Identification and visualization of protein binding regions with the ArDock server

Modeling of Protein Complexes

The recent advances in structural biology, combined with continuously increasing computational capabilities and development of advanced softwares, have drastically simplified the workflow for protein homology modeling. Modeling of individual proteins is nowadays quick and straightforward for a large variety of protein targets, thanks to guided pipelines relying on advanced computational tools and user-friendly interfaces, which have extended and promoted the use of modeling also to scientists not focusing on molecular structures of proteins. Nevertheless, construction of models of multi-protein complexes remains quite challenging for the non-experts, often due to the usage of specific procedures depending on the system under investigation and the need for experimental validation approaches to strengthen the generated output.In this chapter, we provide a brief overview of the approaches enabling generation of multi-protein complex models starting from homology models of individual protein components. Using real-life examples, we include two examples to guide the reader in the generation of homomeric and heteromeric protein models.

In-silico investigation of a novel inhibitors against the antibiotic-resistant Neisseria gonorrhoeae bacteria

Antibiotics are drugs that are used to treat or prevent bacterial infections. They work by either killing or stopping bacteria from spreading. Nevertheless, it appeared in the last decade, Antibiotic-resistant bacteria are bacteria resistant to antibiotics and cannot be controlled or killed by them. In the presence of an antibiotic, they can live and even reproduce. The Neisseria gonorrhoeae bacteria is appearing to be a multidrug-resistant pathogen. Many factors contribute to antibiotic resistance, including unfettered access to antimicrobials, incorrect drug selection, misuse, and low-quality antibiotics. Here, we investigated in-silico docking screening and analysis for ten natural marine fungus extracted compounds. The resulted data were examined for the best binding affinity, toxicity, and chemical interactions. The most superior compound was elipyrone A with six hydrogen bonds, −8.5 of binding affinity, and preferable results in the SWISS-ADME examination. It is well known that “Declining corporate investment and a lack of innovation in the development of new antibiotics are weakening efforts to battle drug-resistant illnesses,” according to the World Health Organization (WHO). So, we extended our effort to predict a new natural compound to overcome the resistance of this bacteria.

From complete cross-docking to partners identification and binding sites predictions

Proteins ensure their biological functions by interacting with each other. Hence, characterising protein interactions is fundamental for our understanding of the cellular machinery, and for improving medicine and bioengineering. Over the past years, a large body of experimental data has been accumulated on who interacts with whom and in what manner. However, these data are highly heterogeneous and sometimes contradictory, noisy, and biased. Ab initio methods provide a means to a "blind" protein-protein interaction network reconstruction. Here, we report on a molecular cross-docking-based approach for the identification of protein partners. The docking algorithm uses a coarse-grained representation of the protein structures and treats them as rigid bodies. We applied the approach to a few hundred of proteins, in the unbound conformations, and we systematically investigated the influence of several key ingredients, such as the size and quality of the interfaces, and the scoring function. We achieved some significant improvement compared to previous works, and a very high discriminative power on some specific functional classes. We provide a readout of the contributions of shape and physico-chemical complementarity, interface matching, and specificity, in the predictions. In addition, we assessed the ability of the approach to account for protein surface multiple usages, and we compared it with a sequence-based deep learning method. This work may contribute to guiding the exploitation of the large amounts of protein structural models now available toward the discovery of unexpected partners and their complex structure characterisation.

Activity of Lymphostatin, A Lymphocyte Inhibitory Virulence Factor of Pathogenic Escherichia coli, is Dependent on a Cysteine Protease Motif

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LifA shares a cysteine protease motif with bacterial toxins and secreted effectors.

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C1480A substituted LifA has reduced inhibitory activity against T cells.

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LifA is cleaved in T cells and this requires C1480 and endosome acidification.

Lymphostatin (LifA) is a 366 kDa protein expressed by attaching & effacing Escherichia coli. It plays an important role in intestinal colonisation and inhibits the mitogen- and antigen-stimulated proliferation of lymphocytes and the synthesis of proinflammatory cytokines. LifA exhibits N-terminal homology with the glycosyltransferase domain of large clostridial toxins (LCTs). A DTD motif within this region is required for lymphostatin activity and binding of the sugar donor uridine diphosphate N-acetylglucosamine. As with LCTs, LifA also contains a cysteine protease motif (C1480, H1581, D1596) that is widely conserved within the YopT-like superfamily of cysteine proteases. By analogy with LCTs, we hypothesised that the CHD motif may be required for intracellular processing of the protein to release the catalytic N-terminal domain after uptake and low pH-stimulated membrane insertion of LifA within endosomes. Here, we created and validated a C1480A substitution mutant in LifA from enteropathogenic E. coli strain E2348/69. The purified protein was structurally near-identical to the wild-type protein. In bovine T lymphocytes treated with wild-type LifA, a putative cleavage product of approximately 140 kDa was detected. Appearance of the putative cleavage product was inhibited in a concentration-dependent manner by bafilomycin A1 and chloroquine, which inhibit endosome acidification. The cleavage product was not observed in cells treated with the C1480A mutant of LifA. Lymphocyte inhibitory activity of the purified C1480A protein was significantly impaired. The data indicate that an intact cysteine protease motif is required for cleavage of lymphostatin and its activity against T cells.

