Improving the Accuracy of the Linear Interaction Energy Method for Solvation Free Energies

The Interaction Mechanism Between C14-Polyacetylene Compounds and the Rat TRPA1 Receptor: An In Silico Study

Polyacetylene (PA) compounds, as natural products, exhibit remarkable properties and distinctive chemical activities. Three structurally similar C14-PA compounds-Echinophorin D, Echinophorin B, and Echinophorin A-extracted from plants demonstrate varying biological activities on the Transient Receptor Potential Channel A1 (TRPA1) protein, which belongs to the TRP (Transient Receptor Potential) family. In the current study, we investigated the binding modes of these three PA compounds with TRPA1 using molecular dynamics (MD), molecular docking, binding free energy calculations, and quantum mechanics/molecular mechanics (QM/MM) methods. Initially, a putative binding site (site-II) in TRPA1 was identified for these compounds; Echinophorin B was found to stabilize the upward A-loop of TRPA1, which is critical for its activation. Furthermore, the binding affinity calculations of PA compounds through molecular fragment decomposition indicate that the arrangement of two triple bonds and one double bond in C14-PA compounds is vital for regulating TRPA1 bioactivity. Additionally, the lipophilic and electronic properties of the three molecules were analyzed in relation to binding affinity, establishing a correlation between TRPA1 activity and these molecular properties.

Solution Ionic Strength Can Modulate Functional Loop Conformations in E. coli Dihydrofolate Reductase

The observation of multiple conformations of a functional loop (termed M20) in the Escherichia coli dihydrofolate reductase (ecDHFR) enzyme triggered the proposition that large-scale motions of protein structural elements contribute to enzyme catalysis. The transition of the M20 loop from a closed conformation to an occluded conformation was thought to aid the rate-limiting release of the products. However, the influence of charged species in the solution environment on the observed M20 loop conformations, independent of charged ligands bound to the enzyme, had not been considered. Molecular dynamics simulations of ecDHFR in model CaCl2 solutions of varying molar ionic strengths IM reveal a substantial free energy barrier between occluded and closed M20 loop states at IM exceeding the E. coli threshold (∼0.24 M). This barrier may facilitate crystallization of ecDHFR in the occluded state, consistent with ecDHFR structures obtained at IM exceeding 0.3 M. At lower IM (≤0.15 M), the M20 loop can explore the occluded state, but prefers an open/partially closed conformation, again consistent with ecDHFR structures. Our findings caution against using ecDHFR structures obtained at nonphysiological ionic strengths in interpreting catalytic events or in structure-based drug design.

Solvation free energies of alcohols in water: temperature and pressure dependences

Solvation free energies μ* of amphiphilic species, methanol and 1,2-hexanediol, are obtained as a function of temperature or pressure based on molecular dynamics simulations combined with efficient free-energy calculation methods. In general, μ* of an amphiphile can be divided into and , the nonpolar and electrostatic contributions, and the former is further divided into and which are the work of cavity formation process and the free energy change due to weak, attractive interactions between the solute molecule and surrounding solvent molecules. We demonstrate that μ* of the two amphiphilic solutes can be obtained accurately using a perturbation combining method, which relies on the exact expressions for and and requires no simulations of intermediate systems between the solute with strong, repulsive interactions and the solute with the van der Waals and electrostatic interactions. The decomposition of μ* gives us several physical insights including that μ* is an increasing function of T due to , that the contributions of hydrophilic groups to the temperature dependence of μ* are additive, and that the contribution of the van der Waals attraction to the solvation volume is greater than that of the electrostatic interactions.

Mechanistic insights into MARK4 inhibition by galantamine toward therapeutic targeting of Alzheimer's disease

Introduction: Hyperphosphorylation of tau is an important event in Alzheimer's disease (AD) pathogenesis, leading to the generation of "neurofibrillary tangles," a histopathological hallmark associated with the onset of AD and related tauopathies. Microtubule-affinity regulating kinase 4 (MARK4) is an evolutionarily conserved Ser-Thr (S/T) kinase that phosphorylates tau and microtubule-associated proteins, thus playing a critical role in AD pathology. The uncontrolled neuronal migration is attributed to overexpressed MARK4, leading to disruption in microtubule dynamics. Inhibiting MARK4 is an attractive strategy in AD therapeutics. Methods: Molecular docking was performed to see the interactions between MARK4 and galantamine (GLT). Furthermore, 250 ns molecular dynamic studies were performed to investigate the stability and conformational dynamics of the MARK4-GLT complex. We performed fluorescence binding and isothermal titration calorimetry studies to measure the binding affinity between GLT and MARK4. Finally, an enzyme inhibition assay was performed to measure the MARK4 activity in the presence and absence of GLT. Results: We showed that GLT, an acetylcholinesterase inhibitor, binds to the active site cavity of MARK4 with an appreciable binding affinity. Molecular dynamic simulation for 250 ns demonstrated the stability and conformational dynamics of the MARK4-GLT complex. Fluorescence binding and isothermal titration calorimetry studies suggested a strong binding affinity. We further show that GLT inhibits the kinase activity of MARK4 significantly (IC50 = 5.87 µM). Conclusion: These results suggest that GLT is a potential inhibitor of MARK4 and could be a promising therapeutic target for AD. GLT's inhibition of MARK4 provides newer insights into the mechanism of GLT's action, which is already used to improve cognition in AD patients.

Xanthatin and 8-epi-xanthatin as new potential colchicine binding site inhibitors: a computational study

CONTEXT

Phytocompounds xanthatin and 8-epi-xanthatin, obtained from Xanthium chinese Mill, showed antitumoral activity in vitro related to the microtubules destabilizing properties of these phytocompounds. Five binding sites for microtubule destabilizing agents have been characterized on tubulin by high-resolution X-ray crystallography: vinca domain, colchicine, pironetin, maytansine site, and more recently, the seventh site. This work aims to develop a comprehensive computational strategy to understand and eventually predict the interaction between xanthatin and 8-epi-xanthatin with the destabilizing-antimitotic binding domain of the tubulin heterodimer. In addition, we propose a putative binding site for these phytocompounds into the microtubule destabilizing binding sites on the tubulin heterodimer. Xanthanolides showed higher stability in the colchicine and pironetin binding sites, whit a greater affinity for the former. In addition, we found that xanthanolides and non-classical colchicine binding site inhibitors share a high structural similarity.

