BIBECHANA Vol. 22, No. 2, August 2025, 159-170 ISSN 2091-0762 (Print), 2382-5340 (Online) Journal homepage: http://nepjol.info/index.php/BIBECHANA Publisher:Dept. of Phys., Mahendra Morang A. M. Campus (Tribhuvan University)Biratnagar Multiscale study of interaction between SARS-CoV-2 protein and human receptor complex Roshan Sigdel, Jhulan Powrel, Nurapati Pantha∗, Narayan Prasad Adhikari Central Department of Physics, Tribhuvan University, Kathmandu, 44618, Nepal ∗Corresponding authors. Email: mrnurapati@gmail.com Abstract This study explores the binding mechanism of key binding pockets (S19 Q24 A475 and T500 R357) in the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spike protein and the Human Angiotensin-Converting Enzyme 2 (hACE2) receptor complex using molec- ular dynamics (MD) simulations and first-principles calculations. The binding pockets were extracted from the 6LZG protein-protein complex, and the input systems were prepared using CHARMM-GUI for molecular dynamics, PyMOL for structural modifications, and a quantum input generator for first-principles calculations. MD simulations were conducted to assess the stability of the binding pockets and to analyze key interactions, including hydrogen bonding, electrostatic, and van der Waals interactions. Root mean square deviation (RMSD) calcula- tions indicated fluctuations within the system. First-principles calculations, based on density functional theory (DFT) and hybrid functionals, were used to compute the binding energies of residues within the binding pockets, which were found to be negative, suggesting no significant intra-pocket binding. Electronic property analysis revealed that the behavior of key amino acid residues is largely governed by p-orbitals and atomic chemical structure, with minimal influence from crystal symmetry near the Fermi level. Moreover, ethylation of residues A475 was determined to be ineffective in disrupting the interaction between SARS-CoV-2 and the hACE2 receptor. Keywords SARS-CoV-2, protein structure, binding pockets, receptor complex, amino acids Article information Manuscript received: February 19, 2025; Accepted: May 22, 2025 DOI https://doi.org/10.3126/bibechana.v22i2.75792 This work is licensed under the Creative Commons CC BY-NC License. https://creativecommons. org/licenses/by-nc/4.0/ 1 Introduction The severe acute respiratory syndrome-like coro- navirus (SARS-CoV-2) epidemic that started the COVID-19 pandemic in Wuhan, China, in Decem- ber 2019 swiftly expanded into a serious global health and economic problem. The virus quickly spread worldwide in 2020, creating widespread ill- nesses, posing a severe threat to the worldwide population, and intensifying into a global crisis, according to reports of the first human transmis- sion in December 2019. According to the WHO, this disease has been responsible for more than 630 million recorded cases and 6.5 million fatalities worldwide so far [1, 2]. There are many different strains of the big virus family known as coron- aviruses. Most viruses affect cats, camels, chickens, and bats. Coronaviruses are unique in that they can spread from one species to another through 159 http://nepjol.info/index.php/BIBECHANA mrnurapati@gmail.com https://doi.org/10.3126/bibechana.v22i2.75792 https://creativecommons.org/licenses/by-nc/4.0/ https://creativecommons.org/licenses/by-nc/4.0/ Roshan Sigdel et al./ BIBECHANA 22 (2025) 159-170 160 “cross-species transmission" or “spillover" mean- ing that a virus that initially infects one species might change in a way that makes it possible to infect another species. The COVID-19 pandemic started this way; the virus probably originated in bats before spreading to humans via an intermedi- ary animal host. Direct, indirect, and close contact with sick people can spread coronaviruses to others. The virus-containing saliva and respiratory secre- tions can be expelled by infected people when they exhale, talk, cough, or sneeze. People can then contract the virus by coming into contact with these infected secretions, either by touching con- taminated surfaces and then touching their mouth, nose, or eyes or by being close to an infected person who expels infected droplets [3–5]. The first human coronaviruses were detected in the 1960s in patients’ nasal discharge linked to the common cold. Named for their crown-like spikes, these viruses caused global outbreaks in the past. Severe acute respiratory syndrome coron- avirus (SARS-CoV) emerged in 2002, while Middle East respiratory syndrome coronavirus (MERS- CoV) surfaced in 2012. In 2019, SARS-CoV-2 was