Molecular Docking Algorithms Research Articles

Comparative Study of Docking Tools for Evaluation of Potential Copper Metallodrugs and Their Interaction with TMPRSS2

COVID-19 has caused over seven million deaths globally due to its high transmission rate. The virus responsible for the disease requires a transmembrane protease serine type II (TMPRSS2-7MEQ) to infiltrate host cells and has been linked to several cancers, particularly prostate cancer. To investigate COVID-19 potential therapies, a series of Casiopeina-like copper complexes containing 1,10-Phenanthroline and amino acids were investigated as TMPRSS2 inhibitors. The molecular structures of twelve Phenanthroline copper complexes were calculated, and their global reactivity indices were analyzed using DFT and conceptual DFT methods. Three molecular docking algorithms were employed to identify the most effective inhibitors by examining their interactions with amino acid residues in the target protein’s catalytic activity triad (Asp345, His296, and Ser441). All complexes are docked above the catalytic site, blocking the interaction with substrates. The Phenanthroline complexes showed better interactions than the Bipyridine complexes, likely due to increased hydrophobic contacts. Analogs’ cationic nature and amino acids’ basic side chains bring them near the active site by interacting with Asp435. The top complexes in this study contain Ornithine, Lysine, and Arginine, making them promising alternatives for researching new drugs for COVID-19 and cancers like prostate cancer.

Identification of mycobacterial Thymidylate kinase inhibitors: a comprehensive pharmacophore, machine learning, molecular docking, and molecular dynamics simulation studies

Thymidylate kinase (TMK) is a pivotal enzyme in Mycobacterium tuberculosis (Mtb), crucial for phosphorylating thymidine monophosphate (dTMP) to thymidine diphosphate (dTDP), thereby playing a critical role in DNA biosynthesis. Dysregulation or inhibition of TMK activity disrupts DNA replication and cell division, making it an attractive target for anti-tuberculosis drug development. In this study, the statistically validated pharmacophore mode was developed from a set of known TMK inhibitors. Further, the robust pharmacophore was considered for screening the Enamine database. The chemical space was reduced through multiple molecular docking approaches, pharmacokinetics, and absolute binding energy estimation. Two different molecular docking algorithms favor the strong binding affinity of the proposed molecules towards TMK. Machine learning-based absolute binding energy also showed the potentiality of the proposed molecules. The binding interactions analysis exposed the strong binding affinity between the proposed molecules and active site amino residues of TMK. Several statistical parameters from all atoms MD simulation explained the stability between proposed molecules and TMK in the dynamic states. The MM-GBSA approach also found a strong binding affinity for each proposed molecule. Therefore, the proposed molecules might be crucial TMK inhibitors for managing Mtb inhibition subjected to in vitro/in vivo validations.

Enhancing plant-based cheese formulation through molecular docking and dynamic simulation of tocopherol and retinol complexes with zein, soy and almond proteins via SVM-machine learning integration

The current study addresses the growing demand for sustainable plant-based cheese alternatives by employing molecular docking and deep learning algorithms to optimize protein-ligand interactions. Focusing on key proteins (zein, soy, and almond protein) along with tocopherol and retinol, the goal was to improve texture, nutritional value, and flavor characteristics via dynamic simulations. The findings demonstrated that the docking analysis presented high accuracy in predicting conformational changes. Flexible docking algorithms provided insights into dynamic interactions, while analysis of energetics revealed variations in binding strengths. Tocopherol exhibited stronger affinity (−5.8Kcal/mol) to zein compared to retinol (−4.1Kcal/mol). Molecular dynamics simulations offered comprehensive insights into stability and behavior over time. The integration of machine learning algorithms improved the classification and the prediction accuracy, achieving a rate of 71.59%. This study underscores the significance of molecular understanding in driving innovation in the plant-based cheese industry, facilitating the development of sustainable alternatives to traditional dairy products.

Exploring the Mechanisms of Sanguinarine in the Treatment of Osteoporosis by Integrating Network Pharmacology Analysis and Deep Learning Technology.

