Mechanistic insights of key host proteins and potential repurposed inhibitors regulating SARS-CoV-2 pathway.

The emergence of pandemic situations originated from severe acute respiratory syndrome (SARS)-CoV-2 and its new variants created worldwide medical emergencies. Due to the non-availability of efficient drugs and vaccines at these emergency hours, repurposing existing drugs can effectively treat patients critically infected by SARS-CoV-2. Finding a suitable repurposing drug with inhibitory efficacy to a host-protein is challenging. A detailed mechanistic understanding of the kinetics, (dis)association pathways, key protein residues facilitating the entry-exit of the drugs with targets are fundamental in selecting these repurposed drugs. Keeping this target as the goal of the paper, the potential repurposing drugs, Nafamostat, Camostat, Silmitasertib, Valproic acid, and Zotatifin with host-proteins HDAC2, CSK22, eIF4E2 are studied to elucidate energetics, kinetics, and dissociation pathways. From an ensemble of independent simulations, we observed the presence of single or multiple dissociation pathways with varying host-proteins-drug systems and quantitatively estimated the probability of unbinding through these specific pathways. We also explored the crucial gateway residues facilitating these dissociation mechanisms. Interestingly, the residues we obtained for HDAC2 and CSK22 are also involved in the catalytic activity. Our results demonstrate how these potential drugs interact with the host machinery and the specific target residues, showing involvement in the mechanism. Most of these drugs are in the preclinical phase, and some are already being used to treat severe COVID-19 patients. Hence, the mechanistic insight presented in this study is envisaged to support further findings of clinical studies and eventually develop efficient inhibitors to treat SARS-CoV-2.

Phase Behavior of Pure PSPC and PEGylated Multicomponent Lipid and Their Interaction with Paclitaxel: An All-Atom MD Study.

Exploring structural behavior of pure 1-palmitoyl-2-stearoyl-sn-glycero-3-phosphocholine (PSPC) and multicomponent PSPC and 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N[amino(poly(ethylene glycol))-2000] (DSPE-PEG2000) membranes and their interaction with pharmaceutically important drugs carry huge importance in drug delivery. Using all-atom molecular dynamics (MD) simulations, we investigated the phase behavior of pure and PEGylated membranes at the temperature range of 280-360 K. We observe a gel-to-liquid crystalline phase transition for pure PSPC between 320 and 330 K, and in the case of multicomponent membranes, at 320 K, a coexistence of order-disorder phases is observed, which gradually transform to a complete liquid crystalline to gel phase between 320 and 310 K. We further studied the interaction of Paclitaxel with pure PSPC and PEGylated bilayers and elucidated the interaction behavior of Paclitaxel at the bilayer interfaces. Understanding of structural and interaction behaviors of the PEGylated bilayers with Paclitaxel will help to explore Paclitaxel-based drug applications in the future.

Kinetics of Ligand-Protein Dissociation from All-Atom Simulations: Are We There Yet?

Large parallel gains in the development of both computational resources and sampling methods have now made it possible to simulate dissociation events in ligand-protein complexes with all-atom resolution. Such encouraging progress, together with the inherent spatiotemporal resolution associated with molecular simulations, has left their use for investigating dissociation processes brimming with potential, both in rational drug design, where it can be an invaluable tool for determining the mechanistic driving forces behind dissociation rate constants, and in force-field development, where it can provide a catalog of transient molecular structures with which to refine force fields. Although much progress has been made in making force fields more accurate, reducing their error for transient structures along a transition path could yet prove to be a critical development helping to make kinetic predictions much more accurate. In what follows, we will provide a state-of-the-art compilation of the enhanced sampling methods based on molecular dynamics (MD) simulations used to investigate the kinetics and mechanisms of ligand-protein dissociation processes. Due to the time scales of such processes being slower than what is accessible using straightforward MD simulations, several ingenious schemes are being devised at a rapid rate to overcome this obstacle. Here we provide an up-to-date compendium of such methods and their achievements and shortcomings in extracting mechanistic insight into ligand-protein dissociation. We conclude with a critical and provocative appraisal attempting to answer the title of this Perspective.

Multi-dimensional spectral gap optimization of order parameters (SGOOP) through conditional probability factorization.