identified in China, causing COVID-19 and spark- ing a pandemic. These coronaviruses are believed to have evolved from bat origins, with some re- ports suggesting rodent origins for CoV-OC43 and HKU1. Studies indicate CoV-OC43’s transmission to humans around 120 years ago, highlighting their prolonged presence in human populations [6, 7]. Coronaviruses are categorized into four genera based on phylogenetic relationships and genomic structure: Alphacoronavirus, Betacoronavirus, Gammacoronavirus, and Deltacoronavirus. Al- phacoronavirus and Betacoronavirus mainly infect mammals, including humans, while Gammacoro- navirus and Deltacoronavirus target birds. Hu- man coronaviruses HCoV-229E, HCoV-NL63 (Al- phacoronavirus), HCoV-OC43, and HCoV-HKU1 (Betacoronavirus) cause common colds. More se- vere outbreaks, like SARS-CoV, MERS-CoV, and SARS-CoV-2, stem from Betacoronavirus. While both alpha-CoVs and beta-CoVs lead to mild ill- nesses, SARS-CoV, MERS-CoV, and SARS-CoV-2 can result in severe, life-threatening diseases [4, 8]. Coronaviruses are composed of positive single- stranded RNA and nucleoprotein (N), encased within a lipid bi-layer containing spike glycoprotein (S), membrane protein (M), envelope glycoprotein (E), and hemagglutinin esterase (HE) [4, 8–10]. The RNA genome’s length ranges from 26 to 32 kb. SARS-CoV-2, a beta-CoV, exhibits a spherical structure with crown-like spikes measuring 60-140 nm in diameter and 9-12 nm in spike length. SARS- CoV-2 shares about 80 % nucleotide sequence iden- tity with SARS-CoV [4,9, 11]. Since the emergence of the COVID-19 pandemic, numerous scientists have devoted their efforts to un- ravelling the complex mechanisms by which SARS- CoV-2 interacts with the hACE2 receptor. De- spite significant progress in vaccine development, many people continue to suffer from the virus, high- lighting the need for a deeper understanding of the virus’s nature and binding mechanisms. Developing more effective vaccines and drugs remains a signifi- cant challenge that requires continued research and innovation. [8, 12]. Here, we perform a multiscale study combining molecular dynamics (MD) simula- tions and Quantum Espresso (QE) calculations to investigate the binding mechanism between SARS- CoV-2 and hACE2. In MD, we analyze hydrogen bonds, van der Waals (VDW), and electrostatic interactions within two key binding pockets: S19 Q24 A475 and R357 T500, where A475 and T500 are SARS-CoV-2 residues, and the rest belong to hACE2. Following this, we use Quantum Expresso codes to conduct quantum mechanical calculations, focusing on the relaxation of these binding sites, estimating the system’s total energy and individ- ual residue contributions, and exploring their elec- tronic properties, such as density of states (DOS) and band structures. Roshan Sigdel et al./ BIBECHANA 22 (2025) 159-170 161 Figure 1: Binding pockets S19 Q24 A475 and R357 T500 in the SARS-CoV-2 protein and hACE2 receptor complex, based on coordinates from the 6LZG.pdb file. 2 Computational Methods 2.1 Molecular Dynamic Simulation Molecular dynamics (MD) simulations were exe- cuted utilizing the NAMD simulation package, em- ploying the 6LZG PDB file from the RCSB Protein Data Bank website, representing the SARS-CoV-2 (Wild type) complex and hACE2 proteins [8, 13]. The simulation system was solvated within a wa- ter environment to mimic the cellular milieu, and periodic boundary conditions were imposed to emu- late an infinite system. To ensure charge neutrality and maintain the desired pH, a balanced number of positive (Na+) and negative (Cl−) ions were intro- duced. Specifically, for the S19 Q24 A475 binding pocket, 2 Na+ ions and 2 Cl− ions were added, and for the R357 T500 binding pocket, 3 Na+ ions and 4 Cl− ions were incorporated. The CHARMM36m force field was chosen to describe the interactions in the simulations, providing a comprehensive rep- resentation of the system’s behaviour. The simu- lation protocol commenced with energy minimiza- tion, spanning 5 ns for each isolated binding pocket and 100 ns for the SARS-CoV-2-hACE2 complex within the NVT ensemble at 310K. Subsequently, a production run of 5ns was carried out for each iso- lated binding pocket and 100ns for the SARS-CoV- 2-hACE2 complex under NPT conditions. The re- sultant data from the production run underwent Vi- sual Molecular Dynamics (VMD) analysis. Struc- tural stability assessment was performed through root mean square deviation (RMSD) calculations using the Rmsd Trajectory Tool in VMD. The simu- lation trajectories used VMD analysis tools to iden- tify and analyze bonded and non-bonded interac- tions. 