Reports of successful applications of machine learning (ML) methods in structure-based virtual screening (SBVS) are increasing. ML methods such as convolutional neural networks show promising results and often outperform traditional methods such as empirical scoring functions in retrospective validation. However, trained ML models are often treated as black boxes and are not straightforwardly interpretable. In most cases, it is unknown which features in the data are decisive and whether a model’s predictions are right for the right reason. Hence, we re-evaluated three widely used benchmark data sets in the context of ML methods and came to the conclusion that not every benchmark data set is suitable. Moreover, we demonstrate on two examples from current literature that bias is learned implicitly and unnoticed from standard benchmarks. On the basis of these results, we conclude that there is a need for eligible validation experiments and benchmark data sets suited to ML for more bias-controlled validation in ML-based SBVS. Therefore, we provide guidelines for setting up validation experiments and give a perspective on how new data sets could be generated.

Upon the ever-increasing number of publicly available experimentally determined and predicted protein and nucleic acid structures, the demand for easy-to-use tools to investigate these structural models is higher than ever before. The ProteinsPlus web server (https://proteins.plus) comprises a growing collection of molecular modeling tools focusing on protein–ligand interactions. It enables quick access to structural investigations ranging from structure analytics and search methods to molecular docking. It is by now well-established in the community and constantly extended. The server gives easy access not only to experts but also to students and occasional users from the field of life sciences. Here, we describe its recently added new features and tools, beyond them a novel method for on-the-fly molecular docking and a search method for single-residue substitutions in local regions of a protein structure throughout the whole Protein Data Bank. Finally, we provide a glimpse into new avenues for the annotation of AlphaFold structures which are directly accessible via a RESTful service on the ProteinsPlus web server.

Supplementary data are available at Bioinformatics online.

In many molecular modeling applications, the standard procedure is still to handle proteins as single, rigid structures. While the importance of conformational flexibility is widely known, handling it remains challenging. Even the crystal structure of a protein usually contains variability exemplified in alternate side chain orientations or backbone segments. This conformational variability is encoded in PDB structure files by so-called alternate locations (AltLocs). Most modeling approaches either ignore AltLocs or resolve them with simple heuristics early on during structure import. We analyzed the occurrence and usage of AltLocs in the PDB and developed an algorithm to automatically handle AltLocs in PDB files enabling all structure-based methods using rigid structures to take the alternative protein conformations described by AltLocs into consideration. A respective software tool named AltLocEnumerator can be used as a structure preprocessor to easily exploit AltLocs. While the amount of data makes it difficult to show impact on a statistical level, handling AltLocs has a substantial impact on a case-by-case basis. We believe that the inspection and consideration of AltLocs is a very valuable approach in many modeling scenarios.

Protein adaptations to extreme environmental conditions are drivers in biotechnological process optimization and essential to unravel the molecular limits of life. Most proteins with such desirable adaptations are found in extremophilic organisms inhabiting extreme environments. The deep sea is such an environment and a promising resource that poses multiple extremes on its inhabitants. Conditions like high hydrostatic pressure and high or low temperature are prevalent and many deep-sea organisms tolerate multiple of these extremes. While molecular adaptations to high temperature are comparatively good described, adaptations to other extremes like high pressure are not well-understood yet. To fully unravel the molecular mechanisms of individual adaptations it is probably necessary to disentangle multifactorial adaptations. In this study, we evaluate differences of protein structures from deepsea organisms and their respective related proteins from nondeep-sea organisms. We created a data collection of 1281 experimental protein structures from 25 deep-sea organisms and paired them with orthologous proteins. We exhaustively evaluate differences between the protein pairs with machine learning and Shapley values to determine characteristic differences in sequence and structure. The results show a reasonable discrimination of deep-sea and nondeep-sea proteins from which we distinguish correlations previously attributed to thermal stability from other signals potentially describing adaptions to high pressure. While some distinct correlations can be observed the overall picture appears intricate.