METHODS

The 3D structures for xanthatin and 8-epi-xanthatin were obtained using DFT with the hybrid functional B3LYP and the base 6-31G (d,p), implemented in Gaussian 09. The 3D coordinates for tubulin proteins were downloaded from PDB. The complexes tubulin-xanthanolides were predicted using a Monte-Carlo iterated search combined with the BFGS gradient-based optimizer implemented in the AutoDock Vina. The xanthanolides-tubulin complexes were energy minimized by molecular dynamics simulations at vacuum, and their stabilities were evaluated by solvated molecular dynamics simulations during 100 ns. All molecular dynamics simulations were performed using the conjugate gradient method implemented in NAMD2 and CHARMM36 forcefield.

Targeting human thymidylate synthase: Ensemble-based virtual screening for drug repositioning and the role of water

A drug repositioning computational approach was carried to search inhibitors for human thymidylate synthase. An ensemble-based virtual screening of FDA-approved drugs showed the drugs Imatinib, Lumacaftor and Naldemedine to be likely candidates for repurposing. The role of water in the drug-receptor interactions was revealed by the application of an extended AutoDock scoring function that included the water forcefield. The binding affinity scores when hydrated ligands were docked were improved in the drugs considered. Further binding free energy calculations based on the Molecular Mechanics Poisson-Boltzmann Surface Area method revealed that Imatinib, Lumacaftor and Naldemedine scored -130.7 ± 28.1, -210.6 ± 29.9 and -238.0 ± 25.4 kJ/mol, respectively, showing good binding affinity for the candidates considered. Overall, the analysis of the molecular dynamics trajectory of the receptor-drug complexes revealed stable structures for Imatinib, Lumacaftor and Naldemedine, for the entire simulation time.

Computational insights for predicting the binding and selectivity of peptidomimetic plasmepsin IV inhibitors against cathepsin D

Plasmepsins (Plms) are aspartic proteases involved in the degradation of human hemoglobin by P. falciparum and are essential for the survival and growth of the parasite. Therefore, Plm enzymes are reported as an important antimalarial drug target. Herein, we have applied molecular docking, molecular dynamics (MD) simulations, and binding free energy with the Linear Interaction Energy (LIE) approach to investigate the binding of peptidomimetic PlmIV inhibitors with a particular focus on understanding their selectivity against the human Asp protease cathepsin D (CatD). The residual decomposition analysis results suggest that amino acid differences in the subsite S3 of PlmIV and CatD are responsible for the higher selectivity of the 5a inhibitor. These findings yield excellent agreement with experimental binding data and provide new details regarding van der Waals and electrostatic interactions of subsite residues as well as structural properties of the PlmIV and CatD systems.

Accurate Binding Free Energy Method from End-State MD Simulations

Herein, we introduce a new strategy to estimate binding free energies using end-state molecular dynamics simulation trajectories. The method is adopted from linear interaction energy (LIE) and ANI-2x neural network potentials (machine learning) for the atomic simulation environment (ASE). It predicts the single-point interaction energies between ligand–protein and ligand–solvent pairs at the accuracy of the wb97x/6-31G* level for the conformational space that is sampled by molecular dynamics (MD) simulations. Our results on 54 protein–ligand complexes show that the method can be accurate and have a correlation of R = 0.87–0.88 to the experimental binding free energies, outperforming current end-state methods with reduced computational cost. The method also allows us to compare BFEs of ligands with different scaffolds. The code is available free of charge (documentation and test files) at https://github.com/otayfuroglu/deepQM.

Finding alternatives to 5-fluorouracil: application of ensemble-based virtual screening for drug repositioning against human thymidylate synthase

5-fluorouracil and analogs are used in the treatment of many solid tumours. However, there are many cases of resistance and high toxicity associated with 5-fluorouracil chemotherapy. Repurposing FDA drugs against human thymidylate synthase revealed a number of FDA drugs that have a potential to be further developed for the treatment of various cancers for which 5-fluorouracil and analogs have been used for chemotherapy. Four FDA drugs prioritized for further validation included Erismodegib, Irinotecan, Conivaptan and Ergotamine. The role of water in mediating drug interactions and its contribution to the total binding energy was also shown. MM-PBSA calculations revealed that the binding affinity was the lowest for the hTS-Ergotamine complex (-66.702 ± 1.807 kJ/mol) suggesting moderate inhibition despite a large energetic contribution from van der Waal interactions (-190.889 ± 1.027 kJ/mol).Communicated by Ramaswamy H. Sarma.

Solvent Effects on the Menshutkin Reaction

The Menshutkin reaction is a methyl transfer reaction relevant in fields ranging from biochemistry to chemical synthesis. In the present work, the energetics and solvent distributions for NH₃+MeCl and Pyr+MeBr reactions were investigated in explicit solvent (water, methanol, acetonitrile, benzene, cyclohexane) by means of reactive molecular dynamics simulations. For polar solvents (water, methanol, and acetonitrile) and benzene, strong to moderate catalytic effects for both reactions were found, whereas apolar and bulky cyclohexane interacts weakly with the solute and does not show pronounced barrier reduction. The calculated barrier heights for the Pyr+MeBr reaction in acetonitrile and cyclohexane are 23.2 and 28.1 kcal/mol compared with experimentally measured barriers of 22.5 and 27.6 kcal/mol, respectively. The solvent distributions change considerably between reactant and TS but comparatively little between TS and product conformations of the solute. As the system approaches the transition state, correlated solvent motions occur which destabilize the solvent-solvent interactions. This is required for the system to surmount the barrier. Finally, it is found that the average solvent-solvent interaction energies in the reactant, TS, and product state geometries are correlated with changes in the solvent structure around the solute.

Prediction of molecular interactions and physicochemical properties relevant for vasopressin V2 receptor antagonism

We have developed two ligand- and receptor-based computational approaches to study the physicochemical properties relevant to the biological activity of vasopressin V2 receptor (V2R) antagonist and eventually to predict the expected binding mode to V2R. The obtained quantitative structure activity relationship (QSAR) model showed a correlation of the antagonist activity with the hydration energy (E_H2O), the polarizability (P), and the calculated partial charge on atom N7 (q6) of the common substructure. The first two descriptors showed a positive contribution to antagonist activity, while the third one had a negative contribution. V2R was modeled and further relaxed on a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocoline (POPC) membrane by molecular dynamics simulations. The receptor antagonist complexes were guessed by molecular docking, and the stability of the most relevant structures was also evaluated by molecular dynamics simulations. As a result, amino acid residues Q96, W99, F105, K116, F178, A194, F307, and M311 were identified with the probably most relevant antagonist-receptor interactions on the studied complexes. The proposed QSAR model could explain the molecular properties relevant to the antagonist activity. The contributions to the antagonist-receptor interaction appeared also in agreement with the binding mode of the complexes obtained by molecular docking and molecular dynamics. These models will be used in further studies to look for new V2R potential antagonist molecules.