Spectral gap optimization of order parameters (SGOOP) [P. Tiwary and B. J. Berne, Proc. Natl. Acad. Sci. U. S. A. 113, 2839 (2016)] is a method for constructing the reaction coordinate (RC) in molecular systems, especially when they are plagued with hard to sample rare events, given a larger dictionary of order parameters or basis functions and limited static and dynamic information about the system. In its original formulation, SGOOP is designed to construct a 1-dimensional RC. Here we extend its scope by introducing a simple but powerful extension based on the notion of conditional probability factorization where known features are effectively washed out to learn additional and possibly hidden features of the energy landscape. We show how SGOOP can be used to proceed in a sequential and bottom-up manner to (i) systematically probe the need for extending the dimensionality of the RC and (ii) if such a need is identified, learn additional coordinates of the RC in a computationally efficient manner. We formulate the method and demonstrate its utility through three illustrative examples, including the challenging and important problem of calculating the kinetics of benzene unbinding from the protein T4L99A lysozyme, where we obtain excellent agreement in terms of dissociation pathway and kinetics with other sampling methods and experiments. In this last case, starting from a larger dictionary of 11 order parameters that are generic for ligand unbinding processes, we demonstrate how to automatically learn a 2-dimensional RC, which we then use in the infrequent metadynamics protocol to obtain 16 independent unbinding trajectories. We believe our method will be a big step in increasing the utility of SGOOP in performing intuition-free sampling of complex systems. Finally, we believe that the utility of our protocol is amplified by its applicability to not just SGOOP but also other generic methods for constructing the RC.

Dendrimer assisted dispersion of carbon nanotubes: a molecular dynamics study.

Various unique physical, chemical, mechanical and electronic properties of carbon nanotubes (CNTs) make them very useful materials for diverse potential application in many fields. Experimentally synthesized CNTs are generally found in bundle geometry with a mixture of different chiralities and present a unique challenge to separate them. In this paper we have proposed the PAMAM dendrimer to be an ideal candidate for this separation. To estimate the efficiency of the dendrimer for the dispersion of CNTs from the bundle geometry, we have calculated potential of mean forces (PMF). Our PMF study of two dendrimer-wrapped CNTs shows lesser binding affinity compared to the two bare CNTs. PMF study shows that the binding affinity decreases for non-protonated dendrimer, and for the protonated case the interaction is fully repulsive in nature. For both the non-protonated as well as protonated cases, the PMF increases gradually with increasing dendrimer generations from 2 to 4 compared to the bare PMF. We have performed PMF calculations with (6,5) and (6,6) chirality to study the chirality dependence of PMF. Our study shows that the PMFs between two (6,5) and two (6,6) CNTs respectively are ∼-29 kcal mol-1 and ∼-27 kcal mol-1. Calculated PMF for protonated dendrimer-wrapped chiral CNTs is more compared to the protonated dendrimer-wrapped armchair CNTs for all the generations studied. However, for non-protonated dendrimer-wrapped CNTs, such chirality dependence is not very prominent. Our study suggests that the dispersion efficiency of the protonated dendrimer is more compared to the non-protonated dendrimer and can be used as an effective dispersing agent for the dispersion of CNTs from the bundle geometry.

Integrated docking and enhanced sampling-based selection of repurposing drugs for SARS-CoV-2 by targeting host dependent factors.

Since the onset of global pandemic, the most focused research currently in progress is the development of potential drug candidates and clinical trials of existing FDA approved drugs for other relevant diseases, in order to repurpose them for the COVID-19. At the same time, several high throughput screenings of drugs have been reported to inhibit the viral components during the early course of infection but with little proven efficacies. Here, we investigate the drug repurposing strategies to counteract the coronavirus infection which involves several potential targetable host proteins involved in viral replication and disease progression. We report the high throughput analysis of literature-derived repurposing drug candidates that can be used to target the genetic regulators known to interact with viral proteins based on experimental and interactome studies. In this work we have performed integrated molecular docking followed by molecular dynamics (MD) simulations and free energy calculations through an expedite in silico process where the number of screened candidates reduces sequentially at every step based on physicochemical interactions. We elucidate that in addition to the pre-clinical and FDA approved drugs that targets specific regulatory proteins, a range of chemical compounds (Nafamostat, Chloramphenicol, Ponatinib) binds to the other gene transcription and translation regulatory proteins with higher affinity and may harbour potential for therapeutic uses. There is a rapid growing interest in the development of combination therapy for COVID-19 to target multiple enzymes/pathways. Our in silico approach would be useful in generating leads for experimental screening for rapid drug repurposing against SARS-CoV-2 interacting host proteins.Communicated by Ramaswamy H. Sarma.

