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Entropy in Molecular Recognition by Proteins
Wednesday, February 15, 2017
Symposium: Protein Dynamics and Allostery 2285-Symp Coupled Residue-Residue Dynamics in Protein Allosteric Mechanisms Donald Hamelberg. Department of Chemistry, Georgia State University, Atlanta, GA, USA. Although the relationship between structure and function in biomolecules is well established, it is not always adequate to provide a complete understanding of biomolecular function. The dynamical fluctuations of biomolecules can also play an essential role in function. Detailed understanding of how conformational dynamics orchestrates function in allosteric regulation of recognition and catalysis at atomic resolution remains ambiguous. The overarching goal is to understand how biomolecular dynamics are coupled to function by using atomistic molecular simulations to complement experiments. In this talk, we will discuss computational studies on members of a ubiquitous family of enzymes that catalyze peptidyl-prolyl bonds and regulate many sub-cellular processes. We analyze large amount of time-dependent multi-dimensional data with a coarse-grained approach and map key dynamical features within individual macrostates by defining dynamics in terms of residue-residue contacts. The effects of substrate binding are observed to be largely sensed at a location over ˚ from the active site, implying its importance in allostery. Using NMR ex15 A periments, we confirm that a dynamic cluster of residues in this distal region is directly coupled to the active site. Furthermore, the dynamical network of interresidue contacts is coupled and temporally dispersed. Mapping these dynamical features and the coupling of dynamics to function has crucial ramifications in understanding allosteric regulation in enzymes and proteins in general. 2286-Symp Entropy in Molecular Recognition by Proteins Joshua Wand. Biochemistry & Biophysics, University of Pennsylvania, Philadelphia, PA, USA. At a fundamental level, biological processes are most often controlled using molecular recognition by proteins. Protein-ligand interactions impact critical events ranging from the catalytic action of enzymes, the assembly of macromolecular structures, complex signaling and allostery, transport phenomena, force generation and so on. The physical origin of high affinity interactions involving proteins continues to be the subject of intense investigation. Conformational entropy represents perhaps the last piece of the thermodynamic puzzle that governs protein structure, stability, dynamics and function. The presence and importance of internal conformational entropy in proteins has been debated for decades but has resisted experimental quantification. Over the past few years we have introduced, developed and validated an NMR-based approach that uses a dynamical proxy to determine changes in conformational entropy. This new approach, which we term the NMR ‘‘entropy meter,’’ requires few assumptions, is empirically calibrated and is apparently robust and universal. Using this ‘‘entropy meter,’’ it can now be quantitatively shown that proteins retain considerable conformational entropy in their native functional states and that this conformational entropy can play a decisive role in the thermodynamics of molecular recognition by proteins. Recent results show that changes conformational entropy of a protein upon binding a high affinity ligand is highly system specific and can vary from strongly inhibiting to even strongly promoting binding and everything in between. Thus one cannot possibly understand comprehensively how proteins work without knowledge of the breadth and underlying principles of the role of conformational entropy in protein function. This approach also allows for the refinement of empirical coefficients that relate changes in accessible surface area to changes in the entropy of water and the determination of the loss of rotational-translational entropy in high affinity protein complexes. Supported by the NIH and the Mathers Foundation.
