Learning probabilistic models of hydrogen bond stability from molecular dynamics simulation trajectories

Hydrogen bonds (H-bonds) play a key role in both the formation and stabilization of protein structures. H-bonds involving atoms from residues that are close to each other in the main-chain sequence stabilize secondary structure elements. H-bonds between atoms from distant residues stabilize a protein’s tertiary structure. However, H-bonds greatly vary in stability. They form and break while a protein deforms. For instance, the transition of a protein from a nonfunctional to a functional state may require some H-bonds to break and others to form. The intrinsic strength of an individual H-bond has been studied from an energetic viewpoint, but energy alone may not be a very good predictor. Other local interactions may reinforce (or weaken) an H-bond. This paper describes inductive learning methods to train a protein-independent probabilistic model of H-bond stability from molecular dynamics (MD) simulation trajectories. The training data describes H-bond occurrences at successive times along these trajectories by the values of attributes called predictors. A trained model is constructed in the form of a regression tree in which each non-leaf node is a Boolean test (split) on a predictor. Each occurrence of an H-bond maps to a path in this tree from the root to a leaf node. Its predicted stability is associated with the leaf node. Experimental results demonstrate that such models can predict H-bond stability quite well. In particular, their performance is roughly 20 % better than that of models based on H-bond energy alone. In addition, they can accurately identify a large fraction of the least stable H-bonds in a give

Binding of Tetracycline and Chlortetracycline to the Enzyme Trypsin: Spectroscopic and Molecular Modeling Investigations

Tetracycline (TC) and chlortetracycline (CTC) are common members of the widely used veterinary drug tetracyclines, the residue of which in the environment can enter human body, being potentially harmful. In this study, we establish a new strategy to probe the binding modes of TC and CTC with trypsin based on spectroscopic and computational modeling methods. Both TC and CTC can interact with trypsin with one binding site to form trypsin-TC (CTC) complex, mainly through van der Waals' interactions and hydrogen bonds with the affinity order: TC>CTC. The bound TC (CTC) can result in inhibition of trypsin activity with the inhibition order: CTC>TC. The secondary structure and the microenvironment of the tryptophan residues of trypsin were also changed. However, the effect of CTC on the secondary structure content of trypsin was contrary to that of TC. Both the molecular docking study and the trypsin activity experiment revealed that TC bound into S1 binding pocket, competitively inhibiting the enzyme activity, and CTC was a non-competitive inhibitor which bound to a non-active site of trypsin, different from TC due to the Cl atom on the benzene ring of CTC which hinders CTC entering into the S1 binding pocket. CTC does not hinder the binding of the enzyme substrate, but the CTC-trypsin-substrate ternary complex can not further decompose into the product. The work provides basic data for clarifying the binding mechanisms of TC (CTC) with trypsin and can help to comprehensively understanding of the enzyme toxicity of different members of tetracyclines in vivo

Predicting P-Glycoprotein-Mediated Drug Transport Based On Support Vector Machine and Three-Dimensional Crystal Structure of P-glycoprotein

Human P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter that confers resistance to a wide range of chemotherapeutic agents in cancer cells by active efflux of the drugs from cells. P-gp also plays a key role in limiting oral absorption and brain penetration and in facilitating biliary and renal elimination of structurally diverse drugs. Thus, identification of drugs or new molecular entities to be P-gp substrates is of vital importance for predicting the pharmacokinetics, efficacy, safety, or tissue levels of drugs or drug candidates. At present, publicly available, reliable in silico models predicting P-gp substrates are scarce. In this study, a support vector machine (SVM) method was developed to predict P-gp substrates and P-gp-substrate interactions, based on a training data set of 197 known P-gp substrates and non-substrates collected from the literature. We showed that the SVM method had a prediction accuracy of approximately 80% on an independent external validation data set of 32 compounds. A homology model of human P-gp based on the X-ray structure of mouse P-gp as a template has been constructed. We showed that molecular docking to the P-gp structures successfully predicted the geometry of P-gp-ligand complexes. Our SVM prediction and the molecular docking methods have been integrated into a free web server (http://pgp.althotas.com), which allows the users to predict whether a given compound is a P-gp substrate and how it binds to and interacts with P-gp. Utilization of such a web server may prove valuable for both rational drug design and screening

