Publications by authors named "Arthur J Olson"

94 Publications

Icosahedral virus structures and the protein data bank.

J Biol Chem 2021 Jan-Jun;296:100554. Epub 2021 Mar 17.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA.

The structural study of icosahedral viruses has a long and impactful history in both crystallographic methodology and molecular biology. The evolution of the Protein Data Bank has paralleled and supported these studies providing readily accessible formats dealing with novel features associated with viral particle symmetries and subunit interactions. This overview describes the growth in size and complexity of icosahedral viruses from the first early studies of small RNA plant viruses and human picornaviruses up to the larger and more complex bacterial phage, insect, and human disease viruses such as Zika, hepatitis B, Adeno and Polyoma virus. The analysis of icosahedral viral capsid protein domain folds has shown striking similarities, with the beta jelly roll motif observed across multiple evolutionarily divergent species. The icosahedral symmetry of viruses drove the development of noncrystallographic symmetry averaging as a powerful phasing method, and the constraints of maintaining this symmetry resulted in the concept of quasi-equivalence in viral structures. Symmetry also played an important early role in demonstrating the power of cryo-electron microscopy as an alternative to crystallography in generating atomic resolution structures of these viruses. The Protein Data Bank has been a critical resource for assembling and disseminating these structures to a wide community, and the virus particle explorer (VIPER) was developed to enable users to easily generate and view complete viral capsid structures from their asymmetric building blocks. Finally, we share a personal perspective on the early use of computer graphics to communicate the intricacies, interactions, and beauty of these virus structures.
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http://dx.doi.org/10.1016/j.jbc.2021.100554DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8081926PMC
July 2021

The AutoDock suite at 30.

Protein Sci 2021 01 12;30(1):31-43. Epub 2020 Sep 12.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA.

The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third-party developers.
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http://dx.doi.org/10.1002/pro.3934DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7737764PMC
January 2021

Art and Science of the Cellular Mesoscale.

Trends Biochem Sci 2020 06 21;45(6):472-483. Epub 2020 Mar 21.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.

Experimental information from microscopy, structural biology, and bioinformatics may be integrated to build structural models of entire cells with molecular detail. This integrative modeling is challenging in several ways: the intrinsic complexity of biology results in models with many closely packed and heterogeneous components; the wealth of available experimental data is scattered among multiple resources and must be gathered, reconciled, and curated; and computational infrastructure is only now gaining the capability of modeling and visualizing systems of this complexity. We present recent efforts to address these challenges, both with artistic approaches to depicting the cellular mesoscale, and development and application of methods to build quantitative models.
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http://dx.doi.org/10.1016/j.tibs.2020.02.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7230070PMC
June 2020

Intrabacterial Metabolism Obscures the Successful Prediction of an InhA Inhibitor of .

ACS Infect Dis 2019 12 5;5(12):2148-2163. Epub 2019 Nov 5.

Department of Pharmacology, Physiology, and Neuroscience , Rutgers University-New Jersey Medical School , Medical Sciences Building, 185 South Orange Avenue , Newark , New Jersey 07103 , United States.

Tuberculosis, caused by (), kills 1.6 million people annually. To bridge the gap between structure- and cell-based drug discovery strategies, we are pioneering a computer-aided discovery paradigm that merges structure-based virtual screening with ligand-based, machine learning methods trained with cell-based data. This approach successfully identified -(3-methoxyphenyl)-7-nitrobenzo[][1,2,5]oxadiazol-4-amine (JSF-2164) as an inhibitor of purified InhA with whole-cell efficacy versus cultured . When the intrabacterial drug metabolism (IBDM) platform was leveraged, mechanistic studies demonstrated that JSF-2164 underwent a rapid FH-dependent biotransformation within to afford intrabacterial nitric oxide and two amines, identified as JSF-3616 and JSF-3617. Thus, metabolism of JSF-2164 obscured the InhA inhibition phenotype within cultured . This study demonstrates a new docking/Bayesian computational strategy to combine cell- and target-based drug screening and the need to probe intrabacterial metabolism when clarifying the antitubercular mechanism of action.
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http://dx.doi.org/10.1021/acsinfecdis.9b00295DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7801736PMC
December 2019

Illustrate: Software for Biomolecular Illustration.

Structure 2019 11 10;27(11):1716-1720.e1. Epub 2019 Sep 10.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA.

The small program Illustrate generates non-photorealistic images of biological molecules for use in dissemination, outreach, and education. The method has been used as part of the "Molecule of the Month," an ongoing educational column at the RCSB Protein Data Bank (http://rcsb.org). Insights from 20 years of application of the program are presented, and the program has been released both as open-source Fortran at GitHub and through an interactive web-based interface.
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http://dx.doi.org/10.1016/j.str.2019.08.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6834899PMC
November 2019

Integrative modeling of the HIV-1 ribonucleoprotein complex.

