Artificial intelligence based de-novo design for novel Plasmodium falciparum plasmepsin (PM) X inhibitors.

Plasmodium falciparum is the leading cause of malaria with 627,000 deaths annually. Invasion and egress are critical stages for successful infection of the host yet depend on proteins that are extensively pre-processed by various maturases. Plasmepsins (Plasmodium pepsins, abbreviated PM, I-X) are pepsin-like aspartic proteases that are involved in almost all stages of the life cycle. The goal of this study was to use de-novo generative modeling techniques to create novel potential PfPMX inhibitors. A total of 4325 compounds were virtually screened by structural-based docking methods. The obtained hits were utilized to refine a structure-based Ligand Neural Network (L-Net) generative model to generate related compounds. The obtained optimal L-Net Compounds with smina scores ≤ -5.00KCalmol-1 and QED ≥ 0.35 were further taken for amplification utilizing Ligand Based Transformer modeling using Deep generative learning (Drug Explorer/DrugEx). The resulting hits were then subjected to XP Glide conventional Molecular docking and QikProp ADMET screening; molecules with XP Docking score ≤ -7.00KCalmol-1 were retained. Based on their Glide ligand efficiency, originality, and uniqueness, 30 compounds were chosen for binding affinity and MM_GBSA energy determination. Following Induced Fit docking (IFD), 7 compounds were taken for 50 ns MD simulations and FEP/MD calculations. This study reported novel potential PfPMX inhibitors with acceptable ADMET profiles and reasonable synthetic accessibility scores, as well as sufficient docking scores against other PMs were generated. The PfPMX inhibitors reported in this article are promising antimalarials for the next stages of drug development, and the first of their kind to be investigated thoroughly.Communicated by Ramaswamy H. Sarma.

An Immunoinformatics-Based Study of Mycobacterium tuberculosis Region of Difference-2 Uncharacterized Protein (Rv1987) as a Potential Subunit Vaccine Candidate for Preliminary Ex Vivo Analysis.

Mycobacterium tuberculosis (Mtb) is the pathogen that causes tuberculosis and develops resistance to many of the existing drugs. The sole licensed TB vaccine, BCG, is unable to provide a comprehensive defense. So, it is crucial to maintain the immunological response to eliminate tuberculosis. Our previous in silico study reported five uncharacterized proteins as potential vaccine antigens. In this article, we considered the uncharacterized Mtb H37Rv regions of difference (RD-2) Rv1987 protein as a promising vaccine candidate. The vaccine quality of the protein was analyzed using reverse vaccinology and immunoinformatics-based quality-checking parameters followed by an ex vivo preliminary investigation. In silico analysis of Rv1987 protein predicted it as surface localized, secretory, single helix, antigenic, non-allergenic, and non-homologous to the host protein. Immunoinformatics analysis of Rv1987 by CD4 + and CD8 + T-cells via MHC-I and MHC-II binding affinity and presence of B-cell epitope predicted its immunogenicity. The docked complex analysis of the 3D model structure of the protein with immune cell receptor TLR-4 revealed the protein's capability for potential interaction. Furthermore, the target protein-encoded gene Rv1987 was cloned, over-expressed, purified, and analyzed by mass spectrometry (MS) to report the target peptides. The qRT-PCR gene expression analysis shows that it is capable of activating macrophages and significantly increasing the production of a number of key cytokines (TNF-α, IL-1ß, and IL-10). Our in-silico analysis and ex vivo preliminary investigations revealed the immunogenic potential of the target protein. These findings suggest that the Rv1987 be undertaken as a potent subunit vaccine antigen and that further animal model immuno-modulation studies would boost the novel TB vaccine discovery and/or BCG vaccine supplement pipeline.

Identification of Potential Drug Targets in Erythrocyte Invasion Pathway of Plasmodium falciparum.

