{"id":40304,"date":"2021-09-30T10:22:27","date_gmt":"2021-09-30T10:22:27","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=40304"},"modified":"2021-10-11T10:30:47","modified_gmt":"2021-10-11T10:30:47","slug":"insights-from-the-interfaces-of-corona-viral-proteins-homomers-versus-heteromers","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol14no3\/insights-from-the-interfaces-of-corona-viral-proteins-homomers-versus-heteromers\/","title":{"rendered":"Insights from the Interfaces of Corona Viral Proteins: Homomers Versus Heteromers"},"content":{"rendered":"

Introduction<\/strong><\/p>\n

Coronavirus in recent times has become an extensively used term all over the world and rightfully so, owing to COVID-19, the disease caused by one of its kind that has been running rampant since late 2019. However, contrary to most people\u2019s assumption that \u2018coronavirus\u2019\u00a0refers to a single species; it is a hyponym encompassing several viruses possessing shared characteristics. The family Coronaviridae<\/em> comprises single-stranded RNA-viruses encapsulated with spike proteins that, under a microscope, are reminiscent of the sun\u2019s corona \u2014 hence,\u00a0according to them, their well-known name. Till date, over 100 million people have been infected with COVID-19 and over 30 million have succumbed to this disease (WHO COVID-19 Weekly Epidemiological Update, 2021), not to mention the immense economic devastation that this\u00a0pandemic has put the world through. With the world fallen into the clutches of the COVID-19-pandemic, it becomes imperative to characterize not just SARS-CoV-2, the etiological agent of COVID-19, but other coronaviruses as well to be equipped in case of a future viral onslaught.\u00a0Many initiatives have already been put into motion to explore the SARS-CoV-2\u2019s constitution to obtain cognizance of the virus modus operandi and leveraging that knowledge to innovate effective counters against the disease. Considerable advances have been made in these pursuits following the advent of the COVID-19 pandemic (Ye et al., 2020).<\/p>\n

A crucial aspect of contriving a therapeutic remedy is assessing the interface interactions of the proteins, which play vital role in the pathogenicity of the organism in question. Proteins are indispensable to almost all biological processes and irregularities in their interface interactions\u00a0manifested as aberrations in their form and function, thus either reducing their efficacy or rendering them completely defunct. Proteins evidently play a key role in any virus virulence. Therefore, it is of great interest to acquire sound insights into the interactions stabilizing the key\u00a0protein-protein interfaces of coronaviruses, as it will unquestionably endow us with necessary intellectual wherewithal to devise the means to curb not just SARS-CoV-2 but any pathogen in consideration. To be able to tamper with and mimic the interface interactions allows us to\u00a0impede the viral life cycle, consequently halting and dispatching the viral load on its path to debilitating its host. Chuck et al. (2013) reported four different nitrile-based peptidomimetic inhibitors with different N-terminal protective groups and different peptide lengths of the 3C-like protease (3CLpro), an integral protein involved in the replication process of coronaviruses. One\u00a0of these peptide inhibitors, Cbz-AVLQ-CN, showed a broad-spectrum inhibition against several of the coronavirus strains known to infect humans. Several potential peptidomimetic inhibitors of 3CLpro of a mutated feline coronavirus (FCoV) strain were thoroughly investigated by St John\u00a0et al. (2015) in their endeavor to discover an effective drug to treat domestic cats suffering from feline infectious peritonitis, a lethal disease caused by this pathogen. Similarities were observed between the substrate-binding sites of the main proteases (Mpro) of transmissible gastroenteritis\u00a0virus (TGEV), human coronavirus (strain 229E), and the SARS-CoV. Anand et al. (2003) venture on the enticing promise of subtle modifications in the drugs available against the former two, for use in the latter. This study proves to be particularly fascinating as it unveils the\u00a0tantalizing prospect that the conserved homology in the key protein interface interactions of several coronaviruses can be exploited to design broad-spectrum drugs suitable for the treatment of several coronavirus-related diseases. In line with characterizing the coronavirus proteins with\u00a0the well-intended goal of reaping the bountiful benefits stemming from it, we through this study seek to understand the homo and heteromeric interface interactions of the said proteins and decipher meaningful inferences from them.\u00a0It\u2019s rarely the case for any protein to function in\u00a0solitude. More often than not, proteins operate in complex coordination with other proteins and this brings into the picture a huge number of interactions all of which must work in a stable and sustained manner for the organism to survive. An important requisite of these vital interactions is\u00a0the formation of protein-protein interfaces. Protein-protein interfaces are defined as the set of residues spanning a region over which two protein subunits, domains, or motifs bind to each other via non-covalent interactions. Covalent interactions, like disulfide bridges, also contribute to interface formation.<\/p>\n

Wide arrays of biological and chemical factors guide the formation of a stable protein interface. Chothia and Janin (1975) from their studies remark that the interfaces of proteins are generally closely packed with hydrophobic residues. These hydrophobic residues are more at the interface\u00a0in comparison to the surface but fewer compared to protein core (Jones & Thornton, 1995; Korn & Burnett, 1991). Mobility, or the lack of it, could be an important feature of protein complexes (Janin & Chothia, 1990). The conformational mobility of the side chains and the main chains of\u00a0the proteins add another dimension to how protein-protein interfaces can be analyzed and distinguished.\u00a0The occurrence of hydrophobic residues at the interface and core of the protein than the surface validates the role of hydrophobic interactions in the protein folding process\u00a0Also, interfaces usually tend to be planar with a surface area that is often proportional to the size of the total protein (Jones and Thornton, 1996).\u00a0From the assessment of the H-bonds and salt bridges in 319 non-redundant protein-protein interfaces Xu, Tsai, and Nussinov (1997) delineate\u00a0the occurrence of side-chain side-chain H-bonds to be more common among the interfaces of the associated proteins. Caffery et al. (2004) meticulously scrutinized a set of 64 protein interfaces based on conservation scores they obtained from two different multiple sequence alignments,\u00a0they observed that protein interfaces are usually more conserved than the surface. However, they noticed that the protein surfaces to be more conserved than the protein interface while using surface-patch analysis. Bahadur et al. (2004) compared the interfaces of protein complexes and\u00a0homodimers and interpreted the close packing contacts in monomers using parameters like the interface area, polar and non-polar composition and interactions, residue propensity, atomic packing density, buried interface atoms and core residues, and hydrophobic interactions. Their\u00a0analyses revealed that in contrast to the crystallographic interfaces, biological ones tend to be large and more hydrophobic, having a higher amount of core residues, possessing more fully buried atoms, and exhibiting better shape complementarity. Elez et al. (2018) showed from their\u00a0survey of biological and crystallographic interfaces that the parameters like intermolecular residue-residue contacts and interaction energies (van der Waals, electrostatic, and desolvation) can effectively discriminate between these interfaces.\u00a0Additionally, Guharoy and Chakrabarti\u00a0(2005) demonstrated that the residues that form the core of a biologically relevant interface are more conserved than the ones in its rim regions. The same isn\u2019t observed for crystalline artifacts. Thus, residue conservation expressed in terms of sequence entropy can be used to ascertain\u00a0whether an interface is truly biologically pertinent or not.\u00a0 Homomeric interfaces appear to be highly populated in hydrogen bonds with non-polar residues while heteromeric interfaces were observed to have polar charged residues with high hydrogen bond density (Zhanhua et al.,\u00a02005).\u00a0Protein heterodimer subunit interaction is important in regulation and catalysis in living cells (Vaishnavi et al., 2010). Guharoy and Chakrabarti (2010) surveyed the interfaces of 121 homodimers and 392 heterocomplexes and concluded that the distribution of conserved residues\u00a0at the interfaces is not random but rather distinctly clustered.\u00a0Hydrogen bonds and salt bridges are the key driving force for molecular recognition and specificity among associated proteins (Kuroda & Gray, 2016). The proportion of the different types of covalent and non-covalent\u00a0interactions differs amongst various proteins based on their origin, composition, and functionality. Nilofer et al. (2019) noted that proteins with small interfaces (area < 1000 \u00c5\u00b2) possess considerable levels of electrostatic interactions in proportion to other non-covalent\u00a0interactions were observed to perform regulatory roles. Interactions aside, other critical interface attributes like solvation potential, interface amino-acid residue propensity, hydrophobicity, planarity, protrusion, and the accessible surface area further distinguish one set of proteins from\u00a0the others and come in handy in protein-protein interface analysis.\u00a0Hence, deciphering the arcane aspects of the coronavirus protein-protein interfaces shall shed light on a new path to combating the advance of coronaviruses. Therefore, we analyzed 366 homomeric and 199 heteromeric\u00a0protein interfaces using six parameters including interface size, interface area, van der Waals, hydrogen bond, electrostatic and total stabilizing energies. To verify the authenticity of our previous finding stating that protein interfaces are predominantly populated with vdW energy\u00a0(Nilofer et al., 2017) and that the small interfaces are rich in electrostatic energy (Nilofer et al., 2019).<\/p>\n

