Method for predicting interaction between RBD in S protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and hACE2 receptor

Abstract

A method for predicting an interaction between a receptor-binding domain (RBD) in a spike (S) protein of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and a human angiotensin-converting enzyme 2 (hACE2) receptor comprises conducting prediction simulation based on a protein structure data and comparing an obtained prediction result with surface plasmon resonance (SPR) experimental data; analysing protein structure simulation and binding simulation based on a sequence data; and analysing protein structure simulation and binding simulation based on a mutation site. The method is based on PROtein binDIng enerGY prediction (PRODIGY) and can predict an interaction mode between the RBD in the S protein and the hACE2 receptor through multiple data types, and can quickly and effectively analyse the infectivity.

Description

TECHNICAL FIELD

[0001] The present disclosure belongs to the technical field of biological analysis and detection, and particularly relates to a method for predicting an interaction between a receptor-binding domain (RBD) in a spike (S) protein of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and a human angiotensin-converting enzyme 2 (hACE2) receptor. BACKGROUND

[0002] Corona virus disease 2019 (COVID-19) is a viral respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The COVID-19 epidemic has broken out around the world, causing a serious impact on global public health, economy, and society. A spike (S) protein of the SARS-CoV-2 serves as a key protein for this virus to enter host cells. Different from that in a SARS-CoV, a receptor-binding domain (RBD) in the S protein of the SARS-CoV-2 has a high affinity and can bind more tightly to a human angiotensin-converting enzyme 2 (hACE2) receptor. Therefore, it is of great significance for the development of vaccines and drugs by analyzing an interaction mechanism between the RBD in the S protein and the hACE2 receptor.

[0003] Molecular dynamics simulation is a calculation method based on Newtonian mechanical principles that can simulate the movement and interaction of molecules and predict their physical and chemical properties and reaction behaviors under different conditions. At present, the molecular dynamics simulation has become one of the important means to study biological macromolecular complexes. The molecular dynamics simulation can obtain information such as the structure, energy, and dynamic behavior of a biological macromolecule complex through calculation, thereby providing important theoretical support for an in-depth understanding on structure and function of the complex. Since the structure and dynamic behavior of macromolecule-macromolecule complexes are more complex, there are relatively few studies on the dynamics simulation of macromolecule-macromolecule complexes.

[0004] PROtein binDIng enerGY prediction (PRODIGY) is a prediction tool for interactions of protein-protein and protein-small molecule based on molecular dynamics simulations. The PRODIGY describes a binding affinity of a protein-protein complex based solely on its structural properties. Studies have shown that there is a correlation between the contact interface of protein-protein complexes and the experimentally obtained binding affinity. Combining this correlation with the properties of non-interacting surfaces can serve as quite desirable predictors for use in molecular dynamics simulations. PRODIGY can calculate binding free energy (BFE) of the complex and predict a binding mode and a stability of the complex. In current SARS-CoV-2-related research, the PRODIGY is mainly used to analyze the interaction between the RBD in the S protein and small-molecule antibodies, while there are fewer studies on the interaction between the RBD of the S protein and the hACE2 receptor. In the present disclosure, a binding mode of the RBD in the S protein and the hACE2 receptor is simulated and analyzed using the PRODIGY, and an obtained analysis result is compared with the existing experimental data. This process has verified a feasibility of the PRODIGY for analyzing the interaction between the RBD in the S protein of SARS-CoV-2 and the hACE2 receptor, thus improving an analysis efficiency of related research and promoting the further development of vaccines and drugs. SUMMARY

[0005] An objective of the present disclosure is to provide a method for predicting an interaction between an RBD in an S protein of a SARS-CoV-2 and an hACE2 receptor, aiming to solve the problems raised in the background.

[0006] To achieve the above objective, the present disclosure provides the following technical solutions:

[0007] The present disclosure provides a method for predicting an interaction between an RBD in an S protein of a SARS-CoV-2 and an hACE2 receptor, including:

[0008] conducting prediction simulation based on a protein structure data and comparing an obtained prediction result with surface plasmon resonance (SPR) experimental data;

[0009] analyzing protein structure simulation and binding simulation based on a sequence data; and

[0010] analyzing protein structure simulation and binding simulation based on a mutation site.