The erythrocyte invasion phase plays a critical role in multiplication, sexual determination, and drug resistance in Plasmodium falciparum. In order to identify the critical genes and pathways in the erythrocyte invasion phase, the gene set (GSE129949) and the RNA-Seq count data for the W2mef strain were used for further analysis. An integrative bioinformatics study was performed to scrutinize genes as potential drug targets. 487 differentially expressed genes (DEGs) with an adjusted P value < 0.001 enriched 47 Gene Ontology (GO) terms that were over-represented based on hyper-geometric analysis P value < 0.01. Protein-Protein interaction network analysis was done using DEGs with higher confidence interactions (PPI score threshold = 0.7). MCODE and cytoHubba apps were utilized to define the hub proteins and rank them based on multiple topological analyses and MCODE scores. Furthermore, Gene Set Enrichment Analysis (GSEA) was carried out by using 322 gene sets from the MPMP database. The genes involved in multiple significant gene sets were determined by leading-edge analysis. Our study identified six genes encoding proteins that could be potential drug targets involved in the erythrocyte invasion phase related to merozoites motility, cell-cycle regulation, G-dependent protein kinase phosphorylation in schizonts, control of microtubule assembly, and sexual commitment. The druggability of those proteins was calculated based on the DCI (Drug Confidence Index) and predicted binding pockets' values. The protein that showed the best binding pocket value was subjected to deep learning-based virtual screening. The study identified the best small molecule inhibitors in terms of drug-binding score against the proteins for inhibitor identification.

Artificial intelligence based virtual screening study for competitive and allosteric inhibitors of the SARS-CoV-2 main protease.

SARS-CoV-2 is a highly contagious and dangerous coronavirus that first appeared in late 2019 causing COVID-19, a pandemic of acute respiratory illnesses that is still a threat to health and the general public safety. We performed deep docking studies of 800 M unique compounds in both the active and allosteric sites of the SARS-COV-2 Main Protease (Mpro) and the 15 M and 13 M virtual hits obtained were further taken for conventional docking and molecular dynamic (MD) studies. The best XP Glide docking scores obtained were -14.242 and -12.059 kcal/mol by CHEMBL591669 and the highest binding affinities were -10.5 kcal/mol (from 444215) and -11.2 kcal/mol (from NPC95421) for active and allosteric sites, respectively. Some hits can bind both sites making them a great area of concern. Re-docking of 8 random allosteric complexes in the active site shows a significant reduction in docking scores with a t-test P value of 2.532 × 10-11 at 95% confidence. Some specific interactions have higher elevations in docking scores. MD studies on 15 complexes show that single-ligand systems are stable as compared to double-ligand systems, and the allosteric binders identified are shown to modulate the active site binding as evidenced by the changes in the interaction patterns and stability of ligands and active site residues. When an allosteric complex was docked to the second monomer to check for homodimer formation, the validated homodimer could not be re-established, further supporting the potential of the identified allosteric binders. These findings could be important in developing novel therapeutics against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Bi-Directional Relationship Between Autophagy and Inflammasomes in Neurodegenerative Disorders.

The innate immune system, as the first line of cellular defense, triggers a protective response called inflammation when encountered with invading pathogens. Inflammasome is a multi-protein cytosolic signaling complex that induces inflammation and is critical for inflammation-induced pyroptotic cell death. Inflammasome activation has been found associated with neurodegenerative disorders (NDs), inflammatory diseases, and cancer. Autophagy is a crucial intracellular quality control and homeostasis process which removes the dysfunctional organelles, damaged proteins, and pathogens by sequestering the cytosolic components in a double-membrane vesicle, which eventually fuses with lysosome resulting in cargo degradation. Autophagy disruption has been observed in many NDs presented with persistent neuroinflammation and excessive inflammasome activation. An interplay between inflammation activation and the autophagy process has been realized over the last decade. In the case of NDs, autophagy regulates neuroinflammation load and cellular damage either by engulfing the misfolded protein deposits, dysfunctional mitochondria, or the inflammasome complex itself. A healthy two-way regulation between both cellular processes has been realized for cell survival and cell defense during inflammatory conditions. Therefore, clinical interest in the modulation of inflammasome activation by autophagy inducers is rapidly growing. In this review, we discuss the structural basis of inflammasome activation and the mechanistic ideas of the autophagy process in NDs. Along with comments on multiple ways of neuroinflammation regulation by microglial autophagy, we also present a perspective on pharmacological opportunities in this molecular interplay pertaining to NDs.

An update on cerebral malaria for therapeutic intervention.

