Predictions of structural elements for the binding of Hin recombinase with the hix site of DNA

Molecular dynamics simulations were coupled with experimental data from biochemistry and genetics to generate a theoretical structure for the binding domain of Hin recombinase complexed with the hix site of DNA. The theoretical model explains the observed sequence specificity of Hin recombinase and leads to a number of testable predictions concerning altered sequence selectivity for various mutants of protein and DNA. Combining molecular dynamics simulations with constraints based on current knowledge of protein structure leads to a theoretical structure of the binding domain of Hin recombinase with the hix site of DNA. The model offers a mechanistic explanation of the presently known characteristics of Hin and predicts the effects of specific mutations of both protein and DNA. The predictions can be tested by currently feasible experiments that should lead to refinements in and improvements on the current theoretical model. Because current experimental and theoretical methods are all limited to providing only partial information about protein-DNA interactions, we believe that this approach of basing molecular simulations on experimental knowledge and using the results of these simulations to design new, more precise experimental tests will be of general utility. These results provide additional evidence for the generality of the helix-turn-helix motif in DNA recognition and stabilization of proteins on DNA

On-resin N-methylation of cyclic peptides for discovery of orally bioavailable scaffolds.

Backbone N-methylation is common among peptide natural products and has a substantial impact on both the physical properties and the conformational states of cyclic peptides. However, the specific impact of N-methylation on passive membrane diffusion in cyclic peptides has not been investigated systematically. Here we report a method for the selective, on-resin N-methylation of cyclic peptides to generate compounds with drug-like membrane permeability and oral bioavailability. The selectivity and degree of N-methylation of the cyclic peptide was dependent on backbone stereochemistry, suggesting that conformation dictates the regiochemistry of the N-methylation reaction. The permeabilities of the N-methyl variants were corroborated by computational studies on a 1,024-member virtual library of N-methyl cyclic peptides. One of the most permeable compounds, a cyclic hexapeptide (molecular mass = 755 Da) with three N-methyl groups, showed an oral bioavailability of 28% in rat

Human liver glycogen phosphorylase inhibitors bind at a new allosteric site

AbstractBackground: Glycogen phosphorylases catalyze the breakdown of glycogen to glucose-1-phosphate for glycolysis. Maintaining control of blood glucose levels is critical in minimizing the debilitating effects of diabetes, making liver glycogen phosphorylase a potential therapeutic target.Results: The binding site in human liver glycogen phosphorylase (HLGP) for a class of promising antidiabetic agents was identified crystallographically. The site is novel and functions allosterically by stabilizing the inactive conformation of HLGP. The initial view of the complex revealed key structural information and inspired the design of a new class of inhibitors which bind with nanomolar affinity and whose crystal structure is also described.Conclusions: We have identified the binding site of a new class of allosteric HLGP inhibitors. The crystal structure revealed the details of inhibitor binding, led to the design of a new class of compounds, and should accelerate efforts to develop therapeutically relevant molecules for the treatment of diabetes

Translational diffusion of cyclic peptides measured using pulsed-field gradient NMR

Cyclic peptides are increasingly being recognized as valuable templates for drug discovery or design. To facilitate efforts in the structural characterization of cyclic peptides, we explore the use of pulse-field gradient experiments as a convenient and noninvasive approach for characterizing their diffusion properties in solution. We present diffusion coefficient measurements of five cyclic peptides, including dichC, SFTI-1, cVc1.1, kB1, and kB2. These peptides range in size from six to 29 amino acids and have various therapeutically interesting activities. We explore the use of internal standards, such as dioxane and acetonitrile, to evaluate the hydrodynamic radius from the diffusion coefficient, and show that 2,2-dimethyl-2-silapentane-5-sulfonic acid, a commonly used chemical shift reference, can be used as an internal standard to avoid spectral overlap issues and simplify data analysis. The experimentally measured hydrodynamic radii correlate with increasing molecular weight and in silico predictions. We further applied diffusion measurements to characterize the self-association of kB2 and showed that it forms oligomers in a concentration-dependent manner, which may be relevant to its mechanism of action. Diffusion coefficient measurements appear to have broad utility in cyclic peptide structural biology, allowing for the rapid characterization of their molecular shape in solution

Building proteins from C_α coordinates using the dihedral probability grid Monte Carlo method

Dihedral probability grid Monte Carlo (DPG‐MC) is a general‐purpose method of conformational sampling that can be applied to many problems in peptide and protein modeling. Here we present the DPG‐MC method and apply it to predicting complete protein structures from Cα coordinates. This is useful in such endeavors as homology modeling, protein structure prediction from lattice simulations, or fitting protein structures to X‐ray crystallographic data. It also serves as an example of how DPG‐MC can be applied to systems with geometric constraints. The conformational propensities for individual residues are used to guide conformational searches as the protein is built from the amino‐terminus to the carboxyl‐terminus. Results for a number of proteins show that both the backbone and side chain can be accurately modeled using DPG‐MC. Backbone atoms are generally predicted with RMS errors of about 0.5 Å (compared to X‐ray crystal structure coordinates) and all atoms are predicted to an RMS error of 1.7 Å or better

Building proteins from C_α coordinates using the dihedral probability grid Monte Carlo method