[0011] Further, a process of conducting the prediction simulation based on the protein structure data and comparing the obtained prediction result with the SPR experimental data includes:

[0012] conducting binding simulation analysis of a pcVOCs S protein-hACE2 complex: obtaining a structural data of the pcVOCs S protein-hACE2 complex from a protein structure database Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB); analyzing the structural data using PROtein binDIng enerGY prediction (PRODIGY), inputting two protein chains that interact in the pcVOCs S protein-hACE2 complex, and recording an obtained PRODIGY analysis result; and

[0013] comparing the PRODIGY analysis result with the existing experimental data: subjecting the existing experimental data and the PRODIGY analysis result to unitary linear regression analysis.

[0014] Further, a process of obtaining the existing experimental data includes:

[0015] conducting retrieval using the SARS-CoV-2, the hACE2, fluorescence resonance energy transfer (FRET), and surface plasmon resonance (SPR) as keywords, and recording variants, experimental methods, and experimental result data used in obtained retrieved literatures.

[0016] Further, a process of analyzing the protein structure simulation and the binding simulation based on the sequence data includes:

[0017] subjecting a local SARS-CoV-2 to high-throughput sequencing, subjecting an obtained sequence to assembly and pedigree judging, and extracting a sequence of an RBD in a corresponding S protein;

[0018] predicting a three-dimensional structure of the S protein with the sequence of the RBD in the corresponding S protein using a SWISS-MODEL online tool based on homology modeling, selecting a homologous model structure during construction of the sequence of the RBD, decomposing a structural data of an hACE2 protein from a 6MOJ complex, conducting rigid docking using ZDOCK Server to obtain a pcVOCs S protein-hACE2 complex structure, analyzing a data of the structure using PRODIGY, and recording an obtained PRODIGY result; and

[0019] verifying the PRODIGY result with a molecular mechanics generalized Born surface area (MM / GBSA)-based HawkDock Server tool.

[0020] Further, a system for analyzing the data of the structure using the PRODIGY has a temperature of 25°C, and binding free energy (BFE) is recorded after the data of the structure is analyzed using the PRODIGY.

[0021] Further, a process of analyzing the protein structure simulation and the binding simulation based on the mutation site includes:

[0022] analyzing an impact of a single mutation site on a binding interaction between the SARS-CoV-2 and the hACE2 receptor by mutation-by-mutation simulation, detecting a binding affinity of the S protein and the hACE2 receptor using an SPR method, and verifying a mutation site that enhances or reduces the binding affinity of the S protein to the hACE2 receptor.

[0023] Further, the SPR method includes: bonding a receptor molecule to a surface of a sensor; causing a solution containing a ligand to flow through the surface of the sensor, such that the ligand binds to the receptor molecule, resulting in an increase in a mass of the receptor molecule bound to the surface of the sensor and then causing an increase in a surface refractive index; and determining a kinetic constant of an intermolecular interaction through a reaction curve.

[0024] Compared with the prior art, beneficial effects of the present disclosure are as follows:

[0025] The PRODIGY can relatively accurately predict an interaction mode between the RBD in the S protein and the hACE2 receptor through multiple data types, and can quickly and effectively analyze the infectivity of different variants. The method provides a reliable tool support for the development of vaccines and drugs for studying SARS-CoV-2 infection. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] To make the objectives, technical solutions and advantages of the present disclosure clearer and more comprehensible, the present disclosure will be further described below in detail in conjunction with embodiments. It should be understood that the specific examples described herein are merely intended to explain the present disclosure, but not to limit the present disclosure.

[0027] The specific implementation of the present disclosure is described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] An example of the present disclosure provides a method for predicting an interaction between an RBD in an S protein of a SARS-CoV-2 and an hACE2 receptor, including:

[0029] conducting prediction simulation based on a protein structure data and comparing an obtained prediction result with surface plasmon resonance (SPR) experimental data;

[0030] analyzing protein structure simulation and binding simulation based on a sequence data; and

[0031] analyzing protein structure simulation and binding simulation based on a mutation site.

[0032] In an example of the present disclosure, previously circulating variants of concern (pcVOCs) are used as a research object.

[0033] As a preferred example of the present disclosure, a process of conducting the prediction simulation based on the protein structure data and comparing the obtained prediction result with the SPR experimental data includes:

[0034] conducting binding simulation analysis of a pcVOCs S protein-hACE2 complex: obtaining a structural data with a higher resolution of the pcVOCs S protein-hACE2 complex from a protein structure database RCSB PDB; analyzing the structural data using PROtein binDIng enerGY prediction (PRODIGY), inputting two protein chains that mainly interact in the pcVOCs S protein-hACE2 complex, and recording BFE and other interaction information, where a system is set to 25°C; and

[0035] comparing the PRODIGY analysis result with the existing experimental data: subjecting the existing experimental data and the PRODIGY analysis result to unitary linear regression analysis.