BACKGROUND: Cerebral malaria is often pronounced as a major life-threatening neurological complication of Plasmodium falciparum infection. The complex pathogenic landscape of the parasite and the associated neurological complications are still not elucidated properly. The growing concerns of drugresistant parasite strains along with the failure of anti-malarial drugs to subdue post-recovery neuro-cognitive dysfunctions in cerebral malaria patients have called for a demand to explore novel biomarkers and therapeutic avenues. Due course of the brain infection journey of the parasite, events such as sequestration of infected RBCs, cytoadherence, inflammation, endothelial activation, and blood-brain barrier disruption are considered critical. METHODS: In this review, we briefly summarize the diverse pathogenesis of the brain-invading parasite associated with loss of the blood-brain barrier integrity. In addition, we also discuss proteomics, transcriptomics, and bioinformatics strategies to identify an array of new biomarkers and drug candidates. CONCLUSION: A proper understanding of the parasite biology and mechanism of barrier disruption coupled with emerging state-of-art therapeutic approaches could be helpful to tackle cerebral malaria.

An update on Cryptosporidium biology and therapeutic avenues.

Cryptosporidium species has been identified as an important pediatric diarrheal pathogen in resource-limited countries, particularly in very young children (0-24 months). However, the only available drug (nitazoxanide) has limited efficacy and can only be prescribed in a medical setting to children older than one year. Many drug development projects have started to investigate new therapeutic avenues. Cryptosporidium's unique biology is challenging for the traditional drug discovery pipeline and requires novel drug screening approaches. Notably, in recent years, new methods of oocyst generation, in vitro processing, and continuous three-dimensional cultivation capacities have been developed. This has enabled more physiologically pertinent research assays for inhibitor discovery. In a short time, many great strides have been made in the development of anti-Cryptosporidium drugs. These are expected to eventually turn into clinical candidates for cryptosporidiosis treatment in the future. This review describes the latest development in Cryptosporidium biology, genomics, transcriptomics of the parasite, assay development, and new drug discovery.

In silico and in vitro study of Mycobacterium tuberculosis H37Rv uncharacterized protein (RipD): an insight on tuberculosis therapeutics.

Tuberculosis caused by Mycobacterium tuberculosis (Mtb) is responsible for the highest global health problem, with the deaths of millions of people. With prevalence of multiple drug resistance (MDR) strains and extended therapeutic times, it is important to discover small molecule inhibitors against novel hypothetical proteins of the pathogen. In this study, a virtual screening protocol was carried out against MtbH37Rv hypothetical protein RipD (Rv1566c) for the identification of potential small molecule inhibitors. The 3D model of the protein structure binding site was used for virtual screening (VS) of inhibitors from the Pathogen Box, followed by its validation through a molecular docking study. The stability of the protein-ligand complex was assessed using a 150 ns molecular dynamics simulation. MM-PBSA and MM-GBSA are the two approaches that were used to perform the trajectory analysis and determine the binding free energies, respectively. The ligand binding was observed to be stable across the entire time frame with an approximate binding free energy of -22.9916 kcal/mol. The drug-likeness of the inhibitors along with a potential anti-tuberculosis compound was validated by ADMET prediction software. Furthermore, a CFU inhibition assay was used to validate the best hit compound's in vitro inhibitory efficacy against a non-pathogenic Mycobacterium smegmatis MC2155 under low nutrient culture conditions. The study reported that the compound proposed in our study (Pathogen Box ID: MMV687700) will be useful for the identification of potential inhibitors against Mtb in future.

QSAR and deep learning model for virtual screening of potential inhibitors against Inosine 5' Monophosphate dehydrogenase (IMPDH) of Cryptosporidium parvum.

Cryptosporidium parvum (Cp) causes a gastro-intestinal disease called Cryptosporidiosis. C. parvum Inosine 5' monophosphate dehydrogenase (CpIMPDH) is responsible for the production of guanine nucleotides. In the present study, 37 known urea-based congeneric compounds were used to build a 2D and 3D QSAR model against CpIMPDH. The built models were validated based on OECD principles. A deep learning model was adopted from a framework called Deep Purpose. The model was trained with 288 known active compounds and validated using a test set. From the training set of the 3D QSAR, a pharmacophore model was built and the best pharmacophore hypotheses were scored and sorted using a phase-hypo score. A phytochemical database was screened using both the pharmacophore model and a deep learning model. The screened compounds were considered for glide XP docking, followed by quantum polarized ligand docking. Finally, the best compound among them was considered for molecular dynamics simulation study.