Symposium: Computational Cardiology 2287-Symp Using Clinical Datasets to Optimize Models of Human Ventricular Electrophysiology: Implications for In Silico Drug Screening Adam P. Hill1, Stefan A. Mann2, Mohammad S. Imtiaz1, Matthew D. Perry3, Jamie I. Vandenberg3. 1 Computational Cardiology, Victor Chang Cardiac Research Institute, Sydney, Australia, 2Cytocentrics Bioscience GmbH, Cologne, Germany, 3 Molecular Cardiology and Biophysics, Victor Chang Cardiac Research Institute, Sydney, Australia. Recent advances in Computational Cardiology mean we can now examine the causes, mechanisms and impact of cardiac dysfunction in silico, particularly in regards to risk stratification and treatment of heart rhythm disturbances. As a result, computational cardiology stands at the threshold of clinical utility. For
465a
example, in-silico models of human cardiac electrophysiology are being considered by the FDA for prediction of proarrhythmic cardiotoxicity as a core component of the preclinical assessment phase of all new drugs. However, one issue that exists with current models is that they each respond differently to insults such as drug block of ion channels or mutation of cardiac ion channel genes. Clearly this poses a problem in relation to the utility of these models in making quantitative predictions that are physiologically or clinically meaningful. To examine this in detail we tested the ability of three models of the human ventricular action potential, the O’hara-Rudy the GrandiBers and the Ten Tusscher models, to reproduce the clinical phenotype of different subtypes of the long QT syndrome. All models, in their original form, produce markedly different and unrealistic predictions of QT prolongation. To address this, we used a global optimization approach to constrain existing in silico models to clinical datasets. After optimization, all models have similar current densities during the action potential, despite differences in kinetic properties of the channels in the different models, and closely reproduced the prolongation of repolarization seen in clinical data. We suggest that models optimized using this approach can be utilized with more confidence in clinical and preclinical applications such as prediction of proarrhythmic risk as part of in silico drug screening, examining pathogenic interactions of electrical dysfunction and structural alteration in the myocardium and assessing the impact of genetic variants in ion channel genes in contributing to heart rhythm disturbances. 2288-Symp Towards in Silico Drug Trials using Human Multiscale Cardiac Models Blanca Rodriguez. University of Oxford, Oxford, United Kingdom. In silico physiological modelling and simulation provide a cheap, useful tool for drug safety and efficacy assessment. In this presentation, we will show evidence of the potential of human multiscale models of the heart for the evaluation of drug safety and efficacy. We will illustrate how in silico trials can be conducted to assess drug safety and efficacy with consideration of population variability and disease conditions. Methodological progress for in silico trials is based on the maturity of multiscale models of human physiology, availability of human recordings to calibrate the human models from the ionic to the whole organ level, and our ability to simulate the consequences of diseases such as myocardial ischemia, heart failure and inherited disease conditions. We will show results using experimentally-calibrated populations of models and high performance computing simulations of diseased human hearts, and their comparison to experimental datasets. In summary, in silico trials using human multiscale cardiac models provide a useful, mechanistic tool for the preclinical assessment of drugs, which enriches and complements current approaches. 2289-Symp Predictive Computational Pharmacology: From Atom to Rhythm Colleen E. Clancy. Department of Pharmacology, University of California, Davis, Davis, CA, USA. Common paroxysmal electrical diseases that affect millions of people worldwide are notoriously difficult to manage with drug therapy, and some drugs intended for therapy can even exacerbate disease. A vital hindrance to safe and effective drug treatment of excitable disorders is that there is currently no way to predict how drugs with complex interactions and multiple subcellular targets will alter the emergent electrical activity of cells and tissues. Our work involves the development of a novel quantitative systems pharmacology approach derived from a combination of experiments, computational biology, high performance computing and clinical observation that allows for probing the mechanisms of action of drugs in the settings of one of the most common excitable diseases: cardiac arrhythmias. These new tools can be applied to preclinical screening of compounds for therapeutic benefit or harm. A computerbased approach can be used to determine mechanisms of drugs, with a specific focus to conduct failure analysis for once promising drugs that have failed clinically. Finally, models are applied to demonstrate utility in guiding therapy for specific clinical situations and to identify optimal ‘‘polypharmacy’’ to inform the common practice of clinical empirical mixing and matching of drugs to create multidrug therapeutic regimens. The computational processes that we have developed are paradigms for how the explosion in systems and computational biology can be utilized to assist drug-screening, determination of mechanisms and to guide therapy. The eventual goal is a scalable, automated platform that will interact with other cutting edge technologies to serve purposes in industry, academia and in clinical medicine that will be widely expanded to pharmacology of other common disorders of excitability such as epilepsy, ataxia and even pain.