Dihedral probability grid Monte Carlo (DPG‐MC) is a general‐purpose method of conformational sampling that can be applied to many problems in peptide and protein modeling. Here we present the DPG‐MC method and apply it to predicting complete protein structures from Cα coordinates. This is useful in such endeavors as homology modeling, protein structure prediction from lattice simulations, or fitting protein structures to X‐ray crystallographic data. It also serves as an example of how DPG‐MC can be applied to systems with geometric constraints. The conformational propensities for individual residues are used to guide conformational searches as the protein is built from the amino‐terminus to the carboxyl‐terminus. Results for a number of proteins show that both the backbone and side chain can be accurately modeled using DPG‐MC. Backbone atoms are generally predicted with RMS errors of about 0.5 Å (compared to X‐ray crystal structure coordinates) and all atoms are predicted to an RMS error of 1.7 Å or better

De novo prediction of polypeptide conformations using dihedral probability grid Monte Carlo methodology

We tested the dihedral probability grid Monte Carlo (DPG‐MC) methodology to determine optimal conformations of polypeptides by applying it to predict the low energy ensemble for two peptides whose solution NMR structures are known: integrin receptor peptide (YGRGDSP, Type II β‐turn) and S3 α‐helical peptide (YMSEDELKAAEAAFKRHGPT). DPG‐MC involves importance sampling, local random stepping in the vicinity of a current local minima, and Metropolis sampling criteria for acceptance or rejection of new structures. Internal coordinate values are based on side‐chain‐specific dihedral angle probability distributions (from analysis of high‐resolution protein crystal structures). Important features of DPG‐MC are: (1) Each DPG‐MC step selects the torsion angles (ϕ, ψ, χ) from a discrete grid that are then applied directly to the structure. The torsion angle increments can be taken as S = 60, 30, 15, 10, or 5°, depending on the application. (2) DPG‐MC utilizes a temperature‐dependent probability function (P) in conjunction with Metropolis sampling to accept or reject new structures. For each peptide, we found close agreement with the known structure for the low energy conformational ensemble located with DPG‐MC. This suggests that DPG‐MC will be useful for predicting conformations of other polypeptides

De novo prediction of polypeptide conformations using dihedral probability grid Monte Carlo methodology

We tested the dihedral probability grid Monte Carlo (DPG‐MC) methodology to determine optimal conformations of polypeptides by applying it to predict the low energy ensemble for two peptides whose solution NMR structures are known: integrin receptor peptide (YGRGDSP, Type II β‐turn) and S3 α‐helical peptide (YMSEDELKAAEAAFKRHGPT). DPG‐MC involves importance sampling, local random stepping in the vicinity of a current local minima, and Metropolis sampling criteria for acceptance or rejection of new structures. Internal coordinate values are based on side‐chain‐specific dihedral angle probability distributions (from analysis of high‐resolution protein crystal structures). Important features of DPG‐MC are: (1) Each DPG‐MC step selects the torsion angles (ϕ, ψ, χ) from a discrete grid that are then applied directly to the structure. The torsion angle increments can be taken as S = 60, 30, 15, 10, or 5°, depending on the application. (2) DPG‐MC utilizes a temperature‐dependent probability function (P) in conjunction with Metropolis sampling to accept or reject new structures. For each peptide, we found close agreement with the known structure for the low energy conformational ensemble located with DPG‐MC. This suggests that DPG‐MC will be useful for predicting conformations of other polypeptides

TorsionNet: A Deep Neural Network to Rapidly Predict Small Molecule Torsion Energy Profiles with the Accuracy of Quantum Mechanics

TorsionNet: A Deep Neural Network to Rapidly Predict Small Molecule Torsion Energy Profiles with the Accuracy of Quantum Mechanics Brajesh K. Rai*,1, Vishnu Sresht1, Qingyi Yang2, Ray Unwalla2, Meihua Tu2, Alan M. Mathiowetz2, and Gregory A. Bakken3 1Simulation and Modeling Sciences and 2Medicine Design, Pfizer Worldwide Research Development and Medical, 610 Main Street, Cambridge, Massachusetts 02139, United States 3Digital, Pfizer, Eastern Point Road, Groton, Connecticut 06340, United States ABSTRACT Fast and accurate assessment of small molecule dihedral energetics is crucial for molecular design and optimization in medicinal chemistry. Yet, accurate prediction of torsion energy profiles remains a challenging task as current molecular mechanics methods are limited by insufficient coverage of druglike chemical space and accurate quantum mechanical (QM) methods are too expensive. To address this limitation, we introduce TorsionNet, a deep neural network (DNN) model specifically developed to predict small molecule torsion energy profiles with QM-level accuracy. We applied active learning to identify nearly 50k fragments (with elements H, C, N, O, F, S, and Cl) that maximized the coverage of our corporate library and leveraged massively parallel cloud computing resources to perform DFT torsion scan of these fragments, generating a training dataset of 1.2 million DFT energies. By training TorsionNet on this dataset, we obtain a model that can rapidly predict the torsion energy profile of typical druglike fragments with DFT-level accuracy. Importantly, our method also provides a direct estimate of the uncertainty in the predicted profiles without any additional calculations. In this report, we show that TorsionNet can reliably identify the preferred dihedral geometries observed in crystal structures. We also present practical applications of TorsionNet that demonstrate how consideration of DNN-based strain energy leads to substantial improvement in existing lead discovery and design workflows. A benchmark dataset (TorsionNet500) comprising 500 chemically diverse fragments with DFT torsion profiles (12k DFT-optimized geometries and energies) has been created and is made freely available.</p