Analysis of a large SARS-CoV-2 (Alpha) outbreak in a Catalan prison using conventional and genomic epidemiology.

Enforcing strict protocols that prevent transmission of airborne infections in prisons is challenging. We examine a large SARS-CoV-2 outbreak in a Catalan penitentiary center from February-March 2021, prior to vaccination deployment. The aim was to describe the evolution of the outbreak using classical and genomic epidemiology and the containment strategy applied. The outbreak was initially detected in one module but spread to four, infecting 7 staff members and 140 incarcerated individuals, 6 of whom were hospitalized (4.4%). Genomic analysis confirmed a single origin (B.1.1.7). Contact tracing identified transmission vectors between modules and prevented further viral spread. In future similar scenarios, the control strategy described here may help limiting transmission of airborne infections in correctional settings.

Diverging co-translational protein complex assembly pathways are governed by interface energy distribution.

Protein-protein interactions are at the heart of all cellular processes, with the ribosome emerging as a platform, orchestrating the nascent-chain interplay dynamics. Here, to study the characteristics governing co-translational protein folding and complex assembly, we combine selective ribosome profiling, imaging, and N-terminomics with all-atoms molecular dynamics. Focusing on conserved N-terminal acetyltransferases (NATs), we uncover diverging co-translational assembly pathways, where highly homologous subunits serve opposite functions. We find that only a few residues serve as "hotspots," initiating co-translational assembly interactions upon exposure at the ribosome exit tunnel. These hotspots are characterized by high binding energy, anchoring the entire interface assembly. Alpha-helices harboring hotspots are highly thermolabile, folding and unfolding during simulations, depending on their partner subunit to avoid misfolding. In vivo hotspot mutations disrupted co-translational complexation, leading to aggregation. Accordingly, conservation analysis reveals that missense NATs variants, causing neurodevelopmental and neurodegenerative diseases, disrupt putative hotspot clusters. Expanding our study to include phosphofructokinase, anthranilate synthase, and nucleoporin subcomplex, we employ AlphaFold-Multimer to model the complexes' complete structures. Computing MD-derived interface energy profiles, we find similar trends. Here, we propose a model based on the distribution of interface energy as a strong predictor of co-translational assembly.

Structural vulnerability in EPCR suggests functional modulation.

The endothelial protein C receptor (EPCR) is a fundamental component of the vascular system in mammals due to its contribution in maintaining blood in a non-prothrombotic state, which is crucial for overall life development. It accomplishes this by enhancing the conversion of protein C (PC) into the anticoagulant activated protein C (APC), with this property being dependent on a known EPCR conformation that enables direct interaction with PC/APC. In this study, we report a previously unidentified conformation of EPCR whereby Tyr154, critical for PC/APC binding, shows a striking non-canonical configuration. This unconventional form is incompatible with PC/APC binding, and reveals, for the first time, a region of structural vulnerability and potential modulation in EPCR. The identification of this malleability enhances our understanding of this receptor, prompting inquiries into the interplay between its plasticity and function, as well as its significance within the broader framework of EPCR's biology, which extends to immune conditions.

Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment.

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homodimers, 3 homo-trimers, 13 heterodimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21 941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their five best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% of the targets compared to 8% two years earlier. This remarkable improvement is due to the wide use of the AlphaFold2 and AlphaFold2-Multimer software and the confidence metrics they provide. Notably, expanded sampling of candidate solutions by manipulating these deep learning inference engines, enriching multiple sequence alignments, or integration of advanced modeling tools, enabled top performing groups to exceed the performance of a standard AlphaFold2-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.

Architecture of the ESCPE-1 membrane coat.

Recycling of membrane proteins enables the reuse of receptors, ion channels and transporters. A key component of the recycling machinery is the endosomal sorting complex for promoting exit 1 (ESCPE-1), which rescues transmembrane proteins from the endolysosomal pathway for transport to the trans-Golgi network and the plasma membrane. This rescue entails the formation of recycling tubules through ESCPE-1 recruitment, cargo capture, coat assembly and membrane sculpting by mechanisms that remain largely unknown. Herein, we show that ESCPE-1 has a single-layer coat organization and suggest how synergistic interactions between ESCPE-1 protomers, phosphoinositides and cargo molecules result in a global arrangement of amphipathic helices to drive tubule formation. Our results thus define a key process of tubule-based endosomal sorting.

Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study.

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

A hotspot for posttranslational modifications on the androgen receptor dimer interface drives pathology and anti-androgen resistance.

Mutations of the androgen receptor (AR) associated with prostate cancer and androgen insensitivity syndrome may profoundly influence its structure, protein interaction network, and binding to chromatin, resulting in altered transcription signatures and drug responses. Current structural information fails to explain the effect of pathological mutations on AR structure-function relationship. Here, we have thoroughly studied the effects of selected mutations that span the complete dimer interface of AR ligand-binding domain (AR-LBD) using x-ray crystallography in combination with in vitro, in silico, and cell-based assays. We show that these variants alter AR-dependent transcription and responses to anti-androgens by inducing a previously undescribed allosteric switch in the AR-LBD that increases exposure of a major methylation target, Arg761. We also corroborate the relevance of residues Arg761 and Tyr764 for AR dimerization and function. Together, our results reveal allosteric coupling of AR dimerization and posttranslational modifications as a disease mechanism with implications for precision medicine.

The multivalency of the glucocorticoid receptor ligand-binding domain explains its manifold physiological activities.

The glucocorticoid receptor (GR) is a ubiquitously expressed transcription factor that controls metabolic and homeostatic processes essential for life. Although numerous crystal structures of the GR ligand-binding domain (GR-LBD) have been reported, the functional oligomeric state of the full-length receptor, which is essential for its transcriptional activity, remains disputed. Here we present five new crystal structures of agonist-bound GR-LBD, along with a thorough analysis of previous structural work. We identify four distinct homodimerization interfaces on the GR-LBD surface, which can associate into 20 topologically different homodimers. Biologically relevant homodimers were identified by studying a battery of GR point mutants including crosslinking assays in solution, quantitative fluorescence microscopy in living cells, and transcriptomic analyses. Our results highlight the relevance of non-canonical dimerization modes for GR, especially of contacts made by loop L1-3 residues such as Tyr545. Our work illustrates the unique flexibility of GR's LBD and suggests different dimeric conformations within cells. In addition, we unveil pathophysiologically relevant quaternary assemblies of the receptor with important implications for glucocorticoid action and drug design.

pyDockDNA: A new web server for energy-based protein-DNA docking and scoring.

Proteins and nucleic acids are essential biological macromolecules for cell life. Indeed, interactions between proteins and DNA regulate many biological processes such as protein synthesis, signal transduction, DNA storage, or DNA replication and repair. Despite their importance, less than 4% of total structures deposited in the Protein Data Bank (PDB) correspond to protein-DNA complexes, and very few computational methods are available to model their structure. We present here the pyDockDNA web server, which can successfully model a protein-DNA complex with a reasonable predictive success rate (as benchmarked on a standard dataset of protein-DNA complex structures, where DNA is in B-DNA conformation). The server implements the pyDockDNA program, as a module of pyDock suite, thus including third-party programs, modules, and previously developed tools, as well as new modules and parameters to handle the DNA properly. The user is asked to enter Protein Data Bank files for protein and DNA input structures (or suitable models) and select the chains to be docked. The server calculations are mainly divided into three steps: sampling by FTDOCK, scoring with new energy-based parameters and the possibility of applying external restraints. The user can select different options for these steps. The final output screen shows a 3D representation of the top 10 models and a table sorting the model according to the scoring function selected previously. All these output files can be downloaded, including the top 100 models predicted by pyDockDNA. The server can be freely accessed for academic use (https://model3dbio.csic.es/pydockdna).

Structural bases for the higher adherence to ACE2 conferred by the SARS-CoV-2 spike Q498Y substitution.

