Identification of potent BRD4-BD1 inhibitors using classical and steered molecular dynamics based free energy analysis.

In the present work a combination of traditional and steered molecular dynamics based techniques were employed to identify potential inhibitors against the human BRD4 protein (BRD4- BD1); an established drug target for multiple illnesses including various malignancies. Quinoline derivatives that were synthesized in-house were tested for their potential as new BRD4-BD1 inhibitors. Initially molecular docking experiments were performed to determine the binding poses of BRD4-BD1 inhibitors. To learn more about the thermodynamics of inhibitor binding to the BRD4-BD1 active site, the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) free energy calculations were conducted afterwards. The findings of the MM-PBSA analysis were further reinforced by performing steered umbrella sampling simulations which revealed crucial details about the binding/unbinding process of the most potent quinoline derivatives at the BRD4-BD1 active site. We report a novel quinoline derivative which can be developed into a fully functional BRD4-BD1 inhibitor after experimental validation. The identified compound (4 g) shows better properties than the standard BRD4-BD1 inhibitors considered in the study. The study also highlights the crucial role of Gln78, Phe79, Trp81, Pro82, Phe83, Gln84, Gln85, Val87, Leu92, Leu94, Tyr97, Met105, Cys136, Asn140, Ile146 and Met149 in inhibitor binding. The study provides a possible lead candidate and key amino acids involved in inhibitor recognition and binding at the active site of BRD4-BD1 protein. The findings might be of significance to medicinal chemists involved in the development of potent BRD4-BD1 inhibitors.

On the construction of LIECE models for the serotonin receptor 5-HT[Formula: see text]R.

Computer-aided approaches to ligand design need to balance accuracy with speed. This is particularly true for one of the key parameters to be optimized during ligand development, the free energy of binding ([Formula: see text]G[Formula: see text]). Here, we developed simple models based on the Linear Interaction Energy approximation to free energy calculation for a G protein-coupled receptor, the serotonin receptor 2A, and critically evaluated their accuracy. Several lessons can be taken from our calculations, providing information on the influence of the docking software used, the conformational state of the receptor, the cocrystallized ligand, and its comparability to the training/test ligands.

Assessing the performance of docking, FEP, and MM/GBSA methods on a series of KLK6 inhibitors.

Kallikrein 6 (KLK6) is an attractive drug target for the treatment of neurological diseases and for various cancers. Herein, we explore the accuracy and efficiency of different computational methods and protocols to predict the free energy of binding (ΔGbind) for a series of 49 inhibitors of KLK6. We found that the performance of the methods varied strongly with the tested system. For only one of the three KLK6 datasets, the docking scores obtained with rDock were in good agreement (R2 ≥ 0.5) with experimental values of ΔGbind. A similar result was obtained with MM/GBSA (using the ff14SB force field) calculations based on single minimized structures. Improved binding affinity predictions were obtained with the free energy perturbation (FEP) method, with an overall MUE and RMSE of 0.53 and 0.68 kcal/mol, respectively. Furthermore, in a simulation of a real-world drug discovery project, FEP was able to rank the most potent compounds at the top of the list. These results indicate that FEP can be a promising tool for the structure-based optimization of KLK6 inhibitors.

Enhanced receptor binding of SARS-CoV-2 through networks of hydrogen-bonding and hydrophobic interactions.

Molecular dynamics and free energy simulations have been carried out to elucidate the structural origin of differential protein-protein interactions between the common receptor protein angiotensin converting enzyme 2 (ACE2) and the receptor binding domains of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [A. E. Gorbalenya et al., Nat. Microbiol. 5, 536-544 (2020)] that causes coronavirus disease 2019 (COVID-19) [P. Zhou et al., Nature 579, 270-273 (2020)] and the SARS coronavirus in the 2002-2003 (SARS-CoV) [T. Kuiken et al., Lancet 362, 263-270 (2003)] outbreak. Analysis of the dynamic trajectories reveals that the binding interface consists of a primarily hydrophobic region and a delicate hydrogen-bonding network in the 2019 novel coronavirus. A key mutation from a hydrophobic residue in the SARS-CoV sequence to Lys417 in SARS-CoV-2 creates a salt bridge across the central hydrophobic contact region, which along with polar residue mutations results in greater electrostatic complementarity than that of the SARS-CoV complex. Furthermore, both electrostatic effects and enhanced hydrophobic packing due to removal of four out of five proline residues in a short 12-residue loop lead to conformation shift toward a more tilted binding groove in the complex in comparison with the SARS-CoV complex. On the other hand, hydrophobic contacts in the complex of the SARS-CoV-neutralizing antibody 80R are disrupted in the SARS-CoV-2 homology complex model, which is attributed to failure of recognition of SARS-CoV-2 by 80R.

