Illuminating the structural basis of human neurokinin 1 receptor (NK1R) antagonism through classical all-atoms molecular dynamics simulations.

Advances in structural biology have bestowed insights into the pleiotropic effects of neurokinin 1 receptors (NK1R) in diverse patho-physiological processes, thereby highlighting the potential therapeutic value of antagonists directed against NK1R. Herein, we investigate the mode of antagonist recognition to discern the obscure atomic facets germane for the function and molecular determinants of NK1R. To commence discernment of potent antagonists and the conformational changes in NK1R, induced upon antagonist binding, state-of-the-art classical all-atoms molecular dynamics (MD) simulations in lipid mimetic bilayers have been utilized. MD simulations of structural ensembles reveals the involvement of TM5 and TM6 in tight anchoring of antagonists through a network of interhelical hydrogen-bonds, while, the extracellular loop 2 (ECL2) governs the overall size and nature of the pocket, thereby modulating NK1R. Consistent comparison between experiments and MD simulation results discerns the predominant role of TM3, TM4, and TM6 in lipid-NK1R interaction. Correlation between hydrophobic index and helicity of TM domains elucidates their importance in maintaining the structural stability in addition to regulating NK1R antagonism. Taken together, we anticipate that our computational study marks a comprehensive structural basis of NK1R antagonism in lipid bilayers, which may facilitate designing of new therapeutics against associated diseases targeting human neurokinin receptors.

Drug repurposing for Mpox: Discovery of small molecules as potential inhibitors against DNA-dependent RNA polymerase using molecular modeling approach.

Mpox (formerly Monkeypox), a zoonotic illness caused by the Mpox virus, belongs to the Orthopoxvirus genus in the family Poxviridae. To design and develop effective antiviral therapeutics against DNA viruses, the DNA-dependent RNA polymerase (DdRp) of poxviruses has emerged as a promising drug target. In the present study, we modeled the three-dimensional (3D) structure of DdRp using a template-based homology approach. After modeling, virtual screening was performed to probe the molecular interactions between 1755 Food and Drug Administration-approved small molecule drugs (≤500 molecular weight) and the DdRp of Mpox. Based on the binding affinity and molecular interaction patterns, five drugs, lumacaftor (-11.7 kcal/mol), conivaptan (-11.7 kcal/mol), betulinic acid (-11.6 kcal/mol), fluspirilene (-11.3 kcal/mol), and imatinib (-11.2 kcal/mol), have been ranked as the top drug compounds interacting with Mpox DdRp. Complexes of these shortlisted drugs with DdRp were further evaluated using state-of-the-art all-atoms molecular dynamics (MD) simulations on 200 nanoseconds followed by principal component analysis (PCA). MD simulations and PCA results revealed highly stable interactions of these small drugs with DdRp. After due validation in wet-lab using available in vitro and in vivo experiments, these repurposed drugs can be further utilized for the treatment of contagious Mpox virus. The outcome of this study may establish a solid foundation to screen repurposed and natural compounds as potential antiviral therapeutics against different highly pathogenic viruses.

Difference in acute and chronic stage ischemic stroke metabolic markers with controls.

BACKGROUND: Acute Ischemic Stroke (AIS), a major cause of disability, was previously associated with multiple metabolomic changes, but many findings were contradictory. Case-control and longitudinal study designs could have played a role in that. To clarify metabolomic changes, we performed a simultaneous comparison of ischemic stroke metabolome in acute, chronic stages of stroke and controls. METHODS: Through the nuclear magnetic resonance (NMR) platform, we evaluated 271 serum metabolites from a cohort of 297 AIS patients in acute and chronic stages and 159 controls. We used Sparse Partial Least Squares-Discriminant analysis (sPLS-DA) to evaluate group disparity; multivariate regression to compare metabolome in acute, chronic stages of stroke and controls; and mixed regression to compare metabolome acute and chronic stages of stroke. We applied false discovery rate (FDR) to our calculations. RESULTS: The sPLS-DA revealed separation of the metabolome in acute, chronic stages of stroke and controls. Regression analysis identified 38 altered metabolites. Ketones, branched-chain amino acids (BCAAs), energy, and inflammatory compounds were mostly elevated, while alanine and glutamine were decreased in the acute stage. These metabolites declined/increased in the chronic stage, often to the same levels as in controls. Levels of fatty acids, phosphatidylcholines, phosphoglycerides, and sphingomyelins did not change between acute and chronic stages, but were different comparing to controls. CONCLUSION: Our pilot study identified metabolites associated with acute stage of ischemic stroke and those that are altered in stroke patients comparing to controls regardless of stroke acuity. Future investigation in a larger independent cohort is needed to validate these findings.

