Scarcity of structural and evolutionary information on protein complexes poses a challenge to deep learning-based structure modelling. We integrate experimental distance restraints obtained by crosslinking mass spectrometry (MS) into AlphaFold-Multimer, by extending AlphaLink to protein complexes. Integrating crosslinking MS data substantially improves modelling performance on challenging targets, by helping to identify interfaces, focusing sampling, and improving model selection. This extends to single crosslinks from whole-cell crosslinking MS, opening the possibility of whole-cell structural investigations driven by experimental data. We demonstrate this by revealing the molecular basis of iron homoeostasis in Bacillus subtilis.

As a critical sensor protein, NLRP3 detects cellular perturbation caused by diverse exogenous and endogenous stimuli. NLRP3 activation requires domain rotation within the NEK7-bound NLRP3 monomer and assembly. However, a detailed molecular mechanism for NLRP3 assembly and activation remains elusive, particularly in terms of dynamics and energetics. In this work, all-atom molecular dynamics (MD) simulations are executed to describe large-amplitude closed-to-open conformational transitions along the rotational pathway. From the MD trajectories, the computed potential of mean force (PMF) shows that NLRP3 activation through monomeric domain rotation is an uphill process, during which the active conformation of the NLRP3-NEK7 monomer cannot be stabilized. Further binding free-energy calculations for two neighboring NLRP3-NEK7 subunits in a disc assembly with the C10 symmetry reveal that the protein self-assembly starts approximately at the 86.5° position on the rotary pathway, along which the NLRP3 activation becomes a downhill process to the active state at 90.5°. The active NLRP3-NEK7 monomeric conformation in the disc assembly is stabilized because of the interactions between the neighboring subunits, involving mainly FISNA loop 1 in one subunit and a \"crocodile-clip\" structure formed by the NBD helix-loop-strand motif (residues 351-373) and the WHD β-hairpin loop (residues 501-521) in the other. Our simulations also demonstrate that NEK7 plays an important role in the NLRP3 cage dissociation in the centrosome, which is consistent with biological experiments. The computational results provide kinetic, energetic, and structural insights into the molecular mechanisms of the activation of NLRP3 and the NEK7-driven dissociation of inactive NLRP3 cages. The activation mechanism of NLRP3 proposed in this work is significantly different from those of previous structural studies.

BACKGROUND: ​​The genus Fusarium poses significant threats to food security and safety worldwide because numerous species of the fungus cause destructive diseases and/or mycotoxin contamination in crops. The adverse effects of climate change are exacerbating some existing threats and causing new problems. These challenges highlight the need for innovative solutions, including the development of advanced tools to identify targets for control strategies.
METHODS: In response to these challenges, we developed the Fusarium Protein Toolkit (FPT), a web-based tool that allows users to interrogate the structural and variant landscape within the Fusarium pan-genome. The tool displays both AlphaFold and ESMFold-generated protein structure models from six Fusarium species. The structures are accessible through a user-friendly web portal and facilitate comparative analysis, functional annotation inference, and identification of related protein structures. Using a protein language model, FPT predicts the impact of over 270 million coding variants in two of the most agriculturally important species, Fusarium graminearum and F. verticillioides. To facilitate the assessment of naturally occurring genetic variation, FPT provides variant effect scores for proteins in a Fusarium pan-genome based on 22 diverse species. The scores indicate potential functional consequences of amino acid substitutions and are displayed as intuitive heatmaps using the PanEffect framework.
CONCLUSIONS: FPT fills a knowledge gap by providing previously unavailable tools to assess structural and missense variation in proteins produced by Fusarium. FPT has the potential to deepen our understanding of pathogenic mechanisms in Fusarium, and aid the identification of genetic targets for control strategies that reduce crop diseases and mycotoxin contamination. Such targets are vital to solving the agricultural problems incited by Fusarium, particularly evolving threats resulting from climate change. Thus, FPT has the potential to contribute to improving food security and safety worldwide.

In this issue of Structure, Walker et al.1 determined the NMR structure of a recently discovered defensin, Pp19, from the venom of an assassin bug. This peptide adopts an α-defensin-like structure, which had not been observed in insects before. Unlike mammalian α-defensins, which are generally antimicrobial, Pp19 has insecticidal activity.

