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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Safe model based optimization balancing exploration and reliability for protein sequence design.

Shuuki Takizawa1, Keita Mori2, Naoto Tanishiki2

  • 1Research Division, Chugai Pharmaceutical Co., Ltd, Yokohama, Japan. takizawa.syuki85@chugai-pharm.co.jp.

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|July 29, 2025
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Summary
This summary is machine-generated.

Protein engineering is accelerated by model-based optimization (MBO), but models can be unreliable. A new method, mean deviation tree-structured Parzen estimator (MD-TPE), improves MBO by penalizing unreliable predictions, leading to better protein designs.

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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Area of Science:

  • Computational biology
  • Protein engineering
  • Machine learning

Background:

  • Protein engineering aims to discover proteins with specific functions, a process often limited by time and the vastness of protein sequence space.
  • Offline Model-Based Optimization (MBO) utilizes proxy models to navigate this space efficiently, but these models can predict unreliable, out-of-distribution values, leading to suboptimal outcomes.
  • Pathological behavior in MBO arises when the proxy model's predictions are too optimistic and deviate significantly from the training data.

Purpose of the Study:

  • To develop a novel optimization approach that addresses the limitations of existing MBO methods in protein engineering.
  • To improve the reliability of proxy models used in protein sequence design.
  • To ensure optimization solutions are found within regions where the proxy model can make accurate predictions.

Main Methods:

  • Introduction of the mean deviation tree-structured Parzen estimator (MD-TPE), a new algorithm for MBO.
  • Integration of Gaussian process (GP) model's predictive distribution deviation into the objective function to penalize unreliable samples.
  • Evaluation of MD-TPE on the GFP dataset and an antibody affinity maturation task.

Main Results:

  • MD-TPE demonstrated a reduction in pathological samples compared to the standard tree-structured Parzen estimator (TPE) when tested on the GFP dataset.
  • The MD-TPE approach successfully identified protein mutants with enhanced binding affinity in the antibody affinity maturation task.
  • The developed method provides a safer optimization strategy by focusing on reliable prediction regions.

Conclusions:

  • The proposed MD-TPE method enhances the safety and reliability of model-based optimization for protein engineering applications.
  • This approach effectively guides the search towards regions with dependable model predictions, improving the discovery of functional proteins.
  • MD-TPE offers a valuable tool for accelerating the design and optimization of proteins with desired functionalities.