AI Medical Compendium Topic:
Protein Conformation

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AIUPred: combining energy estimation with deep learning for the enhanced prediction of protein disorder.

Nucleic acids research
Intrinsically disordered proteins and protein regions (IDPs/IDRs) carry out important biological functions without relying on a single well-defined conformation. As these proteins are a challenge to study experimentally, computational methods play im...

Predicting Functional Surface Topographies Combining Topological Data Analysis and Deep Learning Across the Human Protein Universe.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Characterizing geometric and topological properties of protein structures encompassing surface pockets, interior cavities, and cross channels is important for understanding their functions. Our knowledge of protein structures has been greatly advance...

Geometric epitope and paratope prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying the binding sites of antibodies is essential for developing vaccines and synthetic antibodies. In this article, we investigate the optimal representation for predicting the binding sites in the two molecules and emphasize the ...

Knot or not? Identifying unknotted proteins in knotted families with sequence-based Machine Learning model.

Protein science : a publication of the Protein Society
Knotted proteins, although scarce, are crucial structural components of certain protein families, and their roles continue to be a topic of intense research. Capitalizing on the vast collection of protein structure predictions offered by AlphaFold (A...

De novo protein design-From new structures to programmable functions.

Cell
Methods from artificial intelligence (AI) trained on large datasets of sequences and structures can now "write" proteins with new shapes and molecular functions de novo, without starting from proteins found in nature. In this Perspective, I will disc...

Protein sequence design on given backbones with deep learning.

Protein engineering, design & selection : PEDS
Deep learning methods for protein sequence design focus on modeling and sampling the many- dimensional distribution of amino acid sequences conditioned on the backbone structure. To produce physically foldable sequences, inter-residue couplings need ...

Estimating protein-ligand interactions with geometric deep learning and mixture density models.

Journal of biosciences
Understanding the interactions between a ligand and its molecular target is crucial in guiding the optimization of molecules for any drug design workflow. Multiple experimental and computational methods have been developed to better understand these...

Assessment of Protein-Protein Docking Models Using Deep Learning.

Methods in molecular biology (Clifton, N.J.)
Protein-protein interactions are involved in almost all processes in a living cell and determine the biological functions of proteins. To obtain mechanistic understandings of protein-protein interactions, the tertiary structures of protein complexes ...