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引用本文的文献

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Viruses. 2022 Jan 18;14(2):170. doi: 10.3390/v14020170.

第六届计算结构生物信息学研讨会

The 6th Computational Structural Bioinformatics Workshop.

作者信息

He Jing, Shehu Amarda, Haspel Nurit, Chen Brian

出版信息

BMC Struct Biol. 2013;13 Suppl 1(Suppl 1):I1. doi: 10.1186/1472-6807-13-S1-I1. Epub 2013 Nov 8.

DOI:10.1186/1472-6807-13-S1-I1
PMID:24564893
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3953055/
Abstract

The rapid accumulation of macromolecular structures presents a unique set of challenges and opportunities in the analysis, comparison, modeling, and prediction of macromolecular structures and interactions. The 6th Computational Structural Bioinformatics Workshop (CSBW) was held in Philadelphia on October 4, 2012. This issue includes eleven papers selected from the work presented at the CSBW of 2012. In "Four-body atomic potential for modeling protein-ligand binding affinity: application to enzyme-inhibitor binding energy prediction", a predictive model of free-energy was built to evaluate the binding between a protein and a ligand. In "Unbiased, scalable sampling of protein loop conformations from probabilistic priors", a new Markov chain Monte Carlo algorithm was proposed to generate unbiased conformations of closed protein loops from probabilistic priors. In "Enhancement of accuracy and efficiency for RNA secondary structure prediction by sequence segmentation and MapReduce", authors demonstrate an enhanced method by cutting a long RNA sequence into smaller chunks at strategically selected points, and distributing the tasks to multiple processors. In "A population-based evolutionary search approach to the multiple minima problem in de novo protein structure prediction", the authors present an evolutionary search algorithm to obtain a discrete representation of the protein energy surface in terms of an ensemble of conformations representing local energy minima. In "Estimating loop length from Cryo-EM images at medium resolutions", the authors developed a computational geometry method to simplify the points along the skeleton to measure loop length in 3D images. The paper "A conservation and rigidity based method for detecting critical protein residues" presents a method that combines the rigidity and the evolutionary conservation in detection of the critical residues. In "A conservation and biophysics guided stochastic approach to refining docked multimeric proteins", the authors introduce a refinement method that accepts complexes consisting of any number of monomeric units. In "Elucidating the ensemble of functionally-relevant transitions in protein systems with a robotics-inspired method," the authors present a robotics-inspired tree-based method to sample energetically-credible conformational pathways connecting diverse functional states in multimodal proteins. The paper "An aggregate analysis of many predicted structures to reduce errors in protein structure comparison caused by conformational flexibility" applies protein structure prediction algorithms to enhance the classification of homologous proteins according to their binding preferences. In "DINC: A new AutoDock-based protocol for docking large ligands", an enhanced method was demonstrated for docking large ligands. The paper "Modeling protein conformational transitions by a combination of coarse-grained normal mode analysis and robotics-inspired methods" presents an efficient approach involving robotics concepts.

摘要

大分子结构的快速积累在大分子结构及相互作用的分析、比较、建模和预测方面带来了一系列独特的挑战与机遇。第六届计算结构生物信息学研讨会(CSBW)于2012年10月4日在费城举行。本期包含从2012年CSBW会议上发表的作品中选出的11篇论文。在《用于蛋白质-配体结合亲和力建模的四体原子势:在酶-抑制剂结合能预测中的应用》一文中,构建了一个自由能预测模型来评估蛋白质与配体之间的结合。在《基于概率先验的蛋白质环构象无偏、可扩展采样》中,提出了一种新的马尔可夫链蒙特卡罗算法,用于从概率先验生成封闭蛋白质环的无偏构象。在《通过序列分割和MapReduce提高RNA二级结构预测的准确性和效率》中,作者展示了一种增强方法,即通过在策略性选择的点将长RNA序列切割成较小的片段,并将任务分配给多个处理器。在《基于群体的进化搜索方法解决从头蛋白质结构预测中的多个极小值问题》中,作者提出了一种进化搜索算法,以根据代表局部能量极小值的构象集合获得蛋白质能量表面的离散表示。在《从中等分辨率冷冻电镜图像估计环长度》中,作者开发了一种计算几何方法来简化沿骨架的点,以测量三维图像中的环长度。《一种基于保守性和刚性检测关键蛋白质残基的方法》一文提出了一种在检测关键残基时结合刚性和进化保守性的方法。在《基于保守性和生物物理学引导的随机方法优化对接的多聚体蛋白质》中,作者介绍了一种接受由任意数量单体单元组成的复合物的优化方法。在《用受机器人启发的方法阐明蛋白质系统中功能相关转变的集合》中,作者提出了一种受机器人启发的基于树的方法,以采样连接多模态蛋白质中不同功能状态的能量可信构象途径。《对许多预测结构进行汇总分析以减少构象灵活性导致的蛋白质结构比较误差》一文应用蛋白质结构预测算法,根据同源蛋白质的结合偏好增强其分类。在《DINC:一种基于AutoDock的对接大配体新协议》中,展示了一种对接大配体的增强方法。《通过粗粒度正常模式分析和受机器人启发的方法相结合对蛋白质构象转变进行建模》一文提出了一种涉及机器人概念的有效方法。