Molecular and disease association of gestational diabetes mellitus affected mother and placental datasets reveal a strong link between insulin growth factor (IGF) genes in amino acid transport pathway: A network biology approach.

Discerning the relationship between molecules involved in diseases based on their underlying biological mechanisms is one of the greatest challenges in therapeutic development today. Gestational diabetes mellitus (GDM) is one of the most common complications during pregnancy, which adversely affects both mothers and offspring during and after pregnancy. We have constructed two datasets of (GDM associated genes from affected mother and placenta to systematically analyze and evaluate their interactions like gene-gene, gene-protein, gene-microRNA (miRNA), gene-transcription factors, and gene-associated diseases to enhance our current knowledge, which may lead to further advancements in disease diagnosis, prognosis, and treatment. The results identify the key genes with respect to maternal dataset as insulin receptor, insulin (INS), leptin (LEP), glucokinase, and hepatocyte nuclear factor 1 alpha, whereas from placenta include insulin-like growth factor 1, growth hormone receptor, and breast cancer anti-estrogen resistance protein 1, which are found to be highly enriched in pancreas, ovary, adipocyte, heart, and placental tissues. The key transcription factors include Sp1 transcription factor, pancreatic and duodenal homeobox 1, and hepatocyte nuclear factor 4 alpha, whereas miRNA includes has-miR-5699-5p and has-miR-3158-3p. The study also reveals that GDM has associations with diseases like type I and II diabetes mellitus, obesity, and preeclampsia. More significantly, we could trace out a significant connection between the key molecules like LEP and placental growth hormone from mother and placental dataset, which plays a critical role in INS secretion, INS signaling, and ß-cell dysfunction pathways.

Designing of an epitope-based peptide vaccine against walking pneumonia: an immunoinformatics approach.

Mycoplasma pneumoniae is a substantial respiratory pathogen that develops not only pneumonia but also other respiratory diseases, which mimic viral respiratory syndromes. Nevertheless, vaccine development for this pathogen delays behind as immunity correlated with protection is now predominantly unknown. In the present study, an immunoinformatics pipeline is utilized for epitope-based peptide vaccine design, which can trigger a critical immune response against M. pneumoniae. A total of 105 T-cell epitopes from 12 membrane associated proteins and 7 T-cell epitopes from 5 cytadherence proteins of M. pneumoniae were obtained and validated. Thus, 18 peptides with 9-mer core sequence were identified as best T-cell epitopes by considering the number of residues with > 75% in favored region. Further, the crucial screening studies predicted three peptides with good binding affinity towards HLA molecules as best T-cell and B-cell epitopes. Based on this result, visualization, and dynamic simulation for the three epitopes (WIHGLILLF, VILLFLLLF, and LLAWMLVLF) were assessed. The predicted epitopes needs to be further validated for their adept use as vaccine. Collectively, the study opens up a new horizon with extensive therapeutic application against M. pneumoniae and its associated diseases.

Lipid and cholesterols modulate the dynamics of SARS-CoV-2 viral ion channel ORF3a and its pathogenic variants.

