PredLLPS_PSSM: a novel predictor for liquid–liquid protein separation identification based on evolutionary information and a deep neural network

计算机科学 鉴定(生物学) 深度学习 人工智能 计算生物学 人工神经网络 模式识别(心理学) 生物 植物
作者
Shengming Zhou,Y. Z. Zhou,Tian Liu,Juanjuan Zheng,Cangzhi Jia
出处
期刊:Briefings in Bioinformatics [Oxford University Press]
卷期号:24 (5)
标识
DOI:10.1093/bib/bbad299
摘要

The formation of biomolecular condensates by liquid-liquid phase separation (LLPS) has become a universal mechanism for spatiotemporal coordination of biological activities in cells and has been widely observed to directly regulate the key cellular processes involved in cancer cell pathology. However, the complexity of protein sequences and the diversity of conformations are inherently disordered, which poses great challenges for LLPS protein calculations and experimental research. Herein, we proposed a novel predictor named PredLLPS_PSSM for LLPS protein identification based only on sequence evolution information. Because finding real and reliable samples is the cornerstone of building predictors, we collected anew and collated the LLPS proteins from the latest versions of three databases. By comparing the performance of the position-specific score matrix (PSSM) and word embedding, PredLLPS_PSSM combined PSSM-based information and two deep learning frameworks. Independent tests using three existing independent test datasets and two newly constructed independent test datasets demonstrated the superiority of PredLLPS_PSSM compared with state-of-the-art methods. Furthermore, we tested PredLLPS_PSSM on nine experimentally identified LLPS proteins from three insects that were not included in any of the databases. In addition, the powerful Shapley Additive exPlanation algorithm and heatmap were applied to find the most critical amino acids relevant to LLPS.
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
今后应助清脆安南采纳,获得10
1秒前
5秒前
DJ完成签到,获得积分10
7秒前
ding应助melenda采纳,获得10
8秒前
8秒前
Rwslpy发布了新的文献求助10
9秒前
123完成签到,获得积分10
11秒前
苏卿应助是是是咯采纳,获得10
11秒前
欢呼的书南完成签到,获得积分10
14秒前
完美世界应助g0123采纳,获得10
14秒前
14秒前
dora完成签到,获得积分10
14秒前
清脆安南发布了新的文献求助10
14秒前
15秒前
16秒前
LL完成签到,获得积分10
16秒前
17秒前
慕青应助偷狗的小月亮采纳,获得10
18秒前
今后应助找回自己采纳,获得10
18秒前
唐画完成签到,获得积分10
19秒前
LL发布了新的文献求助10
19秒前
Lalali发布了新的文献求助10
20秒前
汉堡包应助科研通管家采纳,获得30
20秒前
gaogao应助科研通管家采纳,获得10
20秒前
华仔应助科研通管家采纳,获得10
20秒前
科研通AI2S应助科研通管家采纳,获得10
20秒前
小二郎应助科研通管家采纳,获得30
20秒前
不配.应助科研通管家采纳,获得20
21秒前
香蕉觅云应助科研通管家采纳,获得10
21秒前
不配.应助科研通管家采纳,获得20
21秒前
21秒前
21秒前
577610822发布了新的文献求助10
21秒前
李健应助怡然雁凡采纳,获得10
22秒前
VDC发布了新的文献求助30
22秒前
melenda发布了新的文献求助10
22秒前
云云完成签到 ,获得积分10
22秒前
在水一方应助Peng采纳,获得10
24秒前
27秒前
别再熬夜发布了新的文献求助10
27秒前
高分求助中
Spray / Wall-interaction Modelling by Dimensionless Data Analysis 2000
Evolution 3rd edition 1500
保险藏宝图 1000
Lire en communiste 1000
Mantiden: Faszinierende Lauerjäger Faszinierende Lauerjäger 700
PraxisRatgeber: Mantiden: Faszinierende Lauerjäger 700
Mathematics and Finite Element Discretizations of Incompressible Navier—Stokes Flows 500
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 3184564
求助须知:如何正确求助?哪些是违规求助? 2834870
关于积分的说明 8001946
捐赠科研通 2497295
什么是DOI,文献DOI怎么找? 1332766
科研通“疑难数据库(出版商)”最低求助积分说明 636676
邀请新用户注册赠送积分活动 604062