A remarkable number of SARS-CoV-2 variants and other as yet unmonitored lineages harbor amino-acid substitutions with the potential to modulate the interface between the spike receptor-binding domain (RBD) and its receptor ACE2. The naturally occurring Q498Y substitution, which is present in currently circulating SARS-CoV-2 variants, has drawn the attention of several investigations. While computational predictions and in vitro binding studies suggest that Q498Y increases the binding affinity of the spike protein for ACE2, experimental in vivo models of infection have shown that a triple mutant carrying the Q498Y replacement is fatal in mice. To accurately characterize the binding kinetics of the RBD Q498Y-ACE2 interaction, biolayer interferometry analyses were performed. A significant enhancement of the RBD-ACE2 binding affinity relative to a reference SARS-CoV-2 variant of concern carrying three simultaneous replacements was observed. In addition, the RBD Q498Y mutant bound to ACE2 was crystallized. Compared with the structure of its wild-type counterpart, the RBD Q498Y-ACE2 complex reveals the conservation of major hydrogen-bond interactions and a more populated, nonpolar set of contacts mediated by the bulky side chain of Tyr498 that collectively lead to this increase in binding affinity. In summary, these studies contribute to a deeper understanding of the impact of a relevant mutation present in currently circulating SARS-CoV-2 variants which might lead to stronger host-pathogen interactions.

PirePred: An Accurate Online Consensus Tool to Interpret Newborn Screening-Related Genetic Variants in Structural Context.

PirePred is a genetic interpretation tool used for a variety of medical conditions investigated in newborn screening programs. The PirePred server retrieves, analyzes, and displays in real time genetic and structural data on 58 genes/proteins associated with medical conditions frequently investigated in the newborn. PirePred analyzes the predictions generated by 15 pathogenicity predictors and applies an optimized majority vote algorithm to classify any possible nonsynonymous single-nucleotide variant as pathogenic, benign, or of uncertain significance. PirePred predictions for variants of clear clinical significance are better than those of any of the individual predictors considered (based on accuracy, sensitivity, and negative predictive value) or are among the best ones (for positive predictive value and Matthews correlation coefficient). PirePred predictions also outperform the comparable in silico predictions offered as supporting evidence, according to American College of Medical Genetics and Genomics guidelines, by VarSome and Franklin. Also, PirePred has very high prediction coverage. To facilitate the molecular interpretation of the missense, nonsense, and frameshift variants in ClinVar, the changing amino acid residue is displayed in its structural context, which is analyzed to provide functional clues. PirePred is an accurate, robust, and easy-to-use tool for clinicians involved in neonatal screening programs and for researchers of related diseases. The server is freely accessible and provides a user-friendly gateway into the structural/functional consequences of genetic variants at the protein level.

Novel treatment strategy for NRAS-mutated melanoma through a selective inhibitor of CD147/VEGFR-2 interaction.

More than 70% of human NRASmut melanomas are resistant to MEK inhibitors highlighting the crucial need for efficient therapeutic strategies for these tumors. CD147, a membrane receptor, is overexpressed in most cancers including melanoma and is associated with poor prognosis. We show here that CD147i, a specific inhibitor of CD147/VEGFR-2 interaction represents a potential therapeutic strategy for NRASmut melanoma cells. It significantly inhibited the malignant properties of NRASmut melanomas ex vivo and in vivo. Importantly, NRASmut patient's-derived xenografts, which were resistant to MEKi, became sensitive when combined with CD147i leading to decreased proliferation ex vivo and tumor regression in vivo. Mechanistic studies revealed that CD147i effects were mediated through STAT3 pathway. These data bring a proof of concept on the impact of the inhibition of CD147/VEGFR-2 interaction on melanoma progression and represents a new therapeutic opportunity for NRASmut melanoma when combined with MEKi.

New Insights into the Evolution of the Electron Transfer from Cytochrome f to Photosystem I in the Green and Red Branches of Photosynthetic Eukaryotes.

