How accurate is the description of ligand–protein interactions by a hybrid QM/MM approach?

Molecular insight into endosulfan degradation by Ese protein from Arthrobacter: Evidence-based structural bioinformatics and quantum mechanical calculations

Endosulfan is an organochlorine insecticide widely used for agricultural pest control. Many nations worldwide have restricted or completely banned it due to its extreme toxicity to fish and aquatic invertebrates. Arthrobacter sp. strain KW has the ability to degrade α, β endosulfan and its intermediate metabolite endosulfate; this degradation is associated with Ese protein, a two-component flavin-dependent monooxygenase (TC-FDM). Employing in silico tools, we obtained the 3D model of Ese protein, and our results suggest that it belongs to the Luciferase Like Monooxygenase family (LLM). Docking studies showed that the residues V59, V315, D316, and T335 interact with α-endosulfan. The residues: V59, T60, V315, D316, and T335 are implicated in the interacting site with β-endosulfan, and the residues: H17, V315, D316, T335, N364, and Q363 participate in the interaction with endosulfate. Topological analysis of the electron density by means of the Quantum Theory of Atoms in Molecules (QTAIM) and the Non-Covalent Interaction (NCI) index reveals that the Ese-ligands complexes are formed mainly by dispersive forces, where Cl atoms have a predominant role. As Ese is a monooxygenase member, we predict the homodimer formation. However, enzymatic studies must be developed to investigate the Ese protein's enzymatic and catalytic activity.

A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel

The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ganglia (DRGs) and trigeminal ganglia innervating the body and face, respectively, as well as in other tissues and organs including central nervous system. The TRPV1 channel is a versatile receptor that detects harmful heat, pain, and various internal and external ligands. Hence, it operates as a polymodal sensory channel. Many pathological conditions including neuroinflammation, cancer, psychiatric disorders, and pathological pain, are linked to the abnormal functioning of the TRPV1 in peripheral tissues. Intense biomedical research is underway to discover compounds that can modulate the channel and provide pain relief. The molecular mechanisms underlying temperature sensing remain largely unknown, although they are closely linked to pain transduction. Prolonged exposure to capsaicin generates analgesia, hence numerous capsaicin analogs have been developed to discover efficient analgesics for pain relief. The emergence of in silico tools offered significant techniques for molecular modeling and machine learning algorithms to indentify druggable sites in the channel and for repositioning of current drugs aimed at TRPV1. Here we recapitulate the physiological and pathophysiological functions of the TRPV1 channel, including structural models obtained through cryo-EM, pharmacological compounds tested on TRPV1, and the in silico tools for drug discovery and repositioning.

Ligand 1 H NMR Chemical Shifts as Accurate Reporters for Protein-Ligand Binding Interfaces in Solution

The availability of high-resolution 3D structural information is crucial for investigating guest-host systems across a wide range of fields. In the context of drug discovery, the information is routinely used to establish and validate structure-activity relationships, grow initial hits from screening campaigns, and to guide molecular docking. For the generation of protein-ligand complex structural information, X-ray crystallography is the experimental method of choice, however, with limited information on protein flexibility. An experimentally verified structural model of the binding interface in the native solution-state would support medicinal chemists in their molecular design decisions. Here we demonstrate that protein-bound ligand 1 H NMR chemical shifts are highly sensitive and accurate probes for the immediate chemical environment of protein-ligand interfaces. By comparing the experimental ligand 1 H chemical shift values with those computed from the X-ray structure using quantum mechanics methodology, we identify significant disagreements for parts of the ligand between the two experimental techniques. We show that quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) ensembles can be used to refine initial X-ray co-crystal structures resulting in a better agreement with experimental 1 H ligand chemical shift values. Overall, our findings highlight the usefulness of ligand 1 H NMR chemical shift information in combination with a QM/MM MD workflow for generating protein-ligand ensembles that accurately reproduce solution structural data.

Network analysis of chromophore binding site in LOV domain

Photoreceptor proteins are versatile toolbox for developing biosensors for optogenetic applications. These molecular tools get activated upon illumination of blue light, which in turn offers a non-invasive method for gaining high spatiotemporal resolution and precise control of cellular signal transduction. The Light-Oxygen-Voltage (LOV) domain family of proteins is a well-recognized system for constructing optogenetic devices. Translation of these proteins into efficient cellular sensors is possible by tuning their photochemistry lifetime. However, the bottleneck is the need for more understanding of the relationship between the protein environment and photocycle kinetics. Significantly, the effect of the local environment also modulates the electronic structure of chromophore, which perturbs the electrostatic and hydrophobic interaction within the binding site. This work highlights the critical factors hidden in the protein networks, linking with their experimental photocycle kinetics. It presents an opportunity to quantitatively examine the alternation in chromophore's equilibrium geometry and identify details which have substantial implications in designing synthetic LOV constructs with desirable photocycle efficiency.