In this Voices article, we introduce seven impressive young group leaders that presented their work at the recent Gordon Research Conference \"Biophysics and biology of intrinsically disordered proteins\" in Les Diablerets, Switzerland. We asked them to tell us more about their careers and their fascinating research on proteins that do not adopt a single-folded structure.

In humans, 15 genes encode the class B1 family of GPCRs, which are polypeptide hormone receptors characterized by having a large N-terminal extracellular domain (ECD) and receive signals from outside the cell to activate cellular response. For example, the insulinotropic polypeptide (GIP) stimulates the glucose-dependent insulinotropic polypeptide receptor (GIPR), while the glucagon receptor (GCGR) responds to glucagon by increasing blood glucose levels and promoting the breakdown of liver glycogen to induce the production of insulin. The glucagon-like peptides 1 and 2 (GLP-1 and GLP-2) elicit a response from glucagon-like peptide receptor types 1 and 2 (GLP1R and GLP2R), respectively. Since these receptors are implicated in the pathogenesis of diabetes, studying their activation is crucial for the development of effective therapies for the condition. With more structural information being revealed by experimental methods such as X-ray crystallography, cryo-EM, and NMR, the activation mechanism of class B1 GPCRs becomes unraveled. The available crystal and cryo-EM structures reveal that class B1 GPCRs follow a two-step model for peptide binding and receptor activation. The regions close to the C-termini of hormones interact with the N-terminal ECD of the receptor while the regions close to the N-terminus of the peptide interact with the TM domain and transmit signals. This review highlights the structural details of class B1 GPCRs and their conformational changes following activation. The roles of MD simulation in characterizing those conformational changes are briefly discussed, providing insights into the potential structural exploration for future ligand designs.

Small-molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2\'s strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application of AlphaFold2 models in virtual screening and drug discovery remains tentative. Here, we demonstrate an AlphaFold2-based framework combined with all-atom enhanced sampling molecular dynamics and Induced Fit docking, named AF2RAVE-Glide, to conduct computational model-based small-molecule binding of metastable protein kinase conformations, initiated from protein sequences. We demonstrate the AF2RAVE-Glide workflow on three different mammalian protein kinases and their type I and II inhibitors, with special emphasis on binding of known type II kinase inhibitors which target the metastable classical DFG-out state. These states are not easy to sample from AlphaFold2. Here, we demonstrate how with AF2RAVE these metastable conformations can be sampled for different kinases with high enough accuracy to enable subsequent docking of known type II kinase inhibitors with more than 50% success rates across docking calculations. We believe the protocol should be deployable for other kinases and more proteins generally.

We here introduce Ensemble Optimizer (EnOpt), a machine-learning tool to improve the accuracy and interpretability of ensemble virtual screening (VS). Ensemble VS is an established method for predicting protein/small-molecule (ligand) binding. Unlike traditional VS, which focuses on a single protein conformation, ensemble VS better accounts for protein flexibility by predicting binding to multiple protein conformations. Each compound is thus associated with a spectrum of scores (one score per protein conformation) rather than a single score. To effectively rank and prioritize the molecules for further evaluation (including experimental testing), researchers must select which protein conformations to consider and how best to map each compound\'s spectrum of scores to a single value, decisions that are system-specific. EnOpt uses machine learning to address these challenges. We perform benchmark VS to show that for many systems, EnOpt ranking distinguishes active compounds from inactive or decoy molecules more effectively than traditional ensemble VS methods. To encourage broad adoption, we release EnOpt free of charge under the terms of the MIT license.

Knowledge of the solvent accessibility of residues in a protein is essential for different applications, including the identification of interacting surfaces in protein-protein interactions and the characterization of variations. We describe E-pRSA, a novel web server to estimate Relative Solvent Accessibility values (RSAs) of residues directly from a protein sequence. The method exploits two complementary Protein Language Models to provide fast and accurate predictions. When benchmarked on different blind test sets, E-pRSA scores at the state-of-the-art, and outperforms a previous method we developed, DeepREx, which was based on sequence profiles after Multiple Sequence Alignments. The E-pRSA web server is freely available at https://e-prsa.biocomp.unibo.it/main/ where users can submit single-sequence and batch jobs.

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/.