Tailored Parameterization of the LIE Method for Calculating the Binding Free Energy of Vps34-Inhibitor Complexes

Vps34 is the only isoform of the PI3K family in fungi, making this protein an attractive target to develop new treatments against pathogenic fungi. The high structural similarity between the active sites of the human and fungal Vps34 makes repurposing of human Vps34 inhibitors an appealing strategy. Nonetheless, while some of the cross-reactive inhibitors might have the potential to treat fungal infections, a safer approach to prevent undesired side effects would be to identify molecules that specifically inhibit the fungal Vps34. This study presents the parameterization of four LIE models for estimating the binding free energy of Vps34-inhibitor complexes. Two models are parameterized using a multiparametric linear regression leaving one or more free parameters, while the other two are based on the LIE-D model. All of the models show good predictive capacity (R ² > 0.7, r > 0.85) and a low mean absolute error (MAE < 0.71 kcal/mol). The current study highlights the advantages of LIE-D-derived models when predicting the weight of the different contributions to the binding free energy. It is expected that this study will provide researchers with a valuable tool to identify new Vps34 inhibitors for relevant applications such as cancer treatment and the development of new antimicrobial agents.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

Influence of solution ionic strength on the stabilities of M20 loop conformations in apo E. coli dihydrofolate reductase

Interactions among ions and their specific interactions with macromolecular solutes are known to play a central role in biomolecular stability. However, similar effects in the conformational stability of protein loops that play functional roles, such as binding ligands, proteins, and DNA/RNA molecules, remain relatively unexplored. A well-characterized enzyme that has such a functional loop is Escherichia coli dihydrofolate reductase (ecDHFR), whose so-called M20 loop has been observed in three ordered conformations in crystal structures. To explore how solution ionic strengths may affect the M20 loop conformation, we proposed a reaction coordinate that could quantitatively describe the loop conformation and used it to classify the loop conformations in representative ecDHFR x-ray structures crystallized in varying ionic strengths. The Protein Data Bank survey indicates that at ionic strengths (I) below the intracellular ion concentration-derived ionic strength in E. coli (I ≤ 0.237M), the ecDHFR M20 loop tends to adopt open/closed conformations, and rarely an occluded loop state, but when I is >0.237M, the loop tends to adopt closed/occluded conformations. Distance-dependent electrostatic potentials around the most mobile M20 loop region from molecular dynamics simulations of ecDHFR in equilibrated CaCl₂ solutions of varying ionic strengths show that high ionic strengths (I = 0.75/1.5M) can preferentially stabilize the loop in closed/occluded conformations. These results nicely correlate with conformations derived from ecDHFR structures crystallized in varying ionic strengths. Altogether, our results suggest caution in linking M20 loop conformations derived from crystal structures solved at ionic strengths beyond that tolerated by E. coli to the ecDHFR function.

Structural Basis of Inhibition of Human Insulin-Regulated Aminopeptidase (IRAP) by Benzopyran-Based Inhibitors

Inhibition of the insulin-regulated aminopeptidase (IRAP) improves memory and cognition in animal models. The enzyme has recently been crystallized and several series of inhibitors reported. We herein focused on one series of benzopyran-based inhibitors of IRAP known as the HFI series, with unresolved binding mode to IRAP, and developed a robust computational model to explain the structure-activity relationship (SAR) and potentially guide their further optimization. The binding model here proposed places the benzopyran ring in the catalytic binding site, coordinating the Zn²⁺ ion through the oxygen in position 3, in contrast to previous hypothesis. The whole series of HFI compounds was then systematically simulated, starting from this binding mode, using molecular dynamics and binding affinity estimated with the linear interaction energy (LIE) method. The agreement with experimental affinities supports the binding mode proposed, which was further challenged by rigorous free energy perturbation (FEP) calculations. Here, we found excellent correlation between experimental and calculated binding affinity differences, both between selected compound pairs and also for recently reported experimental data concerning the site directed mutagenesis of residue Phe544. The computationally derived structure-activity relationship of the HFI series and the understanding of the involvement of Phe544 in the binding of this scaffold provide valuable information for further lead optimization of novel IRAP inhibitors.

Accelerated Computation of Free Energy Profile at Ab Initio Quantum Mechanical/Molecular Mechanics Accuracy via a Semiempirical Reference Potential. 3. Gaussian Smoothing on Density-of-States

Calculations of the free energy profile, also known as potential of mean force (PMF), along a chosen collective variable (CV) are now routinely applied in the studies of chemical processes, such as enzymatic reactions and chemical reactions in condensed phases. However, if the ab initio quantum mechanical/molecular mechanics (QM/MM) level of accuracy is required for the PMF, it can be formidably demanding even with the most advanced enhanced sampling methods, such as umbrella sampling. To ameliorate this difficulty, we developed a novel method for the computation of the free energy profile based on the reference-potential method recently, in which a low-level reference Hamiltonian is employed for phase space sampling and the free energy profile can be corrected to the level of interest (the target Hamiltonian) by energy reweighting in a nonparametric way. However, when the reference Hamiltonian is very different from the target Hamiltonian, the calculated ensemble averages, including the PMF, often suffer from numerical instability, which mainly comes from the overestimation of the density-of-states (DoS) in the low-energy region. Stochastic samplings of these low-energy configurations are rare events, and some low-energy conformations may get oversampled in simulations of a finite length. In this work, an assumption of Gaussian distribution is applied to the DoS in each CV bin, and the weight of each configuration is rescaled according to the accumulated DoS. The results show that this smoothing process can remarkably reduce the ruggedness of the PMF and increase the reliability of the reference-potential method.

