AIMC Topic: Proteins

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Interpreting forces as deep learning gradients improves quality of predicted protein structures.

Biophysical journal Dec 15, 2023
Protein structure predictions from deep learning models like AlphaFold2, despite their remarkable accuracy, are likely insufficient for direct use in downstream tasks like molecular docking. The functionality of such models could be improved with a c...
Protein Conformation Molecular Dynamics Simulation Deep Learning Proteins
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Regulated Behavior in Living Cells with Highly Aligned Configurations on Nanowrinkled Graphene Oxide Substrates: Deep Learning Based on Interplay of Cellular Contact Guidance.

ACS nano Dec 15, 2023
Micro-/nanotopographical cues have emerged as a practical and promising strategy for controlling cell fate and reprogramming, which play a key role as biophysical regulators in diverse cellular processes and behaviors. Extracellular biophysical facto...
Graphite Mechanotransduction, Cellular Deep Learning Proteins Cell Adhesion Cell Communication
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Bonds and bytes: The odyssey of structural biology.

Current opinion in structural biology Dec 14, 2023
Characterizing structural and dynamic properties of proteins and large macromolecular assemblies is crucial to understand the molecular mechanisms underlying biological functions. In the field of structural biology, no single method comprehensively r...
Artificial Intelligence Computational Biology Models, Molecular Proteins Macromolecular Substances
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DL-PPI: a method on prediction of sequenced protein-protein interaction based on deep learning.

BMC bioinformatics Dec 14, 2023
PURPOSE: Sequenced Protein-Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventi...
Protein Interaction Mapping Amino Acid Sequence Neural Networks, Computer Deep Learning Proteins
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A comprehensive framework for advanced protein classification and function prediction using synergistic approaches: Integrating bispectral analysis, machine learning, and deep learning.

PloS one Dec 14, 2023
Proteins are fundamental components of diverse cellular systems and play crucial roles in a variety of disease processes. Consequently, it is crucial to comprehend their structure, function, and intricate interconnections. Classifying proteins into f...
Deep Learning Proteins Machine Learning Neural Networks, Computer Algorithms
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DeepPPThermo: A Deep Learning Framework for Predicting Protein Thermostability Combining Protein-Level and Amino Acid-Level Features.

Journal of computational biology : a journal of computational molecular cell biology Dec 13, 2023
Using wet experimental methods to discover new thermophilic proteins or improve protein thermostability is time-consuming and expensive. Machine learning methods have shown powerful performance in the study of protein thermostability in recent years....
Proteins Amino Acids Algorithms Deep Learning Neural Networks, Computer
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DeepCompoundNet: enhancing compound-protein interaction prediction with multimodal convolutional neural networks.

Journal of biomolecular structure & dynamics Dec 12, 2023
Virtual screening has emerged as a valuable computational tool for predicting compound-protein interactions, offering a cost-effective and rapid approach to identifying potential candidate drug molecules. Current machine learning-based methods rely o...
Drug Discovery Deep Learning Neural Networks, Computer Computational Biology Proteins Convolutional Neural Networks Protein Binding
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DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction.

Nature methods Dec 8, 2023
Three-dimensional structure modeling from maps is an indispensable step for studying proteins and their complexes with cryogenic electron microscopy. Although the resolution of determined cryogenic electron microscopy maps has generally improved, the...
Microscopy, Electron Protein Conformation Cryoelectron Microscopy Proteins Models, Molecular Deep Learning
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O-GlcNAcPRED-DL: Prediction of Protein O-GlcNAcylation Sites Based on an Ensemble Model of Deep Learning.

Journal of proteome research Dec 6, 2023
O-linked β--acetylglucosamine (O-GlcNAc) is a post-translational modification (i.e., O-GlcNAcylation) on serine/threonine residues of proteins, regulating a plethora of physiological and pathological events. As a dynamic process, O-GlcNAc functions i...
Deep Learning Mice Animals Proteins N-Acetylglucosaminyltransferases Protein Processing, Post-Translational Acetylglucosamine Humans
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AttCON: With better MSAs and attention mechanism for accurate protein contact map prediction.

Computers in biology and medicine Dec 5, 2023
Protein contact map prediction is a critical and vital step in protein structure prediction, and its accuracy is highly contingent upon the feature representations of protein sequence information and the efficacy of deep learning models. In this pape...
Algorithms Computational Biology Proteins Sequence Alignment Neural Networks, Computer
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