Highlight
Peptide-based drug design using generative AI
Advances in AI-driven peptide design are accelerating the discovery of peptide-based drugs with enhanced stability, specificity, and clinical potential.
Nanobodies targeting ion channels: advancing therapeutics through precision and structural insights
Nanobodies offer unprecedented precision for targeting ion channels, with structural biology and AI unlocking new therapeutic avenues.
Computational design of protein complexes: influence of binding affinity
Workflow for predicting the wild-type and mutation induced change in binding affinity of biomolecular complexes using sequence and structure features with AI/ML techniques.
Feature Article
Conditional disorder in proteins: functional transitions between order and disorder
Proteins populate a dynamic continuum of conformations, ranging from fully ordered to intrinsically disordered states, with transitions governed by their specific molecular context.
Decoding fold robustness and thermostability in Thermococcus AMP phosphorylase and its DPBB domains
We dissect thermostability and fold resilience in Thermococcus AMP phosphorylase and its DPBB domains using multiscale simulations, energetic profiling, and rational redesign.
Communication
Hybrid AI/physics pipeline for miniprotein binder prioritization: application to the BRD3 ET domain
AI-based protein design can rapidly generate thousands of candidate binders, but most fail to fold or bind productively, creating a critical need for robust prioritization.
Communication
Zn(II)-metallo-photoantibiotics: experimental and computational approach identifying a therapeutic role for antibacterial and antibiofilm applications
Curcumin-based novel Zn(II)-metallo-photo antibiotics against E. coli and B. subtilis are reported to have antibacterial and antibiofilm properties.
About this collection
The rapid advancement of computational techniques—such as artificial intelligence/machine learning (AI/ML), molecular dynamics simulations, enhanced sampling methods, multiscale modeling and different protein design methods such as de novo protein design, protein redesign, diffusion models, peptidomimetics, and parametric protein design methods—has dramatically enhanced our ability to predict protein structures, design proteins of therapeutic, biotechnological significance, and optimize drug candidates. Moreover, the integration of experimental validation with computational models has paved the way for more accurate predictions, designed proteins for various applications and efficient drug development pipelines.
This special collection, Guest Edited by Aditya K. Padhi (Indian Institute of Technology (BHU) Varanasi, India), Timir Tripathi (North-Eastern Hill University, India) and Seetharaman Jayaraman (UTHSC Center for Cancer Research, USA) explores key areas, including computational protein and antibody design, vaccine design, enzyme engineering, protein-ligand interactions, protein-protein interaction (PPI) modulators, design/redesign of proteins for infectious diseases, cancer, neurodegenerative disorders, and antimicrobial resistance. Moreover, structural insights for mechanistic understanding of the proteins, allosteric modulator design, design of non-natural amino acid-based proteins, applications in synthetic biology, rare diseases and personalized medicine, design and engineering of protein biomaterials and biosensors, case studies on the integration of computational methods with experimental biology, and method centric manuscripts related to the above areas are considered.