Porting Fragmentation Methods to Graphical Processing Units Using an OpenMP Application Programming Interface: Offloading the Fock Build for Low Angular Momentum Functions

A framework to offload four-index two-electron repulsion integrals to graphical processing units (GPUs) using OpenMP is discussed. The method has been applied to the Fock build for low angular momentum s and p functions in both the restricted Hartree-Fock (RHF) and in the effective fragment molecular orbital (EFMO) framework. Benchmark calculations for the GPU code for the pure RHF method show an increasing speedup relative to the existing OpenMP CPU code in GAMESS from 1.04 to 52× for clusters of 70-569 water molecules. The parallel efficiency on 24 NVIDIA V100 GPU boards also increases when increasing the system size: from 75 to 94% for water clusters that contain 303-1120 molecules. In the EFMO framework, the GPU Fock build shows a high linear scalability up to 4608 V100s with a parallel efficiency of 96% for calculations on a solvated mesoporous silica nanoparticle system with ∼67,000 basis functions.

Virtual Screening of Artemisia annua Phytochemicals as Potential Inhibitors of SARS-CoV-2 Main Protease Enzyme

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a human coronaviruses that emerged in China at Wuhan city, Hubei province during December 2019. Subsequently, SARS-CoV-2 has spread worldwide and caused millions of deaths around the globe. Several compounds and vaccines have been proposed to tackle this crisis. Novel recommended in silico approaches have been commonly used to screen for specific SARS-CoV-2 inhibitors of different types. Herein, the phytochemicals of Pakistani medicinal plants (especially Artemisia annua) were virtually screened to identify potential inhibitors of the SARS-CoV-2 main protease enzyme. The X-ray crystal structure of the main protease of SARS-CoV-2 with an N3 inhibitor was obtained from the protein data bank while A. annua phytochemicals were retrieved from different drug databases. The docking technique was carried out to assess the binding efficacy of the retrieved phytochemicals; the docking results revealed that several phytochemicals have potential to inhibit the SARS-CoV-2 main protease enzyme. Among the total docked compounds, the top-10 docked complexes were considered for further study and evaluated for their physiochemical and pharmacokinetic properties. The top-3 docked complexes with the best binding energies were as follows: the top-1 docked complex with a -7 kcal/mol binding energy score, the top-2 docked complex with a -6.9 kcal/mol binding energy score, and the top-3 docked complex with a -6.8 kcal/mol binding energy score. These complexes were subjected to a molecular dynamic simulation analysis for further validation to check the dynamic behavior of the selected top-complexes. During the whole simulation time, no major changes were observed in the docked complexes, which indicated complex stability. Additionally, the free binding energies for the selected docked complexes were also estimated via the MM-GB/PBSA approach, and the results revealed that the total delta energies of MMGBSA were -24.23 kcal/mol, -26.38 kcal/mol, and -25 kcal/mol for top-1, top-2, and top-3, respectively. MMPBSA calculated the delta total energy as -17.23 kcal/mol (top-1 complex), -24.75 kcal/mol (top-2 complex), and -24.86 kcal/mol (top-3 complex). This study explored in silico screened phytochemicals against the main protease of the SARS-CoV-2 virus; however, the findings require an experimentally based study to further validate the obtained results.

GC-MS profiling of Bauhinia variegata major phytoconstituents with computational identification of potential lead inhibitors of SARS-CoV-2 Mpro