Materials and Methods<\/strong><\/p>\n

Dataset<\/strong><\/p>\n

An updated non-redundant structural dataset of 61 homomeric and 129 heteromeric protein complexes (Table 1) ranging from dimers to heptamers pertaining to SARS-CoV-2 were acquired from the Protein Data Bank (PDB) (Figure 2). The entries conform to the following\u00a0refinements: (1) Experimental Method – X-Ray Diffraction; (2) Polymer Entity Type \u2013 Protein; (3) Refinement Resolution (<3.0 \u00c5); and (4) Polymer Entity Sequence Length (> 50 aminoacids). These 61 homomeric and 129 heteromeric protein complexes gave rise to 366\u00a0homomeric and 199 heteromeric interfaces that were examined in this study.<\/p>\n

Table 1: List of Coronaviral (homomeric and heteromeric) protein complexes.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Homo-meric Protein Complexes<\/strong><\/td>\n<\/tr>\n
1WNC<\/td>\n2FAV<\/td>\n2IEQ<\/td>\n3ZBD<\/td>\n5EPW<\/td>\n5YM6<\/td>\n6IVD<\/td>\n6VYO<\/td>\n7CDZ<\/td>\n<\/tr>\n
1WYY<\/td>\n2G9T<\/td>\n2Q6D<\/td>\n4F49<\/td>\n5HIZ<\/td>\n5YM8<\/td>\n6KL2<\/td>\n6W01<\/td>\n7CJD<\/td>\n<\/tr>\n
2AMP<\/td>\n2GA6<\/td>\n2RHB<\/td>\n4KQZ<\/td>\n5JIF<\/td>\n5YVD<\/td>\n6LPA<\/td>\n6WXD<\/td>\n7CMD<\/td>\n<\/tr>\n
2BTL<\/td>\n2GE7<\/td>\n3EAJ<\/td>\n4L3N<\/td>\n5LG6<\/td>\n5ZHY<\/td>\n6LXT<\/td>\n6WZQ<\/td>\n7E35<\/td>\n<\/tr>\n
2BXX<\/td>\n2GE8<\/td>\n3EBN<\/td>\n4MOD<\/td>\n5N4K<\/td>\n5ZUV<\/td>\n6M3M<\/td>\n6XMK<\/td>\n7NIO<\/td>\n<\/tr>\n
2CJR<\/td>\n2GEC<\/td>\n3ETI<\/td>\n4S1T<\/td>\n5RS7<\/td>\n6FV2<\/td>\n6MEA<\/td>\n6Z4U<\/td>\n<\/td>\n<\/tr>\n
2D2D<\/td>\n2GIB<\/td>\n3EWO<\/td>\n4UD1<\/td>\n5XGR<\/td>\n6IVC<\/td>\n6QFY<\/td>\n7C02<\/td>\n<\/td>\n<\/tr>\n
Hetero-meric Protein Complexes<\/strong><\/td>\n<\/tr>\n
1P9U<\/td>\n3C9N<\/td>\n5DO2<\/td>\n6LU7<\/td>\n6XE1<\/td>\n7BEP<\/td>\n7D2Z<\/td>\n7KFY<\/td>\n7LBN<\/td>\n<\/tr>\n
1UK4<\/td>\n3I6K<\/td>\n5E6J<\/td>\n6M5I<\/td>\n6XFN<\/td>\n7BWJ<\/td>\n7D30<\/td>\n7KGJ<\/td>\n7LFZ<\/td>\n<\/tr>\n
1X7Q<\/td>\n3R24<\/td>\n5F22<\/td>\n6PXH<\/td>\n6XKP<\/td>\n7BZ5<\/td>\n7DEO<\/td>\n7KGK<\/td>\n7LG0<\/td>\n<\/tr>\n
2AHM<\/td>\n3SNC<\/td>\n5GMQ<\/td>\n6U7F<\/td>\n6XKQ<\/td>\n7C01<\/td>\n7DET<\/td>\n7KGQ<\/td>\n7LG2<\/td>\n<\/tr>\n
2AMQ<\/td>\n3SNE<\/td>\n5GSB<\/td>\n6U7G<\/td>\n6YLA<\/td>\n7C8B<\/td>\n7DEU<\/td>\n7KGT<\/td>\n7LM8<\/td>\n<\/tr>\n
2BEZ<\/td>\n3VB4<\/td>\n5GSV<\/td>\n6W4H<\/td>\n6YZ5<\/td>\n7C8V<\/td>\n7EAN<\/td>\n7KLW<\/td>\n7LM9<\/td>\n<\/tr>\n
2DD8<\/td>\n3VB6<\/td>\n5V6A<\/td>\n6WAQ<\/td>\n6Z2M<\/td>\n7C8W<\/td>\n7JJC<\/td>\n7KMG<\/td>\n7LOP<\/td>\n<\/tr>\n
2GHW<\/td>\n4KR0<\/td>\n5W8U<\/td>\n6WUU<\/td>\n6ZCZ<\/td>\n7CAN<\/td>\n7JMO<\/td>\n7KMH<\/td>\n7NEG<\/td>\n<\/tr>\n
2Q6G<\/td>\n4M0W<\/td>\n5WFI<\/td>\n6WX4<\/td>\n7B3O<\/td>\n7CDI<\/td>\n7JMP<\/td>\n7KMI<\/td>\n7NEV<\/td>\n<\/tr>\n
2XYQ<\/td>\n4PV8<\/td>\n5YL9<\/td>\n6XA9<\/td>\n7BEH<\/td>\n7CHB<\/td>\n7JMW<\/td>\n7KN5<\/td>\n<\/td>\n<\/tr>\n
2Z3C<\/td>\n4QZV<\/td>\n5YN5<\/td>\n6XAA<\/td>\n7BEI<\/td>\n7CHC<\/td>\n7JN5<\/td>\n7KN6<\/td>\n<\/td>\n<\/tr>\n
2Z3D<\/td>\n4RF1<\/td>\n5YY5<\/td>\n6XBG<\/td>\n7BEJ<\/td>\n7CHF<\/td>\n7JX3<\/td>\n7KN7<\/td>\n<\/td>\n<\/tr>\n
3ATW<\/td>\n4RSP<\/td>\n6BI8<\/td>\n6XBI<\/td>\n7BEL<\/td>\n7CJF<\/td>\n7K9Z<\/td>\n7KVG<\/td>\n<\/td>\n<\/tr>\n
3AVZ<\/td>\n4ZPT<\/td>\n6C6Z<\/td>\n6XC3<\/td>\n7BEM<\/td>\n7CM4<\/td>\n7KFW<\/td>\n7KZB<\/td>\n<\/td>\n<\/tr>\n
3AW0<\/td>\n4ZRO<\/td>\n6IEX<\/td>\n6XC7<\/td>\n7BEN<\/td>\n7CR5<\/td>\n7KFX<\/td>\n7L0N<\/td>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Interface size\u00a0<\/strong><\/p>\n

