Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Protein Structure and Dynamics
Abstract
This paper introduces a novel self-adaptive optimization algorithm designed for modeling biological molecules. Biological systems exhibit intricate and dynamic behaviors, making traditional optimization methods often inadequate. This algorithm addresses this limitation by incorporating a self-adjustment mechanism that dynamically modifies parameters and topology to optimize a system's behavior. We present a framework for automated parameter tuning and topology manipulation, aiming to provide a more flexible and adaptable approach to biological molecular modeling. The algorithm's effectiveness is demonstrated through a series of simulations focusing on protein folding and ligand binding. The resulting results highlight the algorithm's potential for significantly improving model accuracy and robustness.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