SARS-CoV-2 accessory protein, ORF3a is a putative ion channel which immensely contributes to viral pathogenicity by modulating host immune responses and virus-host interactions. Relatively high expression of ORF3a in diseased individuals and implication with inflammasome activation, apoptosis and autophagy inhibition, ratifies as an effective target for developing vaccines and therapeutics. Herein, we present the elusive dynamics of ORF3a-dimeric state using all-atoms molecular dynamics (MD) simulations at µ-seconds scale in a heterogeneous lipid-mimetic system in multiple replicates. Additionally, we also explore the effect of non-synonymous pathogenic mutations on ORF3a ion channel activity and viral pathogenicity in different SARS-CoV-2 variants using various structure-based protein stability (ΔΔG) tools and computational saturation mutagenesis. Our study ascertains the role of phosphatidylcholines and cholesterol in modulating the structure of ORF3a, which perturbs the size and flexibility of the polar cavity that allows permeation of large cations. Discrete trend in ion channel pore radius and area per lipid arises the premise that presence of lipids might also affect the overall conformation of ORF3a. MD structural-ensembles, in some replicates rationalize the crucial role of TM2 in maintaining the native structure of ORF3a. We also infer that loss of structural stability primarily grounds for pathogenicity in more than half of the pathogenic variants of ORF3a. Overall, the effect of mutation on alteration of ion permeability of ORF3a, proposed in this study brings mechanistic insights into variant consequences on viral membrane proteins of SARS-CoV-2, which can be utilized for the development of novel therapeutics to treat COVID-19 and other coronavirus diseases.

Improving Stroke Outcome Prediction Using Molecular and Machine Learning Approaches in Large Vessel Occlusion.

Introduction: Predicting stroke outcomes in acute ischemic stroke (AIS) can be challenging, especially for patients with large vessel occlusion (LVO). Available tools such as infarct volume and the National Institute of Health Stroke Scale (NIHSS) have shown limited accuracy in predicting outcomes for this specific patient population. The present study aimed to confirm whether sudden metabolic changes due to blood-brain barrier (BBB) disruption during LVO reflect differences in circulating metabolites and RNA between small and large core strokes. The second objective was to evaluate whether integrating molecular markers with existing neurological and imaging tools can enhance outcome predictions in LVO strokes. Methods: The infarction volume in patients was measured using magnetic resonance diffusion-weighted images, and the 90-day stroke outcome was defined by a modified Rankin Scale (mRS). Differential expression patterns of miRNAs were identified by RNA sequencing of serum-driven exosomes. Nuclear magnetic resonance (NMR) spectroscopy was used to identify metabolites associated with AIS with small and large infarctions. Results: We identified 41 miRNAs and 11 metabolites to be significantly associated with infarct volume in a multivariate regression analysis after adjusting for the confounders. Eight miRNAs and ketone bodies correlated significantly with infarct volume, NIHSS (severity), and mRS (outcome). Through integrative analysis of clinical, radiological, and omics data using machine learning, our study identified 11 top features for predicting stroke outcomes with an accuracy of 0.81 and AUC of 0.91. Conclusions: Our study provides a future framework for advancing stroke therapeutics by incorporating molecular markers into the existing neurological and imaging tools to improve predictive efficacy and enhance patient outcomes.

Classical molecular dynamics simulation identifies catechingallate as a promising antiviral polyphenol against MPOX palmitoylated surface protein.