[0036] In an example of the present disclosure, results of the binding simulation analysis of the pcVOCs S protein-hACE2 complex are shown in Table 1, where a corresponding conformant structure that has been measured for the Gamma variant strain is not found.

[0037] Table 1 Binding simulation analysis of pcVOCs S protein-hACE2 complex

[0038] pcVOCs Complex ID Complex structure AG (kcal / mol) NC 6M0J RBD single chain + single ACE2 -11.9 B. 1.1.7 / Alpha 7EDJ S trimer + triple ACE2 -10.0 B.1.1.7 / Alpha 7FEM S trimer + single ACE2 -11.5 B.1.351 / Beta 7V80 RBD single chain + single ACE2 -10.2 B.1.351 / Beta 7VX4 RBD single chain + single ACE2 -12.4 P.l / Gamma - - - B.1.617.2 / Delta 7V8B RBD single chain + single ACE2 -11.0 B.1.617.2 / Delta 7TEW RBD single chain + single ACE2 -11.6 B.1.617.2 / Delta 7TEX S trimer + single ACE2 -12.2 B. 1.1.529 / Omicron 7WBP RBD single chain + single ACE2 -11.1 B. 1.1.529 / Omi cron 7WBL RBD single chain + single ACE2 -10.5 B.1.1.529 / Omicron 7T9L RBD single chain + single ACE2 -10.8 BA.l 7U0N RBD single chain + single ACE2 -10.9 BA.2 7UFK RBD single chain + single ACE2 -11.6 BA.2 8DM6 RBD single chain + single ACE2 -10.7 XBB 8IOV RBD single chain + single ACE2 -10.1

[0039] In the existing research [Impact of the temperature on the interactions between common variants of the SARS-CoV-2 receptor binding domain and the human ACE2], the SPR data at 25°C is shown in Table 2. A dissociation constant KD mainly reflects the affinity between molecules. The smaller the value, the stronger the affinity.

[0040] Table 2 SPR data

[0041] pcVOCs 25°C-KD(nM) AG° (kcal / mol) NC 6.73 ±0.03 -10.99 B.1.1.7 / Alpha 0.64 ±0.20 -12.43 B.1.351 / Beta 1.48 ±0.40 -11.71 P.l / Gamma - B.1.617.2 / Delta 198 ±48 -9.00 B. 1.1,529 / Omicron 0.40 ±0.07 -12.43

[0042] In the existing research [Proteomic Approach for Comparative Analysis of the Spike Protein of SARS-CoV-2 Omicron (B. 1.1.529) Variant and Other Pango Lineages], the binding energy analysis of the S protein trimer in an open state is conducted using PRODIGY.

[0043] SPR: Omicron>Alpha>beta>NC>Delta;

[0044] SPRcell: Alpha>gamma>beta>NC>delta>Omicron;

[0045] HADDOCK+PRODIGY: Omicron (-11.8)>Alpha (-10.8)>beta (-10.5)>gamma (-9.5)>delta (-8.3);

[0046] simulation: Omicron (-11.8)>Alpha (-11.5)>Delta (-11.0)>beta (-10.2);

[0047] Except for the different conclusions on Omicron's binding ability in different data, the conclusions are basically consistent with existing reports.

[0048] As a preferred example of the present disclosure, a process of obtaining the existing experimental data includes:

[0049] conducting retrieval using the SARS-CoV-2, the hACE2, FRET, and SPR as keywords, and recording variants, experimental methods, and experimental result data used in obtained retrieved literatures.

[0050] As a preferred example of the present disclosure, a process of analyzing the protein structure simulation and the binding simulation based on the sequence data includes:

[0051] subjecting a local SARS-CoV-2 to high-throughput sequencing, subjecting an obtained sequence to assembly and pedigree judging, and extracting a sequence of an RBD in a corresponding S protein;

[0052] predicting a three-dimensional structure of the S protein with the sequence of the RBD in the corresponding S protein using a SWISS-MODEL online tool based on homology modeling, selecting a homologous model structure during construction of the sequence of the RBD, decomposing a structural data of an hACE2 protein from a 6MOJ complex, conducting rigid docking using ZDOCK Server to obtain a pcVOCs S protein-hACE2 complex structure, analyzing a data of the structure using PRODIGY, and recording BFE and other interaction information, where a system is set to 25°C; and

[0053] verifying the PRODIGY result with an MM / GBSA-based HawkDock Server tool.

