AIMC Topic: Proteins

Clear Filters Showing 1671 to 1680 of 2080 articles

Leveraging AI Advances and Online Tools for Structure-Based Variant Analysis.

Current protocols
Understanding how a gene variant affects protein function is important in life science, as it helps explain traits or dysfunctions in organisms. In a clinical setting, this understanding makes it possible to improve and personalize patient care. Bioi...

CoCoNat: a novel method based on deep learning for coiled-coil prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Coiled-coil domains (CCD) are widespread in all organisms and perform several crucial functions. Given their relevance, the computational detection of CCD is very important for protein functional annotation. State-of-the-art prediction me...

DeepTraSynergy: drug combinations using multimodal deep learning with transformers.

Bioinformatics (Oxford, England)
MOTIVATION: Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells.

ZetaDesign: an end-to-end deep learning method for protein sequence design and side-chain packing.

Briefings in bioinformatics
Computational protein design has been demonstrated to be the most powerful tool in the last few years among protein designing and repacking tasks. In practice, these two tasks are strongly related but often treated separately. Besides, state-of-the-a...

DeepAlgPro: an interpretable deep neural network model for predicting allergenic proteins.

Briefings in bioinformatics
Allergies have become an emerging public health problem worldwide. The most effective way to prevent allergies is to find the causative allergen at the source and avoid re-exposure. However, most of the current computational methods used to identify ...

MMSMAPlus: a multi-view multi-scale multi-attention embedding model for protein function prediction.

Briefings in bioinformatics
Protein is the most important component in organisms and plays an indispensable role in life activities. In recent years, a large number of intelligent methods have been proposed to predict protein function. These methods obtain different types of pr...

DDMut: predicting effects of mutations on protein stability using deep learning.

Nucleic acids research
Understanding the effects of mutations on protein stability is crucial for variant interpretation and prioritisation, protein engineering, and biotechnology. Despite significant efforts, community assessments of predictive tools have highlighted ongo...

DeepAlloDriver: a deep learning-based strategy to predict cancer driver mutations.

Nucleic acids research
Driver mutations can contribute to the initial processes of cancer, and their identification is crucial for understanding tumorigenesis as well as for molecular drug discovery and development. Allostery regulates protein function away from the functi...

[Advances in machine learning for predicting protein functions].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Proteins play a variety of functional roles in cellular activities and are indispensable for life. Understanding the functions of proteins is crucial in many fields such as medicine and drug development. In addition, the application of enzymes in gre...