loops that might affect accuracy. We test our ability to predict soluble and MP loops with the previously published method FREAD. We show that it is possible to predict accurately the structure of MP loops using a database of MP fragments (0.5-1 Å median root-mean-square deviation). The presence of homologous proteins in the database helps prediction accuracy. However, even when homologues are removed better results are still achieved using fragments of MPs (0.8-1.6 Å) rather than SPs (1-4 Å) to model MP loops. We find that many fragments of SPs have shapes similar to their MP counterparts but have very different sequences; however, they do not appear to differ in their substitution patterns. Our findings may allow further improvements to fragment-based loop modeling algorithms for MPs. The current version of our proof-of-concept loop modeling protocol produces high-accuracy loop models for MPs and is available as a web server at http://medeller.info/fread.
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
刚刚
Cosmosurfer完成签到,获得积分0
9秒前
勤奋的香薇完成签到,获得积分10
14秒前
打打应助ping采纳,获得10
20秒前
20秒前
22秒前
飞哥与小佛完成签到,获得积分10
27秒前
开朗曼雁发布了新的文献求助10
36秒前
整齐诺言完成签到,获得积分10
46秒前
46秒前
小二郎应助科研通管家采纳,获得10
48秒前
努力加油煤老八完成签到,获得积分10
57秒前
漂亮的又槐完成签到,获得积分10
1分钟前
1分钟前
1分钟前
MchemG完成签到,获得积分0
1分钟前
小巧慕儿完成签到,获得积分10
1分钟前
1分钟前
cc发布了新的文献求助10
1分钟前
清脆夜阑完成签到,获得积分10
1分钟前
SCI的芷蝶完成签到 ,获得积分10
1分钟前
1分钟前
ping发布了新的文献求助10
1分钟前
ping完成签到,获得积分10
1分钟前
斯文含灵完成签到,获得积分10
1分钟前
2分钟前
诚心荟完成签到,获得积分10
2分钟前
2分钟前
2分钟前
2分钟前
科研通AI6.2应助卿亦佳人采纳,获得10
2分钟前
科研通AI6.2应助卿亦佳人采纳,获得10
2分钟前
ding应助卿亦佳人采纳,获得10
2分钟前
2分钟前
2分钟前
科研通AI6.4应助踏实梦柏采纳,获得10
2分钟前
2分钟前
猪宝发布了新的文献求助30
2分钟前
2分钟前
踏实梦柏发布了新的文献求助10
3分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Nine new races of Peronospora manshurica found on soybeans in the Midwest 1000
Essentials of Carbohydrate Chemistry and Biochemistry, 4th Edition 600
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Eudora Welty and Modern Media 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 计算机科学 化学工程 工程类 有机化学 物理 复合材料 生物化学 内科学 细胞生物学 基因 遗传学 免疫学 冶金 光电子学 癌症研究
热门帖子
关注 科研通微信公众号,转发送积分 7772494
求助须知:如何正确求助?哪些是违规求助? 9314773
关于积分的说明 20339874
捐赠科研通 7357870
什么是DOI,文献DOI怎么找? 3316947
关于科研通互助平台的介绍 2465475
邀请新用户注册赠送积分活动 2331952