Suppr超能文献

用于蛋白质 - DNA 结合亲和力预测的多位点 λ 动力学

Multisite λ-Dynamics for Protein-DNA Binding Affinity Prediction.

作者信息

Al Masri Carmen, Vilseck Jonah Z, Yu Jin, Hayes Ryan L

机构信息

Department of Physics and Astronomy, Uninversity of California, Irvine, California 92697, United States.

Department of Biochemistry and Molecular Biology, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States.

出版信息

J Chem Theory Comput. 2025 Apr 8;21(7):3536-3544. doi: 10.1021/acs.jctc.4c01408. Epub 2025 Mar 24.

Abstract

Transcription factors (TFs) regulate gene expression by binding to specific DNA sequences, playing critical roles in cellular processes and disease pathways. Computational methods, particularly λ-Dynamics, offer a promising approach for predicting TF relative binding affinities. This study evaluates the effectiveness of different λ-Dynamics perturbation schemes in determining binding free energy changes (ΔΔ) of the WRKY transcription factor upon mutating its W-box binding site (GTAA) to a nonspecific sequence (GTAA). Among the schemes tested, the single λ per base pair protocol demonstrated the fastest convergence and highest precision. Extending this protocol to additional mutants (GGTC and GGCAA) yielded ΔΔ values that successfully ranked binding affinities, showcasing its strong potential for high-throughput screening of DNA binding sites.

摘要

转录因子(TFs)通过与特定DNA序列结合来调节基因表达,在细胞过程和疾病通路中发挥关键作用。计算方法,特别是λ动力学,为预测TF相对结合亲和力提供了一种有前景的方法。本研究评估了不同的λ动力学微扰方案在确定WRKY转录因子的W-box结合位点(GTAA)突变为非特异性序列(GTAA)时结合自由能变化(ΔΔ)方面的有效性。在所测试的方案中,每个碱基对单个λ方案表现出最快的收敛速度和最高的精度。将该方案扩展到其他突变体(GGTC和GGCAA)得到了成功对结合亲和力进行排序的ΔΔ值,展示了其在DNA结合位点高通量筛选方面的强大潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fa8/11983716/803b1dde64cb/ct4c01408_0001.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验