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大规模应用自由能微扰计算进行抗体设计。

Large-scale application of free energy perturbation calculations for antibody design.

机构信息

Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, USA.

Global Security Computing Division, Computing Directorate, Lawrence Livermore National Laboratory, Livermore, USA.

出版信息

Sci Rep. 2022 Jul 21;12(1):12489. doi: 10.1038/s41598-022-14443-z.

DOI:10.1038/s41598-022-14443-z
PMID:35864134
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9302960/
Abstract

Alchemical free energy perturbation (FEP) is a rigorous and powerful technique to calculate the free energy difference between distinct chemical systems. Here we report our implementation of automated large-scale FEP calculations, using the Amber software package, to facilitate antibody design and evaluation. In combination with Hamiltonian replica exchange, our FEP simulations aim to predict the effect of mutations on both the binding affinity and the structural stability. Importantly, we incorporate multiple strategies to faithfully estimate the statistical uncertainties in the FEP results. As a case study, we apply our protocols to systematically evaluate variants of the m396 antibody for their conformational stability and their binding affinity to the spike proteins of SARS-CoV-1 and SARS-CoV-2. By properly adjusting relevant parameters, the particle collapse problems in the FEP simulations are avoided. Furthermore, large statistical errors in a small fraction of the FEP calculations are effectively reduced by extending the sampling, such that acceptable statistical uncertainties are achieved for the vast majority of the cases with a modest total computational cost. Finally, our predicted conformational stability for the m396 variants is qualitatively consistent with the experimentally measured melting temperatures. Our work thus demonstrates the applicability of FEP in computational antibody design.

摘要

无化学自由能微扰(FEP)是一种严谨而强大的技术,可以计算不同化学系统之间的自由能差异。在这里,我们报告了我们使用 Amber 软件包实现自动化大规模 FEP 计算的情况,以促进抗体设计和评估。结合哈密顿 replica 交换,我们的 FEP 模拟旨在预测突变对结合亲和力和结构稳定性的影响。重要的是,我们采用了多种策略来准确估计 FEP 结果的统计不确定性。作为案例研究,我们应用我们的方案系统地评估 m396 抗体变体的构象稳定性及其与 SARS-CoV-1 和 SARS-CoV-2 刺突蛋白的结合亲和力。通过适当调整相关参数,避免了 FEP 模拟中的颗粒坍塌问题。此外,通过扩展采样,有效地减少了一小部分 FEP 计算中的大统计误差,从而为绝大多数情况下实现了可接受的统计不确定性,而总计算成本适中。最后,我们对 m396 变体的预测构象稳定性与实验测量的熔点温度定性一致。因此,我们的工作证明了 FEP 在计算抗体设计中的适用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dda/9304423/26a13e9959b8/41598_2022_14443_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dda/9304423/93fa5af2f976/41598_2022_14443_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dda/9304423/a7f1e65a9065/41598_2022_14443_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dda/9304423/a6474d70f403/41598_2022_14443_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dda/9304423/2ccfe2ed4ddf/41598_2022_14443_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dda/9304423/f480e06ab3bc/41598_2022_14443_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dda/9304423/090adc5e07a7/41598_2022_14443_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dda/9304423/26a13e9959b8/41598_2022_14443_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dda/9304423/93fa5af2f976/41598_2022_14443_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dda/9304423/a7f1e65a9065/41598_2022_14443_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dda/9304423/a6474d70f403/41598_2022_14443_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dda/9304423/2ccfe2ed4ddf/41598_2022_14443_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dda/9304423/f480e06ab3bc/41598_2022_14443_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dda/9304423/090adc5e07a7/41598_2022_14443_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1dda/9304423/26a13e9959b8/41598_2022_14443_Fig7_HTML.jpg

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