Glycoconjugates, hypothetical proteins, and post-translational modification: Importance in host-pathogen interaction and antitubercular intervention development.

With the emergence of multidrug-resistant bacteria, insufficiency of the established chemotherapy, and the existing vaccine BCG, tuberculosis (TB) subsists as the chief cause of death in different parts of the world. Thus, identification of novel target proteins is urgently required to develop more effective TB interventions. However, the novel vaccine and drug target knowledge based on the essentiality of the pathogen cell envelope components such as glycoconjugates, glycans, and the peptidoglycan layer of the lipid-rich capsule are limited. Furthermore, most of the genes encoding proteins are characterized as hypothetical and functionally unknown. Correspondingly, some researchers have shown that the lipid and sugar components of the envelope glycoconjugates are largely in charge of TB pathogenesis and encounter many drugs and vaccines. Therefore, in this review we provide an insight into a comprehensive study concerning the importance of cell envelope glycoconjugates and hypothetical proteins, the impact of post-translational modification, and the bioinformatics-based implications for better antitubercular intervention development.

Computational discovery and ex-vivo validation study of novel antigenic vaccine candidates against tuberculosis.

Tuberculosis (TB) is a complex infectious bacterial disease, which has evolved with highly successful mechanisms to interfere with host defenses and existing classes of antibiotics to resist eradication. The single obtainable TB vaccine, Bacille Calmette-Guerin (BCG) has failed to provide regular defense for respiratory TB in adults. In this study, a bioinformatics and immunoinformatics approach was applied on Mycobacterium tuberculosis (Mtb) H37Rv proteomes to discover the potential subunit vaccine candidates that elicit both tuberculosis-specific T-cells and B-cell immune response. A total of 4049 proteins of MtbH37RvMtbH37Rv were retrieved and subjected to in silico sequence-based analysis. Finally, five (P9WL69 (Rv2599), P9WIG1 (Rv0747), P9WLQ1 (Rv1987), O53608 (Rv0063), O06624 (Rv1566c)) novel putative proteins were selected. Among the five putative antigenic vaccine candidates, P9WL69 protein was selected for the ex-vivo validation study. The P9WL69 protein encoding gene was amplified and cloned on pET21b vector. The success of the recombinant clone (pET21b-RV2599) was confirmed by colony PCR, insert release test and sequencing. Furthermore, the identified epitopes of the P9WL69 protein were considered for in silico docking and molecular dynamics simulation study using Toll-like Receptors (TLRs) (TLR-2, TLR-4, TLR-9), Mannose receptor, and Myeloid differentiation 88 (MYD88) to understand their binding affinity towards the development of immunogenic vaccines against tuberculosis.

In-silico analysis of Calcium Dependent Protein Kinase 6 of Cryptosporidium parvum through molecular modeling, docking, and dynamics simulation study.

Calcium Dependent Protein Kinases are found in the Apicomplexan, algae, and plants; however, they are not reported in vertebrates and are regarded as excellent drug targets for pharmaceutical interventions. Calcium Dependent Protein Kinases of Cryptosporidium are probably involved in the regulation of invasion and egress process during the infection of the host cells. The previous study reported that after the Calcium Dependent Protein Kinase 1 gene, Calcium Dependent Protein Kinase 6 of Cryptosporidium parvum is expressed in all stages of the parasite (merozoites/schizonts as well as sexual stages) at a comparable level and makes it as a valid drug target. In this study, an attempt is made to address the similarity in sequences and phylogenetic study of Calcium Dependent Protein Kinase 6 (CDPK6) among Calcium Dependent Protein Kinases of Apicomplexans. Further, the three-dimensional structure determination of CDPK6 of C. parvum was performed through a molecular modeling approach followed by virtual screening of small-molecule inhibitors from different datasets. The best inhibitor from Tres Cantos Antimalarial Set with ID 11730 reported a binding affinity of -8.2 kcal/mol against CDPK6 of C. parvum. Furthermore, the reliability of the binding mode of the inhibitor is validated through a complex molecular dynamics simulation study for a time interval of 100 ns. The simulation study advocates that the inhibitor Tres Cantos Antimalarial Set_11730 formed a stable interaction with the predicted active site residues and can be considered for industrial pharmaceutical research in future.Communicated by Ramaswamy H. Sarma.