Severe acute respiratory syndrome coronavirus 2 was originally identified in Wuhan city of China in December 2019 and it spread rapidly throughout the globe, causing a threat to human life. Since targeted therapies are deficient, scientists all over the world have an opportunity to develop novel drug therapies to combat COVID-19. After the declaration of a global medical emergency, it was established that the Food and Drug Administration (FDA) could permit the use of emergency testing, treatments, and vaccines to decrease suffering, and loss of life, and restore the nation's health and security. The FDA has approved the use of remdesivir and its analogs as an antiviral medication, to treat COVID-19. The primary protease of SARS-CoV-2, which has the potential to regulate coronavirus proliferation, has been a viable target for the discovery of medicines against SARS-CoV-2. The present research deals with the in silico technique to screen phytocompounds from a traditional medicinal plant, Bauhinia variegata for potential inhibitors of the SARS-CoV-2 main protease. Dried leaves of the plant B. variegata were used to prepare aqueous and methanol extract and the constituents were analyzed using the GC-MS technique. A total of 57 compounds were retrieved from the aqueous and methanol extract analysis. Among these, three lead compounds (2,5 dimethyl 1-H Pyrrole, 2,3 diphenyl cyclopropyl methyl phenyl sulphoxide, and Benzonitrile m phenethyl) were shown to have the highest binding affinity (−5.719 to −5.580 kcal/mol) towards SARS-CoV-2 M^pro. The post MD simulation results also revealed the favorable confirmation and stability of the selected lead compounds with M^pro as per trajectory analysis. The Prime MM/GBSA binding free energy supports this finding, the top lead compound 2,3 diphenyl cyclopropyl methyl phenyl sulphoxide showed high binding free energy (−64.377 ± 5.24 kcal/mol) towards M^pro which reflects the binding stability of the molecule with M^pro. The binding free energy of the complexes was strongly influenced by His, Gln, and Glu residues. All of the molecules chosen are found to have strong pharmacokinetic characteristics and show drug-likeness properties. The lead compounds present acute toxicity (LD50) values ranging from 670 mg/kg to 2500 mg/kg; with toxicity classifications of 4 and 5 classes. Thus, these compounds could behave as probable lead candidates for treatment against SARS-CoV-2. However further in vitro and in vivo studies are required for the development of medication against SARS-CoV-2.

QM/MM Energy Decomposition Using the Interacting Quantum Atoms Approach

The interacting quantum atoms (IQA) method decomposes the quantum mechanical (QM) energy of a molecular system in terms of one- and two-center (atomic) contributions within the context of the quantum theory of atoms in molecules. Here, we demonstrate that IQA, enhanced with molecular mechanics (MM) and Poisson-Boltzmann surface-area (PBSA) solvation methods, is naturally extended to the realm of hybrid QM/MM methodologies, yielding intra- and inter-residue energy terms that characterize all kinds of covalent and noncovalent bonding interactions. To test the robustness of this approach, both metal-water interactions and QM/MM boundary artifacts are characterized in terms of the IQA descriptors derived from QM regions of varying size in Zn(II)- and Mg(II)-water clusters. In addition, we analyze a homologous series of inhibitors in complex with a matrix metalloproteinase (MMP-12) by carrying out QM/MM-PBSA calculations on their crystallographic structures followed by IQA energy decomposition. Overall, these applications not only show the advantages of the IQA QM/MM approach but also address some of the challenges lying ahead for expanding the QM/MM methodology.

High throughput virtual screening and molecular dynamics simulation analysis of phytomolecules against BfmR of Acinetobacter baumannii: anti-virulent drug development campaign

Acinetobacter baumannii is a notorious multidrug resistant bacterium responsible for several hospital acquired infections assisted by its capacity to develop biofilms. A. baumannii BfmR (RstA), a response regulator from the BfmR/S two-component signal transduction system, is the major controller of A. baumannii biofilm development and formation. As a result, BfmR represents a novel target for anti-biofilm treatment against A. baumannii. The discovery of the high-resolution crystal structure of BfmR provides a good chance for computational screening of its probable inhibitors. Therefore, in this study we aim to search new, less toxic, and natural BfmR inhibitors from 8450 phytomolecules available in the Indian Medicinal Plants, Phytochemistry and Therapeutic (IMPPAT) database by analyzing molecular docking against BfmR (PDB ID: 6BR7). Out of these 8450 phytomolecules 6742 molecules were successfully docked with BfmR with the docking score range -6.305 kcal/mol to +5.120 kcal/mol. Structure based-molecular docking (SB-MD) and ADMET (absorption, distribution, metabolism, excretion, & toxicity) profile examination revealed that Norepinephrine, Australine, Calystegine B3, 7,7 A-Diepialexine, and Alpha-Methylnoradrenaline phytocompounds strongly binds to the active site residues of BfmR. Furthermore, molecular dynamics simulation (MDS) studies for 100 ns and the binding free energy (MM/GBSA) analysis elucidated the binding mechanism of Calystegine B3, 7,7 A-Diepialexine, and Alpha-Methylnoradrenaline to BfmR. In summary, these phytocompounds seems to have the promising molecules against BfmR, and thus necessitates further verification by both in vitro and in vivo experiments. HighlightsBfmR plays a key role in biofilm development and exopolysaccharide (EPS) synthesis in A. baumannii.Computational approach to search for promising BfmR inhibitors from IMPAAT database.The lead phytomolecules such as Calystegine B3, 7,7 A-Diepialexine, and Alpha-Methylnoradrenaline displayed significant binding with BfmR active site.The outcome of BfmR binding phytomolecules has broadened the scope of hit molecules validation.Communicated by Ramaswamy H. Sarma.

