New molecular modeling approaches, driven by rapidly improving computational platforms, have allowed many success stories for the use of computer-assisted drug design in the discovery of new mechanism- or structure-based drugs. In this overview, we highlight three aspects of the use of molecular docking. First, we discuss the combination of molecular and quantum mechanics to investigate an unusual enzymatic mechanism of a flavoprotein. Second, we present recent advances in anti-infectious agents' synthesis driven by structural insights. At the end, we focus on larger biological complexes made by protein–protein interactions and discuss their relevance in drug design. This review provides information on how these large systems, even in the presence of the solvent, can be investigated with the outlook of drug discovery.

We present the results for CAPRI Round 46, the third joint CASP‐CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo‐oligomers and 6 heterocomplexes. Eight of the homo‐oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher‐order assemblies. These were more difficult to model, as their prediction mainly involved “ab‐initio” docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance “gap” was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template‐based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.

Non-coding (nc) RNAs are increasingly recognized to play important regulatory roles in eukaryotic gene expression. The highly abundant and essential 7SK ncRNA has been shown to negatively regulate RNA Polymerase II transcription by inactivating the positive transcription elongation factor b (P-TEFb) in cellular and Tat-dependent HIV transcription. Here, we identify a more general, P-TEFb-independent role of 7SK RNA in directly affecting the function of the architectural transcription factor and chromatin regulator HMGA1. An important regulatory role of 7SK RNA in HMGA1-dependent cell differentiation and proliferation regulation is uncovered with the identification of over 1500 7SK-responsive HMGA1 target genes. Elevated HMGA1 expression is observed in nearly every type of cancer making the use of a 7SK substructure in the inhibition of HMGA1 activity, as pioneered here, potentially useful in therapy. The 7SK-HMGA1 interaction not only adds an essential facet to the comprehension of transcriptional plasticity at the coupling of initiation and elongation, but also might provide a molecular link between HIV reprogramming of cellular gene expression-associated oncogenesis.

Massive sampling in AlphaFold enables access to increased structural diversity. In combination with its efficient confidence ranking, this unlocks elevated modeling capabilities for monomeric structures and foremost for protein assemblies. However, the approach struggles with GPU cost and data storage. Here we introduce MassiveFold, an optimized and customizable version of AlphaFold that runs predictions in parallel, reducing the computing time from several months to hours. MassiveFold is scalable and able to run on anything from a single computer to a large GPU infrastructure, where it can fully benefit from all the computing nodes.

Protein interactions are essential for cellular processes. In recent years there has been significant progress in computational prediction of 3D structures of individual protein chains, with the best-performing algorithms reaching sub-Ångström accuracy. These techniques are now finding their way into the prediction of protein interactions, adding to the existing modeling approaches. The community-wide Critical Assessment of Predicted Interactions (CAPRI) has been a catalyst for the development of procedures for the structural modeling of protein assemblies by organizing blind prediction experiments. The predicted structures are assessed against unpublished experimentally determined structures using a set of metrics with proven robustness that have been established in the CAPRI community. In addition, several advanced benchmarking databases provide targets against which users can test docking and assembly modeling software. These include the Protein-Protein Docking Benchmark, the CAPRI Scoreset, and the Dockground database, all developed by members of the CAPRI community. Here we present CAPRI-Q, a stand-alone model quality assessment tool, which can be freely downloaded or used via a publicly available web server. This tool applies the CAPRI metrics to assess the quality of query structures against given target structures, along with other popular quality metrics such as DockQ, TM-score and l-DDT, and classifies the models according to the CAPRI model quality criteria. The tool can handle a variety of protein complex types including those involving peptides, nucleic acids, and oligosaccharides. The source code is freely available from https://gitlab.in2p3.fr/cmsb-public/CAPRI-Q and its web interface through the Dockground resource at https://dockground.compbio.ku.edu/assessment/.

β-1,4-Galactosyltransferase 1 (B4GALT1) and ST6 β-galactoside α-2,6-sialyltransferase 1 (ST6GAL1) catalyze the successive addition of terminal β-1,4–linked galactose and α-2,6–linked sialic acid to N-glycans. Their exclusive interaction in the Golgi compartment is a prerequisite for their full catalytic activity, whereas a lack of this interaction is associated with cancers and hypoxia. To date, no structural information exists that shows how glycosyltransferases functionally assemble with each other. Using molecular docking simulations to predict interaction surfaces, along with mutagenesis screens and high-throughput FRET analyses in live cells to validate these predictions, we show here that B4GALT1 and ST6GAL1 interact via highly charged noncatalytic surfaces, leaving the active sites exposed and accessible for donor and acceptor substrate binding. Moreover, we found that the assembly of ST6GAL1 homomers in the endoplasmic reticulum before ST6GAL1 activation in the Golgi utilizes the same noncatalytic surface, whereas B4GALT1 uses its active-site surface for assembly, which silences its catalytic activity. Last, we show that the homomeric and heteromeric B4GALT1/ST6GAL1 complexes can assemble laterally in the Golgi membranes without forming cross-cisternal contacts between enzyme molecules residing in the opposite membranes of each Golgi cisterna. Our results provide detailed mechanistic insights into the regulation of glycosyltransferase interactions, the transitions between B4GALT1 and ST6GAL1 homo- and heteromers in the Golgi, and cooperative B4GALT1/ST6GAL1 function in N-glycan synthesis.