AIMC Topic: Proteins

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Robust principal component analysis-based prediction of protein-protein interaction hot spots.

Proteins Feb 2, 2021
Proteins often exert their function by binding to other cellular partners. The hot spots are key residues for protein-protein binding. Their identification may shed light on the impact of disease associated mutations on protein complexes and help des...
Humans Machine Learning Principal Component Analysis Models, Molecular Protein Interaction Mapping Protein Interaction Domains and Motifs Binding Sites Computational Biology Proteins Databases, Protein ROC Curve Protein Binding
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Protein structure search to support the development of protein structure prediction methods.

Proteins Feb 2, 2021
Protein structure prediction is a long-standing unsolved problem in molecular biology that has seen renewed interest with the recent success of deep learning with AlphaFold at CASP13. While developing and evaluating protein structure prediction metho...
Deep Learning Proteins Benchmarking Humans Research Design Sequence Alignment Structural Homology, Protein Amino Acid Motifs Protein Conformation, alpha-Helical Protein Conformation, beta-Strand
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diSBPred: A machine learning based approach for disulfide bond prediction.

Computational biology and chemistry Jan 27, 2021
The protein disulfide bond is a covalent bond that forms during post-translational modification by the oxidation of a pair of cysteines. In protein, the disulfide bond is the most frequent covalent link between amino acids after the peptide bond. It ...
Algorithms Support Vector Machine Disulfides Proteins Protein Conformation Amino Acid Sequence
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Accelerating Drug Design against Novel Proteins Using Deep Learning.

Journal of chemical information and modeling Jan 25, 2021
In the world plagued by the emergence of new diseases, it is essential that we accelerate the drug design process to develop new therapeutics against them. In recent years, deep learning-based methods have shown some success in ligand-based drug desi...
Deep Learning Catalytic Domain Proteins Humans Ligands Drug Design
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Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation.

Trends in pharmacological sciences Jan 23, 2021
Successful biologics must satisfy multiple properties including activity and particular physicochemical features that are globally defined as developability. These multiple properties must be simultaneously optimized in a very broad design space of p...
Biological Products Humans Proteins Protein Engineering Artificial Intelligence Machine Learning
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TopSuite Web Server: A Meta-Suite for Deep-Learning-Based Protein Structure and Quality Prediction.

Journal of chemical information and modeling Jan 19, 2021
Proteins carry out the most fundamental processes of life such as cellular metabolism, regulation, and communication. Understanding these processes at a molecular level requires knowledge of their three-dimensional structures. Experimental techniques...
Neural Networks, Computer Software Deep Learning Protein Conformation Proteins Crystallography, X-Ray
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Prediction of Protein-ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm.

International journal of molecular sciences Jan 19, 2021
Accurately identifying protein-ATP binding residues is important for protein function annotation and drug design. Previous studies have used classic machine-learning algorithms like support vector machine (SVM) and random forest to predict protein-AT...
Amino Acid Sequence Adenosine Triphosphate Protein Binding Neural Networks, Computer Machine Learning Support Vector Machine Computational Biology Carrier Proteins Algorithms Humans Proteins
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Classification and prediction of protein-protein interaction interface using machine learning algorithm.

Scientific reports Jan 19, 2021
Structural insight of the protein-protein interaction (PPI) interface can provide knowledge about the kinetics, thermodynamics and molecular functions of the complex while elucidating its role in diseases and further enabling it as a potential therap...
Thermodynamics Protein Interaction Mapping Support Vector Machine Proteins Models, Molecular Binding Sites Databases, Protein Molecular Docking Simulation Protein Interaction Maps Protein Binding
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Energy-dependent protein folding: modeling how a protein folding machine may work.

F1000Research Jan 5, 2021
Proteins fold robustly and reproducibly , but many cannot fold in isolation from cellular components. Despite the remarkable progress that has been achieved by the artificial intelligence approaches in predicting the protein native conformations, t...
Protein Folding Proteins Protein Conformation Artificial Intelligence Peptides
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Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes.

Proteins Dec 31, 2020
Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure prediction methods generally leverage predefined structural features to di...
Rotation Ligands Protein Conformation Proteins Neural Networks, Computer Models, Molecular Machine Learning
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