Predicted structural mimicry of spike receptor-binding motifs from highly pathogenic human coronaviruses.

Viruses often encode proteins that mimic host proteins in order to facilitate infection. Little work has been done to understand the potential mimicry of the SARS-CoV-2, SARS-CoV, and MERS-CoV spike proteins, particularly the receptor-binding motifs, which could be important in determining tropism and druggability of the virus. Peptide and epitope motifs have been detected on coronavirus spike proteins using sequence homology approaches; however, comparing the three-dimensional shape of the protein has been shown as more informative in predicting mimicry than sequence-based comparisons. Here, we use structural bioinformatics software to characterize potential mimicry of the three coronavirus spike protein receptor-binding motifs. We utilize sequence-independent alignment tools to compare structurally known protein models with the receptor-binding motifs and verify potential mimicked interactions with protein docking simulations. Both human and non-human proteins were returned for all three receptor-binding motifs. For example, all three were similar to several proteins containing EGF-like domains: some of which are endogenous to humans, such as thrombomodulin, and others exogenous, such as Plasmodium falciparum MSP-1. Similarity to human proteins may reveal which pathways the spike protein is co-opting, while analogous non-human proteins may indicate shared host interaction partners and overlapping antibody cross-reactivity. These findings can help guide experimental efforts to further understand potential interactions between human and coronavirus proteins.

COSMIC Cancer Gene Census 3D database: understanding the impacts of mutations on cancer targets.

Mutations in hallmark genes are believed to be the main drivers of cancer progression. These mutations are reported in the Catalogue of Somatic Mutations in Cancer (COSMIC). Structural appreciation of where these mutations appear, in protein-protein interfaces, active sites or deoxyribonucleic acid (DNA) interfaces, and predicting the impacts of these mutations using a variety of computational tools are crucial for successful drug discovery and development. Currently, there are 723 genes presented in the COSMIC Cancer Gene Census. Due to the complexity of the gene products, structures of only 87 genes have been solved experimentally with structural coverage between 90% and 100%. Here, we present a comprehensive, user-friendly, web interface (https://cancer-3d.com/) of 714 modelled cancer-related genes, including homo-oligomers, hetero-oligomers, transmembrane proteins and complexes with DNA, ribonucleic acid, ligands and co-factors. Using SDM and mCSM software, we have predicted the impacts of reported mutations on protein stability, protein-protein interfaces affinity and protein-nucleic acid complexes affinity. Furthermore, we also predicted intrinsically disordered regions using DISOPRED3.

A novel receptor for platelet-activating factor and lysophosphatidylcholine in Trypanosoma cruzi.

The lipid mediators, platelet-activating factor (PAF) and lysophosphatidylcholine (LPC), play relevant pathophysiological roles in Trypanosoma cruzi infection. Several species of LPC, including C18:1 LPC, which mimics the effects of PAF, are synthesized by T. cruzi. The present study identified a receptor in T. cruzi, which was predicted to bind to PAF, and found it to be homologous to members of the progestin and adiponectin family of receptors (PAQRs). We constructed a three-dimensional model of the T. cruzi PAQR (TcPAQR) and performed molecular docking to predict the interactions of the TcPAQR model with C16:0 PAF and C18:1 LPC. We knocked out T. cruzi PAQR (TcPAQR) gene and confirmed the identity of the expressed protein through immunoblotting and immunofluorescence assays using an anti-human PAQR antibody. Wild-type and knockout (KO) parasites were also used to investigate the in vitro cell differentiation and interactions with peritoneal mouse macrophages; TcPAQR KO parasites were unable to react to C16:0 PAF or C18:1 LPC. Our data are highly suggestive that PAF and LPC act through TcPAQR in T. cruzi, triggering its cellular differentiation and ability to infect macrophages.

ProtCHOIR: a tool for proteome-scale generation of homo-oligomers.

