scholarly journals Impact of Conformational Substates and Energy Landscapes on Understanding Hemoglobin Kinetics and Function

2021 ◽  
Author(s):  
William A. Eaton
Keyword(s):  
Protein Folding ◽  
Protein Function ◽  
Energy Landscape ◽  
Rate Theory ◽  
Energy Landscapes ◽  
New Era ◽  
Energy Landscape Theory ◽  
Conformational Substates ◽  
Landscape Theory ◽  
And Function

AbstractHans Frauenfelder’s discovery of conformational substates in studies of myoglobin carbon monoxide geminate rebinding kinetics at cryogenic temperatures (Austin RH, Beeson KW, Eisenstein L, Frauenfelder H, & Gunsalus IC (1975) Dynamics of Ligand Binding to Myoglobin. Biochemistry 14(24):5355–5373) followed by his introduction of energy landscape theory with Peter Wolynes (Frauenfelder H, Sligar SG, & Wolynes PG (1991) The Energy Landscapes and Motions of Proteins. Science 254(5038):1598–1603) marked the beginning of a new era in the physics and physical chemistry of proteins. Their work played a major role in demonstrating the power and importance of dynamics and of Kramers reaction rate theory for understanding protein function. The biggest impact of energy landscape theory has been in the protein folding field, which is well-known and has been documented in numerous articles and reviews, including a recent one of my own (Eaton WA (2021) Modern Kinetics and Mechanism of Protein Folding: a Retrospective. J. Phys. Chem. B. 125(14):3452–3467). Here I will describe the much less well-known impact of their modern view of proteins on both experimental and theoretical studies of hemoglobin kinetics and function. I will first describe how Frauenfelder’s experiments motivated and influenced my own research on myoglobin, which were key ingredients to my work on understanding hemoglobin.

Recent successes of the energy landscape theory of protein folding and function

2005 ◽  
Vol 38 (4) ◽  
pp. 405-410 ◽  
Author(s):  
P. G. Wolynes
Keyword(s):  
Protein Folding ◽  
Energy Landscape ◽  
Helical Bundles ◽  
Energy Landscape Theory ◽  
Landscape Theory ◽  
Hydrophilic Residues ◽  
And Function ◽  
Quality Of Structure

Protein folding and binding can be understood using energy landscape theory. When seeming deviations from the predictions of the funnel hypothesis are found, landscape theory helps us locate the cause. Sometimes the deviation reflects symmetry effects, allowing extra degeneracies to occur. Such effects seem to explain some kinetic anomalies in helical bundles. When binding processes were found to use apparently non-funneled landscapes this was traced to an inadequate understanding of biomolecular forces. The discrepancy allowed the discovery of new water-mediated forces – some of which act between hydrophilic residues. Introducing such forces into the algorithms greatly improves the quality of structure predictions.

scholarly journals Evolutionary differences in the ACE2 reveals the molecular origins of COVID-19 susceptibility

2021 ◽  
Author(s):  
Ryan R. Cheng ◽  
Esteban Dodero-Rojas ◽  
Michele Di Pierro ◽  
José Nelson Onuchic
Keyword(s):  
Protein Folding ◽  
Protein Sequence ◽  
Energy Landscape ◽  
Spike Protein ◽  
Receptor Protein ◽  
Energy Landscape Theory ◽  
Landscape Theory ◽  
Selection Of

We explore the energetic frustration patterns associated with the binding between the SARS-CoV-2 spike protein and the ACE2 receptor protein in a broad selection of animals. Using energy landscape theory and the concept of energy frustration—theoretical tools originally developed to study protein folding—we are able to identify interactions among residues of the spike protein and ACE2 that result in COVID-19 resistance. This allows us to identify whether or not a particular animal is susceptible to COVID-19 from the protein sequence of ACE2 alone. Our analysis predicts a number of experimental observations regarding COVID-19 susceptibility, demonstrating that this feature can be explained, at least partially, on the basis of theoretical means.

Energy landscapes and solved protein–folding problems

2004 ◽  
Vol 363 (1827) ◽  
pp. 453-467 ◽  
Author(s):  
Peter G. Wolynes
Keyword(s):  
Protein Folding ◽  
Protein Design ◽  
Structure Prediction ◽  
Energy Landscape ◽  
Protein Structures ◽  
Energy Landscapes ◽  
Folding Kinetics ◽  
Energy Functions ◽  
Energy Landscape Theory ◽  
Novel Proteins

Energy–landscape theory has led to much progress in protein folding kinetics, protein structure prediction and protein design. Funnel landscapes describe protein folding and binding and explain how protein topology determines kinetics. Landscape–optimized energy functions based on bioinformatic input have been used to correctly predict low–resolution protein structures and also to design novel proteins automatically.