Accessible High-Throughput Virtual Screening Molecular Docking Software for Students and Educators

We survey low cost high-throughput virtual screening (HTVS) computer programs for instructors who wish to demonstrate molecular docking in their courses. Since HTVS programs are a useful adjunct to the time consuming and expensive wet bench experiments necessary to discover new drug therapies, the topic of molecular docking is core to the instruction of biochemistry and molecular biology. The availability of HTVS programs coupled with decreasing costs and advances in computer hardware have made computational approaches to drug discovery possible at institutional and non-profit budgets. This paper focuses on HTVS programs with graphical user interfaces (GUIs) that use either DOCK or AutoDock for the prediction of DockoMatic, PyRx, DockingServer, and MOLA since their utility has been proven by the research community, they are free or affordable, and the programs operate on a range of computer platforms

Mechanisms of Loss of Functions of Human Angiogenin Variants Implicated in Amyotrophic Lateral Sclerosis

Background: Mutations in the coding region of angiogenin (ANG) gene have been found in patients suffering from Amyotrophic Lateral Sclerosis (ALS). Neurodegeneration results from the loss of angiogenic ability of ANG (protein coded by ANG). In this work, we performed extensive molecular dynamics (MD) simulations of wild-type ANG and disease associated ANG variants to elucidate the mechanism behind the loss of ribonucleolytic activity and nuclear translocation activity, functions needed for angiogenesis. Methodology/Principal Findings: MD simulations were carried out to study the structural and dynamic differences in the catalytic site and nuclear localization signal residues between WT-ANG (Wild-type ANG) and six mutants. Variants K17I, S28N, P112L and V113I have confirmed association with ALS, while T195C and A238G single nucleotide polymorphisms (SNPs) encoding L35P and K60E mutants respectively, have not been associated with ALS. Our results show that loss of ribonucleolytic activity in K17I is caused by conformational switching of the catalytic residue His114 by 99u. The loss of nuclear translocation activity of S28N and P112L is caused by changes in the folding of the residues 31 RRR 33 that result in the reduction in solvent accessible surface area (SASA). Consequently, we predict that V113I will exhibit loss of angiogenic properties by loss of nuclear translocation activity and L35P by loss of both ribonucleolytic activity and nuclear translocation activity. No functional loss was inferred for K60E. The MD simulation results were supported by hydrogen bond interactio

Hexahydroquinolines are antimalarial candidates with potent blood-stage and transmission-blocking activity

Hexahydroquinolines are antimalarial candidates with potent blood-stage and transmission-blocking activityAntimalarial compounds with dual therapeutic and transmission-blocking activity are desired as high-value partners for combination therapies. Here, we report the identification and characterization of hexahydroquinolines (HHQs) that show low nanomolar potency against both pathogenic and transmissible intra-erythrocytic forms of the malaria parasite Plasmodium falciparum. This activity translates into potent transmission-blocking potential, as shown by in vitro male gamete formation assays and reduced oocyst infection and prevalence in Anopheles mosquitoes. In vivo studies illustrated the ability of lead HHQs to suppress Plasmodium berghei blood-stage parasite proliferation. Resistance selection studies, confirmed by CRISPR-Cas9-based gene editing, identified the digestive vacuole membrane-spanning transporter PfMDR1 (P. falciparum multidrug resistance gene-1) as a determinant of parasite resistance to HHQs. Haemoglobin and haem fractionation assays suggest a mode of action that results in reduced haemozoin levels and might involve inhibition of host haemoglobin uptake into intra-erythrocytic parasites. Furthermore, parasites resistant to HHQs displayed increased susceptibility to several first-line antimalarial drugs, including lumefantrine, confirming that HHQs have a different mode of action to other antimalarials drugs for which PfMDR1 is known to confer resistance. This work evokes therapeutic strategies that combine opposing selective pressures on this parasite transporter as an approach to countering the emergence and transmission of multidrug-resistant P. falciparum malaria.The authors thank T.T. Diagana (Novartis Institute for Tropical Diseases, Singapore) for provision of the compounds, the Red Cross (Australia and the USA) for the provision of human blood for cell cultures, and G. Stevenson for assistance with the triaging of compounds following screening. The authors acknowledge the Bill and Melinda Gates Foundation (grant OPP1040399 to D.A.F. and V.M.A. and grant OPP1054480 to E.A.W. and D.A.F.), the National Institutes of Health (grant R01 AI103058 to E.A.W. and D.A.F., grant R01 AI50234 to D.A.F, and R01 AI110329 to T.J.E.), the Australian Research Council (LP120200557 to V.M.A.) and the Medicines for Malaria Venture for their continued support. P.E.F. and M.I.V. are supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER).info:eu-repo/semantics/publishedVersio

Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock

<p>Abstract</p> <p>Background</p> <p>Molecular docking methods are commonly used for predicting binding modes and energies of ligands to proteins. For accurate complex geometry and binding energy estimation, an appropriate method for calculating partial charges is essential. AutoDockTools software, the interface for preparing input files for one of the most widely used docking programs AutoDock 4, utilizes the Gasteiger partial charge calculation method for both protein and ligand charge calculation. However, it has already been shown that more accurate partial charge calculation - and as a consequence, more accurate docking- can be achieved by using quantum chemical methods. For docking calculations quantum chemical partial charge calculation as a routine was only used for ligands so far. The newly developed Mozyme function of MOPAC2009 allows fast partial charge calculation of proteins by quantum mechanical semi-empirical methods. Thus, in the current study, the effect of semi-empirical quantum-mechanical partial charge calculation on docking accuracy could be investigated.</p> <p>Results</p> <p>The docking accuracy of AutoDock 4 using the original AutoDock scoring function was investigated on a set of 53 protein ligand complexes using Gasteiger and PM6 partial charge calculation methods. This has enabled us to compare the effect of the partial charge calculation method on docking accuracy utilizing AutoDock 4 software. Our results showed that the docking accuracy in regard to complex geometry (docking result defined as accurate when the RMSD of the first rank docking result complex is within 2 Å of the experimentally determined X-ray structure) significantly increased when partial charges of the ligands and proteins were calculated with the semi-empirical PM6 method.</p> <p>Out of the 53 complexes analyzed in the course of our study, the geometry of 42 complexes were accurately calculated using PM6 partial charges, while the use of Gasteiger charges resulted in only 28 accurate geometries. The binding affinity estimation was not influenced by the partial charge calculation method - for more accurate binding affinity prediction development of a new scoring function for AutoDock is needed.</p> <p>Conclusion</p> <p>Our results demonstrate that the accuracy of determination of complex geometry using AutoDock 4 for docking calculation greatly increases with the use of quantum chemical partial charge calculation on both the ligands and proteins.</p

A detailed study of antibacterial 3-acyltetramic acids and 3-acylpiperidine-2,4-diones

Inspired by the core fragment of antibacterial natural products such as streptolydigin, 3-acyltetramic acids and 3-acylpiperidine-2,4-diones have been synthesised from the core heterocycle by direct acylation with the substituted carboxylic acids using a strategy which permits ready access to a structurally diverse compound library. The antibacterial activity of these systems has been established against a panel of Gram-positive and Gram-negative bacteria, with activity mostly against the former, which in some cases is very potent. Data consistent with modes of action against undecaprenylpyrophosphate synthase (UPPS) and/or RNA polymerase (RNAP) for a small subset of the library has been obtained. The most active compounds have been shown to exhibit binding at known binding sites of streptolydigin and myxopyronin at UPPS and RNAP. These systems offer potential for their antibacterial activity, and further demonstrate the use of natural products as biologically validated starting points for drug discovery