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
aliu发布了新的文献求助10
1秒前
fgjkl发布了新的文献求助10
2秒前
怡然谷雪发布了新的文献求助20
2秒前
3秒前
4秒前
王鹏飞发布了新的文献求助30
5秒前
5秒前
乐乐发布了新的文献求助10
6秒前
8秒前
欣慰元蝶发布了新的文献求助10
8秒前
迅速采梦完成签到,获得积分10
9秒前
尤川完成签到,获得积分10
9秒前
优雅寻雪发布了新的文献求助10
10秒前
思源应助qx1866583196采纳,获得10
10秒前
凌云发布了新的文献求助10
10秒前
比巴卜溪完成签到,获得积分20
11秒前
深情安青应助郭欣茹采纳,获得10
12秒前
Akim应助热心的汽车采纳,获得10
13秒前
剑影完成签到,获得积分10
14秒前
淡定枫完成签到 ,获得积分10
14秒前
Lily发布了新的文献求助10
14秒前
ziguangrong发布了新的文献求助10
15秒前
17秒前
17秒前
王鹏飞完成签到,获得积分10
18秒前
21秒前
21秒前
21秒前
上官若男应助科研通管家采纳,获得10
21秒前
21秒前
打打应助科研通管家采纳,获得10
21秒前
Lucas应助科研通管家采纳,获得10
21秒前
星辰大海应助科研通管家采纳,获得10
21秒前
21秒前
21秒前
地球发布了新的文献求助10
22秒前
23秒前
fgjkl完成签到 ,获得积分10
24秒前
研友_LMr3An发布了新的文献求助10
24秒前
goodchenlu完成签到 ,获得积分10
26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
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
Elevating Next Generation Genomic Science and Technology using Machine Learning in the Healthcare Industry Applied Machine Learning for IoT and Data Analytics 400
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 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6443547
求助须知:如何正确求助?哪些是违规求助? 8257395
关于积分的说明 17586450
捐赠科研通 5502154
什么是DOI,文献DOI怎么找? 2900906
邀请新用户注册赠送积分活动 1877940
关于科研通互助平台的介绍 1717534