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Mandal N, Surpeta B, Brezovsky J. Reinforcing Tunnel Network Exploration in Proteins Using Gaussian Accelerated Molecular Dynamics. J Chem Inf Model 2024; 64:6623-6635. [PMID: 39143923 DOI: 10.1021/acs.jcim.4c00966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Tunnels are structural conduits in biomolecules responsible for transporting chemical compounds and solvent molecules from the active site. They have been shown to be present in a wide variety of enzymes across all functional and structural classes. However, the study of such pathways is experimentally challenging, because they are typically transient. Computational methods, such as molecular dynamics (MD) simulations, have been successfully proposed to explore tunnels. Conventional MD (cMD) provides structural details to characterize tunnels but suffers from sampling limitations to capture rare tunnel openings on longer time scales. Therefore, in this study, we explored the potential of Gaussian accelerated MD (GaMD) simulations to improve the exploration of complex tunnel networks in enzymes. We used the haloalkane dehalogenase LinB and its two variants with engineered transport pathways, which are not only well-known for their application potential but have also been extensively studied experimentally and computationally regarding their tunnel networks and their importance in multistep catalytic reactions. Our study demonstrates that GaMD efficiently improves tunnel sampling and allows the identification of all known tunnels for LinB and its two mutants. Furthermore, the improved sampling provided insight into a previously unknown transient side tunnel (ST). The extensive conformational landscape explored by GaMD simulations allowed us to investigate in detail the mechanism of ST opening. We determined variant-specific dynamic properties of ST opening, which were previously inaccessible due to limited sampling of cMD. Our comprehensive analysis supports multiple indicators of the functional relevance of the ST, emphasizing its potential significance beyond structural considerations. In conclusion, our research proves that the GaMD method can overcome the sampling limitations of cMD for the effective study of tunnels in enzymes, providing further means for identifying rare tunnels in enzymes with the potential for drug development, precision medicine, and rational protein engineering.
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Affiliation(s)
- Nishita Mandal
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, Poznan 61-614, Poland
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, Warsaw 02-109, Poland
| | - Bartlomiej Surpeta
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, Poznan 61-614, Poland
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, Warsaw 02-109, Poland
| | - Jan Brezovsky
- Laboratory of Biomolecular Interactions and Transport, Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Faculty of Biology, Adam Mickiewicz University, Uniwersytetu Poznanskiego 6, Poznan 61-614, Poland
- International Institute of Molecular and Cell Biology in Warsaw, Ks Trojdena 4, Warsaw 02-109, Poland
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2
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Khan O, Jones G, Lazou M, Joseph-McCarthy D, Kozakov D, Beglov D, Vajda S. Expanding FTMap for Fragment-Based Identification of Pharmacophore Regions in Ligand Binding Sites. J Chem Inf Model 2024; 64:2084-2100. [PMID: 38456842 DOI: 10.1021/acs.jcim.3c01969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
The knowledge of ligand binding hot spots and of the important interactions within such hot spots is crucial for the design of lead compounds in the early stages of structure-based drug discovery. The computational solvent mapping server FTMap can reliably identify binding hot spots as consensus clusters, free energy minima that bind a variety of organic probe molecules. However, in its current implementation, FTMap provides limited information on regions within the hot spots that tend to interact with specific pharmacophoric features of potential ligands. E-FTMap is a new server that expands on the original FTMap protocol. E-FTMap uses 119 organic probes, rather than the 16 in the original FTMap, to exhaustively map binding sites, and identifies pharmacophore features as atomic consensus sites where similar chemical groups bind. We validate E-FTMap against a set of 109 experimentally derived structures of fragment-lead pairs, finding that highly ranked pharmacophore features overlap with the corresponding atoms in both fragments and lead compounds. Additionally, comparisons of mapping results to ensembles of bound ligands reveal that pharmacophores generated with E-FTMap tend to sample highly conserved protein-ligand interactions. E-FTMap is available as a web server at https://eftmap.bu.edu.
