Rapid and Reliable Binding Affinity Prediction of Bromodomain Inhibitors: A Computational Study

Wan, Shunzhou; Bhati, Agastya P.; Zasada, Stefan J.; Wall, Ian D.; Green, Darren V. S.; Bamborough, Paul; Coveney, Peter V.

doi:10.1021/acs.jctc.6b00794

Cited by 75 publications

(170 citation statements)

References 39 publications

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“…ESMACS and TIES [11], [8] are two free energy calculation protocols that implement absolute and relative methods, respectively. Absolute free energy methods calculate the binding affinity of a single drug molecule to a protein, while relative methods calculate the difference in binding affinity between two (usually similar in structure) drug molecules.…”

Section: A Esmacs and Tiesmentioning

confidence: 99%

“…We validated our implementation selecting a subset of the protein ligand systems used in Wan et al [11]: ligand (11) transformations 3 to 1, 4, and 7. We then performed a full simulation on all 3 systems and calculated the binding affinity using HTBAC.…”

Section: Validationmentioning

confidence: 99%

“…The TIES protocol (thermodynamic integration with enhanced sampling) uses the more rigorous "alchemical" thermodynamic integration approach as implemented in NAMD [9], [10]. These protocols have produced statistically meaningful results for industrial computational drug campaign [11].…”

Section: Introductionmentioning

confidence: 99%

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Concurrent and Adaptive Extreme Scale Binding Free Energy Calculations

Dakka

Farkas-Pall

Turilli

et al. 2018

2018 IEEE 14th International Conference on E-Science (E-Science)

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The efficacy of drug treatments depends on how tightly small molecules bind to their target proteins. The rapid and accurate quantification of the strength of these interactions (as measured by 'binding affinity') is a grand challenge of computational chemistry, surmounting which could revolutionize drug design and provide the platform for patient-specific medicine. Recent evidence suggests that molecular dynamics (MD) can achieve useful predictive accuracy (≤ 1 kcal/mol). For this predictive accuracy to impact clinical decision making, binding free energy computational campaigns must provide results rapidly and without loss of accuracy. This demands advances in algorithms, scalable software systems, and efficient utilization of supercomputing resources. We introduce a framework called HTBAC, designed to support accurate and scalable drug binding affinity calculations, while marshaling large simulation campaigns. We show that HTBAC supports the specification and execution of free-energy protocols at scale. This paper makes three main contributions: (1) shows the importance of adaptive execution for ensemble-based free energy protocols to improve binding affinity accuracy; (2) presents and characterizes HTBAC -a software system that enables the scalable and adaptive execution of binding affinity protocols at scale; and (3) for a widely used free-energy protocol (TIES), shows improvements in the accuracy of simulations for a fixed amount of resource, or reduced resource consumption for a fixed accuracy as a consequence of adaptive execution.

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Section: A Esmacs and Tiesmentioning

confidence: 99%

Section: Validationmentioning

confidence: 99%

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Concurrent and Adaptive Extreme Scale Binding Free Energy Calculations

Dakka

Farkas-Pall

Turilli

et al. 2018

2018 IEEE 14th International Conference on E-Science (E-Science)

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“…Our recent work shows that ensemble-based methods are capable of producing rapid, accurate, precise, and reliable binding free energies (33, 34, 39, 40). In these studies, we have used these approaches, termed “enhanced sampling of molecular dynamics with approximation of continuum solvent” (ESMACS) (40) and “thermodynamic integration with enhanced sampling” (TIES) (39).…”

Section: Molecular Dynamics Studies Of Tcr–pmhc Interactionsmentioning

confidence: 99%

The Role of Multiscale Protein Dynamics in Antigen Presentation and T Lymphocyte Recognition

et al. 2017

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T lymphocytes are stimulated when they recognize short peptides bound to class I proteins of the major histocompatibility complex (MHC) protein, as peptide–MHC complexes. Due to the diversity in T-cell receptor (TCR) molecules together with both the peptides and MHC proteins they bind to, it has been difficult to design vaccines and treatments based on these interactions. Machine learning has made some progress in trying to predict the immunogenicity of peptide sequences in the context of specific MHC class I alleles but, as such approaches cannot integrate temporal information and lack explanatory power, their scope will always be limited. Here, we advocate a mechanistic description of antigen presentation and TCR activation which is explanatory, predictive, and quantitative, drawing on modeling approaches that collectively span several length and time scales, being capable of furnishing reliable biological descriptions that are difficult for experimentalists to provide. It is a form of multiscale systems biology. We propose the use of chemical rate equations to describe the time evolution of the foreign and host proteins to explain how the original proteins end up being presented on the cell surface as peptide fragments, while we invoke molecular dynamics to describe the key binding processes on the molecular level, including those of peptide–MHC complexes with TCRs which lie at the heart of the immune response. On each level, complementary methods based on machine learning are available, and we discuss the relationship between these divergent approaches. The pursuit of predictive mechanistic modeling approaches requires experimentalists to adapt their work so as to acquire, store, and expose data that can be used to verify and validate such models.

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“…A diversity of computational approaches, specifically binding free energy calculations which rely on physics-based molecular dynamics simulations (MD) have been developed 2 and blind tests show that many have considerable predictive potential 3,4 . In this context, recent developments in algorithms and hardware that have reduced the cost of these computational approaches have seen an increase in their appeal to the pharmaceutical industry [5][6][7][8][9] . With commercial approaches that claim accuracy of below 1 kcal mol −1 now on the market 10 it is becoming of increasing interest to understand the accuracy of and uncertainties inherent in different approaches 11 .…”

Section: Introductionmentioning

confidence: 99%

Application of ESMACS Binding Free Energy Protocols to Diverse Datasets: Bromodomain-Containing Protein 4

Wright¹,

Wan²,

Meyer³

et al. 2018

Preprint

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As the application of computational methods in drug discovery pipelines becomes more widespread it is increasingly important to understand how reproducible their results are and how sensitive they are to choices made in simulation setup and analysis.Here we use ensemble simulation protocols, termed ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent), to investigate the sensitivity of the popular molecular mechanics Poisson-Boltzmann surface area (MMPBSA) methodology. Using the bromodomain-containing protein 4 (BRD4) system bound to a diverse set of ligands as our target, we show that robust rankings can be produced only through combining ensemble sampling with multiple trajectories and enhanced solvation via an explicit ligand hydration shell.

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Rapid and Reliable Binding Affinity Prediction of Bromodomain Inhibitors: A Computational Study

Cited by 75 publications

References 39 publications

Concurrent and Adaptive Extreme Scale Binding Free Energy Calculations

Concurrent and Adaptive Extreme Scale Binding Free Energy Calculations

The Role of Multiscale Protein Dynamics in Antigen Presentation and T Lymphocyte Recognition

Application of ESMACS Binding Free Energy Protocols to Diverse Datasets: Bromodomain-Containing Protein 4

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