Paper List
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Developing the PsyCogMetrics™ AI Lab to Evaluate Large Language Models and Advance Cognitive Science
This paper addresses the critical gap between sophisticated LLM evaluation needs and the lack of accessible, scientifically rigorous platforms that in...
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Equivalence of approximation by networks of single- and multi-spike neurons
This paper resolves the fundamental question of whether single-spike spiking neural networks (SNNs) are inherently less expressive than multi-spike SN...
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The neuroscience of transformers
提出了Transformer架构与皮层柱微环路之间的新颖计算映射，连接了现代AI与神经科学。
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Framing local structural identifiability and observability in terms of parameter-state symmetries
This paper addresses the core challenge of systematically determining which parameters and states in a mechanistic ODE model can be uniquely inferred ...
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Leveraging Phytolith Research using Artificial Intelligence
This paper addresses the critical bottleneck in phytolith research by automating the labor-intensive manual microscopy process through a multimodal AI...
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Neural network-based encoding in free-viewing fMRI with gaze-aware models
This paper addresses the core challenge of building computationally efficient and ecologically valid brain encoding models for naturalistic vision by ...
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Scalable DNA Ternary Full Adder Enabled by a Competitive Blocking Circuit
This paper addresses the core bottleneck of carry information attenuation and limited computational scale in DNA binary adders by introducing a scalab...
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ELISA: An Interpretable Hybrid Generative AI Agent for Expression-Grounded Discovery in Single-Cell Genomics
This paper addresses the critical bottleneck of translating high-dimensional single-cell transcriptomic data into interpretable biological hypotheses ...
Topological Enhancement of Protein Kinetic Stability
BioISI – Instituto de Biossistemas e Ciências Integrativas and Departamento de Física, Faculdade de Ciências, Universidade de Lisboa, 1749-016, Lisboa, Portugal
30秒速读
IN SHORT: This work addresses the long-standing puzzle of why knotted proteins exist by demonstrating that deep knots provide a functional advantage through enhanced kinetic stability, not equilibrium thermodynamics.
核心创新
- Methodology Introduces a controlled computational framework (LTyP vs. non-LTyP Monte Carlo simulations) to isolate the pure topological effect of knots from sequence, structure, and energetic contributions.
- Biology Reveals a strong, asymmetric dependence on knot depth: deep knots (e.g., YibK) suppress unfolding transitions by >1 order of magnitude, dramatically enhancing kinetic stability, while shallow knots have minimal effect.
- Theory Integrates a reverse evolution model, showing that kinetic stabilization is sequence-dependent, emerging fully only with increased amino acid alphabet complexity, providing an evolutionary rationale for knotted protein conservation.
主要结论
- Deep protein knots (e.g., YibK) enhance kinetic stability (resistance to unfolding) by more than an order of magnitude compared to topology-breaking controls, while shallow knots show minimal effect.
- Kinetic stability increases sharply with knot depth, whereas foldability is only moderately affected, revealing an asymmetric topological constraint favoring native state persistence.
- Kinetic stabilization is sequence-dependent: early, low-complexity (10-letter alphabet) sequences exhibit weaker resistance to unfolding, with stabilization becoming pronounced only with modern (20-letter) alphabet complexity.
摘要: Knotted proteins embed a physical (i.e., open) knot within their native structures. For decades, significant effort has been devoted to elucidating the functional role of knots in proteins, yet no consensus has been reached. Here, using extensive Monte Carlo off-lattice simulations of a simple structure-based model, we isolate the effect of topology by comparing simulations that preserve the linear topology of the chain with simulations that allow chain crossings. This controlled framework enables us to isolate topological effects from sequence, structure and energetic contributions. We show that protein kinetic stability, defined as resistance to unfolding at a fixed temperature, is higher in knotted proteins. Additionally, kinetic stability increases significantly with knot depth, whereas foldability (or folding efficiency) is comparatively less affected. By considering a simple model of protein evolution in which amino-acid alphabet size is used as a proxy for evolutionary time, we find that increasing primary-sequence complexity through the addition of biotic amino acids predominantly enhances kinetic stability. Taken together, these results indicate that kinetic stability is a functional advantage conferred by protein knots and suggest that evolutionary pressure for kinetic stability could contribute to the persistence of knotted proteins.