Domain motions involved in the function of proteins can often be well described as a combination of motions along a handfull of low-frequency modes, that is, with the values of a few normal coordinates. This means that, when the functional motion of a protein is unknown, it should prove possible to predict it, since it amounts to guess a few values. However, without the help of additional experimental data, using normal coordinates for generating accurate conformers far away from the initial one is not so straightforward. To do so, a new approach is proposed: instead of building conformers directly with the values of a subset of normal coordinates, they are built in two steps, the conformer built with normal coordinates being just used for defining a set of distance constraints, the final conformer being built so as to match them. Note that this approach amounts to transform the problem of generating accurate protein conformers using normal coordinates into a better known one: the distance-geometry problem, which is herein solved with the help of the ROSETTA software. In the present study, this approach allowed to rebuild accurately six large amplitude conformational changes, using at most six low-frequency normal coordinates. As a consequence of the low-dimensionality of the corresponding subspace, random exploration also proved enough for generating low-energy conformers close to the known end-point of the conformational change of the LAO binding protein, lysozyme T4 and adenylate kinase.
Scoring-Assisted Generative Exploration for Proteins (SAGE-Prot): A Framework for Multi-Objective Protein Optimization via Iterative Sequence Generation and Evaluation
Effective Protein-Protein Interaction Exploration with PPIretrieval
McGill University（麦吉尔大学）；Mila-Quebec AI Institute（米拉-魁北克人工智能研究所）；Massachusetts Institute of Technology（麻省理工学院）；Université de Montréal（蒙特利尔大学）；DeepMind（深度思维公司）；Shanghai Jiao Tong University（上海交通大学）；Aureka Biotechnologies（奥雷卡生物技术公司）
The NRGTEN Python package: an extensible toolkit for coarse-grained normal mode analysis of proteins, nucleic acids, small molecules and their complexes
摘要：粗粒简正模分析（NMA）是一种研究生物分子动力学的快速计算技术。Najmanovich Research Group Toolkit for Elastic Networks（NRGTEN）。NRGTEN是一个Python工具包，它实现了四种不同的NMA模型，以及流行和新颖的指标，以基准测试和测量这些模型的属性。此外，该工具包作为公共Python包提供，并且可以轻松扩展以开发或实现其他NMA模型。我们在NRGTEN中开发的ENCoM模型（弹性网络接触模型）值得注意，因为它考虑了原子相互作用的特定化学性质。这使得有可能对突变的影响进行一些独特的预测，例如对稳定性（通过振动熵差的变化），对不同构象状态之间的跃迁概率或对整个大分子/复合物的柔性分布（以研究变构和信号传导）。此外，所有NMA模型都可用于从起始结构生成构象系综，以帮助蛋白质-蛋白质、蛋白质-配体或应用中的其他对接研究。NRGTEN通过公共Python包免费提供，该包可以轻松安装在任何现代机器上，并包括在线托管的详细用户指南。可用性和实施：https://github.com/gregorpatof/nrgten_package/联系人：rafael. umontreal.ca
From Supervision to Exploration: What Does Protein Language Model Learn During Reinforcement Learning?
The Chinese University of Hong Kong（香港中文大学）；Peking University（北京大学）；Stanford University（斯坦福大学）；Nanjing University（南京大学）；University of Pennsylvania（宾夕法尼亚大学）；National University of Singapore（新加坡国立大学）；University of Illinois Urbana-Champaign（伊利诺伊大学香槟分校）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）
The eBDIMS path-sampling server: generation, classification and interactive visualization of protein ensembles and transition pathways in 2D-motion space