The number of amino-acid residues that make up the interface is referred to as the interface size.<\/p>\n

Accessible Surface Area (ASA)<\/strong><\/p>\n

The Accessible Surface Area was estimated for each of 366 homomeric and 199 heteromeric interfaces using the NACCESS, a stand-alone program that is capable of calculating the ASA of a molecule obtaining the atomic coordinate information from a PDB file (Hubbard & Thornton,\u00a01993).\u00a0It operates on the premise of the Lee and Richards Method (Lee & Richards, 1971) wherein a probe with 1.4\u00c5 (radius of water molecule is nearly the same) radius (Jones & Thornton, 1995, 1996) is made to roll over the protein complex in monomer and dimer state to find the ASA.<\/p>\n

Interface Area\u00a0<\/strong><\/p>\n

The interface area for the 366 homomeric and 199 heteromeric protein interfaces was calculated from the findings of NACCESS using the formula: {[ASA of Subunit 1 (monomer state) + ASA of Subunit 2 (monomer state)) \u2013 (ASA of the dimer (1 and 2)]} \/ 2.<\/p>\n

Interface Energies<\/strong><\/p>\n

Interface energies (vdW, hydrogen bonds, electrostatic, and total stabilizing energy) were computed for 366 homomeric and 199 heteromeric protein interfaces. The analysis was done using\u00a0PPCheck, a public webserver useful for quantifying the non-covalent interactions between\u00a0any two given proteins\/chains using distance criteria (Sukhwal&Sowdhamini, 2013, 2015). (Note: the role of water at the protein interface was excluded in the calculations).<\/p>\n

Large interfaces and small interfaces\u00a0<\/strong><\/p>\n

Interfaces were categorized as small and large interface based on interface size and interface area. Interfaces with large interface area [1690\u00b1683 (homomeric) 1306\u00b1355 (heteromeric)] and interface size [146\u00b129 (homomeric) 122\u00b129 (heteromeric)] were defined as large interface and\u00a0interfaces with small interface area [472\u00b1174 (homomeric) 310\u00b1199 (heteromeric)] and interface size [60\u00b112 (homomeric) 41\u00b120 (heteromeric)] were defined as small interface.<\/p>\n

Interfaces with dominant and sub-dominant van der Waals energy (vdW) <\/strong><\/p>\n

The small and large interfaces were further categorized as dominant vdW energy and subdominant vdW energy based on the percentage contribution of vdW energy towards total stabilizing energy. Interfaces with the van der Waals energy measuring less than 60% of the total\u00a0stabilizing energy (sum of van der Waals, hydrogen bonds, and electrostatic energies) are defined as sub-dominant interfaces, while those interfaces whose van der Waals energy\u2019s contribution to the total stabilizing energy greater than 60%\u00a0 are defined as the dominant interfaces.<\/p>\n

Statistical analysis\u00a0<\/strong><\/p>\n

Statistical parameters like Mean, Standard Deviation, the cumulative frequency at a defined bin, range, mode, and distribution for the datasets and sub-datasets were assessed using the statistical functions in Microsoft Office Excel (version 2007). Additionally, multiple linear regression\u00a0analyses of interface size against interface area, interface energies were carried out using the statistical analysis tool of Microsoft Office Excel (version 2007). The coefficient of determination (r2<\/sup>) was determined alongside the statistical assessment of significance (p-value)\u00a0through statistical ANOVA test at a 95% confidence limit, the assessment showed significance when p < 0.01.<\/p>\n

Results<\/strong><\/p>\n

We downloaded SARS-CoV-2 viral protein complexes [61 homomers and 129 heteromers with 366 and 199 interfaces respectively (Table 1 and Figure 1)] from PDB using the criteria mentioned in Figure 2. We then characterized the interfaces in terms of six parameters consisting\u00a0of interface area, interface size and interface energies including van der Waals, hydrogen bond, electrostatic and total stabilizing energy. Interface area was calculated using NACCESS program (Lee and Richard method with 1.4\u00c5 as probe radius) and the interface size and interface energies\u00a0were calculated using PPCheck. PPCheck is a computer program; it computes interface energy using distance criteria. We methodically analyzed and compared the interfaces of 366 homomers and 199 heteromers (Figure 1) using six parameters to verify the accuracy of our previous study\u00a0stating the abundance of vdW energy at the protein interface and that the small protein-protein interfaces are rich in electrostatic energy. The relationship between the interface size (number of residues at the interface) and interface area signifies the strength of protein interface. Likewise,\u00a0Figure 3 shows that the interface area increases with interface size in all interfaces of homomer and heteromer with a coefficient of determination of r2<\/sup> \u2265 0.84. It is observed from Figure 4 that the interface size (92\u00b149) and interface area (920\u00b1743 \u00c52<\/sup>) of homomers are greater than the\u00a0interface size [63\u00b129 (hetero)] and interface area [575\u00b1337 \u00c52<\/sup> (hetero)] of heteromers. Moreover, Figure 5 shows majority of interfaces to have interface area <1000 \u00c52<\/sup> in homomers (52%) and heteromers (92%). Furthermore, we calculated the varying percentage contribution of\u00a0interface energy including vdW, hydrogen bonds, electrostatic energy towards total stabilizing energy at the interfaces of homomer and heteromer. We found the interfaces of homomers and heteromers to have a high percentage contribution of vdW (above 76%) and a low percentage\u00a0contribution of hydrogen bonds (above 16%) and electrostatic (above 8%) energies on average (Figure 6). Figure 7 shows that the interfaces of homomer and heteromer are normally distributed and are similar with respect to percent vdW, hydrogen bond and electrostatic energy\u00a0contribution except the distribution of hydrogen bond energy (Figure 7c) in homomers. However, it is intriguing to observe that on average the interfaces of homomer and heteromer are similar in terms of interface size interface area, vdW, hydrogen bond, electrostatic and total stabilizing energy.<\/p>\n\n\n\n
\"Vol14No3_Ins_Chr_fig1\"<\/a><\/td>\nFigure 1: Examples depicting the small and large interfaces of homomeric and heteromeric proteins of SARS-CoV-2. The percentage contributions of interface energies (vdW, hydrogen bond, electrostatic energies) at the interface are also displayed.<\/strong><\/p>\n

Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"Vol14No3_Ins_Chr_fig2\"<\/a><\/td>\nFigure 2: The flowchart with criterion used to create the current data set of 61 homomeric and 129 heteromeric proteins with 366 and 199 interfaces respectively.<\/strong><\/p>\n

Click here to view figure\u00a0<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"Vol14No3_Ins_Chr_fig3\"<\/a><\/td>\nFigure 3: The relationship between interface size and interface area is represented using multiple regression analysis. High coefficients of determination (R2) values were observed with a high confidence limit and significance of fit for the homomers, heteromers, and for overall interfaces.<\/strong><\/p>\n

Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"Vol14No3_Ins_Chr_fig4\"<\/a><\/td>\nFigure 4: The Mean and the Standard Deviation of interface size and interface area of homomeric and heteromeric interfaces of SARS-CoV-2 are depicted.<\/strong><\/p>\n

Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"Vol14No3_Ins_Chr_fig5\"<\/a><\/td>\nFigure 5: The distribution of interface area among homomeric and heteromeric interfaces are illustrated using polynomial distribution. Majority of the homomeric and heteromeric interfaces are found to have 500 \u00c52<\/sup> and 1000 \u00c52<\/sup> on average.<\/strong><\/p>\n

Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"Vol14No3_Ins_Chr_fig6\"<\/td>\nFigure 6: Mean percentage contribution of interface energies at the interface is represented. On an average, the homomeric and heteromeric interfaces are made up of vdW energy (above 76%),\u00a0hydrogen bond energy (above 14%) and electrostatic energy (above 10%). This implies that the protein interfaces are predominantly populated with vdW energy in abundance.<\/strong><\/p>\n

Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"Vol14No3_Ins_Chr_fig7\"<\/a><\/td>\nFigure 7: Distribution of interfaces (in percentage) according to the increasing contributions of vdW, hydrogen bond, and electrostatic energies. The interfaces are distributed normally with vdW and hydrogen bond energy but in the case of electrostatic energy, the interface distribution is reminiscent of a power-law graph.<\/strong><\/p>\n

Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Subsequently, we categorized our interfaces into vdW energy dominant (\u226560%) and vdW energy subdominant (<60%) interfaces based on the contribution of vdW at the interfaces of homomer and heteromer.\u00a0 The majority (91%) of interfaces of homomer and heteromer are vdW energy\u00a0dominant having less than 20% of hydrogen bonds and electrostatic energy contribution towards total energy. On the other hand, the 8% of vdW subdominant interfaces of homomer and heteromer are observed to have more than 20% of hydrogen bonds and electrostatic energy. We\u00a0further found that the interfaces of homomer and heteromer to have three-fold and five-fold more electrostatic energy contribution at the interfaces of homomer and heteromer with small interface area (vdW subdominant) compared to large interface area (vdW dominant) (Figures 8 and 9).\u00a0While, the hydrogen bonds energy contribution are similar at the large and small interfaces of homomer and heteromer. The vdW energy subdominant interfaces of homomer and heteromer are highly pronounced with electrostatic energy contribution unlike the vdW dominant interfaces\u00a0(Figure 10). It is evident from Figure 11 that the interface area and interface size of the large interfaces of homomer and heteromer is four-fold more when compared to the small interfaces. The distribution of interface area among the large and small interfaces of homomer and\u00a0heteromer shows that majority of the interfaces with subdominant vdW energy are observed to have an interface area <1000 \u00c52<\/sup> while interface area of dominant vdW interfaces are >1000 \u00c52<\/sup> (Figure 12). The relationship between the interface size and interface energy is shown using\u00a0Figure 13. We found the correlation of total stabilizing energy and vdW to increase with interface size with r2<\/sup>=0.84. Whereas, hydrogen bonds [r2<\/sup>=0.44 (homomeric) r2<\/sup>=0.37 (heteromeric)] and electrostatic energy [r2<\/sup>=0.24 (homomeric) r2<\/sup>=0.15(heteromeric)] have\u00a0moderate and less correlation with respect to interface size. Hence, to gain in-depth understanding about the interface, we calculated the correlation between interface energy and interface size in large and small interfaces of homomer and heteromer.\u00a0 We found the trend to\u00a0persist in large and small interface with respect to total and vdW energy to have high coefficient of determination at the interfaces of homomer and heteromer. Interestingly, we found the large interfaces of homomer and heteromer with zero electrostatic energy contribution [Figure 14\u00a0(g,h)], while hydrogen bonds being second highly pronounced energy [Figure 14 (e,f)]. Conversely, we found electrostatic energy to be the second highly pronounced energy with respect to the small interfaces of homomer [Figure 14(o)] where the hydrogen bonds energy\u00a0contribution is zero [Figure 14(m)]. However, the contribution of hydrogen bonds and electrostatic energy is equal in the small interfaces of heteromer [Figure 14 (n,p)]. This shows that the small interfaces having restricted interface area and interface size with vdW\u00a0subdominant are abundant with electrostatic energy. This conclusion holds true in case of homomers (small interface, vdW subdominant), but not with heteromers (small interface, vdW subdominant).<\/p>\n\n\n\n
\"Vol14No3_Ins_Chr_fig8\"<\/a><\/td>\nFigure 8: Graph showing interface distribution (in percentage) with high percentage contributions of vdW, hydrogen bond and electrostatic energies. Most of the interfaces are vdW dominant.<\/strong><\/p>\n

Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"Vol14No3_Ins_Chr_fig9\"<\/a><\/td>\nFigure 9: The percentage contribution of hydrogen bond and electrostatic energy at the interfaces of small and large interfaces of homomeric and heteromeric protein complexes are depicted. The contribution of electrostatic energy at the small interfaces (vdW energy subdominant) of homomeric and heteromeric interfaces are three-fold and five-fold more than large interfaces respectively.<\/strong><\/p>\n

Click here to view figure\u00a0<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"Vol14No3_Ins_Chr_fig10\"<\/a><\/td>\nFigure 10: Distribution of interfaces (in percentage) with varying percentages of hydrogen bond and electrostatic energies are shown among the small and large interfaces of homomeric and heteromeric protein complexes.<\/strong><\/p>\n

Click here to view figure\u00a0<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"Vol14No3_Ins_Chr_fig11\"<\/a><\/td>\nFigure 11: The Mean and Standard Deviation of interface size and interface area among the interfaces of homomeric and heteromeric protein complexes are represented.<\/strong><\/p>\n

Click here to view figure\u00a0<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"Vol14No3_Ins_Chr_fig12\"<\/a><\/td>\nFigure 12: Distribution of interfaces (in percentage) among the small and large interfaces of homomeric and heteromeric protein complexes is shown.<\/strong><\/p>\n

Click here to view figure\u00a0<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"Vol14No3_Ins_Chr_fig13\"<\/a><\/td>\nFigure 13: Graph showing the correlation between the interface energies and the interface size for the total interfaces for the homomeric and heteromeric\u00a0protein complexes using multiple regression analysis.<\/strong><\/p>\n

Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n\n\n\n
\"Vol14No3_Ins_Chr_fig14\"<\/a><\/td>\nFigure 14: Graph showing the correlation between the interface energies and the interface size among the small and large interfaces of homomeric and heteromeric protein complexes. The electrostatic energy contribution is more pronounced at the small interfaces of homomer with zero hydrogen bond energy. While the hydrogen bond and electrostatic energy contribution is equal at the small interfaces of heteromeric proteins.<\/strong><\/p>\n

Click here to view figure<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Discussion<\/strong><\/p>\n

Coronavirus has debilitated the health of the human populace and thrown the world\u2019s economy into disarray. We can\u2019t afford to let it or any of its kin hurl in another maelstrom of chaos again. Hence, there is an immediate need to concentrate on finding and delivering new promising\u00a0solutions to deal with coronaviruses. A diverse array of remedies has sprung up from the staunch\u00a0efforts of many researchers (Ahsan et al., 2020; Auwaerter&Casadevall, 2020; Yang, 2021; Odolczyk et al., 2021). Research into the functioning of SARS-CoV-2 has unearthed tantalizing new details about the linchpins in its viral architecture, which serve as potential drug targets\u00a0(Gordon et al., 2020; Xie et al., 2020; Sakkiah et al., 2021). Galvanized by the spirited works happening all over the world, this study endeavors to delineate the homomeric and heteromeric interface interactions of coronavirus proteins, which would prove to be fruitful in the\u00a0development of new and efficacious therapeutic remedies. Understanding the vital interface interactions of coronavirus proteins is a pivotal step in the development of an effective and efficient therapeutic remedy. Hence, it is of interest to explore the protein interfaces of SARS-CoV-2 and to verify our former findings stating that protein interfaces are predominantly dominated with vdW energy while hydrogen bond and electrostatic energy to play a selective role (Nilofer et al., 2017) and also that the small protein interfaces are rich in electrostatic energy\u00a0(Nilofer et al. 2019) using X-ray structures from PDB.<\/p>\n

