Theoretical methods for electron-mediated processes

Electron-driven processes lie at the core of a large variety of physical, biological, and chemical phenomena. Despite their crucial roles in science and technology, detailed description of these processes remains a significant challenge, and there is a need for the development of accurate and efficient computational tools that enable predictive simulation. This work is focused on the development of novel software tools and methodologies aimed at two classes of electron-mediated processes: (i) electron-molecule scattering, and (ii) charge transfer in proteins. The first major focus of this thesis is the electronic structure of autoionizing electronic resonances. The theoretical description of these metastable states is intractable by means of conventional quantum chemistry techniques, and specialized techniques are required in order to accurately describe their energies and lifetimes. In this work, we have utilized the complex absorbing potential (CAP) method, and describe three developments which have advanced the applicability, efficiency, and accessibility of the CAP methodology for molecular resonances: (1) implementation and investigation of the smooth Voronoi potential (2) implementation of CAP in the projected scheme, and (3) development of the OpenCAP package, which extends the CAP methodology to popular electronic structure packages. The second major focus is the identification of electron and hole transfer (ET) pathways in biomolecules. Both experimental and theoretical inquiries into electron/hole transfer processes in biomolecules generally require targeted approaches, which are complicated by the existence of numerous potential pathways. To this end, we have developed an open-source web platform, eMap, which exploits a coarse-grained model of the protein crystal structure to (1) enable pre-screening of potentially efficient ET pathways, and (2) identify shared pathways/motifs in families of proteins. Following introductory chapters on motivation and theoretical background, we devote a chapter to each new methodology mentioned above. The open-source software tools discussed herein are under active development, and have been utilized in published work by several unaffiliated experimental and theoretical groups across the world. We conclude the dissertation with a summary and discussion of the outlook and future directions of the OpenCAP and eMap software packages

Software for the frontiers of quantum chemistry:An overview of developments in the Q-Chem 5 package

This article summarizes technical advances contained in the fifth major release of the Q-Chem quantum chemistry program package, covering developments since 2015. A comprehensive library of exchange–correlation functionals, along with a suite of correlated many-body methods, continues to be a hallmark of the Q-Chem software. The many-body methods include novel variants of both coupled-cluster and configuration-interaction approaches along with methods based on the algebraic diagrammatic construction and variational reduced density-matrix methods. Methods highlighted in Q-Chem 5 include a suite of tools for modeling core-level spectroscopy, methods for describing metastable resonances, methods for computing vibronic spectra, the nuclear–electronic orbital method, and several different energy decomposition analysis techniques. High-performance capabilities including multithreaded parallelism and support for calculations on graphics processing units are described. Q-Chem boasts a community of well over 100 active academic developers, and the continuing evolution of the software is supported by an “open teamware” model and an increasingly modular design

CAP-EOM-CCSD Method with Smooth Voronoi CAP for Metastable Electronic States in Molecular Clusters

The complex absorbing potential (CAP) approach offers a practical tool for characterization of energies and lifetimes of metastable electronic states, such as temporary anions and core ionized states. Here, we present an implementation of the smooth Voronoi CAP combined with equation-of-motion coupled cluster with single and double substitutions method for metastable states. The performance of the smooth Voronoi and a standard box CAPs is compared for different classes of systems: resonances in isolated molecules and in molecular clusters. The results of the benchmark calculations indicate that the choice of the CAP shape should be guided by the character of the metastable states. While Voronoi CAPs yield stable results in the case of a resonance localized on one molecule, their performance in the cases of states delocalized over two or more molecular species can deteriorate due to the CAP leaking into the vacuum region between the moieties. <br /

eMap: A Web Application for Identifying and Visualizing Electron or Hole Hopping Pathways in Proteins

eMap is a web-based platform for identifying and visualizing electron or hole transfer pathways in proteins based on their crystal structures. The underlying model can be viewed as a coarse-grained version of the Pathways model, where each tunneling step between hopping sites represented by electron transfer active (ETA) moieties is described with one effective decay parameter that describes protein-mediated tunneling. ETA moieties include aromatic amino acid residue side chains and aromatic fragments of cofactors that are automatically detected, and, in addition, electron/hole residing sites that can be specified by the users. The software searches for the shortest paths connecting the user-specified electron/hole source to either all surface-exposed ETA residues or to the user-specified target. The identified pathways are ranked based on their length. The pathways are visualized in 2D as a graph, in which each node represents an ETA site, and in 3D using available protein visualization tools. Here, we present the capability and user interface of eMap 1.0, which is available at https://emap.bu.edu.</p

A Computational Approach for Identifying Synergistic Drug Combinations

<div><p>A promising alternative to address the problem of acquired drug resistance is to rely on combination therapies. Identification of the right combinations is often accomplished through trial and error, a labor and resource intensive process whose scale quickly escalates as more drugs can be combined. To address this problem, we present a broad computational approach for predicting synergistic combinations using easily obtainable single drug efficacy, no detailed mechanistic understanding of drug function, and limited drug combination testing. When applied to mutant BRAF melanoma, we found that our approach exhibited significant predictive power. Additionally, we validated previously untested synergy predictions involving anticancer molecules. As additional large combinatorial screens become available, this methodology could prove to be impactful for identification of drug synergy in context of other types of cancers.</p></div

Identification of Synergistic Combinations involving the BRAF Inhibitor PLX4720.

<p>A The observed growth inhibition levels for PLX4720 alone (blue) 17AAG alone (red), PLX4720 varying while 17AAG held constant at 1uM (navy blue), and 17AAG varying while PLX4720 held constant at 1 uM (dark red). B The observed growth inhibition levels for PLX4720 alone (blue) FAK Inhibitor 14 alone (red), PLX4720 varying while FAK Inhibitor 14 held constant at 0.1uM (navy blue), and FAK Inhibitor 14 varying while PLX4720 held constant at 0.1 uM (dark red).</p

Experimental Validation of Predicted BRAF Effective and Synergistic Combinations.

<p>A We selected a set of 11 combinations involving 7 drugs with a diverse set of predictions for experimental validation. These included traditional chemotherapeutic agents (doxorubicin, paclitaxel), targeted agents (PLX4720, gefitinib, FAK Inhibitor 14), a statin (simvastatin), and an antitumor antibiotic (17AAG). B Each drug was tested in combination at medium, and high concentrations, estimated from their GI₁₀, GI₂₅, and GI₅₀ values respectively. The observed growth inhibition levels for all dosage level combinations involving each tested drug combination are shown in violin plots. Violin plots that are colored navy blue are those whose third quantile values where 70% or greater. The predictions for each combination are shown below the plot, with dark blue representing an effective prediction and grey representing an ineffective prediction. C For each tested drug combination, the Chou-Talalay synergy scores were calculated. The observed synergy scores for all dosage level combinations involving each tested drug combination are shown in violin plots. The predictions for each combination are shown below the plot, with green representing a synergy prediction and red representing a non-synergy prediction.</p

Experimental Validation

<p>Experimental Validation</p

Method schematic and Evaluation of individual model performance.

<p>A Our approach integrates a large single drug screen of 150 drugs with a combinatorial drug screen, which tested 780 combinations of 40 unique drugs. This was used to train a random forest model that predicts synergy and genotype-selective efficacy for untested drug combinations. B Receiver operating characteristic (ROC) curves for 10-fold cross-validation of the BRAF-specific effectiveness (top) and synergy (bottom) models. C The effect of randomly removing samples on model accuracy, sensitivity and specificity. At 25% of the original number of combinations was used to train the model, the approach maintained 77.56% accuracy, 89.27% specificity, and 54.91% sensitivity.</p