AIMC Topic: Amino Acid Sequence

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Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides.

BMC bioinformatics May 11, 2021
BACKGROUND: Current methods in machine learning provide approaches for solving challenging, multiple constraint design problems. While deep learning and related neural networking methods have state-of-the-art performance, their vulnerability in decis...
Drug Resistance, Microbial Machine Learning Amino Acid Sequence Antimicrobial Cationic Peptides Pore Forming Cytotoxic Proteins
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CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction.

Biomolecules Apr 27, 2021
The binding affinity of small molecules to receptor proteins is essential to drug discovery and drug repositioning. Chemical methods are often time-consuming and costly, and models for calculating the binding affinity are imperative. In this study, w...
Neural Networks, Computer Forecasting Amino Acid Sequence Ligands Protein Binding Drug Discovery Proteins Deep Learning Drug Repositioning
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Convolutional neural networks with image representation of amino acid sequences for protein function prediction.

Computational biology and chemistry Apr 24, 2021
Proteins are one of the most important molecules that govern the cellular processes in most of the living organisms. Various functions of the proteins are of paramount importance to understand the basics of life. Several supervised learning approache...
Proteins Databases, Protein Amino Acid Sequence Neural Networks, Computer Deep Learning
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An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints.

BioMed research international Apr 24, 2021
Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be e...
Databases, Protein Computational Biology Machine Learning Amino Acid Sequence Position-Specific Scoring Matrices Drug Discovery Drug Interactions Support Vector Machine
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Protein design and variant prediction using autoregressive generative models.

Nature communications Apr 23, 2021
The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for importa...
Antibodies Amino Acid Sequence Protein Engineering Antigens Computational Biology Proteins Mutation Phenotype Genotype Neural Networks, Computer Algorithms Humans
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AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders.

BMC bioinformatics Apr 20, 2021
BACKGROUND: Drug-target interaction (DTI) plays a vital role in drug discovery. Identifying drug-target interactions related to wet-lab experiments are costly, laborious, and time-consuming. Therefore, computational methods to predict drug-target int...
Unsupervised Machine Learning Amino Acid Sequence Drug Discovery Drug Development Algorithms
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Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning.

Nature biomedical engineering Apr 15, 2021
The optimization of therapeutic antibodies is time-intensive and resource-demanding, largely because of the low-throughput screening of full-length antibodies (approximately 1 × 10 variants) expressed in mammalian cells, which typically results in fe...
Humans Protein Binding CRISPR-Cas Systems Amino Acid Sequence Animals Deep Learning Mutagenesis, Site-Directed Protein Engineering Sequence Analysis, Protein Recombinational DNA Repair Antibody Affinity Antigens Trastuzumab Antibody Specificity Receptor, ErbB-2 Hybridomas
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Performance improvement for a 2D convolutional neural network by using SSC encoding on protein-protein interaction tasks.

BMC bioinformatics Apr 12, 2021
BACKGROUND: The interactions of proteins are determined by their sequences and affect the regulation of the cell cycle, signal transduction and metabolism, which is of extraordinary significance to modern proteomics research. Despite advances in expe...
Molecular Docking Simulation Computational Biology Amino Acid Sequence Neural Networks, Computer Software
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iPhosH-PseAAC: Identify Phosphohistidine Sites in Proteins by Blending Statistical Moments and Position Relative Features According to the Chou's 5-Step Rule and General Pseudo Amino Acid Composition.

IEEE/ACM transactions on computational biology and bioinformatics Apr 6, 2021
Protein phosphorylation is one of the key mechanism in prokaryotes and eukaryotes and is responsible for various biological functions such as protein degradation, intracellular localization, the multitude of cellular processes, molecular association,...
Models, Statistical Histidine Neural Networks, Computer Proteins Amino Acid Sequence Computational Biology Phosphorylation Sequence Analysis, Protein
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Machine learning predicts nucleosome binding modes of transcription factors.

BMC bioinformatics Mar 30, 2021
BACKGROUND: Most transcription factors (TFs) compete with nucleosomes to gain access to their cognate binding sites. Recent studies have identified several TF-nucleosome interaction modes including end binding (EB), oriented binding, periodic binding...
Amino Acid Sequence Machine Learning Binding Sites Nucleosomes Protein Binding Transcription Factors
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