Thus far, protein interfaces are defined using several physical and chemical features including hydration, hydrophobicity, hydrophilicity, van der Waals interaction, aromatic residues, hydrogen bonds, electrostatic effects, conserved residues, conformational changes, size, gap\u00a0index, volume, shape complementarity, residue preference, the presence of water molecules and binding energy (Caffrey et al., 2004; Bahadur et al., 2004; Chothia & Janin, 1975; Chakrabarti & Janin, 2002; Guharoy & Chakrabarti, 2010; Chothia et al., 1976; Gromiha et al., 2009; Guharoy\u00a0& Sowmya et al., 2011; Jones, 2012; Janin & Chothia, 1990; Korn & Burnett, 1991; Jones & Thornton, 1995, 1996, 1997a, 1997b; Li et al., 2006; Lo Conte et al., 1999; Miller et al., 1987; Marchetti et al., 2019; Nilofer et al., 2017; Murakami & Jones, 2006; Pal et al., 2007; Robert &\u00a0Janin, 1998; Sowmya et al., 2015; Sowmya & Ranganathan, 2015; Tsai et al., 2008; Taechalertpaisarn et al., 2019; Yang & Gong, 2018; Xu et al., 1997; Zhanhua et al., 2005). The extensive research on interface analysis (Li et al., 2019), protein docking methods (Porter et al.,\u00a02019; Dauzhenka et al., 2018;) and interface feature predictors (Correa Marrero et al., 2019; Bendell et al., 2014; Dai et al., 2016; Daberdaku& Ferrari, 2018; Guo et al., 2016; Garcia-Garcia et al., 2017; Hwang et al., 2016; Humphris&Kortemme, 2008; Li &Kihara, 2012; Jordan et al.,\u00a02012; Qiao et al., 2018; Moreira et al., 2017; Xue et al., 2011Wang et al., 2017;) has deepened our understanding on protein-protein interaction. Hence, the identification of novel structural features of known interfaces using statistical analysis is pertinent to the current scenario dealt\u00a0with the spread of SARS-CoV-2 to understand its protein-protein interaction. Protein binding and folding is more fascinating compared to protein-protein interaction (Li et al., 2005; Lulu et al., 2009). It is of significance to relate molecular function with the structural features of\u00a0homomeric and heteromeric interfaces. Therefore, we performed statistical analysis on 366 homomeric and 199 heteromeric interfaces of SARS-CoV-2 using six parameters including interface area, interface size and interface energies (van der Waals, hydrogen bonds, electrostatic\u00a0and total energy) and identified new structural features in relation to molecular function.<\/p>\n

Protein size play a significant role in protein-protein interaction and protein docking (Martin, 2014). Similarly, proteins with different size and shape come together with the help of stable interface (Vaishnavi et al., 2010). Miller et al., 1987 defined interface area as the change in\u00a0accessible surface area during interface formation and described protein interfaces using interface size. The potency of protein binding is determined by its interface size. We show the interface area to increase with interface size with r2<\/sup>>0.86 (high co-efficient of determination).\u00a0Majority of the interfaces are found to have interface area less than 1000 \u00c52<\/sup> in homomeric and heteromeric protein interfaces. This finding is in contrast to our former results stating most of interfaces to have interface area more than 1000 \u00c52<\/sup>. Each protein interface is unique and has\u00a0varying contribution of interface attributes including interface area, interface size, vdW, hydrogen bonds, electrostatic energy and total stabilizing energy. The quantification of the interface attributes help in identifying the major stabilizing factors at the protein interface which\u00a0in-turn is significant to enhance our understanding of protein-protein interaction. We know that the protein interfaces contain hydrophobic residues (Chothia et al., 1976; Chothia & Janin, 1975; Jones & Thornton, 1995; Korn & Burnett, 1991; Tsai et al., 2008), hydrogen bonds (Xu et al.,\u00a01997; Zhanhua et al., 2005), charge complementarity (Lo Conte, Chothia, & Janin, 1999; Xu et al., 1997), charged, aliphatic and aromatic residues (Gromiha et al., 2009), polar (Petras& Emil, 2006) and non-polar residues (Sowmya & Ranganathan, 2015; Sowmya et al., 2011). The\u00a0presence of hydrogen bonds and electrostatic energy has been documented in literature. However, the contribution of these interface energies towards total stabilizing energy is not accurately reported. Nilofer et al., 2017 described that the interfaces are van der Waals energy\u00a0dominant on average with limited hydrogen bond and electrostatic energy. Subsequently, it was reported that the electrostatic energies are abundant in small protein interfaces (Nilofer et al., 2019). In addition we confirm these findings to hold true with the SARS-CoV-2 dataset showing\u00a0the interfaces of homomeric and heteromeric proteins to be vdW dominant (above 76%) with selective hydrogen bonds (above 16%) and electrostatic energy (above 8%) and also we found small interfaces of homomers to be rich in electrostatic energy with zero hydrogen bond energy\u00a0contribution.We observed majority of interfaces to have dominant vdW energy [90% (homomeric) 92% (heteromeric)] compared to subdominant vdW energy [10% (homomeric) 8% (heteromeric)]. Large interfaces were found to have four times of interface size and interface area than small\u00a0interfaces. It is known that vdW and hydrogen bond energies to increase with interface size in homomeric and heteromeric protein interfaces unlike electrostatic energy (Nilofer et al., 2019). However, electrostatic energy and salt bridges were reported to increase with interface size in regulatory-inhibitory interfaces (Sowmya et al., 2015). We observed large interfaces of homomeric and heteromeric proteins with zero electrostatic energy [r2<\/sup>=0 (homomeric) r2<\/sup>=0(heteromeric)] contribution. Conversely, we observed small interfaces to be rich in\u00a0electrostatic energy [r2<\/sup>=0.50 (homomeric) r2<\/sup>=0.61 (heteromeric)] where the vdW energy is subdominant and hydrogen bond energy is at its minimum. Therefore, we find these findings to be in agreement with our former conclusions stating that protein interfaces to be vdW dominant\u00a0with limited hydrogen bond and electrostatic energies (Nilofer et al., 2017) and also that small protein interfaces with small interface area and interface size are rich in electrostatics (Nilofer et al., 2019).<\/p>\n

Conclusion<\/strong><\/p>\n

Statistical analyses of the homomeric and heteromeric protein-protein interfaces of coronavirus using interface size, area, and energies reveal that these interfaces are largely held together by vdW energy. Of the 366 homomeric and 199 heteromeric protein-protein interfaces surveyed,\u00a091% of interface possesses a large interface size and area, where vdW energy reigns as the dominant contributing energy. However, 9% of the dataset were observed to have small interface size and small interface area with sub-dominant vdW energy. The contributions of hydrogen\u00a0bond and electrostatic energies are remarkable at small interfaces with sub-dominant vdW energy. We found the electrostatic contribution at the small interfaces having small interface area and interface size to be three-fold and five-fold more in homomeric and heteromeric protein\u00a0interfaces respectively. Also, we observed the small interfaces of homomeric interfaces to be\u00a0highly pronounced with electrostatic energy in the absence of hydrogen bond energy. While the contribution of hydrogen bond and electrostatic energies are same in the small interfaces of heteromeric proteins. Hence, our previous finding stating that the protein interfaces are\u00a0predominantly populated with vdW energy and that the small interfaces are rich in electrostatics holds true in case of homomers but not in the case of heteromers. In future, we plan to extend this work by finding the amino acid propensities using in-house python program and predict\u00a0hotspot residues at the interfaces of coronavirus spike glycoprotein and its interacting partner. This prediction will give us more deep insights about the inter-residue interaction which will help us in drug discovery and development process. These insights into the homomeric and\u00a0heteromeric interfaces of coronavirus proteins gained from the statistical analyses of their structural features will undeniably further our understanding of their molecular functioning.<\/p>\n

Acknowledgement<\/strong><\/p>\n

Christina Nilofer thanks VIT University for the support towards the completion of this work and publication. Christina Nilofer extends her gratitude towards Thuraimurugan Hemanand, Chakravarthy Muralidharan Bhargavan and Navaneethan Nandha for their immense contribution and support towards this paper.<\/p>\n

Conflict of Interest\u00a0<\/strong><\/p>\n

The authors declare that there is no financial conflict of interest.<\/p>\n

Funding Source\u00a0<\/strong><\/p>\n

The authors declare that there is no funding support for this publication.<\/p>\n

