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

Interactive drug-design: using advanced computing to evaluate the induced fit effect

This thesis describes the efforts made to provide protein flexibility in a molecular modelling software application, which prior to this work, was operating using rigid proteins and semi flexible ligands. Protein flexibility during molecular modelling simulations is a non-­‐trivial task requiring a great number of floating point operations and it could not be accomplished without the help of supercomputing such as GPGPUs (or possibly Xeon Phi). The thesis is structured as follows. It provides a background section, where the reader can find the necessary context and references in order to be able to understand this report. Next is a state of the art section, which describes what had been done in the fields of molecular dynamics and flexible haptic protein ligand docking prior to this work. An implementation section follows, which lists failed efforts that provided the necessary feedback in order to design efficient algorithms to accomplish this task. Chapter 6 describes in detail an irregular – grid decomposition approach in order to provide fast non-­‐bonded interaction computations for GPGPUs. This technique is also associated with algorithms that provide fast bonded interaction computations and exclusions handling for 1-­‐4 bonded atoms during the non-­‐bonded forces computation part. Performance benchmarks as well as accuracy tables for energy and force computations are provided to demonstrate the efficiency of the methodologies explained in this chapter. Chapter 7 provides an overview of an evolutionary strategy used to overcome the problems associated with the limited capabilities of local search strategies such as steepest descents, which get trapped in the first local minima they find. Our proposed method is able to explore the potential energy landscape in such a way that it can pick competitive uphill solutions to escape local minima in the hope of finding deeper valleys. This methodology is also serving the purpose of providing a good number of conformational updates such that it is able to restore the areas of interaction between the protein and the ligand while searching for optimum global solutions

In Silico Design and Selection of CD44 Antagonists:implementation of computational methodologies in drug discovery and design

Drug discovery (DD) is a process that aims to identify drug candidates through a thorough evaluation of the biological activity of small molecules or biomolecules. Computational strategies (CS) are now necessary tools for speeding up DD. Chapter 1 describes the use of CS throughout the DD process, from the early stages of drug design to the use of artificial intelligence for the de novo design of therapeutic molecules. Chapter 2 describes an in-silico workflow for identifying potential high-affinity CD44 antagonists, ranging from structural analysis of the target to the analysis of ligand-protein interactions and molecular dynamics (MD). In Chapter 3, we tested the shape-guided algorithm on a dataset of macrocycles, identifying the characteristics that need to be improved for the development of new tools for macrocycle sampling and design. In Chapter 4, we describe a detailed reverse docking protocol for identifying potential 4-hydroxycoumarin (4-HC) targets. The strategy described in this chapter is easily transferable to other compounds and protein datasets for overcoming bottlenecks in molecular docking protocols, particularly reverse docking approaches. Finally, Chapter 5 shows how computational methods and experimental results can be used to repurpose compounds as potential COVID-19 treatments. According to our findings, the HCV drug boceprevir could be clinically tested or used as a lead molecule to develop compounds that target COVID-19 or other coronaviral infections. These chapters, in summary, demonstrate the importance, application, limitations, and future of computational methods in the state-of-the-art drug design process

The Monomer Electron Density Force Field (MEDFF) : a physically inspired model for noncovalent interactions

We propose a methodology to derive pairwise-additive noncovalent force fields from monomer electron densities without any empirical input. Energy expressions are based on the symmetry-adapted perturbation theory (SAPT) decomposition of interaction energies. This ensures a physically motivated force field featuring an electrostatic, exchange repulsion, dispersion, and induction contribution, which contains two types of parameters. First, each contribution depends on several fixed atomic parameters, resulting from a partitioning of the monomer electron density. Second, each of the last three contributions (exchange-repulsion, dispersion, and induction) contains exactly one linear fitting parameter. These three so-called interaction parameters in the model are initially estimated separately using SAPT reference calculations for the S66x8 database of noncovalent dimers. In a second step, the three interaction parameters are further refined simultaneously to reproduce CCSD(T)/CBS interaction energies for the same database. The limited number of parameters that are fitted to dimer interaction energies (only three) avoids ill-conditioned fits that plague conventional parameter optimizations. For the exchange repulsion and dispersion component, good results are obtained for all dimers in the S66x8 database using one single value for the associated interaction parameters. The values of those parameters can be considered universal and can also be used for dimers not present in the original database used for fitting. For the induction component such an approach is only viable for the dispersion dominated dimers in the S66x8 database. For other dimers (such as hydrogen-bonded complexes), we show that our methodology remains applicable. However, the interaction parameter needs to be determined on a case-specific basis. As an external validation:, the force field predicts interaction energies in good agreement with CCSD(T)/CBS values for dispersion dominated dimers extracted from an HIV-II protease crystal structure with a bound ligand (indinavir). Furthermore, experimental second virial coefficients of small alkanes and alkenes are well reproduced

Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose.

