AIMC Topic: Proteins

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Biomolecular Actuators for Soft Robots.

Chemical reviews
Biomolecules present promising stimuli-responsive mechanisms to revolutionize soft actuators. Proteins, peptides, and nucleic acids foster specific intermolecular interactions, and their boundless sequence design spaces encode precise actuation capab...

for Investigating Conformational Transitions and Environmental Interactions of Proteins.

Journal of chemical theory and computation
Proteins are inherently dynamic molecules, and their conformational transitions among various states are essential for numerous biological processes, which are often modulated by their interactions with surrounding environments. Although molecular dy...

Machine-learning-guided identification of protein secondary structures using spectral and structural descriptors.

Biomaterials science
Interrogation of the secondary structures of proteins is essential for designing and engineering more effective and safer protein-based biomaterials and other classes of theranostic materials. Protein secondary structures are commonly assessed using ...

Integrating Machine Learning-Based Pose Sampling with Established Scoring Functions for Virtual Screening.

Journal of chemical information and modeling
Physics-based docking methods have long been the cornerstone of structure-based virtual screening (VS). However, the emergence of machine learning (ML)-based docking approaches has opened new possibilities for enhancing VS technologies. In this study...

Deep learning-guided design of dynamic proteins.

Science (New York, N.Y.)
Deep learning has advanced the design of static protein structures, but the controlled conformational changes that are hallmarks of natural signaling proteins have remained inaccessible to de novo design. Here, we describe a general deep learning-gui...

Enhanced Exploration of Protein Conformational Space through Integration of Ultra-Coarse-Grained Models to Multiscale Workflows.

The journal of physical chemistry. B
Computational techniques such as all-atom (AA) molecular dynamics (MD) simulations and coarse-grained (CG) models have been essential to study various biological problems over a wide range of scales. While AA simulations provide detailed insights, th...

CrypTothML: An Integrated Mixed-Solvent Molecular Dynamics Simulation and Machine Learning Approach for Cryptic Site Prediction.

International journal of molecular sciences
Cryptic sites, which are transient binding sites that emerge through protein conformational changes upon ligand binding, are valuable targets for drug discovery, particularly for allosteric modulators. However, identifying these sites remains challen...

Prediction and design of thermostable proteins with a desired melting temperature.

Scientific reports
The stability of proteins at higher temperatures is crucial for their functionality, which is measured by their melting temperature (Tm). The Tm is the temperature at which 50% of the protein loses its native structure and activity. Existing methods ...

High-Throughput Ligand Dissociation Kinetics Predictions Using Site Identification by Ligand Competitive Saturation.

Journal of chemical theory and computation
The dissociation or off rate, , of a drug molecule has been shown to be more relevant to efficacy than affinity for selected systems, motivating the development of predictive computational methodologies. These are largely based on enhanced-sampling m...

Accurate Predictions of Molecular Properties of Proteins via Graph Neural Networks and Transfer Learning.

Journal of chemical theory and computation
Machine learning has emerged as a promising approach for predicting molecular properties of proteins, as it addresses limitations of experimental and traditional computational methods. Here, we introduce GSnet, a graph neural network (GNN) trained to...