ArticleFrontiers in molecular biosciences2019
Comparative Exploratory Analysis of Intrinsically Disordered Protein Dynamics Using Machine Learning and Network Analytic Methods.
Article in Frontiers in molecular biosciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Comparison of Feature Selection Methods in Machine Learning Models of Cancer Information Seeking Among United States Adults: Cross-Sectional Study.JMIR medical informatics · 2026Article
- Comparative analysis of thermal adaptations of extremophilic prolyl oligopeptidases.Biophysical journal · 2024Article
- Production of Distinct Fibrillar, Oligomeric, and Other Aggregation States from Network Models of Multibody Interaction.Journal of chemical theory and computation · 2024Article
- Genetic Algorithm for Automated Parameterization of Network Hamiltonian Models of Amyloid Fibril Formation.The journal of physical chemistry. B · 2024Article
- Clustering Heterogeneous Conformational Ensembles of Intrinsically Disordered Proteins with t-Distributed Stochastic Neighbor Embedding.Journal of chemical theory and computation · 2023Article
- Circuit Topology Approach for the Comparative Analysis of Intrinsically Disordered Proteins.Journal of chemical information and modeling · 2023Article
- Comparative Modeling and Analysis of Extremophilic D-Ala-D-Ala Carboxypeptidases.Biomolecules · 2023Article
- Network Hamiltonian Models for Unstructured Protein Aggregates, with Application to γD-Crystallin.The journal of physical chemistry. B · 2023Article
- Combining enhanced sampling and deep learning dimensionality reduction for the study of the heat shock protein B8 and its pathological mutant K141E.RSC advances · 2022Article
- Highly scalable maximum likelihood and conjugate Bayesian inference for ERGMs on graph sets with equivalent vertices.PloS one · 2022Article
- Neural Upscaling from Residue-Level Protein Structure Networks to Atomistic Structures.Biomolecules · 2021Article
- Testing biological network motif significance with exponential random graph models.Applied network science · 2021Article
- Fibril Surface-Dependent Amyloid Precursors Revealed by Coarse-Grained Molecular Dynamics Simulation.Frontiers in molecular biosciences · 2021Article
- Protein conformational switch discerned via network centrality properties.Computational and structural biotechnology journal · 2021Article
- Sequence Characterization and Molecular Modeling of Clinically Relevant Variants of the SARS-CoV-2 Main Protease.Biochemistry · 2020Article
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3 authors.
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Abstract
Simulations of intrinsically disordered proteins (IDPs) pose numerous challenges to comparative analysis, prominently including highly dynamic conformational states and a lack of well-defined secondary structure. Machine learning (ML) algorithms are especially effective at discriminating among high-dimensional inputs whose differences are extremely subtle, making them well suited to the study of IDPs. In this work, we apply various ML techniques, including support vector machines (SVM) and clustering, as well as related methods such as principal component analysis (PCA) and protein structure network (PSN) analysis, to the problem of uncovering differences between configurational data from molecular dynamics simulations of two variants of the same IDP. We examine molecular dynamics (MD) trajectories of wild-type amyloid beta (Aβ
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