2.2 First Principles Calculations An in-depth analysis of the density of states (DOS) and band structures was conducted for two distinct systems. These systems consisted of amino acid residues from hACE-2, SARS-CoV-2, and their con- stituent amino acids. First-principles calculations were performed using the Quantum ESPRESSO algorithm. The electronic exchange and corre- lation effects were incorporated into the system through the PBEsol functional developed by John P. Perdew. This hybrid density functional, em- ployed by PBEsol, combines the exchange from a Hartree-Fock calculation with the correlation from a density functional description, which are two distinct types of functionals in density functional theory (DFT) [14]. Andrea Dal Corso’s projector augmented wave (PAW) pseudopotential, obtained from the Quan- tum ESPRESSO website, was used to replace the complex effects of the motion of an atom’s core (i.e., non-valence) electrons with an effective potential. This ensured that only the chemically active va- lence electrons were explicitly considered through- Roshan Sigdel et al./ BIBECHANA 22 (2025) 159-170 162 out the entire calculation [15]. The structure was optimized using the Broyden-Fletcher-Goldfarb- Shanno (BFGS) scheme and was allowed to relax until the total energy change between two succes- sive self-consistent field (SCF) steps was less than 10−7 Ry, and each component of the acting force was below 10−4 Ry/Bohrs [16]. A degauss value of 0.001 Ry was used, with “occupation” fixed and “smearing” applied in var- ious systems depending on the number of elec- trons present. The David diagonalization method was employed with the “plain” mixing mode and a mixing value of 0.4 to ensure self-consistency. To prepare for subsequent calculations, the optimal convergence values for the kinetic energy cutoff and k-point sampling were determined. These choices were made to enhance the accuracy of the calcu- lations while minimizing computational expenses. Since the system lacked a crystalline nature, it was necessary to transform it into a crystalline ar- rangement by encapsulating it within a cell. This adaptation was required to enable the accurate ex- ecution of first-principles calculations. Since the system’s crystalline size is not fixed for band calculations, a k-path was generated using the “SeeK-path" tool from Material Cloud [17], following the suggested path Γ–X | Y –Γ–Z | R–Γ–T | U–Γ–V | Γ–X ′ | Y ′–Γ–Z ′ | R′–Γ–T ′ | U ′–Γ–V ′, where Γ represents the Brillouin zone cen- ter, and other points denote symmetry-related loca- tions within the reciprocal space. The band struc- ture analysis primarily focuses on the R–Γ–T seg- ment, with corresponding k-points. 3 Results and Discussion 3.1 Root-Mean-Square Devia- tion(RMSD) 0 2 4 6 8 10 12 14 0 1 2 3 4 5 D is ta n c e ( Å ) Time (ns) Average (a) 0 2 4 6 8 10 12 14 16 18 0 1 2 3 4 5 D is ta n c e ( Å ) Time (ns) Average (b) 1 1.5 2 2.5 3 0 20 40 60 80 100 D is ta n c e ( Å ) Time (ns) Average (c) Figure 2: Time evolution of RMSD of the isolated system of S19 Q24 A475 (2a) and T500 R357 (2b) binding pockets of S protein and human ACE2 complex during the 5 ns simulation run, and of the system containing SARS-CoV-2 spike protein and hACE2 receptor complex during the 100 ns simulation run (2c). The figure in 2 illustrates the RMSD values for a range of systems, including isolated single binding pockets (S19 Q24 A475, and T500 R357) within the S protein and human ACE2 complexes, as well as the complete S protein and human ACE2 com- plexes. Specifically, the RMSD fluctuations for in- Roshan Sigdel et al./ BIBECHANA 22 (2025) 159-170 163 dividual isolated binding pockets, such as S19 Q24 A475 and T500 R357 within the S protein and hu- man ACE2 complexes, exhibit variations spanning 14.25 Å to 0.91 Å and 16.85 Å to 0.74 Å, respec- tively. On the other hand, the RMSD fluctuations for the S protein and hACE2 complexes themselves range from 3.78 Å to 1.115 Å. Regarding average RMSD values, the isolated binding pockets (S19 Q24 A475 and T500 R357) and the S protein and hACE2 complexes demonstrate values of 6.27 Å, 5.78 Å, and 2.05 Å, respectively. Further exami- nation of the RMSD values over time reveals that the isolated system containing only binding pockets (S19 Q24 A475 and T500 R357) fluctuations start at 1.35 Å and 1.19 Å, respectively, and fluctuate in between their respective maximum and minimum values. The most pronounced fluctuations occur between 1.9 ns and 2.3 ns, as well as between 3.99 ns and 4.6 ns for S19 Q24 A475, and between 