Integrating Resonant Recognition Model and Stockwell Transform for Localization of Hotspots in Tubulin

Tubulin is a promising target for designing anti-cancer drugs. Identification of hotspots in multifunctional Tubulin protein provides insights for new drug discovery. Although machine learning techniques have shown significant results in prediction, they fail to identify the hotspots corresponding to a particular biological function. This paper presents a signal processing technique combining resonant recognition model (RRM) and Stockwell Transform (ST) for the identification of hotspots corresponding to a particular functionality. The characteristic frequency (CF) representing a specific biological function is determined using the RRM. Then the spectrum of the protein sequence is computed using ST. The CF is filtered from the ST spectrum using a time-frequency mask. The energy peaks in the filtered sequence represent the hotspots. The hotspots predicted by the proposed method are compared with the experimentally detected binding residues of Tubulin stabilizing drug Taxol and destabilizing drug Colchicine present in the Tubulin protein. Out of the 53 experimentally identified hotspots, 60% are predicted by the proposed method whereas around 20% are predicted by existing machine learning based methods. Additionally, the proposed method predicts some new hot spots, which may be investigated.

R, José M. SQ, D. FGE, Marvin A. SU. Docking Simulations Exhibit Bortezomib and other Boron-containing Peptidomimetics as Potential Inhibitors of SARS-CoV-2 Main Protease

Background::

Treatment of the COVID19 pandemic requires drug development. Boron- containing compounds are attractive chemical agents, some of them act as proteases inhibitors.

Objective::

The present study explores the role of boronic moieties in molecules interacting on the binding site of the SARS-CoV-2 main protease.

Methods::

Conventional docking procedure was applied by assaying boron-free and boron-containing compounds on the recently reported crystal structure of SARS-CoV-2 main protease (PDB code: 6LU7). The set of 150 ligands includes bortezomib and inhibitors of coronavirus proteases.

Results::

Most of the tested compounds share contact with key residues and pose on the cleavage pocket. The compounds with a boron atom in their structure are often estimated to have higher affinity than boron-free analogues.

Conclusion::

Interactions and the affinity of boron-containing peptidomimetics strongly suggest that boron-moieties increase affinity on the main protease, which is tested by in vitro assays. A Bis-boron-containing compound previously tested active on SARS-virus protease and bortezomib were identified as potent ligands. These advances may be relevant to drug designing, in addition to testing available boron-containing drugs in patients with COVID19 infection.

Multiscale Visual Drilldown for the Analysis of Large Ensembles of Multi-Body Protein Complexes

When studying multi-body protein complexes, biochemists use computational tools that can suggest hundreds or thousands of their possible spatial configurations. However, it is not feasible to experimentally verify more than only a very small subset of them. In this paper, we propose a novel multiscale visual drilldown approach that was designed in tight collaboration with proteomic experts, enabling a systematic exploration of the configuration space. Our approach takes advantage of the hierarchical structure of the data - from the whole ensemble of protein complex configurations to the individual configurations, their contact interfaces, and the interacting amino acids. Our new solution is based on interactively linked 2D and 3D views for individual hierarchy levels. At each level, we offer a set of selection and filtering operations that enable the user to narrow down the number of configurations that need to be manually scrutinized. Furthermore, we offer a dedicated filter interface, which provides the users with an overview of the applied filtering operations and enables them to examine their impact on the explored ensemble. This way, we maintain the history of the exploration process and thus enable the user to return to an earlier point of the exploration. We demonstrate the effectiveness of our approach on two case studies conducted by collaborating proteomic experts.

The interactome of stabilized α-synuclein oligomers and neuronal proteins

While it is generally accepted that α-synuclein oligomers (αSOs) play an important role in neurodegeneration in Parkinson's disease, the basis for their cytotoxicity remains unclear. We have previously shown that docosahexaenoic acid (DHA) stabilizes αSOs against dissociation without compromising their ability to colocalize with glutamatergic synapses of primary hippocampal neurons, suggesting that they bind to synaptic proteins. Here, we develop a proteomic screen for putative αSO binding partners in rat primary neurons using DHA-stabilized human αSOs as a bait protein. The protocol involved co-immunoprecipitation in combination with a photoactivatable heterobifunctional sulfo-LC-SDA crosslinker which did not compromise neuronal binding and preserved the interaction between the αSOs-binding partners. We identify in total 29 proteins associated with DHA-αSO of which eleven are membrane proteins, including synaptobrevin-2B (VAMP-2B), the sodium-potassium pump (Na⁺ /K⁺ ATPase), the V-type ATPase, the voltage-dependent anion channel and calcium-/calmodulin-dependent protein kinase type II subunit gamma; only these five hits were also found in previous studies which used unmodified αSOs as bait. We also identified Rab-3A as a target with likely disease relevance. Three out of four selected hits were subsequently validated with dot-blot binding assays. In addition, likely binding sites on these ligands were identified by computational analysis, highlighting a diversity of possible interactions between αSOs and target proteins. These results constitute an important step in the search for disease-modifying treatments targeting toxic αSOs.