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

Hans 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.

Physics of Biomolecular Recognition and Conformational Dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

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

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.

COVID-19: Energy landscape theory of SARS-CoV-2 complexes with Particulate Matter

In the past few days, the global scientific community has made much progress in research for the COVID-19 pandemic, but the new SARS-CoV-2 coronavirus has not yet been correctly characterized thermodynamically and much is still unknown. In particular, the current SARS-CoV-2 models lack the characterization of the virus system within its environment. This is a serious systematic error, which stands in the way of impeding research into the pandemic.In the present work, therefore, we consider the SARS-CoV-2 system with its environment, and we give a correct thermodynamic definition, through analysis and simulations, from air transport to cellular entry through the mechanism of receptor- mediated endocytosis.In studying the aerosol environment of the SARS-CoV-2 virus, we cannot omit the presence of nanoparticles or dust.Therefore, analyzing and comparing the air environments in China and Italy, we note that the Chinese and Italian regions which were at the beginning the most affected by the pandemic are also the most polluted. The same phenomenon is happening today for the United States and Brazil.We therefore propose an energy landscape theory of synergistic complexes of SARS- CoV-2 with particulate matter (PM).This could explain the optimized strategy of deep penetration of interstitial lung cells and the rapid spread of the pandemic in the most polluted areas of the planet. It could also explain the severity and difficulty of treating the forms of interstitial pneumonia occurred in Italy and worldwide.The energy landscape theory of complexes of SARS-CoV-2 with particulate matter (PM), leads to crucial methodological constraints aimed at containing systematic errors in experimental laboratory procedures and in mathematical modeling, which can allow and accelerate the definition of the mechanism of action of the virus and therefore the realization of the appropriate therapies and health protocols.

AWSEM-Suite: a protein structure prediction server based on template-guided, coevolutionary-enhanced optimized folding landscapes

The accurate and reliable prediction of the 3D structures of proteins and their assemblies remains difficult even though the number of solved structures soars and prediction techniques improve. In this study, a free and open access web server, AWSEM-Suite, whose goal is to predict monomeric protein tertiary structures from sequence is described. The model underlying the server’s predictions is a coarse-grained protein force field which has its roots in neural network ideas that has been optimized using energy landscape theory. Employing physically motivated potentials and knowledge-based local structure biasing terms, the addition of homologous template and co-evolutionary restraints to AWSEM-Suite greatly improves the predictive power of pure AWSEM structure prediction. From the independent evaluation metrics released in the CASP13 experiment, AWSEM-Suite proves to be a reasonably accurate algorithm for free modeling, standing at the eighth position in the free modeling category of CASP13. The AWSEM-Suite server also features a front end with a user-friendly interface. The AWSEM-Suite server is a powerful tool for predicting monomeric protein tertiary structures that is most useful when a suitable structure template is not available. The AWSEM-Suite server is freely available at: https://awsem.rice.edu.

Intrinsic Disorder and Degeneracy in Molecular Scale Organization of Biological Membrane

The scale-rich spatiotemporal organization in biological membrane dictates the “molecular encounter” and in turn the larger scale biological processes such as molecular transport, trafficking and cellular signalling. In this work, we explore the degeneracy in lateral organization in lipid bilayer systems from the perspective of energy landscape theory. Our analysis on molecular trajectories show that bilayers with lipids havingin-vivocharacteristics have a highly frustrated energy landscape as opposed to a funnel-like energy landscape inin-vitrosystems. Lattice evolution simulations, with Hamiltonian trained from atomistic trajectories using lipids topology and non-affine displacement measures to characterize the extent of order-disorder in the bilayer, show that the inherent frustration inin-vivolike systems renders them with the ability to access a wide range of nanoscale patterns with equivalent energy penalty. We posit that this structural degeneracy could provide for a larger repository to functionally important molecular organization inin-vivosettings.