Further Exploration of the Quantitative Distance-Energy and Contact Number-Energy Relationships for Predicting the Binding Affinity of the Protein-Ligand Complexes1.

IF 3.2 3区 生物学 Q2 BIOPHYSICS
Yong Xiao Yan, Bao Ting Zhu
{"title":"Further Exploration of the Quantitative Distance-Energy and Contact Number-Energy Relationships for Predicting the Binding Affinity of the Protein-Ligand Complexes<sup>1</sup>.","authors":"Yong Xiao Yan, Bao Ting Zhu","doi":"10.1016/j.bpj.2025.02.021","DOIUrl":null,"url":null,"abstract":"<p><p>Accurate estimation of the strength of the protein-ligand interaction is important in the field of drug discovery. The binding strength can be determined by using experimental binding affinity assays which are both time and labor consuming and costly. Predicting the binding affinity/energy in silico is an alternative approach, particularly for virtual screening of large datasets. In general, the distance-based terms such as electrostatic and van der Waals interactions are among the key determinants of binding energy. In this work, the distance-binding energy relationships, i.e., E ∝ -d<sup>-k</sup>, are further explored, extended and developed for protein-ligand binding affinity prediction. The contributions of different atom-type pairs were considered synthetically and jointly. Additionally, the contact number-energy relationships (E ∝ -n<sup>k</sup>) were also explored for protein-ligand binding affinity prediction. Significantly, the power exponents of the distances or contact numbers in the energy functions are not restricted by the existing theories concerning van der Waals and electrostatic energies (expressed as a a/r<sup>6</sup> - b/r<sup>12</sup> and c/r). The performances of the new distance-based or contact number-based models are better than the performances of those sophisticated non-machine learning-based scoring functions developed before. The exploration and extension of the distance-energy and contact number-energy relationships may offer insights into the development of more effective methods for predicting the protein-ligand binding affinity accurately and for analyzing the protein-ligand interactions rationally.</p>","PeriodicalId":8922,"journal":{"name":"Biophysical journal","volume":" ","pages":""},"PeriodicalIF":3.2000,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biophysical journal","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1016/j.bpj.2025.02.021","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOPHYSICS","Score":null,"Total":0}
引用次数: 0

Abstract

Accurate estimation of the strength of the protein-ligand interaction is important in the field of drug discovery. The binding strength can be determined by using experimental binding affinity assays which are both time and labor consuming and costly. Predicting the binding affinity/energy in silico is an alternative approach, particularly for virtual screening of large datasets. In general, the distance-based terms such as electrostatic and van der Waals interactions are among the key determinants of binding energy. In this work, the distance-binding energy relationships, i.e., E ∝ -d-k, are further explored, extended and developed for protein-ligand binding affinity prediction. The contributions of different atom-type pairs were considered synthetically and jointly. Additionally, the contact number-energy relationships (E ∝ -nk) were also explored for protein-ligand binding affinity prediction. Significantly, the power exponents of the distances or contact numbers in the energy functions are not restricted by the existing theories concerning van der Waals and electrostatic energies (expressed as a a/r6 - b/r12 and c/r). The performances of the new distance-based or contact number-based models are better than the performances of those sophisticated non-machine learning-based scoring functions developed before. The exploration and extension of the distance-energy and contact number-energy relationships may offer insights into the development of more effective methods for predicting the protein-ligand binding affinity accurately and for analyzing the protein-ligand interactions rationally.

准确估计蛋白质与配体相互作用的强度在药物发现领域非常重要。结合强度可以通过实验性结合亲和力测定来确定,这种方法既费时又费力,而且成本高昂。在硅学中预测结合亲和力/能量是一种替代方法,尤其适用于大型数据集的虚拟筛选。一般来说,静电和范德华相互作用等基于距离的因素是决定结合能的关键因素之一。在这项工作中,进一步探索、扩展和发展了距离-结合能关系,即 E ∝ -d-k,用于蛋白质-配体结合亲和力预测。综合并共同考虑了不同原子类型对的贡献。此外,还探讨了用于蛋白质配体结合亲和力预测的接触数-能量关系(E ∝ -nk)。值得注意的是,能量函数中距离或接触数的幂指数不受现有范德华和静电能量理论的限制(以 a/r6 - b/r12 和 c/r 表示)。基于距离或接触数的新模型的性能优于之前开发的基于非机器学习的复杂评分函数。对距离-能量和接触数-能量关系的探索和扩展可能会为开发更有效的方法提供启示,从而准确预测蛋白质与配体的结合亲和力并合理分析蛋白质与配体的相互作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Biophysical journal
Biophysical journal 生物-生物物理
CiteScore
6.10
自引率
5.90%
发文量
3090
审稿时长
2 months
期刊介绍: BJ publishes original articles, letters, and perspectives on important problems in modern biophysics. The papers should be written so as to be of interest to a broad community of biophysicists. BJ welcomes experimental studies that employ quantitative physical approaches for the study of biological systems, including or spanning scales from molecule to whole organism. Experimental studies of a purely descriptive or phenomenological nature, with no theoretical or mechanistic underpinning, are not appropriate for publication in BJ. Theoretical studies should offer new insights into the understanding ofexperimental results or suggest new experimentally testable hypotheses. Articles reporting significant methodological or technological advances, which have potential to open new areas of biophysical investigation, are also suitable for publication in BJ. Papers describing improvements in accuracy or speed of existing methods or extra detail within methods described previously are not suitable for BJ.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信