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Founder's Pitch
"HADES uses Hamiltonian dynamics for efficient protein sequence optimization, enhancing drug and enzyme development."
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Why It Matters
Efficient protein optimization is crucial for developing better pharmaceuticals and industrial enzymes, addressing the high-dimensional complexity of protein folding and function while reducing cost and time.
Product Angle
Turn HADES into a SaaS platform providing protein optimization tools for R&D labs in biotech and pharma sectors, integrating wet-lab and in-silico results.
Disruption
HADES could replace traditional iterative protein engineering methods, like directed evolution, which are costly and time-intensive.
Product Opportunity
The protein engineering market is substantial, driven by demand for faster drug development and industrial enzyme optimization. Companies pay for tools that reduce costs and time to market.
Use Case Idea
Develop a platform to enable pharmaceutical companies to rapidly design and test protein-based drugs with enhanced efficacy and reduced side effects.
Science
The paper introduces HADES, a Bayesian optimization technique leveraging Hamiltonian dynamics to efficiently explore protein sequences. It uses a structure-aware approach by simulating physical movements in a continuous state system, then discretizing to protein sequences. This method outperforms traditional methods on key metrics by efficiently navigating the complex fitness landscape of proteins.
Method & Eval
Comparative in-silico evaluations showed that HADES significantly outperformed existing methods across various metrics, such as maximum fitness, mean fitness, and fitness diversity using benchmark datasets like GB1 and PhoQ.
Caveats
The technique's reliance on accurate structure prediction models is a limitation; any errors in these models could propagate, affecting overall outcomes.
Author Intelligence
Jiahao Wang
LEAD Shanghai Jiao Tong University
jiahaowang@sjtu.edu.cn
Shuangjia Zheng
Shanghai Jiao Tong University
shuangjia.zheng@sjtu.edu.cn