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Computational Design of Novel Nanobodies Targeting the Receptor Binding Domain of Variants of Concern of SARS-CoV-2

Phoomintara Longsompurana, Thanyada Rungrotmongkol, Nongluk Plongthongkum, Kittikhun Wangkanont, Peter Wolschann, Rungtiva P. Poo-arporn 


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 haemagglutinins (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.


The emergence of COVID-19, caused by SARS-CoV-2, has resulted in a severe global public health emergency [1–3]. The infection process involves the receptor binding motif (RBM) of the SARS-CoV-2 RBD spike protein (S protein) attaching to human ACE2, a transmembrane protein receptor, resulting in virus entry through receptor-mediated endocytosis into the cell [4]. Mutations in RBD, particularly in VOCs such as Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529); BA.1 and BA.2, have been shown to increase virus transmissibility, evade host defenses, and confer the ability to escape immune responses [4–6]. As a result, the development of high-specific neutralizing molecules such as nanobodies (Nbs) is critical to combat the emergence of VOCs and their impact on global public health [7–9].

Materials and methods

Evaluation of the performance of docking programs

The protein data sets of 29 Nbs and 86 antibodies (Abs) in complex with RBDs for blind docking were retrieved from Protein Data Bank (PDB) ( [15] (S1 Table) in the supplementary information. The RBD data sets included Wuhan-Hu-1 (Wh) and six VOC RBDs: Alpha, Beta, Delta, Gamma, Omicron BA.1 sub-lineage, and Omicron BA.2 sub-lineage (S2 Table). A list of ACE2 and other viral RBDs/HAs (S3 Table) was compiled to evaluate cross-binding. Before performing blind docking, we removed heteroatoms/molecules, such as metal ions, small molecules, water molecules, and His-tag, from all complexes, prepared the protein chains of RBDs and ligands (Nbs or antibodies) separately using Discovery Studio software, and retrieved the missing amino acids in the protein chain using the SWISS-MODEL expert system ( [16].


Evaluation of the performance of docking programs

The docking program’s scoring function algorithm is a crucial factor that directly affects the accuracy of docking results. To evaluate the docking precision of seven selected docking programs, 115 ligand-RBD complex data sets were used, and 100 poses were generated from each program for each complex. These poses were ranked based on the scoring function and the RMSD from lowest to highest, and the first rank pose based on the scoring function of each program was termed "top pose", while the pose with the lowest RMSD among all the predictably generated poses was termed "best pose". However, the best docking program does not always generate the top pose and best pose at the same rank accordingly. Hence, we considered three primary criteria: the median of RMSD of 100 docking poses, the effect of the number of docking outputs, and the RMSD cut-off for evaluation.


In this research, we have identified four notable findings regarding the Nb/RBD docking approach. Firstly, we determined that the protein-protein molecular docking technique utilizing HDOCK was suitable for evaluating the interaction between Nb/RBD affinity during Nb screening and engineering, enabling the achievement of broad specificity towards VOC RBDs [20, 50]. Secondly, as part of the lead Nb selection process, we identified two prominent Nbs with the lowest HDOCK scores, namely Nb17.1 and Nb23.1, out of the 29 Nbs evaluated. These Nbs displayed exceptional binding affinity and successfully engaged with the RBM of all RBDs. The third finding of this study involves the further engineering the lead Nbs to generate novel Nbs capable of effectively interacting with all VOC RBDs. A significant improvement in docking scores for Nb/RBD interactions was achieved by incorporating single-point mutations at specific residues on the Nb interface. Additionally, the multi-point engineered Nbs displayed exceptional binding affinity and demonstrated high specificity towards all VOC RBDs [51]. 


P.L. would like to thank to Petchra Pra Jom Klao Ph.D. Research Scholarship from King Mongkut’s University of Technology Thonburi. We extend our gratitude to Dr. Nitchakan Darai and Miss Hathaichanok Chuntakaruk (Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University) for their insightful discussions and technical assistance. We thank the ASEAN-European Academic University Network (ASEAUNINET) for a short visit grant. The Vienna Scientific Cluster (VSC) is acknowledged for facilities and computing resources.

Citation: Longsompurana P, Rungrotmongkol T, Plongthongkum N, Wangkanont K, Wolschann P, Poo-arporn RP (2023) Computational design of novel nanobodies targeting the receptor binding domain of variants of concern of SARS-CoV-2. PLoS ONE 18(10): e0293263.

Editor: Sheikh Arslan Sehgal, The Islamia University of Bahawalpur Pakistan, PAKISTAN

Received: June 3, 2023; Accepted: October 9, 2023; Published: October 24, 2023

Copyright: © 2023 Longsompurana et al. This is an open access article 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.

Data Availability: All relevant data for this study are within the paper, its Supporting information files, the OSF repository (, the figshare repository (, and the repository (

Funding: Research project is supported by the National Research Council of Thailand (NRCT) and King Mongkut’s University of Technology Thonburi: N42A650316, the Research Strengthening Project of the Faculty of Engineering and Thailand Science Research and Innovation (TSRI), Basic Research Fund: Fiscal year 2023 (Program Smart Healthcare). P.L. gratefully acknowledge the financial support provided by the Petchra Pra Jom Klao Ph.D. Research Scholarship from King Mongkut’s University of Technology Thonburi. T.R. is financially supported by the National Research Council of Thailand (NRCT, grant number N42A650231). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

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