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Amino Acid Sequence

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Super High-Throughput Screening of Enzyme Variants by Spectral Graph Convolutional Neural Networks.

Journal of chemical theory and computation
Finding new enzyme variants with the desired substrate scope requires screening through a large number of potential variants. In a typical enzyme engineering workflow, it is possible to scan a few thousands of variants, and gather several candidates...

Evolutionary-scale prediction of atomic-level protein structure with a language model.

Science (New York, N.Y.)
Recent advances in machine learning have leveraged evolutionary information in multiple sequence alignments to predict protein structure. We demonstrate direct inference of full atomic-level protein structure from primary sequence using a large langu...

AlphaFold2 and its applications in the fields of biology and medicine.

Signal transduction and targeted therapy
AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most chal...

In Silico Screening and Optimization of Cell-Penetrating Peptides Using Deep Learning Methods.

Biomolecules
Cell-penetrating peptides (CPPs) have great potential to deliver bioactive agents into cells. Although there have been many recent advances in CPP-related research, it is still important to develop more efficient CPPs. The development of CPPs by in s...

RiRPSSP: A unified deep learning method for prediction of regular and irregular protein secondary structures.

Journal of bioinformatics and computational biology
Protein secondary structure prediction (PSSP) is an important and challenging task in protein bioinformatics. Protein secondary structures (SSs) are categorized in regular and irregular structure classes. Regular SSs, representing nearly 50% of amino...

Protein Design Using Physics Informed Neural Networks.

Biomolecules
The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function. Recent advancements in machine learning techniques have been su...

ProteInfer, deep neural networks for protein functional inference.

eLife
Predicting the function of a protein from its amino acid sequence is a long-standing challenge in bioinformatics. Traditional approaches use sequence alignment to compare a query sequence either to thousands of models of protein families or to large ...

Hierarchical graph learning for protein-protein interaction.

Nature communications
Protein-Protein Interactions (PPIs) are fundamental means of functions and signalings in biological systems. The massive growth in demand and cost associated with experimental PPI studies calls for computational tools for automated prediction and und...

Predicting mechanical properties of silk from its amino acid sequences via machine learning.

Journal of the mechanical behavior of biomedical materials
The silk fiber is increasingly being sought for its superior mechanical properties, biocompatibility, and eco-friendliness, making it promising as a base material for various applications. One of the characteristics of protein fibers, such as silk, i...

The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors.

Current opinion in structural biology
The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between...