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Affiliation(s)
- Omeir Khan
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States
| | - George Jones
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, United States
| | - Maria Lazou
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - Diane Joseph-McCarthy
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Dmitri Beglov
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
- Acpharis Inc., Holliston, Massachusetts 01746, United States
| | - Sandor Vajda
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, United States
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215, United States
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3
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Bekar-Cesaretli AA, Khan O, Nguyen T, Kozakov D, Joseph-Mccarthy D, Vajda S. Conservation of Hot Spots and Ligand Binding Sites in Protein Models by AlphaFold2. J Chem Inf Model 2024; 64:960-973. [PMID: 38253327 PMCID: PMC10922769 DOI: 10.1021/acs.jcim.3c01761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The neural network-based program AlphaFold2 (AF2) provides high accuracy structure prediction for a large fraction of globular proteins. An important question is whether these models are accurate enough for reliably docking small ligands. Several recent papers and the results of CASP15 reveal that local conformational errors reduce the success rates of direct ligand docking. Here, we focus on the ability of the models to conserve the location of binding hot spots, regions on the protein surface that significantly contribute to the binding free energy of the protein-ligand interaction. Clusters of hot spots predict the location and even the druggability of binding sites, and hence are important for computational drug discovery. The hot spots are determined by protein mapping that is based on the distribution of small fragment-sized probes on the protein surface and is less sensitive to local conformation than docking. Mapping models taken from the AlphaFold Protein Structure Database show that identifying binding sites is more reliable than docking, but the success rates are still 5% to 10% lower than based on mapping X-ray structures. The drop in accuracy is particularly large for models of multidomain proteins. However, both the model binding sites and the mapping results can be substantially improved by generating AF2 models for the ligand binding domains of interest rather than the entire proteins and even more if using forced sampling with multiple initial seeds. The mapping of such models tends to reach the accuracy of results obtained by mapping the X-ray structures.
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Affiliation(s)
| | - Omeir Khan
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, US
| | - Thu Nguyen
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, US
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, US
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, US
| | - Diane Joseph-Mccarthy
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215
| | - Sandor Vajda
- Department of Chemistry, Boston University, Boston, Massachusetts 02215, US
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215
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Amaya-Rodriguez CA, Carvajal-Zamorano K, Bustos D, Alegría-Arcos M, Castillo K. A journey from molecule to physiology and in silico tools for drug discovery targeting the transient receptor potential vanilloid type 1 (TRPV1) channel. Front Pharmacol 2024; 14:1251061. [PMID: 38328578 PMCID: PMC10847257 DOI: 10.3389/fphar.2023.1251061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 12/14/2023] [Indexed: 02/09/2024] Open
Abstract
The heat and capsaicin receptor TRPV1 channel is widely expressed in nerve terminals of dorsal root ganglia (DRGs) and trigeminal ganglia innervating the body and face, respectively, as well as in other tissues and organs including central nervous system. The TRPV1 channel is a versatile receptor that detects harmful heat, pain, and various internal and external ligands. Hence, it operates as a polymodal sensory channel. Many pathological conditions including neuroinflammation, cancer, psychiatric disorders, and pathological pain, are linked to the abnormal functioning of the TRPV1 in peripheral tissues. Intense biomedical research is underway to discover compounds that can modulate the channel and provide pain relief. The molecular mechanisms underlying temperature sensing remain largely unknown, although they are closely linked to pain transduction. Prolonged exposure to capsaicin generates analgesia, hence numerous capsaicin analogs have been developed to discover efficient analgesics for pain relief. The emergence of in silico tools offered significant techniques for molecular modeling and machine learning algorithms to indentify druggable sites in the channel and for repositioning of current drugs aimed at TRPV1. Here we recapitulate the physiological and pathophysiological functions of the TRPV1 channel, including structural models obtained through cryo-EM, pharmacological compounds tested on TRPV1, and the in silico tools for drug discovery and repositioning.