PLoS Comput Biol 2019 06 13;15(6):e1007150. Epub 2019 Jun 13.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America.

A coarse-grain computational method integrates biophysical and structural data to generate models of HIV-1 genomic RNA, nucleocapsid and integrase condensed into a mature ribonucleoprotein complex. Several hypotheses for the initial structure of the genomic RNA and oligomeric state of integrase are tested. In these models, integrase interaction captures features of the relative distribution of gRNA in the immature virion and increases the size of the RNP globule, and exclusion of nucleocapsid from regions with RNA secondary structure drives an asymmetric placement of the dimerized 5'UTR at the surface of the RNP globule.
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http://dx.doi.org/10.1371/journal.pcbi.1007150DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6592547PMC
June 2019

Novel Intersubunit Interaction Critical for HIV-1 Core Assembly Defines a Potentially Targetable Inhibitor Binding Pocket.

mBio 2019 03 12;10(2). Epub 2019 Mar 12.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA

HIV-1 capsid protein (CA) plays critical roles in both early and late stages of the viral replication cycle. Mutagenesis and structural experiments have revealed that capsid core stability significantly affects uncoating and initiation of reverse transcription in host cells. This has led to efforts in developing antivirals targeting CA and its assembly, although none of the currently identified compounds are used in the clinic for treatment of HIV infection. A specific interaction that is primarily present in pentameric interfaces in the HIV-1 capsid core was identified and is reported to be important for CA assembly. This is shown by multidisciplinary characterization of CA site-directed mutants using biochemical analysis of virus-like particle formation, transmission electron microscopy of assembly, crystallographic studies, and molecular dynamic simulations. The data are consistent with a model where a hydrogen bond between CA residues E28 and K30' from neighboring N-terminal domains (CAs) is important for CA pentamer interactions during core assembly. This pentamer-preferred interaction forms part of an -terminal omain nterface (NDI) pocket that is amenable to antiviral targeting. Precise assembly and disassembly of the HIV-1 capsid core are key to the success of viral replication. The forces that govern capsid core formation and dissociation involve intricate interactions between pentamers and hexamers formed by HIV-1 CA. We identified one particular interaction between E28 of one CA and K30' of the adjacent CA that appears more frequently in pentamers than in hexamers and that is important for capsid assembly. Targeting the corresponding site could lead to the development of antivirals which disrupt this interaction and affect capsid assembly.
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http://dx.doi.org/10.1128/mBio.02858-18DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6414707PMC
March 2019

Massive-Scale Binding Free Energy Simulations of HIV Integrase Complexes Using Asynchronous Replica Exchange Framework Implemented on the IBM WCG Distributed Network.

J Chem Inf Model 2019 04 22;59(4):1382-1397. Epub 2019 Feb 22.

Center for Biophysics and Computational Biology and Department of Chemistry , Temple University , Philadelphia , Pennsylvania 19122 , United States.

To perform massive-scale replica exchange molecular dynamics (REMD) simulations for calculating binding free energies of protein-ligand complexes, we implemented the asynchronous replica exchange (AsyncRE) framework of the binding energy distribution analysis method (BEDAM) in implicit solvent on the IBM World Community Grid (WCG) and optimized the simulation parameters to reduce the overhead and improve the prediction power of the WCG AsyncRE simulations. We also performed the first massive-scale binding free energy calculations using the WCG distributed computing grid and 301 ligands from the SAMPL4 challenge for large-scale binding free energy predictions of HIV-1 integrase complexes. In total there are ∼10000 simulated complexes, ∼1 million replicas, and ∼2000 μs of aggregated MD simulations. Running AsyncRE MD simulations on the WCG requires accepting a trade-off between the number of replicas that can be run (breadth) and the number of full RE cycles that can be completed per replica (depth). As compared with synchronous Replica Exchange (SyncRE) running on tightly coupled clusters like XSEDE, on the WCG many more replicas can be launched simultaneously on heterogeneous distributed hardware, but each full RE cycle requires more overhead. We compared the WCG results with that from AutoDock and more advanced RE simulations including the use of flattening potentials to accelerate sampling of selected degrees of freedom of ligands and/or receptors related to slow dynamics due to high energy barriers. We propose a suitable strategy of RE simulations to refine high throughput docking results which can be matched to corresponding computing resources: from HPC clusters, to small or medium-size distributed campus grids, and finally to massive-scale computing networks including millions of CPUs like the resources available on the WCG.
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http://dx.doi.org/10.1021/acs.jcim.8b00817DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6496938PMC
April 2019

CellPAINT: Interactive Illustration of Dynamic Mesoscale Cellular Environments.

