In silico design of dehydrophenylalanine containing peptide activators of glucokinase using pharmacophore modelling, molecular dynamics and machine learning: implications in type 2 diabetes

Abstract

Diabetes represents a significant global health challenge associated with substantial healthcare costs and therapeutic complexities. Current diabetes therapies often entail adverse effects, necessitating the exploration of novel agents. Glucokinase (GK), a key enzyme in glucose homeostasis, primarily regulates blood glucose levels in hepatocytes and pancreatic cells. Unlike other hexokinases, GK exhibits unique kinetic properties, such as a high Km and lack of feedback inhibition, allowing it to function as a glucose sensor Glucokinase activators (GKAs) have emerged as promising candidates for managing type-2 diabetes by allosterically enhancing GK activity. Despite initial promise, existing GKAs face significant safety concerns, driving the need for compounds with improved safety profiles. This study introduces a novel chemical scaffold within the GKA landscape: peptide-based GKAs incorporating non-standard amino acid residues such as α,β-dehydrophenylalanine (ΔPhe/ΔF). A virtual library containing 3,368,000 peptides was constructed and screened using a hybrid pharmacophore, namely DHRR (D: donor; H: hydrogen; R: aromatic ring). Molecular docking and molecular dynamics simulations assisted in identifying three peptides, Pep-11, Pep-15, and Pep-16, which depicted stable binding at the allosteric site of Glucokinase. These peptides were synthesized using a combination of solid and solution phase synthesis methods. In vitro enzymatic activity of glucokinase was increased by at least 1.5 times in the presence of these peptides. Several machine learning algorithms were explored as alternatives to conventional in-silico methods for predicting GK activity. Regression and tree-based algorithms outperformed other methods, with the logistic regression and random forest classifiers both achieving an ROC-AUC of 0.98.