The rapid developments in gene sequencing technologies achieved in the recent decades, along with the expansion of knowledge on the three-dimensional structures of proteins, have enabled the construction of proteome-scale databases of protein models such as the Genome3D and ModBase. Nevertheless, although gene products are usually expressed as individual polypeptide chains, most biological processes are associated with either transient or stable oligomerisation. In the PDB databank, for example, ~40% of the deposited structures contain at least one homo-oligomeric interface. Unfortunately, databases of protein models are generally devoid of multimeric structures. To tackle this particular issue, we have developed ProtCHOIR, a tool that is able to generate homo-oligomeric structures in an automated fashion, providing detailed information for the input protein and output complex. ProtCHOIR requires input of either a sequence or a protomeric structure that is queried against a pre-constructed local database of homo-oligomeric structures, then extensively analyzed using well-established tools such as PSI-Blast, MAFFT, PISA and Molprobity. Finally, MODELLER is employed to achieve the construction of the homo-oligomers. The output complex is thoroughly analyzed taking into account its stereochemical quality, interfacial stabilities, hydrophobicity and conservation profile. All these data are then summarized in a user-friendly HTML report that can be saved or printed as a PDF file. The software is easily parallelizable and also outputs a comma-separated file with summary statistics that can straightforwardly be concatenated as a spreadsheet-like document for large-scale data analyses. As a proof-of-concept, we built oligomeric models for the Mabellini Mycobacterium abscessus structural proteome database. ProtCHOIR can be run as a web-service and the code can be obtained free-of-charge at http://lmdm.biof.ufrj.br/protchoir.

Improving Blind Docking in DOCK6 through an Automated Preliminary Fragment Probing Strategy.

Probing protein surfaces to accurately predict the binding site and conformation of a small molecule is a challenge currently addressed through mainly two different approaches: blind docking and cavity detection-guided docking. Although cavity detection-guided blind docking has yielded high success rates, it is less practical when a large number of molecules must be screened against many detected binding sites. On the other hand, blind docking allows for simultaneous search of the whole protein surface, which however entails the loss of accuracy and speed. To bridge this gap, in this study, we developed and tested BLinDPyPr, an automated pipeline which uses FTMap and DOCK6 to perform a hybrid blind docking strategy. Through our algorithm, FTMap docked probe clusters are converted into DOCK6 spheres for determining binding regions. Because these spheres are solely derived from FTMap probes, their locations are contained in and specific to multiple potential binding pockets, which become the regions that are simultaneously probed and chosen by the search algorithm based on the properties of each candidate ligand. This method yields pose prediction results (45.2-54.3% success rates) comparable to those of site-specific docking with the classic DOCK6 workflow (49.7-54.3%) and is half as time-consuming as the conventional blind docking method with DOCK6.

Inhibiting Mycobacterium tuberculosis CoaBC by targeting an allosteric site.

Coenzyme A (CoA) is a fundamental co-factor for all life, involved in numerous metabolic pathways and cellular processes, and its biosynthetic pathway has raised substantial interest as a drug target against multiple pathogens including Mycobacterium tuberculosis. The biosynthesis of CoA is performed in five steps, with the second and third steps being catalysed in the vast majority of prokaryotes, including M. tuberculosis, by a single bifunctional protein, CoaBC. Depletion of CoaBC was found to be bactericidal in M. tuberculosis. Here we report the first structure of a full-length CoaBC, from the model organism Mycobacterium smegmatis, describe how it is organised as a dodecamer and regulated by CoA thioesters. A high-throughput biochemical screen focusing on CoaB identified two inhibitors with different chemical scaffolds. Hit expansion led to the discovery of potent and selective inhibitors of M. tuberculosis CoaB, which we show to bind to a cryptic allosteric site within CoaB.

ProCarbDB: a database of carbohydrate-binding proteins.

Carbohydrate-binding proteins play crucial roles across all organisms and viruses. The complexity of carbohydrate structures, together with inconsistencies in how their 3D structures are reported, has led to difficulties in characterizing the protein-carbohydrate interfaces. In order to better understand protein-carbohydrate interactions, we have developed an open-access database, ProCarbDB, which, unlike the Protein Data Bank (PDB), clearly distinguishes between the complete carbohydrate ligands and their monomeric units. ProCarbDB is a comprehensive database containing over 5200 3D X-ray crystal structures of protein-carbohydrate complexes. In ProCarbDB, the complete carbohydrate ligands are annotated and all their interactions are displayed. Users can also select any protein residue in the proximity of the ligand to inspect its interactions with the carbohydrate ligand and with other neighbouring protein residues. Where available, additional curated information on the binding affinity of the complex and the effects of mutations on the binding have also been provided in the database. We believe that ProCarbDB will be an invaluable resource for understanding protein-carbohydrate interfaces. The ProCarbDB web server is freely available at http://www.procarbdb.science/procarb.