[0054] In an example of the present disclosure, binding free energy (BFE) AG = molecular mechanical energy Emm (van der Waals force Evdw + electrostatic interaction Eele) + solvation free energy Gsolvation (Gpolar + Gnonpolar) - TSmm.

[0055] The differences in mutation sites of local variants are shown in Table 3:

[0056] Table 3 Differences in mutation sites of local variants

[0057] RBD site Spro site 28 346 53 371 58 376 87 405 90 408 128 446 134 452 168 486 175 493 178 496 BA.1.17 R L T D R S L F R S BA.2.2 R F A N S G L F R G BA.2.12.1 R F A N s G Q F R G BA.5.2 R F A N s G R V Q G BF.7.14 T F A N R G R V Q G

[0058] The results of the binding simulation analysis of the S protein-hACE2 complex of the local variants are shown in Table 4:

[0059] Table 4 Binding simulation analysis of S protein-hACE2 complex of local variants

[0060] Virus strains Templates AG (kcal / mol) HawkDock Server MM / GBSA 22 / 01 / 10 complexBA. 1.17 71 ww. 1 .A -19.5 -92.01 22 / 03 / 17 complex_BA.2.2 71ww. 1 .A -21.5 -104.99 22 / 06 / 08 complex_BA.2.12.1 71 ww. 1 .A -22.7 -102.76 22 / 10 / 09 complex_BA.5.2 71 ww. 1 .A -18.8 -78.2 23 / 02 / 08 complexBF.7,14 71 ww. 1 .A -20.6 -87.85

[0061] Comparing BA.2.2 and BA.2.12.1, L452Q leads to an increase in binding capacity, changing from hydrophobic leucine to hydrophilic glutamine, which is consistent with relevant reports.

[0062] BA.5.2, which is further away from its ancestors on the evolutionary tree, has reduced binding ability compared to BA.2.2, and the two differ by 3 amino acids in the RBD region. These overall results are consistent with the results of the MM / GBSA method provided by HawkDock Server.

[0063] As a preferred example of the present disclosure, a process of analyzing the protein structure simulation and the binding simulation based on the mutation site includes:

[0064] analyzing an impact of a single mutation site on a binding interaction between the SARS-CoV-2 and the hACE2 receptor by mutation-by-mutation simulation, detecting a binding affinity of the S protein and the hACE2 receptor using an SPR method, and verifying a mutation site that enhances or reduces the binding affinity of the S protein to the hACE2 receptor;

[0065] The SPR method includes: bonding a receptor molecule to a surface of a sensor; causing a solution containing a ligand to flow through the surface of the sensor, such that the ligand binds to the receptor molecule, resulting in an increase in a mass of the receptor molecule bound to the surface of the sensor and then causing an increase in a surface refractive index (in resonance unit, RU); and determining a kinetic constant of an intermolecular interaction through a reaction curve.

[0066] In an example of the present disclosure, point mutation results are preferably shown in Table 5.

[0067] Table 5 Impact of single mutation site

[0068] Virus strains Templates AG (kcal mol'1) complexes 71ww.l.A -21.5 R493Q 71ww. l.A -21.5 L452R 71ww.l.A -14.9 F486V 71ww.l.A -18 L452R+F486V 71ww.l.A -17.8 F486V+R493Q 71ww.l.A -21.4 L452R+R493Q 71ww. l.A -16.8 complex_BA.5.2 71ww.l.A -18.8

[0069] From the comparison of the single point mutation results with BA.2.2, L452R has caused a decrease in binding ability, F486V has caused a decrease in binding ability, and R493Q shows no impact on the binding ability.

[0070] From the comparison of the double-site mutation results with BA.5.2, L452R has caused a decrease in binding ability, F486V has caused an increase in binding ability, and R493Q has caused an increase in binding ability.

[0071] It is reported in the literature that L452R causes an increase in binding ability, F486V causes a decrease in binding ability, and R493Q causes an increase in binding ability. F486V improves the immune evasion ability of the virus by changing its conformation, but reduces the binding ability of S protein and hace2.

