AIMC Topic: Amino Acid Sequence

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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 Amino Acid Sequence Proteins Amino Acids Algorithms
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MMDB: Multimodal dual-branch model for multi-functional bioactive peptide prediction.

Analytical biochemistry Mar 7, 2024
Bioactive peptides can hinder oxidative processes and microbial spoilage in foodstuffs and play important roles in treating diverse diseases and disorders. While most of the methods focus on single-functional bioactive peptides and have obtained prom...
Amino Acid Sequence Deep Learning Peptides
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AbDPP: Target-oriented antibody design with pretraining and prior biological structure knowledge.

Proteins Mar 5, 2024
Antibodies represent a crucial class of complex protein therapeutics and are essential in the treatment of a wide range of human diseases. Traditional antibody discovery methods, such as hybridoma and phage display technologies, suffer from limitatio...
Amino Acid Sequence Antigens Antibodies Humans Deep Learning Algorithms
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Convolutions are competitive with transformers for protein sequence pretraining.

Cell systems Feb 29, 2024
Pretrained protein sequence language models have been shown to improve the performance of many prediction tasks and are now routinely integrated into bioinformatics tools. However, these models largely rely on the transformer architecture, which scal...
Neural Networks, Computer Peer Review Amino Acid Sequence Computational Biology
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Machine Learning Accelerates De Novo Design of Antimicrobial Peptides.

Interdisciplinary sciences, computational life sciences Feb 28, 2024
Efficient and precise design of antimicrobial peptides (AMPs) is of great importance in the field of AMP development. Computing provides opportunities for peptide de novo design. In the present investigation, a new machine learning-based AMP predicti...
Drug Design Amino Acid Sequence Microbial Sensitivity Tests Machine Learning Antimicrobial Peptides
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Automated model building and protein identification in cryo-EM maps.

Nature Feb 26, 2024
Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention in three-dimensional computer graphics programs. Here we present ModelAngelo, a machine-learning approa...
Computer Graphics Markov Chains Cryoelectron Microscopy Protein Conformation Models, Molecular Proteins Amino Acid Sequence Machine Learning Neural Networks, Computer
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SME-MFP: A novel spatiotemporal neural network with multiangle initialization embedding toward multifunctional peptides prediction.

Computational biology and chemistry Feb 20, 2024
As a promising alternative to conventional antibiotic drugs in the biomedical field, functional peptide has been widely used in disease treatment owing to its low toxicity, high absorption rate, and biological activity. Recently, several machine lear...
Amino Acid Sequence Peptides Neural Networks, Computer Machine Learning
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Machine learning for functional protein design.

Nature biotechnology Feb 15, 2024
Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and structure data have radically transformed computational protein design. New methods promise to escape the constraints of natural and laboratory evolution, accelera...
Machine Learning Proteins Biotechnology Amino Acid Sequence Antibodies
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CCL-DTI: contributing the contrastive loss in drug-target interaction prediction.

BMC bioinformatics Jan 30, 2024
BACKGROUND: The Drug-Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention. These methods have two modul...
Knowledge Drug Interactions Entropy Amino Acid Sequence Machine Learning
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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...
Neural Networks, Computer Protein Stability Proteins Machine Learning Amino Acid Sequence
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