AIMC Topic: Amino Acid Sequence

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Antibody Clustering Using a Machine Learning Pipeline that Fuses Genetic, Structural, and Physicochemical Properties.

Advances in experimental medicine and biology Jan 1, 2020
Antibody V domain clustering is of paramount importance to a repertoire of immunology-related areas. Although several approaches have been proposed for antibody clustering, still no consensus has been reached. Numerous attempts use information from g...
Antigen-Antibody Complex Complementarity Determining Regions Machine Learning Amino Acid Sequence Molecular Conformation Cluster Analysis
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Antioxidant Proteins' Identification Based on Support Vector Machine.

Combinatorial chemistry & high throughput screening Jan 1, 2020
BACKGROUND: Evidence have increasingly indicated that for human disease, cell metabolism are deeply associated with proteins. Structural mutations and dysregulations of these proteins contribute to the development of the complex disease. Free radical...
Humans Antioxidants Databases, Protein Principal Component Analysis Amino Acid Sequence Support Vector Machine Proteins Sequence Analysis, Protein
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An Overview on Predicting Protein Subchloroplast Localization by using Machine Learning Methods.

Current protein & peptide science Jan 1, 2020
The chloroplast is a type of subcellular organelle of green plants and eukaryotic algae, which plays an important role in the photosynthesis process. Since the function of a protein correlates with its location, knowing its subchloroplast localizatio...
Models, Statistical Plants Chloroplasts Datasets as Topic Computational Biology Amino Acid Sequence Gene Expression Regulation, Plant Protein Transport Proteome Chloroplast Proteins Machine Learning
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Deep learning on chaos game representation for proteins.

Bioinformatics (Oxford, England) Jan 1, 2020
MOTIVATION: Classification of protein sequences is one big task in bioinformatics and has many applications. Different machine learning methods exist and are applied on these problems, such as support vector machines (SVM), random forests (RF) and ne...
Neural Networks, Computer Deep Learning Amino Acid Sequence Support Vector Machine Sequence Analysis, Protein Proteins Game Theory
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Machine learning can be used to distinguish protein families and generate new proteins belonging to those families.

The Journal of chemical physics Nov 7, 2019
Proteins are classified into families based on evolutionary relationships and common structure-function characteristics. Availability of large data sets of gene-derived protein sequences drives this classification. Sequence space is exponentially lar...
Machine Learning Amino Acid Sequence Protein Conformation Proteins
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Develop machine learning-based regression predictive models for engineering protein solubility.

Bioinformatics (Oxford, England) Nov 1, 2019
MOTIVATION: Protein activity is a significant characteristic for recombinant proteins which can be used as biocatalysts. High activity of proteins reduces the cost of biocatalysts. A model that can predict protein activity from amino acid sequence is...
Protein Engineering Amino Acid Sequence Solubility Machine Learning Proteins
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DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks.

Bioinformatics (Oxford, England) Sep 15, 2019
MOTIVATION: Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy and interpretability.
Amino Acid Sequence Deep Learning Neural Networks, Computer Proteins Software
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Multifaceted protein-protein interaction prediction based on Siamese residual RCNN.

Bioinformatics (Oxford, England) Jul 15, 2019
MOTIVATION: Sequence-based protein-protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Base...
Proteins Amino Acid Sequence Protein Binding Neural Networks, Computer Computational Biology Algorithms
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Improving prediction of protein secondary structure, backbone angles, solvent accessibility and contact numbers by using predicted contact maps and an ensemble of recurrent and residual convolutional neural networks.

Bioinformatics (Oxford, England) Jul 15, 2019
MOTIVATION: Sequence-based prediction of one dimensional structural properties of proteins has been a long-standing subproblem of protein structure prediction. Recently, prediction accuracy has been significantly improved due to the rapid expansion o...
Computational Biology Solvents Neural Networks, Computer Protein Structure, Secondary Proteins Amino Acid Sequence
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The PSIPRED Protein Analysis Workbench: 20 years on.

Nucleic acids research Jul 2, 2019
The PSIPRED Workbench is a web server offering a range of predictive methods to the bioscience community for 20 years. Here, we present the work we have completed to update the PSIPRED Protein Analysis Workbench and make it ready for the next 20 year...
Amino Acid Sequence Binding Sites Protein Conformation, beta-Strand History, 21st Century Protein Binding Sequence Alignment Protein Interaction Domains and Motifs Sequence Homology, Amino Acid Proteins Models, Molecular Protein Conformation, alpha-Helical Molecular Sequence Annotation Internet Gene Ontology Software
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