Ringtail: A Python Tool for Efficient Management and Storage of Virtual Screening Results.

Virtual screening using molecular docking is now routinely used for the rapid evaluation of very large ligand libraries in early stage drug discovery. As the size of compound libraries which can feasibly be screened grows, so do the challenges in result management and storage. Here we introduce Ringtail, a new Python tool in the AutoDock Suite for efficient storage and analysis of virtual screening data based on portable SQLite databases. Ringtail is designed to work with AutoDock-GPU and AutoDock Vina out-of-the-box. Its modular design also allows for easy extension to support input file types from other docking software, different storage solutions, and incorporation into other applications. Ringtail's SQLite database output can dramatically reduce the required disk storage (36-46 fold) by selecting individual poses to store and by taking advantage of the relational database format. Filtering times are also dramatically reduced, requiring minutes to filter millions of ligands. Thus, Ringtail is a tool that can immediately integrate into existing virtual screening pipelines using AutoDock-GPU and Vina, and is scriptable and modifiable to fit specific user needs.

Performance evaluation of flexible macrocycle docking in AutoDock.

Macrocycles represent an important class of ligands, both in natural products and designed drugs. In drug design, macrocyclizations can impart specific ligand conformations and contribute to passive permeation by encouraging intramolecular H-bonds. AutoDock-GPU and Vina can model macrocyclic ligands flexibly, without requiring the enumeration of macrocyclic conformers before docking. Here, we characterize the performance of the method for handling macrocyclic compounds, which is implemented and the default behaviour for ligand preparation with our ligand preparation pipeline, Meeko. A pseudoatom is used to encode bond geometry and produce an anisotropic closure force for macrocyclic rings. This method is evaluated on a diverse set of small molecule and peptide macrocycles, ranging from 7- to 33-membered rings, showing little accuracy loss compared to rigid redocking of the X-ray macrocycle conformers. This suggests that for conformationally flexible macrocycles with unknown binding modes, this method can be effectively used to predict the macrocycle conformation.

Benchmarking the Performance of Irregular Computations in AutoDock-GPU Molecular Docking.

Irregular applications can be found in different scientific fields. In computer-aided drug design, molecular docking simulations play an important role in finding promising drug candidates. AutoDock is a software application widely used for predicting molecular interactions at close distances. It is characterized by irregular computations and long execution runtimes. In recent years, a hardware-accelerated version of AutoDock, called AutoDock-GPU, has been under active development. This work benchmarks the recent code and algorithmic enhancements incorporated into AutoDock-GPU. Particularly, we analyze the impact on execution runtime of techniques based on early termination. These enable AutoDock-GPU to explore the molecular space as necessary, while safely avoiding redundant computations. Our results indicate that it is possible to achieve average runtime reductions of 50% by using these techniques. Furthermore, a comprehensive literature review is also provided, where our work is compared to relevant approaches leveraging hardware acceleration for molecular docking.

AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings.

AutoDock Vina is arguably one of the fastest and most widely used open-source programs for molecular docking. However, compared to other programs in the AutoDock Suite, it lacks support for modeling specific features such as macrocycles or explicit water molecules. Here, we describe the implementation of this functionality in AutoDock Vina 1.2.0. Additionally, AutoDock Vina 1.2.0 supports the AutoDock4.2 scoring function, simultaneous docking of multiple ligands, and a batch mode for docking a large number of ligands. Furthermore, we implemented Python bindings to facilitate scripting and the development of docking workflows. This work is an effort toward the unification of the features of the AutoDock4 and AutoDock Vina programs. The source code is available at https://github.com/ccsb-scripps/AutoDock-Vina.

Accelerating AutoDock4 with GPUs and Gradient-Based Local Search.

AutoDock4 is a widely used program for docking small molecules to macromolecular targets. It describes ligand-receptor interactions using a physics-inspired scoring function that has been proven useful in a variety of drug discovery projects. However, compared to more modern and recent software, AutoDock4 has longer execution times, limiting its applicability to large scale dockings. To address this problem, we describe an OpenCL implementation of AutoDock4, called AutoDock-GPU, that leverages the highly parallel architecture of GPU hardware to reduce docking runtime by up to 350-fold with respect to a single-threaded process. Moreover, we introduce the gradient-based local search method ADADELTA, as well as an improved version of the Solis-Wets random optimizer from AutoDock4. These efficient local search algorithms significantly reduce the number of calls to the scoring function that are needed to produce good results. The improvements reported here, both in terms of docking throughput and search efficiency, facilitate the use of the AutoDock4 scoring function in large scale virtual screening.