Cumulative global prevalence of the emergent monkeypox (MPX) infection in the non-endemic countries has been professed as a global public health predicament. Lack of effective MPX-specific treatments sets the baseline for designing the current study. This research work uncovers the effective use of known antiviral polyphenols against MPX viral infection, and recognises their mode of interaction with the target F13 protein, that plays crucial role in formation of enveloped virions. Herein, we have employed state-of-the-art machine learning based AlphaFold2 to predict the three-dimensional structure of F13 followed by molecular docking and all-atoms molecular dynamics (MD) simulations to investigate the differential mode of F13-polyphenol interactions. Our extensive computational approach identifies six potent polyphenols Rutin, Epicatechingallate, Catechingallate, Quercitrin, Isoquecitrin and Hyperoside exhibiting higher binding affinity towards F13, buried inside a positively charged binding groove. Intermolecular contact analysis of the docked and MD simulated complexes divulges three important residues Asp134, Ser137 and Ser321 that are observed to be involved in ligand binding through hydrogen bonds. Our findings suggest that ligand binding induces minor conformational changes in F13 to affect the conformation of the binding site. Concomitantly, essential dynamics of the six-MD simulated complexes reveals Catechin gallate, a known antiviral agent as a promising polyphenol targeting F13 protein, dominated with a dense network of hydrophobic contacts. However, assessment of biological activities of these polyphenols need to be confirmed through in vitro and in vivo assays, which may pave the way for development of new novel antiviral drugs.

All-atoms molecular dynamics study to screen potent efflux pump inhibitors against KpnE protein of Klebsiella pneumoniae.

The Small Multidrug Resistance efflux pump protein KpnE, plays a pivotal role in multi-drug resistance in Klebsiella pneumoniae. Despite well-documented study of its close homolog, EmrE, from Escherichia coli, the mechanism of drug binding to KpnE remains obscure due to the absence of a high-resolution experimental structure. Herein, we exclusively elucidate its structure-function mechanism and report some of the potent inhibitors through drug repurposing. We used molecular dynamics simulation to develop a dimeric structure of KpnE and explore its dynamics in lipid-mimetic bilayers. Our study identified both semi-open and open conformations of KpnE, highlighting its importance in transport process. Electrostatic surface potential map suggests a considerable degree of similarity between KpnE and EmrE at the binding cleft, mostly occupied by negatively charged residues. We identify key amino acids Glu14, Trp63 and Tyr44, indispensable for ligand recognition. Molecular docking and binding free energy calculations recognizes potential inhibitors like acarbose, rutin and labetalol. Further validations are needed to confirm the therapeutic role of these compounds. Altogether, our membrane dynamics study uncovers the crucial charged patches, lipid-binding sites and flexible loop that could potentiate substrate recognition, transport mechanism and pave the way for development of novel inhibitors against K. pneumoniae.Communicated by Ramaswamy H. Sarma.

Polygenic Risk Score Assessment for Coronary Artery Disease in Asian Indians.

We evaluated the performance of various polygenic risk score (PRS) models derived from European (EU), South Asian (SA), and Punjabi Asian Indians (AI) studies on 13,974 subjects from AI ancestry. While all models successfully predicted Coronary artery disease (CAD) risk, the AI, SA, and EU + AI were superior predictors and more transportable than the EU model; the predictive performance in training and test sets was 18% and 22% higher in AI and EU + AI models, respectively than in EU. Comparing individuals with extreme PRS quartiles, the AI and EU + AI captured individuals with high CAD risk showed 2.6 to 4.6 times higher efficiency than the EU. Interestingly, including the clinical risk score did not significantly change the performance of any genetic model. The enrichment of diversity variants in EU PRS improves risk prediction and transportability. Establishing population-specific normative and risk factors and inclusion into genetic models would refine the risk stratification and improve the clinical utility of CAD PRS.

Contribution of circulating Mfge8 to human T2DM and cardiovascular disease.

