AlphaFold-guided molecular replacement for solving challenging crystal structures

分子置换 切断 集合(抽象数据类型) 计算机科学 相似性(几何) 晶体结构 蛋白质数据库 移相器 算法 蛋白质数据库 序列(生物学) 蛋白质结构 计算生物学 化学 人工智能 结晶学 物理 生物 立体化学 生物化学 图像(数学) 光学 量子力学 程序设计语言
作者
Wei Wang,Zhen Gong,Wayne A. Hendrickson
标识
DOI:10.1107/s2059798324011999
摘要

Molecular replacement (MR) is highly effective for biomolecular crystal structure determination, increasingly so as the database of known structures has increased. For candidates without recognizable similarity to known structures, however, crystal structure analyses have nearly always required experiments for de novo phase evaluation. Now, with the unprecedented accuracy of AlphaFold predictions of protein structures from amino-acid sequences, an appreciable expansion of the reach of MR for proteins is realized. Here, we sought to automate an AlphaFold -guided MR procedure that tailors predictions to the MR problem at hand. We first optimized the reliability cutoff parameters for residue inclusion as tested in application to a previously MR-intractable problem. We then examined cases where AlphaFold by default predicts a conformation alternative to that of the candidate structure, devising tests for MR solution either from domain-specific predictions or from predictions based on diverse sequence subclusters. We tested subclustering procedures on an enzyme system that entails multiple MR-challenging conformations. The overall process as implemented in Phenix automatically surveys a succession of trials of increasing computational complexity until an MR solution is found or the options are exhausted. Validated MR solutions were found for 92% of one set of 158 challenging problems from the PDB and 93% of those from a second set of 215 challenges. Thus, many crystal structure analyses that previously required experimental phase evaluation can now be solved by AlphaFold -guided MR. In effect, this and related MR approaches are de novo phasing methods.

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
LingMg发布了新的文献求助10
1秒前
纸万完成签到,获得积分10
1秒前
2秒前
麦辣鸡腿堡完成签到,获得积分10
2秒前
acuis发布了新的文献求助10
2秒前
王镇发布了新的文献求助10
2秒前
HOAN应助科研通管家采纳,获得10
2秒前
桐桐应助科研通管家采纳,获得10
3秒前
BowieHuang应助科研通管家采纳,获得10
3秒前
完美世界应助科研通管家采纳,获得10
3秒前
打打应助科研通管家采纳,获得10
3秒前
JayceHe应助科研通管家采纳,获得10
3秒前
852应助科研通管家采纳,获得10
3秒前
科研通AI6应助科研通管家采纳,获得10
3秒前
Orange应助科研通管家采纳,获得10
3秒前
BowieHuang应助科研通管家采纳,获得10
3秒前
holly完成签到,获得积分10
3秒前
传奇3应助科研通管家采纳,获得10
3秒前
汉堡包应助科研通管家采纳,获得10
3秒前
Virgil完成签到,获得积分10
3秒前
完美世界应助科研通管家采纳,获得10
3秒前
虚幻导师发布了新的文献求助10
3秒前
大模型应助科研通管家采纳,获得10
3秒前
3秒前
李健应助科研通管家采纳,获得10
3秒前
完美世界应助科研通管家采纳,获得10
3秒前
桐桐应助科研通管家采纳,获得10
3秒前
慕青应助科研通管家采纳,获得10
3秒前
Akim应助科研通管家采纳,获得10
3秒前
4秒前
JamesPei应助科研通管家采纳,获得10
4秒前
BowieHuang应助科研通管家采纳,获得10
4秒前
BowieHuang应助科研通管家采纳,获得10
4秒前
爱笑如蓉完成签到,获得积分10
4秒前
kiminonawa应助科研通管家采纳,获得10
4秒前
4秒前
HOAN应助科研通管家采纳,获得10
4秒前
4秒前
小柠完成签到,获得积分10
4秒前
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
Clinical Microbiology Procedures Handbook, Multi-Volume, 5th Edition 2000
The Cambridge History of China: Volume 4, Sui and T'ang China, 589–906 AD, Part Two 1000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1000
Russian Foreign Policy: Change and Continuity 800
Real World Research, 5th Edition 800
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5717887
求助须知:如何正确求助?哪些是违规求助? 5248869
关于积分的说明 15283627
捐赠科研通 4867961
什么是DOI,文献DOI怎么找? 2613978
邀请新用户注册赠送积分活动 1563880
关于科研通互助平台的介绍 1521369