Victoria A. Feher

Computational approaches to mapping allosteric pathways.

Allosteric signaling occurs when chemical and/or physical changes at an allosteric site alter the activity of a primary orthosteric site often many Ångströms distant. A number of recently developed computational techniques, including dynamical network analysis, novel topological and molecular dynamics methods, and hybrids of these methods, are useful for elucidating allosteric signaling pathways at the atomistic level. No single method prevails as best to identify allosteric signal propagation path(s), rather each has particular strengths in characterizing signals that occur over specific timescale ranges and magnitudes of conformational fluctuation. With continued improvement in accuracy and predictive power, these computational techniques aim to become useful drug discovery tools that will allow researchers to identify allostery critical residues for subsequent pharmacological targeting.

D3R grand challenge 2015: Evaluation of protein–ligand pose and affinity predictions

The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. Two targets served as the framework to test community docking and scoring methods: (1) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (2) MAP4K4, donated by Genentech. The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand–protein poses. An additional sub-challenge provided small groups of chemically similar HSP90 compounds amenable to alchemical calculations of relative binding free energy. Unlike previous blinded Challenges, we did not provide cognate receptors or receptors prepared with hydrogens and likewise did not require a specified crystal structure to be used for pose or affinity prediction in Stage 1. Given the freedom to select from over 200 crystal structures of HSP90 in the PDB, participants employed workflows that tested not only core docking and scoring technologies, but also methods for addressing water-mediated ligand–protein interactions, binding pocket flexibility, and the optimal selection of protein structures for use in docking calculations. Nearly 40 participating groups submitted over 350 prediction sets for Grand Challenge 2015. This overview describes the datasets and the organization of the challenge components, summarizes the results across all submitted predictions, and considers broad conclusions that may be drawn from this collaborative community endeavor.

Emerging Computational Methods for the Rational Discovery of Allosteric Drugs

Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.

D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.

The Drug Design Data Resource (D3R) ran Grand Challenge 2 (GC2) from September 2016 through February 2017. This challenge was based on a dataset of structures and affinities for the nuclear receptor farnesoid X receptor (FXR), contributed by F. Hoffmann-La Roche. The dataset contained 102 IC50 values, spanning six orders of magnitude, and 36 high-resolution co-crystal structures with representatives of four major ligand classes. Strong global participation was evident, with 49 participants submitting 262 prediction submission packages in total. Procedurally, GC2 mimicked Grand Challenge 2015 (GC2015), with a Stage 1 subchallenge testing ligand pose prediction methods and ranking and scoring methods, and a Stage 2 subchallenge testing only ligand ranking and scoring methods after the release of all blinded co-crystal structures. Two smaller curated sets of 18 and 15 ligands were developed to test alchemical free energy methods. This overview summarizes all aspects of GC2, including the dataset details, challenge procedures, and participant results. We also consider implications for progress in the field, while highlighting methodological areas that merit continued development. Similar to GC2015, the outcome of GC2 underscores the pressing need for methods development in pose prediction, particularly for ligand scaffolds not currently represented in the Protein Data Bank (http://www.pdb.org), and in affinity ranking and scoring of bound ligands.

A 3-Dimensional Trimeric β-Barrel Model for Chlamydia MOMP Contains Conserved and Novel Elements of Gram-Negative Bacterial Porins

Chlamydia trachomatis is the most prevalent cause of bacterial sexually transmitted diseases and the leading cause of preventable blindness worldwide. Global control of Chlamydia will best be achieved with a vaccine, a primary target for which is the major outer membrane protein, MOMP, which comprises ∼60% of the outer membrane protein mass of this bacterium. In the absence of experimental structural information on MOMP, three previously published topology models presumed a16-stranded barrel architecture. Here, we use the latest β-barrel prediction algorithms, previous 2D topology modeling results, and comparative modeling methodology to build a 3D model based on the 16-stranded, trimeric assumption. We find that while a 3D MOMP model captures many structural hallmarks of a trimeric 16-stranded β-barrel porin, and is consistent with most of the experimental evidence for MOMP, MOMP residues 320–334 cannot be modeled as β-strands that span the entire membrane, as is consistently observed in published 16-stranded β-barrel crystal structures. Given the ambiguous results for β-strand delineation found in this study, recent publications of membrane β-barrel structures breaking with the canonical rule for an even number of β-strands, findings of β-barrels with strand-exchanged oligomeric conformations, and alternate folds dependent upon the lifecycle of the bacterium, we suggest that although the MOMP porin structure incorporates canonical 16-stranded conformations, it may have novel oligomeric or dynamic structural changes accounting for the discrepancies observed.

Structural Characterisation of Tpx from Yersinia Pseudotuberculosis Reveals Insights Into the Binding of Salicylidene Acylhydrazide Compounds.

Thiol peroxidase, Tpx, has been shown to be a target protein of the salicylidene acylhydrazide class of antivirulence compounds. In this study we present the crystal structures of Tpx from Y. pseudotuberculosis (ypTpx) in the oxidised and reduced states, together with the structure of the C61S mutant. The structures solved are consistent with previously solved atypical 2-Cys thiol peroxidases, including that for “forced” reduced states using the C61S mutant. In addition, by investigating the solution structure of ypTpx using small angle X-ray scattering (SAXS), we have confirmed that reduced state ypTpx in solution is a homodimer. The solution structure also reveals flexibility around the dimer interface. Notably, the conformational changes observed between the redox states at the catalytic triad and at the dimer interface have implications for substrate and inhibitor binding. The structural data were used to model the binding of two salicylidene acylhydrazide compounds to the oxidised structure of ypTpx. Overall, the study provides insights into the binding of the salicylidene acylhydrazides to ypTpx, aiding our long-term strategy to understand the mode of action of this class of compounds.

Molecular Docking to Flexible Targets

It is widely accepted that protein receptors exist as an ensemble of conformations in solution. How best to incorporate receptor flexibility into virtual screening protocols used for drug discovery remains a significant challenge. Here, stepwise methodologies are described to generate and select relevant protein conformations for virtual screening in the context of the relaxed complex scheme (RCS), to design small molecule libraries for docking, and to perform statistical analyses on the virtual screening results. Methods include equidistant spacing, RMSD-based clustering, and QR factorization protocols for ensemble generation and ROC analysis for ensemble selection.

Seeing the Unseen: Sampling the Excited State of T4 Lysozyme L99A with Simulations on the Anton Supercomputer

Proteins are the workhorses of the cell, converting small molecules into energy, harnessing energy for macromolecular synthesis, and interacting with one another to relay important cellular messages. All of these tasks are performed through the coordinated movement of a proteins atoms, which guide a protein from one conformational state to another. While experimental techniques provide insight into protein atomic motions, experiments cannot track all atomic-level motions across the nanosecond (ns) to millisecond (ms) time regimes. Even for proteins like T4 lysozyme, which have been extensively characterized (1-3), mysteries remain about the mechanism of converting between different protein states. The Leu99⇒Ala (L99A) mutant of T4 lysozyme is a model system for studying rarely seen, hidden excited states, even though the structure of its excited state has never been solved (4-9). Here we have sampled the excited state of L99A at atomic resolution with computational simulation on the Anton supercomputer (10). In this simulation, phenylalanine 114 (F114) and helices F and G undergo conformational changes that are indicative of a transition to the excited state. This MD-generated excited state also reproduces known excited state chemical shifts (R2 = 0.95) and agrees with multiple lines of experiment (4, 7, 11, 12). Our results of the L99A excited state cohere decades of data on this invisible conformation. We anticipate that this structure will provide experimentalists studying this protein with the tools to understand the atomic level details underlying L99A dynamics.