MFGE8 is a major exosome (EV) protein known to mediate inflammation and atherosclerosis in type 2 diabetes mellitus (T2DM) in animal studies. The pathophysiological role of this protein in obesity, T2DM, and cardiovascular disease is less investigated in humans. Earlier we reported a rare Asian Indian population-specific missense variant (rs371227978; Arg148His) in the MFGE8 gene associated with increased circulating Mfge8 and T2DM. We have further investigated the role of Mfge8 with T2DM risk in additional Asian Indians (n = 4897) and Europeans and other multiethnic cohorts from UK Biobank (UKBB) (n = 455,808) and the US (n = 1150). We also evaluated the exposure of Mfge8-enriched human EVs in zebrafish (ZF) for their impact on cardiometabolic organ system. Most individual carriers of Arg148His variant not only had high circulating Mfge8 but also revealed a positive significant correlation with glucose (r = 0.42; p = 4.9 × 10-04), while the non-carriers showed a negative correlation of Mfge8 with glucose (r = -0.38; p = 0.001) in Asian Indians. The same variant was monomorphic in non-South Asian ethnicities. Even without the variant, serum Mfge8 correlated significantly with blood glucose in other non-South Asian ethnicities (r = 0.47; p = 2.2 × 10-13). Since Mfge8 is an EV marker, we tested the exposure of Mfge8-enriched human EVs to ZF larvae as an exploratory study. The ZF larvae showed rapid effects on insulin-sensitive organs, developing fatty liver disease, heart hypertrophy and exhibiting redundant growth with poor muscular architecture with and without the high-fat diet (HFD). In contrast, the control group fishes developed fatty liver disease and heart hypertrophy only after the HFD feeding. Backed with strong support from animal studies on the role of Mfge8 in obesity, insulin resistance, and atherosclerosis, the current research suggests that circulating Mfge8 may become a potential marker for predicting the risk of T2DM and cardiovascular disease in humans.

Molecular docking and MD simulations reveal protease inhibitors block the catalytic residues in Prp8 intein of Aspergillus fumigatus: a potential target for antimycotics.

Resistance to azoles and amphotericin B especially in Aspergillus fumigatus is a growing concern towards the treatment of invasive fungal infection. At this critical juncture, intein splicing would be a productive, and innovative target to establish therapies against resistant strains. Intein splicing is the central event for the activation of host protein, essential for the growth and survival of various microorganisms including A. fumigatus. The splicing process is a four-step protease-like nucleophilic cascade. Thus, we hypothesise that protease inhibitors would successfully halt intein splicing and potentially restrict the growth of the aforementioned pathogen. Using Rosetta Fold and molecular dynamics simulations, we modelled Prp8 intein structure; resembling classic intein fold with horse shoe shaped splicing domain. To fully comprehend the active site of Afu Prp8 intein, C1, T62, H65, H818, N819 from intein sequences and S820, the first C-extein residue are selected. Molecular docking shows that two FDA-approved drugs, i.e. Lufotrelvir and Remdesivir triphosphate efficiently interact with Prp8 intein from the assortment of 212 protease inhibitors. MD simulation portrayed that Prp8 undergoes conformational change upon ligand binding, and inferred the molecular recognition and stability of the docked complexes. Per-residue decomposition analysis confirms the importance of F: block R802, V803, and Q807 binding pocket in intein splicing domain towards recognition of inhibitors, along with active site residues through strong hydrogen bonds and hydrophobic contacts. However, in vitro and in vivo assays are required to confirm the inhibitory action on Prp8 intein splicing; which may pave the way for the development of new antifungals for A. fumigatus.Communicated by Ramaswamy H. Sarma.

Discovery and validation of circulating stroke metabolites by NMR-based analyses using patients from the MISS and UK Biobank.

BACKGROUND: Stroke is a significant health issue in the United States, and identifying biomarkers for the prevention and functional recovery after an acute stroke remains the highest priority. This study aims to identify circulating metabolite signatures that may be associated with stroke pathophysiology by performing discovery and validation studies. METHODS: We performed targeted metabolomics profiling of 420 participants of the discovery dataset of Metabolome in an Ischemic Stroke Study (MISS) using high-throughput nuclear magnetic resonance (NMR) spectroscopy. A validation study of significantly altered metabolites was conducted using an independent cohort of 117,988 participants from the UK Biobank, whose metabolomics profiles were generated using the same NMR technology. RESULTS AND CONCLUSION: Our study identified 16 metabolites to be significantly perturbed during acute stroke. Amino acid phenylalanine was significantly increased, while glutamine and histidine were significantly lowered in stroke. Serum levels of apolipoprotein A-1, HDL particles, small HDL particles, essential fatty acids, and phosphatidylcholine were reduced, while ketone bodies like 3-hydroxybutyrate and acetoacetate were markedly increased in stroke. Based on the robust validation in a large independent UK Biobank dataset, some of these analytes may become clinically meaningful biomarkers to predict or prevent stroke in humans.