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research.

Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic.

QMCPACK: an open source ab initio quantum Monte Carlo package for the electronic structure of atoms, molecules and solids.

QMCPACK is an open source quantum Monte Carlo package for ab initio electronic structure calculations. It supports calculations of metallic and insulating solids, molecules, atoms, and some model Hamiltonians. Implemented real space quantum Monte Carlo algorithms include variational, diffusion, and reptation Monte Carlo. QMCPACK uses Slater-Jastrow type trial wavefunctions in conjunction with a sophisticated optimizer capable of optimizing tens of thousands of parameters. The orbital space auxiliary-field quantum Monte Carlo method is also implemented, enabling cross validation between different highly accurate methods. The code is specifically optimized for calculations with large numbers of electrons on the latest high performance computing architectures, including multicore central processing unit and graphical processing unit systems. We detail the program's capabilities, outline its structure, and give examples of its use in current research calculations. The package is available at http://qmcpack.org.

Simple Model for the Benzene Hexafluorobenzene Interaction.

While the experimental intermolecular distance distribution functions of pure benzene and pure hexafluorobenzene are well described by transferable all-atom force fields, the interaction between the two molecules (in a 1:1 mixture) is not well simulated. We demonstrate that the parameters of the transferable force fields are adequate to describe the intermolecular distance distribution if the charges are replaced by a set of charges that are not located at the atoms. The simplest model that well describes the experimental distance distribution, between benzene and hexafluorobenzene, is that of a single ellipsoid for each molecule, representing the van der Waals interactions, and a set of three point charges (on the axis perpendicular to the arene plane) which give the same quadrupole moment as do the all atom charges from the transferable force fields.

Nonlinearities of organic electro-optic materials in nanoscale slots and implications for the optimum modulator design.

The performance of highly nonlinear organic electro-optic (EO) materials incorporated into nanoscale slots is examined. It is shown that EO coefficients as large as 190 pm/V can be obtained in 150 nm wide plasmonic slot waveguides but that the coefficients decrease for narrower slots. Possible mechanism that lead to such a decrease are discussed. Monte-Carlo computer simulations are performed, confirming that chromophore-surface interactions are one important factor influencing the EO coefficient in narrow plasmonic slots. These highly nonlinear materials are of particular interest for applications in optical modulators. However, in modulators the key parameters are the voltage-length product UπL and the insertion loss rather than the linear EO coefficients. We show record-low voltage-length products of 70 Vµm and 50 Vµm for slot widths in the order of 50 nm for the materials JRD1 and DLD164, respectively. This is because the nonlinear interaction is enhanced in narrow slot and thereby compensates for the reduced EO coefficient. Likewise, it is found that lowest insertion losses are observed for slot widths in the range 60 to 100 nm.

Systematic Generation of Anisotropic Coarse-Grained Lennard-Jones Potentials and Their Application to Ordered Soft Matter.

We have developed an approach to coarse-grained (CG) modeling of the van der Waals (vdW) type of interactions among molecules by representing groups of atoms within those molecules in terms of ellipsoids (rather than spheres). Our approach systematically translates an arbitrary underlying all-atom (AA) representation of a molecular system to a multisite ellipsoidal potential within the family of Gay-Berne type potentials. As the method enables arbitrary levels of coarse-graining, or even multiple levels of coarse-graining within a single simulation, we describe the method as a Level of Detail (LoD) model. The LoD model, as integrated into our group's Metropolis Monte Carlo computational package, is also capable of reducing the complexity of the molecular electrostatics by means of a multipole expansion of charges obtained from an AA force field (or directly from electronic structure calculations) of the charges within each ellipsoid. Electronic polarizability may additionally be included. The present CG representation does not include transformation of bonded interactions; ellipsoids are connected at the fully atomistic bond sites by freely rotating links that are constrained to maintain a constant distance. The accuracy of the method is demonstrated for three distinct types of self-assembling or self-organizing molecular systems: (1) the interaction between benzene and perfluorobenzene (dispersion interactions), (2) linear hydrocarbon chains (a system with large conformational flexibility), and (3) the self-organization of ethylene carbonate (a highly polar liquid). Lastly, the method is applied to the interaction of large (∼100 atom) molecules, which are typical of organic nonlinear optical chromophores, to demonstrate the effect of different CG models on molecular assembly.

