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Protein Conformation

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Label-free SERS detection of proteins based on machine learning classification of chemo-structural determinants.

The Analyst
Establishing standardized methods for a consistent analysis of spectral data remains a largely underexplored aspect in surface-enhanced Raman spectroscopy (SERS), particularly applied to biological and biomedical research. Here we propose an effectiv...

OPUS-Rota3: Improving Protein Side-Chain Modeling by Deep Neural Networks and Ensemble Methods.

Journal of chemical information and modeling
Side-chain modeling is critical for protein structure prediction since the uniqueness of the protein structure is largely determined by its side-chain packing conformation. In this paper, differing from most approaches that rely on rotamer library sa...

Spatiotemporal identification of druggable binding sites using deep learning.

Communications biology
Identification of novel protein binding sites expands druggable genome and opens new opportunities for drug discovery. Generally, presence or absence of a binding site depends on the three-dimensional conformation of a protein, making binding site id...

Conformational changes of β-thalassemia major hemoglobin and oxidative status of plasma after in vitro exposure to extremely low-frequency electromagnetic fields: An artificial neural network analysis.

Electromagnetic biology and medicine
Electromagnetic fields (EMF) can generate reactive oxygen species and induce oxidative modifications. We investigated the effects of extremely low-frequency electromagnetic fields (ELF-EMF) on oxidative status of plasma and erythrocytes in β-thalasse...

Characterizing chromatin folding coordinate and landscape with deep learning.

PLoS computational biology
Genome organization is critical for setting up the spatial environment of gene transcription, and substantial progress has been made towards its high-resolution characterization. The underlying molecular mechanism for its establishment is much less u...

Discovering Protein Conformational Flexibility through Artificial-Intelligence-Aided Molecular Dynamics.

The journal of physical chemistry. B
Proteins sample a variety of conformations distinct from their crystal structure. These structures, their propensities, and the pathways for moving between them contain an enormous amount of information about protein function that is hidden from a pu...

Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies.

The journal of physical chemistry letters
The current pandemic demands a search for therapeutic agents against the novel coronavirus SARS-CoV-2. Here, we present an efficient computational strategy that combines machine learning (ML)-based models and high-fidelity ensemble docking studies to...

Predicting protein model correctness in Coot using machine learning.

Acta crystallographica. Section D, Structural biology
Manually identifying and correcting errors in protein models can be a slow process, but improvements in validation tools and automated model-building software can contribute to reducing this burden. This article presents a new correctness score that ...

Anomaly Detection-Based Recognition of Near-Native Protein Structures.

IEEE transactions on nanobioscience
The three-dimensional structures populated by a protein molecule determine to a great extent its biological activities. The rich information encoded by protein structure on protein function continues to motivate the development of computational appro...

Machine Learning Driven Analysis of Large Scale Simulations Reveals Conformational Characteristics of Ubiquitin Chains.

Journal of chemical theory and computation
Understanding the conformational characteristics of protein complexes in solution is crucial for a deeper insight in their biological function. Molecular dynamics simulations performed on high performance computing plants and with modern simulation t...