Molecular dynamics-driven drug discovery: leaping forward with confidence

Molecular Dynamics for Antimicrobial Peptide Discovery

Although antimicrobial resistance is an increasingly significant public health concern, there have only been two new classes of antibiotics approved for human use since the 1960s. Understanding the mechanisms of action of antibiotics is critical for novel antibiotic discovery, but novel approaches are needed that do not exclusively rely on experiments. Molecular dynamics simulation is a computational tool that uses simple models of the atoms in a system to discover nanoscale insights into the dynamic relationship between mechanism and biological function. Such insights can lay the framework for elucidating the mechanism of action and optimizing antibiotic templates. Antimicrobial peptides represent a promising solution to escalating antimicrobial resistance, given their lesser tendency to induce resistance than that of small-molecule antibiotics. Simulations of these agents have already revealed how they interact with bacterial membranes and the underlying physiochemical features directing their structure and function. In this minireview, we discuss how traditional molecular dynamics simulation works and its role and potential for the development of new antibiotic candidates with an emphasis on antimicrobial peptides.

Bioinformatic analysis of subfamily-specific regions in 3D-structures of homologs to study functional diversity and conformational plasticity in protein superfamilies

Local 3D-structural differences in homologous proteins contribute to functional diversity observed in a superfamily, but so far received little attention as bioinformatic analysis was usually carried out at the level of amino acid sequences. We have developed Zebra3D - the first-of-its-kind bioinformatic software for systematic analysis of 3D-alignments of protein families using machine learning. The new tool identifies subfamily-specific regions (SSRs) - patterns of local 3D-structure (i.e. single residues, loops, or secondary structure fragments) that are spatially equivalent within families/subfamilies, but are different among them, and thus can be associated with functional diversity and function-related conformational plasticity. Bioinformatic analysis of protein superfamilies by Zebra3D can be used to study 3D-determinants of catalytic activity and specific accommodation of ligands, help to prepare focused libraries for directed evolution or assist development of chimeric enzymes with novel properties by exchange of equivalent regions between homologs, and to characterize plasticity in binding sites. A companion Mustguseal web-server is available to automatically construct a 3D-alignment of functionally diverse proteins, thus reducing the minimal input required to operate Zebra3D to a single PDB code. The Zebra3D + Mustguseal combined approach provides the opportunity to systematically explore the value of SSRs in superfamilies and to use this information for protein design and drug discovery. The software is available open-access at https://biokinet.belozersky.msu.ru/Zebra3D.

A novel antiplasmodial compound: integration of in silico and in vitro assays

Malaria is a disease caused by Plasmodium genus. which P. falciparum is responsible for the most severe form of the disease, cerebral malaria. In 2018, 405,000 people died of malaria. Antimalarial drugs have serious adverse effects and limited efficacy due to multidrug-resistant strains. One way to overcome these limitations is the use of computational approaches for prioritizing candidates to phenotypic assays and/or in vitro assays against validated targets. Plasmodium falciparum Enoyl-ACP reductase (PfENR) is noteworthy because it catalyzes the rate-limiting step of the biosynthetic pathway of fatty acid. Thus, the study aimed to identify potential PfENR inhibitors by ligand (2D molecular similarity and pharmacophore models) and structure-based virtual screening (molecular docking). 2D similarity-based virtual screening using Tanimoto Index (> 0.45) selected 29,236 molecules from natural products subset available in ZINC database (n = 181,603). Next, 10 pharmacophore models for PfENR inhibitors were generated and evaluated based on the internal statistical parameters from GALAHAD™ and ROC/AUC curve. These parameters selected a suitable pharmacophore model with one hydrophobic center and two hydrogen bond acceptors. The alignment of the filtered molecules on best pharmacophore model resulted in the selection of 10,977 molecules. These molecules were directed to the docking-based virtual screening by AutoDock Vina 1.1.2 program. These strategies selected one compound to phenotypic assays against parasite. ZINC630259 showed EC₅₀ = 0.12 ± 0.018 µM in antiplasmodial assays and selective index similar to other antimalarial drugs. Finally, MM/PBSA method showed stability of molecule within PfENR binding site (ΔG_binding=-57.337 kJ/mol).Communicated by Ramaswamy H. Sarma.

Testing automatic methods to predict free binding energy of host-guest complexes in SAMPL7 challenge

The design of new host–guest complexes represents a fundamental challenge in supramolecular chemistry. At the same time, it opens new opportunities in material sciences or biotechnological applications. A computational tool capable of automatically predicting the binding free energy of any host–guest complex would be a great aid in the design of new host systems, or to identify new guest molecules for a given host. We aim to build such a platform and have used the SAMPL7 challenge to test several methods and design a specific computational pipeline. Predictions will be based on machine learning (when previous knowledge is available) or a physics-based method (otherwise). The formerly delivered predictions with an RMSE of 1.67 kcal/mol but will require further work to identify when a specific system is outside of the scope of the model. The latter is combines the semiempirical GFN2B functional, with docking, molecular mechanics, and molecular dynamics. Correct predictions (RMSE of 1.45 kcal/mol) are contingent on the identification of the correct binding mode, which can be very challenging for host–guest systems with a large number of degrees of freedom. Participation in the blind SAMPL7 challenge provided fundamental direction to the project. More advanced versions of the pipeline will be tested against future SAMPL challenges.

Cheminformatics-Based Identification of Potential Novel Anti-SARS-CoV-2 Natural Compounds of African Origin

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome virus 2 (SARS-CoV-2) has impacted negatively on public health and socioeconomic status, globally. Although, there are currently no specific drugs approved, several existing drugs are being repurposed, but their successful outcomes are not guaranteed. Therefore, the search for novel therapeutics remains a priority. We screened for inhibitors of the SARS-CoV-2 main protease and the receptor-binding domain of the spike protein from an integrated library of African natural products, compounds generated from machine learning studies and antiviral drugs using AutoDock Vina. The binding mechanisms between the compounds and the proteins were characterized using LigPlot+ and molecular dynamics simulations techniques. The biological activities of the hit compounds were also predicted using a Bayesian-based approach. Six potential bioactive molecules NANPDB2245, NANPDB2403, fusidic acid, ZINC000095486008, ZINC0000556656943 and ZINC001645993538 were identified, all of which had plausible binding mechanisms with both viral receptors. Molecular dynamics simulations, including molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) computations revealed stable protein-ligand complexes with all the compounds having acceptable free binding energies <-15 kJ/mol with each receptor. NANPDB2245, NANPDB2403 and ZINC000095486008 were predicted as antivirals; ZINC000095486008 as a membrane permeability inhibitor; NANPDB2403 as a cell adhesion inhibitor and RNA-directed RNA polymerase inhibitor; and NANPDB2245 as a membrane integrity antagonist. Therefore, they have the potential to inhibit viral entry and replication. These drug-like molecules were predicted to possess attractive pharmacological profiles with negligible toxicity. Novel critical residues identified for both targets could aid in a better understanding of the binding mechanisms and design of fragment-based de novo inhibitors. The compounds are proposed as worthy of further in vitro assaying and as scaffolds for the development of novel SARS-CoV-2 therapeutic molecules.