An immunoinformatic approach towards development of a potent and effective multi-epitope vaccine against monkeypox virus (MPXV).

Monkeypox is a viral zoonotic disease, often transmitted to humans from animals. While the whole world is haggling with the COVID-19 pandemic, the emergence of the monkeypox virus (MPXV) arose as a new challenge to mankind. Till date, numerous cases related to the MPXV have been reported in several countries across the globe, but, its momentary distribution in the current time has left everyone in fright with increasing mortality and limited clinically approved treatments. Therefore, it is of immense importance to develop a potent and highly effective vaccine capable of inducing desired immunogenic responses against the highly contagious MPXV. Herein, using various immunoinformatic and computational biology tools, we made an attempt to develop a multi-epitope vaccine construct against the MPXV which is antigenic, non-allergen and non-toxic in nature and capable of exhibiting immunogenic behavior. The sequence of vaccine construct was designed using the proposed 4 MHC-I, 3 MHC-II and 4 B-cell epitopes linked with suitable adjuvant and linkers. The modeled structure of the vaccine construct was used to assess its interaction with the Toll-like Receptor 4 (TLR4) using ClusPro and HADDOCK. All-atoms molecular dynamics simulation of the MPXV vaccine construct-TLR4 complex followed by a high level of gene expression of the construct within the bacterial system affirmed its stability along with induction of immunogenic response within the host cell. Altogether, our immunoinformatic approach aid in the development of a stable chimeric vaccine construct against MPXV and needs further experimental validation for its immunological relevance and usefulness as a vaccine candidate.Communicated by Ramaswamy H. Sarma.

Exploiting the potential of natural polyphenols as antivirals against monkeypox envelope protein F13 using machine learning and all-atoms MD simulations.

The re-emergence of monkeypox (MPX), in the era of COVID-19 pandemic is a new global menace. Regardless of its leniency, there are chances of MPX expediting severe health deterioration. The role of envelope protein, F13 as a critical component for production of extracellular viral particles makes it a crucial drug target. Polyphenols, exhibiting antiviral properties have been acclaimed as an effective alternative to the traditional treatment methods for management of viral diseases. To facilitate the development of potent MPX specific therapeutics, herein, we have employed state-of-the-art machine learning techniques to predict a highly accurate 3-dimensional structure of F13 as well as identify binding hotspots on the protein surface. Additionally, we have effectuated high-throughput virtual screening methodology on 57 potent natural polyphenols having antiviral activities followed by all-atoms molecular dynamics (MD) simulations, to substantiate the mode of interaction of F13 protein and polyphenol complexes. The structure-based virtual screening based on Glide SP, XP and MM/GBSA scores enables the selection of six potent polyphenols having higher binding affinity towards F13. Non-bonded contact analysis, of pre- and post- MD complexes propound the critical role of Glu143, Asp134, Asn345, Ser321 and Tyr320 residues in polyphenol recognition, which is well supported by per-residue decomposition analysis. Close-observation of the structural ensembles from MD suggests that the binding groove of F13 is mostly hydrophobic in nature. Taken together, this structure-based analysis from our study provides a lead on Myricetin, and Demethoxycurcumin, which may act as potent inhibitors of F13. In conclusion, our study provides new insights into the molecular recognition and dynamics of F13-polyphenol bound states, offering new promises for development of antivirals to combat monkeypox. However, further in vitro and in vivo experiments are necessary to validate these results.

Comparison of Acute and Chronic Stage Ischemic Stroke Metabolome with Controls.