Sanguinarine (SAN) has been reported to have antioxidant, antiinflammatory, and antimicrobial activities with potential for the treatment of osteoporosis (OP). This work purposed to unravel the molecular mechanisms of SAN in the treatment of OP. OP-related genes and SAN-related targets were predicted from public databases. Differential expression analysis and VennDiagram were adopted to detect SAN-related targets against OP. Protein-protein interaction (PPI) network was served for core target identification. Molecular docking and DeepPurpose algorithm were further adopted to investigate the binding ability between core targets and SAN. Gene pathway scoring of these targets was calculated utilizing gene set variation analysis (GSVA). Finally, we explored the effect of SAN on the expressions of core targets in preosteoblastic MC3T3-E1 cells. A total of 21 candidate targets of SAN against OP were acquired. Furthermore, six core targets were identified, among which CASP3, CTNNB1, and ERBB2 were remarkably differentially expressed in OP and healthy individuals. The binding energies of SAN with CASP3, CTNNB1, and ERBB2 were -6, -6.731, and -7.162 kcal/mol, respectively. Moreover, the GSVA scores of the Wnt/calcium signaling pathway were significantly lower in OP cases than in healthy individuals. In addition, the expression of CASP3 was positively associated with Wnt/calcium signaling pathway. CASP3 and ERBB2 were significantly lower expressed in SAN group than in DMSO group, whereas the expression of CTNNB1 was in contrast. CASP3, CTNNB1, and ERBB2 emerge as potential targets of SAN in OP prevention and treatment.

A Combined Approach for Predicting Binding Affinity of Zika Virus NS2B-NS3 Protease Inhibitors Using Semi-Empirical Methods and Molecular Docking Algorithms

Zika virus (ZIKV) is a mosquito-borne virus that has emerged as a major public health concern due to its association with severe neurological disorders. In recent years, there has been increasing interest in exploring natural products as potential therapeutics for ZIKV infection. This study aimed to predict the binding affinity of natural compounds to the ZIKV NS2B-NS3 protease (NS2B-NS3pro) using multiple molecular docking algorithms and semiempirical quantum mechanical methods. Our results demonstrate that semiempirical methods can improve the accuracy of molecular docking studies for natural compounds. In this particular case, the PM7 (Parametric Method Number 7) method showed a significant improvement in the coefficient of determination (R2 = 0.85). We expect this combined approach to aid in the development of natural product-based therapies for ZIKV infection and to highlight the importance of continued research in this field.

Computational studies of tyrosinase inhibitors.

Computational studies have significantly advanced the understanding of tyrosinase (TYR) function, mechanism, and inhibition, accelerating the development of more effective and selective inhibitors. This chapter provides an overview of in silico studies on TYR inhibitors, emphasizing key inhibitory chemotypes and the main residues involved in ligand-target interactions. The chapter discusses tools applied in the context of TYR inhibitor development, e.g., structure-based virtual screening, molecular docking, artificial intelligence, and machine learning algorithms.

Unveiling the multitargeted potency of Sodium Danshensu against cervical cancer: a multitargeted docking-based, structural fingerprinting and molecular dynamics simulation study

Cervical Cancer (CC) is one of the most common types of cancer in women worldwide, with a significant number of deaths reported yearly. Despite the various treatment options available, the high mortality rate associated with CC highlights the need to develop new and effective therapeutic agents. In this study, we have screened the complete prepared FDA library against the Mitotic kinesin-like protein 1, Cyclin B1, DNA polymerase, and MCM10-ID using three glide-based molecular docking algorithms: HTVS, SP and XP to produce a robust calculation. All four proteins are crucial proteins that actively participate in CC development, and inhibiting them together can be a game-changer step for multitargeted drug designing. Our multitargeted screening identified Sodium (Na) Danshensu, a natural FDA-approved phenolic compound of caffeic acid derivatives isolated from Salvia miltiorrhiza. The docking score ranges from −5.892 to −13.103 Kcal/mol, and the screening study was evaluated with the pharmacokinetics and interaction fingerprinting to identify the pattern of interactions that revealed that the compound has bound to the best site it can be fitted to where maximum bonds were created to make the complex stable. The molecular dynamics simulations for 100 ns were then extended to validate the stability of the protein-ligand complexes. The results provide insight into the repurposing, and Na-danshensu exhibited strong binding affinity and stable complex formation with the target proteins, indicating its potential as a multitargeted drug against CC. Communicated by Ramaswamy H. Sarma

Identifying USP1 Inhibitors with Allosteric Effect on Its Triple Catalytic Center through Virtual Screening

