AMMOS: Automated Molecular Mechanics Optimization tool for in silico Screening

<p>Abstract</p> <p>Background</p> <p>Virtual or <it>in silico </it>ligand screening combined with other computational methods is one of the most promising methods to search for new lead compounds, thereby greatly assisting the drug discovery process. Despite considerable progresses made in virtual screening methodologies, available computer programs do not easily address problems such as: structural optimization of compounds in a screening library, receptor flexibility/induced-fit, and accurate prediction of protein-ligand interactions. It has been shown that structural optimization of chemical compounds and that post-docking optimization in multi-step structure-based virtual screening approaches help to further improve the overall efficiency of the methods. To address some of these points, we developed the program AMMOS for refining both, the 3D structures of the small molecules present in chemical libraries and the predicted receptor-ligand complexes through allowing partial to full atom flexibility through molecular mechanics optimization.</p> <p>Results</p> <p>The program AMMOS carries out an automatic procedure that allows for the structural refinement of compound collections and energy minimization of protein-ligand complexes using the open source program AMMP. The performance of our package was evaluated by comparing the structures of small chemical entities minimized by AMMOS with those minimized with the Tripos and MMFF94s force fields. Next, AMMOS was used for full flexible minimization of protein-ligands complexes obtained from a mutli-step virtual screening. Enrichment studies of the selected pre-docked complexes containing 60% of the initially added inhibitors were carried out with or without final AMMOS minimization on two protein targets having different binding pocket properties. AMMOS was able to improve the enrichment after the pre-docking stage with 40 to 60% of the initially added active compounds found in the top 3% to 5% of the entire compound collection.</p> <p>Conclusion</p> <p>The open source AMMOS program can be helpful in a broad range of <it>in silico </it>drug design studies such as optimization of small molecules or energy minimization of pre-docked protein-ligand complexes. Our enrichment study suggests that AMMOS, designed to minimize a large number of ligands pre-docked in a protein target, can successfully be applied in a final post-processing step and that it can take into account some receptor flexibility within the binding site area.</p

Studying the effects of DNA methylation on adduct formation using molecular modeling

The goal of this study was to use molecular modeling to compare and analyze the molecular structure of a double-stranded DNA fragment, and the effects of DNA methylation to adduct formation, which may eventually lead to disease-related genetic mutations. Specifically, the research work in this thesis focuses on using molecular modeling to simulate the experimental results in a recent report in which DNA adduction occurs with BPDE (benzo[a]pyrene diol epoxide) within a specific double-stranded DNA fragment that contained various methylation patterns and was quantitatively measured. The ability to use molecular modeling to correlate the pattern of DNA methylation and the locations of the most frequent adduction sites with genotoxic compounds can be very useful to further advance the study of genetic mutation, prevention of diseases, and so on. In this study the MMFF94s force field was used to run molecular dynamics simulations on dsDNA, and the results were analyzed to determine changes in the rotation of specific base pairs and the distances between the base pairs as a result of DNA methylation. The results show that there is a significant change in those two characteristics between non-methylated DNA and methylated DNA which might lead to adduct formation with BPDE

PubChem3D: a new resource for scientists

<p>Abstract</p> <p>Background</p> <p>PubChem is an open repository for small molecules and their experimental biological activity. PubChem integrates and provides search, retrieval, visualization, analysis, and programmatic access tools in an effort to maximize the utility of contributed information. There are many diverse chemical structures with similar biological efficacies against targets available in PubChem that are difficult to interrelate using traditional 2-D similarity methods. A new layer called PubChem3D is added to PubChem to assist in this analysis.</p> <p>Description</p> <p>PubChem generates a 3-D conformer model description for 92.3% of all records in the PubChem Compound database (when considering the parent compound of salts). Each of these conformer models is sampled to remove redundancy, guaranteeing a minimum (non-hydrogen atom pair-wise) RMSD between conformers. A diverse conformer ordering gives a maximal description of the conformational diversity of a molecule when only a subset of available conformers is used. A pre-computed search per compound record gives immediate access to a set of 3-D similar compounds (called "Similar Conformers") in PubChem and their respective superpositions. Systematic augmentation of PubChem resources to include a 3-D layer provides users with new capabilities to search, subset, visualize, analyze, and download data.</p> <p>A series of retrospective studies help to demonstrate important connections between chemical structures and their biological function that are not obvious using 2-D similarity but are readily apparent by 3-D similarity.</p> <p>Conclusions</p> <p>The addition of PubChem3D to the existing contents of PubChem is a considerable achievement, given the scope, scale, and the fact that the resource is publicly accessible and free. With the ability to uncover latent structure-activity relationships of chemical structures, while complementing 2-D similarity analysis approaches, PubChem3D represents a new resource for scientists to exploit when exploring the biological annotations in PubChem.</p

Evolutionary Monte Carlo of QM properties in chemical space: Electrolyte design

Optimizing a target function over the space of organic molecules is an important problem appearing in many fields of applied science, but also a very difficult one due to the vast number of possible molecular systems. We propose an Evolutionary Monte Carlo algorithm for solving such problems which is capable of straightforwardly tuning both exploration and exploitation characteristics of an optimization procedure while retaining favourable properties of genetic algorithms. The method, dubbed MOSAiCS (Metropolis Optimization by Sampling Adaptively in Chemical Space), is tested on problems related to optimizing components of battery electrolytes, namely minimizing solvation energy in water or maximizing dipole moment while enforcing a lower bound on the HOMO-LUMO gap; optimization was done over sets of molecular graphs inspired by QM9 and Electrolyte Genome Project (EGP) datasets. MOSAiCS reliably generated molecular candidates with good target quantity values, which were in most cases better than the ones found in QM9 or EGP. While the optimization results presented in this work sometimes required up to $10^{6}$ QM calculations and were thus only feasible thanks to computationally efficient ab initio approximations of properties of interest, we discuss possible strategies for accelerating MOSAiCS using machine learning approaches