IEEE Comput Graph Appl 2018 Nov-Dec;38(6):51-66

CellPAINT allows nonexpert users to create interactive mesoscale illustrations that integrate a variety of biological data. Like popular digital painting software, scenes are created using a palette of molecular "brushes." The current release allows creation of animated scenes with an HIV virion, blood plasma, and a simplified T-cell.
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http://dx.doi.org/10.1109/MCG.2018.2877076DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6456043PMC
July 2019

Perspectives on Structural Molecular Biology Visualization: From Past to Present.

Authors:
Arthur J Olson

J Mol Biol 2018 10 23;430(21):3997-4012. Epub 2018 Jul 23.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA. Electronic address:

Visualization has been a key technology in the progress of structural molecular biology for as long as the field has existed. This perspective describes the nature of the visualization process in structural studies, how it has evolved over the years, and its relationship to the changes in technology that have supported and driven it. It focuses on how technical advances have changed the way we look at and interact with molecular structure, and how structural biology has fostered and challenged that technology.
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http://dx.doi.org/10.1016/j.jmb.2018.07.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6186497PMC
October 2018

Lattice Models of Bacterial Nucleoids.

J Phys Chem B 2018 05 25;122(21):5441-5447. Epub 2018 Jan 25.

Department of Integrative Structural and Computational Biology , The Scripps Research Institute , 10550 North Torrey Pines Road , La Jolla , California , United States.

Mesoscale molecular modeling is providing a new window into the inner workings of living cells. Modeling of genomes, however, remains a technical challenge, due to their large size and complexity. We describe a lattice method for rapid generation of bacterial nucleoid models that integrates experimental data from a variety of biophysical techniques and provides a starting point for simulation and hypothesis generation. The current method builds models of a circular bacterial genome with supercoiled plectonemes, packed within the small space of the bacterial cell. Lattice models are generated for Mycoplasma genitalium and Escherichia coli nucleoids, and used to simulate interaction data. The method is rapid enough to allow generation of multiple models when analyzing structure/function relationships, and we demonstrate use of the lattice models in creation of an all-atom representation of an entire cell.
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http://dx.doi.org/10.1021/acs.jpcb.7b11770DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5980677PMC
May 2018

Dense Array of Spikes on HIV-1 Virion Particles.

J Virol 2017 07 26;91(14). Epub 2017 Jun 26.

Department of Immunology and Microbial Science, The Scripps Research Institute, La Jolla, California, USA

HIV-1 is rare among viruses for having a low number of envelope glycoprotein (Env) spikes per virion, i.e., ∼7 to 14. This exceptional feature has been associated with avoidance of humoral immunity, i.e., B cell activation and antibody neutralization. Virus-like particles (VLPs) with increased density of Env are being pursued for vaccine development; however, these typically require protein engineering that alters Env structure. Here, we used instead a strategy that targets the producer cell. We employed fluorescence-activated cell sorting (FACS) to sort for cells that are recognized by trimer cross-reactive broadly neutralizing antibody (bnAb) and not by nonneutralizing antibodies. Following multiple iterations of FACS, cells and progeny virions were shown to display higher levels of antigenically correct Env in a manner that correlated between cells and cognate virions ( = 0.027). High-Env VLPs, or hVLPs, were shown to be monodisperse and to display more than a 10-fold increase in spikes per particle by electron microscopy (average, 127 spikes; range, 90 to 214 spikes). Sequencing revealed a partial truncation in the C-terminal tail of Env that had emerged in the sort; however, iterative rounds of "cell factory" selection were required for the high-Env phenotype. hVLPs showed greater infectivity than standard pseudovirions but largely similar neutralization sensitivity. Importantly, hVLPs also showed superior activation of Env-specific B cells. Hence, high-Env HIV-1 virions, obtained through selection of producer cells, represent an adaptable platform for vaccine design and should aid in the study of native Env. The paucity of spikes on HIV is a unique feature that has been associated with evasion of the immune system, while increasing spike density has been a goal of vaccine design. Increasing the density of Env by modifying it in various ways has met with limited success. Here, we focused instead on the producer cell. Cells that stably express HIV spikes were screened on the basis of high binding by bnAbs and low binding by nonneutralizing antibodies. Levels of spikes on cells correlated well with those on progeny virions. Importantly, high-Env virus-like particles (hVLPs) were produced with a manifest array of well-defined spikes, and these were shown to be superior in activating desirable B cells. Our study describes HIV particles that are densely coated with functional spikes, which should facilitate the study of HIV spikes and their development as immunogens.
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http://dx.doi.org/10.1128/JVI.00415-17DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5487557PMC
July 2017

A New Class of Allosteric HIV-1 Integrase Inhibitors Identified by Crystallographic Fragment Screening of the Catalytic Core Domain.

