Exploring Stereochemical and Conformational Requirements at Cannabinoid Receptors for Synthetic Cannabinoids Related to SDB-006, 5F-SDB-006, CUMYL-PICA, and 5F-CUMYL-PICA.

Synthetic cannabinoid receptor agonists (SCRAs) represent the most rapidly expanding class of new psychoactive substances (NPSs). Despite the prevalence and potency of recent chiral indole-3-carboxamide SCRAs, few pharmacological data are available regarding the enantiomeric bias of these NPSs toward human CB1 and CB2 receptors. A series of homochiral indole-3-carboxamides derived from (S)- and (R)-α-methylbenzylamine and featuring variation of the 1-alkyl substituent were prepared, pharmacologically evaluated, and compared to related achiral congeners derived from cumyl- and benzylamine. Competitive binding assays demonstrated that all analogues derived from either enantiomer of α-methylbenzylamine (14-17) showed affinities for CB1 (Ki = 47.9-813 nM) and CB2 (Ki = 47.9-347 nM) that were intermediate to that of the corresponding benzylic (10-13, CB1 Ki = 550 nM to >10 µM; CB2 Ki = 61.7 nM to >10 µM) and cumyl derivatives (6-9, CB1 Ki = 12.6-21.4 nM; CB2 Ki = 2.95-24.5 nM). In a fluorometric membrane potential assay, all α-methylbenzyl analogues (excluding 17) were potent, efficacious agonists of CB1 (EC50 = 32-464 nM; Emax = 89-104%) and low efficacy agonists of CB2 (EC50 = 54-500 nM; Emax = 52-77%), with comparable or greater potency than the benzyl analogues and much lower potency than the cumyl derivatives, consistent with binding trends. The relatively greater affinity and potency of (S)-14-17 compared to (R)-14-17 analogues at CB1 highlighted an enantiomeric bias for this series of SCRAs. Molecular dynamics simulations provided a conformational basis for the observed differences in agonist potency at CB1 pending benzylic substitution.

Personalized Pharmacoperones for Lysosomal Storage Disorder: Approach for Next-Generation Treatment.

Lysosomal storage disorders (LSDs) are a collection of inborn errors of metabolic disorders affected by mutations in lysosome functional genes, commonly acid hydrolases. From the past decades, many approaches like enzyme replacement therapy, substrate reduction therapy are followed to treat these conditions. However, all these approaches have their own limitations. Proof-of-concept studies on pharmacological chaperone therapy (PCT) is now transformed into clinical practice to treat LSDs. Furthermore, it is narrowed with individuals to chaperone sensitive, specific mutations. Hence, personalizing the PCT will be a new direction to combat LSDs. In this review, we have discussed the available clinical strategies and pointed the light on how pharmacological chaperones can be personalized and hopeful to be a next-generation approach to address LSDs.

Evolution- and structure-based computational strategy reveals the impact of deleterious missense mutations on MODY 2 (maturity-onset diabetes of the young, type 2).

Heterozygous mutations in the central glycolytic enzyme glucokinase (GCK) can result in an autosomal dominant inherited disease, namely maturity-onset diabetes of the young, type 2 (MODY 2). MODY 2 is characterised by early onset: it usually appears before 25 years of age and presents as a mild form of hyperglycaemia. In recent years, the number of known GCK mutations has markedly increased. As a result, interpreting which mutations cause a disease or confer susceptibility to a disease and characterising these deleterious mutations can be a difficult task in large-scale analyses and may be impossible when using a structural perspective. The laborious and time-consuming nature of the experimental analysis led us to attempt to develop a cost-effective computational pipeline for diabetic research that is based on the fundamentals of protein biophysics and that facilitates our understanding of the relationship between phenotypic effects and evolutionary processes. In this study, we investigate missense mutations in the GCK gene by using a wide array of evolution- and structure-based computational methods, such as SIFT, PolyPhen2, PhD-SNP, SNAP, SNPs&GO, fathmm, and Align GVGD. Based on the computational prediction scores obtained using these methods, three mutations, namely E70K, A188T, and W257R, were identified as highly deleterious on the basis of their effects on protein structure and function. Using the evolutionary conservation predictors Consurf and Scorecons, we further demonstrated that most of the predicted deleterious mutations, including E70K, A188T, and W257R, occur in highly conserved regions of GCK. The effects of the mutations on protein stability were computed using PoPMusic 2.1, I-mutant 3.0, and Dmutant. We also conducted molecular dynamics (MD) simulation analysis through in silico modelling to investigate the conformational differences between the native and the mutant proteins and found that the identified deleterious mutations alter the stability, flexibility, and solvent-accessible surface area of the protein. Furthermore, the functional role of each SNP in GCK was identified and characterised using SNPeffect 4.0, F-SNP, and FASTSNP. We hope that the observed results aid in the identification of disease-associated mutations that affect protein structure and function. Our in silico findings provide a new perspective on the role of GCK mutations in MODY2 from an evolution-based structure-centric point of view. The computational architecture described in this paper can be used to predict the most appropriate disease phenotypes for large-genome sequencing projects and to provide individualised drug therapy for complex diseases such as diabetes.

Mapping the structural topology of IRS family cascades through computational biology.

Structural topologies of proteins play significant roles in analyzing their biological functions. Converting the amino acid data in a protein sequence into structural information to outline the function of a protein is a major challenge in post-genome research which can add an extra room in understanding the protein sequence-structure-function relationships. In this study, we performed a comprehensive bioinformatics analysis of structural topology of the IRS family members such as IRS-1, IRS-2, IRS-3, IRS-4, IRS-5 and IRS-6. Based on this assessment, we found that IRS-2 encloses the highest number of α helices, ß sheets and ß turns in the secondary structure topology compared to IRS-1 and IRS-6. IRS family members are rich in serine or leucine residues. Among the IRS family members, the highest percentage of serine and leucine was observed in IRS-1 (15%) and IRS-5 (10%), respectively. Notably, the highest number of disulphide bonds was observed in IRS-1 (10) which is responsible for structural stability of the protein. Hydrogen bond pattern in α helices and ß sheet was recorded in IRS-1, IRS-2 and IRS-6. By conservation analysis, the longest protein IRS-3 was found to be highly conserved among the IRS family members. The cluster of sequence logo present in the N terminus of these cascades was noted, and highly conserved residues in N-terminal region help in the formation of the two highly conserved domains such as PH domain and PTB domain. Results generated from this analysis will be more beneficial to researchers in understanding more about insulin signalling mechanism(s) as well as insulin resistance pathway. We discuss here that bioinformatics tools utilized in this study can play a vital role in addressing the complexity of structural topology to understand structure-function relationships in insulin signalling cascades.