Automation of absolute protein-ligand binding free energy calculations for docking refinement and compound evaluation

Absolute binding free energy calculations with explicit solvent molecular simulations can provide estimates of protein-ligand affinities, and thus reduce the time and costs needed to find new drug candidates. However, these calculations can be complex to implement and perform. Here, we introduce the software BAT.py, a Python tool that invokes the AMBER simulation package to automate the calculation of binding free energies for a protein with a series of ligands. The software supports the attach-pull-release (APR) and double decoupling (DD) binding free energy methods, as well as the simultaneous decoupling-recoupling (SDR) method, a variant of double decoupling that avoids numerical artifacts associated with charged ligands. We report encouraging initial test applications of this software both to re-rank docked poses and to estimate overall binding free energies. We also show that it is practical to carry out these calculations cheaply by using graphical processing units in common machines that can be built for this purpose. The combination of automation and low cost positions this procedure to be applied in a relatively high-throughput mode and thus stands to enable new applications in early-stage drug discovery.

Databases for the study of biofilms: current status and potential applications

Biofilms play an important role in health, being associated with >80% of all microbial infections in the body and in the development of antibiotic resistance. Research in this field has continuously produced large volumes of data. Being able to handle all this information will be paramount for progress in this field. However, this places a heavy burden on the development of strategies to gather, organize and make this information available in a way that can be readily and effectively used by those requiring it. Lately, efforts towards this goal have been reported, particularly with the development of Quorumpeps, BiofOmics, BaAMPs, QSPpred, dPABBs, aBiofilm and the Biofilms Structural Database. This work reviews these databases and highlights their applicability and potential, while stressing some of the challenges for the coming years in database development and usage brought about by the use of big data and machine learning.

Molecular Dynamics Simulations in Drug Discovery and Pharmaceutical Development

Molecular dynamics (MD) simulations have become increasingly useful in the modern drug development process. In this review, we give a broad overview of the current application possibilities of MD in drug discovery and pharmaceutical development. Starting from the target validation step of the drug development process, we give several examples of how MD studies can give important insights into the dynamics and function of identified drug targets such as sirtuins, RAS proteins, or intrinsically disordered proteins. The role of MD in antibody design is also reviewed. In the lead discovery and lead optimization phases, MD facilitates the evaluation of the binding energetics and kinetics of the ligand-receptor interactions, therefore guiding the choice of the best candidate molecules for further development. The importance of considering the biological lipid bilayer environment in the MD simulations of membrane proteins is also discussed, using G-protein coupled receptors and ion channels as well as the drug-metabolizing cytochrome P450 enzymes as relevant examples. Lastly, we discuss the emerging role of MD simulations in facilitating the pharmaceutical formulation development of drugs and candidate drugs. Specifically, we look at how MD can be used in studying the crystalline and amorphous solids, the stability of amorphous drug or drug-polymer formulations, and drug solubility. Moreover, since nanoparticle drug formulations are of great interest in the field of drug delivery research, different applications of nano-particle simulations are also briefly summarized using multiple recent studies as examples. In the future, the role of MD simulations in facilitating the drug development process is likely to grow substantially with the increasing computer power and advancements in the development of force fields and enhanced MD methodologies.

Machine Learning of Allosteric Effects: The Analysis of Ligand-Induced Dynamics to Predict Functional Effects in TRAP1

Allosteric molecules provide a powerful means to modulate protein function. However, the effect of such ligands on distal orthosteric sites cannot be easily described by classical docking methods. Here, we applied machine learning (ML) approaches to expose the links between local dynamic patterns and different degrees of allosteric inhibition of the ATPase function in the molecular chaperone TRAP1. We focused on 11 novel allosteric modulators with similar affinities to the target but with inhibitory efficacy between the 26.3 and 76%. Using a set of experimentally related local descriptors, ML enabled us to connect the molecular dynamics (MD) accessible to ligand-bound (perturbed) and unbound (unperturbed) systems to the degree of ATPase allosteric inhibition. The ML analysis of the comparative perturbed ensembles revealed a redistribution of dynamic states in the inhibitor-bound versus inhibitor-free systems following allosteric binding. Linear regression models were built to quantify the percentage of experimental variance explained by the predicted inhibitor-bound TRAP1 states. Our strategy provides a comparative MD–ML framework to infer allosteric ligand functionality. Alleviating the time scale issues which prevent the routine use of MD, a combination of MD and ML represents a promising strategy to support in silico mechanistic studies and drug design.

Integrating molecular modelling methods to advance influenza A virus drug discovery

Since the discovery of the anti-influenza drugs oseltamivir and zanamivir using computer-aided drug design methods, there have been significant applications of molecular modelling methodologies applied to influenza A virus drug discovery, such as molecular dynamics (MD) simulation, molecular docking, and virtual screening (VS). In this review, we provide a brief general introduction to molecular modelling in the context of drug discovery and then focus on the advances and impact of integrating these methods with specific reference to potential influenza A antiviral drug targets.

Novel 2-amino-1,4-naphthoquinone hybrids: Design, synthesis, cytotoxicity evaluation and in silico studies

In the present work, a novel series of 2-amino-1,4-naphthoquinones bearing oxyphenyl moiety (5a-5m) were designed and synthesized via a two-step route and evaluated for their in vitro cytotoxic activity against three different cancer cell lines (MCF-7, HL-60 and U937) and normal human cell line (HEK-293) by MTT assay. Compounds 5b (4-nitro-benzyl-) and 5k (4-bromo-benzyl-) were identified to possess the highest cytotoxic activity against MCF-7 cancerous cells (IC₅₀ values of 27.76 and 27.86 μM, respectively). At the same time, none of the compounds exert significant toxicity against HEK-293 normal human kidney cells. Cell cycle analysis showed that the selected derivatives increased the population of MCF-7 cells in the S phase at 25 and 50 μM concentrations. Annexin V-FITC/PI staining assay also confirmed that compounds 5b and 5k induced apoptosis in the cell death pathway. Molecular docking and molecular dynamics studies were also performed to evaluate the probable interactions between the hybrids and human ATP binding domain of topo IIα protein. Our findings may provide new insight for further development of novel naphthoquinone-containing compounds.

