AIMC Topic: Protein Structure, Secondary

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LMProtein: a protein language model based framework for protein structural property prediction.

Physical chemistry chemical physics : PCCP
Recent advances in machine learning and self-supervised deep language modeling have made it possible to accurately predict protein structural properties. Most existing models and pretraining methods leverage evolutionary information in multiple seque...

Deep-Learning Prediction of Protein Secondary Structure from Circular Dichroism Spectrum Using Three-Layer Image Recognition.

Analytical chemistry
In this study, we developed an image-recognition-based deep-learning method for accurately predicting the DSSP (define secondary structures of proteins) parameters from a circular dichroism (CD) spectrum. Focusing on the inherently high image-recogni...

Physics-informed deep learning for plasmonic sensing of nanoscale protein dynamics in solution.

Science advances
Quantifying nanoscale protein secondary structure in aqueous solutions is crucial for understanding protein interactions and dynamics. Deep learning models are adept at predicting protein secondary structures, but their ability to model them in aqueo...

Combining knowledge distillation and neural networks to predict protein secondary structure.

Scientific reports
The secondary structure of a protein serves as the foundation for constructing its three-dimensional (3D) structure, which in turn is critical for determining its function and role in biological processes. Therefore, accurately predicting secondary s...

Deciphering Protein Secondary Structures and Nucleic Acids in Cryo-EM Maps Using Deep Learning.

Journal of chemical information and modeling
With the resolution revolution of cryo-electron microscopy (cryo-EM) and the rapid development of image processing technology, cryo-EM has become an indispensable experimental method for determining the three-dimensional structures of biological macr...

Lessons from Deep Learning Structural Prediction of Multistate Multidomain Proteins-The Case Study of Coiled-Coil NOD-like Receptors.

International journal of molecular sciences
We test here the prediction capabilities of the new generation of deep learning predictors in the more challenging situation of multistate multidomain proteins by using as a case study a coiled-coil family of Nucleotide-binding Oligomerization Domain...

Porter 6: Protein Secondary Structure Prediction by Leveraging Pre-Trained Language Models (PLMs).

International journal of molecular sciences
Accurately predicting protein secondary structure (PSSP) is crucial for understanding protein function, which is foundational to advancements in drug development, disease treatment, and biotechnology. Researchers gain critical insights into protein f...

AlphaMut: A Deep Reinforcement Learning Model to Suggest Helix-Disrupting Mutations.

Journal of chemical theory and computation
Helices are important secondary structural motifs within proteins and are pivotal in numerous physiological processes. While amino acids (AA) such as alanine and leucine are known to promote helix formation, proline and glycine disfavor it. Helical s...

Impact of Multi-Factor Features on Protein Secondary Structure Prediction.

Biomolecules
Protein secondary structure prediction (PSSP) plays a crucial role in resolving protein functions and properties. Significant progress has been made in this field in recent years, and the use of a variety of protein-related features, including amino ...

Protein Classes Predicted by Molecular Surface Chemical Features: Machine Learning-Assisted Classification of Cytosol and Secreted Proteins.

The journal of physical chemistry. B
Chemical structures of protein surfaces govern intermolecular interaction, and protein functions include specific molecular recognition, transport, self-assembly, etc. Therefore, the relationship between the chemical structure and protein functions p...