Adaptive Protein Design Protocols and Middleware

2025-10-07

AI Summary

Protein design faces challenges of low sampling efficiency and poor convergence in structure generation/prediction due to the vast sequence–structure space. This paper proposes an AI–HPC co-designed adaptive protein design protocol and a lightweight middleware system, integrating machine learning–driven dynamic resource scheduling, asynchronous task orchestration, and real-time structural evaluation to enable closed-loop iterative optimization between generative models and large-scale predictors (e.g., AlphaFold2). Key contributions are: (1) the first implementation of joint adaptive control of computational resources and model inference within the design pipeline; and (2) middleware-mediated decoupling of algorithmic logic from heterogeneous underlying hardware, significantly improving throughput and structural consistency. Experiments demonstrate ≥40% reduction in computational cost while preserving design quality, enabling efficient, scalable design of protein libraries at the thousand-protein scale.

Technology Category

• Machine Learning: Hardware-aware ML
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• Addressing computational challenges in protein sequence and structure sampling
• Developing adaptive protocols for improved protein design quality and throughput
• Integrating AI with high-performance computing for efficient protein evaluation

Innovation

• Integrated AI with high-performance computing systems
• Implemented adaptive protein design protocols
• Enabled dynamic resource allocation for protein design