The Specialized High-Performance Network on Anton 3

Shim, Keun Sup; Greskamp, Brian; Towles, Brian; Edwards, Bruce M.; Grossman, J. P.; Shaw, David E.

doi:10.1109/hpca53966.2022.00092

Cited by 6 publications

(3 citation statements)

References 36 publications

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“…Molecular dynamics (MD) simulations provide an atomic‐resolution lens into how proteins behave in solution at room or physiological temperatures (McCammon et al, 1977 ; Voelz et al, 2010 ; Zimmerman et al, 2021 ). Recent advances in custom hardware like Anton and massively parallel distributed computing platforms like folding@home have helped MD simulations reach unprecedented scales (Shim et al, 2022 ; Voelz et al, 2023 ). Several tools have been developed to facilitate the comparison of ensembles.…”

Section: Predicting Sequence–function Relationships From An Ensemble ...mentioning

confidence: 99%

Toward physics‐based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants

Meller,

Kelly,

Smith

et al. 2024

Protein Science

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The goal of precision medicine is to utilize our knowledge of the molecular causes of disease to better diagnose and treat patients. However, there is a substantial mismatch between the small number of food and drug administration (FDA)‐approved drugs and annotated coding variants compared to the needs of precision medicine. This review introduces the concept of physics‐based precision medicine, a scalable framework that promises to improve our understanding of sequence–function relationships and accelerate drug discovery. We show that accounting for the ensemble of structures a protein adopts in solution with computer simulations overcomes many of the limitations imposed by assuming a single protein structure. We highlight studies of protein dynamics and recent methods for the analysis of structural ensembles. These studies demonstrate that differences in conformational distributions predict functional differences within protein families and between variants. Thanks to new computational tools that are providing unprecedented access to protein structural ensembles, this insight may enable accurate predictions of variant pathogenicity for entire libraries of variants. We further show that explicitly accounting for protein ensembles, with methods like alchemical free energy calculations or docking to Markov state models, can uncover novel lead compounds. To conclude, we demonstrate that cryptic pockets, or cavities absent in experimental structures, provide an avenue to target proteins that are currently considered undruggable. Taken together, our review provides a roadmap for the field of protein science to accelerate precision medicine.

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Section: Predicting Sequence–function Relationships From An Ensemble ...mentioning

confidence: 99%

Toward physics‐based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants

Meller,

Kelly,

Smith

et al. 2024

Protein Science

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show abstract

“…However, this strategy is precluded by the vast computation cost, partly due to the inaccurate energy function and the enormous space of possible structural conformations. For example, the Shaw group performed the molecular dynamics simulation on a specially-designed high-performance computer [33] , and Duan et al acquired the simulation of one millisecond for a short protein with only 36 amino acids, which costed two months on a Cray machine with 256 processors [34] .…”

Section: Protein Structure Prediction Approaches: Rationale Categorie...mentioning

confidence: 99%

Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms

Huang

Kong

Wang

et al. 2023

Genomics, Proteomics &Amp; Bioinformatics

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Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields, including biochemistry, medicine, physics, mathematics, and computer science. These researchers adopt various research paradigms to attack the same structure prediction problem: biochemists and physicists attempt to reveal the principles governing protein folding; mathematicians, especially statisticians, usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure, while computer scientists formulate protein structure prediction as an optimization problem — finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native structure. These research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman, namely, data modeling and algorithmic modeling. Recently, we have also witnessed the great success of deep learning in protein structure prediction. In this review, we present a survey of the efforts for protein structure prediction. We compare the research paradigms adopted by researchers from different fields, with an emphasis on the shift of research paradigms in the era of deep learning. In short, the algorithmic modeling techniques, especially deep neural networks, have considerably improved the accuracy of protein structure prediction; however, theories interpreting the neural networks and knowledge on protein folding are still highly desired.

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“…For this reason, broad and agile scientific progress hinges upon MD simulations being run on generalpurpose commodity platforms (i.e., CPUs and GPUs), which are readily available in HPC-grade configurations from cloud computing providers. However, commodity platforms are currently up to 1000× slower than DSAs [35,37].…”

Section: Introductionmentioning

confidence: 99%

Characterizing Molecular Dynamics Simulation on Commodity Platforms

Peverelli

Conficconi

Bartolini³

et al. 2022

2022 IEEE International Symposium on Workload Characterization (IISWC)

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Molecular Dynamics (MD) simulation is an essential tool driving innovation in key scientific domains such as physics, materials science, biochemistry, and drug discovery. Enabling larger, longer, and more accurate MD simulations can directly impact scientific discovery and innovation. While domain-specific architectures for MD exist, they are not widely accessible, and MD performance on commodity platforms (i.e., CPUs and GPUs) remains critical for supporting broad and agile scientific progress.This paper aims at characterizing MD simulation on commodity platforms with a benchmark campaign on modern systems available in public cloud offerings. We focus on LAMMPS, one of the prevalent MD frameworks, and characterize several representative and diverse MD experiments. We find that the benchmarked CPU instance provides good scalability to many cores, while the reference LAMMPS GPU implementation struggles with scaling to multiple devices. Additionally, we evaluate the performance impact of application-specific parameters such as error threshold and arithmetic precision.Our study indicates that key drivers for further improvement of LAMMPS performance on commodity systems are: 1) improving scalability and offload efficiency in multiaccelerator systems and 2) reducing work imbalance in the CPU parallelization. Set initial positions and velocities Initial integration step: Get x(t+Δt), v(t+(½)Δt) Apply boundary conditions Update neighbor list data structures Compute non-bonded forces with an appropriate force field, compute bonded forces

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The Specialized High-Performance Network on Anton 3

Cited by 6 publications

References 36 publications

Toward physics‐based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants

Toward physics‐based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants

Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms

Characterizing Molecular Dynamics Simulation on Commodity Platforms

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