Understanding protein folding with energy landscape theory Part II: Quantitative aspects

2002 ◽  
Vol 35 (3) ◽  
pp. 205-286 ◽  
Author(s):  
Steven S. Plotkin ◽  
José N. Onuchic
Keyword(s):  
Phase Diagram ◽  
Free Energy ◽  
Protein Folding ◽  
Energy Landscape ◽  
Rate Theory ◽  
Functional Theory ◽  
Random Energy Model ◽  
Energy Landscape Theory ◽  
Random Energy ◽  
Basic Concepts

1. Introduction 2062. Quantifying the notions behind the energy landscape 2062.1 Basic concepts of the Random Energy Model (REM) 2062.2 Replica-symmetric partition functions and densities of states 2092.3 The RHP phase diagram and avoided phase transitions 2102.4 Basic concepts of the entropy of topologically constrained polymers 2123. Beyond the Random Energy Model 2193.1 The GREM and the glass transition in a finite RHP 2224. Basics of configurational diffusion for RHPs and proteins 2274.1 Kinetics on a correlated energy landscape 2315. Thermodynamics and kinetics of protein folding 2345.1 A protein Hamiltonian with cooperative interactions 2345.2 Variance of native contact energies 2355.3 Thermodynamics of protein folding 2365.4 Free-energy surfaces and dynamics for a Hamiltonian with pair-wise interactions 2405.5 The effects of cooperativity on folding 2425.6 Transition-state drift 2425.7 Phase diagram for a model protein 2455.8 A non-Arrhenius folding-rate curve for proteins 2466. Non-Markovian configurational diffusion and reaction coordinates in protein folding 2476.1 An illustrative example 2506.2 Non-Markovian rate theory and reaction surfaces 2516.3 Application of non-Markovian rate theory to simulation data 2577. Structural and energetic heterogeneity in the folding mechanism 2597.1 The general strategy 2617.2 An illustrative example 2637.3 Free-energy functional 2647.4 Dependence of the barrier height on mean loop length (contact order) and structural variance 2687.5 Illustrations using lattice model proteins and functional theory 2697.6 Connections of functional theory with experiments 2718. Conclusions and future prospects 2739. Acknowledgments 27410. AppendicesA. Table of common symbols 275B. GREM construction for the glass transition 276C. Effect of a Q-dependent diffusion coefficient 279D. A frequency-dependent Einstein relation 27911. References 281We have seen in Part I of this review that the energy landscape theory of protein folding is a statistical description of a protein's complex potential energy surface, where individual folding events are sampled from an ensemble of possible routes on the landscape. We found that the most likely global structure for the landscape of a protein can be described as that of a partially random heteropolymer with a rugged, yet funneled landscape towards the native structure. Here we develop some quantitative aspects of folding using tools from the statistical mechanics of disordered systems, polymers, and phase transitions in finite-sized systems. Throughout the text we will refer to concepts and equations developed in Part I of the review, and the reader is advised to at least survey its contents before proceeding here. Sections, figures or equations from Part I are often prefixed with I- [e.g. Section I-1.1, Fig. I-1, Eq. (I-1.1)].

The energy landscape theory of protein folding: Insights into folding mechanisms and scenarios

2000 ◽  
pp. 87-152 ◽  
Author(s):  
José Nelson Onuchic ◽  
Hugh Nymeyer ◽  
Angel E. García ◽  
Jorge Chahine ◽  
Nicholas D. Socci
Keyword(s):  
Protein Folding ◽  
Energy Landscape ◽  
Energy Landscape Theory ◽  
Landscape Theory

Energy Landscapes: An Overview

2001 ◽  
Vol 700 ◽  
Author(s):  
David J. Wales
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Energy Surface ◽  
Dynamic Properties ◽  
Energy Landscape ◽  
Global Optimisation ◽  
Energy Landscapes ◽  
Energy Landscape Theory ◽  
Landscape Theory ◽  
Optimisation Algorithms

AbstractThe goal of energy landscape theory is to relate observable thermodynamic and dynamic properties to features of the underlying potential energy surface. Here we illustrate the approach with reference to the annealing of C60 and indicate how it may be used to design improved global optimisation algorithms.

scholarly journals Learning To Fold Proteins Using Energy Landscape Theory

2014 ◽  
Vol 54 (8-9) ◽  
pp. 1311-1337 ◽  
Author(s):  
Nicholas P. Schafer ◽  
Bobby L. Kim ◽  
Weihua Zheng ◽  
Peter G. Wolynes
Keyword(s):  
Energy Landscape ◽  
Energy Landscape Theory ◽  
Landscape Theory
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