Abstract
データ駆動型解析により，タンパク質のダイナミクスと進化に共通する特徴の物理的起源が明らかになり，頑健性と可塑性のトレードオフが示された．さらに，AlphaFoldによって予測されたタンパク質構造データベースを基に，タンパク質進化の統計的傾向を分析し，進化的次元削減を実証し，生物学的複雑性の普遍的法則を強調した．
Proteins exhibit structural variations due to thermal noise (dynamics) and genetic mutations (evolution). Using extensive protein structure databases, it is observed that native-state dynamics of proteins exhibit long-range correlations, resembling critical points in physical systems, which contribute to significant conformational changes like allosteric regulation. Additionally, data-driven analysis revealed correspondences between protein dynamics and evolution, shedding light on the robustness-plasticity tradeoff of proteins. Furthermore, based on AlphaFold structure predictions, the statistical trends in protein evolution were observed, highlighted by evolutionary dimensionality reduction. These findings underscore the universal principles of biocomplexity and may lead to advancements of protein engineering technologies.
Proteins exhibit structural variations due to thermal noise (dynamics) and genetic mutations (evolution). Using extensive protein structure databases, it is observed that native-state dynamics of proteins exhibit long-range correlations, resembling critical points in physical systems, which contribute to significant conformational changes like allosteric regulation. Additionally, data-driven analysis revealed correspondences between protein dynamics and evolution, shedding light on the robustness-plasticity tradeoff of proteins. Furthermore, based on AlphaFold structure predictions, the statistical trends in protein evolution were observed, highlighted by evolutionary dimensionality reduction. These findings underscore the universal principles of biocomplexity and may lead to advancements of protein engineering technologies.
| Translated title of the contribution | Universal Principles of Protein Dynamics and Evolution Revealed by Data-Driven Studies |
|---|---|
| Original language | Japanese |
| Pages (from-to) | 291-294 |
| Number of pages | 4 |
| Journal | Seibutsu Butsuri |
| Volume | 64 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - Dec 2024 |
User-Defined Keywords
- protein evolution
- protein dynamics
- long-range correlation
- normal mode analysis
- AlphaFold