AIMC Topic: Proteins

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xCAPT5: protein-protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language model.

BMC bioinformatics Mar 10, 2024
BACKGROUND: Predicting protein-protein interactions (PPIs) from sequence data is a key challenge in computational biology. While various computational methods have been proposed, the utilization of sequence embeddings from protein language models, wh...
Neural Networks, Computer Algorithms Amino Acids Proteins Amino Acid Sequence
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Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network.

Interdisciplinary sciences, computational life sciences Mar 8, 2024
As one of the most important post-translational modifications (PTMs), protein phosphorylation plays a key role in a variety of biological processes. Many studies have shown that protein phosphorylation is associated with various human diseases. There...
Phosphorylation Alzheimer Disease Proteins Humans Computational Biology Neural Networks, Computer Algorithms Protein Processing, Post-Translational ROC Curve
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GraphsformerCPI: Graph Transformer for Compound-Protein Interaction Prediction.

Interdisciplinary sciences, computational life sciences Mar 8, 2024
Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable pred...
Animals Drug Design Deep Learning Protein Binding Humans Algorithms Proteins Caenorhabditis elegans
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DeePNAP: A Deep Learning Method to Predict Protein-Nucleic Acid Binding Affinity from Their Sequences.

Journal of chemical information and modeling Mar 8, 2024
Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity ...
Proteins Mutation Deep Learning Protein Binding Nucleic Acids
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Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence space.

Proceedings of the National Academy of Sciences of the United States of America Mar 7, 2024
Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions, from catalyzing reactions to recognizing pathogens. The ability to evolve proteins rapidly and inexpensively toward improved properties is a common objectiv...
Gene Library Machine Learning Proteins Mutation Protein Engineering
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Heterogeneous sampled subgraph neural networks with knowledge distillation to enhance double-blind compound-protein interaction prediction.

Structure (London, England : 1993) Mar 5, 2024
Identifying binding compounds against a target protein is crucial for large-scale virtual screening in drug development. Recently, network-based methods have been developed for compound-protein interaction (CPI) prediction. However, they are difficul...
Humans Neural Networks, Computer Protein Binding Proteins
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Identification of Protein-Protein Interaction Associated Functions Based on Gene Ontology.

The protein journal Mar 4, 2024
Protein-protein interactions (PPIs) involve the physical or functional contact between two or more proteins. Generally, proteins that can interact with each other always have special relationships. Some previous studies have reported that gene ontolo...
Humans Protein Interaction Maps Gene Ontology Protein Interaction Mapping Proteins Computational Biology Databases, Protein
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A Comprehensive Review on Machine Learning Techniques for Protein Family Prediction.

The protein journal Mar 1, 2024
Proteomics is a field dedicated to the analysis of proteins in cells, tissues, and organisms, aiming to gain insights into their structures, functions, and interactions. A crucial aspect within proteomics is protein family prediction, which involves ...
Proteins Databases, Protein Computational Biology Humans Proteomics Machine Learning
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Building Representation Learning Models for Antibody Comprehension.

Cold Spring Harbor perspectives in biology Mar 1, 2024
Antibodies are versatile proteins with both the capacity to bind a broad range of targets and a proven track record as some of the most successful therapeutics. However, the development of novel antibody therapeutics is a lengthy and costly process. ...
Proteins Comprehension Humans Machine Learning
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Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process.

Journal of molecular graphics & modelling Feb 28, 2024
Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, de novo design can help ide...
Proteins Artificial Intelligence Drug Design Small Molecule Libraries Drug Discovery
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