Browsing by Author "Thorpe, Ian F."
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Item Allosteric Inhibitors Have Distinct Effects, but Also Common Modes of Action, in the HCV Polymerase(2016-11-15) Davis, Brittny C.; Brown, Jodian A.; Thorpe, Ian F.The RNA-dependent RNA polymerase from the Hepatitis C Virus (gene product NS5B) is a validated drug target because of its critical role in genome replication. There are at least four distinct allosteric sites on the polymerase to which several small molecule inhibitors bind. In addition, numerous crystal structures have been solved with different allosteric inhibitors bound to the polymerase. However, the molecular mechanisms by which these small molecules inhibit the enzyme have not been fully elucidated. There is evidence that allosteric inhibitors alter the intrinsic motions and distribution of conformations sampled by the enzyme. In this study we use molecular dynamics simulations to understand the structural and dynamic changes that result when inhibitors are bound at three different allosteric binding sites on the enzyme. We observe that ligand binding at each site alters the structure and dynamics of NS5B in a distinct manner. Nonetheless, our studies also highlight commonalities in the mechanisms of action of the different inhibitors. Each inhibitor alters the conformational states sampled by the enzyme, either by rigidifying the enzyme and preventing transitions between functional conformational states or by destabilizing the enzyme and preventing functionally relevant conformations from being adequately sampled. By illuminating the molecular mechanisms of allosteric inhibition, these studies delineate the intrinsic functional properties of the enzyme and pave the way for designing novel and more effective polymerase inhibitors. This information may also be important to understand how allosteric regulation occurs in related viral polymerases and other enzymes.Item Block Cyclic Distribution of Data in pbdR and its Effects on Computational Efficiency(2013) Bachmann, Matthew G.; Dyas, Ashley D.; Kilmer, Shelby C.; Sass, Julian; Raim, Andrew; Neerchal, Nagaraj K.; Adragni, Kofi P.; Ostrouchov, George; Thorpe, Ian F.Programming with big data in R (pbdR), a package used to implement high-performance computing in the statistical software R, uses block cyclic distribution to organize large data across many processes. Because computations performed on large matrices are often not associative, a systematic approach must be used during parallelization to divide the matrix correctly. The block cyclic distribution method stresses a balanced load across processes by allocating sections of data to a corresponding node. This method achieves well divided data that each process computes individually and calculates a final result more efficiently. A nontrivial problem occurs when using block cyclic distribution: Which combinations of different block sizes and grid layouts are most effective? These two factors greatly influence computational efficiency, and therefore it is crucial to study and understand their relationship. To analyze the effects of block size and processor grid layout, we carry out a performance study of the block cyclic process used to compute a principal components analysis (PCA). We apply PCA both to a large simulated data set and to data involving the analysis of single nucleotide polymorphisms (SNPs). We implement analysis of variance (ANOVA) techniques in order to distinguish the variability associated with each grid layout and block distribution. Once the nature of these factors is determined, predictions about the performance for much larger data sets can be made. Our final results demonstrate the relationship between computational efficiency and both block distribution and processor grid layout, and establish a benchmark regarding which combinations of these factors are most effective.Item Identifying Nonlinear Correlations in High Dimensional Data with Application to Protein Molecular Dynamics Simulations(2013) Bailey, William J.; Chambless, Claire A.; Cho, Brandynne M.; Smith, Jesse D.; Raim, Andrew M.; Adragni, Kofi P.; Thorpe, Ian F.Complex biomolecules such as proteins can respond to changes in their environment through a process called allostery, which plays an important role in regulating the function of these biomolecules. Allostery occurs when an event at a specific location in a macromolecule produces an effect at a location in the molecule some distance away. An important component of allostery is the coupling of protein sites. Such coupling is one mechanism by which allosteric effects can be transmitted over long distances. To understand this phenomenon, molecular dynamic simulations are carried out with a large number of atoms, and the trajectories of these atoms are recorded over time. Simple correlation methods have