AIMC Topic: Proteins

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Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations.

Molecules (Basel, Switzerland) Feb 13, 2024
The rank ordering of ligands remains one of the most attractive challenges in drug discovery. While physics-based in silico binding affinity methods dominate the field, they still have problems, which largely revolve around forcefield accuracy and sa...
Ligands Proteins Binding Sites Cost-Benefit Analysis Retrospective Studies Protein Binding Machine Learning Thermodynamics Physics
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Predicting DNA structure using a deep learning method.

Nature communications Feb 9, 2024
Understanding the mechanisms of protein-DNA binding is critical in comprehending gene regulation. Three-dimensional DNA structure, also described as DNA shape, plays a key role in these mechanisms. In this study, we present a deep learning-based meth...
Protein Binding DNA Machine Learning Deep Learning Proteins
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Revolutionizing protein-protein interaction prediction with deep learning.

Current opinion in structural biology Feb 7, 2024
Protein-protein interactions (PPIs) are pivotal for driving diverse biological processes, and any disturbance in these interactions can lead to disease. Thus, the study of PPIs has been a central focus in biology. Recent developments in deep learning...
Proteins Computational Biology Protein Interaction Mapping Deep Learning Genomics Algorithms
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De novo design of cavity-containing proteins with a backbone-centered neural network energy function.

Structure (London, England : 1993) Feb 6, 2024
The design of small-molecule-binding proteins requires protein backbones that contain cavities. Previous design efforts were based on naturally occurring cavity-containing backbone architectures. Here, we designed diverse cavity-containing backbones ...
Models, Molecular Protein Conformation Proteins Neural Networks, Computer
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An atlas of protein homo-oligomerization across domains of life.

Cell Feb 6, 2024
Protein structures are essential to understanding cellular processes in molecular detail. While advances in artificial intelligence revealed the tertiary structure of proteins at scale, their quaternary structure remains mostly unknown. We devise a s...
Bacteria Proteome Artificial Intelligence Humans Archaea Eukaryota Proteins
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Learning the shape of protein microenvironments with a holographic convolutional neural network.

Proceedings of the National Academy of Sciences of the United States of America Feb 1, 2024
Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from its sequence or struct...
Neural Networks, Computer Proteins Amino Acids Algorithms Machine Learning
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AnnoPRO: a strategy for protein function annotation based on multi-scale protein representation and a hybrid deep learning of dual-path encoding.

Genome biology Feb 1, 2024
Protein function annotation has been one of the longstanding issues in biological sciences, and various computational methods have been developed. However, the existing methods suffer from a serious long-tail problem, with a large number of GO famili...
Molecular Sequence Annotation Proteins Software Humans Computational Biology Deep Learning
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Deep learning for protein structure prediction and design-progress and applications.

Molecular systems biology Jan 30, 2024
Proteins are the key molecular machines that orchestrate all biological processes of the cell. Most proteins fold into three-dimensional shapes that are critical for their function. Studying the 3D shape of proteins can inform us of the mechanisms th...
Proteins Deep Learning Protein Conformation
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Transfer learning to leverage larger datasets for improved prediction of protein stability changes.

Proceedings of the National Academy of Sciences of the United States of America Jan 29, 2024
Amino acid mutations that lower a protein's thermodynamic stability are implicated in numerous diseases, and engineered proteins with enhanced stability can be important in research and medicine. Computational methods for predicting how mutations per...
Protein Stability Neural Networks, Computer Amino Acid Sequence Proteins Machine Learning
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Numerical stability of DeepGOPlus inference.

PloS one Jan 29, 2024
Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (DNN) architectures available and achieve state-of-the-art performance for many problems. Originally applied to computer vision tasks, CNNs work well wi...
Neural Networks, Computer Amino Acid Sequence Monte Carlo Method Proteins Reproducibility of Results
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