Previous studies have shown that downregulation of ubiquitin-specific protease 1 (USP1) can inhibit cell proliferation and promote apoptosis, indicating that targeting USP1 with drugs is a potential treatment for cancer, especially breast and ovarian cancer. In this study, we performed virtual screening on a database containing about 1.37 million molecules using the pharmacophore model, multiple precision molecular docking algorithms, molecular mechanics/generalized born surface area (MM/GBSA), strain energy, and ADMET screening methods. We found that Z28551960, Z423064076, Z1037335610, and Z1129923146 compounds showed excellent performance in terms of binding energy with USP1, ADMET property evaluation, and binding dynamics with USP1, had high potential as USP1 inhibitors, and were identified as hits in virtual screening. In addition, we demonstrated for the first time through molecular dynamics simulations that those molecules can enhance the structural flexibility of the USP1 protein and reduce its structural compactness, providing a basis for protein conformational changes. In summary, our discovery of potential USP1 inhibitors with novel scaffolds provides a theoretical basis for further development of USP1 inhibitors.

Vina-FPGA: A Hardware-Accelerated Molecular Docking Tool With Fixed-Point Quantization and Low-Level Parallelism

Molecular docking (MD) is one of the core steps in the expensive and time-consuming process of drug design, which is basically an optimization problem based on scoring functions. AutoDock series MD software is widely accepted by academia and industry, among which AutoDock Vina (Vina) is the latest and most popular version due to its accuracy and relatively high speed. However, contrast to its prior version, i.e., AutoDock4, hardware acceleration approaches of Vina are rarely reported. In this article, we propose Vina-field-programmable gate array (FPGA), a hardware-accelerated Vina implementation with FPGA that exploits the low-level parallelism. First, the fixed-point quantization is analyzed and realized to accelerate the MD algorithm with a better energy efficiency in hardware. To boost the performance of the module-level computation, multiple in- module hardware pipelines have been designed and implemented. Besides, a strategy for fast accessing to block RAM (BRAM) is implemented by utilizing the layout of data, which brings four times memory access speed to the intermolecular and intramolecular energy computing modules. Under the same 140 ligand–receptor benchmarks, Vina-FPGA performs up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.9\times $ </tex-math></inline-formula> (average <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.7\times$ </tex-math></inline-formula> ) faster than a state-of-the-art CPU does while consuming only 2.5% energy with similar docking accuracies. Compared to the GPU-accelerated implementation or Vina-GPU, the average energy consumption of Vina-FPGA is merely 45%.

Neural Networks in the Design of Molecules with Affinity to Selected Protein Domains

Drug design with machine learning support can speed up new drug discoveries. While current databases of known compounds are smaller in magnitude (approximately 108), the number of small drug-like molecules is estimated to be between 1023 and 1060. The use of molecular docking algorithms can help in new drug development by sieving out the worst drug-receptor complexes. New chemical spaces can be efficiently searched with the application of artificial intelligence. From that, new structures can be proposed. The research proposed aims to create new chemical structures supported by a deep neural network that will possess an affinity to the selected protein domains. Transferring chemical structures into SELFIES codes helped us pass chemical information to a neural network. On the basis of vectorized SELFIES, new chemical structures can be created. With the use of the created neural network, novel compounds that are chemically sensible can be generated. Newly created chemical structures are sieved by the quantitative estimation of the drug-likeness descriptor, Lipinski's rule of 5, and the synthetic Bayesian accessibility classifier score. The affinity to selected protein domains was verified with the use of the AutoDock tool. As per the results, we obtained the structures that possess an affinity to the selected protein domains, namely PDB IDs 7NPC, 7NP5, and 7KXD.

A study of essential computational software in medicinal chemistry

Computational software tools are now routinely used to investigate the structure, dynamics, surface properties, and thermodynamics of inorganic, biological, and polymeric systems. These tools are a vital part of the guide for drug discovery and are widely used in rational drug design and structure-based drug design processes. The process of drug design and discovery is essential in the invention of a new chemical entity. For this process, plenty of computational tools are available globally. These computational software tools are fast, free, open online access, and paid. Pharmaceutical software decreases human efforts, errors, and time utilization in a particular task without compromising the quality of work, with great accuracy and efficiency. This software is utilized by various institutes globally related to science and medicine. A computer application applies a library of medicinal chemical transformation rules to an input structure and then permits evaluation of the resulting output structures. Computer-Aided Drug Design (CADD) is now widely recognized as a viable alternative and complements high throughput screening. The search for new molecular entities has led to the construction of high-quality datasets and design libraries that may be optimized for molecular diversity or similarity. Conversely, advances in molecular docking algorithms, combined with improvements in computational infrastructure, are enabling rapid improvement in screening throughput.

