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
1秒前
FashionBoy应助larder采纳,获得10
1秒前
MMM发布了新的文献求助10
2秒前
浅夏初晴完成签到 ,获得积分10
2秒前
2秒前
5秒前
玩命的咖啡完成签到,获得积分20
6秒前
7秒前
7秒前
7秒前
机智的乌发布了新的文献求助10
7秒前
8秒前
8秒前
天天快乐应助悠悠采纳,获得10
9秒前
Bighen完成签到 ,获得积分10
9秒前
10秒前
11秒前
11秒前
Verano_4关注了科研通微信公众号
12秒前
12秒前
啦啦发布了新的文献求助10
12秒前
14秒前
14秒前
考拉完成签到,获得积分10
16秒前
YU完成签到,获得积分10
16秒前
16秒前
17秒前
18秒前
happyboy2008完成签到 ,获得积分10
19秒前
fei发布了新的文献求助30
19秒前
XYY发布了新的文献求助10
20秒前
赘婿应助Dr郑迅采纳,获得10
21秒前
科研通AI2S应助科研通管家采纳,获得10
21秒前
Orange应助科研通管家采纳,获得10
21秒前
adazbd发布了新的文献求助10
21秒前
英姑应助科研通管家采纳,获得10
21秒前
科研通AI2S应助科研通管家采纳,获得10
21秒前
研友_VZG7GZ应助科研通管家采纳,获得10
22秒前
张点心发布了新的文献求助10
22秒前
ESLG应助科研通管家采纳,获得10
22秒前
高分求助中
Spray / Wall-interaction Modelling by Dimensionless Data Analysis 2000
ALA生合成不全マウスでの糖代謝異常の分子機構解析 520
安全防范技术与工程 500
Mathematics and Finite Element Discretizations of Incompressible Navier—Stokes Flows 500
2-Acetyl-1-pyrroline: an important aroma component of cooked rice 500
A real-time energy management strategy based on fuzzy control and ECMS for PHEVs 400
2024 Medicinal Chemistry Reviews 400
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 3191143
求助须知:如何正确求助?哪些是违规求助? 2840488
关于积分的说明 8028774
捐赠科研通 2503831
什么是DOI,文献DOI怎么找? 1337224
科研通“疑难数据库(出版商)”最低求助积分说明 638034
邀请新用户注册赠送积分活动 606497