Computationally modeling how mutations affect protein–protein binding not only helps uncover the biophysics of protein interfaces, but also enables the redesign and optimization of protein interactions. Traditional high‐throughput methods for estimating binding free energy changes are currently limited to mutations directly at the interface due to difficulties in accurately modeling how long‐distance mutations propagate their effects through the protein structure. However, the modeling and design of such mutations is of substantial interest as it allows for greater control and flexibility in protein design applications. We have developed a method that combines high‐throughput Rosetta‐based side‐chain optimization with conformational sampling using classical molecular dynamics simulations, finding significant improvements in our ability to accurately predict long‐distance mutational perturbations to protein binding. Our approach uses an analytical framework grounded in alchemical free energy calculations while enabling exploration of a vastly larger sequence space. When comparing to experimental data, we find that our method can predict internal long‐distance mutational perturbations with a level of accuracy similar to that of traditional methods in predicting the effects of mutations at the protein–protein interface. This work represents a new and generalizable approach to optimize protein free energy landscapes for desired biological functions.
This content will become publicly available on September 1, 2024
Due to improvements in high-performance computing (HPC) capabilities, many of today’s applications produce petabytes worth of data, causing bottlenecks within the system. Importance-based sampling methods, including our spatio-temporal hybrid data sampling method, are capable of resolving these bottlenecks. While our hybrid method has been shown to outperform existing methods, its effectiveness relies heavily on user parameters, such as histogram bins, error threshold, or number of regions. Moreover, the throughput it demonstrates must be higher to avoid becoming a bottleneck itself. In this article, we resolve both of these issues. First, we assess the effects of several user input parameters and detail techniques to help determine optimal parameters. Next, we detail and implement accelerated versions of our method using OpenMP and CUDA. Upon analyzing our implementations, we find 9.8× to 31.5× throughput improvements. Next, we demonstrate how our method can accept different base sampling algorithms and the effects these different algorithms have. Finally, we compare our sampling methods to the lossy compressor cuSZ in terms of data preservation and data movement.
more » « less- Award ID(s):
- 2018069
- NSF-PAR ID:
- 10487816
- Publisher / Repository:
- SAGE
- Date Published:
- Journal Name:
- The International Journal of High Performance Computing Applications
- Volume:
- 37
- Issue:
- 5
- ISSN:
- 1094-3420
- Page Range / eLocation ID:
- 539 to 559
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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