0.36 ns and 0.85 ns, as well as between 3.75 ns and 4 ns for T500 R357. Eventually, the RMSD values sta- bilize at a relatively constant level. Similarly, the RMSD values for the S protein and hACE2 com- plexes initially fluctuate, with significant variations between 26.8 ns and 36.8 ns and between 59.6 ns and 62 ns. Ultimately, these values also reach a steady state. The results indicate that both the S protein and hACE2 complexes and the isolated system containing only binding pockets S19 Q24 A475 and T500 R357 maintain relatively consis- tent RMSD values, suggesting structural stability in the molecular dynamics simulation. The more significant fluctuations observed in the isolated sys- tem can be attributed to the absence of interactions with other amino acids in the S protein and hACE2 complexes. 3.2 Hydrogen Bonds 0 0.5 1 1.5 2 2.5 3 3.5 4 0 1 2 3 4 5 N u m b e r o f H -b o n d Time (ns) Average (a) 0 0.5 1 1.5 2 2.5 0 20 40 60 80 100 N u m b e r o f H -b o n d Time (ns) Average (b) 0 0.5 1 1.5 2 2.5 3 0 1 2 3 4 5 N u m b e r o f H -b o n d Time (ns) Average (c) Figure 3: Time evolution of the number of hydrogen bonds (H-bonds) formed between S protein and hACE2 (S19 Q24 A475 and T500 R357) for the isolated system during 5ns simulation runs (3a), and (3c) and the system containing SARS-CoV-2 Spike protein and hACE2 receptor complex for pocket S19 Q24 A475 during 100 ns simulation runs (3b). To calculate the number of hydrogen bonds be- tween amino acid residues of binding pockets A475 S19 Q24 and T500 R357 of the SARS-CoV-2 S pro- tein and the ACE2 receptor, we utilized the VMD extension Hydrogen Bonds with a 3.5 Å cut-off dis- tance for hydrogen bond formation. Roshan Sigdel et al./ BIBECHANA 22 (2025) 159-170 164 Table 1: Occupancy Percentage of Hydrogen Bonds in the S19 Q24 A475 and T500 R357 Binding Pocket of the S Protein-hACE2 Complex Hydrogen Bonds Occupancy % For S19 Q24 A475 Binding Pocket SER19-Side—ALA475-Side 1.60% ALA475-Main—GLN24-Main 0.80% ALA475-Main—GLN24-Side 2.40% SER19-Main—ALA475-Main 4.00% SER19-Main—ALA475-Side 4.00% GLN24-Main—ALA475-Side 0.80% For S19 Q24 A475 Binding Pocket from Total SARS-CoV-2 Spike Protein-hACE2 Complex SER19-Side—ALA475-Main 41.12% SER19-Main—ALA475-Main 7.19% GLN24-Side—ALA475-Main 6.99% For T500 R357 Binding Pocket ARG357-Main—THR500-Main 3.20% ARG357-Main—THR500-Side 6.40% THR500-Side—ARG357-Side 0.80% ARG357-Side—THR500-Main 2.40% ARG357-Side—THR500-Side 3.20% Figure 3 illustrates the dynamic changes in hydro- gen bond counts for various binding pockets within the S protein and human ACE2 complex. The isolated systems of S19 Q24 A475 and T500 R357 exhibit fluctuating hydrogen bond counts ranging from 0 to 4 and 0 to 3, respectively. Similarly, the binding pocket within the SARS-CoV-2 spike protein and hACE2 receptor complex, namely S19 Q24 A475, experiences hydrogen bond fluctuations from 0 to 2. Notably, no hydrogen bonds were ob- served between amino acid residues T500 and R357 in the T500 R357 binding pocket of the SARS- CoV-2 spike protein and hACE2 receptor complex. The total number of hydrogen bonds between the hACE2 receptor and S protein amino acid residues for the isolated systems of S19 Q24 A475 and T500 R357 binding pockets are 6 and 5, respectively. The corresponding binding pockets within the SARS- CoV-2 spike protein and hACE2 receptor complex are 3 and 0, respectively. Detailed hydrogen bond occupancy percentages are summarized in Table 1. From Table 1, the isolated binding pockets ex- hibit minimal hydrogen bonding with very low occupancy percentages. This indicates that the hydrogen bonds involving amino acids S19, Q24, and A475 are insignificant enough to confirm sta- ble binding. However, the same binding pocket comprising S19, Q24, and A475 in the complete complex shows a significant hydrogen bond con- tribution, particularly between SER19 (side chain) and ALA475 (main chain). This discrepancy arises because isolating a specific binding pocket by re- moving all other binding pockets alters the system’s environment, demonstrating that amino acids from other binding pockets also influence the hydrogen bonding within a given binding pocket. Similarly, the binding site involving T500 and R357 in the complete complex does not exhibit any hydrogen bonding. However, small hydrogen bonds are observed in the system containing only the T500–R357 binding pocket, isolated from all other binding sites. This behaviour reinforces that interactions between binding pockets are critical in modulating hydrogen bonding within individual pockets. 