Predicting routes of phase I and II metabolism based on quantum mechanics and machine learning

1. Unexpected metabolism could lead to the failure of many late-stage drug candidates or even the withdrawal of approved drugs. Thus, it is critical to predict and study the dominant routes of metabolism in the early stages of research. In this study, we describe the development and validation of a 'WhichEnzyme' model that accurately predicts the enzyme families most likely to be responsible for a drug-like molecule's metabolism. Furthermore, we combine this model with our previously published regioselectivity models for Cytochromes P450, Aldehyde Oxidases, Flavin-containing Monooxygenases, UDP-glucuronosyltransferases and Sulfotransferases - the most important Phase I and Phase II drug metabolising enzymes - and a 'WhichP450' model that predicts the Cytochrome P450 isoform(s) responsible for a compound's metabolism. The regioselectivity models are based on a mechanistic understanding of these enzymes' actions, and use quantum mechanical simulations with machine learning methods to accurately predict sites of metabolism and the resulting metabolites. We train heuristic based on the outputs of the 'WhichEnzyme', 'WhichP450', and regioselectivity models to determine the most likely routes of metabolism and metabolites to be observed experimentally. Finally, we demonstrate that this combination delivers high sensitivity in identifying experimentally reported metabolites and higher precision than other methods for predicting in vivo metabolite profiles.

Fibpredictor: a computational method for rapid prediction of amyloid fibril structures

Amyloid fibrils are important in diseases such as Alzheimer's disease and Parkinson's disease, and are also a common instability in peptide and protein drug products. Despite their importance, experimental structures of amyloid fibrils in atomistic detail are rare. To address this limitation, we have developed a novel, rapid computational method to predict amyloid fibril structures (Fibpredictor). The method combines β-sheet model building, β-sheet replication, and symmetry operations with side-chain prediction and statistical scoring functions. When applied to nine amyloid fibrils with experimentally determined structures, the method predicted the correct structures of amyloid fibrils and enriched those among the top-ranked structures. These models can be used as the initial heuristic structures for more complicated computational studies. Fibpredictor is available at http://nanohub.org/resources/fibpredictor .

Structural transitions and interactions in the early stages of human glucagon amyloid fibrillation

A mechanistic understanding of the intermolecular interactions and structural changes during fibrillation is crucial for the design of safe and efficacious glucagon formulations. Amide hydrogen/deuterium exchange with mass spectrometric analysis was used to identify the interactions and amino acids involved in the initial stages of glucagon fibril formation at acidic pH. Kinetic measurements from intrinsic and thioflavin T fluorescence showed sigmoidal behavior. Secondary structural measurement of fibrillating glucagon using far-UV circular dichroism spectroscopy showed changes in structure from random coil → α-helix → β-sheet, with increase in α-helix content during the lag phase followed by increase in β-sheet content during the growth phase. Hydrogen/deuterium exchange with mass spectrometric analysis of fibrillating glucagon suggested that C-terminal residues 22-29 are involved in interactions during the lag phase, during which N-terminal residues 1-6 showed no changes. Molecular dynamics simulations of glucagon fragments showed C-terminal to C-terminal interactions with greater α-helix content for the 20-29 fragment, with hydrophobic and aromatic residues (Phe-22, Trp-25, Val-23, and Met-27) predominantly involved. Overall, the study shows that glucagon interactions during the early phase of fibrillation are mediated through C-terminal residues, which facilitate the formation of α-helix-rich oligomers, which further undergo structural rearrangement and elongation to form β-sheet-rich mature fibrils.

Application of information theory to a three-body coarse-grained representation of proteins in the PDB: insights into the structural and evolutionary roles of residues in protein structure

Knowledge-based methods for analyzing protein structures, such as statistical potentials, primarily consider the distances between pairs of bodies (atoms or groups of atoms). Considerations of several bodies simultaneously are generally used to characterize bonded structural elements or those in close contact with each other, but historically do not consider atoms that are not in direct contact with each other. In this report, we introduce an information-theoretic method for detecting and quantifying distance-dependent through-space multibody relationships between the sidechains of three residues. The technique introduced is capable of producing convergent and consistent results when applied to a sufficiently large database of randomly chosen, experimentally solved protein structures. The results of our study can be shown to reproduce established physico-chemical properties of residues as well as more recently discovered properties and interactions. These results offer insight into the numerous roles that residues play in protein structure, as well as relationships between residue function, protein structure, and evolution. The techniques and insights presented in this work should be useful in the future development of novel knowledge-based tools for the evaluation of protein structure.

Are distance-dependent statistical potentials considering three interacting bodies superior to two-body statistical potentials for protein structure prediction?

Distance-based statistical potentials have long been used to model condensed matter systems, e.g. as scoring functions in differentiating native-like protein structures from decoys. These scoring functions are based on the assumption that the total free energy of the protein can be calculated as the sum of pairwise free energy contributions derived from a statistical analysis of pair-distribution functions. However, this fundamental assumption has been challenged theoretically. In fact the free energy of a system with N particles is only exactly related to the N-body distribution function. Based on this argument coarse-grained multi-body statistical potentials have been developed to capture higher-order interactions. Having a coarse representation of the protein and using geometric contacts instead of pairwise interaction distances renders these models insufficient in modeling details of multi-body effects. In this study, we investigated if extending distance-dependent pairwise atomistic statistical potentials to corresponding interaction functions that are conditional on a third interacting body, defined as quasi-three-body statistical potentials, could model details of three-body interactions. We also tested if this approach could improve the predictive capabilities of statistical scoring functions for protein structure prediction. We analyzed the statistical dependency between two simultaneous pairwise interactions and showed that there is surprisingly little if any dependency of a third interacting site on pairwise atomistic statistical potentials. Also the protein structure prediction performance of these quasi-three-body potentials is comparable with their corresponding two-body counterparts. The scoring functions developed in this study showed better or comparable performances compared to some widely used scoring functions for protein structure prediction.

Design, Synthesis and Pharmacological Evaluation of Novel 2-[2-(2-Chlorophenoxy) phenyl]-1,3,4-oxadiazole Derivatives as Benzodiazepine Receptor Agonists

New derivatives of 2-[2-(2-Chlorophenoxy)phenyl]-1,3,4-oxadiazole as candidates for agonistic effect on benzodiazepine receptors were synthesized. Conformational analysis and superimposition of energy minima conformers of the novel compounds on estazolam, a known benzodiazepine agonist, revealed that the main proposed benzodiazepine pharmacophores were well matched. In pharmacological evaluation, anticonvulsant activity of the compounds determined by pentylenetetrazole-induced lethal convulsion and maximal electroshock tests. The results showed that the introduction of an amino substituent in position 5 of 1,3,4- oxadiazole ring generates compound 6 that has a considerable effect. Compound 8 with a hydroxyl substituent on position 5 of 1,3,4- oxadiazole ring showed a relatively mild anticonvulsant activity, which was significantly weaker than that of diazepam and compound 6. Anticonvulsant effects of active compounds were antagonized by flumazenil, an antagonist of benzodiazepine receptors, indicating the involvement of benzodiazepine receptors in these effects.