Estimating the ligand-binding affinity via λ-dependent umbrella sampling simulations

The umbrella sampling (US) approach has been demonstrated to be a very efficient method for estimating the ligand-binding affinity. However, most of the calculated values overestimate experimental ones that are probably caused by the inaccurate representation of the interaction between the ligand and the surrounding molecules. The issue can be resolved via the implementation aspects of λ-alteration simulation into the US approach, which we call the λ-dependent umbrella sampling (λUS) scheme. In particular, the electrostatic and van der Waals interactions were simultaneously changed by using the coupling parameter λ during λUS simulations. The mean value of obtained results, ∆ G US λ = 0.20 = - 11.59 ± 1.51 kcal mol^-1 , is in good fitting to the mean value of respective experiments, ∆G_EXP =  - 11.26 ± 0.89 kcal mol^-1 . Moreover, the correlation between the proposed approach and experiment is quite good with a value of R US λ = 0.20 = 0.82 ± 0.10 . The λUS scheme significantly enhances the calculated accuracy since the RMSE of the proposed scheme is smaller than traditional US simulations, RMSE US λ = 0.20 = 2.99 ± 0.82 kcal mol^-1 versus RMSE US λ = 0.00 = 5.48 ± 0.81 kcal mol^-1 . Furthermore, the precision is increased since the computed error via λUS approach, δ US λ = 0.20 = 1.51 kcal mol^-1 , was smaller than those of the US simulation, δ US λ = 0.00 = 1.78 kcal mol^-1 . Overall, the proposed approach perhaps provides an efficient way to accurately and precisely estimate the ligand-binding free energy.

A structure-based computational workflow to predict liability and binding modes of small molecules to hERG

Off-target interactions of drugs with the human ether-à-go-go related gene 1 (hERG1) channel have been associated with severe cardiotoxic conditions leading to the withdrawal of many drugs from the market over the last decades. Consequently, predicting drug-induced hERG-liability is now a prerequisite in any drug discovery campaign. Understanding the atomic level interactions of drug with the channel is essential to guide the efficient development of safe drugs. Here we utilize the recent cryo-EM structure of the hERG channel and describe an integrated computational workflow to characterize different drug-hERG interactions. The workflow employs various structure-based approaches and provides qualitative and quantitative insights into drug binding to hERG. Our protocol accurately differentiated the strong blockers from weak and revealed three potential anchoring sites in hERG. Drugs engaging in all these sites tend to have high affinity towards hERG. Our results were cross-validated using a fluorescence polarization kit binding assay and with electrophysiology measurements on the wild-type (WT-hERG) and on the two hERG mutants (Y652A-hERG and F656A-hERG), using the patch clamp technique on HEK293 cells. Finally, our analyses show that drugs binding to hERG disrupt and hijack certain native—structural networks in the channel, thereby, gaining more affinity towards hERG.

How to make an undruggable enzyme druggable: lessons from ras proteins

Significant advances have been made toward discovering allosteric inhibitors for challenging drug targets such as the Ras family of membrane-associated signaling proteins. Malfunction of Ras proteins due to somatic mutations is associated with up to a quarter of all human cancers. Computational techniques have played critical roles in identifying and characterizing allosteric ligand-binding sites on these proteins, and to screen ligand libraries against those sites. These efforts, combined with a wide range of biophysical, structural, biochemical and cell biological experiments, are beginning to yield promising inhibitors to treat malignancies associated with mutated Ras proteins. In this chapter, we discuss some of these developments and how the lessons learned from Ras might be applied to similar other challenging drug targets.

Contemporary Formulation Development for Inhaled Pharmaceuticals

Pulmonary delivery has gained increased interests over the past few decades. For respiratory conditions, targeted drug delivery directly to the site of action can achieve a high local concentration for efficacy with reduced systemic exposure and adverse effects. For systemic conditions, the unique physiology of the lung evolutionarily designed for rapid gaseous exchange presents an entry route for systemic drug delivery. Although the development of inhaled formulations has come a long way over the last few decades, many aspects of it remain to be elucidated. In particular, a reliable and well-understood method for in vitro-in vivo correlations remains to be established. With the rapid and ongoing advancement of technology, there is much potential to better utilise computational methods including different types of modelling and simulation approaches to support inhaled formulation development. This review intends to provide an introduction on some fundamental concepts in pulmonary drug delivery and inhaled formulation development followed by discussions on some challenges and opportunities in the translation of inhaled pharmaceuticals from preclinical studies to clinical development. The review concludes with some recent advancements in modelling and simulation approaches that could play an increasingly important role in modern formulation development of inhaled pharmaceuticals.

EasyAmber: A comprehensive toolbox to automate the molecular dynamics simulation of proteins

Conformational plasticity of the functionally important regions and binding sites in protein/enzyme structures is one of the key factors affecting their function and interaction with substrates/ligands. Molecular dynamics (MD) can address the challenge of accounting for protein flexibility by predicting the time-dependent behavior of a molecular system. It has a potential of becoming a particularly important tool in protein engineering and drug discovery, but requires specialized training and skills, what impedes practical use by many investigators. We have developed the easyAmber - a comprehensive set of programs to automate the molecular dynamics routines implemented in the Amber package. The toolbox can address a wide set of tasks in computational biology struggling to account for protein flexibility. The automated workflow includes a complete set of steps from the initial "static" molecular model to the MD "production run": the full-atom model building, optimization/equilibration of the molecular system, classical/conventional and accelerated molecular dynamics simulations. The easyAmber implements advanced MD protocols, but is highly automated and easy-to-operate to attract a broad audience. The toolbox can be used on a personal desktop station equipped with a compatible gaming GPU-accelerator, as well as help to manage huge workloads on a powerful supercomputer. The software provides an opportunity to operate multiple simulations of different proteins at the same time, thus significantly increasing work efficiency. The easyAmber takes the molecular dynamics to the next level in terms of usability for complex processing of large volumes of data, thus supporting the recent trend away from inefficient "static" approaches in biology toward a deeper understanding of the dynamics in protein structures. The software is freely available for download at https://biokinet.belozersky.msu.ru/easyAmber, no login required.