3.3 VDW or Electrostatic Interac- tions Electrostatic and van der Waals interactions are critical non-covalent interactions prevalent in bi- ological systems. These interactions are crucial in both intermolecular and intramolecular bind- ing between biomolecules. In proteins, charged amino acids attract or repel each other due to elec- trostatic interactions. Meanwhile, van der Waals interactions occur due to the temporary asymmetry of electron density around an atom, which creates a dipole moment. This dipole moment is responsible for the attractive force at longer distances and the repulsive force at shorter distances. Our study analyzed VDW, or electrostatic interac- tions between amino acid residues within two bind- ing pockets (S19 Q24 A475 and T500 R357) found in the spike protein and hACE2 receptor complex. We examined the electrostatic and van der Waals forces between these residues using an isolated sys- tem approach. We compared the results of our anal- ysis with those obtained from systems containing Roshan Sigdel et al./ BIBECHANA 22 (2025) 159-170 165 the S19 Q24 A475 and T500 R357 binding pock- ets of the SARS-CoV-2 spike protein and hACE2 receptor complex, respectively. In both the iso- lated system and the system containing the SARS- CoV-2 spike protein and hACE2 receptor complex, the interactions among amino acid residues within the S19 Q24 A475 and T500 R357 binding pockets are characterized by significant non-bonded inter- action energies, which are primarily composed of van der Waals and electrostatic contributions. For the system involving the SARS-CoV-2 spike protein and hACE2 receptor complex, the average electro- static contribution, van der Waals interaction, and non-bonded interaction energies are (-12.64 ± 0.19) kcal/mol, (-0.9996 ± 0.043) kcal/mol, and (-13.64 ± 0.18) kcal/mol, respectively, for the S19 Q24 A475 binding pocket; and (1.61 ± 0.13) kcal/mol, (-0.93 ± 0.02) kcal/mol, and (0.675 ± 0.124) kcal/mol, re- spectively, for the T500 R357 binding pocket. On the other hand, the isolated system of the S19 Q24 A475 and T500 R357 binding pockets shows negligi- ble electrostatic contribution, van der Waals inter- action, and non-bonded interaction energies. The average values for these interactions are (-5.46 ± 1.92) kcal/mol, (-0.022 ± 0.042) kcal/mol, and (- 5.48 ± 1.91) kcal/mol, respectively, for the S19 Q24 A475 binding pocket; and (-8.104 ± 1.797) kcal/mol, (0.017 ± 0.0788) kcal/mol, and (-8.09 ± 1.76) kcal/mol, respectively, for the T500 R357 binding pocket. -100 -50 0 50 0 1 2 3 4 5 E n e rg y ( k c a l/ m o l) Time (ns) Elec VdW Non-bonded (a) -30 -25 -20 -15 -10 -5 0 5 10 15 0 20 40 60 80 100 E n e rg y ( k c a l/ m o l) Time (ns) Elec VdW Non-bonded (b) -80 -60 -40 -20 0 20 40 0 1 2 3 4 5 E n e rg y ( k c a l/ m o l) Time (ns) Elec VdW Non-bonded (c) -15 -10 -5 0 5 10 15 0 20 40 60 80 100 E n e rg y ( k c a l/ m o l) Time (ns) Elec VdW Non-bonded (d) Figure 4: Variation of electrostatic (Elec) and van der Waals (vdW) interaction energies between the amino acid residue of S protein and amino acid residue of hACE2 for the isolated system of S19 Q24 A475 and T500 R357 binding pocket, (4a) and (4c), and for the system containing SARS-CoV-2 Spike protein and hACE2 receptor complex for S19 Q24 A475 and T500 R357 binding pocket, (4b) and (4d). The nonbonded interaction in both the isolated sys- tems and the system containing the SARS-CoV- 2 spike protein and hACE2 receptor complex are primarily driven by van der Waals and electro- static interactions. However, the magnitudes of nonbonded interaction energies are higher in the latter case. The van der Waals interaction remains relatively stable by examining the electrostatic, van der Waals, and total nonbonded interaction plot in Figure 4. At the same time, fluctuations are more pronounced in the electrostatic interaction. Conse- quently, these fluctuations lead to considerable vari- ations in the overall nonbonded interaction energy, the sum of van der Waals and electrostatic interac- tions. Notably, these fluctuations are less intense in the isolated systems of binding pockets within the S protein and human ACE2 complex compared to the system containing the SARS-CoV-2 spike pro- Roshan Sigdel et al./ BIBECHANA 22 (2025) 159-170 166 tein and hACE2 receptor complex. 