Department of Physics, University of Trento, via Sommarive 14, I-38123 Trento, Italy.
INFN-TIFPA, Trento Institute for Fundamental Physics and Applications, via Sommarive 14, I-38123 Trento, Italy.
J Chem Inf Model. 2023 Feb 27;63(4):1260-1275. doi: 10.1021/acs.jcim.2c01311. Epub 2023 Feb 3.
In recent years, a few multiple-resolution modeling strategies have been proposed, in which functionally relevant parts of a biomolecule are described with atomistic resolution, with the remainder of the system being concurrently treated using a coarse-grained model. In most cases, the parametrization of the latter requires lengthy reference all-atom simulations and/or the usage of off-shelf coarse-grained force fields, whose interactions have to be refined to fit the specific system under examination. Here, we overcome these limitations through a novel multiresolution modeling scheme for proteins, dubbed coarse-grained anisotropic network model for variable resolution simulations, or CANVAS. This scheme enables a user-defined modulation of the resolution level throughout the system structure; a fast parametrization of the potential without the necessity of reference simulations; and the straightforward usage of the model on the most commonly used molecular dynamics platforms. The method is presented and validated with two case studies, the enzyme adenylate kinase and the therapeutic antibody pembrolizumab, by comparing the results obtained with the CANVAS model against fully atomistic simulations. The modeling software, implemented in Python, is made freely available for the community on a collaborative github repository.
近年来，已经提出了几种多分辨率建模策略，其中生物分子的功能相关部分用原子分辨率描述，而系统的其余部分则同时用粗粒模型处理。在大多数情况下，后者的参数化需要进行冗长的全原子模拟和/或使用现成的粗粒力场，这些力场的相互作用必须进行细化以适应所研究的特定系统。在这里，我们通过一种新的蛋白质多分辨率建模方案克服了这些限制，该方案称为可变分辨率模拟的粗粒各向异性网络模型，或简称 CANVAS。该方案允许在整个系统结构中用户定义分辨率水平的调制；无需参考模拟即可快速参数化势能；并且可以在最常用的分子动力学平台上直接使用该模型。该方法通过与完全原子模拟的比较，用两个案例研究，即酶腺苷酸激酶和治疗性抗体 pembrolizumab 进行了介绍和验证。该建模软件是用 Python 实现的，并在协作的 github 存储库上免费提供给社区。
