Automated Protocol for Large-Scale Modeling of Gene Expression Data.

With the continued rise of phenotypic- and genotypic-based screening projects, computational methods to analyze, process, and ultimately make predictions in this field take on growing importance. Here we show how automated machine learning workflows can produce models that are predictive of differential gene expression as a function of a compound structure using data from A673 cells as a proof of principle. In particular, we present predictive models with an average accuracy of greater than 70% across a highly diverse ∼1000 gene expression profile. In contrast to the usual in silico design paradigm, where one interrogates a particular target-based response, this work opens the opportunity for virtual screening and lead optimization for desired multitarget gene expression profiles.

Relative Binding Free Energy Calculations Applied to Protein Homology Models.

A significant challenge and potential high-value application of computer-aided drug design is the accurate prediction of protein-ligand binding affinities. Free energy perturbation (FEP) using molecular dynamics (MD) sampling is among the most suitable approaches to achieve accurate binding free energy predictions, due to the rigorous statistical framework of the methodology, correct representation of the energetics, and thorough treatment of the important degrees of freedom in the system (including explicit waters). Recent advances in sampling methods and force fields coupled with vast increases in computational resources have made FEP a viable technology to drive hit-to-lead and lead optimization, allowing for more efficient cycles of medicinal chemistry and the possibility to explore much larger chemical spaces. However, previous FEP applications have focused on systems with high-resolution crystal structures of the target as starting points-something that is not always available in drug discovery projects. As such, the ability to apply FEP on homology models would greatly expand the domain of applicability of FEP in drug discovery. In this work we apply a particular implementation of FEP, called FEP+, on congeneric ligand series binding to four diverse targets: a kinase (Tyk2), an epigenetic bromodomain (BRD4), a transmembrane GPCR (A2A), and a protein-protein interaction interface (BCL-2 family protein MCL-1). We apply FEP+ using both crystal structures and homology models as starting points and find that the performance using homology models is generally on a par with the results when using crystal structures. The robustness of the calculations to structural variations in the input models can likely be attributed to the conformational sampling in the molecular dynamics simulations, which allows the modeled receptor to adapt to the "real" conformation for each ligand in the series. This work exemplifies the advantages of using all-atom simulation methods with full system flexibility and offers promise for the general application of FEP to homology models, although additional validation studies should be performed to further understand the limitations of the method and the scenarios where FEP will work best.

Small molecule-facilitated degradation of ANO1 protein: a new targeting approach for anticancer therapeutics.

ANO1, a calcium-activated chloride channel, is highly expressed and amplified in human cancers and is a critical survival factor in these cancers. The ANO1 inhibitor CaCCinh-A01 decreases proliferation of ANO1-amplified cell lines; however, the mechanism of action remains elusive. We explored the mechanism behind the inhibitory effect of CaCCinh-A01 on cell proliferation using a combined experimental and in silico approach. We show that inhibition of ANO1 function is not sufficient to diminish proliferation of ANO1-dependent cancer cells. We report that CaCCinh-A01 reduces ANO1 protein levels by facilitating endoplasmic reticulum-associated, proteasomal turnover of ANO1. Washout of CaCCinh-A01 rescued ANO1 protein levels and resumed cell proliferation. Proliferation of newly derived CaCCinh-A01-resistant cell pools was not affected by CaCCinh-A01 as compared with the parental cells. Consistently, CaCCinh-A01 failed to reduce ANO1 protein levels in these cells, whereas ANO1 currents were still inhibited by CaCCinh-A01, indicating that CaCCinh-A01 inhibits cell proliferation by reducing ANO1 protein levels. Furthermore, we employed in silico methods to elucidate novel biological functions of ANO1 inhibitors. Specifically, we derived a pharmacophore model to describe inhibitors capable of promoting ANO1 degradation and report new inhibitors of ANO1-dependent cell proliferation. In summary, our data demonstrate that inhibition of the channel activity of ANO1 is not sufficient to inhibit ANO1-dependent cell proliferation, indicating that the role of ANO1 in cancer only partially depends on its function as a channel. Our results provide an impetus for gaining a deeper understanding of ANO1 modulation in cells and introduce a new targeting approach for antitumor therapy in ANO1-amplified cancers.

