Carbonic anhydrase 2 (CA2) enzyme deficiency caused by CA2 gene mutations is a inherited disorder characterized by symptoms like osteopetrosis, renal tubular acidosis, and cerebral calcification. To better understand the impact of CA2 deficiency causal mutations on the CA2 protein structure, mutations were collected and screened to know their pathogenicity potential using diverse computational programs. Subsequently, mutant 3D models were built, and structural divergence, protein stability, and molecular dynamics analyses were performed. This study found M-CAP as the best sensitive prediction method to measure the deleterious potential of CA2 missense mutations. Free energy dynamics of tertiary structure models of CA2 mutants with DUET, mCSM, and SDM based consensus methods predicted only 50% of the variants as destabilizing. Superimposition of native and mutant CA2 models revealed the minor structural fluctuations at the amino acid residue level but not at the whole protein structure level. Near native molecular dynamic simulation analysis indicated that CA2 causative missense variants result in residue level fluctuation pattern in the protein structure. This study expands the known understanding of genotype-protein phenotype correlations underlying CA2 variant pathogenicity and presents a potential avenue for modifying the CA2 deficiency by targeting biophysical structural features of CA2 protein.

The somatic mutations in ATP binding cleft of the tyrosine kinase binding domain of EGFR are known to occur in 15-40% of non-small cell lung cancer (NSCLC) patients. Although first and second generation anti-EGFR inhibitors are widely used to treat these patients, their therapeutic efficacy is modest and often results in adverse effects or drug resistance. Therefore, there is a need to develop novel as well as safe anti-EGFR drugs. The rapid emergence of computational drug designing provided a great opportunity to both discover and predict the efficacy of novel EGFR inhibitors from plant sources. In the present study, we designed several chemical analogues of edible curcumin (CUCM) compound and assessed their drug likeliness, ADME and toxicity properties using a diverse range of advanced computational methods. We also have examined the structural plasticity and binding characteristics of EGFR wild-type and mutant forms (S769L and K846R) against ligand molecules like Gefitinib, native CUCM, and different CUCM analogues. Through multidimensional experimental approaches, we conclude that CUCM-36 ((1E,4Z,6E)-1-(3,4-Diphenoxyphenyl)-5-hydroxy-7-(4-hydroxy-3-phenoxyphenyl)-1,4,6-heptatrien-3-one) is the best anti-EGFR compound with high drug-likeness, ADME properties, and low toxicity properties. CUCM-36 compound has demonstrated better affinity towards both wild-type (ΔG is -8.5 kcal/Mol) and mutant forms (V769L & K846R; ΔG for both is >-9.20 kcal/Mol) compared to natural CUCM and Gefitinib inhibitor. This study advises the future laboratory assays to develop CUCM-36 as a novel drug compound for treating EGFR positive non-small cell lung cancer patients.

Obesity and type 2 and diabetes mellitus (T2D) are two dual epidemics whose shared genetic pathological mechanisms are still far from being fully understood. Therefore, this study is aimed at discovering key genes, molecular mechanisms, and new drug targets for obesity and T2D by analyzing the genome wide gene expression data with different computational biology approaches. In this study, the RNA-sequencing data of isolated primary human adipocytes from individuals who are lean, obese, and T2D was analyzed by an integrated framework consisting of gene expression, protein interaction network (PIN), tissue specificity, and druggability approaches. Our findings show a total of 1932 unique differentially expressed genes (DEGs) across the diabetes versus obese group comparison (p≤0.05). The PIN analysis of these 1932 DEGs identified 190 high centrality network (HCN) genes, which were annotated against 3367 GO terms and functional pathways, like response to insulin signaling, phosphorylation, lipid metabolism, glucose metabolism, etc. (p≤0.05). By applying additional PIN and topological parameters to 190 HCN genes, we further mapped 25 high confidence genes, functionally connected with diabetes and obesity traits. Interestingly, ERBB2, FN1, FYN, HSPA1A, HBA1, and ITGB1 genes were found to be tractable by small chemicals, antibodies, and/or enzyme molecules. In conclusion, our study highlights the potential of computational biology methods in correlating expression data to topological parameters, functional relationships, and druggability characteristics of the candidate genes involved in complex metabolic disorders with a common etiological basis.

Mitochondrial disorders (MIDs) shows overlapping clinical presentations owing to the genetic and metabolic defects of mitochondria. However, specific relationship between inherited mutations in nuclear encoded mitochondrial proteins and their functional impacts in terms of metabolic defects in patients is not yet well explored. Therefore, using high throughput whole exome sequencing (WES), we screened a chronic kidney disease (CKD) and sensorineural hearing loss (SNHL) patient, and her family members to ascertain the mode of inheritance of the mutation, and healthy population controls to establish its rare frequency. The impact of mutation on biophysical characteristics of the protein was further studied by mapping it in 3D structure. Furthermore, LC-MS tandem mass spectrophotometry based untargeted metabolomic profiling was done to study the fluctuations in plasma metabolites relevant to disease causative mutations and kidney damage. We identified a very rare homozygous c.631G > A (p.Val211Met) pathogenic mutation in RMND1 gene in the proband, which is inherited in an autosomal recessive fashion. This gene is involved in the mitochondrial translational pathways and contribute in mitochondrial energy metabolism. The p.Val211Met mutation is found to disturb the structural orientation (RMSD is -2.95 Å) and stability (ΔΔG is -0.552 Kcal/mol) of the RMND1 protein. Plasma metabolomics analysis revealed the aberrant accumulation of metabolites connected to lipid and amino acid metabolism pathways. Of these metabolites, pathway networking has discovered ceramide, a metabolite of sphingolipids, which plays a role in different signaling cascades including mitochondrial membrane biosynthesis, is highly elevated in this patient. This study suggests that genetic defects in RMND1 gene alters the mitochondrial energy metabolism leading to the accumulation of ceramide, and subsequently promote dysregulated apoptosis and tissue necrosis in kidneys.