From Protein Structure Predictions to Dynamics From Protein Structure Predictions to Dynamics Overview Designed enzymes with new catalytic functions have the potential to revolutionize synthesis strategies in organic chemistry. A significant step toward this goal has been the recent development of computational methods that accurately predict atomistic protein structures. However, the catalytic efficiency of structure-based enzyme designs remains several orders of magnitude lower than for naturally evolved enzymes, which feature optimized structures and conformational transitions, i.e. protein dynamics, that enhance enzyme efficiency. The role of protein dynamics in computational design strategies is currently limited due to the lack of accurate predictions of dynamical properties and their impact on enzyme efficiency. In this project, we address this need with the development of new methods that analyze anharmonic motion to predict protein dynamics and conformational transitions from molecular dynamics simulations. Our methods are readily integrated in computational protein design strategies to allow for the simultaneous optimization of structure and dynamics. We further use our methods to study multiple cases in which enzyme activity is strongly associated with dynamics to develop a framework that determines which dynamical features are support desired enzyme functions. Intellectual Merit Our strategy to predict protein dynamics is based on the development of novel methods that eliminate the need for harmonic approximations in the analysis of vibrations in atomistic molecular dynamics simulations. Our analysis utilizes a time-correlation formalism that: a) reveals anharmonic degrees of freedom prone to large amplitude fluctuations; b) resolves thermally excited vibrational dynamics in the time and frequency domain; and c) analyzes correlations between distinct anharmonic vibrations. To predict conformational dynamics with relevance to enzyme function, we use this information to decode the relationship between the high-dimensional protein potential energy surface and stochastic dynamics on timescales exceeding the simulation time. A second requirement for the optimization of dynamics towards increased enzymatic efficiency in newly designed enzymes, is the selection of dynamical features that are beneficial for function. To identify structure-dynamics-function relationships, we will use our newly developed methods to study multiple examples in which dynamics are known to be associated with protein function. These cases include a) the sudden onset of enzymatic activity and anharmonic motion in RNase A with temperature during the protein dynamical transition; b) a change in the far-infrared spectrum of lysozyme upon inhibitor binding; c) changes in catalytic activity and dynamics during enzyme evolution. Broader Impact Establishing a relationship between protein structure, dynamics and function is of general interest in the field of biophysics beyond the design of novel enzymes. Proteins evolved for an optimal balance between stability and activity in their native solvation environment, which greatly affects protein dynamics. However, the characterization of protein dynamics is often limited to quantitative measures of protein flexibility, which does not reveal the dynamic degrees of freedom critical for function. The methods developed within this project, which will be made publicly available including detailed documentation and protocols for automation, will allow for the analysis of protein vibrations and their slower stochastic conformational transitions in this broader context, e.g. for proteins in varying solvation environments or to compare mesophilic and thermophilic enzyme variants. Further, we will use automated simulation and analysis protocols for the development of undergraduate research projects with a specific focus on students in ASUs online biochemistry program. These projects will be part of a greater effort to establish group-based research experiences for online undergraduate students in ASUs Online Undergraduate Research Scholars (OURS) program to close a significant opportunity gap between online and on-campus student populations regarding extracurricular research experiences.
|Effective start/end date||5/1/22 → 4/30/25|
- National Science Foundation (NSF): $481,740.00
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