MD simulation of the interaction between sialoglycans and the second sialic acid binding site of influenza A virus N1 neuraminidase.

The neuraminidases (NAs) of avian influenza viruses (IAVs) contain a second sialic acid-binding site (2SBS), historically known as the hemadsorption site, which is separated from the sialyl-hydrolase catalytic site and serves to facilitate NA catalytic activity towards multivalent sialyl-capped glycoconjugates. Transmission and adaptation of avian IAVs to humans decreases hemadsorption and catalytic activities of the NA. Here, we report the molecular recognition features of the NA 2SBS of two pandemic H1N1 IAVs, A/Brevig Mission /1/1918 (BM18) and A/California/04/2009 (CA09), differing by their 2SBS activity. Using explicit solvent MD simulation, molecular mechanics, and glycosidic conformation analysis we initially analyzed the interactions of BM18 2SBS with two sialyllacto-N-tetraose pentasaccharides, 3'SLN-LC and 6'SLN-LC, which are models for the glycan receptors of IAVs in birds and humans, respectively. These studies characterize the binding specificity of BM18 2SBS towards human-type and avian-type receptors and identifies the key amino acids that affects binding. We next compared the interactions of the 2SBSs of BM18 and CA09 with 6'SLN-LC, revealing the critical effect of amino acid 372 on binding. Our results expand the current knowledge of the molecular features of NA 2SBSs and its alteration during the adaptation of avian IAVs to humans.

Molecular Dynamics Simulations of Curved Lipid Membranes.

Eukaryotic cells contain membranes with various curvatures, from the near-plane plasma membrane to the highly curved membranes of organelles, vesicles, and membrane protrusions. These curvatures are generated and sustained by curvature-inducing proteins, peptides, and lipids, and describing these mechanisms is an important scientific challenge. In addition to that, some molecules can sense membrane curvature and thereby be trafficked to specific locations. The description of curvature sensing is another fundamental challenge. Curved lipid membranes and their interplay with membrane-associated proteins can be investigated with molecular dynamics (MD) simulations. Various methods for simulating curved membranes with MD are discussed here, including tools for setting up simulation of vesicles and methods for sustaining membrane curvature. The latter are divided into methods that exploit scaffolding virtual beads, methods that use curvature-inducing molecules, and methods applying virtual forces. The variety of simulation tools allow researcher to closely match the conditions of experimental studies of membrane curvatures.

Why Does the Novel Coronavirus Spike Protein Interact so Strongly with the Human ACE2? A Thermodynamic Answer.

The SARS-CoV-2 pandemic is the biggest health concern today, but until now there is no treatment. One possible drug target is the receptor binding domain (RBD) of the coronavirus' spike protein, which recognizes the human angiotensin-converting enzyme 2 (hACE2). Our in silico study discusses crucial structural and thermodynamic aspects of the interactions involving RBDs from the SARS-CoV and SARS-CoV-2 with the hACE2. Molecular docking and molecular dynamics simulations explain why the chemical affinity of the new SARS-CoV-2 for hACE2 is much higher than in the case of SARS-CoV, revealing an intricate pattern of hydrogen bonds and hydrophobic interactions and estimating a free energy of binding, which is consistently much more negative in the case of SARS-CoV-2. This work presents a chemical reason for the difficulty in treating the SARS-CoV-2 virus with drugs targeting its spike protein and helps to explain its infectiousness.

Oxygen binding isotope effects of triazole-based HIV-1 reverse transcriptase inhibitors indicate the actual binding site.

Binding isotope effects (BIEs) associated with binding of four triazole-based ligands to HIV-1 reverse transcriptase have been calculated at the QM/MM MD level of theory. Two main binding sites: allosteric cavity and RNase H active site, as well as three other sites reported in the literature (the Knuckles, the NNRTI Adjacent, and Incoming Nucleotide Binding) have been considered. The interactions between inhibitors and these protein sites have been quantified by binding free energies obtained from free energy perturbation (FEP) calculations, supported by interaction energy analysis. It has been shown that binding in the allosteric cavity can be distinguished from binding to other sites based on BIEs as it is associated with normal 18O-BIEs of the carbonyl oxygen atom while binding to RNase H active site is characterized by inverse binding isotope effect (18O-BIE < 1). For other sites 18O-BIEs close to unity are predicted. This information points to oxygen binding isotope effects of carbonyl group as indicative of the actual binding site of studied inhibitors.

pH modulates the TGF-ß ligands binding to the receptors: a computational analysis.

