Protein Modeling and Structure-Based Drug Design

Structure-based drug design attempts to design small molecule ligands by docking them directly into the binding pocket of a target protein. This requires a 3D structure of the reference protein. The goal is to optimally fill the binding pocket with a ligand by satisfying non-bonded interactions with the functional groups of the binding site residues. The structure-based design process starts with a detailed analysis of the binding pocket to elucidate the hot spots for the putative interactions with the protein. Either experimental methods or computational tools can be used to perform a mapping of the active site using molecular probes or small solvent-like molecules. In an iterative process of structure determination, modeling of modified ligands, docking and screening, synthesis, and biological testing, the properties of small molecule ligands are refined to optimize their binding to the target protein. Databases have been developed to retrieve and compare structural information from the exponentially growing body of structural data on protein-ligand complexes. They allow comparison of binding poses, active site interaction geometries, protein-ligand binding motifs, and the original solvation structures in the binding pocket of the protein. Proteins can be compared in terms of their exposed binding pockets. The shape and exposure of groups with special physicochemical properties in binding pockets are compared and help to design small molecule ligands with the desired selectivity. Ideas for isosteric replacements on the ligand scaffold can also be generated in this way. If an experimentally determined structure of the target protein is not available, a homology model can be constructed using a related protein of known architecture as a template. The accuracy and success of such a homology model is strongly dependent on the homology of the sequence to the structure of the template. Tools for secondary structure prediction and amino acid substitution propensities have been developed to improve the reliability of the sequence assignment of proteins to the 3D structure of the reference template. Recently, AI-programs have been developed based on neural networks. They were trained to learn structure predictions based on experimentally determined protein structures. De novo design approaches start with a small molecule seed or fragment and grow them into putative ligands in the binding pocket. Two non-overlapping fragments can also be linked together to form a larger ligand with improved binding properties. In all structure-based design strategies, the geometry of a constructed protein-ligand complex must be evaluated in terms of expected binding affinity. A variety of scoring functions are used to predict binding affinity based on the geometry of the formed complex. https://sn.pub/0alfsy