Submicrosecond time resolution atomic force microscopy for probing nanoscale dynamics.

We propose, simulate, and experimentally validate a new mechanical detection method to analyze atomic force microscopy (AFM) cantilever motion that enables noncontact discrimination of transient events with ~100 ns temporal resolution without the need for custom AFM probes, specialized instrumentation, or expensive add-on hardware. As an example application, we use the method to screen thermally annealed poly(3-hexylthiophene):phenyl-C(61)-butyric acid methyl ester photovoltaic devices under realistic testing conditions over a technologically relevant performance window. We show that variations in device efficiency and nanoscale transient charging behavior are correlated, thereby linking local dynamics with device behavior. We anticipate that this method will find application in scanning probe experiments of dynamic local mechanical, electronic, magnetic, and biophysical phenomena.

Quantum dot/plasmonic nanoparticle metachromophores with quantum yields that vary with excitation wavelength.

Coupled plasmonic/chromophore systems are of interest in applications ranging from fluorescent biosensors to solar photovoltaics and photoelectrochemical cells because near-field coupling to metal nanostructures can dramatically alter the optical performance of nearby materials. We show that CdSe quantum dots (QDs) near single silver nanoprisms can exhibit photoluminescence lifetimes and quantum yields that depend on the excitation wavelength, in apparent violation of the Kasha-Vavilov rule. We attribute the variation in QD lifetime with excitation wavelength to the wavelength-dependent coupling of higher-order plasmon modes to different spatial subpopulations of nearby QDs. At the QD emission wavelength, these subpopulations are coupled to far-field radiation with varying efficiency by the nanoprism dipolar resonance. These results offer an easily accessible new route to design metachromophores with tailored optical properties.

Surface characterization of polythiophene:fullerene blends on different electrodes using near edge X-ray absorption fine structure.

We study the top surface composition of blends of the conjugated polymer regioregular poly-3-hexylthiophene (P3HT) with the fullerene (6,6)-phenyl-C(61)-butyric acid methyl ester (PCBM), an important model system for organic photovoltaics (OPVs), using near-edge X-ray absorption fine structure spectroscopy (NEXAFS). We compare the ratio of P3HT to PCBM near the air/film interface that results from preparing blend films on two sets of substrates: (1) poly(3,4-ethylenedioxythiophene) poly(styrenesulfonate) (PEDOT:PSS) coated indium tin oxide (ITO) as is commonly used in conventional OPV structures and (2) ZnO substrates that are either unmodified or modified with a C(60)-like self-assembled monolayer, similar to those that have been recently reported in inverted OPV structures. We find that the top surface (the film/air interface) is enriched in P3HT compared to the bulk, regardless of substrate or annealing conditions, indicating that changes in device performance due to substrate modification treatments should be attributed to the buried substrate/film interface and the bulk of the film rather than the exposed film/air interface.

Spectral control of plasmonic emission enhancement from quantum dots near single silver nanoprisms.

The near-field effects of plasmonic optical antennas are being explored in applications ranging from biosensors to solar cells. We demonstrate that photoluminescence emission enhancement from CdSe quantum dots (QDs) can be obtained in the absence of any excitation enhancement near single silver nanoprisms. The spectral dependence of the radiative and nonradiative decay rate of the QDs closely follows the silver nanoparticle plasmon scattering spectrum. Using both experiment and theory we show that, in the absence of excitation enhancement, the ratio of radiative to nonradiative decay rate enhancement is proportional to the silver nanoparticle scattering efficiency. These results provide guidelines both for separating excitation and emission enhancement effects in sensing and device applications and for tailoring emission enhancement effects using plasmonic nanostructures.