A computational approach to validate novel drug targets of gentianine from Swertiya chirayita in Plasmodium falciparum.

Gentianine is one of the compounds found in the plant Swertiya chirayita that is known for its antimalarial activity. However, its exact molecular mechanism of action is yet to be understood. In our present study, we applied several computational approaches to filter out and determine possible targets of gentianine in Plasmodium falciparum 3D7. Protein-protein networks formed the basis of one of our strategies along with orthologous protein analysis to establish essentiality. Out of 6 essential proteins from unique pathways, haloacid dehalogenase like-hydrolase (PfHAD1), phosphoenolpyruvate carboxykinase (PfPEPCK) and fumarate hydratase (PfFH) were screened as drug targets through this approach. Through our other strategy we established the predicted IC50 (PIC50) value of gentianine with a set of molecular descriptors from 123 Pathogen Box anti-malarial compounds. Afterwards through 2D structural similarity, L-lactate dehydrogenase (PfLDH) was established as another possible target. In our work, we performed in silico docking and analysed the binding of gentianine to the proteins. All of the proteins were reported with favourable binding results and were considered for complex molecular dynamics simulation approach. Our research clears up the molecular mechanism of antimalarial activity of gentianine to some extent paving way for experimental validation of the same in future.

An immunoinformatics approach for design and validation of multi-subunit vaccine against Cryptosporidium parvum.

An immunoinformatics-based approach is explored for potential multi-subunit vaccine candidates against Cryptosporidium parvum. We performed protein structure based systematic methodology for the development of a proficient multi-subunit vaccine candidate against C. parvum based on their probability of antigenicity, allergenicity and transmembrane helices as the screening criteria. The best-screened epitopes like B-cell epitopes (BCL), Helper T-lymphocytes (HTL) and cytotoxic T- lymphocytes (CTL) were joined by using the appropriate linkers to intensify and develop the presentation and processing of the antigenic molecules. Modeller software was used to generate the best 3D model of the subunit protein. RAMPAGE and other web servers were employed for the validation of the modeled protein. Furthermore, the predicted modeled structure was docked with the two known receptors like TLR2 and TLR4 through ClusPro web server. Based on the docking score, the multi-subunit vaccine docked with TLR2 was subjected to energy minimization by molecular dynamics (MD) simulation to examine their stability within a solvent system. From the simulation study, we found that the residue Glu-107 of subunit vaccine formed a hydrogen bond interaction with Arg-299 of the TLR2 receptor throughout the time frame of the MD simulation. The overall results showed that the multi-subunit vaccine could be an efficient vaccine candidate against C. parvum.

Identification of adaptive inhibitors of Cryptosporidium parvum fatty acyl-coenzyme A synthetase isoforms by virtual screening.

Cryptosporidiosis is a significant cause of gastroenteritis in both humans and livestock in developing countries. The only FDA-approved drug available against the same is nitazoxanide, with questionable efficacy in malnourished children and immunocompromised patients. Recent in vitro studies have indicated the viability of Triacsin C as a potential drug candidate, which targets the parasite's long-chain fatty acyl coenzyme A synthetase enzyme (LC-FACS), a critical component of the fatty acid metabolism pathway. We have used this molecule as a baseline to propose more potent versions thereof. We have applied a combined approach of substructure replacement, literature search, and database screening to come up with 514 analogs of Triacsin C. A virtual screening protocol was carried out which lead us to identify a potential hit compound. This was further subjected to a 100-ns molecular dynamics simulation in complex to determine its stability and binding characteristics. After which, the ADME/tox properties were predicted to assess its viability as a drug. The molecule R134 was identified as the best hit due to its highest average binding affinity, stability in complex when subjected to MD simulations, and reasonable predicted ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) properties comparable to those of the Triacsin C parent molecule. We have proposed R134 as a putative drug candidate against the Cryptosporidium parvum LC-FACS enzyme isoforms, following an in silico protocol. We hope the results will be helpful when planning future in vitro experiments for identifying drugs against Cryptosporidium.

