Use of simplified models for theoretical prediction of the interactions between available antibodies and the receptor-binding domain of SARS-CoV-2 spike protein

Abstract

The negative impact of infectious diseases like COVID-19 on public health and the global economy is evident. This pandemic represents a significant challenge for the scientific community to develop new practical analytical methods for accurately diagnosing emerging cases. Due to their selectivity and sensitivity, new methodologies based on antigen/antibody interactions to detect COVID-19 biomarkers are necessary. In this context, the theoretical, computational modeling reduces experimental efforts and saves resources for rational biosensor design. This study proposes using molecular dynamics to predict the interactions between the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike protein simplified model and a set of highly characterized antibodies. The binding free energy of the antigen/antibody complexes was calculated for the simplified models and compared against the complete SARS-CoV-2 ectodomain to validate the methodology. The structural data derived from our molecular dynamics and end-point free energy calculations showed a positive correlation between both approximations, with a 0.82 Pearson correlation coefficient; t = 3.661, df = 3, p-value = 0.03522, with a 95% confident interval. Furthermore, we identified the interfacial residues that could generate covalent bonds with a specific chemical surface without perturbing the binding dynamics to develop highly sensitive and specific diagnostic devices.

Communicated by Ramaswamy H. Sarma.

Graphical Abstract

Keywords:

• Simplified models
• COVID-19
• SARS-CoV-2
• molecular dynamics
• binding free energy

Data and software availability

A listing of available data, construction and execution scripts and input structures files are available on the figshare repository: 10.6084/m9.figshare.15026352

All the third-party software used in this work is stated in the ‘Methods and computational tools’ section of the Manuscript. Here is a list of used software: Schrödinger Maestro v. 2019-4; Amber18 CUDA version software (Fee Waiver License for Abraham Vidal-Limon); R Statistical package v. 4.0.1, and CHARMM-GUI server was used for recover of SARS-Cov-2 spike protein full model (https://charmm-gui.org/).

Author contributions

Guillermo A. Huerta Miranda: Formal writing, Reviewing and Editing, Visualization, Investigation.: Wendy I. García-García: Reviewing and Editing, Investigation Writing – Original draft preparation.: Abraham Vidal-Limon: Conceptualization, Methodology, Writing – Original draft preparation.: Margarita Miranda-Hernández: Supervision, Project administration.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the [CONACYT] under Grant [A1-S-15877] and [PAPIIT-DGAPA-UNAM] under Grant [IN104621]. The authors are grateful for access to computational resources on the Miztli supercomputer through the project UNAM-DGTIC-COVID19-009 and UNAM-DGTIC-347; also an acknowledgment to Centro Nacional de Supercomputo-IPICyT, A.C. (National Supercomputing Center-CNS) for facilities at Thubat-Kaal 2 supercomputer. Special thanks to Engineer Irving Álvarez at DGTIC-UNAM; Engineer Jesús Alaniz at ThubatKaal 2.0 at CNS and MSc. Aldo Rodríguez at CNyN-UNAM for their technical support. A. Vidal-Limon and G.A. Huerta-Miranda thank CONACYT A1-S-15877 for the financial support of their research internship. W.I. García-García thanks the PhD scholarship granted by CONACYT.