AIMC Topic: Proteins

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Hybrid Models for the simulation and prediction of chromatographic processes for protein capture.

Journal of chromatography. A May 13, 2021
The biopharmaceutical industries are continuously faced with the pressure to reduce the development costs and accelerate development time scales. The traditional approach of heuristic-based or platform process-based optimization is soon getting obsol...
Computer Simulation Adsorption Proteins Kinetics Neural Networks, Computer Chromatography Chemistry Techniques, Analytical
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DeepBSP-a Machine Learning Method for Accurate Prediction of Protein-Ligand Docking Structures.

Journal of chemical information and modeling May 12, 2021
In recent years, machine-learning-based scoring functions have significantly improved the scoring power. However, many of these methods do not perform well in distinguishing the native structure from docked decoy poses due to the lack of decoy struct...
Molecular Docking Simulation Protein Binding Proteins Ligands Machine Learning
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Drug Target Identification with Machine Learning: How to Choose Negative Examples.

International journal of molecular sciences May 12, 2021
Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target I...
Protein Interaction Mapping Proteins Pharmaceutical Preparations Software Drug Discovery Computational Biology Support Vector Machine Humans Machine Learning
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MultiPredGO: Deep Multi-Modal Protein Function Prediction by Amalgamating Protein Structure, Sequence, and Interaction Information.

IEEE journal of biomedical and health informatics May 11, 2021
Protein is an essential macro-nutrient for perceiving a wide range of biochemical activities and biological regulations in living cells. In this work, we have presented a novel multi-modal approach, named MultiPredGO, for predicting protein functions...
Neural Networks, Computer Proteins
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Accurate prediction of protein-ATP binding residues using position-specific frequency matrix.

Analytical biochemistry May 7, 2021
Knowledge of protein-ATP interaction can help for protein functional annotation and drug discovery. Accurately identifying protein-ATP binding residues is an important but challenging task to gain the knowledge of protein-ATP interactions, especially...
Algorithms Protein Binding Humans Neural Networks, Computer Adenosine Triphosphate Computational Biology Proteins
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Bingham deep neural and oppositional fish swarm optimized protein structure prediction.

Journal of biomolecular structure & dynamics May 6, 2021
It is familiar that essential proteins take part in managing cellular activities in living organisms. Moreover, protein structure prediction from its amino acid sequence is advantageous to the comprehending of cellular functions. Formerly, several es...
Neural Networks, Computer Fish Proteins Proteins Databases, Protein Algorithms Animals
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CopulaNet: Learning residue co-evolution directly from multiple sequence alignment for protein structure prediction.

Nature communications May 5, 2021
Residue co-evolution has become the primary principle for estimating inter-residue distances of a protein, which are crucially important for predicting protein structure. Most existing approaches adopt an indirect strategy, i.e., inferring residue co...
Models, Molecular Mutation Computational Biology Proteins Protein Structure, Tertiary Humans Caspases Neural Networks, Computer Sequence Alignment Machine Learning
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Computational generation of proteins with predetermined three-dimensional shapes using ProteinSolver.

STAR protocols Apr 28, 2021
Computational generation of new proteins with a predetermined three-dimensional shape and computational optimization of existing proteins while maintaining their shape are challenging problems in structural biology. Here, we present a protocol that u...
Databases, Protein Computational Biology Neural Networks, Computer Proteins Software
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ML-DTI: Mutual Learning Mechanism for Interpretable Drug-Target Interaction Prediction.

The journal of physical chemistry letters Apr 27, 2021
Deep learning (DL) provides opportunities for the identification of drug-target interactions (DTIs). The challenges of applying DL lie primarily with the lack of interpretability. Also, most of the existing DL-based methods formulate the drug and tar...
Pharmaceutical Preparations Deep Learning Organic Chemicals Protein Binding 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...
Proteins Drug Repositioning Neural Networks, Computer Deep Learning Forecasting Amino Acid Sequence Protein Binding Drug Discovery Ligands
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