Identification of novel Aedes aegypti odorant-binding protein 1 modulators by ligand and structure-based approaches and bioassays

Arboviruses are a group of viruses (e.g. Dengue, Chikungunya and Yellow fever virus) that are transmitted by arthropod vectors, which Aedes aegipty is the vector of main viruses in Americas. This vector is responsible to 2.4 millions of arboviruses cases in Brazil with less than a thousand deaths annually. Despite of epidemiological data, arboviruses treatment is symptomatic and the vaccine control is not effective, which makes the vector control against A. aegipty a promising strategy to diseases control. One way to achieve this goal is to development of A. aegipty sensitive olfactory modulators. Odorant binding protein 1 from A. aegypti (AaegOBP1) is essential in sensory communication, and is the first filter in odorant selection, which makes this target promising to development of new repellents. For this reason, hierarchical virtual screening (ligand-based pharmacophore model and molecular docking) together volatility filter was applied at Sigma-Aldrich database (n = 126.851) to prioritize potential molecules to repellency assays. Three compounds showed adequate stereo-electronic requirements (QFIT> 81.53), score to AaegOBP1 binding site (Score > 36.0) and volatile properties and it was chosen for repellency assays. ZINC00170981 and ZINC00131924 showed a dose-response behavior, while ZINC01621824 did not showed activity in repellency assays. Finally, Molecular Dynamics (MD) was employed to hypothesize the stability of protein-ligand complexes. According to RMSD, RMSF and binding free energy data, ZINC00170981 and ZINC00131924 were able to stabilize AaegOBP1 binding-site during the trajectory by interactions with key residues such as His77, Leu89 and Trp114). Communicated by Ramaswamy H. Sarma.

Identification of potential Leishmania chagasi superoxide dismutase allosteric modulators by structure-based computational approaches: homology modelling, molecular dynamics and pharmacophore-based virtual screening

The visceral form of Leishmaniasis, also known as kala-azar, caused by Leishmania chagasi is the main etiological agent of this form in Brazil responsible for 30,000 annual deaths. Despite its epidemiological impact, treatment of the disease is limited by resistance, species-dependent efficacy and serious adverse effects. The application of computational tools to prioritize potential bioactive molecules based on 3D structural of biological target is a viable alternative. Among the L. chagasi validated targets, Fe + 2 superoxide dismutase B2 (LcFeSODB2) is the first parasite enzyme against oxidative stress and it is involved in essential metabolic processes for its survival. Due to substrate binding-site volume (superoxide ion) and consequent difficulty in its active site modulation for small molecules, the search for allosteric sites at LcFeSODB2 3D structure is a promising strategy. As there are no 3D structures of LcFeSODB2, comparative modeling was applied to build 3D models by SWISS-MODEL and MODELLER version 9.19. Next, the best 3D model was used in molecular dynamics (MD) routines with multiple probes on GROMACS version 5.1.2. In addition, potential allosteric sites predicted by FTMap and Metapocket web servers were used with probe occupancy maps from MD to select an allosteric binding site and propose a pharmacophore model. Next, it was used as a template in virtual screening by UNITY^® module available on SYBYL-X version 2.1.1 at Sigma-Aldrich CPR™ subset of ZINC12 database. The pharmacophore-based virtual screening resulted in the selection of two potential allosteric LcFeSOD compounds with partial pharmacophoric requirements, drug-like properties and commercial availability for enzymatic assays. Communicated by Ramaswamy H. Sarma.

Hitting a Moving Target: Simulation and Crystallography Study of ATAD2 Bromodomain Blockers

Small molecule ligand binding to the ATAD2 bromodomain is investigated here through the synergistic combination of molecular dynamics and protein crystallography. A previously unexplored conformation of the binding pocket upon rearrangement of the gatekeeper residue Ile1074 has been found. Further, our investigations reveal how minor structural differences in the ligands result in binding with different plasticity of the ZA loop for this difficult-to-drug bromodomain.

Protein-ligand binding with the coarse-grained Martini model

The detailed understanding of the binding of small molecules to proteins is the key for the development of novel drugs or to increase the acceptance of substrates by enzymes. Nowadays, computer-aided design of protein–ligand binding is an important tool to accomplish this task. Current approaches typically rely on high-throughput docking essays or computationally expensive atomistic molecular dynamics simulations. Here, we present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein–ligand interactions of small drug-like molecules. Remarkably, we achieve high accuracy without the need of any a priori knowledge of binding pockets or pathways. Our approach is applied to a range of systems from the well-characterized T4 lysozyme over members of the GPCR family and nuclear receptors to a variety of enzymes. The presented results open the way to high-throughput screening of ligand libraries or protein mutations using the coarse-grained Martini model.

Computer-aided design of protein-ligand binding is important for the development of novel drugs. Here authors present an approach to use the recently re-parametrized coarse-grained Martini model to perform unbiased millisecond sampling of protein-ligand binding interactions of small drug-like molecules.

Identification of Novel Key Targets and Candidate Drugs in Oral Squamous Cell Carcinoma

Background:

Oral Squamous Cell Carcinoma (OSCC) is the most common malignant epithelial neoplasm. It is located within the top 10 ranking incidence of cancers with a poor prognosis and low survival rates. New breakthroughs of therapeutic strategies are therefore needed to improve the survival rate of OSCC harboring patients.

Objective:

Since targeted therapy is considered as the most promising therapeutic strategies in cancer, it is of great significance to identify novel targets and drugs for the treatment of OSCC.

Methods:

A series of bioinformatics approaches were launched to identify the hub proteins and their potential agents. Microarray analysis and several online functional activity network analysis were firstly utilized to recognize drug targets in OSCC. Subsequently, molecular docking was used to screen their potential drugs from the specs chemistry database. At the same time, the assessment of ligand-based virtual screening model was also evaluated.

