AIMC Topic: Proteins

Clear Filters Showing 1591 to 1600 of 2080 articles

Afpdb: an efficient structure manipulation package for AI protein design.

Bioinformatics (Oxford, England)
MOTIVATION: The advent of AlphaFold and other protein Artificial Intelligence (AI) models has transformed protein design, necessitating efficient handling of large-scale data and complex workflows. Using existing programming packages that predate rec...

[ protein design in the age of artificial intelligence].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Proteins with specific functions and characteristics play a crucial role in biomedicine and nanotechnology. protein design enables the customization of sequences to produce proteins with desired structures that do not exist in the nature. In recent ...

Do protein language models learn phylogeny?

Briefings in bioinformatics
Deep machine learning demonstrates a capacity to uncover evolutionary relationships directly from protein sequences, in effect internalising notions inherent to classical phylogenetic tree inference. We connect these two paradigms by assessing the ca...

DeepPFP: a multi-task-aware architecture for protein function prediction.

Briefings in bioinformatics
Deriving protein function from protein sequences poses a significant challenge due to the intricate relationship between sequence and function. Deep learning has made remarkable strides in predicting sequence-function relationships. However, models t...

Artificial intelligence in cryo-EM protein particle picking: recent advances and remaining challenges.

Briefings in bioinformatics
Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling the determination of high-resolution 3-Dimensional (3D) structures of large biological macromolecules. Protein particle picking, the process of identifying individua...

ET-PROTACs: modeling ternary complex interactions using cross-modal learning and ternary attention for accurate PROTAC-induced degradation prediction.

Briefings in bioinformatics
MOTIVATION: Accurately predicting the degradation capabilities of proteolysis-targeting chimeras (PROTACs) for given target proteins and E3 ligases is important for PROTAC design. The distinctive ternary structure of PROTACs presents a challenge to t...

EuDockScore: Euclidean graph neural networks for scoring protein-protein interfaces.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interactions are essential for a variety of biological phenomena including mediating biochemical reactions, cell signaling, and the immune response. Proteins seek to form interfaces which reduce overall system energy. Alth...

Deep learning model for protein multi-label subcellular localization and function prediction based on multi-task collaborative training.

Briefings in bioinformatics
The functional study of proteins is a critical task in modern biology, playing a pivotal role in understanding the mechanisms of pathogenesis, developing new drugs, and discovering novel drug targets. However, existing computational models for subcel...

A two-task predictor for discovering phase separation proteins and their undergoing mechanism.

Briefings in bioinformatics
Liquid-liquid phase separation (LLPS) is one of the mechanisms mediating the compartmentalization of macromolecules (proteins and nucleic acids) in cells, forming biomolecular condensates or membraneless organelles. Consequently, the systematic ident...

GORetriever: reranking protein-description-based GO candidates by literature-driven deep information retrieval for protein function annotation.

Bioinformatics (Oxford, England)
SUMMARY: The vast majority of proteins still lack experimentally validated functional annotations, which highlights the importance of developing high-performance automated protein function prediction/annotation (AFP) methods. While existing approache...