Novel B, C-ring truncated deguelin derivatives reveals as potential inhibitors of cyclin D1 and cyclin E using molecular docking and molecular dynamic simulation

The overexpression of cyclin D1 and cyclin E due to their oncogenic potential and amplification has been associated with a higher mortality rate in many cancers. The deguelin is a natural compound, has shown promising anti-cancer activity by directly binding cyclin D1 and cyclin E and thus suppressing its function. The C7a atomic position of deguelin structure contains a proton that generates stabilized radical, as a result, decomposed deguelin reduces its structural stability and significantly decreases its biological activity. To design deguelin derivatives with the reduced potential side effect, series of B, C-ring truncated derivatives were investigated as cyclin D1 and cyclin E inhibitors. R-group-based enumeration was implemented in the deguelin scaffold using the R-group enumeration module of Schrödinger. Drug-Like filters like, REOS and PAINs series were applied to the enumerated compound library to remove compounds containing reactive functional groups. Further, screened compounds were docked within the ligand-binding cavity of cyclin D1 and cyclin E crystal structure, using Glide SP and XP protocol to obtain docking poses. Enrichment calculations were done using SchrÖdinger software, with 1000 decoy compounds (from DUD.E database) and 60 compounds (XP best poses) along with deguelin, to validate the docking protocol. The receiver operating characteristic (ROC) curve indicates R² = 0.94 for cyclin D1 and R² = 0.79 for cyclin E, suggesting that the docking protocol is valid. Besides, we explored molecular dynamics simulation to probe the binding stability of deguelin and its derivatives within the binding cavity of cyclin D1 and cyclin E structures which are associated with the cyclin D1 and cyclin E inhibitory mechanism.

An in silico scientific basis for LL-37 as a therapeutic for Covid-19

A multi‐pronged approach with help in all forms possible is essential to completely overcome the Covid‐19 pandemic. There is a requirement to research as many new and different types of approaches as possible to cater to the entire world population, complementing the vaccines with promising results. The need is also because SARS‐CoV‐2 has several unknown or variable facets which get revealed from time to time. In this work, in silico scientific findings are presented, which are indicative of the potential for the use of the LL‐37 human anti‐microbial peptide as a therapeutic against SARS‐CoV‐2. This indication is based on the high structural similarity of LL‐37 to the N‐terminal helix, with which the virus interacts, of the receptor for SARS‐CoV‐2, Angiotensin Converting Enzyme 2. Moreover, there is positive prediction of binding of LL‐37 to the receptor‐binding domain of SARS‐CoV‐2; this is the first study to have described this interaction. In silico data on the safety of LL‐37 are also reported. As Vitamin D is known to upregulate the expression of LL‐37, the vitamin is a candidate preventive molecule. This work provides the possible basis for an inverse correlation between Vitamin D levels in the body and the severity of or susceptibility to Covid‐19, as widely reported in literature. With the scientific link put forth herein, Vitamin D could be used at an effective, medically prescribed, safe dose as a preventive. The information in this report would be valuable in bolstering the worldwide efforts to eliminate the pandemic as early as possible.

Delineating binding potential, stability of Sulforaphane-N-acetyl-cysteine in the active site of histone deacetylase 2 and testing its cytotoxicity against distinct cancer lines through stringent molecular dynamics, DFT and cell-based assays

Histone deacetylase 2 (HDAC2), an isozyme of Class I HDACs has potent imputations in actuating neurodegenerative signaling. Currently, there are sizeable therapeutic disquiets with the use of synthetic histone deacetylase inhibitors in disease management. This strongly suggests the unfulfilled medical necessity of plant substitutes for therapeutic intervention. Sulforaphane-N-acetyl-cysteine (SFN-N-acetylcysteine or SFN-NAC), a sulforaphane metabolite has shown significantly worthier activity against HDACs under in vitro conditions. However, the atomistic studies of SFN-NAC against HDAC2 are currently lacking. Thus, the present study employed a hybrid strategy including extra-precision (XP) grid-based flexible molecular docking, molecular mechanics generalized born surface area (MM-GBSA), e-Pharmacophores method, and molecular dynamics simulation for exploring the binding strengh, mode of interaction, e-Pharmacophoric features, and stability of SFN-NAC towards HDAC2. Further, the globally acknowledged density functional theory (DFT) study was performed on SFN-NAC and entinostat individually in complex state with HDAC2. Apart from this, these inhibitors were tested against three distinct cancer cell models and one transformed cell line for cytotoxic activity. Moreover, double mutant of HDAC2 was generated and the binding orientation and interaction of SFN-NAC was scrutinized in this state. On the whole, this study unbosomed and explained the comparatively higher binding affinity of entinostat for HDAC2 and its wide spectrum cytotoxicity than SFN-NAC.