Cracking a cancer code histone deacetylation in epigenetic: the implication from molecular dynamics simulations on efficacy assessment of histone deacetylase inhibitors

Epigenetic changes, histone acetylation and deacetylation in chromatin have been intensively studied due to their significance in regulating the gene expression. According to the type of tumor, the levels of histone deacetylases (HDAC) are varied. HDAC inhibitors are a new promising class of compounds that inhibit the proliferation of tumor cells. In this study, the inhibitory efficacy of some HDAC inhibitors such as vorinostat, panobinostat, abexinostat, belinostat, resminostat, dacinostat and pracinostat was studied using molecular dynamics simulation. The inhibitory efficacy was estimated by computing the enzyme's stability, positional stability of the individual amino acids and interaction energies of HDLP-inhibitor complexes. It is hoped that this investigation may improve our understanding of the atomic-level description of the inhibitor binding site and how the HDAC inhibitors change the environment of the enzyme's active site. The results obtained from the root-mean-square deviation, the radius of gyration, solvent-accessible surface area, root-mean-square fluctuation, stride server and Ramachandran plot have revealed that the stability of HDLP enzyme with vorinostat, panobinostat and abexinostat is higher than the other studied complexes. According to the calculated values for MM-PBSA, LIE, semi-LIE binding free energies and interaction energies, the stability of the HDLP enzyme varies as panobinostat > abexinostat > vorinostat where resminostat complex showed relatively low stability. The ligandability and drugability values also give the same trend as above. The findings revealed that the panobinostat and abexinostat are potential lead compounds as reference inhibitor vorinostat. Therefore, it is possible to use these drugs as HDAC inhibitors in clinical practices. Also, the outcomes of this study could be utilized to identify new inhibitors for clinical research.Communicated by Ramaswamy H. Sarma.

A computational strategy to understand structure-activity relationship of 1,3-disubstituted imidazole [1,5-α] pyrazine derivatives described as ATP competitive inhibitors of the IGF-1 receptor related to Ewing sarcoma

We followed a comprehensive computational strategy to understand and eventually predict the structure-activity relationship of thirty-three 1,3-disubstituted imidazole [1,5-α] pyrazine derivatives described as ATP competitive inhibitors of the IGF-1 receptor related to Ewing sarcoma. The quantitative structure-activity relationship model showed that the inhibitory potency is correlated with the molar volume, a steric descriptor and the net charge calculated value on atom C1 (q1) and N4 (q4) of the pharmacophore, all of them appearing to give a positive contribution to the inhibitory activity. According to experimental and calculated values, the most potent compound would be 3-[4-(azetidin-2-ylmethyl) cyclohexyl]-1-[3-(benzyloxy) phenyl] imidazo [1,5-α]pyrazin-8-amine (compound 23). Docking was used to guess important residues involved in the ATP-competitive inhibitory activity. It was validated by 200 ns of molecular dynamics (MD) simulation using improved linear interaction energy (LIE) method. MD of previously preferred structures by docking shows that the most potent ligand could establish hydrogen bonds with the ATP-binding site of the receptor, and the Ser979 and Ser1059 residues contribute favourably to the binding stability of compound 23. MD simulation also gave arguments about the chemical structure of the compound 23 being able to fit in the ATP-binding pocket, expecting to remain stable into it during the entire simulation and allowing us to hint the significant contribution expected to be given by electrostatic and hydrophobic interactions to the ligand-receptor complex stability. This computational combined strategy here described could represent a useful and effective prime approach to guide the identification of tyrosine kinase inhibitors as new lead compounds.

How Good is Jarzynski's Equality for Computer-Aided Drug Design?

Accurate determination of the binding affinity of the ligand to the receptor remains a difficult problem in computer-aided drug design. Here, we study and compare the efficiency of Jarzynski’s equality (JE) combined with steered molecular dynamics and the linear interaction energy (LIE) method by assessing the binding affinity of 23 small compounds to six receptors, including β-lactamase, thrombin, factor Xa, HIV-1 protease (HIV), myeloid cell leukemia-1, and cyclin-dependent kinase 2 proteins. It was shown that Jarzynski’s nonequilibrium binding free energy ΔG_neq^Jar correlates with the available experimental data with the correlation levels R = 0.89, 0.86, 0.83, 0.80, 0.83, and 0.81 for six data sets, while for the binding free energy ΔG_LIE obtained by the LIE method, we have R = 0.73, 0.80, 0.42, 0.23, 0.85, and 0.01. Therefore, JE is recommended to be used for ranking binding affinities as it provides accurate and robust results. In contrast, LIE is not as reliable as JE, and it should be used with caution, especially when it comes to new systems.

Recent Developments in Linear Interaction Energy Based Binding Free Energy Calculations

The linear interaction energy (LIE) approach is an end-point method to compute binding affinities. As such it combines explicit conformational sampling (of the protein-bound and unbound-ligand states) with efficiency in calculating values for the protein-ligand binding free energy ΔG _bind . This perspective summarizes our recent efforts to use molecular simulation and empirically calibrated LIE models for accurate and efficient calculation of ΔG _bind for diverse sets of compounds binding to flexible proteins (e.g., Cytochrome P450s and other proteins of direct pharmaceutical or biochemical interest). Such proteins pose challenges on ΔG _bind computation, which we tackle using a previously introduced statistically weighted LIE scheme. Because calibrated LIE models require empirical fitting of scaling parameters, they need to be accompanied with an applicability domain (AD) definition to provide a measure of confidence for predictions for arbitrary query compounds within a reference frame defined by a collective chemical and interaction space. To enable AD assessment of LIE predictions (or other protein-structure and -dynamic based ΔG _bind calculations) we recently introduced strategies for AD assignment of LIE models, based on simulation and training data only. These strategies are reviewed here as well, together with available tools to facilitate and/or automate LIE computation (including software for combined statistically-weighted LIE calculations and AD assessment).

Binding Dynamics of siRNA with Selected Lipopeptides: A Computer-Aided Study of the Effect of Lipopeptides' Functional Groups and Stereoisomerism

The engineering issues pertaining to nanoparticle systems toward targeted gene therapies have not been fully probed. Recent experiments have identified specific structural characteristics of a novel class of lipopeptides (LP) that may lead to potent nanocarriers intended as RNAi therapeutics, albeit the molecular mechanism that underlies their performance remains unexplored. We conducted molecular dynamics simulations in atomistic detail coupled with free energy computations to study the dynamics and thermodynamics of an acrylate- and an epoxide-derived LP, members of the aforesaid class, upon their binding to siRNA in aqueous solution aiming at examining structure-potency relations. We found that the entropic part of the free energy of binding predominates; moreover, the first LP class tends to disrupt the Watson-Crick base pairing of siRNA, whereas the latter leaves the double helix intact. Moreover, the identified tug-of-war effect between LP-water and LP-siRNA hydrogen bonding in the supramolecular complex can underpin synthesis routes toward tuning the association dynamics. Our simulations on two diastereomers of the epoxide-derived LP showed significant structural and energetics differences upon binding, as a result of steric effects imposed by the different absolute configurations at their chiral centers. These findings may serve as crucial design parameters toward modulating the interplay between complex stability and ease of releasing the nucleic acid drug into the cell.

