Open AccessComputational Design of Nanobody Binding to Cortisol to Improve Their Binding Affinity Using Molecular Docking and Molecular Dynamics Simulations
Author(s) -
Umi Baroroh,
Nur Asni Setiani,
Irma Mardiah,
Dewi Astriany,
Muhammad Yusuf
Publication year - 2022
Publication title -
indonesian journal of chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.273
H-Index - 14
eISSN - 2460-1578
pISSN - 1411-9420
DOI - 10.22146/ijc.71480
Subject(s) - chemistry , molecular dynamics , docking (animal) , binding affinities , mutant , binding site , affinities , molecular model , affinity maturation , ligand (biochemistry) , binding energy , stereochemistry , biophysics , computational chemistry , biochemistry , receptor , gene , medicine , physics , nursing , biology , nuclear physics
Currently, nanobody binding cortisol has been deposited in the database. Unfortunately, the affinity is still in micromolar order. Substituting hydrophobic residues in the binding pocket and utilizing CDR2 and CDR3 is the strategy to improve the affinity. A single and double substitution at positions 53 and 101 have been introduced to the nanobody structure through molecular modeling. The affinity toward cortisol was evaluated using molecular docking to get the binding pose. The highest binding energy pose was used as the initial coordinate to analyze further using 100 ns molecular dynamics simulations. The binding affinities calculated by MMGBSA showed that MT3, MT5, and MT6 have better binding affinity than WT. In contrast, the ligand movement through MD simulations reveals that MT1, MT3, and MT5 are relatively stable. Hence, docking and MD simulations showed that MT3 is the best mutant than others. This mutant is substituting the threonine to isoleucine at position 53. New hydrophobic interactions occurred and caused the increase of binding. Eventually, this study provides valuable structural information to improve the binding affinity of nanobody binding cortisol for further development of this molecule to antibody-based biosensor design.