Exploring the therapeutic potential of GC–MS separated compounds from Dracaena cinnabari against dengue virus and Aedes aegypti using in silico tools

The paper's main aim was to investigate bioactive molecules in Dracaena cinnabari extract using gas chromatography-mass spectroscopy (GC–MS) and to assess their therapeutic potential using molecular docking algorithm, ProTox II and ADME studies on dengue virus and Aedes aegypti. Molecular docking was carried out using AutoDock Vina, followed by drug-likeness potential and toxicity using in silico tools (ProTox II and ADME). A total of 25 differentcompounds were detected in the methanol extract, and the major compounds were cis-13-Octadecenoic acid (19.04 %), n-Hexadecanoic acid (16.5 %), beta-Sitosterol (10.5 %), and n-Heptadecanol-1 (9.74 %). Molecular docking revealed that beta-Sitosterol and stigmasterol are the lead compounds and scored the highest docking value among the compounds. The best-docked ligand score for dengue virus was recorded for 4V0Q (stigmasterol, −9.0 kcal/mol), whereas the best-docked ligand score for Ae. agypti was recorded for 1PZ4 (beta-Sitosterol, −9.9 kcal/mol). The toxicity prediction for the beta-Sitosterol and 4,4′-Dihydroxy-2 methoxydihydrochalcone did not violate the Lipinski rules. The values of LD50 predicted using ProTox II revealed that stigmasterol, 4,4′-dihydroxy-2-methoxydihydrochalcone, beta-Sitosterol, and vitamin E ranged from 890 to 5000 mg kg − 1in a rat model. This study depicts the potential of promising molecules of D. cinnabari. However, in vivo and in vitro investigation is needed to support the results of this study.

Identification of Potential Antimalarial Drug Candidates Targeting Falcipain-2 Protein of Malaria Parasite-A Computational Strategy.

Falcipain-2 (FP-2) is one of the main haemoglobinase of P. falciparum which is an important molecular target for the treatment of malaria. In this study, we have screened alkaloids to identify potential inhibitors against FP-2 since alkaloids possess great potential as anti-malarial agents. A total of 340 alkaloids were considered for the study using a series of computational pipelines. Initially, pharmacokinetics and toxicity risk assessment parameters were applied to screen compounds. Subsequently, molecular docking algorithms were utilised to understand the binding efficiency of alkaloids against FP-2. Further, oral toxicity prediction was done using the pkCSM tool, and 3D pharmacophore features were analysed using the PharmaGist server. Finally, MD simulation was performed for Artemisinin and the top 3 drug candidates (Noscapine, Reticuline, Aclidinium) based on docking scores to understand the functional impact of the complexes, followed by a binding site interaction residues study. Overall analysis suggests that Noscapine conceded good pharmacokinetics and oral bioavailability properties. Also, it showed better binding efficiency with FP-2 when compared to Artemisinin. Interestingly, structure alignment analysis with artemisinin revealed that Noscapine, Reticuline, and Aclidinium might possess similar biological action. Molecular dynamics and free energy calculations revealed that Noscapine could be a potent antimalarial agent targeting FP-2 that can be used for the treatment of malaria and need to be studied experimentally in the future.