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Affiliation(s)
- Cesar A. Amaya-Rodriguez
- Centro Interdisciplinario de Neurociencia de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Departamento de Fisiología y Comportamiento Animal, Facultad de Ciencias Naturales, Exactas y Tecnología, Universidad de Panamá, Ciudad de Panamá, Panamá
| | - Karina Carvajal-Zamorano
- Centro Interdisciplinario de Neurociencia de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
| | - Daniel Bustos
- Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Vicerrectoría de Investigación y Postgrado Universidad Católica del Maule, Talca, Chile
- Laboratorio de Bioinformática y Química Computacional, Departamento de Medicina Traslacional, Facultad de Medicina, Universidad Católica del Maule, Talca, Chile
| | - Melissa Alegría-Arcos
- Núcleo de Investigación en Data Science, Facultad de Ingeniería y Negocios, Universidad de las Américas, Santiago, Chile
| | - Karen Castillo
- Centro Interdisciplinario de Neurociencia de Valparaíso, Facultad de Ciencias, Universidad de Valparaíso, Valparaíso, Chile
- Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Vicerrectoría de Investigación y Postgrado Universidad Católica del Maule, Talca, Chile
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Ahmed F, Brooks CL. FASTDock: A Pipeline for Allosteric Drug Discovery. J Chem Inf Model 2023; 63:7219-7227. [PMID: 37939386 PMCID: PMC10773972 DOI: 10.1021/acs.jcim.3c00895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Allostery is involved in innumerable biological processes and plays a fundamental role in human disease. Thus, the exploration of allosteric modulation is crucial for research on biological mechanisms and in the development of novel therapeutics. The development of small-molecule allosteric effectors can be used as tools to probe biological mechanisms of interest. One of the main limitations in targeting allosteric sites is the difficulty in uncovering them for specific receptors. Furthermore, upon discovery of novel allosteric modulation, early lead generation is made more difficult as compared to that at orthosteric sites because there is likely no information about the types of molecules that can bind at the site. In the work described here, we present a novel drug discovery pipeline, FASTDock, which allows one to uncover ligandable sites as well as small molecules that target the given site without requiring pre-existing knowledge of ligands that can bind in the targeted site. By using a hierarchical screening strategy, this method has the potential to enable high-throughput screens of an exceptionally large database of targeted ligand space.
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Affiliation(s)
- Furyal Ahmed
- Biophysics Program, University of Michigan, Ann Arbor, MI 48103
| | - Charles L. Brooks
- Department of Chemistry and Biophysics Program, University of Michigan, Ann Arbor, MI 48103
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Nussinov R, Zhang M, Liu Y, Jang H. AlphaFold, allosteric, and orthosteric drug discovery: Ways forward. Drug Discov Today 2023; 28:103551. [PMID: 36907321 PMCID: PMC10238671 DOI: 10.1016/j.drudis.2023.103551] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 02/27/2023] [Accepted: 03/07/2023] [Indexed: 03/13/2023]
Abstract
Drug discovery is arguably a highly challenging and significant interdisciplinary aim. The stunning success of the artificial intelligence-powered AlphaFold, whose latest version is buttressed by an innovative machine-learning approach that integrates physical and biological knowledge about protein structures, raised drug discovery hopes that unsurprisingly, have not come to bear. Even though accurate, the models are rigid, including the drug pockets. AlphaFold's mixed performance poses the question of how its power can be harnessed in drug discovery. Here we discuss possible ways of going forward wielding its strengths, while bearing in mind what AlphaFold can and cannot do. For kinases and receptors, an input enriched in active (ON) state models can better AlphaFold's chance of rational drug design success.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Mingzhen Zhang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Yonglan Liu
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
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7
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Durham J, Zhang J, Humphreys IR, Pei J, Cong Q. Recent advances in predicting and modeling protein-protein interactions. Trends Biochem Sci 2023; 48:527-538. [PMID: 37061423 DOI: 10.1016/j.tibs.2023.03.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 03/03/2023] [Accepted: 03/17/2023] [Indexed: 04/17/2023]
Abstract
Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.
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Affiliation(s)
- Jesse Durham
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Jing Zhang
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ian R Humphreys
- Department of Biochemistry, University of Washington, Seattle, WA, USA; Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Jimin Pei
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qian Cong
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, USA; Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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8
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Protein-Ligand Interactions in Scarcity: The Stringent Response from Bacteria to Metazoa, and the Unanswered Questions. Int J Mol Sci 2023; 24:ijms24043999. [PMID: 36835415 PMCID: PMC9965611 DOI: 10.3390/ijms24043999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/06/2023] [Accepted: 02/14/2023] [Indexed: 02/18/2023] Open
Abstract
The stringent response, originally identified in Escherichia coli as a signal that leads to reprogramming of gene expression under starvation or nutrient deprivation, is now recognized as ubiquitous in all bacteria, and also as part of a broader survival strategy in diverse, other stress conditions. Much of our insight into this phenomenon derives from the role of hyperphosphorylated guanosine derivatives (pppGpp, ppGpp, pGpp; guanosine penta-, tetra- and tri-phosphate, respectively) that are synthesized on starvation cues and act as messengers or alarmones. These molecules, collectively referred to here as (p)ppGpp, orchestrate a complex network of biochemical steps that eventually lead to the repression of stable RNA synthesis, growth, and cell division, while promoting amino acid biosynthesis, survival, persistence, and virulence. In this analytical review, we summarize the mechanism of the major signaling pathways in the stringent response, consisting of the synthesis of the (p)ppGpp, their interaction with RNA polymerase, and diverse factors of macromolecular biosynthesis, leading to differential inhibition and activation of specific promoters. We also briefly touch upon the recently reported stringent-like response in a few eukaryotes, which is a very disparate mechanism involving MESH1 (Metazoan SpoT Homolog 1), a cytosolic NADPH phosphatase. Lastly, using ppGpp as an example, we speculate on possible pathways of simultaneous evolution of alarmones and their multiple targets.