Background: Acute Ischemic Stroke (AIS), a major cause of disability, was previously associated with multiple metabolomic changes, but many findings were contradictory. Case-control and longitudinal study designs could have played a role in that. To clarify metabolomic changes, we performed a simultaneous comparison of ischemic stroke metabolome in acute, chronic stages of stroke and controls. Methods: Through the nuclear magnetic resonance (NMR) platform, we evaluated 271 serum metabolites from a cohort of 297 AIS patients in acute and chronic stages and 159 controls. We used Sparse Partial Least Squares-Discriminant analysis (sPLS-DA) to evaluate group disparity; multivariate regression to compare metabolome in acute, chronic stages of stroke and controls; and mixed regression to compare metabolome acute and chronic stages of stroke. We applied false discovery rate (FDR) to our calculations. Results: The sPLS-DA revealed separation of the metabolome in acute, chronic stages of stroke and controls. Regression analysis identified 38 altered metabolites. Ketone bodies, branched-chain amino acids (BCAAs), energy, and inflammatory compounds were elevated in the acute stage, but declined in the chronic stage, often to the same levels as in controls. Levels of other amino acids, phosphatidylcholines, phosphoglycerides, and sphingomyelins mainly did not change between acute and chronic stages, but was different comparing to controls. Conclusion: Our pilot study identified metabolites associated with acute stage of ischemic stroke and those that are altered in stroke patients comparing to controls regardless of stroke acuity. Future investigation in a larger independent cohort is needed to validate these findings.

Machine learning and classical MD simulation to identify inhibitors against the P37 envelope protein of monkeypox virus.

Monkeypox virus (MPXV) outbreak is a serious public health concern that requires international attention. P37 of MPXV plays a pivotal role in DNA replication and acts as one of the promising targets for antiviral drug design. In this study, we intent to screen potential analogs of existing FDA approved drugs of MPXV against P37 using state-of-the-art machine learning and computational biophysical techniques. AlphaFold2 guided all-atoms molecular dynamics simulations optimized P37 structure is used for molecular docking and binding free energy calculations. Similar to members of Phospholipase-D family , the predicted P37 structure also adopts a ß-α-ß-α-ß sandwich fold, harbouring strongly conserved HxKxxxxD motif. The binding pocket comprises of Tyr48, Lys86, His115, Lys117, Ser130, Asn132, Trp280, Asn240, His325, Lys327 and Tyr346 forming strong hydrogen bonds and dense hydrophobic contacts with the screened analogs and is surrounded by positively charged patches. Loops connecting the two domains and C-terminal region exhibit high degree of flexibility. In some structural ensembles, the partial disorderness in the C-terminal region is presumed to be due to its low confidence score, acquired during structure prediction. Transition from loop to ß-strands (244-254 aa) in P37-Cidofovir and its analog complexes advocates the need for further investigations. MD simulations support the accuracy of the molecular docking results, indicating the potential of analogs as potent binders of P37. Taken together, our results provide preferable understanding of molecular recognition and dynamics of ligand-bound states of P37, offering opportunities for development of new antivirals against MPXV. However, the need of in vitro and in vivo assays for confirmation of these results still persists.Communicated by Ramaswamy H. Sarma.

Assessing the prediction of type 2 diabetes risk using polygenic and clinical risk scores in South Asian study populations.