In spite of showing high sequence similarity and forming structurally similar ternary complex in vitro, the in vivo role of TGF-ß1 and TGF-ß3 ligands suggests against their functional redundancy and necessitates the importance for the study of the specificity of these ligands. A comparative computational analysis of binary and ternary complexes of these two ligands shows that anchor residues of ligand and receptor at TGF-ß:TßR2 interface are similar in both complexes. However, the potential anchor residues of TGF-ß at TGF-ß:TßR1 interface are different, Tyr50 and Lys51 in TGF-ß3 complex and Lys60 and Tyr6 in TGF-ß1 complex. Pro55 and Asp57 of TßRI may act as anchor residues in complexes of both ligands along with Ile54 for TGF-ß3 complex and Val61 for TGF-ß1 complex. Arg58 of TßR1 acts as a potential hot residue for TGF-ß3 ternary complex but not for TGF-ß1 ternary complex formation whereas Pro55 and Phe60 may act as hot residues for both complexes. The Delphi analysis of the pH dependence of the binding energy indicates that pH has a remarkable effect on the binding energy of TßR2 to the open form of TGF-ß3. Lowering of pH from 7 to 4 favors binding of the open form of TGF-ß3 to TßR2. Now, apart from the residues at pH 7, residues Arg25, Lys31 and Arg94 of TGF-ß3 and Asp118 and Glu119 of TßR2 also contribute significantly to the binding energy. Contrary to the binding energy of TßR2 to TGF-ß3/TGF-ß1, TßR1 shows appreciable pH dependence for its binding in ternary complex of TGF-ß3/TGF-ß1. In TGF-ß3 ternary complex, the TßR1 electrostatic interaction energy disfavors complex formation at pH 7 while it is favored at pH 4.

ProBASS - a language model with sequence and structural features for predicting the effect of mutations on binding affinity.

Protein-protein interactions (PPIs) govern virtually all cellular processes. Even a single mutation within PPI can significantly influence overall protein functionality and potentially lead to various types of diseases. To date, numerous approaches have emerged for predicting the change in free energy of binding (ΔΔGbind) resulting from mutations, yet the majority of these methods lack precision. In recent years, protein language models (PLMs) have been developed and shown powerful predictive capabilities by leveraging both sequence and structural data from protein-protein complexes. Yet, PLMs have not been optimized specifically for predicting ΔΔGbind. We developed an approach to predict effects of mutations on PPI binding affinity based on two most advanced protein language models ESM2 and ESM-IF1 that incorporate PPI sequence and structural features, respectively. We used the two models to generate embeddings for each PPI mutant and subsequently fine-tuned our model by training on a large dataset of experimental ΔΔGbind values. Our model, ProBASS (Protein Binding Affinity from Structure and Sequence) achieved a correlation with experimental ΔΔGbind values of 0.83 ± 0.05 for single mutations and 0.69 ± 0.04 for double mutations when model training and testing was done on the same PDB. Moreover, ProBASS exhibited very high correlation (0.81 ± 0.02) between prediction and experiment when training and testing was performed on a dataset containing 2325 single mutations in 132 PPIs. ProBASS surpasses the state-of-the-art methods in correlation with experimental data and could be further trained as more experimental data becomes available. Our results demonstrate that the integration of extensive datasets containing ΔΔGbind values across multiple PPIs to refine the pre-trained PLMs represents a successful approach for achieving a precise and broadly applicable model for ΔΔGbind prediction, greatly facilitating future protein engineering and design studies.

Combining Complementarity and Binding Energetics in the Assessment of Protein Interactions: EnCPdock-A Practical Manual.

The combined effect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong determinant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and nonlocal complementarity (EC) in PPIs using the complementarity plot. It further derives an AI-based ΔGbinding with a prediction accuracy comparable to the state of the art. This book chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.

Exploring the Synergistic Mechanism of AP2A2 Transcription Factor Inhibition via Molecular Modeling and Simulations as a Novel Computational Approach for Combating Breast Cancer: In Silico Interpretations.