Results:

In this study, two microarray data (GSE31056, GSE23558) were firstly selected and analyzed to get consensus candidate genes including 681 candidate genes. Additionally, we selected 33 candidate genes based on whether they belong to the kinases and transcription factors and further clustered candidate hub targets based on functions and signaling pathways with significant enrichment analysis by using DAVID and STRING online databases. Then, core PPI network was then identified and we manually selected GRB2 and IGF1 as the key drug targets according to the network analysis and previous references. Lastly, virtual screening was performed to identify potential small molecules which could target these two targets, and such small molecules can serve as the promising candidate agents for future drug development.

Conclusion:

In summary, our study might provide novel insights for understanding of the underlying molecular events of OSCC, and our discovered candidate targets and candidate agents could be used as the promising therapeutic strategies for the treatment of OSCC.

Toward Simple, Predictive Understanding of Protein-Ligand Interactions: Electronic Structure Calculations on Torpedo Californica Acetylcholinesterase Join Forces with the Chemist's Intuition

Contemporary efforts for empirically-unbiased modeling of protein-ligand interactions entail a painful tradeoff - as reliable information on both noncovalent binding factors and the dynamic behavior of a protein-ligand complex is often beyond practical limits. We demonstrate that information drawn exclusively from static molecular structures can be used for reproducing and predicting experimentally-measured binding affinities for protein-ligand complexes. In particular, inhibition constants (Ki) were calculated for seven different competitive inhibitors of Torpedo californica acetylcholinesterase using a multiple-linear-regression-based model. The latter, incorporating five independent variables - drawn from QM cluster, DLPNO-CCSD(T) calculations and LED analyses on the seven complexes, each containing active amino-acid residues found within interacting distance (3.5 Å) from the corresponding ligand - is shown to recover 99.9% of the sum of squares for measured Ki values, while having no statistically-significant residual errors. Despite being fitted to a small number of data points, leave-one-out cross-validation statistics suggest that it possesses surprising predictive value (Q2LOO=0.78, or 0.91 upon removal of a single outlier). This thus challenges ligand-invariant definitions of active sites, such as implied in the lock-key binding theory, as well as in alternatives highlighting shape-complementarity without taking electronic effects into account. Broader implications of the current work are discussed in dedicated appendices.

Identification of novel antiplasmodial compound by hierarquical virtual screening and in vitro assays

Malaria is an infectious disease caused by protozoa of the genus Plasmodium spp. with approximately 219 million cases in 2017. P. falciparum is main responsible for the most severe form of the disease, cerebral malaria. Despite of public health impacts, chemotherapy against malaria is still limited due to the emergence of drug resistance cases used in monotherapy and combination therapies. Thus, the development of new antimalarial drugs becomes emergency. One way of achieve this goal is to explore essential and/or unique therapeutic targets of the parasite, or at least sufficiently different to ensure selective inhibition. Enoil-ACP reductase (ENR) is a NADH-dependent enzyme responsible for the limiting step of the type II fatty acid biosynthetic pathway (FAS II). Thus, pharmacophore and docking based virtual screening were applied to prioritize molecules for in vitro assays against P. falciparum W2 strain. The application of successive filters at OOCC database (n = 618) resulted in the identification of one molecule (13) (EC₅₀ = 0.098 ± 0.021 μM) with similar biological activity to artemether. The molecule 13 is a typical drug repurposing case due to previous other approved therapeutic uses on Chinese medicine as a non-specific cholinergic antagonist, thus it could be accelerated the drug development process. Additionally, molecular dynamics studies were used to confirm stability of the molecular interactions identified by molecular docking. Thus, representative structures of P. falciparum ENR can be used in a study to propose new derivatives for evaluation of biological activity in vitro and in vivo. Communicated by Ramaswamy H. Sarma.

Improving Protein-Ligand Docking Results with High-Throughput Molecular Dynamics Simulations

Structure-based virtual screening relies on classical scoring functions that often fail to reliably discriminate binders from nonbinders. In this work, we present a high-throughput protein-ligand complex molecular dynamics (MD) simulation that uses the output from AutoDock Vina to improve docking results in distinguishing active from decoy ligands in a directory of useful decoy-enhanced (DUD-E) dataset. MD trajectories are processed by evaluating ligand-binding stability using root-mean-square deviations. We select 56 protein targets (of 7 different protein classes) and 560 ligands (280 actives, 280 decoys) and show 22% improvement in ROC AUC (area under the curve, receiver operating characteristics curve), from an initial value of 0.68 (AutoDock Vina) to a final value of 0.83. The MD simulation demonstrates a robust performance across all seven different protein classes. In addition, some predicted ligand-binding modes are moderately refined during MD simulations. These results systematically validate the reliability of a physics-based approach to evaluate protein-ligand binding interactions.

In silico studies of the inhibition mechanism of dengue with papain

Dengue virus is becoming a major global disease; the envelope protein is the major target for vaccine development against Dengue. Nowadays, the attention has focused on developing inhibitors based on Papain is a promising target for treating Dengue. In the present work, the theoretical studies of E-protein(Cys74-Glu79;Lys110)…Papain(Cys25, Asn175 and His159) complexes are analysed by Density Functional Theory (M06-2X/cc-pVDZ) method. Among the E-protein(Cys74-Glu79;Lys110)…Papain(Cys25, Asn175 and Hys159) complexes, E-protein(Glu76)…Papain(Cys25) complex has the highest interaction value of -352.22 kcal/mol. Moreover, the natural bond orbital analysis also supports the above results. The 100 ns Molecular Dynamics simulation reveals that, E-protein(Ala54-Ile129)…Papain(Cys25) complex had the lowest root mean square deviation value of 1 Å compared to the E-protein(Ala54-Ile129)… Papain(Asn175 & His159) complexes. The salt bridge formation between the Asp103 and Lys110 residues are the important stabilizing factor in E-protein(Ala54-Ile129)…Papain(Cys25) complex. This result can extend our knowledge of the functional behaviour of Papain and provides structural insight to target Envelope protein as forthcoming drug targets in Dengue.

User-Friendly Quantum Mechanics: Applications for Drug Discovery

Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields. Thanks to the continuous increase of computational power offered by GPUs and to the development of sophisticated algorithms, QM methods are becoming part of the standard tools used in computer-aided drug design (CADD). We present the most used QM methods and software packages, and we discuss recent representative applications in drug design and drug discovery.

An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45

Our information-driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modeling process, and to the robustness of its scoring function. We participated in CAPRI both as server and manual predictors. In CAPRI rounds 38-45, we have used various strategies depending on the available information. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template-based refinement/CA-CA restraint-guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse-grained force field in HADDOCK. Overall, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable or higher-quality models when considering the top 10) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces. An analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models.