Application of InterCriteria Analysis to Assess the Performance of Scoring Functions in Molecular Docking Software Packages

(1) Background: In silico approaches to rational drug design are among the fastest evolving ones. Depending on the available structural information for the biomacromolecule and the small molecule, the in silico methods are classified as ligand- and structure-based. The latter predict ligand–receptor binding using 3D structures of both molecules, whose computational simulation is referred to as molecular docking. It aims at estimating the binding affinity (approximated by scoring function) and the ligand binding pose in the receptor’s active site, which postulates a key role of the scoring functions in molecular docking algorithms. This study focuses on the performance of different types of scoring functions implemented in molecular modelling software packages. (2) Methods: An InterCriteria analysis (ICrA) was applied to assess the performance of the scoring functions available in MOE, GOLD, SeeSAR, and AutoDock Vina software platforms. The InterCriteria analysis was developed to distinguish possible relations between pairs of criteria when multiple objects are considered. All 12 investigated scoring functions were tested by docking a set of protease inhibitors in the binding sites of two protein targets. The dataset consisted of 88 benzamidine-type compounds with experimentally measured inhibitory constants for thrombin and trypsin, which allows for the objective assessment of the scoring functions performance. The results generated by the molecular docking were subjected to ICrA in order to analyze both docking energies as approximations of the binding affinities and RMSDs (root-mean-square deviation) as measures of the experimental binding pose proximity between the compounds and the co-crystalized ligand, based on the atoms in the common scaffold. (3) Results: The results obtained for the best poses, the average of the best 5 or 30 poses retained after docking, were analyzed. A comparison with the experimentally observed inhibitory effects was also performed. The InterCriteria analysis application confirms that the performance of the scoring functions for the same dataset of ligands depends on the studied protein. The analysis reveals that none of the studied scoring functions is a good predictor of the compounds’ binding affinities for the considered protein targets. (4) Conclusion: In terms of this analysis, the investigated scoring functions do not produce equivalent results, which suggests the necessity for their combined use in consensus docking studies.

Insilico Identification of H5N1 Avian Influenza Neuraminidase Inhibitors

Background: H5N1 avian influenza virus is influenza-A virus subtype that is highly contagious and fatal among birds and humans. Although human-human transmission of H5N1 is rare, but once the virus has acquired this ability, a devastating pandemic may be inevitable. The virus is currently in WHO phase 3 pandemic alerts: A new influenza virus subtype causing disease in humans, but is yet to spread efficiently and sustainably among humans. Oseltamivir is a single most-win WHO recommended drug for the treatment and prophylaxis of H5N1 influenza virus infection. However, several oseltamivir resistant strains of H5N1 have been reported. Methods: In silico molecular docking algorithm and pharmacophore modelling simulations were used to identify a novel H5N1 avian influenza neuraminidase inhibitors. Results: Of the 518-neuraminidase analogue, five (eflornithine, eltrombopag, entecavir, aminosalicylic acid and pregabalin) demonstrated excellent binding affinities against viral neuraminidase similar to that of oseltamivir. Again, these drugs like oseltamivir interact mainly with arginine, tyrosine and glutamine at different positions of the neuraminidase. Similarly, oseltamivir and the five selected drugs superimpose to the active pocket of neuraminidase, further buttressing the previous findings. Furthermore, oseltamivir and the five drugs had common generated pharmacophores. Conclusion: This study was the first to demonstrate the anti-viral neuraminidase effect of eflornithine, eltrombopag, entecavir, aminosalicylic acid and pregabalin. Keywords: Neuraminidase, Oseltamivir, H5N1 avian influenza, Pharmacophore, molecular docking.

Anti-COVID-19 and antidiabetic activities of new oleanane and ursane-type triterpenoids from Salvia grossheimii: an in-silico approach

Salvia grossheimii is a perennial herb with antidiabetic and cytotoxic constituents. In continuation of our study on S. grosshiemii to identify the bioactive phytochemicals, we have reported the characterization of seven undescribed triterpenoids. The aerial parts of the plant were extracted in dichloromethane and its constituents were isolated using chromatography techniques. The structures of compounds were identified using 1D, 2D NMR, and ESI-MS spectral data. Seven new oleanane- and ursane-type triterpenoids (1–7) were identified in S. grossheimii. The structures of 1–7 were characterized as; 2α-hydroxy-3β-acetoxy-olean-9(11),12-diene (1), 2α-acetoxy-3β-hydroxy-olean-9(11),12-diene (2), 3β-acetoxy-olean-18-ene,2α,11α-diol (3), 2α-hydroxy-3β-acetoxy-urs-9(11),12-diene (4), 2α-acetoxy-3β-hydroxy-urs-9(11),12-diene (5), 2α,3β-diacetoxy-urs-12-ene-11α,20β-diol (6), 2α,3β-diacetoxy-urs-9(11),12-diene-20β-ol (7). Triterpenoids (2, 5, and 7) were intramolecular transesterification or dehydration products of their corresponding isomers or allylic alcohol in the C rings, respectively, produced in-situ during NMR spectroscopy. Virtual screening of 1–7 was performed with molecular docking analysis to identify the potential SARS-CoV-2 and α-glucosidase inhibitors using the smina molecular docking algorithm. The best binding energy values (kcal/mol) against COVID-19 main protease Mpro were calculated for 6 (-8.77) and 7 (-8.68), and the higher binding affinities toward human α-glucosidase were obtained for 2 (-9.39) and 6 (-8.63). This study suggests S. grossheimii as a rich source of bioactive triterpenoids and introduces new natural compounds. Considering the high binding energy values of 2, 6, and 7, these structures could be candidates for anti-COVID-19 and antidiabetic drug development in the future.