Ligand-guided homology modeling drives identification of novel histamine H3 receptor ligands

In this study, we report a ligand-guided homology modeling approach allowing the analysis of relevant binding site residue conformations and the identification of two novel histamine H3 receptor ligands with binding affinity in the nanomolar range. The newly developed method is based on exploiting an essential charge interaction characteristic for aminergic G-protein coupled receptors for ranking 3D receptor models appropriate for the discovery of novel compounds through virtual screening

Molecular dynamics simulations and in silico peptide ligand screening of the Elk-1 ETS domain

Background: The Elk-1 transcription factor is a member of a group of proteins called ternary complex factors, which serve as a paradigm for gene regulation in response to extracellular signals. Its deregulation has been linked to multiple human diseases including the development of tumours. The work herein aims to inform the design of potential peptidomimetic compounds that can inhibit the formation of the Elk-1 dimer, which is key to Elk-1 stability. We have conducted molecular dynamics simulations of the Elk-1 ETS domain followed by virtual screening. Results: We show the ETS dimerisation site undergoes conformational reorganisation at the a1b1 loop. Through exhaustive screening of di- and tri-peptide libraries against a collection of ETS domain conformations representing the dynamics of the loop, we identified a series of potential binders for the Elk-1 dimer interface. The di-peptides showed no particular preference toward the binding site; however, the tri-peptides made specific interactions with residues: Glu17, Gln18 and Arg49 that are pivotal to the dimer interface. Conclusions: We have shown molecular dynamics simulations can be combined with virtual peptide screening to obtain an exhaustive docking protocol that incorporates dynamic fluctuations in a receptor. Based on our findings, we suggest experimental binding studies to be performed on the 12 SILE ranked tri-peptides as possible compounds for the design of inhibitors of Elk-1 dimerisation. It would also be reasonable to consider the score ranked tri-peptides as a comparative test to establish whether peptide size is a determinant factor of binding to the ETS domain

Complex macrocycle exploration: parallel, heuristic, and constraint-based conformer generation using ForceGen.

ForceGen is a template-free, non-stochastic approach for 2D to 3D structure generation and conformational elaboration for small molecules, including both non-macrocycles and macrocycles. For conformational search of non-macrocycles, ForceGen is both faster and more accurate than the best of all tested methods on a very large, independently curated benchmark of 2859 PDB ligands. In this study, the primary results are on macrocycles, including results for 431 unique examples from four separate benchmarks. These include complex peptide and peptide-like cases that can form networks of internal hydrogen bonds. By making use of new physical movements ("flips" of near-linear sub-cycles and explicit formation of hydrogen bonds), ForceGen exhibited statistically significantly better performance for overall RMS deviation from experimental coordinates than all other approaches. The algorithmic approach offers natural parallelization across multiple computing-cores. On a modest multi-core workstation, for all but the most complex macrocycles, median wall-clock times were generally under a minute in fast search mode and under 2 min using thorough search. On the most complex cases (roughly cyclic decapeptides and larger) explicit exploration of likely hydrogen bonding networks yielded marked improvements, but with calculation times increasing to several minutes and in some cases to roughly an hour for fast search. In complex cases, utilization of NMR data to constrain conformational search produces accurate conformational ensembles representative of solution state macrocycle behavior. On macrocycles of typical complexity (up to 21 rotatable macrocyclic and exocyclic bonds), design-focused macrocycle optimization can be practically supported by computational chemistry at interactive time-scales, with conformational ensemble accuracy equaling what is seen with non-macrocyclic ligands. For more complex macrocycles, inclusion of sparse biophysical data is a helpful adjunct to computation

Simulation and comparative analysis of binding modes of nucleoside and non-nucleoside agonists at the A2B adenosine receptor

PURPOSE: A(2B) receptor agonists are studied as possible therapeutic tools for a variety of pathological conditions. Unfortunately, medicinal chemistry efforts have led to the development of a limited number of potent agonists of this receptor, in most cases with a low or no selectivity versus the other adenosine receptor subtypes. Among the developed molecules, two structural families of compounds have been identified based on nucleoside and non-nucleoside (pyridine) scaffolds. The aim of this work is to analyse the binding mode of these molecules at 3D models of the human A(2B) receptor to identify possible common interaction features and the key receptor residues involved in ligand interaction. METHODS: The A(2B) receptor models are built by using two recently published crystal structures of the human A(2A) receptor in complex with two different agonists. The developed models are used as targets for molecular docking studies of nucleoside and non-nucleoside agonists. The generated docking conformations are subjected to energy minimization and rescoring by using three different scoring functions. Further analysis of top-score conformations are performed with a tool evaluating the interaction energy between the ligand and the binding site residues. RESULTS: Results suggest a set of common interaction points between the two structural families of agonists and the receptor binding site, as evidenced by the superimposition of docking conformations and by analysis of interaction energy with the receptor residues. CONCLUSIONS: The obtained results show that there is a conserved pattern of interaction between the A(2B) receptor and its agonists. These information and can provide useful data to support the design and the development of A(2B) receptor agonists belonging to nucleoside or non-nucleoside structural families. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/2193-9616-1-24) contains supplementary material, which is available to authorized users