Studies have shown that transcription factor AP2A2 (activator protein-2 alpha-2) is involved in the expression of DLEC1, a tumor suppressor gene, which, when mutated, will cause breast cancer and is thus an excellent target for breast cancer studies. Therefore, in the present research, a synergistic approach toward combating breast cancer is proposed by blocking AP2A2 factor, and allowing the cancer cells to be sensitive to anti-cancer drugs. The effect of AP2A2 on breast cancer was first understood via gene analysis from cBioPortal. AP2A2 was then modeled using RaptorX and its structure was validated from Ramachandran plots. Using all ligands from MolPort database, molecular docking was performed against AP2A2, from which the top three best docked ligands were studied for toxicity in humans using Protox-II. Once the ligands passed these tests, the best complexes were simulated at 200ns in Desmond Maestro, to comprehend their stabilities, followed by the computations of free energies of binding via Molecular mechanics- Generalized Born Solvent Accessibility method (MM-GBSA). The results showed that molecules MolPort-005-945-556 (sachharolipids), MolPort-001-741-124 (flavonoids), and MolPort-005-944-667 (lignan glycosides) with AP2A2 passed toxicity evaluation and belonged to toxicity classes 6, 5, and 5, respectively, with good docking energies. 200 ns simulations revealed stable complexes with slight conformational changes. Stability of ligands was confirmed via snapshots at every 20 ns of the trajectory. Radial distribution of these molecules against the protein revealed very slight deviation from binding pocket. Additionally, the free binding energies for these complexes were found to be - 54.93 ± 12.982 kcal/mol, - 44.39 ± 14.393 kcal/mol, and - 66.51 ± 13.522 kcal/mol, respectively. A preliminary computational validation of the inability of AP2A2 to bind to DLEC1 in the presence of ligands offers beneficial insights into the potential of these ligands. Therefore, this study sheds light on the potential natural molecules that could stably block AP2A2 with least deviation and act in synergy to aid anti-cancer drugs work on breast cancer cells.

Investigating the Molecular Interactions of Quinoline Derivatives for Antibacterial Activity Against Bacillus subtilis: Computational Biology and In Vitro Study Interpretations.

Bacterial infections are evolving and one of the chief problems is emergence and prevalence of antibacterial resistance. Moreover, certain strains of Bacillus subtilis have become resistant to several antibiotics. To counteract this menace, the present work aimed to comprehend the antibacterial activity of synthesized two quinoline derivatives against Bacillus subtilis. Toxicity predictions via Protox II, SwissADME and T.E.S.T (Toxicity Estimation Software Tool) revealed that these derivatives were non-toxic and had little to no adverse effects. Molecular docking studies carried out in Schrodinger with two quinoline derivatives (referred Q1 and Q2) docked against selected target proteins (PDB IDs: 2VAM and1FSE) of B. subtilis demonstrated ideal binding energies (2VAM-Q1: - 4.63 kcal/mol and 2VAM-Q2: - 4.46 kcal/mol, and 1FSE-Q1: - 3.51 kcal/mol, 1FSE-Q2: - 6.34 kcal/mol). These complexes were simulated at 100 ns and the outcomes revealed their stability with slight conformational changes. Anti-microbial assay via disc diffusion method revealed zones of inhibition showing that B. subtilis was inhibited by both Q1 and Q2, with Q2 performing slightly better than Q1, pointing towards its effectiveness against this organism and necessitating further study on other bacteria in prospective studies. Thus, this study demonstrates that our novel quinoline derivatives exhibit antibacterial properties against Bacillus subtilis and can act as potent anti-bacterials.

Computational Mutagenesis at the SARS-CoV-2 Spike Protein/Angiotensin-Converting Enzyme 2 Binding Interface: Comparison with Experimental Evidence.

