As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning (ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

Gut microbiota of higher vertebrates is host-specific. The number and diversity of the organisms residing within the gut ecosystem are defined by physiological and environmental factors, such as host genotype, habitat, and diet. Recently, culture-independent sequencing techniques have added a new dimension to the study of gut microbiota and the challenge to analyze the large volume of sequencing data is increasingly addressed by the development of novel computational tools and methods. Interestingly, gut microbiota maintains a constant relative abundance at operational taxonomic unit (OTU) levels and altered bacterial abundance has been associated with complex diseases such as symptomatic atherosclerosis, type 2 diabetes, obesity, and colorectal cancer. Therefore, the study of gut microbial population has emerged as an important field of research in order to ultimately achieve better health. In addition, there is a spontaneous, non-linear, and dynamic interaction among different bacterial species residing in the gut. Thus, predicting the influence of perturbed microbe–microbe interaction network on health can aid in developing novel therapeutics. Here, we summarize the population abundance of gut microbiota and its variation in different clinical states, computational tools available to analyze the pyrosequencing data, and gut microbe–microbe interaction networks.

The emergence of drug-resistant strains of Mycobacterium tuberculosis makes identification and validation of newer drug targets a global priority. Phosphoserine phosphatase (PSP), a key essential metabolic enzyme involved in conversion of O-phospho-l-serine to l-serine, was characterized in this study. The M. tuberculosis genome harbors all enzymes involved in l-serine biosynthesis including two PSP homologs: Rv0505c (SerB1) and Rv3042c (SerB2). In the present study, we have biochemically characterized SerB2 enzyme and developed malachite green-based high throughput assay system to identify SerB2 inhibitors. We have identified 10 compounds that were structurally different from known PSP inhibitors, and few of these scaffolds were highly specific in their ability to inhibit SerB2 enzyme, were noncytotoxic against mammalian cell lines, and inhibited M. tuberculosis growth in vitro. Surface plasmon resonance experiments demonstrated the relative binding for these inhibitors. The two best hits identified in our screen, clorobiocin and rosaniline, were bactericidal in activity and killed intracellular bacteria in a dose-dependent manner. We have also identified amino acid residues critical for these SerB2-small molecule interactions. This is the first study where we validate that M. tuberculosis SerB2 is a druggable and suitable target to pursue for further high throughput assay system screening.

Fungal allergy is considered as serious health problem worldwide and is increasing at an alarming rate in the industrialized areas. Rhizopus oyzae is a ubiquitously present airborne pathogenic mold and an important source of inhalant allergens for the atopic population of India. Here, we report the biochemical and immunological features of its 44 kDa sero-reactive aspartic protease allergen, which is given the official designation ‘Rhi o 1’. The natural Rhi o 1 was purified by sequential column chromatography and its amino acid sequence was determined by mass spectrometry and N-terminal sequencing. Based on its amino acid sequence, the cDNA sequence was identified, cloned and expressed to produce recombinant Rhi o 1. The allergenic activity of rRhi o 1 was assessed by means of its IgE reactivity and histamine release ability. The biochemical property of Rhi o 1 was studied by enzyme assay. IgE-inhibition experiments were performed to identify its cross-reactivity with the German cockroach aspartic protease allergen Bla g 2. For precise characterization of the cross-reactive epitope, we used anti-Bla g 2 monoclonal antibodies for their antigenic specificity towards Rhi o 1. A homology based model of Rhi o 1 was built and mapping of the cross-reactive conformational epitope was done using certain in silico structural studies. The purified natural nRhi o 1 was identified as an endopeptidase. The full length allergen cDNA was expressed and purified as recombinant rRhi o 1. Purified rRhi o 1 displayed complete allergenicity similar to the native nRhi o 1. It was recognized by the serum IgE of the selected mold allergy patients and efficiently induced histamine release from the sensitized PBMC cells. This allergen was identified as an active aspartic protease functional in low pH. The Rhi o 1 showed cross reactivity with the cockroach allergen Bla g 2, as it can inhibit IgE binding to rBla g 2 up to certain level. The rBla g 2 was also found to cross-stimulate histamine release from the effector cells sensitized with anti-Rhi o 1 serum IgE. This cross-reactivity was found to be mediated by a common mAb4C3 recognizable conformational epitope. Bioinformatic studies revealed high degree of structural resemblances between the 4C3 binding sites of both the allergens. The present study reports for the first time anew fungal aspartic protease allergen designated as Rhi o 1, which triggers IgE-mediated sensitization leading to various allergic diseases. Here we have characterized the recombinant Rhi o 1 and its immunological features including cross-reactive epitope information that will facilitate the component-resolved diagnosis of mold allergy.

Targeting bacterial virulence mechanisms without compromising bacterial growth is a promising strategy to prevent drug resistance. LysR-type transcriptional regulators (LTTRs) possess structural conservation across bacterial species and regulate virulence in numerous pathogens, making them attractive targets for antimicrobial agents. We targeted AphB, a Vibrio cholerae LTTR, which regulates the expression of genes encoding cholera toxin and toxin-co-regulated pilus for inhibitor designing. Since AphB ligand is unknown, we followed a molecular fragment-based approach for ligand designing using FDA-approved drugs and subsequent screen to identify molecules that exhibited high-affinity binding to AphB ligand-binding pocket. Among the identified compounds, ribavirin, an anti-viral drug, antagonized AphB functions. Ribavirin perturbed Vibrio cholerae pathogenesis in animal models. The inhibitory effects of the drug was limited to the bacteria expressing wild type AphB, but not its constitutively active mutant (AphB_N100E), which represents the ligand-bound state, suggesting that ribavirin binds to the active site of AphB to exert its inhibitory role and there exists no AphB-independent mechanism of its action. Similarly, ribavirin suppressed the functions of Salmonella Typhi LTTR Hrg, indicating its broad spectrum efficacy. Moreover, ribavirin did not affect the bacterial viability in culture. This study cites an example of drug repurposing for anti-infective therapy.

Helicobacter pylori is a gastric mucosal pathogen and is associated with diseases like peptic ulcer and gastric cancer. To combat H. pylori infection, there is an urgent need for new class of antibiotics due to the emergence of drug-resistant strains. Enzymes involved in bacterial lysine biosynthetic pathways may be potential targets for antibacterial drug development, since lysine is an essential component of the bacterial peptidoglycan cell wall. No pathway exists for lysine biosynthesis in humans; hence, the inhibitors targeting bacterial enzymes may have selective toxicity. dapE-encoded N-succinyl-L,L-diaminopimelic acid desuccinylase (DapE) is a critical enzyme of this pathway and deletion of DapE gene is lethal to H. pylori, since the organism has no alternative pathway for lysine biosynthesis. In this study, we reported a 3D model structure of H. pylorie DapE, which consisted of a catalytic domain and a dimerization domain generated by MODELLER software. We also confirmed the stability of the modeled structure through 10 ns molecular dynamics simulation using GROMACS software. Next, to identify potential small molecule inhibitors of DapE, drug-like small molecule-screening library was generated. This was performed by Tanimoto-based similarity searching in the PubChem Database with DapE substrate L,L-SDAP as a query molecule, followed by fragment-based docking approach using GLIDE XP. This approach identified two potential substrate-competitive small molecule inhibitors of DapE. These new molecules may provide a starting point to search for novel therapeutics.