J Biol Chem 2016 Nov 19;291(45):23569-23577. Epub 2016 Sep 19.

From the Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey 08854,

HIV-1 integrase (IN) is essential for virus replication and represents an important multifunctional therapeutic target. Recently discovered quinoline-based allosteric IN inhibitors (ALLINIs) potently impair HIV-1 replication and are currently in clinical trials. ALLINIs exhibit a multimodal mechanism of action by inducing aberrant IN multimerization during virion morphogenesis and by competing with IN for binding to its cognate cellular cofactor LEDGF/p75 during early steps of HIV-1 infection. However, quinoline-based ALLINIs impose a low genetic barrier for the evolution of resistant phenotypes, which highlights a need for discovery of second-generation inhibitors. Using crystallographic screening of a library of 971 fragments against the HIV-1 IN catalytic core domain (CCD) followed by a fragment expansion approach, we have identified thiophenecarboxylic acid derivatives that bind at the CCD-CCD dimer interface at the principal lens epithelium-derived growth factor (LEDGF)/p75 binding pocket. The most active derivative (5) inhibited LEDGF/p75-dependent HIV-1 IN activity in vitro with an IC of 72 μm and impaired HIV-1 infection of T cells at an EC of 36 μm The identified lead compound, with a relatively small molecular weight (221 Da), provides an optimal building block for developing a new class of inhibitors. Furthermore, although structurally distinct thiophenecarboxylic acid derivatives target a similar pocket at the IN dimer interface as the quinoline-based ALLINIs, the lead compound, 5, inhibited IN mutants that confer resistance to quinoline-based compounds. Collectively, our findings provide a plausible path for structure-based development of second-generation ALLINIs.
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http://dx.doi.org/10.1074/jbc.M116.753384DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5095411PMC
November 2016

Fragment-Based Analysis of Ligand Dockings Improves Classification of Actives.

J Chem Inf Model 2016 08 25;56(8):1597-607. Epub 2016 Jul 25.

Integrative Structural and Computational Biology, The Scripps Research Institute , La Jolla, California 92037, United States.

We describe ADChemCast, a method for using results from virtual screening to create a richer representation of a target binding site, which may be used to improve ranking of compounds and characterize the determinants of ligand-receptor specificity. ADChemCast clusters docked conformations of ligands based on shared pairwise receptor-ligand interactions within chemically similar structural fragments, building a set of attributes characteristic of binders and nonbinders. Machine learning is then used to build rules from the most informational attributes for use in reranking of compounds. In this report, we use ADChemCast to improve the ranking of compounds in 11 diverse proteins from the Database of Useful Decoys-Enhanced (DUD-E) and demonstrate the utility of the method for characterizing relevant binding attributes in HIV reverse transcriptase.
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http://dx.doi.org/10.1021/acs.jcim.6b00248DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5023760PMC
August 2016

Proteome-wide covalent ligand discovery in native biological systems.

Nature 2016 06 15;534(7608):570-4. Epub 2016 Jun 15.

Department of Chemical Physiology, The Scripps Research Institute. La Jolla, California 92307, USA.

Small molecules are powerful tools for investigating protein function and can serve as leads for new therapeutics. Most human proteins, however, lack small-molecule ligands, and entire protein classes are considered 'undruggable'. Fragment-based ligand discovery can identify small-molecule probes for proteins that have proven difficult to target using high-throughput screening of complex compound libraries. Although reversibly binding ligands are commonly pursued, covalent fragments provide an alternative route to small-molecule probes, including those that can access regions of proteins that are difficult to target through binding affinity alone. Here we report a quantitative analysis of cysteine-reactive small-molecule fragments screened against thousands of proteins in human proteomes and cells. Covalent ligands were identified for >700 cysteines found in both druggable proteins and proteins deficient in chemical probes, including transcription factors, adaptor/scaffolding proteins, and uncharacterized proteins. Among the atypical ligand-protein interactions discovered were compounds that react preferentially with pro- (inactive) caspases. We used these ligands to distinguish extrinsic apoptosis pathways in human cell lines versus primary human T cells, showing that the former is largely mediated by caspase-8 while the latter depends on both caspase-8 and -10. Fragment-based covalent ligand discovery provides a greatly expanded portrait of the ligandable proteome and furnishes compounds that can illuminate protein functions in native biological systems.
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http://dx.doi.org/10.1038/nature18002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4919207PMC
June 2016

Computational protein-ligand docking and virtual drug screening with the AutoDock suite.

Nat Protoc 2016 May 14;11(5):905-19. Epub 2016 Apr 14.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA.