Background: Genome-wide polygenic risk scores (PRS) have shown high specificity and sensitivity in predicting type 2 diabetes (T2D) risk in Europeans. However, the PRS-driven information and its clinical significance in non-Europeans are underrepresented. We examined the predictive efficacy and transferability of PRS models using variant information derived from genome-wide studies of Asian Indians (AIs) (PRSAI) and Europeans (PRSEU) using 13,974 AI individuals. Methods: Weighted PRS models were constructed and analyzed on 4602 individuals from the Asian Indian Diabetes Heart Study/Sikh Diabetes Study (AIDHS/SDS) as discovery/training and test/validation datasets. The results were further replicated in 9372 South Asian individuals from UK Biobank (UKBB). We also assessed the performance of each PRS model by combining data of the clinical risk score (CRS). Results: Both genetic models (PRSAI and PRSEU) successfully predicted the T2D risk. However, the PRSAI revealed 13.2% odds ratio (OR) 1.80 [95% confidence interval (CI) 1.63-1.97; p = 1.6 × 10-152] and 12.2% OR 1.38 (95% CI 1.30-1.46; p = 7.1 × 10-237) superior performance in AIDHS/SDS and UKBB validation sets, respectively. Comparing individuals of extreme PRS (ninth decile) with the average PRS (fifth decile), PRSAI showed about two-fold OR 20.73 (95% CI 10.27-41.83; p = 2.7 × 10-17) and 1.4-fold OR 3.19 (95% CI 2.51-4.06; p = 4.8 × 10-21) higher predictability to identify subgroups with higher genetic risk than the PRSEU. Combining PRS and CRS improved the area under the curve from 0.74 to 0.79 in PRSAI and 0.72 to 0.75 in PRSEU. Conclusion: Our data suggest the need for extending genetic and clinical studies in varied ethnic groups to exploit the full clinical potential of PRS as a risk prediction tool in diverse study populations.

Diabetes mellitus susceptibility with varied diseased phenotypes and its comparison with phenome interactome networks.

An emerging area of interest in understanding disease phenotypes is systems genomics. Complex diseases such as diabetes have played an important role towards understanding the susceptible genes and mutations. A wide number of methods have been employed and strategies such as polygenic risk score and allele frequencies have been useful, but understanding the candidate genes harboring those mutations is an unmet goal. In this perspective, using systems genomic approaches, we highlight the application of phenome-interactome networks in diabetes and provide deep insights. LINC01128, which we previously described as candidate for diabetes, is shown as an example to discuss the approach.

Ethnic differences in ApoC-III concentration and the risk of cardiovascular disease: No evidence for the cardioprotective role of rare/loss of function APOC3 variants in non-Europeans.

Background: Hypertriglyceridemia is as an independent risk factor for cardiovascular disease (CVD). Apolipoprotein C-III (ApoC-III) is known to regulate triglyceride (TG) metabolism. However, the causal association between ApoC-III and CVD development is unclear. The objectives were to examine the impact of ApoC-III concentration on TG and lipoproteins and investigate the role of known rare loss-of-function APOC3 variants for modulating ApoC-III, TG concentrations and CVD risk in different ethnic groups. Methods: Plasma ApoC-III levels were measured in a multiethnic sample of 518 individuals comprising 271 Asian Indians (Sikhs), 87 Caucasians, 80 African Americans, and 80 Hispanics. Results: ApoC-III levels showed a robust association with TG in Asian Indians (r = 0.5, p = 1.1 × 10-23), Caucasians (r = 0.4, p = 7.2 × 10-4), and Hispanics (r = 0.9, p = 2.7x × 10-28). African Americans had lowest ApoC-III and TG concentrations and highest (44%) prevalence of coronary artery disease (CAD). ApoC-III levels correlated with fasting blood glucose (r = 0.25, p = 6.1 × 10-5) in Asian Indians and central adiposity in Hispanics (waist: r = 0.22, p = 0.05; waist-hip ratio: r = 0.24, p = 0.04). The carriers of rare variants IVS1-2G-A (rs373975305); A43T (rs147210663) and IVS3 + 1G-T (rs140621530) showed high TG but not low ApoC-III levels in Asian Indians and Caucasians. Conclusion: These results highlight the challenges of generalizing antisense ApoC-III inhibition for treating atherosclerotic disease in dyslipidemia that may benefit only specific sub-populations. The observed ethnic differences in ApoC-III concentrations and CAD risk factors, emphasize in-depth genetic and metabolomics evaluations on diverse ancestries.