References<\/strong><\/p>\n

    \n
  1. Ahsan, W., Alhazmi, H. A., Patel, K. S., Mangla, B., Al Bratty, M., Javed, S., Najmi, A., Sultan, M. H., Makeen, H. A., Khalid, A., Mohan, S., Taha, M., & Sultana, S. (2020). Recent Advancements in the Diagnosis, Prevention, and Prospective Drug Therapy of COVID-19. Frontiers in public health, 8, 384. org\/10.3389\/fpubh.2020.00384
    \n    CrossRef<\/a><\/li>\n
  2. Anand, K., Ziebuhr, J., Wadhwani, P., Mesters, J. R., &Hilgenfeld, R. (2003). Coronavirus main proteinase (3CLpro) structure: basis for design of anti-SARS drugs. Science (New York, N.Y.), 300(5626), 1763\u20131767. doi.org\/10.1126\/science.1085658
    \n    CrossRef<\/a><\/li>\n
  3. Auwaerter, P. G., Casadevall, A. (2020). Is the Coronavirus Treatable? Johns Hopkins Medicine. https:\/\/www.hopkinsmedicine.org\/health\/conditions-and-diseases\/coronavirus\/coronavirus-treatment-whats-in-development<\/li>\n
  4. Bahadur, R. P., Chakrabarti, P., Rodier, F., & Janin, J. (2004). A dissection of specific and non-specific protein\u2013protein interfaces. Journal of Molecular Biology, 336(4), 943\u2013955. doi:10.1016\/j.jmb.2003.12.073
    \n    CrossRef<\/a><\/li>\n
  5. Bendell, C. J., Liu, S., Aumentado-Armstrong, T., Istrate, B., Cernek, P. T., Khan, S., \u2026 Murgita, R. A. (2014). Transient protein\u2013protein interface prediction: Datasets, features, algorithms, and the RAD-T predictor. BMC Bioinformatics, 15(1), 82. doi:10.1186\/1471-2105-15-82
    \n    CrossRef<\/a><\/li>\n
  6. Caffrey, D. R., Somaroo, S., Hughes, J. D., Mintseris, J., & Huang, E. S. (2004). Are protein\u2013protein interfaces more conserved in sequence than the rest of the protein surface? Protein Science, 13(1), 190\u2013202. doi:10.1110\/ps.03323604
    \n    CrossRef<\/a><\/li>\n
  7. Chakrabarti, P., & Janin, J. (2002). Dissecting protein\u2013protein recognition sites. Proteins: Structure, Function, and Genetics, 47(3), 334\u2013343. doi:10. 1002\/prot.10085
    \n    CrossRef<\/a><\/li>\n
  8. Chothia, C., & Janin, J. (1975). Principles of protein\u2013protein recognition. Nature, 256(5520), 705\u2013708. doi:10.1038\/256705a0
    \n    CrossRef<\/a><\/li>\n
  9. Chothia, C., Wodak, S., & Janin, J. (1976). Role of subunit interfaces in the allosteric mechanism of hemoglobin. Proceedings of the National Academy of Sciences of the United States of America, 73(11), 3793\u20133797. doi:10.1073\/pnas.73.11.3793
    \n    CrossRef<\/a><\/li>\n
  10. Chuck, C. P., Chen, C., Ke, Z., Wan, D. C., Chow, H. F., & Wong, K. B. (2013). Design, synthesis and crystallographic analysis of nitrile-based broad-spectrum peptidomimetic inhibitors for coronavirus 3C-like proteases.European journal of medicinal chemistry, 59, 1\u20136. doi.org\/10.1016\/j.ejmech.2012.10.053
    \n    CrossRef<\/a><\/li>\n
  11. Correa Marrero, M., Immink, R. G. H., de Ridder, D., & van Dijk, A. D. J. (2019). Improved inference of intermolecular contacts through protein\u2013protein interaction prediction using co-evolutionary analysis. Bioinformatics, 35, 2036\u20132042. doi:10.1093\/bioinformatics\/bty924
    \n    CrossRef<\/a><\/li>\n
  12. Dai, W., Wu, A., Ma, L., Li, Y. X., Jiang, T., & Li, Y. Y. (2016). A novel index of protein\u2013protein interface propensity improves interface residue recognition. BMC Systems Biology, 10(S4), 112. doi:10.1186\/s12918- 016-0351-7
    \n    CrossRef<\/a><\/li>\n
  13. Daberdaku, S., & Ferrari, C. (2018). Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction. BMC Bioinformatics, 19(1), 35. doi:10.1186\/s12859-018-2043-3
    \n    CrossRef<\/a><\/li>\n
  14. Dauzhenka, T., Kundrotas, P. J., &Vakser, I. A. (2018). Computational feasibility of an exhaustive search of side-chain conformations in protein\u2013protein docking. Journal of Computational Chemistry, 39(24), 2012\u20132021. doi:10.1002\/jcc.25381
    \n    CrossRef<\/a><\/li>\n
  15. Elez, K., Bonvin, A., &Vangone, A. (2018). Distinguishing crystallographic from biological interfaces in protein complexes: role of intermolecular contacts and energetics for classification. BMC bioinformatics, 19(Suppl 15), 438.
    \n    CrossRef<\/a><\/li>\n
  16. Garcia-Garcia, J., Valls-Comamala, V., Guney, E., Andreu, D., Munoz, F. J., ~ Fernandez-Fuentes, N., \u2026 Oliva, B. (2017). iFrag: A protein\u2013protein interface prediction server based on sequence fragments. Journal of Molecular Biology, 429(3), 382\u2013389. doi:10.1016\/j.jmb.2016.11.034
    \n    CrossRef<\/a><\/li>\n
  17. Gordon, D. E., Hiatt, J., Bouhaddou, M., Rezelj, V. V., Ulferts, S., Braberg, H., Jureka, A. S., Obernier, K., Guo, J. Z., Batra, J., Kaake, R. M., Weckstein, A. R., Owens, T. W., Gupta, M., Pourmal, S., Titus, E. W., Cakir, M., Soucheray, M., McGregor, M., Cakir, Z., \u2026 Krogan, N. J. (2020). Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms. Science (New York, N.Y.), 370(6521), eabe9403. org\/10.1126\/science.abe9403
    \n    CrossRef<\/a><\/li>\n
  18. Gromiha, M. M., Yokota, K., & Fukui, K. (2009). Energy based approach for understanding the recognition mechanism in protein\u2013protein complexes. Molecular Biosystems, 5, 1779\u20131786. doi:10.1039\/b904161n
    \n    CrossRef<\/a><\/li>\n
  19. Guharoy, M., & Chakrabarti, P. (2005). Conservation and relative importance of residues across protein-protein interfaces. Proceedings of the National Academy of Sciences of the United States of America, 102(43), 15447\u201315452. doi:10.1073\/pnas.0505425102
    \n    CrossRef<\/a><\/li>\n
  20. Guharoy, M., & Chakrabarti, P. (2010). Conserved residue clusters at protein\u2013protein interfaces and their use in binding site identification. BMC Bioinformatics, 11(1), 286. doi:10.1186\/1471-2105-11-286
    \n    CrossRef<\/a><\/li>\n
  21. Guo, F., Ding, Y., Li, S. C., Shen, C., & Wang, L. (2016). Protein\u2013protein interface prediction based on hexagon structure similarity. Computational Biology and Chemistry, 63, 83\u201388. doi:10.1016\/j.compbiolchem.2016.02.008
    \n    CrossRef<\/a><\/li>\n
  22. Hubbard, S., & Thornton, J. (1993). NACCESS, computer program. London: Department of Biochemistry Molecular Biology, University College.<\/li>\n
  23. Humphris, E. L., &Kortemme, T. (2008). Prediction of protein\u2013protein interface sequence diversity using flexible backbone computational protein design. Structure, 16(12), 1777\u20131788. doi:10.1016\/j.str.2008.09.012
    \n    CrossRef<\/a><\/li>\n
  24. Hwang, H., Petrey, D., & Honig, B. (2016). A hybrid method for protein\u2013protein interface prediction. Protein Science, 25(1), 159\u2013165. doi: 10.1002\/pro.2744
