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计算机蛋白质设计的工具

发布: 2021-07-27 1,881阅读 228评论

近年来随着计算机运算能力的提升以及先进算法的涌现，计算机辅助蛋白设计逐渐成为蛋白质工程的研究热点。其中DBTL（design-build-test-learn）循环（图1）是计算机辅助蛋白设计在挖掘新功能酶或改造现有酶的主要思路。

以结构模拟与能量计算为基础的蛋白质计算设计不但能改造酶的底物特异性与热稳定性，还可从头设计具有特定功能的人工酶。在学习和设计模块中，一系列的计算方法，如易于操作的结构预测和分子对接、大规模分子动力学(MD)模拟、高精度量子力学(QM)计算、数据驱动的机器学习和用户友好的网络工具，极大地帮助蛋白质工程改进甚至定制各种功能。

图1 计算辅助加速蛋白质工程

目前应用于蛋白质工程的主要计算工具总结如表1所示。表1列出了目前较为常用的蛋白质结构预测工具、分子对接工具、蛋白质通道计算工具、配体通道工具、分子动力学模拟工具、量子力学与分子力学结合模拟工具、突变靶点确认工具以及可完成多目的Rosetta工具包。其中配体通道工具的是基于分子对接方法预测配体与蛋白之间结合与解结合过程的结构变化，目前应用于蛋白从头设计工程。Gaussian作为QM量化软件，同时支持AMBER等力场，以ONIOM方式计算QM/MM；ORCA同样作为QM量化软件则支持CHARMM力场。另外，QM/MM一项中的chemshell工具是一种接口。当QM量化软件和MM没有相应接口时，如DL-POLY和Gaussian不能同时使用，即想用Gaussian算QM，想用DL-POLY算MM无法实现同时对QM/MM进行计算，chemshell则作为平台出现，可以同时调用上述两个量化软件，支持QM/MM同时进行。

表 1 蛋白质工程的计算工具

Target property Tools Distribution URL
protein structure MODELLER SM https://salilab.org/modeller
I-TASSER WS http://zhanglab.ccmb.med.umich.edu/I-TASSER
trRosetta WS https://yanglab.nankai.edu.cn/trRosetta
molecular docking AutoDock SM http://autodock.scripps.edu
UCSF DOCK SL http://dock.compbio.ucsf.edu
rDock SL http://rdock.sourceforge.net
protein channel Caver Analyst SM http://www.caver.cz
CHEXVIS WS http://vgl.serc.iisc.ernet.in/chexvis
ligand transport CaverDock WS https://loschmidt.chemi.muni.cz/caverweb
MM GROMACS SL http://www.gromacs.org/
AMBER SL http://ambermd.org
NAMD SL http://www.ks.uiuc.edu/Research/namd
QM and MM Gaussian SM https://gaussian.com
ORCA SL https://orcaforum.kofo.mpg.de
QM/MM ChemShell SL https://orcaforum.kofo.mpg.de
protein design HotSpot Wizard SL https://loschmidt.chemi.muni.cz/hotspotwizard
multipurpose Rosetta SL http://www.rosettacommons.org
在蛋白质工程中，确定突变位点后，下一步就是评估该位点特定突变对蛋白质目标属性的影响。根据评估功能的类型对工具进行了分组，一共7个工具，这些工具可用于根据特定位点突变的效果预测，如表2所示：

表2 用于预测突变效果的工具

Target property Tools URL
protein–protein interactions MutaBind http://www.ncbi.nlm.nih.gov/projects/mutabind/
iSEE Be provided as an R-model
mCSM-PPI2 http://biosig.unimelb.edu.au/mmcsm_ppi
interactions of the protein with nucleic acids and ligands mCSM-NA http://structure.bioc.cam.ac.uk/mcsm na.
PremPDI https://lilab.jysw.suda.edu.cn/research/PremPDI/
mCSM-lig http://structure.bioc.cam.ac.uk/mcsm_lig
protein dynamics and stability DynaMut http://biosig.unimelb.edu.au/dynamut/
这工具的功能主要包括预测蛋白-蛋白相互作用、蛋白与核酸或其他配体的相互作用以及蛋白质动力学及稳定性。

2018年，全球蛋白质工程市场评估为19亿美元，预计2024年将达到39亿美元，同年复合增长率(CAGR)将达到12.4%。而目前针对蛋白质的智能化计算设计研究，国内外都仍处在起步阶段，是我国蛋白质设计领域比肩世界水平的机遇。因此，处理好人工智能在蛋白质设计改造领域的应用，开发蛋白质计算设计新技术，解决关键技术难题，是蛋白质计算设计今后发展的主要道路。

参考文献：

1.Computer- aidedunderstanding  and engineering of enzymatic selectivity. DOI: 10.1016/j.biotechadv.2021.107793. 2021.
2.CSM- NA: predictingthe effects of mutations on protein  nucleic acids interactions. DOI: 10.1093/nar/gkx236.2017.
3.mmCSM-  PPI: predictingthe effects of multiple point mutations on protein-  protein interactions. DOI: 10.1093/nar/gkab273.2021.
4.iSEE: Interfacestructure, evolution, and energy-  based machine learning predictor of bindingaffinity changes upon mutations. DOI: 10.1002/prot.25630. 2018.
5.MutaBind estimatesand interprets the effects of sequence variants on protein- protein interactions.10.1093/nar/gkw374. 2016.
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