Computational docking can be used to predict bound conformations and free energies of binding for small-molecule ligands to macromolecular targets. Docking is widely used for the study of biomolecular interactions and mechanisms, and it is applied to structure-based drug design. The methods are fast enough to allow virtual screening of ligand libraries containing tens of thousands of compounds. This protocol covers the docking and virtual screening methods provided by the AutoDock suite of programs, including a basic docking of a drug molecule with an anticancer target, a virtual screen of this target with a small ligand library, docking with selective receptor flexibility, active site prediction and docking with explicit hydration. The entire protocol will require ∼5 h.
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http://dx.doi.org/10.1038/nprot.2016.051DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4868550PMC
May 2016

AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility.

PLoS Comput Biol 2015 Dec 2;11(12):e1004586. Epub 2015 Dec 2.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, United States of America.

Automated docking of drug-like molecules into receptors is an essential tool in structure-based drug design. While modeling receptor flexibility is important for correctly predicting ligand binding, it still remains challenging. This work focuses on an approach in which receptor flexibility is modeled by explicitly specifying a set of receptor side-chains a-priori. The challenges of this approach include the: 1) exponential growth of the search space, demanding more efficient search methods; and 2) increased number of false positives, calling for scoring functions tailored for flexible receptor docking. We present AutoDockFR-AutoDock for Flexible Receptors (ADFR), a new docking engine based on the AutoDock4 scoring function, which addresses the aforementioned challenges with a new Genetic Algorithm (GA) and customized scoring function. We validate ADFR using the Astex Diverse Set, demonstrating an increase in efficiency and reliability of its GA over the one implemented in AutoDock4. We demonstrate greatly increased success rates when cross-docking ligands into apo receptors that require side-chain conformational changes for ligand binding. These cross-docking experiments are based on two datasets: 1) SEQ17 -a receptor diversity set containing 17 pairs of apo-holo structures; and 2) CDK2 -a ligand diversity set composed of one CDK2 apo structure and 52 known bound inhibitors. We show that, when cross-docking ligands into the apo conformation of the receptors with up to 14 flexible side-chains, ADFR reports more correctly cross-docked ligands than AutoDock Vina on both datasets with solutions found for 70.6% vs. 35.3% systems on SEQ17, and 76.9% vs. 61.5% on CDK2. ADFR also outperforms AutoDock Vina in number of top ranking solutions on both datasets. Furthermore, we show that correctly docked CDK2 complexes re-create on average 79.8% of all pairwise atomic interactions between the ligand and moving receptor atoms in the holo complexes. Finally, we show that down-weighting the receptor internal energy improves the ranking of correctly docked poses and that runtime for AutoDockFR scales linearly when side-chain flexibility is added.
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http://dx.doi.org/10.1371/journal.pcbi.1004586DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4667975PMC
December 2015

Self-assembly gets physical.

Authors:
Arthur J Olson

Nat Nanotechnol 2015 Aug;10(8):728

Department of Integrative Structural and Computational Biology at The Scripps Research Institute, La Jolla, 92037 California, USA.

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http://dx.doi.org/10.1038/nnano.2015.172DOI Listing
August 2015

Covalent docking using autodock: Two-point attractor and flexible side chain methods.

Protein Sci 2016 Jan 7;25(1):295-301. Epub 2015 Jul 7.

Molecular Graphics Lab, Department of Integrative Structural and Computational Biology, MB-112, the Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California, 92037-1000.

We describe two methods of automated covalent docking using Autodock4: the two-point attractor method and the flexible side chain method. Both methods were applied to a training set of 20 diverse protein-ligand covalent complexes, evaluating their reliability in predicting the crystallographic pose of the ligands. The flexible side chain method performed best, recovering the pose in 75% of cases, with failures for the largest inhibitors tested. Both methods are freely available at the AutoDock website (http://autodock.scripps.edu).
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http://dx.doi.org/10.1002/pro.2733DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4815316PMC
January 2016

Computational challenges of structure-based approaches applied to HIV.

Curr Top Microbiol Immunol 2015 ;389:31-51

MGL, Department of Integrative Structural and Computational Biology and HIV Interaction and Viral Evolution Center, The Scripps Research Institute, 10550 N. Torrey Pines Rd., La Jolla, CA, 92037, USA.

Here, we review some of the opportunities and challenges that we face in computational modeling of HIV therapeutic targets and structural biology, both in terms of methodology development and structure-based drug design (SBDD). Computational methods have provided fundamental support to HIV research since the initial structural studies, helping to unravel details of HIV biology. Computational models have proved to be a powerful tool to analyze and understand the impact of mutations and to overcome their structural and functional influence in drug resistance. With the availability of structural data, in silico experiments have been instrumental in exploiting and improving interactions between drugs and viral targets, such as HIV protease, reverse transcriptase, and integrase. Issues such as viral target dynamics and mutational variability, as well as the role of water and estimates of binding free energy in characterizing ligand interactions, are areas of active computational research. Ever-increasing computational resources and theoretical and algorithmic advances have played a significant role in progress to date, and we envision a continually expanding role for computational methods in our understanding of HIV biology and SBDD in the future.
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http://dx.doi.org/10.1007/82_2015_432DOI Listing
July 2015

A virtual screen discovers novel, fragment-sized inhibitors of Mycobacterium tuberculosis InhA.

