Darré Leonardo, Machado Matías Rodrigo, Brandner Astrid Febe, González Humberto Carlos, Ferreira Sebastián, Pantano Sergio
Institut Pasteur de Montevideo , Montevideo, Uruguay.
Department of Chemistry, King's College , London, United Kingdom.
J Chem Theory Comput. 2015 Feb 10;11(2):723-39. doi: 10.1021/ct5007746.
Modeling of macromolecular structures and interactions represents an important challenge for computational biology, involving different time and length scales. However, this task can be facilitated through the use of coarse-grained (CG) models, which reduce the number of degrees of freedom and allow efficient exploration of complex conformational spaces. This article presents a new CG protein model named SIRAH, developed to work with explicit solvent and to capture sequence, temperature, and ionic strength effects in a topologically unbiased manner. SIRAH is implemented in GROMACS, and interactions are calculated using a standard pairwise Hamiltonian for classical molecular dynamics simulations. We present a set of simulations that test the capability of SIRAH to produce a qualitatively correct solvation on different amino acids, hydrophilic/hydrophobic interactions, and long-range electrostatic recognition leading to spontaneous association of unstructured peptides and stable structures of single polypeptides and protein-protein complexes.
对大分子结构和相互作用进行建模是计算生物学面临的一项重大挑战,涉及不同的时间和长度尺度。然而,通过使用粗粒度(CG)模型可以推动这项任务,该模型减少了自由度的数量,并允许对复杂的构象空间进行高效探索。本文介绍了一种名为SIRAH的新型CG蛋白质模型,其开发目的是用于显式溶剂,并以拓扑无偏的方式捕捉序列、温度和离子强度效应。SIRAH在GROMACS中实现,相互作用使用经典分子动力学模拟的标准成对哈密顿量进行计算。我们展示了一组模拟,测试了SIRAH在不同氨基酸上产生定性正确的溶剂化、亲水/疏水相互作用以及导致无结构肽自发缔合和单链多肽及蛋白质-蛋白质复合物稳定结构的长程静电识别的能力。