Symposium: Protein Dynamics and Allostery 2285-Symp Coupled Residue-Residue Dynamics in Protein Allosteric Mechanisms Donald Hamelberg. Department of Chemistry, Georgia State University, Atlanta, GA, USA. Although the relationship between structure and function in biomolecules is well established, it is not always adequate to provide a complete understanding of biomolecular function. The dynamical fluctuations of biomolecules can also play an essential role in function. Detailed understanding of how conformational dynamics orchestrates function in allosteric regulation of recognition and catalysis at atomic resolution remains ambiguous. The overarching goal is to understand how biomolecular dynamics are coupled to function by using atomistic molecular simulations to complement experiments. In this talk, we will discuss computational studies on members of a ubiquitous family of enzymes that catalyze peptidyl-prolyl bonds and regulate many sub-cellular processes. We analyze large amount of time-dependent multi-dimensional data with a coarse-grained approach and map key dynamical features within individual macrostates by defining dynamics in terms of residue-residue contacts. The effects of substrate binding are observed to be largely sensed at a location over ˚ from the active site, implying its importance in allostery. Using NMR ex15 A periments, we confirm that a dynamic cluster of residues in this distal region is directly coupled to the active site. Furthermore, the dynamical network of interresidue contacts is coupled and temporally dispersed. Mapping these dynamical features and the coupling of dynamics to function has crucial ramifications in understanding allosteric regulation in enzymes and proteins in general. 2286-Symp Entropy in Molecular Recognition by Proteins Joshua Wand. Biochemistry & Biophysics, University of Pennsylvania, Philadelphia, PA, USA. At a fundamental level, biological processes are most often controlled using molecular recognition by proteins. Protein-ligand interactions impact critical events ranging from the catalytic action of enzymes, the assembly of macromolecular structures, complex signaling and allostery, transport phenomena, force generation and so on. The physical origin of high affinity interactions involving proteins continues to be the subject of intense investigation. Conformational entropy represents perhaps the last piece of the thermodynamic puzzle that governs protein structure, stability, dynamics and function. The presence and importance of internal conformational entropy in proteins has been debated for decades but has resisted experimental quantification. Over the past few years we have introduced, developed and validated an NMR-based approach that uses a dynamical proxy to determine changes in conformational entropy. This new approach, which we term the NMR ‘‘entropy meter,’’ requires few assumptions, is empirically calibrated and is apparently robust and universal. Using this ‘‘entropy meter,’’ it can now be quantitatively shown that proteins retain considerable conformational entropy in their native functional states and that this conformational entropy can play a decisive role in the thermodynamics of molecular recognition by proteins. Recent results show that changes conformational entropy of a protein upon binding a high affinity ligand is highly system specific and can vary from strongly inhibiting to even strongly promoting binding and everything in between. Thus one cannot possibly understand comprehensively how proteins work without knowledge of the breadth and underlying principles of the role of conformational entropy in protein function. This approach also allows for the refinement of empirical coefficients that relate changes in accessible surface area to changes in the entropy of water and the determination of the loss of rotational-translational entropy in high affinity protein complexes. Supported by the NIH and the Mathers Foundation.