J Chem Inf Model 2015 Mar 17;55(3):645-59. Epub 2015 Feb 17.

†Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California 92037, United States.

Isoniazid (INH) is usually administered to treat latent Mycobacterium tuberculosis (Mtb) infections and is used in combination therapy to treat active tuberculosis (TB). Unfortunately, resistance to this drug is hampering its clinical effectiveness. INH is a prodrug that must be activated by Mtb catalase-peroxidase (KatG) before it can inhibit InhA (Mtb enoyl-acyl-carrier-protein reductase). Isoniazid-resistant cases of TB found in clinical settings usually involve mutations in or deletion of katG, which abrogate INH activation. Compounds that inhibit InhA without requiring prior activation by KatG would not be affected by this resistance mechanism and hence would display continued potency against these drug-resistant isolates of Mtb. Virtual screening experiments versus InhA in the GO Fight Against Malaria (GO FAM) project were designed to discover new scaffolds that display base-stacking interactions with the NAD cofactor. GO FAM experiments included targets from other pathogens, including Mtb, when they had structural similarity to a malaria target. Eight of the 16 soluble compounds identified by docking against InhA plus visual inspection were modest inhibitors and did not require prior activation by KatG. The best two inhibitors discovered are both fragment-sized compounds and displayed Ki values of 54 and 59 μM, respectively. Importantly, the novel inhibitors discovered have low structural similarity to known InhA inhibitors and thus help expand the number of chemotypes on which future medicinal chemistry efforts can be focused. These new fragment hits could eventually help advance the fight against INH-resistant Mtb strains, which pose a significant global health threat.
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http://dx.doi.org/10.1021/ci500672vDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4386068PMC
March 2015

Small-molecule library screening by docking with PyRx.

Methods Mol Biol 2015 ;1263:243-50

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA, 92037-1000, USA,

Virtual molecular screening is used to dock small-molecule libraries to a macromolecule in order to find lead compounds with desired biological function. This in silico method is well known for its application in computer-aided drug design. This chapter describes how to perform small-molecule virtual screening by docking with PyRx, which is open-source software with an intuitive user interface that runs on all major operating systems (Linux, Windows, and Mac OS). Specific steps for using PyRx, as well as considerations for data preparation, docking, and data analysis, are also described.
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http://dx.doi.org/10.1007/978-1-4939-2269-7_19DOI Listing
September 2015

cellPACK: a virtual mesoscope to model and visualize structural systems biology.

Nat Methods 2015 Jan 1;12(1):85-91. Epub 2014 Dec 1.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA.

cellPACK assembles computational models of the biological mesoscale, an intermediate scale (10-100 nm) between molecular and cellular biology scales. cellPACK's modular architecture unites existing and novel packing algorithms to generate, visualize and analyze comprehensive three-dimensional models of complex biological environments that integrate data from multiple experimental systems biology and structural biology sources. cellPACK is available as open-source code, with tools for validation of models and with 'recipes' and models for five biological systems: blood plasma, cytoplasm, synaptic vesicles, HIV and a mycoplasma cell. We have applied cellPACK to model distributions of HIV envelope protein to test several hypotheses for consistency with experimental observations. Biologists, educators and outreach specialists can interact with cellPACK models, develop new recipes and perform packing experiments through scripting and graphical user interfaces at http://cellPACK.org/.
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http://dx.doi.org/10.1038/nmeth.3204DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4281296PMC
January 2015

3D molecular models of whole HIV-1 virions generated with cellPACK.

Faraday Discuss 2014 25;169:23-44. Epub 2014 Sep 25.

University of California, San Francisco, CA 94143, USA.

As knowledge of individual biological processes grows, it becomes increasingly useful to frame new findings within their larger biological contexts in order to generate new systems-scale hypotheses. This report highlights two major iterations of a whole virus model of HIV-1, generated with the cellPACK software. cellPACK integrates structural and systems biology data with packing algorithms to assemble comprehensive 3D models of cell-scale structures in molecular detail. This report describes the biological data, modeling parameters and cellPACK methods used to specify and construct editable models for HIV-1. Anticipating that cellPACK interfaces under development will enable researchers from diverse backgrounds to critique and improve the biological models, we discuss how cellPACK can be used as a framework to unify different types of data across all scales of biology.
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http://dx.doi.org/10.1039/c4fd00017jDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4569901PMC
June 2015

Distinguishing binders from false positives by free energy calculations: fragment screening against the flap site of HIV protease.