Molecular docking and simulation studies on SARS-CoV-2 Mpro reveals Mitoxantrone, Leucovorin, Birinapant, and Dynasore as potent drugs against COVID-19

The outbreak of novel coronavirus (COVID-19), which began from Wuhan City, Hubei, China, and declared as a Public Health Emergency of International Concern by World Health Organization (WHO) on 30^th January 2020. The present study describes how the available drug candidates can be used as a potential SARS-CoV-2 M^pro inhibitor by molecular docking and molecular dynamic simulation studies. Drug repurposing strategy is applied by using the library of antiviral and FDA approved drugs retrieved from the Selleckchem Inc. (Houston, TX, http://www.selleckchem.com) and DrugBank database respectively. Computational methods like molecular docking and molecular dynamics simulation were used. The molecular docking calculations were performed using LeadIT FlexX software. The molecular dynamics simulations of 100 ns were performed to study conformational stability for all complex systems. Mitoxantrone and Leucovorin from FDA approved drug library and Birinapant and Dynasore from anti-viral drug libraries interact with SARS-CoV-2 M^pro at higher efficiency as a result of the improved steric and hydrophobic environment in the binding cavity to make stable complex. Also, the molecular dynamics simulations of 100 ns revealed the mean RMSD value of 2.25 Å for all the complex systems. This shows that lead compounds bound tightly within the M^pro cavity and thus having conformational stability. Glutamic acid (Glu166) of M^pro is a key residue to hold and form a stable complex of reported lead compounds by forming hydrogen bonds and salt bridge. Our findings suggest that Mitoxantrone, Leucovorin, Birinapant, and Dynasore represents potential inhibitors of SARS-CoV-2 M^pro.

Discovery and design of soft polymeric bio-inspired materials with multiscale simulations and artificial intelligence

Establishing the “Materials 4.0” paradigm requires intimate knowledge of the virtual space in materials design.

Diarylcyclopropane hydroxamic acid inhibitors of histone deacetylase 4 designed by combinatorial approach and QM/MM calculations

Inhibitors of histone deacetylase superfamily (HDAC), which induce cell cycle arrest, trigger cell death and reduce angiogenesis appear as promising anti-cancer drugs targeting the epigenetic regulation of gene expression. Approved HDAC inhibitors were found effective against haematological and solid malignancies, other HDACIs are currently in clinical trials for the treatment of neurological diseases or immune disorders. Among those, diarylcyclopropane hydroxamic acids (DCHA) were found to be potent and selective inhibitors of the class IIa HDACs, specifically HDAC4, a pharmacological target for the treatment of Huntington's disease and muscular atrophy. Crystallographic analysis revealed that one of the aryl groups of the DCHA fills the lower specificity pocket of the HDAC4 catalytic site that is specific for the class IIa HDACs. We have used computer-assisted combinatorial chemistry, hybrid quantum mechanics/molecular mechanics (QM/MM) with implicit solvation and QSAR models to optimize DCHA inhibitors and propose more potent DCHA analogues. The QM/MM approach has been selected since the process of inhibitor binding to the catalytic zinc and polar amino acid residues of the deacetylase active site induces considerable rearrangement of electron density of the inhibitor. Virtual combinatorial library consisting of 12180 DCHA analogues was focused by means of structure-based evaluation to form a small combinatorial subset enriched in potentially interesting inhibitor candidates. Two validated QSAR models making use of computed relative binding affinities of the DCHA inhibitors to the HDAC4 (ΔΔG_com^QM/MM) were utilized to estimate the inhibitory potencies of the new analogues. The predicted half-maximal inhibitory concentrations (IC₅₀^pre) of the designed analogues fall into the low nanomolar concentration range and their predicted ADME properties are also favourable. The best designed DCHA analogues contain indazole, phenylpiperidine, phenyloxazole or hydroxypyridine moieties and stabilize bound inhibitors by hydrogen bonds to the catalytic water molecule and backbone carbonyl groups of the deacetylase active site residues. This makes them more potent and more specific inhibitors towards the HDAC4 isoform than the known diarylcyclopropane hydroxamic acids. The analogues are recommended for synthesis and experimental verification of inhibitory potencies in medicinal chemistry laboratories.