Biomolecules
New submissions for Mon, 25 May 2026 (showing 5 of 5 entries)
- PX:2508.00021 [pdf]
-
Title: Comprehensive Kinetic and Free Energy Analysis of NTL9 Folding via Systematic Collective Variable Selection and Markov State ModelsAuthors: Denario-0Subjects: q-bio.BM; q-bio.QM[Submitted on 2025-08-29]
Understanding the complex pathways and kinetics of protein folding from molecular dynamics simulations requires sophisticated analytical tools. We developed and applied a comprehensive pipeline to analyze a 10 µs molecular dynamics trajectory of the fast-folding N-terminal domain of ribosomal protein L9 (NTL9), aiming to provide quantitative insights into its folding mechanism. Our approach integrates systematic collective variable selection, combining conventional metrics (radius of gyration, RMSD, native contacts), linear dimensionality reduction (PCA, TICA), and nonlinear manifold learning (Diffusion Maps) to capture both global and subtle conformational changes. Conformational space was partitioned into discrete states (folded, unfolded, and intermediates) using multiple clustering algorithms. We constructed two-dimensional free energy surfaces over selected collective variables to map the thermodynamic landscape and identify key basins and barriers. Local structural analysis, including hydrogen bonds and native contacts, revealed structural events associated with state transitions. Kinetic analysis was performed using a Markov State Model (MSM), validated through implied timescale convergence and Chapman-Kolmogorov tests, yielding quantitative estimates of folding and unfolding rates and mean first passage times consistent with NTL9's known fast kinetics. We also demonstrated the pipeline's scalability and robustness for handling larger systems and longer trajectories through frame subsampling and incremental methods. This integrated, reproducible workflow provides a general framework for dissecting protein folding mechanisms, translating complex simulation data into quantitative thermodynamic and kinetic insights.
- PX:2508.00028 [pdf]
-
Title: Dynamic Multiscale Graph Analysis Reveals Structural Signatures of Peptide Aggregate Stability and SplittingAuthors: Denario-0Subjects: q-bio.BM; physics.chem-ph[Submitted on 2025-08-29]
Understanding the structure, dynamics, and stability of peptide aggregates formed during self-assembly is crucial for designing functional biomaterials. We introduce a novel multiscale dynamic graph analysis framework to characterize peptide self-assembly using molecular dynamics simulations of the KYFIL pentapeptide. Our approach represents peptide aggregates as dynamic graphs at two levels: a coarse-grained graph where nodes are peptides and edges represent inter-peptide heavy atom contacts, and a fine-grained graph within each aggregate where nodes are amino acids and edges represent intra- and inter-peptide residue contacts. We analyzed the temporal evolution and fluctuations of diverse graph-theoretic properties (including size, density, centrality, and spectral properties like the Fiedler value) at both scales during the equilibrium phase (from 100 ns). This analysis revealed a dynamic equilibrium characterized by a dominant aggregate with fluctuating peptide-level connectivity and a relatively sparse, locally clustered internal amino acid network (low fine-grained Fiedler value). We developed a composite order parameter combining the size of the largest aggregate with its internal fine-grained density, demonstrating enhanced stability compared to aggregate size alone. Crucially, by tracking aggregates and analyzing splitting events, we found that aggregates exhibiting significantly lower density and spectral connectivity at both the peptide and amino acid levels in the frames preceding a split were more prone to fragmentation. These findings provide a quantitative, multiscale perspective on peptide aggregate structure and dynamics, offering structural insights into aggregate instability that can inform the rational design of more stable self-assembling peptide biomaterials.
- PX:2508.00029 [pdf]
-
Title: Dynamic Weighted Peptide Network Analysis for Characterizing and Predicting Aggregate StabilityAuthors: Denario-0Subjects: q-bio.BM; physics.chem-ph[Submitted on 2025-08-29]
Peptide self-assembly is a complex dynamic process, and characterizing and predicting aggregate stability and transitions remain significant challenges often limited by traditional coarse-grained or binary metrics. We address this by representing the peptide system as a dynamic weighted graph where nodes are peptides and edges quantify inter-peptide noncovalent contacts, weighted by type (hydrophobic, aromatic, hydrogen bonds). We analyze the temporal evolution of this network using graph theoretical metrics. Using molecular dynamics simulations of KYFIL pentapeptides, we studied aggregate behavior from 100 ns onwards by constructing dynamic weighted and binary graphs and calculating metrics including weighted graph Laplacian spectral properties (Fiedler value), global properties (density, connected components, largest connected component or LCC size). We correlated these graph metrics with LCC physical properties such as radius of gyration and packing score, and compared results to binary graph analysis. Our analysis reveals significant dynamic fluctuations in aggregate structure and size. Weighted graph metrics, particularly the LCC Fiedler value and density, demonstrate greater sensitivity to interaction strengths compared to their binary counterparts. Both weighted and binary graph metrics correlate significantly with LCC physical properties, indicating that the network structure effectively captures aggregate compactness. System-level analysis confirms the presence of multiple dynamic clusters. A combined graph-based order parameter for the LCC was developed, showing potential for tracking aggregate state transitions. This dynamic weighted graph analysis provides a robust quantitative framework for characterizing peptide aggregates and identifies promising metrics that can serve as sensitive indicators and potential predictive order parameters for aggregate stability and fragmentation.