Effective estimation of the inhibitor affinity of HIV-1 protease via a modified LIE approach

The inhibition of the Human Immunodeficiency Virus Type 1 Protease (HIV-1 PR) can prevent the synthesis of new viruses. Computer-aided drug design (CADD) would enhance the discovery of new therapies, through which the estimation of ligand-binding affinity is critical to predict the most efficient inhibitor. A time-consuming binding free energy method would reduce the usefulness of CADD. The modified linear interaction energy (LIE) approach emerges as an appropriate protocol that performs this task. In particular, the polar interaction free energy, which is obtained via numerically resolving the linear Poisson-Boltzmann equation, plays as an important role in driving the binding mechanism of the HIV-1 PR + inhibitor complex. The electrostatic interaction energy contributes to the attraction between two molecules, but the vdW interaction acts as a repulsive factor between the ligand and the HIV-1 PR. Moreover, the ligands were found to adopt a very strong hydrophobic interaction with the HIV-1 PR. Furthermore, the results obtained corroborate the high accuracy and precision of computational studies with a large correlation coefficient value R = 0.83 and a small RMSE δ RMSE = 1.25 kcal mol-1. This method is less time-consuming than the other end-point methods, such as the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) and free energy perturbation (FEP) approaches. Overall, the modified LIE approach would provide ligand-binding affinity with HIV-1 PR accurately, precisely, and rapidly, resulting in a more efficient design of new inhibitors.

Macrocyclic peptidomimetics as inhibitors of insulin-regulated aminopeptidase (IRAP)

Macrocyclic analogues of the linear hexapeptide, angiotensin IV (AngIV) have proved to be potent inhibitors of insulin-regulated aminopeptidase (IRAP, oxytocinase, EC 3.4.11.3). Along with higher affinity, macrocycles may also offer better metabolic stability, membrane permeability and selectivity, however predicting the outcome of particular cycle modifications is challenging. Here we describe the development of a series of macrocyclic IRAP inhibitors with either disulphide, olefin metathesis or lactam bridges and variations of ring size and other functionality. The binding mode of these compounds is proposed based on molecular dynamics analysis. Estimation of binding affinities (ΔG) and relative binding free energies (ΔΔG) with the linear interaction energy (LIE) method and free energy perturbation (FEP) method showed good general agreement with the observed inhibitory potency. Experimental and calculated data highlight the cumulative importance of an intact N-terminal peptide, the specific nature of the macrocycle, the phenolic oxygen and the C-terminal functionality.

Identification of a C2-symmetric diol based human immunodeficiency virus protease inhibitor targeting Zika virus NS2B-NS3 protease

Zika virus (ZIKV) is an emerging mosquito-borne flavivirus and infection by ZIKV Asian lineage is known to cause fetal brain anomalies and Guillain-Barrés syndrome. The WHO declared ZIKV a global public health emergency in 2016. However, currently neither vaccines nor antiviral prophylaxis/treatments are available. In this study, we report the identification of a C2-symmetric diol-based Human immunodeficiency virus type-1 (HIV) protease inhibitor active against ZIKV NS2B-NS3 protease. The compound, referred to as 9b, was identified by in silico screening of a library of 6265 protease inhibitors. Molecular dynamics (MD) simulation studies revealed that compound 9b formed a stable complex with ZIKV protease. Interaction analysis of compound 9b's binding pose from the cluster analysis of MD simulations trajectories predicted that 9b mostly interacted with ZIKV NS3. Although designed as an aspartyl protease inhibitor, compound 9b was found to inhibit ZIKV serine protease in vitro with IC₅₀ = 143.25 ± 5.45 µM, in line with the in silico results. Additionally, linear interaction energy method (LIE) was used to estimate binding affinities of compounds 9b and 86 (a known panflavivirus peptide hybrid with IC₅₀ = 1.64 ± 0.015 µM against ZIKV protease). The LIE method correctly predicted the binding affinity of compound 86 to be lower than that of 9b, proving to be superior to the molecular docking methods in scoring and ranking compounds. Since most of the reported ZIKV protease inhibitors are positively charged peptide-hybrids, with our without electrophilic warheads, compound 9b represents a less polar and more drug-like non-peptide hit compound useful for further optimization.Communicated by Ramaswamy Sarma.

Two genes in a pathogenicity gene cluster encoding secreted proteins are required for appressorial penetration and infection of the maize anthracnose fungus Colletotrichum graminicola

To avoid pathogen-associated molecular pattern recognition, the hemibiotrophic maize pathogen Colletotrichum graminicola secretes proteins mediating the establishment of biotrophy. Targeted deletion of 26 individual candidate genes and seven gene clusters comprising 32 genes of C. graminicola identified a pathogenicity cluster (CLU5) of five co-linear genes, all of which, with the exception of CLU5b, encode secreted proteins. Targeted deletion of all genes of CLU5 revealed that CLU5a and CLU5d are required for full appressorial penetration competence, with virulence deficiencies independent of the host genotype and organ inoculated. Cytorrhysis experiments and microscopy showed that Δclu5a mutants form pressurized appressoria, but they are hampered in forming penetration pores and fail to differentiate a penetration peg. Whereas Δclu5d mutants elicited WT-like papillae, albeit at increased frequencies, papillae induced by Δclu5a mutants were much smaller than those elicited by the WT. Synteny of CLU5 is not only conserved in Colletotrichum spp. but also in additional species of Sordariomycetes including insect pathogens and saprophytes suggesting importance of CLU5 for fungal biology. Since CLU5a and CLU5d also occur in non-pathogenic fungi and since they are expressed prior to plant invasion and even in vegetative hyphae, the encoded proteins probably do not act primarily as effectors.

