Adaptive Protein Design Protocols and Middleware

Computational protein design is experiencing a transformation driven by AI/ML. However, the range of potential protein sequences and structures is astronomically vast, even for moderately sized proteins. Hence, achieving convergence between generated and predicted structures demands substantial comp...

Full description

Saved in:
Bibliographic Details
Published in:2025 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) pp. 1011 - 1015
Main Authors: Alsaadi, Aymen, Ash, Jonathan, Titov, Mikhail, Turilli, Matteo, Merzky, Andre, Jha, Shantenu, Khare, Sagar
Format: Conference Proceeding
Language:English
Published: IEEE 03.06.2025
Subjects:
Adaptation models
Computational modeling
Data models
High performance computing
hpc
Machine learning
machinelearning
Middleware
protein design
Proteins
Protocols
Resource management
Throughput
workflows
ISSN:2995-066X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Computational protein design is experiencing a transformation driven by AI/ML. However, the range of potential protein sequences and structures is astronomically vast, even for moderately sized proteins. Hence, achieving convergence between generated and predicted structures demands substantial computational resources for sampling. The Integrated Machine-Learning for Protein Structures at Scale (IMPRESS) offers methods and advanced computing systems for coupling AI to high-performance computing tasks, enabling the ability to evaluate the effectiveness of protein designs as they are developed, as well as the models and simulations used to generate data and train models. This paper introduces IMPRESS and demonstrates the development and implementation of an adaptive protein design protocol and its supporting computing infrastructure. This leads to increased consistency in the quality of protein design and enhanced throughput of protein design due to dynamic resource allocation and asynchronous workload execution.
ISSN:2995-066X
DOI:10.1109/IPDPSW66978.2025.00157