AI Medical Compendium Topic:
Proteins

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Predicting mutational function using machine learning.

Mutation research. Reviews in mutation research
Genetic variations are one of the major causes of phenotypic variations between human individuals. Although beneficial as being the substrate of evolution, germline mutations may cause diseases, including Mendelian diseases and complex diseases such ...

Predicting gene and protein expression levels from DNA and protein sequences with Perceiver.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The functions of an organism and its biological processes result from the expression of genes and proteins. Therefore quantifying and predicting mRNA and protein levels is a crucial aspect of scientific research. Concerning ...

Prediction of enzymatic function with high efficiency and a reduced number of features using genetic algorithm.

Computers in biology and medicine
The post-genomic era has raised a growing demand for efficient procedures to identify protein functions, which can be accomplished by applying machine learning to the characteristics set extracted from the protein. This approach is feature-based and ...

Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies.

Current opinion in structural biology
Computational protein design facilitates the discovery of novel proteins with prescribed structure and functionality. Exciting designs were recently reported using novel data-driven methodologies that can be roughly divided into two categories: evolu...

Protein structure prediction with in-cell photo-crosslinking mass spectrometry and deep learning.

Nature biotechnology
While AlphaFold2 can predict accurate protein structures from the primary sequence, challenges remain for proteins that undergo conformational changes or for which few homologous sequences are known. Here we introduce AlphaLink, a modified version of...

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...

HeadTailTransfer: An efficient sampling method to improve the performance of graph neural network method in predicting sparse ncRNA-protein interactions.

Computers in biology and medicine
Noncoding RNA (ncRNA) is a functional RNA derived from DNA transcription, and most transcribed genes are transcribed into ncRNA. ncRNA is not directly involved in the translation of proteins, but it can participate in gene expression in cells and aff...

Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility.

Journal of chemical information and modeling
Protein-ligand docking is an essential tool in structure-based drug design with applications ranging from virtual high-throughput screening to pose prediction for lead optimization. Most docking programs for pose prediction are optimized for redockin...

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...

DeepDetect: Deep Learning of Peptide Detectability Enhanced by Peptide Digestibility and Its Application to DIA Library Reduction.

Analytical chemistry
In tandem mass spectrometry-based proteomics, proteins are digested into peptides by specific protease(s), but generally only a fraction of peptides can be detected. To characterize detectable proteotypic peptides, we have developed a series of metho...