    \n    CrossRef<\/a><\/li>\n
  25. Janin, J., & Chothia, C. (1990). The structure of protein\u2013protein recognition sites. The Journal of Biological Chemistry, 265(27), 16027\u201316030. Jones, S. (2012). Computational and structural characterisation of protein associations. Advances in Experimental Medicine and Biology, 747, 42\u201354. doi:10.1007\/978-1-4614-3229-6_3
    \n    CrossRef<\/a><\/li>\n
  26. Jones, S., & Thornton, J. M. (1995). Protein\u2013protein interactions: A review of protein dimer structures. Progress in Biophysics and Molecular Biology, 63(1), 31\u201365. doi:10.1016\/0079-6107(94)00008-W
    \n    CrossRef<\/a><\/li>\n
  27. Jones, S., & Thornton, J. M. (1996). Principles of protein\u2013protein interactions. Proceedings of the National Academy of Sciences of the United States of America, 93(1), 13\u201320. doi:10.1073\/pnas.93.1.13
    \n    CrossRef<\/a><\/li>\n
  28. Jones, S., & Thornton, J. M. (1997a). Analysis of protein\u2013protein interaction sites using surface patches. Journal of Molecular Biology, 272(1), 121\u2013132. doi:10.1006\/jmbi.1997.1234
    \n    CrossRef<\/a><\/li>\n
  29. Jones, S., & Thornton, J. M. (1997b). Prediction of protein\u2013protein interaction sites using patch analysis. Journal of Molecular Biology, 272(1), 133\u2013143. doi:10.1006\/jmbi.1997.1233
    \n    CrossRef<\/a><\/li>\n
  30. Jordan, R. A., El-Manzalawy, Y., Dobbs, D., &Honavar, V. (2012). Predicting protein\u2013protein interface residues using local surface structural similarity. BMC Bioinformatics, 13(1), 41. doi:10.1186\/1471-2105- 13-41
    \n    CrossRef<\/a><\/li>\n
  31. Korn, A. P., & Burnett, R. M. (1991). Distribution and complementarity of hydropathy in multisubunit proteins. Proteins, 9(1), 37\u201355. doi:10. 1002\/prot.340090106
    \n    CrossRef<\/a><\/li>\n
  32. Kuroda, D., & Gray, J. J. (2016). Shape complementarity and hydrogen bond preferences in protein-protein interfaces: implications for antibody modeling and protein-protein docking. Bioinformatics (Oxford, England), 32(16), 2451\u20132456. https:\/\/doi.org\/10.1093\/bioinformatics\/btw197
    \n    CrossRef<\/a><\/li>\n
  33. Lee, B., & Richards, F. M. (1971). The interpretation of protein structures: Estimation of static accessibility. Journal of Molecular Biology, 55(3), 379\u2013400. doi:10.1016\/0022-2836(71)90324-x
    \n    CrossRef<\/a><\/li>\n
  34. Li, B., &Kihara, D. (2012). Protein docking prediction using predicted protein\u2013protein interface. BMC Bioinformatics, 13(1), 7.doi:10.1186\/1471- 2105-13-7
    \n    CrossRef<\/a><\/li>\n
  35. Li, L., Gunasekaran, K., Gan, J. G.-K., Zhanhua, C., Shapshak, P., Sakharkar, M. K., \u2026 Kangueane, P. (2005). Structural features differentiate the mechanisms between 2S (2 state) and 3S (3 state) folding homodimers. Bioinformation, 1(2), 42\u201349. doi:10.6026\/97320630001042
    \n    CrossRef<\/a><\/li>\n
  36. Li, L., Zhao, B., Cui, Z., Gan, J., Sakharkar, M. K., & Kangueane, P. (2006). Identification of hot spot residues at protein\u2013protein interface. Bioinformation, 1(4), 121\u2013126. doi:10.6026\/97320630001121
    \n    CrossRef<\/a><\/li>\n
  37. Li, M., He, Q., Ma, J., He, F., Zhu, Y., Chang, C., \u2026 Chen, T. (2019). PPICurator: A tool for extracting comprehensive protein\u2013protein interaction information. Proteomics, 19(4), 1800291.
    \n    CrossRef<\/a><\/li>\n
  38. Lo Conte, L., Chothia, C., & Janin, J. (1999). The atomic structure of protein\u2013protein recognition sites. Journal of Molecular Biology, 285(5), 2177\u20132198. doi:10.1006\/jmbi.1998.2439
    \n    CrossRef<\/a><\/li>\n
  39. Lulu, S., Suresh, A., Karthikraja, V., Arumugam, M., Kayathri, R., & Kangueane, P. (2009). Structural features for homodimer folding mechanism. Journal of Molecular Graphics and Modelling, 28(2), 88\u201394. doi:10.1016\/j.jmgm.2009.04.002
    \n    CrossRef<\/a><\/li>\n
  40. Marchetti, F., Capelli, R., Rizzato, F., Laio, A., & Colombo, G. (2019). The subtle trade-off between evolutionary and energetic constraints in protein\u2013protein interactions. The Journal of Physical Chemistry Letters, 10(7), 1489\u20131497. doi:10.1021\/acs.jpclett.9b00191
    \n    CrossRef<\/a><\/li>\n
  41. Martin, J. (2014). Benchmarking protein\u2013protein interface predictions: Why you should care about protein size. Proteins: Structure, Function, and Bioinformatics, 82(7), 1444\u20131452. doi:10.1002\/prot.24512
    \n    CrossRef<\/a><\/li>\n
  42. Miller, S., Lesk, A. M., Janin, J., & Chothia, C. (1987). The accessible surface area and stability of oligomeric proteins. Nature, 328(6133), 834\u2013836. doi:10.1038\/328834a0
    \n    CrossRef<\/a><\/li>\n
  43. Moreira, I. S., Koukos, P. I., Melo, R., Almeida, J. G., Preto, A. J., Schaarschmidt, J., \u2026 Bonvin, A. M. J. J. (2017). SpotOn: High accuracy identification of protein\u2013protein interface hot-spots. Scientific Reports, 7(1), 8007. doi:10.1038\/s41598-017-08321-2
    \n    CrossRef<\/a><\/li>\n
  44. Murakami, Y., & Jones, S. (2006). SHARP2: Protein\u2013protein interaction predictions using patch analysis. Bioinformatics, 22(14), 1794\u20131795. doi: 10.1093\/bioinformatics\/btl171
    \n    CrossRef<\/a><\/li>\n
  45. Nilofer, C., Sukhwal, A., Mohanapriya, A., & Kangueane, P. (2017). Protein\u2013protein interfaces are vdW dominant with selective H-bonds and (or) electrostatic towards broad functional specificity. Bioinformation, 13(6), 164\u2013173. doi:10.6026\/97320630013164
    \n    CrossRef<\/a><\/li>\n
  46. Odolczyk, N., Marzec, E., Winiewska-Szajewska, M., Pozna\u0144ski, J., &Zielenkiewicz, P. (2021). Native Structure-Based Peptides as Potential Protein-Protein Interaction Inhibitors of SARS-CoV-2 Spike Protein and Human ACE2 Receptor. Molecules (Basel, Switzerland), 26(8), 2157. https:\/\/doi.org\/10.3390\/molecules26082157
    \n    CrossRef<\/a><\/li>\n
  47. Organization WHO (2021, May 18). COVID-19 Weekly Epidemiological Update.<\/li>\n
  48. Pal, A., Chakrabarti, P., Bahadur, R., Rodier, F., & Janin, J. (2007). Peptide segments in protein\u2013protein interfaces. Journal of Biosciences, 32(1), 101\u2013111.
    \n    CrossRef<\/a><\/li>\n
  49. Petras, J. K., & Emil, A. (2006). Electrostatic properties of protein\u2013protein complexes. Biophysical Journal, 91, 1724\u20131736. doi:10.1529\/biophysj. 106.086025
    \n    CrossRef<\/a><\/li>\n