Accelerating Molecular Dynamics Enrichments of High-Affinity Ligands for Proteins.

Molecular docking algorithms are used to seek the most active compounds from a pool of ligands. In principle, molecular dynamics (MD) simulations with accurate physical potentials and sampling could yield better enrichments, but they are computationally expensive. Here, we describe a method called MELD-Bracket that utilizes biased replica exchange ladders in MD in order to compete different ligands against each other within a fast bracket style "binding tournament". MELD-Bracket finds best-binders rapidly when ligands are well separated in their binding affinities.

Prophylactic and therapeutic roles of Glycyrrhiza glabra in the prevention and treatment of COVID19

Introduction ‐COVID19 has become one of the world's leading problems. The severe acute respiratory syndrome coronavirus (SARS‐CoV)‐2 enters the human body through droplet transmission and oroanal route. The pathological manifestations of SARS‐CoV‐2 is mediated through key proteins involved in viral entry into the host and replication of the virus within the host. Specifically, the RNA Dependent RNA Polymerase (RdRp) employs a replicative function in multiplying and transmitting the virus, aided by other nsps, nsp7 and 8 forming functional complex; similarly, the spike protein is necessary for the virus to enter the host by binding to ACE receptors. Given the limited available treatments and urgency for the development of prophylactic agents and therapeutic drugs for the control of SARS‐CoV‐2 and treatment of COVID19, traditional treatment methods using natural resources are decisive. To address the pandemic, a drug targeting the function of these key proteins and protein complexes will be potentially more successful and effective. Use of medically important plants with antiviral activity including, Glycyrrhiza glabra (G glabra) and isolation of potential natural active ingredients is more beneficial and effective. Here we elucidated the potential interactions of known phytochemicals from G glabra and key SARS‐CoV‐2 proteins.Methods ‐A molecular docking algorithm, PatchDock, was used to study the interactions between viral target proteins and phytochemicals of G. glabra. The molecular display program, Chimera was used to display the interactions between the phytochemicals of G. glabra and viral proteins.Results ‐Of the 20 reported compounds (GG1 ‐ GG20) that we used for our studies, 13 of the compounds have effectively bound to inhibit the SARS‐CoV‐2 proteins, nsp7/8/12 complex including RdRp and/or spike protein. Specifically, the compounds GG3 and GG4 achieved lower ACE scores than ‐100 binding to all three key proteins of the SARS‐COV‐2. Compound GG4 binds to the Spike Protein that mediate binding of SARS‐COV‐2 to the ACE Receptor of host cell, thereby inhibiting the entry of virus. GG4 also binds to close to the active site of nsp‐complex. However, GG3 binds close to the active site of the RdRp where the thumb, finger, and palm domains meet. GG3 also binds close to the nsp7/8/12 complex in which chains 7 and 12 join together, forming a more effective complex to inhibit the viral replication. Our additional docking studies of phytochemicals of G. glabra and viral proteins will show effective binding of phytochemicals with viral proteins that inhibit either the entry and/or replication and transmission of the SARS‐CoV‐2.Conclusion –In conclusion, GG3 and GG4 from Glycyrrhiza glabra have demonstrated the ability to prevent the entry and replication of SARS‐COV‐2 proteins by inhibiting the functions of Spike and RdRp proteins in silico.

In Silico Comparison of Separate or Combinatorial Effects of Potential Inhibitors of the SARS-CoV-2 Binding Site of ACE2.

