Ferro-Costas David, Mosquera-Lois Irea, Fernández-Ramos Antonio
Centro Singular de Investigación en Química Biolóxica e Materiais Moleculares (CIQUS), Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain.
J Cheminform. 2021 Dec 24;13(1):100. doi: 10.1186/s13321-021-00578-0.
In this work, we introduce TorsiFlex, a user-friendly software written in Python 3 and designed to find all the torsional conformers of flexible acyclic molecules in an automatic fashion. For the mapping of the torsional potential energy surface, the algorithm implemented in TorsiFlex combines two searching strategies: preconditioned and stochastic. The former is a type of systematic search based on chemical knowledge and should be carried out before the stochastic (random) search. The algorithm applies several validation tests to accelerate the exploration of the torsional space. For instance, the optimized structures are stored and this information is used to prevent revisiting these points and their surroundings in future iterations. TorsiFlex operates with a dual-level strategy by which the initial search is carried out at an inexpensive electronic structure level of theory and the located conformers are reoptimized at a higher level. Additionally, the program takes advantage of conformational enantiomerism, when possible. As a case study, and in order to exemplify the effectiveness and capabilities of this program, we have employed TorsiFlex to locate the conformers of the twenty proteinogenic amino acids in their neutral canonical form. TorsiFlex has produced a number of conformers that roughly doubles the amount of the most complete work to date.
在这项工作中，我们介绍了TorsiFlex，这是一款用Python 3编写的用户友好型软件，旨在自动找出柔性无环分子的所有扭转构象。为了绘制扭转势能面，TorsiFlex中实现的算法结合了两种搜索策略：预处理搜索和随机搜索。前者是一种基于化学知识的系统搜索，应在随机（无规）搜索之前进行。该算法应用了多种验证测试来加速对扭转空间的探索。例如，优化后的结构会被存储起来，这些信息可用于防止在未来的迭代中重新访问这些点及其周围区域。TorsiFlex采用双层次策略运行，即初始搜索在成本较低的电子结构理论水平上进行，找到的构象在更高水平上重新优化。此外，该程序在可能的情况下利用构象对映体现象。作为一个案例研究，为了举例说明该程序的有效性和能力，我们使用TorsiFlex来定位二十种蛋白质原氨基酸的中性标准形式的构象。TorsiFlex生成的构象数量大约是迄今为止最完整研究的两倍。