The coronavirus disease-2019 (COVID-19) pandemic, caused by the pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), started in China during late 2019 and swiftly spread worldwide. Since COVID-19 emergence, many therapeutic regimens have been relentlessly explored, and although two vaccines have just received emergency use authorization by different governmental agencies, antiviral therapeutics based neutralizing antibodies and small-drug inhibitors can still be vital viable options to prevent and treat SARS-CoV-2 infections. The viral spike glycoprotein (S-protein) is the key molecular player that promotes human host cellular invasion via recognition of and binding to the angiotensin-converting enzyme 2 gene (ACE2). In this work, we report the results obtained by mutating in silico the 18 ACE2 residues and the 14 S-protein receptor binding domain (S-RBDCoV-2) residues that contribute to the receptor/viral protein binding interface. Specifically, each wild-type protein-protein interface residue was replaced by a hydrophobic (isoleucine), polar (serine and threonine), charged (aspartic acid/glutamic acid and lysine/arginine), and bulky (tryptophan) residue, respectively, in order to study the different effects exerted by nature, shape, and dimensions of the mutant amino acids on the structure and strength of the resulting binding interface. The computational results were next validated a posteriori against the corresponding experimental data, yielding an overall agreement of 92%. Interestingly, a non-negligible number of mis-sense variations were predicted to enhance ACE2/S-RBDCoV-2 binding, including the variants Q24T, T27D/K/W, D30E, H34S7T/K, E35D, Q42K, L79I/W, R357K, and R393K on ACE2 and L455D/W, F456K/W, Q493K, N501T, and Y505W on S-RBDCoV-2, respectively.

Novel σ1 antagonists designed for tumor therapy: Structure - activity relationships of aminoethyl substituted cyclohexanes.

Depending on the substitution pattern and stereochemistry, 1,3-dioxanes 1 with an aminoethyl moiety in 4-position represent potent σ1 receptor antagonists. In order to increase the stability, a cyclohexane ring first replaced the acetalic 1, 3-dioxane ring of 1. A large set of aminoethyl substituted cyclohexane derivatives was prepared in a six-step synthesis. All enantiomers and diastereomers were separated by chiral HPLC at the stage of the primary alcohol 7, and their absolute configuration was determined by CD spectroscopy. Neither the relative nor the absolute configuration had a large impact on the σ1 affinity. The highest σ1 affinity was found for cis-configured benzylamines (1R,3S)-11 (Ki = 0.61 nM) and (1S,3R)-11 (Ki = 1.3 nM). Molecular dynamics simulations showed that binding of (1R,3S)-11 at the σ1 receptor is stabilized by the typical polar interaction of the protonated amino moiety with the carboxy group of E172 which is optimally oriented by an H-bond interaction with Y103. The lipophilic interaction of I124 with the N-substituent also contributes to the high σ1 affinity of the benzylamines. The antagonistic activity was determined in a Ca2+ influx assay in retinal ganglion cells. The enantiomeric cis-configured benzylamines (1R,3S)-11 and (1S,3R)-11 were able to inhibit the growth of DU145 cells, a highly aggressive human prostate tumor cell line. Moreover, cis-11 could also inhibit the growth of further human tumor cells expressing σ1 receptors. The experimentally determined logD7.4 value of 3.13 for (1R,3S)-11 is in a promising range regarding membrane penetration. After incubation with mouse liver microsomes and NADPH for 90 min, 43% of the parent (1R,3S)-11 remained unchanged, indicating intermediate metabolic stability. Altogether, nine metabolites including one glutathione adduct were detected by means of LC-MS analysis.

Computational Alanine Scanning and Structural Analysis of the SARS-CoV-2 Spike Protein/Angiotensin-Converting Enzyme 2 Complex.

The recent emergence of the pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent for the coronavirus disease 2019 (COVID-19), is causing a global pandemic that poses enormous challenges to global public health and economies. SARS-CoV-2 host cell entry is mediated by the interaction of the viral transmembrane spike glycoprotein (S-protein) with the angiotensin-converting enzyme 2 gene (ACE2), an essential counter-regulatory carboxypeptidase of the renin-angiotensin hormone system that is a critical regulator of blood volume, systemic vascular resistance, and thus cardiovascular homeostasis. Accordingly, this work reports an atomistic-based, reliable in silico structural and energetic framework of the interactions between the receptor-binding domain of the SARS-CoV-2 S-protein and its host cellular receptor ACE2 that provides qualitative and quantitative insights into the main molecular determinants in virus/receptor recognition. In particular, residues D38, K31, E37, K353, and Y41 on ACE2 and Q498, T500, and R403 on the SARS-CoV-2 S-protein receptor-binding domain are determined as true hot spots, contributing to shaping and determining the stability of the relevant protein-protein interface. Overall, these results could be used to estimate the binding affinity of the viral protein to different allelic variants of ACE2 receptors discovered in COVID-19 patients and for the effective structure-based design and development of neutralizing antibodies, vaccines, and protein/protein inhibitors against this terrible new coronavirus.

Role of Structural Features in Oligomerization, Active-Site Integrity and Ligand Binding of Ribose-1,5-Bisphosphate Isomerase.