In Vitro Antidiabetic, Anti-Obesity and Antioxidant Analysis of Ocimum basilicum Aerial Biomass and in Silico Molecular Docking Simulations with Alpha-Amylase and Lipase Enzymes

The present study explored phytochemicals, porcine pancreatic α-amylase (PPA) and lipase (PPL) inhibitory activities and antioxidant potential of polar and nonpolar extracts of the leaves and flowers of Ocimum basilicum and the in-silico mode of interaction between these enzymes and the major chemical constituents of the herb. The hexane extract (HE) and hydro-ethanolic extract (EE) obtained sequentially were used to estimate PPA and PPL inhibitory and antioxidant activities, total phenolic content (TPC) and total flavonoid content (TFC). Chemical constituents of the essential oils and HE were determined by GC-MS (Gas Chromatography-Mass Spectrometry). For PPA inhibition, IC₅₀ (µg/mL) of the extracts were 0.27-0.37, which were close to 0.24 of acarbose, while for PPL inhibition, IC₅₀ (µg/mL) of the extracts were 278.40-399.65, and that of Orlistat 145.72. The flowers EE was most potent antioxidant followed by leaves EE. The leaves EE had highest TPC and TFC followed of flowers EE. The essential oil of flowers had higher estragole (55%) than linalool (37%), while the essential oil of the leaves had higher linalool (42%) than estragole (38%). The HE of the flowers contained higher estragole (42%) than linalool (23%), while of the HE of the leaves too had higher estragole (65%) than linalool (18%). The in-silico molecular docking study showed linalool and estragole to have considerable PPA and PPL binding potential, which were further investigated through molecular dynamics simulations and binding free energy calculations. The PPA and PPL inhibitory activities of O. basilicum extracts and their notable antioxidant potential propose the herb as a multi-target complimentary medicine for diabetes, obesity and oxidative stress.

Development and Validation of a New High-Performance Liquid Chromatography Method for the Simultaneous Quantification of Coenzyme Q10, Phosphatidylserine, and Vitamin C from a Cutting-Edge Liposomal Vehiculization

A high-performance liquid chromatography (HPLC) method was developed to simultaneously quantify three widely used active substances such as coenzyme Q10, phosphatidylserine, and vitamin C. This new method optimizes current timing and costs in the analyses of these three active substances. Additionally, since the analyzed compounds were encapsulated on a cutting-edge liposomal formulation, further processing was necessary to be developed prior to HPLC analyses. The technique was studied and adequately validated in accordance with the guidelines of the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) regarding selectivity, linearity, accuracy, precision, and robustness. After data treatment of results, linear regressions for all active substances showed an optimal linearity with a correlation coefficient of >0.999 in the concentration range between 70 to 130% of the liposomal formulation and less than a 3% relative standard deviation (RSD) in accuracy and precision.

Pyridinylimidazoles as GSK3β Inhibitors: The Impact of Tautomerism on Compound Activity via Water Networks

Glycogen synthase kinase-3β (GSK3β) is involved in many pathological conditions and represents an attractive drug target. We previously reported dual GSK3β/p38α mitogen-activated protein kinase inhibitors and identified N-(4-(4-(4-fluorophenyl)-2-methyl-1H-imidazol-5-yl)pyridin-2-yl)cyclopropanecarboxamide (1) as a potent dual inhibitor of both target kinases. In this study, we aimed to design selective GSK3β inhibitors based on our pyridinylimidazole scaffold. Our efforts resulted in several novel and potent GSK3β inhibitors with IC₅₀ values in the low nanomolar range. 5-(2-(Cyclopropanecarboxamido)pyridin-4-yl)-4-cyclopropyl-1H-imidazole-2-carboxamide (6g) displayed very good kinase selectivity as well as metabolical stability and inhibited GSK3β activity in neuronal SH-SY5Y cells. Interestingly, we observed the importance of the 2-methylimidazole's tautomeric state for the compound activity. Finally, we reveal how this crucial tautomerism effect is surmounted by imidazole-2-carboxamides, which are able to stabilize the binding via enhanced water network interactions, regardless of their tautomeric state.

Perspectives towards antiviral drug discovery against Ebola virus

Ebola virus disease (EVD), caused by Ebola viruses, resulted in more than 11 500 deaths according to a recent 2018 WHO report. With mortality rates up to 90%, it is nowadays one of the most deadly infectious diseases. However, no Food and Drug Administration‐approved Ebola drugs or vaccines are available yet with the mainstay of therapy being supportive care. The high fatality rate and absence of effective treatment or vaccination make Ebola virus a category‐A biothreat pathogen. Fortunately, a series of investigational countermeasures have been developed to control and prevent this global threat. This review summarizes the recent therapeutic advances and ongoing research progress from research and development to clinical trials in the development of small‐molecule antiviral drugs, small‐interference RNA molecules, phosphorodiamidate morpholino oligomers, full‐length monoclonal antibodies, and vaccines. Moreover, difficulties are highlighted in the search for effective countermeasures against EVD with additional focus on the interplay between available in silico prediction methods and their evidenced potential in antiviral drug discovery.

Getting Docking into Shape Using Negative Image-Based Rescoring

The failure of default scoring functions to ensure virtual screening enrichment is a persistent problem for the molecular docking algorithms used in structure-based drug discovery. To remedy this problem, elaborate rescoring and postprocessing schemes have been developed with a varying degree of success, specificity, and cost. The negative image-based rescoring (R-NiB) has been shown to improve the flexible docking performance markedly with a variety of drug targets. The yield improvement is achieved by comparing the alternative docking poses against the negative image of the target protein's ligand-binding cavity. In other words, the shape and electrostatics of the binding pocket is directly used in the similarity comparison to rank the explicit docking poses. Here, the PANTHER/ShaEP-based R-NiB methodology is tested with six popular docking softwares, including GLIDE, PLANTS, GOLD, DOCK, AUTODOCK, and AUTODOCK VINA, using five validated benchmark sets. Overall, the results indicate that R-NiB outperforms the default docking scoring consistently and inexpensively, demonstrating that the methodology is ready for wide-scale virtual screening usage.