  50. Porter, K. A., Desta, I., Kozakov, D., & Vajda, S. (2019). What method to use for protein-protein docking? Current Opinion in Structural Biology, 55, 1\u20137. doi:10.1016\/j.sbi.2018.12.010
    \n    CrossRef<\/a><\/li>\n
  51. Qiao, Y., Xiong, Y., Gao, H., Zhu, X., & Chen, P. (2018). Protein\u2013protein interface hot spots prediction based on a hybrid feature selection strategy. BMC Bioinformatics, 19(1), 14. doi:10.1186\/s12859-018-2009-5
    \n    CrossRef<\/a><\/li>\n
  52. Robert, C. H., & Janin, J. (1998). A soft, mean-field potential derived from crystal contacts for predicting protein-protein interactions. Journal of Molecular Biology, 283(5), 1037\u20131047. doi:10.1006\/jmbi.1998.2152
    \n    CrossRef<\/a><\/li>\n
  53. Sakkiah, S., Guo, W., Pan, B., Ji, Z., Yavas, G., Azevedo, M., Hawes, J., Patterson, T. A., & Hong, H. (2021). Elucidating Interactions Between SARS-CoV-2 Trimeric Spike Protein and ACE2 Using Homology Modeling and Molecular Dynamics Simulations. Frontiers in chemistry, 8, 622632.
    \n    CrossRef<\/a><\/li>\n
  54. Sowmya, G., Anita, S., & Kangueane, P. (2011). Insights from the structural analysis of protein heterodimer interfaces. Bioinformation, 6(4), 137\u2013143. doi:10.6026\/97320630006137
    \n    CrossRef<\/a><\/li>\n
  55. Sowmya, G., Breen, E. J., & Ranganathan, S. (2015). Linking structural features of protein complexes and biological function. Protein Science, 24(9), 1486\u20131494. doi:10.1002\/pro.2736
    \n    CrossRef<\/a><\/li>\n
  56. Sowmya, G., & Ranganathan, S. (2015). Discrete structural features among interface residue-level classes. BMC Bioinformatics, 16(S18), S8. doi:10.1186\/1471-2105-16-S18-S8
    \n    CrossRef<\/a><\/li>\n
  57. St John, S. E., Therkelsen, M. D., Nyalapatla, P. R., Osswald, H. L., Ghosh, A. K., &Mesecar, A. D. (2015). X-ray structure and inhibition of the feline infectious peritonitis virus 3C-like protease: Structural implications for drug design. Bioorganic & medicinal chemistry letters, 25(22), 5072\u20135077. doi.org\/10.1016\/j.bmcl.2015.10.023
    \n    CrossRef<\/a><\/li>\n
  58. Sukhwal, A., & Sowdhamini, R. (2013). Oligomerisation status and evolutionary conservation of interfaces of protein structural domain superfamilies. Molecular Biosystems, 9(7), 1652\u20131661. doi:10.1039\/c3mb25484d
    \n    CrossRef<\/a><\/li>\n
  59. Sukhwal, A., & Sowdhamini, R. (2015). PPCheck: A webserver for the quantitative analysis of protein\u2013protein interfaces and prediction of residue hotspot. Bioinformatics and Biology Insights, 9, 141\u2013151. doi: 10.4137\/BBI.S25928
    \n    CrossRef<\/a><\/li>\n
  60. Taechalertpaisarn, J., Lyu, R.-L., Arancillo, M., Lin, C.-M., Perez, L. M., Ioerger, T. R., & Burgess, K. (2019). Correlations between secondary structure- and protein\u2013protein interface-mimicry: The interface mimicry hypothesis. Organic & Biomolecular Chemistry, 17(12), 3267\u20133274. doi:10.1039\/C9OB00204A
    \n    CrossRef<\/a><\/li>\n
  61. Tsai, C. J., Lin, S. L., Wolfson, H. J., &Nussinov, R. (2008). Studies of protein\u2013protein interfaces: A statistical analysis of the hydrophobic effect. Protein Science, 6(1), 53\u201364. doi:10.1002\/pro.5560060106
    \n    CrossRef<\/a><\/li>\n
  62. Vaishnavi, A., Sowmya, G., Kalaivanii, J., Ilakya, S., Kangueane, U., & Kangueane, P. (2010). Interaction modes at protein hetero-dimer interfaces. Bioinformation, 4(7), 310\u2013319. doi:10.6026\/ 97320630004310
    \n    CrossRef<\/a><\/li>\n
  63. Wang, W., Yang, Y., Yin, J., & Gong, X. (2017). Different protein\u2013protein interface patterns predicted by different machine learning methods. Scientific Reports, 7(1), 16023. doi:10.1038\/s41598-017-16397-z
    \n    CrossRef<\/a><\/li>\n
  64. Xie, Y., Karki, C. B., Du, D., Li, H., Wang, J., Sobitan, A., Teng, S., Tang, Q., & Li, L. (2020). Spike Proteins of SARS-CoV and SARS-CoV-2 Utilize Different Mechanisms to Bind With Human ACE2. Frontiers in molecular biosciences, 7, 591873.
    \n    CrossRef<\/a><\/li>\n
  65. Xu, D., Tsai, C. J., &Nussinov, R. (1997). Hydrogen bonds and salt bridges across protein-protein interfaces. Protein Engineering Design and Selection, 10(9), 999\u20131012. doi:10.1093\/protein\/10.9.999
    \n    CrossRef<\/a><\/li>\n
  66. Xue, L. C., Dobbs, D., &Honavar, V. (2011). HomPPI: A class of sequence homology based protein-protein interface prediction methods. BMC Bioinformatics, 12(1), 244. doi:10.1186\/1471-2105-12-244
    \n    CrossRef<\/a><\/li>\n
  67. Ye, T., Zhong, Z., Garc\u00eda-Sastre, A., Schotsaert, M., & De Geest, B. G. (2020). Current Status of COVID-19 (Pre)Clinical Vaccine Development.AngewandteChemie (International ed. in English), 59(43), 18885\u201318897. org\/10.1002\/anie.202008319
    \n    CrossRef<\/a><\/li>\n
  68. Yang D. (2021). Application of Nanotechnology in the COVID-19 Pandemic. International journal of nanomedicine, 16, 623\u2013649. org\/10.2147\/IJN.S296383
    \n    CrossRef<\/a><\/li>\n
  69. Yang, Y., & Gong, X. (2018). A new probability method to understand protein\u2013protein interface formation mechanism at amino acid level. Journal of Theoretical Biology, 436, 18\u201325. doi:10.1016\/j.jtbi.2017. 09.026
    \n    CrossRef<\/a><\/li>\n
  70. Zhanhua, C., Gan, J. G., Lei, L., Mathura, V. S., Sakharkar, M. K., & Kangueane, P. (2005). Protein subunit interfaces: Heterodimers versus homodimers. Bioinformation, 1(2), 28\u201339. doi:10.6026\/97320630001028
    \n    CrossRef<\/a><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    Introduction Coronavirus in recent times has become an extensively used […]<\/p>\n","protected":false},"author":14,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[93],"tags":[],"class_list":["post-40304","post","type-post","status-publish","format-standard","hentry","category-vol14no3"],"_links":{"self":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/40304","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/users\/14"}],"replies":[{"embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/comments?post=40304"}],"version-history":[{"count":5,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/40304\/revisions"}],"predecessor-version":[{"id":41240,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/40304\/revisions\/41240"}],"wp:attachment":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/media?parent=40304"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/categories?post=40304"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/tags?post=40304"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}