been used in the literature to identify coupled motions between protein sites. We implement a recently developed statistical method for dimension reduction called principal fitted components (PFC) in the statistical programming language R to identify both linear and non-linear correlations between protein sites while dealing efficiently with the high dimensionality of the data. PFC models reduce the dimensionality of data while capturing linear and nonlinear dependencies among predictors (atoms) using a flexible set of basis functions. For faster processing, we implement the PFC algorithm using parallel computing through the Programming with Big Data in R (pbdR) package for R. We demonstrate the methods’ effectiveness on simulated datasets, and apply the routine to time series data from Molecular Dynamic (MD) simulations to identify coupled motion among the atoms.Item Inhibitors for the hepatitis C virus RNA polymerase explored by SAR with advanced machine learning methods(2014-06-01) Weidlich, Iwona E.; Filippov, Igor V.; Brown, Jodian; Basu, Neerja Kaushik; Krishnan, Ramalingam; Nicklaus, Marc C.; Thorpe, Ian F.Hepatitis C virus (HCV) is a global health challenge, affecting approximately 200 million people worldwide. In this study we developed SAR models with advanced machine learning classifiers Random Forest and k Nearest Neighbor Simulated Annealing for 679 small molecules with measured inhibition activity for NS5B genotype 1b. The activity was expressed as a binary value (active/inactive), where actives were considered molecules with IC50 ≤ 0.95 μM. We applied our SAR models to various drug-like databases and identified novel chemical scaffolds for NS5B inhibitors. Subsequent in vitro antiviral assays suggested a new activity for an existing prodrug, Candesartan cilexetil, which is currently used to treat hypertension and heart failure but has not been previously tested for anti-HCV activity. We also identified NS5B inhibitors with two novel non-nucleoside chemical motifs.Item Nonlinear Measures of Correlation and Dimensionality Reduction with Application to Protein Motion(2014) Hong, Nancy; Jasien, Emily; Pagan, Christopher; Xie, Daniel; Coulibaly, Zana; Adragni, Kofi P.; Thorpe, Ian F.The study of allostery, a regulatory process that occurs in complex macromolecules such as proteins, is of particular interest as it has a key role in determining the function of these macromolecules. Allostery produces motional correlations that can be analyzed using different statistical methods. We implement a program in the statistical programming language R that uses polynomial regression and leave-one-out cross-validation to model relationships in data obtained from different sites in the protein, using the square root of the coefficient of determination to detect both linear and non-linear trends. The performance of the program will be studied on a simulated data set with linear and non-linear relationships and the effectiveness of the implemented methods as it relates to this problem will be assessed.Item Thumb inhibitor binding eliminates functionally important dynamics in the hepatitis C virus RNA polymerase(Wiley, 2012-07-31) Davis, Brittny C.; Thorpe, Ian F.Hepatitis C virus (HCV) has infected almost 200 million people worldwide, typically causing chronic liver damage and severe complications such as liver failure. Currently, there are few approved treatments for viral infection. Thus, the HCV RNA‐dependent RNA polymerase (gene product NS5B) has emerged as an important target for small molecule therapeutics. Potential therapeutic agents include allosteric inhibitors that bind distal to the enzyme active site. While their mechanism of action is not conclusively known, it has been suggested that certain inhibitors prevent a conformational change in NS5B that is crucial for RNA replication. To gain insight into the molecular origin of long‐range allosteric inhibition of NS5B, we employed molecular dynamics simulations of the enzyme with and without an inhibitor bound to the thumb domain. These studies indicate that the presence of an inhibitor in the thumb domain alters both the structure and internal motions of NS5B. Principal components analysis identified motions that are severely attenuated by inhibitor binding. These motions may have functional relevance by facilitating interactions between NS5B and RNA template or nascent RNA duplex, with presence of the ligand leading to enzyme conformations with narrower and thus less accessible RNA binding channels. This study provides the first evidence for a mechanistic basis of allosteric inhibition in NS5B. Moreover, we present evidence that allosteric inhibition of NS5B results from intrinsic features of the enzyme free energy landscape, suggesting a common mechanism for the action of diverse allosteric ligands. Proteins 2013. © 2012 Wiley Periodicals, Inc.