3.4 Energy Calculations We employed an SCF input file for a comprehen- sive quantum mechanical analysis, calculating the total energy alongside Fermi energy, Kohn-Sham states, and energy eigenvalues. Our objective was to uncover the binding energy within two specific binding pockets of the SARS-CoV-2 spike protein and hACE2 receptor complex. This was achieved through a range of first-principles calculation se- tups utilizing Quantum ESPRESSO, adjusting lat- tice parameters (a, b, and c) based on system size (Table 2). Table 2: Lattice parameters, crystal structures, and total energies of various systems for two binding pockets. System Lattice Parameters (a=b=c) in Å Crystal Structure Total Energy of Systems of binding pockets (Ry) Ethane - - -42.8747 For S19 Q24 A475 Binding Pocket S19 Q24 A475 11.9112 Cubic -544.2074 S19 Q24 11.9112 Cubic -412.7575 S19 5.07 Cubic -172.6133 Q24 8.1787 Cubic -240.1767 A475 4.9275 Cubic -131.4759 Ethylated A475 6.4988 Cubic -172.4018 Ethane 5.5738 Cubic -42.8747 For T500 R357 Binding Pocket T500 R357 15.9037 Cubic -471.5230 R357 11.790 Cubic -278.4724 T500 5.5738 Cubic -193.1114 Ethane 5.5738 Cubic -42.8747 The binding energy of each binding pocket can be determined by utilizing the ground state energy val- ues of the individual amino acid residues of the SARS-CoV-2 spike protein and the hACE2 recep- tor. The binding energy for the systems is calcu- lated using the following equation: • Binding energy for S19 Q24 A475 binding pocket Ebinding = ES19+EQ24+EA475−ES19−Q24−A475 (1) Ebinding = −0.0584Ry • Binding energy for T500 R357 binding pocket Ebinding = ET500 +ER357 −ET500−R357 (2) Ebinding = −0.0607Ry • Binding energy for Ethylated A475 Ebinding = EA475 + EC2H6 − EEthylatedA475 (3) Ebinding = −1.9488Ry Where, ES19, EQ24, and ER357 represent the energies of the amino acid residues of the hACE2 receptor, EA475 and ET500 represent the energies of the amino acid residues of the SARS-CoV-2 spike protein, and ethylated en- ergy of EA475 is EEthylatedA475. Addition- ally, ES19−Q24−A475 and ET500−R357 repre- sent the total system energies of each binding pockets. Understanding the interaction between the SARS- CoV-2 spike protein and the hACE2 receptor com- plex is vital for understanding viral infection mech- anisms, but predicting amino acid binding accu- rately was challenging due to the absence of a fixed geometry. Analysis of the S19 Q24 A45 pocket revealed that the calculated binding energy of −0.0584 Ry is negative, suggesting no binding, which contradicts the actual situation within the pocket. In reality, the amino acid residues are bound through hydrogen bonds, Van der Waals forces, and electrostatic interactions [8]. Similar in- consistencies are seen in pockets T500 R357 (with binding energy −0.0607 Ry), which bind via Van der Waals or electrostatic interactions. Factors like body fluids, temperature, and bulk protein inter- actions that are unaccounted for in our system can influence binding. Our isolated site approach might lead to negative binding energies, indicating un- binding. The effect of ethylation was investigated on residue A475. The A475 ethylated complex was -1.9488 Ry in binding energy, indicating a state of instability. The ethylation modification did not suc- ceed in enhancing the stabilization of the hACE2- SARS-CoV-2 binding complex, suggesting that the strategy is ineffective in destabilizing the binding interface between the amino acid residues of the SARS-CoV-2 protein and the hACE2 receptor com- plex. Roshan Sigdel et al./ BIBECHANA 22 (2025) 159-170 167 3.5 Density of States (DOS) And Bands The band structure of solids, stemming from quan- tum forces on isolated atoms, governs their elec- tronic and optical traits, pivotal in materials sci- ence. Electrons adhere to Pauli exclusion, occu- pying valence bands while leaving the conduction band accessible. The band gap, devoid of orbitals, separates them and emerges from electron-ion in- teractions. Materials are classified based on the band gap: conductors (small/zero gap), semicon- ductors (0.1-4 eV), and insulators (>4 eV). Conduc- tors allow easy electron flow, semiconductors suit electronics, and insulators hinder electricity [18]. The density of States (DOS) characterizes available quantum states in a solid’s energy range, impacting electronic, transport, and magnetic traits. Calcu- lating DOS reveals energy band distribution, shed- ding light on electron behaviour. 