A Comparative Linear Interaction Energy and MM/PBSA Study on SIRT1-Ligand Binding Free Energy Calculation

Binding free energy (ΔGbind) computation can play an important role in prioritizing compounds to be evaluated experimentally on their affinity for target proteins, yet fast and accurate ΔGbind calculation remains an elusive task. In this study, we compare the performance of two popular end-point methods, i.e., linear interaction energy (LIE) and molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA), with respect to their ability to correlate calculated binding affinities of 27 thieno[3,2-d]pyrimidine-6-carboxamide-derived sirtuin 1 (SIRT1) inhibitors with experimental data. Compared with the standard single-trajectory setup of MM/PBSA, our study elucidates that LIE allows to obtain direct ("absolute") values for SIRT1 binding free energies with lower compute requirements, while the accuracy in calculating relative values for ΔGbind is comparable (Pearson's r = 0.72 and 0.64 for LIE and MM/PBSA, respectively). We also investigate the potential of combining multiple docking poses in iterative LIE models and find that Boltzmann-like weighting of outcomes of simulations starting from different poses can retrieve appropriate binding orientations. In addition, we find that in this particular case study the LIE and MM/PBSA models can be optimized by neglecting the contributions from electrostatic and polar interactions to the ΔGbind calculations.

Exploring Amyloid-β Dimer Structure Using Molecular Dynamics Simulations

A major hallmark of Alzheimer's disease (AD) is the aggregation of amyloid-β peptides in the brains of people afflicted by the disease. The exact pathway to this catastrophic event is unknown. In this work, a total of 9.5 μs molecular dynamics simulations have been performed to investigate the structure and dynamics of the smallest form of toxic Aβ oligomers, i.e., the Aβ dimers. This study suggests that specific hydrophobic regions are vital in the aggregation process. Different possible structures for Aβ dimers are reported along with their relative binding affinity. These data may be used to design better Aβ-aggregation inhibitors. The diversity of the dimer structures suggests several aggregation pathways.

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.

Adequate prediction for inhibitor affinity of Aβ40 protofibril using the linear interaction energy method

The search for efficient inhibitors targeting Aβ oligomers and fibrils is an important issue in Alzheimer's disease treatment. As a consequence, an accurate and computationally cheap approach to estimate the binding affinity for many ligands interacting with Aβ peptides is very important. Here, the calculated binding free energies of 30 ligands interacting with 12Aβ11-40 peptides using the linear interaction energy (LIE) approach are found to be in good correlation with experimental data (R = 0.79). The binding affinities of these complexes are also calculated by using free energy perturbation (FEP) and molecular mechanic/Poisson-Boltzmann surface area (MM/PBSA) methods. The time-consuming FEP method provides results with similar correlation (R = 0.72), whereas MM/PBSA calculations show very low correlation with experimental data (R = 0.27). In all complexes, van der Waals interactions contribute much more than electrostatic interactions. The LIE model, which is much less time-consuming than both the FEP and MM/PBSA methods, opens the door to accurate and rapid affinity prediction of ligands with Aβ peptides and the design of new ligands.

Discovery of potent and selective inhibitors of the Escherichia coli M1-aminopeptidase via multicomponent solid-phase synthesis of tetrazole-peptidomimetics

The Escherichia coli neutral M1-aminopeptidase (ePepN) is a novel target identified for the development of antimicrobials. Here we describe a solid-phase multicomponent approach which enabled the discovery of potent ePepN inhibitors. The on-resin protocol, developed in the frame of the Distributed Drug Discovery (D3) program, comprises the implementation of parallel Ugi-azide four-component reactions with resin-bound amino acids, thus leading to the rapid preparation of a focused library of tetrazole-peptidomimetics (TPMs) suitable for biological screening. By dose-response studies, three compounds were identified as potent and selective ePepN inhibitors, as little inhibitory effect was exhibited for the porcine ortholog aminopeptidase. The study allowed for the identification of the key structural features required for a high ePepN inhibitory activity. The most potent and selective inhibitor (TPM 11) showed a non-competitive inhibition profile of ePepN. We predicted that both diastereomers of compound TPM 11 bind to a site distinct from that occupied by the substrate. Theoretical models suggested that TPM 11 has an alternative inhibition mechanism that doesn't involve Zn coordination. On the other hand, the activity landscape analysis provided a rationale for our findings. Of note, compound TMP 2 showed in vitro antibacterial activity against Escherichia coli. Furthermore, none of the three identified inhibitors is a potent haemolytic agent, and only two compounds showed moderate cytotoxic activity toward the murine myeloma P3X63Ag cells. These results point to promising compounds for the future development of rationally designed TPMs as antibacterial agents.

Computational Studies on the Inhibitor Selectivity of Human JAMM Deubiquitinylases Rpn11 and CSN5

Deubiquitinylases (DUBs) are highly specialized enzymes which are responsible for removal of covalently attached ubiquitin(s) from the targeted proteins. DUBs play an important role in maintaining the protein homeodynamics. Recently, DUBs have emerged as novel therapeutic targets in cancer, inflammation, diabetes, and neurodegeneration. Among the different families of DUBs, the metalloprotease group or JAB1/MOV34/MPR1 (JAMMs) proteases are unique in terms of catalytic mechanism. JAMMs exhibit a Zn²⁺-dependent deubiquitinylase activity. Within the JAMM family, deubiquitinylases Rpn11 and CSN5 are constituents of large bimolecular complexes, namely the 26S proteasome and COP9 signalosome (CSN), respectively. Rpn11 and CSN5 are potential drug targets in cancer and selective inhibitors of both proteins have been reported in the literature. However, the selectivity of JAMM inhibitors (capzimin for RPN11 and CSN5i-3 for CSN5) has not been structurally resolved yet. In the present work, we have explored the binding modes of capzimin and CSN5i-3 and rationalize their selectivity for Rpn11 and CSN5 targets. We found that capzimin interacts with the active site Zn⁺² of Rpn11 in a bidentate manner and also interacts with the residues in the distal ubiquitin binding site. MD simulations studies and binding energy analysis revealed that the selective binding of the inhibitors can be only explained by the consideration of larger heterodimeric complexes of Rpn11 (Rpn8-Rpn11) and CSN5 (CSN5-CSN6). Simulation of these protein-protein complexes is necessary to avoid unrealistic large conformational changes. The selective binding of inhibitors is mainly governed by residues in the distal ubiquitin binding site. This study demonstrates that selective inhibitor binding design for Rpn11 and CSN5 JAMM proteases requires consideration of heterodimeric protein-protein target structures.

d-Amino Acid Pseudopeptides as Potential Amyloid-Beta Aggregation Inhibitors

A causative factor for neurotoxicity associated with Alzheimer's disease is the aggregation of the amyloid-β (Aβ) peptide into soluble oligomers. Two all d-amino acid pseudo-peptides, SGB1 and SGD1, were designed to stop the aggregation. Molecular dynamics (MD) simulations have been carried out to study the interaction of the pseudo-peptides with both Aβ13⁻23 (the core recognition site of Aβ) and full-length Aβ1⁻42. Umbrella sampling MD calculations have been used to estimate the free energy of binding, ∆G, of these peptides to Aβ13⁻23. The highest ∆Gbinding is found for SGB1. Each of the pseudo-peptides was also docked to Aβ1⁻42 and subjected up to seven microseconds of all atom molecular dynamics simulations. The resulting structures lend insight into how the dynamics of Aβ1⁻42 are altered by complexation with the pseudo-peptides and confirmed that SGB1 may be a better candidate for developing into a drug to prevent Alzheimer's disease.

