Recent advances in predicting and modeling protein–protein interactions

计算机科学 计算生物学 蛋白质-蛋白质相互作用 蛋白质组 人工智能 机器学习 功能(生物学) 计算模型 结构生物信息学 蛋白质结构 生物信息学 生物 遗传学 生物化学
作者
Jesse Durham,Jing Zhang,Ian R. Humphreys,Jimin Pei,Qian Cong
出处
期刊:Trends in Biochemical Sciences [Elsevier]
卷期号:48 (6): 527-538 被引量:20
标识
DOI:10.1016/j.tibs.2023.03.003
摘要

Deciphering coevolutionary signals in protein sequences and applying deep learning methods such as AlphaFold have led to breakthroughs in modeling protein structures and interactions. The accuracy of interaction partner detection and structural modeling or protein complexes by computational methods now approaches experimental methods, and we are entering a new era where computation will play an essential role in both tasks. We expect rapid progress in characterizing human PPIs, thus enabling biomedical applications such as interpreting pathogenic variants, developing drugs to target PPIs, and designing protein binders to regulate protein function. We still face challenges in modeling transient and weak interactions, understanding the interactions mediated by intrinsically disordered regions (IDRs), expanding to other molecules such as polysaccharides and lipids, and moving towards modeling the entire cell. Protein–protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale. Protein–protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale. the phenomenon where two different positions (residues) in a protein or two proteins reciprocally affect each other's evolution, which usually results from direct contact between residues in the 3D structures of proteins. a class of neural networks that are frequently used in image processing. Neural networks are computational methods inspired by biological neural networks. a branch of machine learning that comprises multiple layers of neural networks. methods that attempt to find a mutual orientation of the 3D structures of two interacting proteins that minimize an energy function over the protein–protein interaction (PPI) interface. energy functions derived from statistical analysis of observed states in existing systems (e.g., experimentally determined protein structures). They are designed to be efficient in computing, and more frequently observed states are evaluated more favorably. the total energy of a particular system computed as a function of the state of the system. proteins that function together, not necessarily through physical interaction. regions in a protein that do not adopt a fixed or ordered 3D structure. a concatenated multiple sequence alignment (MSA) of proteins A and B used as inputs for coevolution analysis or deep learning networks such as AlphaFold. In this concatenated MSA, the homologs of protein A and protein B are paired by being placed in the same row of the MSA. The generation of a paired MSA converts the problem of modeling two proteins to a problem similar to that of modeling one protein. proteins interact through direct binding to each other.
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
更新
大幅提高文件上传限制,最高150M (2024-4-1)

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
anan完成签到 ,获得积分10
5秒前
6秒前
张zzz完成签到,获得积分10
6秒前
只羊完成签到,获得积分10
6秒前
萝卜丁完成签到 ,获得积分10
7秒前
Julia完成签到,获得积分10
7秒前
xxxx0414完成签到 ,获得积分10
9秒前
LIVE完成签到,获得积分10
10秒前
子非鱼完成签到,获得积分10
10秒前
byron完成签到,获得积分10
10秒前
大力完成签到 ,获得积分10
11秒前
科目三应助顺心的水云采纳,获得10
11秒前
14秒前
贱小贱完成签到,获得积分10
14秒前
如约而至完成签到 ,获得积分10
15秒前
加油吧少年完成签到,获得积分10
16秒前
亚威完成签到,获得积分10
16秒前
xiying完成签到 ,获得积分10
16秒前
东方不败完成签到,获得积分10
17秒前
liangguangyuan完成签到 ,获得积分10
17秒前
默默的巧蕊完成签到,获得积分20
17秒前
董小天天完成签到,获得积分10
18秒前
东野先森发布了新的文献求助10
18秒前
强壮的小牙签完成签到,获得积分10
19秒前
冰冰完成签到 ,获得积分10
19秒前
Orange应助科研通管家采纳,获得10
19秒前
firewood完成签到,获得积分10
19秒前
severus完成签到 ,获得积分10
20秒前
时尚雨兰完成签到,获得积分10
22秒前
圆圆完成签到,获得积分10
22秒前
徐橙橙完成签到,获得积分10
23秒前
Mt完成签到,获得积分10
24秒前
酷酷菲音完成签到,获得积分10
24秒前
落落大方完成签到,获得积分10
25秒前
25秒前
笑一笑完成签到 ,获得积分10
27秒前
朱诗源完成签到 ,获得积分10
27秒前
Singularity应助我有一只猫采纳,获得10
27秒前
义气小白菜完成签到 ,获得积分10
29秒前
左丘完成签到,获得积分10
30秒前
高分求助中
Sustainability in Tides Chemistry 2800
The Young builders of New china : the visit of the delegation of the WFDY to the Chinese People's Republic 1000
Rechtsphilosophie 1000
Bayesian Models of Cognition:Reverse Engineering the Mind 888
Le dégorgement réflexe des Acridiens 800
Defense against predation 800
A Dissection Guide & Atlas to the Rabbit 600
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3134060
求助须知:如何正确求助?哪些是违规求助? 2784861
关于积分的说明 7769049
捐赠科研通 2440325
什么是DOI,文献DOI怎么找? 1297361
科研通“疑难数据库(出版商)”最低求助积分说明 624959
版权声明 600792