Pentose bisphosphate pathway, exclusively found in archaea, is similar to the pentose phosphate pathway present in bacteria and eukarya. In pentose bisphosphate pathway, the conversion of ribose moieties of nucleosides into 3-phosphoglycerate (3-PGA) involves multiple steps; one of them being the conversion of ribose-1,5-bisphosphate (R15P) to ribulose-1,5-bisphosphate (RuBP) catalyzed by an enzyme ribose-1,5-bisphosphate isomerase (R15Pi). The availability of the three-dimensional structure of R15Pi had facilitated the understanding of various structural and functional aspects of the enzyme. Nevertheless, the structure of R15Pi also left several significant questions unanswered that would aid in understanding the structure-function relationship of the enzyme. Thus, we have taken up a computational approach to further understand the role of various structural features of the enzyme R15Pi. Results obtained from molecular dynamics (MD) simulations aided in understanding the obligation of the enzyme R15Pi to oligomerize and also in deciphering the role of catalytic residue(s) in structural stability. Identification of invariant water molecules of the enzyme R15Pi helped in discerning their significance at the active-site pocket and structurally important regions. Further, molecular docking studies allowed the identification of the amino acid residues essential for holding the substrate (R15P) or product (RuBP) in the vicinity of the active site of the enzyme R15Pi. Interestingly, results of the molecular docking studies assisted in the identification of an "alternate binding site" for the substrate, R15P. Finally, based on these results, we propose a mechanism of "substrate sliding" for the enzyme R15Pi.

Tailoring the Variational Implicit Solvent Method for New Challenges: Biomolecular Recognition and Assembly.

Predicting solvation free energies and describing the complex water behavior that plays an important role in essentially all biological processes is a major challenge from the computational standpoint. While an atomistic, explicit description of the solvent can turn out to be too expensive in large biomolecular systems, most implicit solvent methods fail to capture "dewetting" effects and heterogeneous hydration by relying on a pre-established (i.e., guessed) solvation interface. Here we focus on the Variational Implicit Solvent Method, an implicit solvent method that adds water "plasticity" back to the picture by formulating the solvation free energy as a functional of all possible solvation interfaces. We survey VISM's applications to the problem of molecular recognition and report some of the most recent efforts to tailor VISM for more challenging scenarios, with the ultimate goal of including thermal fluctuations into the framework. The advances reported herein pave the way to make VISM a uniquely successful approach to characterize complex solvation properties in the recognition and binding of large-scale biomolecular complexes.

Conformational and energy evaluations of novel peptides binding to dengue virus envelope protein.

Effective novel peptide inhibitors which targeted the domain III of the dengue envelope (E) protein by blocking dengue virus (DENV) entry into target cells, were identified. The binding affinities of these peptides towards E-protein were evaluated by using a combination of docking and explicit solvent molecular dynamics (MD) simulation methods. The interactions of these complexes were further investigated by using the Molecular Mechanics-Poisson Boltzmann Surface Area (MMPBSA) and Molecular Mechanics Generalized Born Surface Area (MMGBSA) methods. Free energy calculations of the peptides interacting with the E-protein demonstrated that van der Waals (vdW) and electrostatic interactions were the main driving forces stabilizing the complexes. Interestingly, calculated binding free energies showed good agreement with the experimental dissociation constant (Kd) values. Our results also demonstrated that specific residues might play a crucial role in the effective binding interactions. Thus, this study has demonstrated that a combination of docking and molecular dynamics simulations can accelerate the identification process of peptides as potential inhibitors of dengue virus entry into host cells.

Ligand efficiency metrics in drug discovery: the pros and cons from a practical perspective.

INTRODUCTION: Ligand efficiency metrics are almost universally accepted as a valuable indicator of compound quality and an aid to reduce attrition. Areas covered: In this review, the authors describe ligand efficiency metrics giving a balanced overview on their merits and points of weakness in order to enable the readers to gain an informed opinion. Relevant theoretical breakthroughs and drug-like properties are also illustrated. Several recent exemplary case studies are discussed in order to illustrate the main fields of application of ligand efficiency metrics. Expert opinion: As a medicinal chemist guide, ligand efficiency metrics perform in a context- and chemotype-dependent manner; thus, they should not be used as a magic box. Since the 'big bang' of efficiency metrics occurred more or less ten years ago and the average time to develop a new drug is over the same period, the next few years will give a clearer outlook on the increased rate of success, if any, gained by means of these new intriguing tools.