Symposium: Computational Cardiology 2287-Symp Using Clinical Datasets to Optimize Models of Human Ventricular Electrophysiology: Implications for In Silico Drug Screening Adam P. Hill1, Stefan A. Mann2, Mohammad S. Imtiaz1, Matthew D. Perry3, Jamie I. Vandenberg3. 1 Computational Cardiology, Victor Chang Cardiac Research Institute, Sydney, Australia, 2Cytocentrics Bioscience GmbH, Cologne, Germany, 3 Molecular Cardiology and Biophysics, Victor Chang Cardiac Research Institute, Sydney, Australia. Recent advances in Computational Cardiology mean we can now examine the causes, mechanisms and impact of cardiac dysfunction in silico, particularly in regards to risk stratification and treatment of heart rhythm disturbances. As a result, computational cardiology stands at the threshold of clinical utility. For
465a
example, in-silico models of human cardiac electrophysiology are being considered by the FDA for prediction of proarrhythmic cardiotoxicity as a core component of the preclinical assessment phase of all new drugs. However, one issue that exists with current models is that they each respond differently to insults such as drug block of ion channels or mutation of cardiac ion channel genes. Clearly this poses a problem in relation to the utility of these models in making quantitative predictions that are physiologically or clinically meaningful. To examine this in detail we tested the ability of three models of the human ventricular action potential, the O’hara-Rudy the GrandiBers and the Ten Tusscher models, to reproduce the clinical phenotype of different subtypes of the long QT syndrome. All models, in their original form, produce markedly different and unrealistic predictions of QT prolongation. To address this, we used a global optimization approach to constrain existing in silico models to clinical datasets. After optimization, all models have similar current densities during the action potential, despite differences in kinetic properties of the channels in the different models, and closely reproduced the prolongation of repolarization seen in clinical data. We suggest that models optimized using this approach can be utilized with more confidence in clinical and preclinical applications such as prediction of proarrhythmic risk as part of in silico drug screening, examining pathogenic interactions of electrical dysfunction and structural alteration in the myocardium and assessing the impact of genetic variants in ion channel genes in contributing to heart rhythm disturbances. 2288-Symp Towards in Silico Drug Trials using Human Multiscale Cardiac Models Blanca Rodriguez. University of Oxford, Oxford, United Kingdom. In silico physiological modelling and simulation provide a cheap, useful tool for drug safety and efficacy assessment. In this presentation, we will show evidence of the potential of human multiscale models of the heart for the evaluation of drug safety and efficacy. We will illustrate how in silico trials can be conducted to assess drug safety and efficacy with consideration of population variability and disease conditions. Methodological progress for in silico trials is based on the maturity of multiscale models of human physiology, availability of human recordings to calibrate the human models from the ionic to the whole organ level, and our ability to simulate the consequences of diseases such as myocardial ischemia, heart failure and inherited disease conditions. We will show results using experimentally-calibrated populations of models and high performance computing simulations of diseased human hearts, and their comparison to experimental datasets. In summary, in silico trials using human multiscale cardiac models provide a useful, mechanistic tool for the preclinical assessment of drugs, which enriches and complements current approaches. 2289-Symp Predictive Computational Pharmacology: From Atom to Rhythm Colleen E. Clancy. Department of Pharmacology, University of California, Davis, Davis, CA, USA. Common paroxysmal electrical diseases that affect millions of people worldwide are notoriously difficult to manage with drug therapy, and some drugs intended for therapy can even exacerbate disease. A vital hindrance to safe and effective drug treatment of excitable disorders is that there is currently no way to predict how drugs with complex interactions and multiple subcellular targets will alter the emergent electrical activity of cells and tissues. Our work involves the development of a novel quantitative systems pharmacology approach derived from a combination of experiments, computational biology, high performance computing and clinical observation that allows for probing the mechanisms of action of drugs in the settings of one of the most common excitable diseases: cardiac arrhythmias. These new tools can be applied to preclinical screening of compounds for therapeutic benefit or harm. A computerbased approach can be used to determine mechanisms of drugs, with a specific focus to conduct failure analysis for once promising drugs that have failed clinically. Finally, models are applied to demonstrate utility in guiding therapy for specific clinical situations and to identify optimal ‘‘polypharmacy’’ to inform the common practice of clinical empirical mixing and matching of drugs to create multidrug therapeutic regimens. The computational processes that we have developed are paradigms for how the explosion in systems and computational biology can be utilized to assist drug-screening, determination of mechanisms and to guide therapy. The eventual goal is a scalable, automated platform that will interact with other cutting edge technologies to serve purposes in industry, academia and in clinical medicine that will be widely expanded to pharmacology of other common disorders of excitability such as epilepsy, ataxia and even pain.