J Phys Chem B 2015 Jan 17;119(3):976-88. Epub 2014 Sep 17.

Center for Biophysics & Computational Biology/ICMS, ‡Department of Chemistry, Temple University , Philadelphia, Pennsylvania19122, United States.

Molecular docking is a powerful tool used in drug discovery and structural biology for predicting the structures of ligand-receptor complexes. However, the accuracy of docking calculations can be limited by factors such as the neglect of protein reorganization in the scoring function; as a result, ligand screening can produce a high rate of false positive hits. Although absolute binding free energy methods still have difficulty in accurately rank-ordering binders, we believe that they can be fruitfully employed to distinguish binders from nonbinders and reduce the false positive rate. Here we study a set of ligands that dock favorably to a newly discovered, potentially allosteric site on the flap of HIV-1 protease. Fragment binding to this site stabilizes a closed form of protease, which could be exploited for the design of allosteric inhibitors. Twenty-three top-ranked protein-ligand complexes from AutoDock were subject to the free energy screening using two methods, the recently developed binding energy analysis method (BEDAM) and the standard double decoupling method (DDM). Free energy calculations correctly identified most of the false positives (≥83%) and recovered all the confirmed binders. The results show a gap averaging ≥3.7 kcal/mol, separating the binders and the false positives. We present a formula that decomposes the binding free energy into contributions from the receptor conformational macrostates, which provides insights into the roles of different binding modes. Our binding free energy component analysis further suggests that improving the treatment for the desolvation penalty associated with the unfulfilled polar groups could reduce the rate of false positive hits in docking. The current study demonstrates that the combination of docking with free energy methods can be very useful for more accurate ligand screening against valuable drug targets.
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http://dx.doi.org/10.1021/jp506376zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4306491PMC
January 2015

AutoDock4(Zn): an improved AutoDock force field for small-molecule docking to zinc metalloproteins.

J Chem Inf Model 2014 Aug 18;54(8):2371-9. Epub 2014 Jul 18.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute , La Jolla, California 92037, United States.

Zinc is present in a wide variety of proteins and is important in the metabolism of most organisms. Zinc metalloenzymes are therapeutically relevant targets in diseases such as cancer, heart disease, bacterial infection, and Alzheimer's disease. In most cases a drug molecule targeting such enzymes establishes an interaction that coordinates with the zinc ion. Thus, accurate prediction of the interaction of ligands with zinc is an important aspect of computational docking and virtual screening against zinc containing proteins. We have extended the AutoDock force field to include a specialized potential describing the interactions of zinc-coordinating ligands. This potential describes both the energetic and geometric components of the interaction. The new force field, named AutoDock4Zn, was calibrated on a data set of 292 crystal complexes containing zinc. Redocking experiments show that the force field provides significant improvement in performance in both free energy of binding estimation as well as in root-mean-square deviation from the crystal structure pose. The new force field has been implemented in AutoDock without modification to the source code.
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http://dx.doi.org/10.1021/ci500209eDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4144784PMC
August 2014

Blind prediction of HIV integrase binding from the SAMPL4 challenge.

J Comput Aided Mol Des 2014 Apr 5;28(4):327-45. Epub 2014 Mar 5.

Department of Pharmaceutical Sciences and Department of Chemistry, University of California, Irvine, 147 Bison Modular, Irvine, CA, 92697, USA,

Here, we give an overview of the protein-ligand binding portion of the Statistical Assessment of Modeling of Proteins and Ligands 4 (SAMPL4) challenge, which focused on predicting binding of HIV integrase inhibitors in the catalytic core domain. The challenge encompassed three components--a small "virtual screening" challenge, a binding mode prediction component, and a small affinity prediction component. Here, we give summary results and statistics concerning the performance of all submissions at each of these challenges. Virtual screening was particularly challenging here in part because, in contrast to more typical virtual screening test sets, the inactive compounds were tested because they were thought to be likely binders, so only the very top predictions performed significantly better than random. Pose prediction was also quite challenging, in part because inhibitors in the set bind to three different sites, so even identifying the correct binding site was challenging. Still, the best methods managed low root mean squared deviation predictions in many cases. Here, we give an overview of results, highlight some features of methods which worked particularly well, and refer the interested reader to papers in this issue which describe specific submissions for additional details.
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http://dx.doi.org/10.1007/s10822-014-9723-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331050PMC
April 2014

Virtual screening of integrase inhibitors by large scale binding free energy calculations: the SAMPL4 challenge.

J Comput Aided Mol Des 2014 Apr 7;28(4):475-90. Epub 2014 Feb 7.