In cyanobacteria and most green algae of the eukaryotic green lineage, the copper-protein plastocyanin (Pc) alternatively replaces the heme-protein cytochrome c6 (Cc6) as the soluble electron carrier from cytochrome f (Cf) to photosystem I (PSI). The functional and structural equivalence of 'green' Pc and Cc6 has been well established, representing an example of convergent evolution of two unrelated proteins. However, plants only produce Pc, despite having evolved from green algae. On the other hand, Cc6 is the only soluble donor available in most species of the red lineage of photosynthetic organisms, which includes, among others, red algae and diatoms. Interestingly, Pc genes have been identified in oceanic diatoms, probably acquired by horizontal gene transfer from green algae. However, the mechanisms that regulate the expression of a functional Pc in diatoms are still unclear. In the green eukaryotic lineage, the transfer of electrons from Cf to PSI has been characterized in depth. The conclusion is that in the green lineage, this process involves strong electrostatic interactions between partners, which ensure a high affinity and an efficient electron transfer (ET) at the cost of limiting the turnover of the process. In the red lineage, recent kinetic and structural modeling data suggest a different strategy, based on weaker electrostatic interactions between partners, with lower affinity and less efficient ET, but favoring instead the protein exchange and the turnover of the process. Finally, in diatoms the interaction of the acquired green-type Pc with both Cf and PSI may not yet be optimized.

UEP: an open-source and fast classifier for predicting the impact of mutations in protein-protein complexes.

MOTIVATION: Single protein residue mutations may reshape the binding affinity of protein-protein interactions. Therefore, predicting its effects is of great interest in biotechnology and biomedicine. Unfortunately, the availability of experimental data on binding affinity changes upon mutation is limited, which hampers the development of new and more precise algorithms. Here, we propose UEP, a classifier for predicting beneficial and detrimental mutations in protein-protein complexes trained on interactome data. RESULTS: Regardless of the simplicity of the UEP algorithm, which is based on a simple three-body contact potential derived from interactome data, we report competitive results with the gold standard methods in this field with the advantage of being faster in terms of computational time. Moreover, we propose a consensus selection procedure by involving the combination of three predictors that showed higher classification accuracy in our benchmark: UEP, pyDock and EvoEF1/FoldX. Overall, we demonstrate that the analysis of interactome data allows predicting the impact of protein-protein mutations using UEP, a fast and reliable open-source code. AVAILABILITY AND IMPLEMENTATION: UEP algorithm can be found at: https://github.com/pepamengual/UEP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Docking-based identification of small-molecule binding sites at protein-protein interfaces.

Protein-protein interactions play an essential role in many biological processes, and their perturbation is a major cause of disease. The use of small molecules to modulate them is attracting increased attention, but protein interfaces generally do not have clear cavities for binding small compounds. A proposed strategy is to target interface hot-spot residues, but their identification through computational approaches usually require the complex structure, which is not often available. In this context, pyDock energy-based docking and scoring can predict hot-spots on the unbound proteins, thus not requiring the complex structure. Here, we have devised a new strategy to detect protein-protein inhibitor binding sites, based on the integration of molecular dynamics for the generation of transient cavities, and docking-based interface hot-spot prediction for the selection of the suitable cavities. This integrative approach has been validated on a test set formed by protein-protein complexes with known inhibitors for which complete structural data of unbound molecules and complexes is available. The results show that local conformational sampling with short molecular dynamics can generate transient cavities similar to the known inhibitor binding sites, and that docking simulations can identify the best cavities with similar predictive accuracy as when knowing the real interface. In a few cases, these predicted pockets are shown to be suitable for protein-ligand docking. The proposed strategy will be useful for many protein-protein complexes for which there is no available structure, as long as the the unbound proteins do not deviate dramatically from the bound conformations.

Docking approaches for modeling multi-molecular assemblies.

Computational docking approaches aim to overcome the limited availability of experimental structural data on protein-protein interactions, which are key in biology. The field is rapidly moving from the traditional docking methodologies for modeling of binary complexes to more integrative approaches using template-based, data-driven modeling of multi-molecular assemblies. We will review here the predictive capabilities of current docking methods in blind conditions, based on the results from the most recent community-wide blind experiments. Integration of template-based and ab initio docking approaches is emerging as the optimal strategy for modeling protein complexes and multimolecular assemblies. We will also review the new methodological advances on ab initio docking and integrative modeling.

Modeling of Protein Complexes and Molecular Assemblies with pyDock.