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Increased slow dynamics defines ligandability of BTB domains. Nat Commun 2022; 13:6989. [PMID: 36384931 PMCID: PMC9668832 DOI: 10.1038/s41467-022-34599-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 10/25/2022] [Indexed: 11/17/2022] Open
Abstract
Efficient determination of protein ligandability, or the propensity to bind small-molecules, would greatly facilitate drug development for novel targets. Ligandability is currently assessed using computational methods that typically consider the static structural properties of putative binding sites or by experimental fragment screening. Here, we evaluate ligandability of conserved BTB domains from the cancer-relevant proteins LRF, KAISO, and MIZ1. Using fragment screening, we discover that MIZ1 binds multiple ligands. However, no ligands are uncovered for the structurally related KAISO or LRF. To understand the principles governing ligand-binding by BTB domains, we perform comprehensive NMR-based dynamics studies and find that only the MIZ1 BTB domain exhibits backbone µs-ms time scale motions. Interestingly, residues with elevated dynamics correspond to the binding site of fragment hits and recently defined HUWE1 interaction site. Our data argue that examining protein dynamics using NMR can contribute to identification of cryptic binding sites, and may support prediction of the ligandability of novel challenging targets.
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Wakefield AE, Kozakov D, Vajda S. Mapping the binding sites of challenging drug targets. Curr Opin Struct Biol 2022; 75:102396. [PMID: 35636004 PMCID: PMC9790766 DOI: 10.1016/j.sbi.2022.102396] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/20/2022] [Accepted: 04/25/2022] [Indexed: 02/03/2023]
Abstract
An increasing number of medically important proteins are challenging drug targets because their binding sites are too shallow or too polar, are cryptic and thus not detectable without a bound ligand or located in a protein-protein interface. While such proteins may not bind druglike small molecules with sufficiently high affinity, they are frequently druggable using novel therapeutic modalities. The need for such modalities can be determined by experimental or computational fragment based methods. Computational mapping by mixed solvent molecular dynamics simulations or the FTMap server can be used to determine binding hot spots. The strength and location of the hot spots provide very useful information for selecting potentially successful approaches to drug discovery.
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Affiliation(s)
- Amanda E. Wakefield
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215,Department of Chemistry, Boston University, Boston, Massachusetts 02215
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA NY, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215,Department of Chemistry, Boston University, Boston, Massachusetts 02215
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Jones G, Wakefield AE, Triplett J, Idrissa K, Goebel J, Kozakov D, Vajda S. API Development Increases Access to Shared Computing Resources at Boston University. JOURNAL OF SOFTWARE ENGINEERING AND APPLICATIONS 2022; 15:197-207. [PMID: 36568682 PMCID: PMC9779984 DOI: 10.4236/jsea.2022.156011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Within the last few decades, increases in computational resources have contributed enormously to the progress of science and engineering (S & E). To continue making rapid advancements, the S & E community must be able to access computing resources. One way to provide such resources is through High-Performance Computing (HPC) centers. Many academic research institutions offer their own HPC Centers but struggle to make the computing resources easily accessible and user-friendly. Here we present SHABU, a RESTful Web API framework that enables S & E communities to access resources from Boston University's Shared Computing Center (SCC). The SHABU requirements are derived from the use cases described in this work.
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Affiliation(s)
- George Jones
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, USA
| | - Amanda E. Wakefield
- Department of Chemistry, Boston University, Boston, USA,Department of Biomedical Engineering, Boston University, Boston, USA
| | | | | | - James Goebel
- College of Engineering, Boston University, Boston, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, USA
| | - Sandor Vajda
- Department of Chemistry, Boston University, Boston, USA,Department of Biomedical Engineering, Boston University, Boston, USA,Corresponding author.
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12
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Computational Resources for Molecular Biology 2022. J Mol Biol 2022; 434:167625. [PMID: 35569508 DOI: 10.1016/j.jmb.2022.167625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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