Comparative Analysis of Protein Surface Hydrophobicity Maps Determined by Sparse Sampling INDUS and Spatial Aggregation Propensity

生物系统 背景(考古学) 化学 折叠(DSP实现) 表面蛋白 蛋白质折叠 曲面(拓扑) 疏水效应 化学物理 有机化学 数学 生物化学 几何学 生物 病毒学 古生物学 工程类 电气工程
作者
Imee Sinha,Shekhar Garde,Steven M. Cramer
出处
期刊:Journal of Physical Chemistry B [American Chemical Society]
卷期号:127 (48): 10304-10314 被引量:3
标识
DOI:10.1021/acs.jpcb.3c04902
摘要

Protein surface hydrophobicity plays a central role in various biological processes such as protein folding and aggregation, as well as in the design and manufacturing of biotherapeutics. While the hydrophobicity of protein surface patches has been linked to their constituent residue hydropathies, recent research has shown that protein surface hydrophobicity is more complex and characterized by the response of water to these surfaces. In this work, we employ water density perturbations to map the surface hydrophobicity of a set of model proteins using sparse indirect umbrella sampling simulations (SSI). This technique is used to identify hydrophobic surface patches for the set of model proteins, and the results are compared to those obtained from the widely adopted spatial aggregation propensity (SAP) technique. While SAP-based calculations show agreement with SSI in some cases, there are several examples of disagreement. We identify four general classes of difference in behavior and study factors that contribute to these differences. We find that the SAP method can sometimes mask the effect of weakly nonpolar or isolated nonpolar residues that can lead to strong hydrophobic patches on the protein surface. In addition, hydrophobic patches identified by SAP can exhibit shifts in both position and strength on the SSI map. Our results demonstrate that the combination of topography and chemical context controls the hydrophobicity of a given patch above and beyond the intrinsic polarity of the residues present on the patch surface. The availability of more accurate protein hydrophobicity maps in concert with new classes of hydrophobic molecular descriptors may create significant opportunities for in silico prediction of protein behavior for a range of applications, such as protein design, biomanufacturability, and downstream bioprocessing.
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
1秒前
zzs发布了新的文献求助10
1秒前
wanci应助ooa4321采纳,获得10
1秒前
lemonrlq发布了新的文献求助30
2秒前
Kylin发布了新的文献求助10
3秒前
347发布了新的文献求助10
4秒前
4秒前
5秒前
阳光的紫丝完成签到 ,获得积分10
5秒前
lin完成签到,获得积分10
6秒前
充电宝应助积极念波采纳,获得10
6秒前
6秒前
吕培森完成签到 ,获得积分10
6秒前
7秒前
大意的书兰完成签到 ,获得积分10
8秒前
8秒前
9秒前
9秒前
9秒前
科研通AI6.2应助王豆豆采纳,获得10
10秒前
10秒前
11秒前
深情安青应助彩霞采纳,获得10
12秒前
12秒前
jjj1234发布了新的文献求助10
13秒前
yin景景发布了新的文献求助10
14秒前
14秒前
积极念波完成签到,获得积分20
14秒前
所所应助王富贵采纳,获得10
14秒前
华仔应助受伤的千凝采纳,获得10
15秒前
15秒前
苏远烟发布了新的文献求助10
15秒前
顾矜应助钟鸿盛Domi采纳,获得10
16秒前
白瑾发布了新的文献求助10
16秒前
Janice完成签到,获得积分20
16秒前
梦M完成签到 ,获得积分10
17秒前
18秒前
害怕的怜翠完成签到,获得积分10
18秒前
Cszdyeyoushen发布了新的文献求助10
18秒前
大模型应助我测你码采纳,获得10
18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
Autoparametric Resonance in Mechanical Systems 1000
基于锂离子电池正极材料回收的绿色溶剂开发及工程化应用研究 800
Social Psychology 600
Cosmos as Art Object: Studies in Plato's Timaeus and Other Dialogues 600
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7644284
求助须知:如何正确求助?哪些是违规求助? 9217188
关于积分的说明 19774670
捐赠科研通 7209505
什么是DOI,文献DOI怎么找? 3276786
关于科研通互助平台的介绍 2438296
邀请新用户注册赠送积分活动 2274627