Automated ligand- and structure-based protocol for in silico prediction of human serum albumin binding.

Plasma protein binding has a profound impact on the pharmacokinetic and pharmacodynamic properties of many drug candidates and is thus an integral component of drug discovery. Nevertheless, extant methods to examine small-molecule interactions with plasma protein have various limitations, thus creating a need for alternative methods. Herein we present a comprehensive and cross-validated in silico workflow for the prediction of small-molecule binding to Human Serum Albumin (HSA), the most ubiquitous plasma protein. This protocol reliably predicts small-molecule interactions with HSA, including a binding affinity calculation using multiple linear regression methods, binding site prediction using a naive-Bayes classifier, and a three-dimensional binding pose using induced fit docking. Furthermore, this workflow is implemented in a portable and automated format that can be downloaded and used by other end users, either as is or with customization.

Polarized Raman spectroscopy of oligothiophene crystals to determine unit cell orientation.

Raman spectra were recorded experimentally and calculated theoretically for bithiophene, terthiophene, and quaterthiophene samples as a function of excitation polarization. Distinct spectral signatures were assigned and correlated to the molecular/unit cell orientation as determined by X-ray diffraction. The ability to predict molecular/unit cell orientation within organic crystals using polarized Raman spectroscopy was evaluated by predicting the unit cell orientation in a simulated terthiophene crystal given a random set of simulated polarized Raman spectra. Polarized Raman spectroscopy offers a promising tool to quickly and economically determine the unit cell orientation in known organic crystals and crystalline thin films. Implications of our methodologies for studying individual molecule conformations are discussed.

Quantitative DFT modeling of the enantiomeric excess for dioxirane-catalyzed epoxidations.

Herein we report the first fully quantum mechanical study of enantioselectivity for a large data set. We show that transition state modeling at the UB3LYP-DFT/6-31G* level of theory can accurately model enantioselectivity for various dioxirane-catalyzed asymmetric epoxidations. All the synthetically useful high selectivities are successfully "predicted" by this method. Our results hint at the utility of this method to further model other asymmetric reactions and facilitate the discovery process for the experimental organic chemist. Our work suggests the possibility of using computational methods not simply to explain organic phenomena, but also to predict them quantitatively.

Using a two-step hydride transfer to achieve 1,4-reduction in the catalytic hydrogenation of an acyl pyridinium cation.

The stoichiometric reduction of N-carbophenoxypyridinium tetraphenylborate (6) by CpRu(P-P)H (Cp = eta(5)-cyclopentadienyl; P-P = dppe, 1,2-bis(diphenylphosphino)ethane, or dppf, 1,1'-bis(diphenylphosphino)ferrocene), and Cp*Ru(P-P)H (Cp* = eta(5)-pentamethylcyclopentadienyl; P-P = dppe) gives mixtures of 1,2- and 1,4-dihydropyridines. The stoichiometric reduction of 6 by Cp*Ru(dppf)H (5) gives only the 1,4-dihydropyridine, and 5 catalyzes the exclusive formation of the 1,4-dihydropyridine from 6, H(2), and 2,2,6,6-tetramethylpiperidine. In the stoichiometric reductions, the ratio of 1,4 to 1,2 product increases as the Ru hydrides become better one-electron reductants, suggesting that the 1,4 product arises from a two-step (e(-)/H(*)) hydride transfer. Calculations at the UB3LYP/6-311++G(3df,3pd)//UB3LYP/6-31G* level support this hypothesis, indicating that the spin density in the N-carbophenoxypyridinium radical (13) resides primarily at C4, while the positive charge in 6 resides primarily at C2 and C6. The isomeric dihydropyridines thus result from the operation of different mechanisms: the 1,2 product from a single-step H(-) transfer and the 1,4 product from a two-step (e(-)/H(*)) transfer.

Discovery and Optimization of a Selective Ligand for the Switch/Sucrose Nonfermenting-Related Bromodomains of Polybromo Protein-1 by the Use of Virtual Screening and Hydration Analysis.