On the convergence of multi-scale free energy simulations

In this work we employ simple model systems to evaluate the relative performance of two of the most important free energy methods: The Zwanzig equation (also known as "Free energy perturbation") and Bennett's acceptance ratio method (BAR). Although our examples should be transferable to other kinds of free energy simulations, we focus on applications of multi-scale free energy simulations. Such calculations are especially complex, since they connect two different levels of theory with very different requirements in terms of speed, accuracy, sampling and parallelizability. We try to reconcile all those different factors by developing some simple criteria to guide the early stages of the development of a free energy protocol. This is accomplished by quantifying how many λ intermediate steps and how many potential energy evaluations are necessary in order to reach a certain level of convergence.

Structural Basis of Inhibition of Human Insulin-Regulated Aminopeptidase (IRAP) by Aryl Sulfonamides

The insulin-regulated aminopeptidase (IRAP) is a membrane-bound zinc metallopeptidase with many important regulatory functions. It has been demonstrated that inhibition of IRAP by angiotensin IV (Ang IV) and other peptides, as well as more druglike inhibitors, improves cognition in several rodent models. We recently reported a series of aryl sulfonamides as small-molecule IRAP inhibitors and a promising scaffold for pharmacological intervention. We have now expanded with a number of derivatives, report their stability in liver microsomes, and characterize the activity of the whole series in a new assay performed on recombinant human IRAP. Several compounds, such as the new fluorinated derivative 29, present submicromolar affinity and high metabolic stability. Starting from the two binding modes previously proposed for the sulfonamide scaffold, we systematically performed molecular dynamics simulations and binding affinity estimation with the linear interaction energy method for the full compound series. The significant agreement with experimental affinities suggests one of the binding modes, which was further confirmed by the excellent correlation for binding affinity differences between the selected pair of compounds obtained by rigorous free energy perturbation calculations. The new experimental data and the computationally derived structure-activity relationship of the sulfonamide series provide valuable information for further lead optimization of novel IRAP inhibitors.

Molecular Mechanisms in the Selectivity of Nonsteroidal Anti-Inflammatory Drugs

Nonsteroidal anti-inflammatory drugs (NSAIDs) inhibit cyclooxygenase (COX) 1 and 2 with varying degrees of selectivity. A group of COX-2 selective inhibitors-coxibs-binds in a time-dependent manner through a three-step mechanism, utilizing a side pocket in the binding site. Coxibs have been extensively probed to identify the structural features regulating the slow tight-binding mechanism responsible for COX-2 selectivity. In this study, we further probe a structurally and kinetically diverse data set of COX inhibitors in COX-2 by molecular dynamics and free energy simulations. We find that the features regulating the high affinities associated with time-dependency in COX depend on the inhibitor kinetics. In particular, most time-dependent inhibitors share a common structural binding mechanism, involving an induced-fit rotation of the side-chain of Leu531 in the main binding pocket. The high affinities of two-step slow tight-binding inhibitors and some slow reversible inhibitors can thus be explained by the increased space in the main binding pocket after this rotation. Coxibs that belong to a separate class of slow tight-binding inhibitors benefit more from the displacement of the neighboring side-chain of Arg513, exclusive to the COX-2 side-pocket. This displacement further stabilizes the aforementioned rotation of Leu531 and can explain the selectivity of coxibs for COX-2.

Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations

We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource ( https://drugdesigndata.org/ ). The challenge was focused on the ligands of the farnesoid X receptor (FXR), a highly flexible nuclear receptor of the cholesterol derivative chenodeoxycholic acid. FXR is considered an important therapeutic target for metabolic, inflammatory, bowel and obesity related diseases (Expert Opin Drug Metab Toxicol 4:523-532, 2015), but in the context of this competition it is also interesting due to the significant ligand-induced conformational changes displayed by the protein. To deal with these conformational changes we employed multiple simulations of molecular dynamics (MD). Our MD-based protocols were top-ranked in estimating the free energy of binding of the ligands and FXR protein. Our approach was ranked second in the prediction of the binding poses where we also combined MD with molecular docking and artificial neural networks. Our approach showed mediocre results for high-throughput scoring of interactions.

Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors

Cytochrome P450 aromatase (CYP19A1) plays a key role in the development of estrogen dependent breast cancer, and aromatase inhibitors have been at the front line of treatment for the past three decades. The development of potent, selective and safer inhibitors is ongoing with in silico screening methods playing a more prominent role in the search for promising lead compounds in bioactivity-relevant chemical space. Here we present a set of comprehensive binding affinity prediction models for CYP19A1 using our automated Linear Interaction Energy (LIE) based workflow on a set of 132 putative and structurally diverse aromatase inhibitors obtained from a typical industrial screening study. We extended the workflow with machine learning methods to automatically cluster training and test compounds in order to maximize the number of explained compounds in one or more predictive LIE models. The method uses protein-ligand interaction profiles obtained from Molecular Dynamics (MD) trajectories to help model search and define the applicability domain of the resolved models. Our method was successful in accounting for 86% of the data set in 3 robust models that show high correlation between calculated and observed values for ligand-binding free energies (RMSE < 2.5 kJ mol-1), with good cross-validation statistics.