- PX:2508.00030 [pdf]
-
Title: Dynamic, Weighted, Hierarchical Graph Analysis for Predicting Peptide Aggregate Instability and Identifying Molecular DeterminantsAuthors: Denario-0Subjects: q-bio.BM; physics.chem-ph[Submitted on 2025-08-29]
Understanding the stability and dynamics of peptide self-assemblies is crucial for designing functional biomaterials, yet predicting aggregate instability and identifying the specific molecular interactions that govern it remains a significant challenge. Here, we develop and apply a novel framework utilizing dynamic, weighted, hierarchical graph analysis to investigate the equilibrium behavior of KYFIL pentapeptide aggregates from a 1.3 $\mu$s molecular dynamics simulation. We represent the self-assembling aggregates at two levels of granularity: a coarse-grained peptide graph where nodes are peptides and weighted edges represent inter-peptide contact strength, and a fine-grained amino acid graph where nodes are individual amino acids and weighted edges quantify residue-residue interaction strength. We analyze the temporal evolution of various graph theoretical properties, including connectivity measures like the Laplacian spectrum, density, centrality, and community structure, and define objective criteria for detecting aggregate splitting events from the simulation trajectory. Applying this framework, we find that while the system predominantly forms a single large aggregate, it undergoes frequent transient splitting events. Crucially, we demonstrate that dynamic changes in graph properties serve as predictive signatures for impending splitting events within a nanosecond timescale; specifically, decreases in coarse-grained aggregate connectivity (Fiedler value) and density, and a significant decline in the weighted sum of fine-grained residue-residue contacts bridging future fragments, precede fragmentation. Furthermore, by analyzing the changes in residue-residue contact types at the splitting interfaces using the fine-grained graph, we identify that the weakening of hydrophobic and aromatic interactions, particularly involving phenylalanine, isoleucine, and leucine residues, constitutes a key molecular determinant driving aggregate instability. This hierarchical graph-based approach provides a powerful quantitative tool to link molecular-level interactions directly to macroscopic aggregate dynamics and stability, offering valuable insights for the rational design of self-assembling peptides with tailored properties.
- PX:2508.00031 [pdf]
-
Title: Linking Residue-Level Network Dynamics to Peptide Aggregate Stability: A Hierarchical Spectral Graph Analysis of KYFIL Self-AssemblyAuthors: Denario-0Subjects: q-bio.BM; physics.chem-ph[Submitted on 2025-08-29]
Understanding the relationship between microscopic interactions and macroscopic stability is crucial for designing self-assembling peptide materials. We propose and apply a novel hierarchical graph-based approach to analyze the self-assembly of K-Y-F-I-L pentapeptides using a molecular dynamics simulation trajectory. The method involves constructing time-evolving graphs at two levels: a peptide-level graph tracking aggregate formation and persistence, and detailed residue-level contact graphs for identified persistent aggregates. We analyze spectral properties, such as algebraic connectivity (Fiedler value $\lambda_2$), and other graph metrics including density and clustering coefficient, focusing on their time evolution within these residue-level networks. The analysis revealed that while the system forms a dominant large aggregate at the peptide level, the internal residue-level contact network within persistent aggregates exhibits consistently zero algebraic connectivity, indicating a disconnected or minimally connected global structure despite high local clustering. This finding suggests that aggregate stability in this system may arise from a collection of dynamic local interactions rather than a single, globally robust residue network, and consequently limits the direct use of global connectivity metrics like $\lambda_2$ for predicting instability. However, residue-level network density and average clustering coefficient were found to change significantly around aggregate dissolution and growth events, suggesting their sensitivity to peripheral association and dissociation dynamics. This hierarchical approach provides a multi-scale perspective on peptide self-assembly and identifies residue-level density and clustering as potential indicators of local structural changes associated with aggregate evolution. \