AIMC Topic: Protein Structure, Secondary

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AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences.

Nucleic acids research
The AlphaFold Database Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released...

HN-PPISP: a hybrid network based on MLP-Mixer for protein-protein interaction site prediction.

Briefings in bioinformatics
MOTIVATION: Biological experimental approaches to protein-protein interaction (PPI) site prediction are critical for understanding the mechanisms of biochemical processes but are time-consuming and laborious. With the development of Deep Learning (DL...

HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures.

Bioinformatics (Oxford, England)
MOTIVATION: Protein and peptide engineering has become an essential field in biomedicine with therapeutics, diagnostics and synthetic biology applications. Helices are both abundant structural feature in proteins and comprise a major portion of bioac...

LambdaPP: Fast and accessible protein-specific phenotype predictions.

Protein science : a publication of the Protein Society
The availability of accurate and fast artificial intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first in...

GeoPacker: A novel deep learning framework for protein side-chain modeling.

Protein science : a publication of the Protein Society
Atomic interactions play essential roles in protein folding, structure stabilization, and function performance. Recent advances in deep learning-based methods have achieved impressive success not only in protein structure prediction, but also in prot...

Deep learning models for RNA secondary structure prediction (probably) do not generalize across families.

Bioinformatics (Oxford, England)
MOTIVATION: The secondary structure of RNA is of importance to its function. Over the last few years, several papers attempted to use machine learning to improve de novo RNA secondary structure prediction. Many of these papers report impressive resul...

Prior knowledge facilitates low homologous protein secondary structure prediction with DSM distillation.

Bioinformatics (Oxford, England)
MOTIVATION: Protein secondary structure prediction (PSSP) is one of the fundamental and challenging problems in the field of computational biology. Accurate PSSP relies on sufficient homologous protein sequences to build the multiple sequence alignme...

NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning.

Nucleic acids research
Recent advances in machine learning and natural language processing have made it possible to profoundly advance our ability to accurately predict protein structures and their functions. While such improvements are significantly impacting the fields o...

SSpro/ACCpro 6: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, deep learning and structural similarity.

Bioinformatics (Oxford, England)
MOTIVATION: Accurately predicting protein secondary structure and relative solvent accessibility is important for the study of protein evolution, structure and an early-stage component of typical protein 3D structure prediction pipelines.

CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Coiled-coil is composed of two or more helices that are wound around each other. It widely exists in proteins and has been discovered to play a variety of critical roles in biology processes. Generally, there are three types of structural...