3.5.1 For S19 Q24 A475 Binding Pocket The figures (5a and 5b), (5c and 5d), (5e and 5f), (5g and 5h), (5i and 5j), and (5k and 5l) depict the band and DOS structures, along with PDOS highlighting orbital and atomic contributions, for the hACE2 receptor’s amino acid residue (S19 and Q24), SARS-CoV-2’s amino acid residue (A475), S19 Q24, S19 Q24 A45, and ethylated A475 re- spectively. Starting with S19, it exhibits n-type semiconductor behaviour with a band gap of 2.52 eV. The presence of conduction bands close to the Fermi surface suggests it can act as an electron donor. The band contributions in the conduction and valence bands, as seen in Figures 5a and 5b, are primarily attributed to the p-orbital of carbon and nitrogen atoms, respectively. The band struc- tures near the Fermi level do not significantly vary with high symmetric points, indicating that crys- tal symmetry does not significantly affect electronic states. Moving on to the Q24 system, it falls within the wide energy gap semiconductor category with a band gap of 3.3798 eV. Like S19, the p-orbital of carbon and oxygen atoms predominantly con- tributes to the conduction band, while the p-orbital of oxygen mainly influences the valence band. Like before, the band structures around the Fermi level remain relatively unchanged across high symmet- ric points. A475, on the other hand, exhibits n- type semiconductor behaviour with a band gap of 2.4808 eV. Its conduction bands’ proximity to the Fermi level facilitates electron movement, making it an effective electron donor. The p-orbitals of car- bon and nitrogen atoms play a significant role in the conduction and valence bands, similar to S19 and Q24. Again, the band structures near the Fermi level show minimal variation with high symmetric points. In the S19 Q24 system, the interaction be- tween S19 and Q24 results in a system with a band gap of 3.0851 eV. This places it in the wide en- ergy gap semiconductor regime. The p-orbital of carbon atoms and some contribution from oxygen are notable in the conduction band, while the va- lence band’s behaviour is akin to that of S19 and Q24. Despite the binding, the electronic proper- ties align more with Q24, and the band structures near the Fermi level remain largely consistent. The S19 Q24 A475 system also demonstrates semicon- ductor characteristics with a band gap of 2.2652 eV. The band contributions in the conduction and va- lence bands resemble those of the individual amino acids. Importantly, after the interaction, the sys- tem’s semiconductor nature still aligns with that of the hACE2 receptor, indicating a shift in prop- erties towards hACE2 characteristics. The band structures around the Fermi level exhibit no sig- nificant changes due to the interaction. Finally, ethylated A475 exhibits p-type semiconductor be- haviour with a band gap of 3.4036 eV. The valence band’s proximity to the Fermi level indicates elec- tron acceptance characteristics. Interestingly, the ethylation of A475 transforms its properties from n-type to wide energy gap p-type semiconductors. Despite this shift, the band contributions from the p-orbitals of carbon and oxygen atoms persist, and the band structures near the Fermi level display slight variations across high symmetric points. This suggests that ethylation does not dramatically im- pact the electronic structure at these energy levels. Roshan Sigdel et al./ BIBECHANA 22 (2025) 159-170 168 -8 -6 -4 -2 0 2 4 6 8 R Γ T Fermi level E -E f (e V ) High Symmetric Points -8 -6 -4 -2 0 2 4 6 8 0 5 10 15 20 25 Fermi level DOS (No. of states per eV) (a) 0.00 2.00 4.00 6.00 8.00 10.00 12.00 14.00 16.00 18.00 -6.00 -4.00 -2.00 0.00 2.00 4.00 6.00 P D O S ( N o . o f p ro je c te d s ta te s p e r e V ) E-Ef (eV) sum s-orbital p-orbital 0.00 2.00 4.00 6.00 8.00 10.00 12.00 -6.00 -4.00 -2.00 0.00 2.00 4.00 6.00 P D O S ( N o . o f s ta te s p e r e V ) E-Ef (eV) Carbon Nitrogen Hydrogen Oxygen (b) -6 -4 -2 0 2 4 6 R Γ T Fermi level E -E f (e V ) High Symmetric Points -6 -4 -2 0 2 4 6 0 50 100 150 200 250 300 Fermi level DOS (No. of states per eV) (c) 0 50 100 150 200 250 300 350 400 -8 -6 -4 -2 0 2 4 6 8 P D O S ( N o . o f p ro je c te d s ta te s p e r e V ) E-Ef (eV) sum s-orbital p-orbital Zoomed Region 0 50 100 150 200 -8 -6 -4 -2 0 2 4 6 8 P D O S ( N o . o f s ta te s p e r e V ) E-Ef (eV) Carbon Nitrogen Hydrogen Oxygen Zoomed Region (d) -8 -6 -4 -2 0 2 4 6 8 R Γ T Fermi level E -E f (e V ) High Symmetric Points -8 -6 -4 -2 0 2 4 6 8 0 5 10 15 20 25 Fermi level DOS (No. of states per eV) (e) 0 2 4 6 8 10 12 14 -8 -6 -4 -2 0 2 4 6 8 P D O S ( N o . o f p ro je c te d s ta te s p e r e V ) E-Ef (eV) sum s-orbital p-orbital 0 1 2 3 4 5 6 -8 -6 -4 -2 0 2 4 6 8 P D O S ( N o . o f s ta te s p e r e V ) E-Ef (eV) Carbon Nitrogen Hydrogen Oxygen (f) -5 -4 -3 -2 -1 0 1 2 3 4 R Γ T Fermi level E -E f (e V ) High Symmetric Points -5 -4 -3 -2 -1 0 1 2 3 4 0 2000 4000 6000 8000 10000 Fermi level DOS (No. of states per eV) (g) 0 200 400 600 800 1000 1200 -5 -4 -3 -2 -1 0 1 2 3 4 P D O S ( N o . o f s ta te s p e r e V ) E-Ef (eV) sum s-orbital p-orbital Zoomed Region 0 100 200 300 400 500 600 700 800 -5 -4 -3 -2 -1 0 1 2 3 4 P D O S ( N o . o f s ta te s p e r e V ) E-Ef (eV) Carbon Nitrogen Hydrogen Oxygen Zoomed Region (h) Roshan Sigdel et al./ BIBECHANA 22 (2025) 159-170 169 -3 -2 -1 0 1 2 3 4 R Γ T Fermi level E -E f (e V ) High Symmetric Points -3 -2 -1 0 1 2 3 4 0 500 1000 1500 2000 2500 Fermi level DOS (No. of states per eV) (i) 0 200 400 600 800 1000 1200 1400 -5 -4 -3 -2 -1 0 1 2 3 4 P D O S ( N o . o f p ro je c te d s ta te s p e r e V ) E-Ef (eV) sum s-orbital p-orbital Zoomed Region 0 200 400 600 800 1000 -5 -4 -3 -2 -1 0 1 2 3 4 P D O S ( N o . o f s ta te s p e r e V ) E-Ef (eV) Carbon Nitrogen Hydrogen Oxygen Zoomed Region (j) -4 -2 0 2 4 6 8 R Γ T Fermi level E -E f (e V ) High Symmetric Points -4 -2 0 2 4 6 8 0 20 40 60 80 100 Fermi level DOS (No. of states per eV) (k) 0 5 10 15 20 -6 -4 -2 0 2 4 6 8 10 P D O S ( N o . o f p ro je c te d s ta te s p e r e V ) E-Ef (eV) sum s-orbital p-orbital 0 2 4 6 8 10 -6 -4 -2 0 2 4 6 8 10 P D O S ( N o . o f s ta te s p e r e V ) E-Ef (eV) Carbon Nitrogen Hydrogen Oxygen (l) Figure 5: Figures (5a), (5c), (5e), (5g), (5i), and (5k) illustrate the Band and DOS structures for the S19, Q24, A475, S19 Q24, S19 Q24 A475, and Ethylated A475 systems, respectively. Figures (5b), (5d), (5f), (5h), (5j), and (5l) display the Orbital-projected density of states and Atom-projected density of states for the S19, Q24, A475, S19 Q24, S19 Q24 A475, and Ethylated A475 systems. 3.5.2 For T500 R357 Binding Pocket R357 has a wide energy gap n-type semiconductor nature with a 3.3505 eV band gap, featuring bands at the Fermi level. Band contributions near the Fermi level stem mainly from p-orbitals, Oxygen, and Nitrogen atoms. The preserved band struc- ture symmetry suggests a critical role in the SARS- CoV-2 binding mechanism. The semi-metallic na- ture and Fermi-level states suggest potential inter- actions with other molecules. T500, a p-type semi- conductor, possesses a 2.881 eV band gap with Car- bon and Nitrogen-driven conduction and valence bands near the Fermi level. Symmetry analysis reveals minimal changes near the Fermi level, im- plying consistent electronic properties. T500 R357 exhibits a 3.4137 eV band gap with n-type semi- conductor behaviour, mirroring hACE2’s proper- ties post-interaction. Band contributions involve p-orbitals, Carbon, Nitrogen, and Oxygen atoms. The consistent band structure near the Fermi level hints at the modification after binding, indicating potential for drug design. 4 Conclusions Our study involved three molecular dynamics sim- ulations: two on isolated binding pockets (S19 Q24 A475 and T500 R357) and one on the full SARS-CoV-2 spike protein-hACE2 receptor com- plex. These simulations (5ns, 5ns, and 100ns) aimed to reveal binding mechanisms by assess- ing stability and amino acid interactions, including hydrogen bonds, electrostatic, and van der Waals forces. RMSD values confirmed stability, with the isolated S19 Q24 A475 system averaging 6.27 Å, 5.78 Å for T500 R357 system, and the full com- plex averaging 2.05 Å. Non-bonded interactions, particularly van der Waals and electrostatic forces were stronger in the full complex than in isolated pockets, with significant electrostatic fluctuations. First-principles calculations revealed negative bind- ing energies for specific amino acid interactions, though real-world conditions could alter these re- sults. Ethylation of A475 was found to have min- imal impact on binding. Density functional the- ory analyses highlighted the electronic properties of key amino acids, showing strong p-orbital contri- butions with limited crystal symmetry effects near the Fermi level. These findings deepen our under- standing of the binding mechanisms between SARS- CoV-2 and hACE2, offering valuable insights for developing therapeutics aimed at disrupting viral interactions. Roshan Sigdel et al./ BIBECHANA 22 (2025) 159-170 170 Acknowledgements R. Sigdel sincerely expresses his gratitude to the University Grants Commission (UGC) for its finan- cial support. 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Oxford Univer- sity Press, 2001. https://covid19.who.int/ https://covid19.who.int/ Introduction Computational Methods Molecular Dynamic Simulation First Principles Calculations Results and Discussion Root-Mean-Square Deviation(RMSD) Hydrogen Bonds VDW or Electrostatic Interactions Energy Calculations Density of States (DOS) And Bands For S19 Q24 A475 Binding Pocket For T500 R357 Binding Pocket Conclusions