PERCEPTRON: an open-source GPU-accelerated proteoform identification pipeline for top-down proteomics.

PERCEPTRON is a next-generation freely available web-based proteoform identification and characterization platform for top-down proteomics (TDP). PERCEPTRON search pipeline brings together algorithms for (i) intact protein mass tuning, (ii) de novo sequence tags-based filtering, (iii) characterization of terminal as well as post-translational modifications, (iv) identification of truncated proteoforms, (v) in silico spectral comparison, and (vi) weight-based candidate protein scoring. High-throughput performance is achieved through the execution of optimized code via multiple threads in parallel, on graphics processing units (GPUs) using NVidia Compute Unified Device Architecture (CUDA) framework. An intuitive graphical web interface allows for setting up of search parameters as well as for visualization of results. The accuracy and performance of the tool have been validated on several TDP datasets and against available TDP software. Specifically, results obtained from searching two published TDP datasets demonstrate that PERCEPTRON outperforms all other tools by up to 135% in terms of reported proteins and 10-fold in terms of runtime. In conclusion, the proposed tool significantly enhances the state-of-the-art in TDP search software and is publicly available at https://perceptron.lums.edu.pk. Users can also create in-house deployments of the tool by building code available on the GitHub repository (http://github.com/BIRL/Perceptron).

SPECTRUM - A MATLAB Toolbox for Proteoform Identification from Top-Down Proteomics Data.

Top-Down Proteomics (TDP) is an emerging proteomics protocol that involves identification, characterization, and quantitation of intact proteins using high-resolution mass spectrometry. TDP has an edge over other proteomics protocols in that it allows for: (i) accurate measurement of intact protein mass, (ii) high sequence coverage, and (iii) enhanced identification of post-translational modifications (PTMs). However, the complexity of TDP spectra poses a significant impediment to protein search and PTM characterization. Furthermore, limited software support is currently available in the form of search algorithms and pipelines. To address this need, we propose 'SPECTRUM', an open-architecture and open-source toolbox for TDP data analysis. Its salient features include: (i) MS2-based intact protein mass tuning, (ii) de novo peptide sequence tag analysis, (iii) propensity-driven PTM characterization, (iv) blind PTM search, (v) spectral comparison, (vi) identification of truncated proteins, (vii) multifactorial coefficient-weighted scoring, and (viii) intuitive graphical user interfaces to access the aforementioned functionalities and visualization of results. We have validated SPECTRUM using published datasets and benchmarked it against salient TDP tools. SPECTRUM provides significantly enhanced protein identification rates (91% to 177%) over its contemporaries. SPECTRUM has been implemented in MATLAB, and is freely available along with its source code and documentation at https://github.com/BIRL/SPECTRUM/.

Perspectives towards antiviral drug discovery against Ebola virus.

Ebola virus disease (EVD), caused by Ebola viruses, resulted in more than 11 500 deaths according to a recent 2018 WHO report. With mortality rates up to 90%, it is nowadays one of the most deadly infectious diseases. However, no Food and Drug Administration-approved Ebola drugs or vaccines are available yet with the mainstay of therapy being supportive care. The high fatality rate and absence of effective treatment or vaccination make Ebola virus a category-A biothreat pathogen. Fortunately, a series of investigational countermeasures have been developed to control and prevent this global threat. This review summarizes the recent therapeutic advances and ongoing research progress from research and development to clinical trials in the development of small-molecule antiviral drugs, small-interference RNA molecules, phosphorodiamidate morpholino oligomers, full-length monoclonal antibodies, and vaccines. Moreover, difficulties are highlighted in the search for effective countermeasures against EVD with additional focus on the interplay between available in silico prediction methods and their evidenced potential in antiviral drug discovery.

Evolution of efficacious pangenotypic hepatitis C virus therapies.

Hepatitis C compromises the quality of life of more than 350 million individuals worldwide. Over the last decade, therapeutic regimens for treating hepatitis C virus (HCV) infections have undergone rapid advancements. Initially, structure-based drug design was used to develop molecules that inhibit viral enzymes. Subsequently, establishment of cell-based replicon systems enabled investigations into various stages of HCV life cycle including its entry, replication, translation, and assembly, as well as role of host proteins. Collectively, these approaches have facilitated identification of important molecules that are deemed essential for HCV life cycle. The expanded set of putative virus and host-encoded targets has brought us one step closer to developing robust strategies for efficacious, pangenotypic, and well-tolerated medicines against HCV. Herein, we provide an overview of the development of various classes of virus and host-directed therapies that are currently in use along with others that are undergoing clinical evaluation.

In silico Structure-based Identification of Novel Acetylcholinesterase Inhibitors Against Alzheimer's Disease.

OBJECTIVE AND BACKGROUND: Inhibition of acetylcholinesterase (AChE) has gained much importance since the discovery of the involvement of peripheral anionic site as an allosteric regulator of AChE. Characterized by the formation of ß-amyloid plaques, Alzheimer's disease (AD) is currently one of the leading causes of death across the world. Progression in this neurodegenerative disorder causes deficit in the cholinergic activity that leads towards cognitive decline. Therapeutic interventions in AD are largely focused upon AChE inhibitors designed essentially to prevent the loss of cholinergic function. The multifactorial AD pathology calls for Multitarget-directed ligands (MTDLs) to follow up on various components of the disease. Considering this approach, other related AD targets were also selected. Structure-based virtual screening was relied upon for the identification of lead compounds with anti-AD effect. METHOD: Several chemoinformatics approaches were used in this study, reporting four multi-target inhibitors: MCULE-7149246649-0-1, MCULE-6730554226-0-4, MCULE-1176268617-0-6 and MCULE-8592892575-0-1 with high binding energies that indicate better AChE inhibitory activity. Additional in-silico analysis hypothesized the abundant presence of aromatic interactions to be pivotal for interaction of selected compounds to the acetyl-cholinesterase. Additionally, we presented an alternative approach to determine protein-ligand stability by calculating the Gibbs-free energy change over time. Furthermore, this allows to rank potential hits for further in-vitro testing. RESULTS AND CONCLUSION: With no predicted indication of adverse effects on humans, this study unravels four active multi-target inhibitors against AChE with promising affinities and good ADMET profile for the potential use in AD treatment.

Structure-based virtual screening and molecular docking for the identification of potential multi-targeted inhibitors against breast cancer.

Breast cancer is characterized by an uncontrolled growth of cells in breast tissue. Genes that foster cell growth in breast cells are overexpressed, giving rise to breast tumors. The identification of effective inhibitors represents a rational chemopreventive strategy. The current in silico study provides a pharmacoinformatic approach for the identification of active compounds against a co-chaperone HSP90 and the human epidermal growth factor receptors EGFR and HER2/neu receptor. The elevated levels of expression of these target proteins have been documented in breast cancer. The utilization of drug-likeness filters helped to evaluate the pharmacological activity of potential lead compounds. Those fulfilling this criterion were subjected to energy minimization for 1000 steepest descent steps at a root means square gradient of 0.02 with an Amber ff12SB force field. Based on molecular docking results and binding interaction analysis, this study represents five chemical compounds (S-258282355, S-258012947, S-259417539, S-258002927, and S-259411474) that indicate high binding energies that range between -8.7 to -10.3 kcal/mol. With high cytochrome P inhibitory promiscuity activity, these multi-targeted potential hits portray not only good physiochemical interactions but also an excellent profile of absorption, distribution, metabolism, excretion, and toxicity, which hypothesizes that these compounds can be developed as anticancer drugs in the near future.