We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket constructed of molecular fragments. The problem of mutual ligand alignment is addressed in a general way, and optimal model parameters and ligand poses are identified through multiple-instance machine learning. We provide algorithmic details along with performance results on sixteen structure-activity data sets covering many pharmaceutically relevant targets. In particular, we show how models initially induced from small data sets can extrapolatively identify potent new ligands with novel underlying scaffolds with very high specificity. Further, we show that combining predictions from QuanSA models with those from physics-based simulation approaches is synergistic. QuanSA predictions yield binding affinities, explicit estimates of ligand strain, associated ligand pose families, and estimates of structural novelty and confidence. The method is applicable for fine-grained lead optimization as well as potent new lead identification

Applications and Improvements in the Molecular Modeling of Protein and Ligand Interactions

Understanding protein and ligand interactions is fundamental to treat disease and avoid toxicity in biological organisms. Molecular modeling is a helpful but imperfect tool used in computer-aided toxicology and drug discovery. In this work, molecular docking and structural informatics have been integrated with other modeling methods and physical experiments to better understand and improve predictions for protein and ligand interactions. Results presented as part of this research include: 1.) an application of single-protein docking for an intermediate state structure, specifically, modeling an intermediate state structure of alpha-1-antitrypsin and using the resulting model to virtually screen for chemical inhibitors that can treat alpha-1-antitrypsin deficiency, 2.) an application of multi-protein docking and metabolism prediction, specifically, modeling the cytochrome P450 metabolism and estrogen receptor activity of an environmental pollutant (PCB-30), and 3.) providing evidence to support the inclusion of anion-pi interactions in molecular modeling by demonstrating the biological roles of anion-pi interactions in stabilizing protein and protein-ligand structures. This work has direct applications for mitigating disease and toxicity, but it also demonstrates useful ways of integrating computational and experimental data to improve upon modeling protein and ligand interactions

Developing and validating Fuzzy-Border continuum solvation model with POlarizable Simulations Second order Interaction Model (POSSIM) force field for proteins

The accurate, fast and low cost computational tools are indispensable for studying the structure and dynamics of biological macromolecules in aqueous solution. The goal of this thesis is development and validation of continuum Fuzzy-Border (FB) solvation model to work with the Polarizable Simulations Second-order Interaction Model (POSSIM) force field for proteins developed by Professor G A Kaminski. The implicit FB model has advantages over the popularly used Poisson Boltzmann (PB) solvation model. The FB continuum model attenuates the noise and convergence issues commonly present in numerical treatments of the PB model by employing fixed position cubic grid to compute interactions. It also uses either second or first-order approximation for the solvent polarization which is similar to the second-order explicit polarization applied in POSSIM force field. The FB model was first developed and parameterized with nonpolarizable OPLS-AA force field for small molecules which are not only important in themselves but also building blocks of proteins and peptide side chains. The hydration parameters are fitted to reproduce the experimental or quantum mechanical hydration energies of the molecules with the overall average unsigned error of ca. 0.076kcal/mol. It was further validated by computing the absolute pKa values of 11 substituted phenols with the average unsigned error of 0.41pH units in comparison with the quantum mechanical error of 0.38pH units for this set of molecules. There was a good transferability of hydration parameters and the results were produced only with fitting of the specific atoms to the hydration energy and pKa targets. This clearly demonstrates the numerical and physical basis of the model is good enough and with proper fitting can reproduce the acidity constants for other systems as well. After the successful development of FB model with the fixed charges OPLS-AA force field, it was expanded to permit simulations with Polarizable Simulations Second-order Interaction Model (POSSIM) force field. The hydration parameters of the small molecules representing analogues of protein side chains were fitted to their solvation energies at 298.15K with an average error of ca.0.136kcal/mol. Second, the resulting parameters were used to reproduce the pKa values of the reference systems and the carboxylic (Asp7, Glu10, Glu19, Asp27 and Glu43) and basic residues (Lys13, Lys29, Lys34, His52 and Lys55) of the turkey ovomucoid third domain (OMTKY3) protein. The overall average unsigned error in the pKa values of the acid residues was found to be 0.37pH units and the basic residues was 0.38 pH units compared to 0.58pH units and 0.72 pH units calculated previously using polarizable force field (PFF) and Poisson Boltzmann formalism (PBF) continuum solvation model. These results are produced with fitting of specific atoms of the reference systems and carboxylic and basic residues of the OMTKY3 protein. Since FB model has produced improved pKa shifts of carboxylic residues and basic protein residues in OMTKY3 protein compared to PBF/PFF, it suggests the methodology of first-order FB continuum solvation model works well in such calculations. In this study the importance of explicit treatment of the electrostatic polarization in calculating pKa of both acid and basic protein residues is also emphasized. Moreover, the presented results demonstrate not only the consistently good degree of accuracy of protein pKa calculations with the second-degree POSSIM approximation of the polarizable calculations and the first-order approximation used in the Fuzzy-Border model for the continuum solvation energy, but also a high degree of transferability of both the POSSIM and continuum solvent Fuzzy Border parameters. Therefore, the FB model of solvation combined with the POSSIM force field can be successfully applied to study the protein and protein-ligand systems in water

Molecular simulation of protein-ligand complexes

Computational methods provide important contributions to modern drug discovery projects. In this thesis, we discuss the insights into protein-ligand interactions afforded by methods such as molecular docking, molecular dynamics (MD) and alchemical free energy calculations, which expedite the process of lead compound design and optimisation. These methods are applied to two case studies of biomolecular systems of therapeutic interest. The targets of the studies are the integrin αvβ6 and the bromodomain-containing protein 4 (BRD4). As the accuracy of molecular mechanics based methods relies on the quality of the force field in which the potential energy is calculated from, we focus on developing force field parameters for a series of small molecule inhibitors of αvβ6. Parameters are then applied to MD and relative free energy perturbation (FEP) simulations. MD simulations highlight the importance of hydrogen bonds, metal chelate interactions and cation