Paper List
-
STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings
This paper addresses the core challenge of generalizing protein function prediction to unseen or newly introduced Gene Ontology (GO) terms by overcomi...
-
Incorporating indel channels into average-case analysis of seed-chain-extend
This paper addresses the core pain point of bridging the theoretical gap for the widely used seed-chain-extend heuristic by providing the first rigoro...
-
Competition, stability, and functionality in excitatory-inhibitory neural circuits
This paper addresses the core challenge of extending interpretable energy-based frameworks to biologically realistic asymmetric neural networks, where...
-
Enhancing Clinical Note Generation with ICD-10, Clinical Ontology Knowledge Graphs, and Chain-of-Thought Prompting Using GPT-4
This paper addresses the core challenge of generating accurate and clinically relevant patient notes from sparse inputs (ICD codes and basic demograph...
-
Learning From Limited Data and Feedback for Cell Culture Process Monitoring: A Comparative Study
This paper addresses the core challenge of developing accurate real-time bioprocess monitoring soft sensors under severe data constraints: limited his...
-
Cell-cell communication inference and analysis: biological mechanisms, computational approaches, and future opportunities
This review addresses the critical need for a systematic framework to navigate the rapidly expanding landscape of computational methods for inferring ...
-
Generating a Contact Matrix for Aged Care Settings in Australia: an agent-based model study
This study addresses the critical gap in understanding heterogeneous contact patterns within aged care facilities, where existing population-level con...
-
Emergent Spatiotemporal Dynamics in Large-Scale Brain Networks with Next Generation Neural Mass Models
This work addresses the core challenge of understanding how complex, brain-wide spatiotemporal patterns emerge from the interaction of biophysically d...
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.