Probing the Time Dependency of Cyclooxygenase-1 Inhibitors by Computer Simulations

Time-dependent inhibition of the cyclooxygenases (COX) by a range of nonsteroidal anti-inflammatory drugs has been described since the first experimental assays of COX were performed. Slow tight-binding inhibitors of COX-1 bind in a two-step mechanism in which the EI → EI* transition is slow and practically irreversible. Since then, various properties of the inhibitors have been proposed to cause or affect the time dependency. Conformational changes in the enzyme have also been proposed to cause the time dependency, but no particular structural feature has been identified. Here, we investigated a series of inhibitors of COX-1 that are either time-independent or time-dependent using a combination of molecular dynamics simulations, binding free energy calculations, and potential of mean force calculations. We find that the time-dependent inhibitors stabilize a conformational change in the enzyme mainly identified by the rotation of a leucine side chain adjacent to the binding pocket. The induced conformation has been previously shown to be essential for the high binding affinities of tight-binding inhibitors in COX-1. The results of this work show that the structural features of the enzyme involved in both time-dependent and tight-binding inhibition are identical and further identify a structural mechanism responsible for the transition between the two enzyme-inhibitor complexes characteristic of slow tight-binding COX-1 inhibitors.

Interfacial solvation thermodynamics

Previous studies have reached conflicting conclusions regarding the interplay of cavity formation, polarizability, desolvation, and surface capillary waves in driving the interfacial adsorptions of ions and molecules at air-water interfaces. Here we revisit these questions by combining exact potential distribution results with linear response theory and other physically motivated approximations. The results highlight both exact and approximate compensation relations pertaining to direct (solute-solvent) and indirect (solvent-solvent) contributions to adsorption thermodynamics, of relevance to solvation at air-water interfaces, as well as a broader class of processes linked to the mean force potential between ions, molecules, nanoparticles, proteins, and biological assemblies.

Aryl Sulfonamide Inhibitors of Insulin-Regulated Aminopeptidase Enhance Spine Density in Primary Hippocampal Neuron Cultures

The zinc metallopeptidase insulin regulated aminopeptidase (IRAP), which is highly expressed in the hippocampus and other brain regions associated with cognitive function, has been identified as a high-affinity binding site of the hexapeptide angiotensin IV (Ang IV). This hexapeptide is thought to facilitate learning and memory by binding to the catalytic site of IRAP to inhibit its enzymatic activity. In support of this hypothesis, low molecular weight, nonpeptide specific inhibitors of IRAP have been shown to enhance memory in rodent models. Recently, it was demonstrated that linear and macrocyclic Ang IV-derived peptides can alter the shape and increase the number of dendritic spines in hippocampal cultures, properties associated with enhanced cognitive performance. After screening a library of 10 500 drug-like substances for their ability to inhibit IRAP, we identified a series of low molecular weight aryl sulfonamides, which exhibit no structural similarity to Ang IV, as moderately potent IRAP inhibitors. A structural and biological characterization of three of these aryl sulfonamides was performed. Their binding modes to human IRAP were explored by docking calculations combined with molecular dynamics simulations and binding affinity estimations using the linear interaction energy method. Two alternative binding modes emerged from this analysis, both of which correctly rank the ligands according to their experimental binding affinities for this series of compounds. Finally, we show that two of these drug-like IRAP inhibitors can alter dendritic spine morphology and increase spine density in primary cultures of hippocampal neurons.

Computational Approaches to the Chemical Equilibrium Constant in Protein-ligand Binding

The physiological role played by protein-ligand recognition has motivated the development of several computational approaches to the ligand binding affinity. Some of them, termed rigorous, have a strong theoretical foundation but involve too much computation to be generally useful. Some others alleviate the computational burden by introducing strong approximations and/or empirical calibrations, which also limit their general use. Most importantly, there is no straightforward correlation between the predictive power and the level of approximation introduced. Here, we present a general framework for the quantitative interpretation of protein-ligand binding based on statistical mechanics. Within this framework, we re-derive self-consistently the fundamental equations of some popular approaches to the binding constant and pinpoint the inherent approximations. Our analysis represents a first step towards the development of variants with optimum accuracy/efficiency ratio for each stage of the drug discovery pipeline.

Improved QM/MM Linear-Interaction Energy Model for Substrate Recognition in Zinc-Containing Metalloenzymes

One of the essential challenges in the description of receptor-drug interactions in the presence of various polyvalent cations (such as zinc, magnesium, or iron) is the accurate assessment of the electronic effects due to cofactor binding. The effects can range from partial electronic polarization of the proximal atoms in a receptor and bound substrate to long-range effects related to partial charge transfer and electronic delocalization effects between the cofactor and the drug. Here, we examine the role of the explicit account for electronic effects for a panel of small-molecule inhibitors binding to the zinc-aminopeptidase PfA-M1, an essential target for antimalarial drug development. Our study on PfA-M1:inhibitor interactions at the QM level reveals that the partial charge and proton transfer due to bound zinc ion are important mechanisms in the inhibitors' recognition and catalysis. The combination of classical MD simulations with a posteriori QM/MM corrections with novel DFTB parameters for the zinc cation and the linear-interaction energy (LIE) approach offers by far the most accurate estimates for the PfA-M1:inhibitor binding affinities, opening the door for future inhibitor design.

Glossary of terms used in computational drug design, part II (IUPAC Recommendations 2015)

Computational drug design is a rapidly changing field that plays an increasingly important role in medicinal chemistry. Since the publication of the first glossary in 1997, substantial changes have occurred in both medicinal chemistry and computational drug design. This has resulted in the use of many new terms and the consequent necessity to update the previous glossary. For this purpose a Working Party of eight experts was assembled. They produced explanatory definitions of more than 150 new and revised terms.

Accurate Prediction of Ligand Affinities for a Proton-Dependent Oligopeptide Transporter

Membrane transporters are critical modulators of drug pharmacokinetics, efficacy, and safety. One example is the proton-dependent oligopeptide transporter PepT1, also known as SLC15A1, which is responsible for the uptake of the β-lactam antibiotics and various peptide-based prodrugs. In this study, we modeled the binding of various peptides to a bacterial homolog, PepT_St, and evaluated a range of computational methods for predicting the free energy of binding. Our results show that a hybrid approach (endpoint methods to classify peptides into good and poor binders and a theoretically exact method for refinement) is able to accurately predict affinities, which we validated using proteoliposome transport assays. Applying the method to a homology model of PepT1 suggests that the approach requires a high-quality structure to be accurate. Our study provides a blueprint for extending these computational methodologies to other pharmaceutically important transporter families.

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A hierarchical computational approach determines ligand affinities to transporters

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Lysine-containing dipeptides proposed to bind vertically like a tripeptide

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Experimental structures are vital for the accurate prediction of affinities

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A model of prodrug interactions to human PepT1 is suggested