In silico study of M18 aspartyl amino peptidase (M18AAP) of Plasmodium vivax as an antimalarial drug target.

Plasmodium vivax (Pv) is the second most malaria causing pathogen among Plasmodium species. M18 aspartic aminopeptidase (M18AAP) protein is a single gene copy present in Plasmodium. This protein is functional at the terminal stage of hemoglobin degradation of host and completes the hydrolysis process which makes it an important target for new chemotherapeutics. No experimental and structural study on M18AAP protein of P. vivax is reported till today. This paper advocates the application of multiple computational approaches like protein model prediction, ligand-based 3D QSAR study, pharmacophore, structure-based virtual screening and molecular docking simulation for identification of potent lead molecules against the enzyme. The 3D QSAR model was developed using known bioactive compounds against the PvM18AAP protein which statistically signify the k-NN model with q^2 = 0.7654. The study reports a lead molecule from ligand-centric approach with good binding affinity and possessing lowest docking score. The findings will be helpful for in-vivo and in-vitro validations and development of potent anti-malarial molecules against the drug resistant strains of malaria parasite.

Plasmodium falciparum: Multidrug resistance.

Malaria is the most lethal and debilitating disease caused by the protozoan parasite Plasmodium worldwide. The most severe forms of disease and the incidence rates of mortality are associated with P. falciparum infections. With the identification of disease source and symptoms, many chemical entities were developed naturally and synthetically for administration as a potential antimalarial drug. The major classes of approved antimalarial drugs that are governed as first-line treatment in tropical and subtropical areas include quinolines, naphthoquinones, antifolates, 8-aminoquinolines, and endoperoxides. However, the efficacy of antimalarial drugs has decreased due to ongoing multidrug resistance problem to current drugs. With increasing resistance to the current antimalarial artemisinin and its combination therapies, malaria prophylaxis has declined gradually. New-generation antimalarial and novel drug target are required to check the incidence of malaria resistance. This review summarizes the emergence of multidrug resistance to known antimalarial and the development of new antimalarial to resolve drug resistance condition. Few essential proteins are also discussed that can be considered as novel drug target against malaria in future.

In silico analysis of plasmodium falciparum CDPK5 protein through molecular modeling, docking and dynamics.

Calcium-Dependent Protein Kinase 5 (CDPK5) protein is one of the family members of a calcium-dependent protein kinase that is found in plants and some species of protozoa which includes Plasmodium falciparum (Pf), the pathogen responsible for malaria. CDPKs regulate many biological processes in Apicomplexans such as Plasmodium, Toxoplasma or Cryptosporidium. The study addresses the similarity in sequences and evolutionary relationship of CDPK5 across Apicomplexans. Further, the three-dimensional structural conformation of PfCDPK5 is generated through homology modeling. Molecular dynamics simulation of the homology model for a time interval of 40 ns resulted in a stable conformation of the PfCDPK5 protein. Inhibitor identification was carried out from computational screening of known anti-malarial compounds. The reliability of the binding mode for the best inhibitor compound MMV687246 was validated through a complex molecular dynamics study. This findings advocates that MMV687246 from Pathogen Box as the best inhibitor against PfCDPK5 protein and can be considered for experimental validation study in future.

In-silico screening of small molecule inhibitors against Lactate Dehydrogenase (LDH) of Cryptosporidium parvum.

Cryptosporidium parvum is a protozoan parasite which causes waterborne diseases known as Cryptosporidiosis. It is an acute enteric diarrheal disease being severe in the case of immunocompromised individuals and children. C. parvum mainly depends on the glycolysis process for energy production and LDH (Lactate Dehydrogenase) is a key controller of this process. In this study from different in-silico approaches such as structure-based, ligand-based and de novo drug design; a total of 40 compounds were selected for docking studies against LDH. The study reported a compound CHEMBL1784973 from Pathogen Box as the best inhibitor in terms of docking score and pharmacophoric features. Furthermore, the binding mode of the best-reported inhibitor was validated through molecular dynamics simulation for a time interval of 70 ns in water environment. The findings resulted in the stable conformation of the inhibitor in the active site of the protein. This study will be helpful for experimental validation.