The study of the 3D structural details of protein interactions is essential to understand biomolecular functions at the molecular level. In this context, the limited availability of experimental structures of protein-protein complexes at atomic resolution is propelling the development of computational docking methods that aim to complement the current structural coverage of protein interactions. One of these docking approaches is pyDock, which uses van der Waals, electrostatics, and desolvation energy to score docking poses generated by a variety of sampling methods, typically FTDock or ZDOCK. The method has shown a consistently good prediction performance in community-wide assessment experiments like CAPRI or CASP, and has provided biological insights and insightful interpretation of experiments by modeling many biomolecular interactions of biomedical and biotechnological interest. Here, we describe in detail how to perform structural modeling of protein assemblies with pyDock, and the application of its modules to different biomolecular recognition phenomena, such as modeling of binding mode, interface, and hot-spot prediction, use of restraints based on experimental data, inclusion of low-resolution structural data, binding affinity estimation, or modeling of homo- and hetero-oligomeric assemblies.

Modelling the Evolution of COVID-19 in High-Incidence European Countries and Regions: Estimated Number of Infections and Impact of Past and Future Intervention Measures.

A previously developed mechanistic model of COVID-19 transmission has been adapted and applied here to study the evolution of the disease and the effect of intervention measures in some European countries and territories where the disease has had a major impact. A clear impact of the major intervention measures on the reproduction number (Rt) has been found in all studied countries and territories, as already suggested by the drop in the number of deaths over time. Interestingly, the impact of such major intervention measures seems to be the same in most of these countries. The model has also provided realistic estimates of the total number of infections, active cases and future outcomes. While the predictive capabilities of the model are much more uncertain before the peak of the outbreak, we could still reliably predict the evolution of the disease after a major intervention by assuming the subsequent reproduction number from the current study. A greater challenge is to foresee the long-term impact of softer intervention measures, but this model can estimate the outcome of different scenarios and help to plan changes for the implementation of control measures in a given country or region.

Structural Characterization of Protein-Protein Interactions with pyDockSAXS.

Structural characterization of protein-protein interactions can provide essential details to understand biological functions at the molecular level and to facilitate their manipulation for biotechnological and biomedical purposes. Unfortunately, the 3D structure is available for only a small fraction of all possible protein-protein interactions, due to the technical limitations of high-resolution structural determination methods. In this context, low-resolution structural techniques, such as small-angle X-ray scattering (SAXS), can be combined with computational docking to provide structural models of protein-protein interactions at large scale. In this chapter, we describe the pyDockSAXS web server ( https://life.bsc.es/pid/pydocksaxs ), which uses pyDock docking and scoring to provide structural models that optimally satisfy the input SAXS data. This server, which is freely available to the scientific community, provides an automatic pipeline to model the structure of a protein-protein complex from SAXS data.

Structural basis for the dominant or recessive character of GLIALCAM mutations found in leukodystrophies.

Megalencephalic leukoencephalopathy with subcortical cysts (MLC) is a type of leukodystrophy characterized by white matter edema, and it is caused mainly by recessive mutations in MLC1 and GLIALCAM genes. These variants are called MLC1 and MLC2A with both types of patients sharing the same clinical phenotype. In addition, dominant mutations in GLIALCAM have also been identified in a subtype of MLC patients with a remitting phenotype. This variant has been named MLC2B. GLIALCAM encodes for an adhesion protein containing two immunoglobulin (Ig) domains and it is needed for MLC1 targeting to astrocyte-astrocyte junctions. Most mutations identified in GLIALCAM abolish GlialCAM targeting to junctions. However, it is unclear why some mutations behave as recessive or dominant. Here, we used a combination of biochemistry methods with a new developed anti-GlialCAM nanobody, double-mutants and cysteine cross-links experiments, together with computer docking, to create a structural model of GlialCAM homo-interactions. Using this model, we suggest that dominant mutations affect different GlialCAM-GlialCAM interacting surfaces in the first Ig domain, which can occur between GlialCAM molecules present in the same cell (cis) or present in neighbouring cells (trans). Our results provide a framework that can be used to understand the molecular basis of pathogenesis of all identified GLIALCAM mutations.