AIMC Topic: Proteins

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How Deep Learning Tools Can Help Protein Engineers Find Good Sequences.

The journal of physical chemistry. B Jun 9, 2021
The deep learning revolution introduced a new and efficacious way to address computational challenges in a wide range of fields, relying on large data sets and powerful computational resources. In protein engineering, we consider the challenge of com...
Proteins Deep Learning Amino Acid Sequence Protein Engineering
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Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning.

Scientific reports Jun 4, 2021
We developed a method to improve protein thermostability, "loop-walking method". Three consecutive positions in 12 loops of Burkholderia cepacia lipase were subjected to random mutagenesis to make 12 libraries. Screening allowed us to identify L7 as ...
Protein Engineering Burkholderia cepacia Hydrolases Mutagenesis, Site-Directed Lipase Polymerase Chain Reaction Hot Temperature Mutagenesis Molecular Conformation Enzyme Stability Plasmids Kinetics Molecular Dynamics Simulation Machine Learning Computational Biology Proteins Mutation Escherichia coli
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Computational representations of protein-ligand interfaces for structure-based virtual screening.

Expert opinion on drug discovery Jun 3, 2021
: Structure-based virtual screening (SBVS) is an essential strategy for hit identification. SBVS primarily uses molecular docking, which exploits the protein-ligand binding mode and associated affinity score for compound ranking. Previous studies hav...
Humans Protein Binding Proteins Ligands Machine Learning Molecular Docking Simulation
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Deep Scoring Neural Network Replacing the Scoring Function Components to Improve the Performance of Structure-Based Molecular Docking.

ACS chemical neuroscience Jun 3, 2021
Accurate prediction of protein-ligand interactions can greatly promote drug development. Recently, a number of deep-learning-based methods have been proposed to predict protein-ligand binding affinities. However, these methods independently extract t...
Molecular Docking Simulation Neural Networks, Computer Protein Binding Ligands Proteins
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AlphaFold - A Personal Perspective on the Impact of Machine Learning.

Journal of molecular biology Jun 2, 2021
I outline how over my career as a protein scientist Machine Learning has impacted my area of science and one of my pastimes, chess, where there are some interesting parallels. In 1968, modelling of three-dimensional structures was initiated based on ...
Humans Animals Proteins Models, Molecular Machine Learning Software Chymotrypsin Protein Folding Protein Conformation Trypsin
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Statistical Learning from Single-Molecule Experiments: Support Vector Machines and Expectation-Maximization Approaches to Understanding Protein Unfolding Data.

The journal of physical chemistry. B Jun 2, 2021
Single-molecule force spectroscopy has become a powerful tool for the exploration of dynamic processes that involve proteins; yet, meaningful interpretation of the experimental data remains challenging. Owing to low signal-to-noise ratio, experimenta...
Motivation Protein Unfolding Proteins Models, Molecular Support Vector Machine
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Current directions in combining simulation-based macromolecular modeling approaches with deep learning.

Expert opinion on drug discovery May 31, 2021
: Structure-guided drug discovery relies on accurate computational methods for modeling macromolecules. Simulations provide means of predicting macromolecular folds, of discovering function from structure, and of designing macromolecules to serve as ...
Humans Neural Networks, Computer Drug Discovery Deep Learning Algorithms Proteins
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Protein Structure Prediction: Conventional and Deep Learning Perspectives.

The protein journal May 28, 2021
Protein structure prediction is a way to bridge the sequence-structure gap, one of the main challenges in computational biology and chemistry. Predicting any protein's accurate structure is of paramount importance for the scientific community, as the...
Proteins Software Deep Learning Databases, Protein Computational Biology Protein Conformation
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LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites.

BioMed research international May 28, 2021
Lysine succinylation is a typical protein post-translational modification and plays a crucial role of regulation in the cellular process. Identifying succinylation sites is fundamental to explore its functions. Although many computational methods wer...
Area Under Curve Sensitivity and Specificity Algorithms Internet Protein Processing, Post-Translational Reproducibility of Results Computational Biology Animals Succinic Acid Deep Learning Humans Proteins Escherichia coli Neural Networks, Computer
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Recent progress on the prospective application of machine learning to structure-based virtual screening.

Current opinion in chemical biology May 27, 2021
As more bioactivity and protein structure data become available, scoring functions (SFs) using machine learning (ML) to leverage these data sets continue to gain further accuracy and broader applicability. Advances in our understanding of the optimal...
Proteins Ligands Machine Learning Molecular Docking Simulation
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