Department of Chemistry and Chemical Biology, Rutgers The State University of New Jersey, Piscataway, NJ, 08854, USA,

As part of the SAMPL4 blind challenge, filtered AutoDock Vina ligand docking predictions and large scale binding energy distribution analysis method binding free energy calculations have been applied to the virtual screening of a focused library of candidate binders to the LEDGF site of the HIV integrase protein. The computational protocol leveraged docking and high level atomistic models to improve enrichment. The enrichment factor of our blind predictions ranked best among all of the computational submissions, and second best overall. This work represents to our knowledge the first example of the application of an all-atom physics-based binding free energy model to large scale virtual screening. A total of 285 parallel Hamiltonian replica exchange molecular dynamics absolute protein-ligand binding free energy simulations were conducted starting from docked poses. The setup of the simulations was fully automated, calculations were distributed on multiple computing resources and were completed in a 6-weeks period. The accuracy of the docked poses and the inclusion of intramolecular strain and entropic losses in the binding free energy estimates were the major factors behind the success of the method. Lack of sufficient time and computing resources to investigate additional protonation states of the ligands was a major cause of mispredictions. The experiment demonstrated the applicability of binding free energy modeling to improve hit rates in challenging virtual screening of focused ligand libraries during lead optimization.
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http://dx.doi.org/10.1007/s10822-014-9711-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4137862PMC
April 2014

Virtual screening with AutoDock Vina and the common pharmacophore engine of a low diversity library of fragments and hits against the three allosteric sites of HIV integrase: participation in the SAMPL4 protein-ligand binding challenge.

J Comput Aided Mol Des 2014 Apr 4;28(4):429-441. Epub 2014 Feb 4.

Department of Integrative Structural & Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.

To rigorously assess the tools and protocols that can be used to understand and predict macromolecular recognition, and to gain more structural insight into three newly discovered allosteric binding sites on a critical drug target involved in the treatment of HIV infections, the Olson and Levy labs collaborated on the SAMPL4 challenge. This computational blind challenge involved predicting protein-ligand binding against the three allosteric sites of HIV integrase (IN), a viral enzyme for which two drugs (that target the active site) have been approved by the FDA. Positive control cross-docking experiments were utilized to select 13 receptor models out of an initial ensemble of 41 different crystal structures of HIV IN. These 13 models of the targets were selected using our new "Rank Difference Ratio" metric. The first stage of SAMPL4 involved using virtual screens to identify 62 active, allosteric IN inhibitors out of a set of 321 compounds. The second stage involved predicting the binding site(s) and crystallographic binding mode(s) for 57 of these inhibitors. Our team submitted four entries for the first stage that utilized: (1) AutoDock Vina (AD Vina) plus visual inspection; (2) a new common pharmacophore engine; (3) BEDAM replica exchange free energy simulations, and a Consensus approach that combined the predictions of all three strategies. Even with the SAMPL4's very challenging compound library that displayed a significantly lower amount of structural diversity than most libraries that are conventionally employed in prospective virtual screens, these approaches produced hit rates of 24, 25, 34, and 27 %, respectively, on a set with 19 % declared binders. Our only entry for the second stage challenge was based on the results of AD Vina plus visual inspection, and it ranked third place overall according to several different metrics provided by the SAMPL4 organizers. The successful results displayed by these approaches highlight the utility of the computational structure-based drug discovery tools and strategies that are being developed to advance the goals of the newly created, multi-institution, NIH-funded center called the "HIV Interaction and Viral Evolution Center".
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http://dx.doi.org/10.1007/s10822-014-9709-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053500PMC
April 2014

Crystallographic fragment-based drug discovery: use of a brominated fragment library targeting HIV protease.

Chem Biol Drug Des 2014 Feb 30;83(2):141-8. Epub 2013 Oct 30.

Department of Integrative Computational and Structural Biology, TSRI, 10550 N. Torrey Pines Rd., La Jolla, CA, USA.

A library of 68 brominated fragments was screened against a new crystal form of inhibited HIV-1 protease in order to probe surface sites in soaking experiments. Often, fragments are weak binders with partial occupancy, resulting in weak, difficult-to-fit electron density. The use of a brominated fragment library addresses this challenge, as bromine can be located unequivocally via anomalous scattering. Data collection was carried out in an automated fashion using AutoDrug at SSRL. Novel hits were identified in the known surface sites: 3-bromo-2,6-dimethoxybenzoic acid (Br6) in the flap site and 1-bromo-2-naphthoic acid (Br27) in the exosite, expanding the chemistry of known fragments for development of higher affinity potential allosteric inhibitors. At the same time, mapping the binding sites of a number of weaker binding Br-fragments provides further insight into the nature of these surface pockets.
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http://dx.doi.org/10.1111/cbdd.12227DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3898673PMC
February 2014
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