In silico predictions and optimization of aptamers against Streptococcus agalactiae surface protein using computational docking

Abstract

Abstract Aptamers are single-stranded RNA or DNA oligonucleotides that form unique three-dimensional (3D) structures that can bind a wide range of molecules and compounds The development of aptamers against targeted biomolecules using computational approach is a novel method to obtain viable chemical-based antibody substitutes. The conventional method to derive aptamers using SELEX technology involves multiple rounds of selection and enrichment, which is laborious, costly and time-consuming. It is also inefficient as it will often fail to isolate high binding aptamers. We evaluated a new approach to improve aptamer designs by using in silico refinement of the desired binding requirement on the biomolecule target. The three-dimensional (3D) structures of single strand oligonucleotides were modelled to contain hairpin(s) and predicted to retain folding stability base on free energy secondary structure formation. The size and number of hairpins were then optimized with aptamer sequence length to derive the highest binding score. We utilized the method against Streptoccocus agalactiae surface protein as test model and generated binding score values by molecular docking using AutoDock Vina. The aptamer binding affinities were then evaluated for binding capacity improvement via specific structural design. Molecular docking experiments indicated the feasibility of using in silico technique to select aptamers that can function as synthetic antibodies and revealed 3D structural conformity as an essential requirement in aptamer design.