Experimental and computational methods for identification of novel fungal histone acetyltransferase Rtt109 inhibitors

University of Minnesota Ph.D. dissertation. February 2014. Major: Medicinal Chemistry. Advisor: Elizabeth A. Amin. 1 computer file (PDF); xii, 180 pages.Rtt109 is a fungal-specific histone acetyltransferase that catalyzes histone H3 lysine 56 acetylation and is a promising antifungal drug target. To identify novel Rtt109 inhibitors as potential drug scaffolds, we employed in vitro high throughput screening (HTS) and various computer-assisted strategies, including molecular dynamics, docking, three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis, pharmacophore modeling, and Support Vector Machine (SVM) mining. An initial experimental screening of 82,861 compounds (HTS1) yielded hits with activity ranging from 0.49 - 17.5 µM against Rtt109. The molecular dynamics simulation of Rtt109 suggested that the histone lysine tunnel, a potential inhibitor binding site, was not flexible and thus the use of a rigid protein structure of Rtt109 was appropriate for docking studies. From a virtual screen using Surflex-Dock, we have identified 878 additional compounds as potential hits, with predicted Kd values of 0.1 nm or lower. Based on preliminary experimental data from HTS1, validated pharmacophore maps were developed and used to pinpoint potential Rtt109 ligand-receptor interactions. 3D-QSAR CoMFA and CoMSIA models that were also derived from the hit series generated in the initial experimental HTS display high self-consistency (r2 = 0.985 [CoMFA] and r2 = 0.976 [CoMSIA]) and robust internal predictivity (rcv2 = 0.754 [CoMFA] and rcv2 = 0.654 [CoMSIA]). Importantly, key features identified in both the pharmacophore hypotheses and the 3D-QSAR models agreed well with each other and with experimentally defined structural features in the Rtt109 lysine-binding tunnel. In addition, our optimized SVM models demonstrated high predictive power against the external test sets for Rtt109 with accuracy of 91.1%. We also identified novel features with significant differentiating ability to separate Rtt109 inhibitors from non-inhibitors

Generation of multiple pharmacophore hypotheses using multiobjective optimisation techniques

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Generation of multiple pharmacophore hypotheses using multiobjective optimisation techniques

Pharmacophore methods provide a way of establishing a structure--activity relationship for a series of known active ligands. Often, there are several plausible hypotheses that could explain the same set of ligands and, in such cases, it is important that the chemist is presented with alternatives that can be tested with different synthetic compounds. Existing pharmacophore methods involve either generating an ensemble of conformers and considering each conformer of each ligand in turn or exploring conformational space on-the-fly. The ensemble methods tend to produce a large number of hypotheses and require considerable effort to analyse the results, whereas methods that vary conformation on-the-fly typically generate a single solution that represents one possible hypothesis, even though several might exist. We describe a new method for generating multiple pharmacophore hypotheses with full conformational flexibility being explored on-the-fly. The method is based on multiobjective evolutionary algorithm techniques and is designed to search for an ensemble of diverse yet plausible overlays which can then be presented to the chemist for further investigation

Enumeration, conformation sampling and population of libraries of peptide macrocycles for the search of chemotherapeutic cardioprotection agents

Peptides are uniquely endowed with features that allow them to perturb previously difficult to drug biomolecular targets. Peptide macrocycles in particular have seen a flurry of recent interest due to their enhanced bioavailability, tunability and specificity. Although these properties make them attractive hit-candidates in early stage drug discovery, knowing which peptides to pursue is non‐trivial due to the magnitude of the peptide sequence space. Computational screening approaches show promise in their ability to address the size of this search space but suffer from their inability to accurately interrogate the conformational landscape of peptide macrocycles. We developed an in‐silico compound enumerator that was tasked with populating a conformationally laden peptide virtual library. This library was then used in the search for cardio‐protective agents (that may be administered, reducing tissue damage during reperfusion after ischemia (heart attacks)). Our enumerator successfully generated a library of 15.2 billion compounds, requiring the use of compression algorithms, conformational sampling protocols and management of aggregated compute resources in the context of a local cluster. In the absence of experimental biophysical data, we performed biased sampling during alchemical molecular dynamics simulations in order to observe cyclophilin‐D perturbation by cyclosporine A and its mitochondrial targeted analogue. Reliable intermediate state averaging through a WHAM analysis of the biased dynamic pulling simulations confirmed that the cardio‐protective activity of cyclosporine A was due to its mitochondrial targeting. Paralleltempered solution molecular dynamics in combination with efficient clustering isolated the essential dynamics of a cyclic peptide scaffold. The rapid enumeration of skeletons from these essential dynamics gave rise to a conformation laden virtual library of all the 15.2 Billion unique cyclic peptides (given the limits on peptide sequence imposed). Analysis of this library showed the exact extent of physicochemical properties covered, relative to the bare scaffold precursor. Molecular docking of a subset of the virtual library against cyclophilin‐D showed significant improvements in affinity to the target (relative to cyclosporine A). The conformation laden virtual library, accessed by our methodology, provided derivatives that were able to make many interactions per peptide with the cyclophilin‐D target. Machine learning methods showed promise in the training of Support Vector Machines for synthetic feasibility prediction for this library. The synergy between enumeration and conformational sampling greatly improves the performance of this library during virtual screening, even when only a subset is used

The application of multiobjective optimisation to protein-ligand docking

Despite the intense efforts that have been devoted to the development of scoring functions for protein-ligand docking, they are still limited in their ability to identify the correct binding pose of a ligand within a protein binding site. A deeper understanding of the intricacies of scoring functions is therefore essential in order to develop these effectively. The aim of the work described in this thesis is to analyse the individual interaction energy types which form the individual components of a force field-based scoring function. To do this, & protein-ligand docking algorithm that is based on multiobjective optimisation has been developed. Multiobjective optimisation allows for the optimisation of several objectives simultaneously and this has been applied to the individual interaction energy types of the GRID scoring function. Traditionally these interaction energy types are summed together and the total energy is used to guide the search. By using individual energy types during optimisation, their roles can be better understood. The interaction energy types that have been used here are the electrostatic and hydrogen bond interactions combined, and van der Waals interactions. The algorithm is first tested on two datasets containing twenty complexes. The results show that the different interaction energy types have varying influences when it comes to successfully docking certain complexes, and that it is important to fmd the right balance of interaction energy types so as to find correct solutions. Ofthe twenty complexes, the algorithm found correct solutions for fifteen. To improve the performance of the algorithm, a few enhancements were introduced. This includes a simplex minimisation process with a Lamarckian element. The algorithm was retested on the twenty complexes, and the newer version was found to outperform the original version, finding correct solutions for seventeen of the twenty complexes. To extensively study the capabilities of the algorithm, it was tested on varied datasets, including the FlexX dataset. The algoritlun's performance was also compared to a single-objective docking tool, Q-fit. The comparison betw~en the multiobjective and single-objective methodologies revealed that single-objective methods can sometimes fail at finding correct docked solutions because they are unable to correctly balance the interaction energy types comprising a scoring function. The study also showed that a multiobjective optimisation method can reveal the reasons why a given docking algorithm may fail at fmding a correct solution. Finally, the algorithm was extended to incorporate desolvation energy as a third objective. Though these results are preliminary, they revealed some interesting relationships between the different objectives.EThOS - Electronic Theses Online ServiceGBUnited Kingdo