Sep 06, 2023
Computational design of novel nanobodies targeting the receptor binding domain of variants of concern of SARS-CoV-2
- Phoomintara Longsomboon1,
- Thanyada Rungrotmongkol2,
- Nongluk Plongthongkum1,
- Kittikhun Wangkanont3,
- Peter Wolschann4,
- Rungtiva P. Poo-arporn1
- 1Biological Engineering Program, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand;
- 2Center of Excellence in Biocatalyst and Sustainable Biotechnology, Department of Biochemistry, Faculty of Science, and Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University;
- 3Center of Excellence for Molecular Biology and Genomics of Shrimp, Department of Biochemistry, Faculty of Science, Chulalongkorn University, Thailand;
- 4Institute of Theoretical Chemistry, University of Vienna, Vienna, Austria
Protocol Citation: Phoomintara Longsomboon, Thanyada Rungrotmongkol, Nongluk Plongthongkum, Kittikhun Wangkanont, Peter Wolschann, Rungtiva P. Poo-arporn 2023. Computational design of novel nanobodies targeting the receptor binding domain of variants of concern of SARS-CoV-2. protocols.io https://dx.doi.org/10.17504/protocols.io.4r3l22q4jl1y/v1
Manuscript citation:
[PONE-D-23-16784] Computational design of novel nanobodies targeting the receptor binding domain of variants of concern of SARS-CoV-2
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: August 28, 2023
Last Modified: September 06, 2023
Protocol Integer ID: 87067
Keywords: COVID-19, Nanobodies, Variants of concern, Molecular modeling, Receptor binding domain, protein docking, respiratory syndrome coronavirus, computational design of novel nanobody, novel nanobody, effective nbs against sar, rbd docking, neutralizing nanobody, strategies against sar, potential of molecular modeling, molecular modeling, viral rbd, binding affinity, receptor, performance of different protein, protein, affinity than human angiotensin
Funders Acknowledgements:
National Research Council of Thailand
Grant ID: N42A650316
National Research Council of Thailand
Grant ID: N42A650231
Research Strengthening Project of the Faculty of Engineering and Thailand Science Research and Innovation (TSRI), Basic Research Fund: Fiscal year 2023, Program Smart Healthcare
Grant ID: FRB660073/0164
Petchra Pra Jom Klao Ph.D. Research Scholarship from King Mongkut’s University of Technology Thonburi
Grant ID: -
Abstract
The COVID-19 pandemic has created an urgent need for effective therapeutic
and diagnostic strategies to manage the disease caused by the severe acute
respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the emergence of
numerous variants of concern (VOCs) has made it challenging to develop targeted
therapies that are broadly specific in neutralizing the virus. In this study, we
aimed to develop neutralizing nanobodies (Nbs) using computational techniques
that can effectively neutralize the receptor-binding domain (RBD) of SARS-CoV-2
VOCs. We evaluated the performance of different protein-protein docking
programs and identified HDOCK as the most suitable program for Nb/RBD docking
with high accuracy. Using this approach, we designed 14 novel Nbs with high
binding affinity to the VOC RBDs. The Nbs were engineered with mutated amino
acids that interacted with key amino acids of the RBDs, resulting in higher
binding affinity than human angiotensin-converting enzyme 2 (ACE2) and other
viral RBDs or hemagglutinins (HAs). The successful development of these Nbs
demonstrates the potential of molecular modeling as a low-cost and
time-efficient method for engineering effective Nbs against SARS-CoV-2. The
engineered Nbs have the potential to be employed in RBD-neutralizing assays,
facilitating the identification of novel treatment, prevention, and diagnostic
strategies against SARS-CoV-2.
1. Validation of protein-protein docking server
Prepare the protein datasets consisting of 29 nanobody (Nbs) and 86 antibodies (Abs) complexed with RBDs from the Protein Data Bank (PDB) (https://www.rcsb.org/) for blind docking.
Remove heteroatoms/molecules, including metal ions, small molecules, water molecules, and His-tags, from all complexes.
Prepare the protein chains of RBDs and ligands (Nbs or antibodies) separately using Discovery Studio software.
The missing amino acids in the protein chain are remodeled using the SWISS-MODEL expert system (https://swissmodel.expasy.org/).
Perform blind docking using seven protein-protein docking programs including;
1) HDOCK (http://hdock.phys.hust.edu.cn/)
2) ATTRACT (http://www.attract.ph.tum.de/services/ATTRACT/attract.html)
3) pyDockWEB (https://life.bsc.es/pid/pydockweb)
4) GRAMM-X (http://vakser.compbio.ku.edu/resources/gramm/grammx/)
5) PatchDock (https://bioinfo3d.cs.tau.ac.il/PatchDock/)
6) FRODOCK (http://frodock.chaconlab.org/) and
7) ZDOCK (https://zdock.umassmed.edu/).
The root mean square deviation (RMSD) values of the ligands (Nb or Ab) are calculated using the Discovery Studio program.
2. Selection of lead Nbs
The 29 Nbs are redocked with each targeted RBD using a blind docking method using HDOCK.
Calculate the RMSD values to assess the accuracy of the docking poses of the 29 Nbs with respect to all targeted RBDs. Present the docking scores and RMSD values for each RBD in terms of the mean.
The similarity of amino acid sequences of 29 Nbs is analyzed using the Clustal Omega server (https://www.ebi.ac.uk/Tools/msa/clustalo/).
The lead Nbs are selected based on the best mean docking score, lowest RMSD, and diverse amino acid sequences, which are then employed for structure-based engineering.
3. Structural-based engineering and broad specific binding of Nbs
To improve the binding affinity of Nbs to all targeted RBDs, the two lead Nbs are mutated using the site-direct mutagenesis feature on the Discovery Studio program.
Nb residues that had no interaction and repulsion with RBD are considered for the mutation process.
The AMBER ff14SB force field is applied for structural energy minimization before the docking process of mutated Nbs.
Calculate the ΔHDOCK value, and choose the mutated residue at a specific position that exhibited the lowest ΔHDOCK for multi-point mutation.
To investigate the broad specific binding of engineered Nbs, cross-docking between Nb and all targeted RBDs, ACE2, and other viral RBDs/HAs is performed using the HDOCK program.
4. Physicochemical properties prediction of engineered Nbs
The contact surface amino acids and chemical interactions are determined using the PDBsum server (http://www.ebi.ac.uk/thornton-srv/databases/pdbsum/Generate.html).
The physicochemical properties are predicted using the ProtParam (ExPASy) tool (https://web.expasy.org/protparam/).
The PI value is calculated using the Protein–Sol web server (https://protein-sol.manchester.ac.uk/).