Mabellini: a genome-wide database for understanding the structural proteome and evaluating prospective antimicrobial targets of the emerging pathogen Mycobacterium abscessus.

Mycobacterium abscessus, a rapid growing, multidrug resistant, nontuberculous mycobacteria, can cause a wide range of opportunistic infections, particularly in immunocompromised individuals. M. abscessus has emerged as a growing threat to patients with cystic fibrosis, where it causes accelerated inflammatory lung damage, is difficult and sometimes impossible to treat and can prevent safe transplantation. There is therefore an urgent unmet need to develop new therapeutic strategies. The elucidation of the M. abscessus genome in 2009 opened a wide range of research possibilities in the field of drug discovery that can be more effectively exploited upon the characterization of the structural proteome. Where there are no experimental structures, we have used the available amino acid sequences to create 3D models of the majority of the remaining proteins that constitute the M. abscessus proteome (3394 proteins and over 13 000 models) using a range of up-to-date computational tools, many developed by our own group. The models are freely available for download in an on-line database, together with quality data and functional annotation. Furthermore, we have developed an intuitive and user-friendly web interface (http://www.mabellinidb.science) that enables easy browsing, querying and retrieval of the proteins of interest. We believe that this resource will be of use in evaluating the prospective targets for design of antimicrobial agents and will serve as a cornerstone to support the development of new molecules to treat M. abscessus infections.

Key Topics in Molecular Docking for Drug Design.

Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.

The A-Z of Zika drug discovery.

Despite the recent outbreak of Zika virus (ZIKV), there are still no approved treatments, and early-stage compounds are probably many years away from approval. A comprehensive A-Z review of the recent advances in ZIKV drug discovery efforts is presented, highlighting drug repositioning and computationally guided compounds, including discovered viral and host cell inhibitors. Promising ZIKV molecular targets are also described and discussed, as well as targets belonging to the host cell, as new opportunities for ZIKV drug discovery. All this knowledge is not only crucial to advancing the fight against the Zika virus and other flaviviruses but also helps us prepare for the next emerging virus outbreak to which we will have to respond.

Dataset showing the impact of the protonation states on molecular dynamics of HIV protease.

The data described here supports the research article "Unraveling HIV Protease Flaps Dynamics by Constant pH Molecular Dynamics Simulations" (Soares et al., 2016) [1]. The data involves both standard Molecular Dynamics (MD) and Constant pH Molecular Dynamics (CpHMD) to elucidate the effect of protonation states of catalytic dyad on the HIV-PR conformation. The data obtained from MD simulation demonstrate that the protonation state of the two aspartic acids (Asp25/Asp25') has a strong influence on the dynamics of the HIV-PR. Regarding the CpHMD simulation, we performed pka calculations for HIV-PR and the data indicate that only one catalytic aspartate should be protonated.

Unraveling HIV protease flaps dynamics by Constant pH Molecular Dynamics simulations.

The active site of HIV protease (HIV-PR) is covered by two flaps. These flaps are known to be essential for the catalytic activity of the HIV-PR, but their exact conformations at the different stages of the enzymatic pathway remain subject to debate. Understanding the correct functional dynamics of the flaps might aid the development of new HIV-PR inhibitors. It is known that, the HIV-PR catalytic efficiency is pH-dependent, likely due to the influence of processes such as charge transfer and protonation/deprotonation of ionizable residues. Several Molecular Dynamics (MD) simulations have reported information about the HIV-PR flaps. However, in MD simulations the protonation of a residue is fixed and thus it is not possible to study the correlation between conformation and protonation state. To address this shortcoming, this work attempts to capture, through Constant pH Molecular Dynamics (CpHMD), the conformations of the apo, substrate-bound and inhibitor-bound HIV-PR, which differ drastically in their flap arrangements. The results show that the HIV-PR flaps conformations are defined by the protonation of the catalytic residues Asp25/Asp25' and that these residues are sensitive to pH changes. This study suggests that the catalytic aspartates can modulate the opening of the active site and substrate binding.