Successful oral delivery of poorly water-soluble drugs both depends on the intraluminal behavior of drugs and of appropriate advanced drug delivery systems

Poorly water-soluble drugs continue to be a problematic, yet important class of pharmaceutical compounds for treatment of a wide range of diseases. Their prevalence in discovery is still high, and their development is usually limited by our lack of a complete understanding of how the complex chemical, physiological and biochemical processes that occur between administration and absorption individually and together impact on bioavailability. This review defines the challenge presented by these drugs, outlines contemporary strategies to solve this challenge, and consequent in silico and in vitro evaluation of the delivery technologies for poorly water-soluble drugs. The next steps and unmet needs are proposed to present a roadmap for future studies for the field to consider enabling progress in delivery of poorly water-soluble compounds.

Molecular Dynamics Simulations of Adenosine Receptors: Advances, Applications and Trends

:

Adenosine receptors (ARs) are transmembrane proteins that belong to the G protein-coupled receptors (GPCRs) superfamily and mediate the biological functions of adenosine. To date, four AR subtypes are known, namely A1, A2A, A2B and A3 that exhibit different signaling pathways, tissue localization, and mechanisms of activation. Moreover, the widespread ARs and their implication in numerous physiological and pathophysiological conditions had made them pivotal therapeutic targets for developing clinically effective agents.

:

The crystallographic success in identifying the 3D crystal structures of A2A and A1 ARs has dramatically enriched our understanding of their structural and functional properties such as ligand binding and signal transduction. This, in turn, has provided a structural basis for a larger contribution of computational methods, particularly molecular dynamics (MD) simulations, toward further investigation of their molecular properties and designing bioactive ligands with therapeutic potential. MD simulation has been proved to be an invaluable tool in investigating ARs and providing answers to some critical questions. For example, MD has been applied in studying ARs in terms of ligand-receptor interactions, molecular recognition, allosteric modulations, dimerization, and mechanisms of activation, collectively aiding in the design of subtype selective ligands.

:

In this review, we focused on the advances and different applications of MD simulations utilized to study the structural and functional aspects of ARs that can foster the structure-based design of drug candidates. In addition, relevant literature was briefly discussed which establishes a starting point for future advances in the field of drug discovery to this pivotal group of drug targets.

Importance of protein dynamics in the structure-based drug discovery of class A G protein-coupled receptors (GPCRs)

Demand for novel GPCR modulators is increasing as the association between the GPCR signaling pathway and numerous diseases such as cancers, psychological and metabolic disorders continues to be established. In silico structure-based drug design (SBDD) offers an outlet where researchers could exploit the accumulating structural information of GPCR to expedite the process of drug discovery. The coupling of structure-based approaches such as virtual screening and molecular docking with molecular dynamics and/or Monte Carlo simulation aids in reflecting the dynamics of proteins in nature into previously static docking studies, thus enhancing the accuracy of rationally designed ligands. This review will highlight recent computational strategies that incorporate protein flexibility into SBDD of GPCR-targeted ligands.

From Microscale to Macroscale: Nine Orders of Magnitude for a Comprehensive Modeling of Hydrogels for Controlled Drug Delivery

Because of their inherent biocompatibility and tailorable network design, hydrogels meet an increasing interest as biomaterials for the fabrication of controlled drug delivery devices. In this regard, mathematical modeling can highlight release mechanisms and governing phenomena, thus gaining a key role as complementary tool for experimental activity. Starting from the seminal contribution given by Flory-Rehner equation back in 1943 for the determination of matrix structural properties, over more than 70 years, hydrogel modeling has not only taken advantage of new theories and the increasing computational power, but also of the methods offered by computational chemistry, which provide details at the fundamental molecular level. Simulation techniques such as molecular dynamics act as a "computational microscope" and allow for obtaining a new and deeper understanding of the specific interactions between the solute and the polymer, opening new exciting possibilities for an in silico network design at the molecular scale. Moreover, system modeling constitutes an essential step within the "safety by design" paradigm that is becoming one of the new regulatory standard requirements also in the field-controlled release devices. This review aims at providing a summary of the most frequently used modeling approaches (molecular dynamics, coarse-grained models, Brownian dynamics, dissipative particle dynamics, Monte Carlo simulations, and mass conservation equations), which are here classified according to the characteristic length scale. The outcomes and the opportunities of each approach are compared and discussed with selected examples from literature.

Protobiotic Systems Chemistry Analyzed by Molecular Dynamics

Systems chemistry has been a key component of origin of life research, invoking models of life's inception based on evolving molecular networks. One such model is the graded autocatalysis replication domain (GARD) formalism embodied in a lipid world scenario, which offers rigorous computer simulation based on defined chemical kinetics equations. GARD suggests that the first pre-RNA life-like entities could have been homeostatically-growing assemblies of amphiphiles, undergoing compositional replication and mutations, as well as rudimentary selection and evolution. Recent progress in molecular dynamics has provided an experimental tool to study complex biological phenomena such as protein folding, ligand-receptor interactions, and micellar formation, growth, and fission. The detailed molecular definition of GARD and its inter-molecular catalytic interactions make it highly compatible with molecular dynamics analyses. We present a roadmap for simulating GARD's kinetic and thermodynamic behavior using various molecular dynamics methodologies. We review different approaches for testing the validity of the GARD model by following micellar accretion and fission events and examining compositional changes over time. Near-future computational advances could provide empirical delineation for further system complexification, from simple compositional non-covalent assemblies towards more life-like protocellular entities with covalent chemistry that underlies metabolism and genetic encoding.

Relative Binding Energies Predict Crystallographic Binding Modes of Ethionamide Booster Lead Compounds

Transcriptional repressor EthR from Mycobacterium tuberculosis is a valuable target for antibiotic booster drugs. We previously reported a virtual screening campaign to identify EthR inhibitors for development. Two ligand binding orientations were often proposed, though only the top scoring pose was utilized for filtering of the large data set. We obtained biophysically validated hits, some of which yielded complex crystal structures. In some cases, the crystallized binding mode and top scoring mode agree, while for others an alternate ligand binding orientation was found. In this contribution, we combine rigid docking, molecular dynamics simulations, and the linear interaction energy method to calculate binding free energies and derive relative binding energies for a number of EthR inhibitors in both modes. This strategy allowed us to correctly predict the most favorable orientation. Therefore, this widely applicable approach will be suitable to triage multiple binding modes within EthR and other potential drug targets with similar characteristics.