Metadynamics-based enhanced sampling protocol for virtual screening: case study for 3CLpro protein for SARS-CoV-2.

In recent times, computational methods played an important role in the down selection of chemical compounds, which could be a potential drug candidate with a high affinity to target proteins. However, the screening methodologies, including docking, often fails to identify the most effective compound, which could be a ligand for the target protein. To solve that, here we have integrated meta-dynamics, an enhanced sampling molecular simulation method, with all-atom molecular dynamics to determine a specific compound that could target the main protease of novel severe acute respiratory syndrome coronavirus 2 (SARS-COV-2). This combined computational approach uses the enhanced sampling to explore the free energy surface associated with the protein's binding site (including the ligand) in an explicit solvent. We have implemented this method to find new chemical entities that exhibit high specificity of binding to the 3-chymotrypsin-like cysteine protease (3CLpro) present in the SARS-CoV-2 and segregated to the most strongly bound ligands based on free energy and scoring functions (defined and implemented) from a set of 17 ligands which were prescreened for synthesizability and druggability. Additionally, we have compared these 17 ligands' affinities against controls, N3 and 13b α-ketoamide inhibitors, for which experimental crystal structures are available. Based on our results and analysis from the combined molecular simulation approach, we could identify the best compound which could be further taken as a potential candidate for experimental validation.Communicated by Ramaswamy H. Sarma.

Can One Trust Kinetic and Thermodynamic Observables from Biased Metadynamics Simulations?: Detailed Quantitative Benchmarks on Millimolar Drug Fragment Dissociation.

Understanding ligand dissociation mechanisms at an atomic resolution is a highly desired but difficult to achieve objective in experiments as well as in computer simulations. Structural details of the dissociation process are in general hard to capture in experiments, while the relevant time scales are far beyond molecular dynamics (MD) simulations even with the most powerful supercomputers. As such, many different specialized enhanced sampling methods have been proposed that make it possible to efficiently calculate the dissociation mechanisms in protein-ligand systems. However, accurate benchmarks against long unbiased MD simulations are either not reported yet or simply not feasible due to the extremely long time scales. In this manuscript, we consider one such recent method, "infrequent metadynamics", and benchmark in detail the thermodynamic and kinetic information obtained from this method against extensive unbiased MD simulations for the dissociation dynamics of two different millimolar fragments from the protein FKBP in explicit water with residence times in the nanoseconds to microseconds regime. We find that the metadynamics approach gives the same binding free energy profile, dissociation pathway, and ligand residence time as the unbiased MD, albeit using only 6-50 times lower computational resources. Furthermore, we demonstrate how the metadynamics approach can self-consistently be used to ascertain whether the reweighted kinetic constants are reliable or not. We thus conclude that the answer to the question posed in the title of this manuscript is, statistically speaking, yes.

DNA-Assisted Dispersion of Carbon Nanotubes and Comparison with Other Dispersing Agents.

Separation and sorting of pristine carbon nanotubes (CNTs) from bundle geometry is a very challenging task due to the insoluble and nondispersive nature of CNTs in aqueous medium. Recently, many studies have been performed to address this problem using various organic and inorganic solutions, surfactant molecules, and biomolecules as dispersing agents. Recent experimental studies have reported the DNA to be highly efficient in dispersing CNTs from bundle geometry. However, there is no microscopic study and also quantitative estimation of the dispersion efficiency of the DNA. Using all-atom molecular dynamics simulation, we study the structure and stability of single-stranded DNA (ssDNA)-single-walled carbon nanotube (SWNT) (6,5) complex. To quantify the dispersion efficiency of various DNA sequences, we perform potential of mean forces (PMF) calculation between two bare SWNTs as well ssDNA-wrapped CNTs for different base sequences. From the PMF calculation, we find the PMF between two bare (6,5) SWNTs to be approximately -29 kcal/mol. For the ssDNA-wrapped SWNTs, the PMF reduces significantly and becomes repulsive. In the presence of ssDNA of different polynucleotide bases (A, T, G, and C), we present a microscopic picture of the ssDNA-SWNT (6,5) complex and also a quantitative estimate of the interaction strength between nanotubes from PMF calculation. From PMF, we show the sequence of dispersion efficiency for four different nucleic bases to be T > A > C > G. We have also presented a comparison of the dispersion efficiencies of ssDNA, flavin mononucleotide surfactant, and poly(amidoamine) (PAMAM) dendrimer by comparing their respective PMF values.