AIMC Topic: Amino Acid Sequence

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Models and data of AMPlify: a deep learning tool for antimicrobial peptide prediction.

BMC research notes Feb 2, 2023
OBJECTIVES: Antibiotic resistance is a rising global threat to human health and is prompting researchers to seek effective alternatives to conventional antibiotics, which include antimicrobial peptides (AMPs). Recently, we have reported AMPlify, an a...
Anti-Bacterial Agents Animals Antimicrobial Peptides Rana catesbeiana Deep Learning Amino Acid Sequence
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Improving DNA-Binding Protein Prediction Using Three-Part Sequence-Order Feature Extraction and a Deep Neural Network Algorithm.

Journal of chemical information and modeling Jan 31, 2023
Identification of the DNA-binding protein (DBP) helps dig out information embedded in the DNA-protein interaction, which is significant to understanding the mechanisms of DNA replication, transcription, and repair. Although existing computational met...
Neural Networks, Computer DNA-Binding Proteins Algorithms Amino Acid Sequence
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Transformer-based deep learning for predicting protein properties in the life sciences.

eLife Jan 18, 2023
Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap b...
Amino Acid Sequence Biological Science Disciplines Deep Learning Amino Acids Language
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ISPRED-SEQ: Deep Neural Networks and Embeddings for Predicting Interaction Sites in Protein Sequences.

Journal of molecular biology Jan 13, 2023
The knowledge of protein-protein interaction sites (PPIs) is crucial for protein functional annotation. Here we address the problem focusing on the prediction of putative PPIs considering as input protein sequences. The issue is important given the h...
Deep Learning Proteins Molecular Sequence Annotation Amino Acid Sequence Protein Interaction Mapping
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MM-StackEns: A new deep multimodal stacked generalization approach for protein-protein interaction prediction.

Computers in biology and medicine Jan 3, 2023
Accurate in-silico identification of protein-protein interactions (PPIs) is a long-standing problem in biology, with important implications in protein function prediction and drug design. Current computational approaches predominantly use a single da...
Proteins Amino Acid Sequence Learning Saccharomyces cerevisiae Humans Neural Networks, Computer
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Using machine learning to predict the effects and consequences of mutations in proteins.

Current opinion in structural biology Jan 3, 2023
Machine and deep learning approaches can leverage the increasingly available massive datasets of protein sequences, structures, and mutational effects to predict variants with improved fitness. Many different approaches are being developed, but syste...
Machine Learning Algorithms Neural Networks, Computer Amino Acid Sequence Mutation
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Artificial intelligence-based recognition for variant pathogenicity of BRCA1 using AlphaFold2-predicted structures.

Theranostics Jan 1, 2023
With the surge of the high-throughput sequencing technologies, many genetic variants have been identified in the past decade. The vast majority of these variants are defined as variants of uncertain significance (VUS), as their significance to the fu...
BRCA1 Protein Artificial Intelligence Genetic Predisposition to Disease Virulence Amino Acid Sequence Humans
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AbAgIntPre: A deep learning method for predicting antibody-antigen interactions based on sequence information.

Frontiers in immunology Dec 22, 2022
INTRODUCTION: Antibody-mediated immunity is an essential part of the immune system in vertebrates. The ability to specifically bind to antigens allows antibodies to be widely used in the therapy of cancers and other critical diseases. A key step in a...
Antibodies Antigens Amino Acid Sequence Animals Neural Networks, Computer Deep Learning
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Improved compound-protein interaction site and binding affinity prediction using self-supervised protein embeddings.

BMC bioinformatics Dec 16, 2022
BACKGROUND: Compound-protein interaction site and binding affinity predictions are crucial for drug discovery and drug design. In recent years, many deep learning-based methods have been proposed for predications related to compound-protein interacti...
Proteins Amino Acid Sequence Drug Discovery Binding Sites Neural Networks, Computer
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Feedback-AVPGAN: Feedback-guided generative adversarial network for generating antiviral peptides.

Journal of bioinformatics and computational biology Dec 14, 2022
In this study, we propose , a system that aims to computationally generate novel antiviral peptides (AVPs). This system relies on the key premise of the Generative Adversarial Network (GAN) model and the Feedback method. GAN, a generative modeling ap...
Computational Chemistry Network Pharmacology Feedback Amino Acid Sequence Deep Learning Antimicrobial Peptides Algorithms Antiviral Agents
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