Background:The COVID-19 is a pandemic viral infection with a high morbidity rate, leading to many worldwide deaths since the end of 2019. The RBD (Receptor Binding Domain) of SARS-CoV-2 through its spike utilizes several host molecules to enter host cells. One of the most important ones is the angiotensin-converting enzyme 2 (ACE2), an enzyme normally engaged in renin angiotensin pathway and is responsible for hypertension regulation. As different articles have analyzed separate compounds which can bind ACE2 as the potential virus entry blockers, and each one with a different molecular docking algorithm, in this study we compared all candidate compounds individually as well as their combinations using a unique validated software to introduce most promising ones.Methods:We collected and prepared a list of all available compounds which potentially can inhibit RBD binding site of the ACE2 from different studies and then reanalyzed and compared them using the Patchdock (ver. 1.3) as a suitable molecular docking algorithm for analysis of separate compounds or their combinations.Results:Saikosaponin A (e.g. in Bupleurum chinense), Baicalin (e.g. in several species in the genus Scutellaria), Glycyrrhizin (Glycyrrhiza glabra), MLN-4760 and Umifenovir better occupied ACE2 to inhibit viral RBD binding and are suggested as the top five inhibitors of the SARS-CoV-2 binding site of ACE2. Their combinatory effects were also inspiring concurrent ACE2 blockade.Conclusion:The results propose greatest compounds and their combinatory anti-SARS-CoV-2 effects in order to decrease the time and expenses required for further experimental designs.

Efficacy of Tinospora cordifolia in treating SARS‐CoV‐2: in silico studies

IntroductionSARS‐CoV‐2 has struck as a major global pandemic causing COVID19 with acute respiratory distress syndrome (ARDS) and other respiratory diseases gradually affecting multiple organs. Currently, SARS‐CoV‐2 is affecting over 85.1 million global cases of SARS‐CoV‐2 (20.7 millions in US) infections with 1.84 million (0.352 million in US) deaths globally. SARS‐CoV‐2 genome is 30kb in size and makes 29 proteins including key Spike protein (SP) and RNA dependent RNA polymerase (RdRP). The SP facilitates the entry of the virus into human host cells. Interactions of potential drugs with the SP can prevent or inhibit the entry of the virus into the host cells by blocking the interaction. Similarly, the non‐structural proteins (nsp) 7 and 8 facilitate the function of the RdRp/nsp12 that is essential for the replication of viral genome leading to multiply and spread of the virus within and from the host. In this study, we have selected 26 known phytochemicals (TC1 ‐ TC26) from medicinally important Ayurvedic plant, Tinospora cordifolia (TC), to study the interactions of these phytochemicals with key SARS‐CoV‐2 proteins using in silico methods. We have identified promising interactions between TC phytochemicals, and the key SARS‐CoV2 proteins that can potentially block the viral entry and/or replication of SARS‐CoV‐2 thereby preventing and treating COVID19.MethodsA molecular docking algorithm, PatchDock, was used to study the interactions between target proteins and phytochemicals of TC. To filter out the redundant docking predictions resulting from the patch matching system, a RMSD clustering calculation is used. The molecular display program, Chimera, was used to display the interactions between the phytochemicals and SARS‐CoV‐2 viral proteins.ResultsOf the 26 compounds tested (TC1 ‐ TC26), 11 showed effective binding to inhibit the SARS‐CoV‐2 proteins, SP or nsp7/8/12 complex including RdRp. The effectiveness of the docking compounds was measured in terms of atomic contact energy (ACE) Scores and rankings based on clusters. The compounds TC1, TC4, TC10 and TC14 achieved lower scores (of binding) less than ‐100 showing potential binding to all three viral proteins/complexes, SP, RdRp and nsp7/8/12. Further, analysis indicates that TC4 and TC10 are binding remarkably close to the active site of the SP that binds to the ACE2 receptor of host cells, providing evidence for effective inhibition of the SP. TC1 and TC14 binds between the N‐terminal, palm domain, and thumb domain of the RdRp, creating an interaction inside the active site, substantially inhibiting its critical replication function.ConclusionFor the first time, we present in silico evidence that TC4 and TC10, and TC1 and TC14 potentially impair the entry and replication of SARS‐COV‐2 proteins respectively by inhibiting the functions of Spike and RdRp/nsp7‐8/12 proteins respectively.