Bromodomains (BRDs) are epigenetic interaction domains currently recognized as emerging drug targets for development of anticancer or anti-inflammatory agents. In this study, development of a selective ligand of the fifth BRD of polybromo protein-1 (PB1(5)) related to switch/sucrose nonfermenting (SWI/SNF) chromatin remodeling complexes is presented. A compound collection was evaluated by consensus virtual screening and a hit was identified. The biophysical study of protein-ligand interactions was performed using X-ray crystallography and isothermal titration calorimetry. Collective data supported the hypothesis that affinity improvement could be achieved by enhancing interactions of the complex with the solvent. The derived SAR along with free energy calculations and a consensus hydration analysis using WaterMap and SZmap algorithms guided rational design of a set of novel analogues. The most potent analogue demonstrated high affinity of 3.3 µM and an excellent selectivity profile, thus comprising a promising lead for the development of chemical probes targeting PB1(5).

Accurate Force Field Development for Modeling Conjugated Polymers.

The modeling of the conformational properties of conjugated polymers entails a unique challenge for classical force fields. Conjugation imposes strong constraints upon bond rotation. Planar configurations are favored, but the concomitantly shortened bond lengths result in moieties being brought into closer proximity than usual. The ensuing steric repulsions are particularly severe in the presence of side chains, straining angles, and stretching bonds to a degree infrequently found in nonconjugated systems. We herein demonstrate the resulting inaccuracies by comparing the LMP2-calculated inter-ring torsion potentials for a series of substituted stilbenes and bithiophenes to those calculated using standard classical force fields. We then implement adjustments to the OPLS-2005 force field in order to improve its ability to model such systems. Finally, we show the impact of these changes on the dihedral angle distributions, persistence lengths, and conjugation length distributions observed during molecular dynamics simulations of poly[2-methoxy-5-(2'-ethylhexyloxy)-p-phenylene vinylene] (MEH-PPV) and poly 3-hexylthiophene (P3HT), two of the most widely used conjugated polymers.

Continuous Localized Orbital Corrections to Density Functional Theory: B3LYP-CLOC.

Our previous works have demonstrated the ability of our localized orbital correction (LOC) methodology to greatly improve the accuracy of various thermochemical properties at the stationary points of the Density Functional Theory (DFT) reaction coordinate (RC). Herein we extend this methodology from stationary points to the entire RC connecting any stationary points by developing continuous localized orbital corrections (CLOCs). We show that the resultant method, DFT-CLOC, is capable of producing RCs with far greater accuracy than uncorrected DFT and yet requires negligible computational cost beyond the uncorrected DFT calculations. Various post-Hartree-Fock (post-HF) reaction coordinate profiles were used, including a sigmatropic shift, Diels-Alder reaction, electrocyclization, carbon radical and three hydrogen radical reactions to show that this method is robust across multiple reaction types of general interest.

Localized orbital corrections for the calculation of barrier heights in density functional theory.

This work describes the extension of a previously reported empirical localized orbital correction model for density functional theory (DFT-LOC) for atomization energies, ionization potentials, electron affinities, and reaction enthalpies to the correction of barrier heights for chemical reactions of various types including cycloadditions, cycloreversions, dipolar cycloadditions, S(N)2's, carbon radical reactions, hydrogen radical reactions, sigmatropic shifts, and electrocyclizations. The B3LYP localized orbital correction version of the model (B3LYP-LOC) reduces the number of outliers and overall mean unsigned error (MUE) vs. experiment or ab initio values from 3.2 to 1.3 kcal/mole for barrier heights and from 5.1 to 1.1 kcal/mole for reaction enthalpies versus B3LYP. Furthermore, the new model has essentially zero additional computational cost beyond standard DFT calculations. Although the model is heuristic and is based on multiple linear regression to experimental or ab initio data, each of the parameters is justified on chemical grounds and provides insight into the fundamental limitations of DFT, most importantly the failure of current DFT methods to accurately account for nondynamical electron correlation.