MOTIVATION: Binding sites are the key interfaces that determine a protein's biological activity, and therefore common targets for therapeutic intervention. Techniques that help us detect, compare, and contextualize binding sites are hence of immense interest to drug discovery. RESULTS: Here, we present an approach that integrates protein language models with a 3D tessellation technique to derive rich and versatile representations of binding sites that combine functional, structural, and evolutionary information with unprecedented detail. We demonstrate that the associated similarity metrics induce meaningful pocket clusterings by balancing local structure against global sequence effects. The resulting embeddings are shown to simplify a variety of downstream tasks: they help organize the 'pocketome' in a way that efficiently contextualizes new binding sites, construct performant druggability models, and define challenging train-test splits for believable benchmarking of pocket-centric machine-learning models. AVAILABILITY AND IMPLEMENTATION: A Python package that implements the EPoCS method is freely available at https://github.com/tugceoruc/epocs.

MOTIVATION: Effective use of evolutionary information has recently led to tremendous progress in computational prediction of three-dimensional (3D) structures of proteins and their complexes. Despite the progress, the accuracy of predicted structures tends to vary considerably from case to case. Since the utility of computational models depends on their accuracy, reliable estimates of deviation between predicted and native structures are of utmost importance. RESULTS: For the first time, we present a deep convolutional neural network (CNN) constructed on a Voronoi tessellation of 3D molecular structures. Despite the irregular data domain, our data representation allows us to efficiently introduce both convolution and pooling operations and train the network in an end-to-end fashion without precomputed descriptors. The resultant model, VoroCNN, predicts local qualities of 3D protein folds. The prediction results are competitive to state of the art and superior to the previous 3D CNN architectures built for the same task. We also discuss practical applications of VoroCNN, for example, in recognition of protein binding interfaces. AVAILABILITY AND IMPLEMENTATION: The model, data and evaluation tests are available at https://team.inria.fr/nano-d/software/vorocnn/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Despite the progress made in studying protein-ligand interactions and the widespread application of docking and affinity prediction tools, improving their precision and efficiency still remains a challenge. Computational approaches based on the scoring of docking conformations with statistical potentials constitute a popular alternative to more accurate but costly physics-based thermodynamic sampling methods. In this context, a minimalist and fast sidechain-free knowledge-based potential with a high docking and screening power can be very useful when screening a big number of putative docking conformations. RESULTS: Here, we present a novel coarse-grained potential defined by a 3D joint probability distribution function that only depends on the pairwise orientation and position between protein backbone and ligand atoms. Despite its extreme simplicity, our approach yields very competitive results with the state-of-the-art scoring functions, especially in docking and screening tasks. For example, we observed a twofold improvement in the median 5% enrichment factor on the DUD-E benchmark compared to Autodock Vina results. Moreover, our results prove that a coarse sidechain-free potential is sufficient for a very successful docking pose prediction. AVAILABILITYAND IMPLEMENTATION: The standalone version of KORP-PL with the corresponding tests and benchmarks are available at https://team.inria.fr/nano-d/korp-pl/ and https://chaconlab.org/modeling/korp-pl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.