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AMMOS:用于计算机筛选的自动分子力学优化工具。

AMMOS: Automated Molecular Mechanics Optimization tool for in silico Screening.

作者信息

Pencheva Tania, Lagorce David, Pajeva Ilza, Villoutreix Bruno O, Miteva Maria A

机构信息

INSERM U648, Bioinformatics-MTI University Paris Diderot, 5 rue Marie-Andrée Lagroua, 75205 Paris Cedex 13, France.

出版信息

BMC Bioinformatics. 2008 Oct 16;9:438. doi: 10.1186/1471-2105-9-438.

DOI:10.1186/1471-2105-9-438
PMID:18925937
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2588602/
Abstract

BACKGROUND

Virtual or in silico ligand screening combined with other computational methods is one of the most promising methods to search for new lead compounds, thereby greatly assisting the drug discovery process. Despite considerable progresses made in virtual screening methodologies, available computer programs do not easily address problems such as: structural optimization of compounds in a screening library, receptor flexibility/induced-fit, and accurate prediction of protein-ligand interactions. It has been shown that structural optimization of chemical compounds and that post-docking optimization in multi-step structure-based virtual screening approaches help to further improve the overall efficiency of the methods. To address some of these points, we developed the program AMMOS for refining both, the 3D structures of the small molecules present in chemical libraries and the predicted receptor-ligand complexes through allowing partial to full atom flexibility through molecular mechanics optimization.

RESULTS

The program AMMOS carries out an automatic procedure that allows for the structural refinement of compound collections and energy minimization of protein-ligand complexes using the open source program AMMP. The performance of our package was evaluated by comparing the structures of small chemical entities minimized by AMMOS with those minimized with the Tripos and MMFF94s force fields. Next, AMMOS was used for full flexible minimization of protein-ligands complexes obtained from a mutli-step virtual screening. Enrichment studies of the selected pre-docked complexes containing 60% of the initially added inhibitors were carried out with or without final AMMOS minimization on two protein targets having different binding pocket properties. AMMOS was able to improve the enrichment after the pre-docking stage with 40 to 60% of the initially added active compounds found in the top 3% to 5% of the entire compound collection.

CONCLUSION

The open source AMMOS program can be helpful in a broad range of in silico drug design studies such as optimization of small molecules or energy minimization of pre-docked protein-ligand complexes. Our enrichment study suggests that AMMOS, designed to minimize a large number of ligands pre-docked in a protein target, can successfully be applied in a final post-processing step and that it can take into account some receptor flexibility within the binding site area.

摘要

背景

虚拟或计算机辅助配体筛选与其他计算方法相结合,是寻找新先导化合物最具前景的方法之一,从而极大地辅助了药物发现过程。尽管虚拟筛选方法取得了显著进展,但现有的计算机程序并不容易解决诸如:筛选库中化合物的结构优化、受体灵活性/诱导契合以及蛋白质-配体相互作用的准确预测等问题。研究表明,化合物的结构优化以及基于结构的多步虚拟筛选方法中的对接后优化有助于进一步提高方法的整体效率。为了解决其中的一些问题,我们开发了AMMOS程序,通过分子力学优化允许部分到完全原子灵活性,来优化化学库中存在的小分子的三维结构以及预测的受体-配体复合物。

结果

AMMOS程序执行一个自动过程,使用开源程序AMMP对化合物集合进行结构优化,并对蛋白质-配体复合物进行能量最小化。通过将AMMOS最小化的小化学实体的结构与使用Tripos和MMFF94s力场最小化的结构进行比较,评估了我们软件包的性能。接下来,AMMOS用于对从多步虚拟筛选获得的蛋白质-配体复合物进行完全灵活的最小化。对含有60%最初添加抑制剂的选定对接前复合物进行富集研究,在两个具有不同结合口袋性质的蛋白质靶点上,对接前复合物有无最终的AMMOS最小化处理。AMMOS能够在对接前阶段后提高富集效果,在前3%至5%的整个化合物集合中发现40%至60%最初添加的活性化合物。

结论

开源的AMMOS程序可有助于广泛的计算机辅助药物设计研究,如小分子优化或对接前蛋白质-配体复合物的能量最小化。我们的富集研究表明,旨在最小化大量对接在蛋白质靶点上的配体的AMMOS,可成功应用于最终的后处理步骤,并且它可以考虑结合位点区域内的一些受体灵活性。

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