Systems Biology

In neuropharmacology and drug development, in silico methods have become increasingly vital, particularly for studying receptor–ligand interactions at the molecular level. Membrane proteins such as GABA (A) receptors play a central role in neuronal signaling and are key targets for therapeutic intervention. While experimental techniques like electrophysiology and radioligand binding provide valuable functional data, they often fall short in resolving the structural complexity of membrane proteins and can be time-consuming, costly, and inaccessible in many research settings. This study presents a comprehensive computational workflow for investigating membrane protein–ligand interactions, demonstrated using the GABA (A) receptor α5β2γ2 subtype and mitragynine, an alkaloid from Mitragyna speciosa (Kratom), as a case study. The protocol includes homology modeling of the receptor based on a high-resolution template, followed by structure optimization and validation. Ligand docking is then used to predict binding sites and affinities at known modulatory interfaces. Finally, molecular dynamics (MD) simulations assess the stability and conformational dynamics of receptor–ligand complexes over time. Overall, this workflow offers a robust, reproducible approach for structural analysis of membrane protein–ligand interactions, supporting early-stage drug discovery and mechanistic studies across diverse membrane protein targets.

Genome-wide screens using yeast or phage displays are powerful tools for identifying protein–ligand interactions, including drug or vaccine targets, ligand receptors, or protein–protein interactions. However, assembling libraries for genome-wide screens can be challenging and often requires unbiased cloning of 10⁵–10⁷ DNA fragments for a complete representation of a eukaryote genome. A sub-optimal genomic library can miss key genomic sequences and thus result in biased screens. Here, we describe an efficient method to generate genome-wide libraries for yeast surface display using Gibson assembly. The protocol entails genome fragmentation, ligation of adapters, library cloning using Gibson assembly, library transformation, library DNA recovery, and a streamlined Oxford nanopore library sequencing procedure that covers the length of the cloned DNA fragments. We also describe a computational pipeline to analyze the library coverage of the genome and predict the proportion of expressed proteins. The method allows seamless library transfer among multiple vectors and can be easily adapted to any expression system.