OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features in simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations with only a modest increase in cost.

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost.

TALAIA: a 3D visual dictionary for protein structures.

MOTIVATION: Graphical analysis of the molecular structure of proteins can be very complex. Full-atom representations retain most geometric information but are generally crowded, and key structural patterns can be challenging to identify. Non-full-atom representations could be more instructive on physicochemical aspects but be insufficiently detailed regarding shapes (e.g. entity beans-like models in coarse grain approaches) or simple properties of amino acids (e.g. representation of superficial electrostatic properties). In this work, we present TALAIA a visual dictionary that aims to provide another layer of structural representations.TALAIA offers a visual grammar that combines simple representations of amino acids while retaining their general geometry and physicochemical properties. It uses unique objects, with differentiated shapes and colors to represent amino acids. It makes easier to spot crucial molecular information, including patches of amino acids or key interactions between side chains. Most conventions used in TALAIA are standard in chemistry and biochemistry, so experimentalists and modelers can rapidly grasp the meaning of any TALAIA depiction. RESULTS: We propose TALAIA as a tool that renders protein structures and encodes structure and physicochemical aspects as a simple visual grammar. The approach is fast, highly informative, and intuitive, allowing the identification of possible interactions, hydrophobic patches, and other characteristic structural features at first glance. The first implementation of TALAIA can be found at https://github.com/insilichem/talaia.

TeachOpenCADD 2022: open source and FAIR Python pipelines to assist in structural bioinformatics and cheminformatics research.

Computational pipelines have become a crucial part of modern drug discovery campaigns. Setting up and maintaining such pipelines, however, can be challenging and time-consuming-especially for novice scientists in this domain. TeachOpenCADD is a platform that aims to teach domain-specific skills and to provide pipeline templates as starting points for research projects. We offer Python-based solutions for common tasks in cheminformatics and structural bioinformatics in the form of Jupyter notebooks, based on open source resources only. Including the 12 newly released additions, TeachOpenCADD now contains 22 notebooks that cover both theoretical background as well as hands-on programming. To promote reproducible and reusable research, we apply software best practices to our notebooks such as testing with automated continuous integration and adhering to the idiomatic Python style. The new TeachOpenCADD website is available at https://projects.volkamerlab.org/teachopencadd and all code is deposited on GitHub.

TeachOpenCADD-KNIME: A Teaching Platform for Computer-Aided Drug Design Using KNIME Workflows.

Open-source workflows have become more and more an integral part of computer-aided drug design (CADD) projects since they allow reproducible and shareable research that can be easily transferred to other projects. Setting up, understanding, and applying such workflows involves either coding or using workflow managers that offer a graphical user interface. We previously reported the TeachOpenCADD teaching platform that provides interactive Jupyter Notebooks (talktorials) on central CADD topics using open-source data and Python packages. Here we present the conversion of these talktorials to KNIME workflows that allow users to explore our teaching material without any line of code. TeachOpenCADD KNIME workflows are freely available on the KNIME Hub: https://hub.knime.com/volkamerlab/space/TeachOpenCADD .

Discovery of processive catalysis by an exo-hydrolase with a pocket-shaped active site.

Substrates associate and products dissociate from enzyme catalytic sites rapidly, which hampers investigations of their trajectories. The high-resolution structure of the native Hordeum exo-hydrolase HvExoI isolated from seedlings reveals that non-covalently trapped glucose forms a stable enzyme-product complex. Here, we report that the alkyl ß-D-glucoside and methyl 6-thio-ß-gentiobioside substrate analogues perfused in crystalline HvExoI bind across the catalytic site after they displace glucose, while methyl 2-thio-ß-sophoroside attaches nearby. Structural analyses and multi-scale molecular modelling of nanoscale reactant movements in HvExoI reveal that upon productive binding of incoming substrates, the glucose product modifies its binding patterns and evokes the formation of a transient lateral cavity, which serves as a conduit for glucose departure to allow for the next catalytic round. This path enables substrate-product assisted processive catalysis through multiple hydrolytic events without HvExoI losing contact with oligo- or polymeric substrates. We anticipate that such enzyme plasticity could be prevalent among exo-hydrolases.