[0072] In summary, the method can relatively accurately predict an interaction mode between the RBD in the S protein and the hACE2 receptor through multiple data types, and provides reliable method support and technical guidance for the development of vaccines and drugs for studying SARS-CoV-2 infection.

[0073] It should be noted that those skilled in the art may further make several variations and improvements without departing from the scope of the present disclosure, but such variations and improvements should also be deemed as falling within the protection scope of the present disclosure without affecting the implementation effect and practicability of the patent.

Claims

1. A method for predicting an interaction between a receptor-binding domain (RBD) in a spike (S) protein of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and a human angiotensin-converting enzyme 2 (hACE2) receptor, comprising:conducting prediction simulation based on a protein structure data and comparing an obtained prediction result with surface plasmon resonance (SPR) experimental data;analyzing protein structure simulation and binding simulation based on a sequence data; and analyzing protein structure simulation and binding simulation based on a mutation site.

2. The method for predicting an interaction between an RBD in an S protein of a SARS-CoV-2 and an hACE2 receptor according to claim I, wherein a process of conducting the prediction simulation based on the protein structure data and comparing the obtained prediction result with the SPR experimental data comprises:conducting binding simulation analysis of a pcVOCs S protein-hACE2 complex: obtaining a structural data of the pcVOCs S protein-hACE2 complex from a protein structure database Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB); analyzing the structural data using PROtein binDIng enerGY prediction (PRODIGY), inputting two protein chains that interact in the pcVOCs S protein-hACE2 complex, and recording an obtained PRODIGY analysis result; andcomparing the PRODIGY analysis result with the existing experimental data: subjecting the existing experimental data and the PRODIGY analysis result to unitary linear regression analysis.

3. The method for predicting an interaction between an RBD in an S protein of a SARS-CoV-2 and an hACE2 receptor according to claim 2, wherein a process of obtaining the existing experimental data comprises:conducting retrieval using the SARS-CoV-2, the hACE2, fluorescence resonance energy transfer (FRET), and surface plasmon resonance (SPR) as keywords, and recording variants, experimental methods, and experimental result data used in obtained retrieved literatures.

4. The method for predicting an interaction between an RBD in an S protein of a SARS-CoV-2 and an hACE2 receptor according to claim 2, wherein a process of analyzing the protein structure simulation and the binding simulation based on the sequence data comprises:subjecting a local SARS-CoV-2 to high-throughput sequencing, subjecting an obtained sequence to assembly and pedigree judging, and extracting a sequence of an RBD in acorresponding S protein;predicting a three-dimensional structure of the S protein with the sequence of the RBD in the corresponding S protein using a SWISS-MODEL online tool based on homology modeling, selecting a homologous model structure during construction of the sequence of the RBD, decomposing a structural data of an hACE2 protein from a 6M0J complex, conducting rigid docking using ZDOCK Server to obtain a pcVOCs S protein-hACE2 complex structure, analyzing a data of the structure using PRODIGY, and recording an obtained PRODIGY result; andverifying the PRODIGY result with a molecular mechanics generalized Bom surface area (MM / GBSA)-based Hawk Dock Server tool.

5. The method for predicting an interaction between an RBD in an S protein of a SARS-CoV-2 and an hACE2 receptor according to claim 4, wherein a system for analyzing the data of the structure using the PRODIGY has a temperature of 25°C, and binding free energy (BFE) is recorded after the data of the structure is analyzed using the PRODIGY.

6. The method for predicting an interaction between an RBD in an S protein of a SARS-CoV-2 and an hACE2 receptor according to claim 1, wherein a process of analyzing the protein structure simulation and the binding simulation based on the mutation site comprises:analyzing an impact of a single mutation site on a binding interaction between the SARS-CoV-2 and the hACE2 receptor by mutation-by-mutation simulation, detecting a binding affinity of the S protein and the hACE2 receptor using an SPR method, and verifying a mutation site that enhances or reduces the binding affinity of the S protein to the hACE2 receptor.

7. The method for predicting an interaction between an RBD in an S protein of a SARS-CoV-2 and an hACE2 receptor according to claim 6, wherein the SPR method comprises: bonding a receptor molecule to a surface of a sensor; causing a solution containing a ligand to flow through the surface of the sensor, such that the ligand binds to the receptor molecule, resulting in an increase in a mass of the receptor molecule bound to the surface of the sensor and then causing an increase in a surface refractive index; and determining a kinetic constant of an intermolecular interaction through a reaction curve.