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Optimisation of human VH domain antibodies specific to Mycobacterium tuberculosis heat shock protein (HSP16.3)

Soong JX, Chan SK, Lim TS, Choong YS

J Comput Aided Mol Des, 2019 03;33(3):375-385.
PMID: 30689080 DOI: 10.1007/s10822-019-00186-z

Mycobacterium tuberculosis (Mtb) 16.3 kDa heat shock protein 16.3 (HSP16.3) is a latency-associated antigen that can be targeted for latent tuberculosis (TB) diagnostic and therapeutic development. We have previously developed human VH domain antibodies (dAbs; clone E3 and F1) specific against HSP16.3. In this work, we applied computational methods to optimise and design the antibodies in order to improve the binding affinity with HSP16.3. The VH domain antibodies were first docked to the dimer form of HSP16.3 and further sampled using molecular dynamics simulation. The calculated binding free energy of the HSP16.3-dAb complexes showed non-polar interactions were responsible for the antigen-antibody association. Per-residue free energy decomposition and computational alanine scanning have identified one hotspot residue for E3 (Y391) and 4 hotspot residues for F1 (M394, Y396, R397 and M398). These hotspot residues were then mutated and evaluated by binding free energy calculations. Phage ELISA assay was carried out on the potential mutants (E3Y391W, F1M394E, F1R397N and F1M398Y). The experimental assay showed improved binding affinities of E3Y391W and F1M394E against HSP16.3 compared with the wild type E3 and F1. This case study has thus showed in silico methods are able to assist in optimisation or improvement of antibody-antigen binding.
Theoretical study of the interactions between peptide tyrosine tyrosine [PYY (1-36)], a newly identified modulator in type 2 diabetes pathophysiology, with receptors NPY1R and NPY4R

Choong YS, Lim YY, Soong JX, Savoo N, Guida C, Rhyman L, et al.

Hormones (Athens), 2021 Sep;20(3):557-569.
PMID: 33782920 DOI: 10.1007/s42000-021-00278-2

PURPOSE: Diabetes mellitus is a common condition in the clinically obese. Bariatric surgery is one of the ways to put type 2 diabetes in remission. Recent findings propose the appetite-regulator peptide tyrosine tyrosine (PYY) as a therapeutic option for patients with type 2 diabetes. This novel gut hormone restores impaired insulin and glucagon secretion in pancreatic islets and is implicated in type 2 diabetes reversal after bariatric surgery. The current study elucidates the interactions between PYY and the NPY1R and NPY4R receptors using computational methods.
METHODS: Protein structure prediction, molecular docking simulation, and molecular dynamics (MD) simulation were performed to elucidate the interactions of PYY with NPY1R and NPY4R.
RESULTS: The predicted binding models of PYY-NPY receptors are in agreement with those described in the literature, although different interaction partners are presented for the C-terminal tail of PYY. Non-polar interactions are predicted to drive the formation of the protein complex. The calculated binding energies show that PYY has higher affinity for NPY4R (ΔGGBSA = -65.08 and ΔGPBSA = -87.62 kcal/mol) than for NPY1R (ΔGGBSA = -23.11 and ΔGPBSA = -50.56 kcal/mol).
CONCLUSIONS: Based on the constructed models, the binding conformations obtained from docking and MD simulation for both the PYY-NPY1R and PYY-NPY4R complexes provide a detailed map of possible interactions. The calculated binding energies show a higher affinity of PYY for NPY4R. These findings may help to understand the mechanisms behind the improvement of diabetes following bariatric surgery.
Computer-Aided Antibody Design: An Overview

Choong YS, Lee YV, Soong JX, Law CT, Lim YY

Adv Exp Med Biol, 2017;1053:221-243.
PMID: 29549642 DOI: 10.1007/978-3-319-72077-7_11

The use of monoclonal antibody as the next generation protein therapeutics with remarkable success has surged the development of antibody engineering to design molecules for optimizing affinity, better efficacy, greater safety and therapeutic function. Therefore, computational methods have become increasingly important to generate hypotheses, interpret and guide experimental works. In this chapter, we discussed the overall antibody design by computational approches.

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