In silico structural elucidation of RNA-dependent RNA polymerase towards the identification of potential Crimean-Congo Hemorrhagic Fever Virus inhibitors

The Crimean-Congo Hemorrhagic Fever virus (CCHFV) is a segmented negative single-stranded RNA virus (-ssRNA) which causes severe hemorrhagic fever in humans with a mortality rate of ~50%. To date, no vaccine has been approved. Treatment is limited to supportive care with few investigational drugs in practice. Previous studies have identified viral RNA dependent RNA Polymerase (RdRp) as a potential drug target due to its significant role in viral replication and transcription. Since no crystal structure is available yet, we report the structural elucidation of CCHFV-RdRp by in-depth homology modeling. Even with low sequence identity, the generated model suggests a similar overall structure as previously reported RdRps. More specifically, the model suggests the presence of structural/functional conserved RdRp motifs for polymerase function, the configuration of uniform spatial arrangement of core RdRp sub-domains, and predicted positively charged entry/exit tunnels, as seen in sNSV polymerases. Extensive pharmacophore modeling based on per-residue energy contribution with investigational drugs allowed the concise mapping of pharmacophoric features and identified potential hits. The combination of pharmacophoric features with interaction energy analysis revealed functionally important residues in the conserved motifs together with in silico predicted common inhibitory binding modes with highly potent reference compounds.

Multi-target, ensemble-based virtual screening yields novel allosteric KRAS inhibitors at high success rate

RAS mutations account for >15% of all human tumors, and of these ~85% are due to mutations in a particular RAS gene: KRAS. Recent studies revealed that KRAS harbors four druggable allosteric sites. Here, we have (a) used molecular simulations to generate ensembles of wild type and four major oncogenic KRAS mutants (G12V, G12D, G13D, and Q61H); (b) characterized the druggability of each allosteric pocket in each protein; (c) conducted extensive ensemble-based virtual screening using pocket-tailored ligand libraries; (d) prioritized hits through hierarchical postdocking analysis; and (e) validated predicted hits with NMR. Of the 785 diverse potential hits identified by our in silico analysis, we tested 90 for their ability to bind KRAS using NMR and found that nine cause backbone amide chemical shift perturbations of residues near the functionally responsive switch loops, suggesting potential binding. We conducted detailed biophysical analyses on a novel indole-based compound to demonstrate the potential of our workflow to yield lead compounds. We believe the detailed information documented in this work regarding the druggability profile of each allosteric site and the chemical fingerprints of compounds that target them will serve as vital resources for future structure-based drug design efforts against KRAS, a high-value target for cancer therapy.

Coumarin derivatives as acetyl- and butyrylcholinestrase inhibitors: An in vitro, molecular docking, and molecular dynamics simulations study

Alzheimer's disease is an irreversible and progressive brain disease that can cause problems with memory and thinking skills. It is characterized by loss of cognitive ability and severe behavioral abnormalities, and could lead to death. Cholinesterases (ChEs) play a crucial role in the control of cholinergic transmission, and subsequently, the acetylcholine level in the brain is upgraded by inhibition of ChEs. Coumarins have been shown to display potential cholinesterase inhibitory action, where the aromatic moiety has led to the design of new candidates that could inhibit Aβ aggregation. Accordingly, the present work is an in vitro activity, along with docking and molecular dynamics (MD) simulation studies of synthesized coumarin derivatives, to explore the plausible binding mode of these compounds inside the cholinesterase enzymes. For this purpose, a series of previously prepared N1-(coumarin-7-yl) derivatives were screened in vitro for acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitory activities. The assayed compounds exhibited moderate inhibitory activity against AChE, with IC₅₀ values ranging from 42.5 ± 2.68 to 442 ± 3.30 μM. On the other hand, the studied compounds showed remarkable activity against BChE with IC₅₀ values ranging from 2.0 ± 1.4 nM to 442 ± 3.30 μM. In order to better understand the ligand binding site interaction of compounds and the stability of protein-ligand complexes, a molecular docking with molecular dynamics simulation of 5000 ps in an explicit solvent system was carried out for both cholinesterases. We concluded that the tested coumarin derivatives are potential candidates as leads for potent and efficacious ChEs inhibitors.

In Silico Evaluation of Ibuprofen and Two Benzoylpropionic Acid Derivatives with Potential Anti-Inflammatory Activity

Inflammation is a complex reaction involving cellular and molecular components and an unspecific response to a specific aggression. The use of scientific and technological innovations as a research tool combining multidisciplinary knowledge in informatics, biotechnology, chemistry and biology are essential for optimizing time and reducing costs in the drug design. Thus, the integration of these in silico techniques makes it possible to search for new anti-inflammatory drugs with better pharmacokinetic and toxicological profiles compared to commercially used drugs. This in silico study evaluated the anti-inflammatory potential of two benzoylpropionic acid derivatives (MBPA and DHBPA) using molecular docking and their thermodynamic profiles by molecular dynamics, in addition to predicting oral bioavailability, bioactivity and toxicity. In accordance to our predictions the derivatives proposed here had the potential capacity for COX-2 inhibition in the human and mice enzyme, due to containing similar interactions with the control compound (ibuprofen). Ibuprofen showed toxic predictions of hepatotoxicity (in human, mouse and rat; toxicophoric group 2-arylacetic or 3-arylpropionic acid) and irritation of the gastrointestinal tract (in human, mouse and rat; toxicophoric group alpha-substituted propionic acid or ester) confirming the literature data, as well as the efficiency of the DEREK 10.0.2 program. Moreover, the proposed compounds are predicted to have a good oral bioavailability profile and low toxicity (LD50 < 700 mg/kg) and safety when compared to the commercial compound. Therefore, future studies are necessary to confirm the anti-inflammatory potential of these compounds.

Application of ESMACS binding free energy protocols to diverse datasets: Bromodomain-containing protein 4

As the application of computational methods in drug discovery pipelines becomes more widespread it is increasingly important to understand how reproducible their results are and how sensitive they are to choices made in simulation setup and analysis. Here we use ensemble simulation protocols, termed ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent), to investigate the sensitivity of the popular molecular mechanics Poisson-Boltzmann surface area (MMPBSA) methodology. Using the bromodomain-containing protein 4 (BRD4) system bound to a diverse set of ligands as our target, we show that robust rankings can be produced only through combining ensemble sampling with multiple trajectories and enhanced solvation via an explicit ligand hydration shell.