AIMC Topic: Protein Conformation

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Harnessing AlphaFold to reveal hERG channel conformational state secrets.

eLife
To design safe, selective, and effective new therapies, there must be a deep understanding of the structure and function of the drug target. One of the most difficult problems to solve has been the resolution of discrete conformational states of tran...

CoBdock-2: enhancing blind docking performance through hybrid feature selection combining ensemble and multimodel feature selection approaches.

Journal of computer-aided molecular design
Identifying orthosteric binding sites and predicting small molecule affinities remains a key challenge in virtual screening. While blind docking explores the entire protein surface, its precision is hindered by the vast search space. Cavity detection...

Rprot-Vec: a deep learning approach for fast protein structure similarity calculation.

BMC bioinformatics
BACKGROUND: Predicting protein structural similarity and detecting homologous sequences remain fundamental and challenging tasks in computational biology. Accurate identification of structural homologs enables function inference for newly discovered ...

Integrating Protein Language Models and Geometric Deep Learning for Peptide Toxicity Prediction.

Journal of chemical information and modeling
Peptide toxicity prediction is a critical task in biomedical research, influencing drug safety and therapeutic development. Traditional methods, relying on sequence similarity or handcrafted features, struggle to capture the complex relationship betw...

Harnessing protein language model for structure-based discovery of highly efficient and robust PET hydrolases.

Nature communications
Plastic waste, particularly polyethylene terephthalate (PET), presents significant environmental challenges, driving extensive research into enzymatic biodegradation. However, existing PET hydrolases (PETases) are limited by narrow sequence diversity...

Mapping the Conformational Heterogeneity Intrinsic to the Protein Native Ensemble.

Biochemistry
In the AlphaFold era, there is a significant momentum in predicting protein structures, functionality, and mutational hotspots from deep learning approaches. In this review, we highlight how structural information is only a starting point in understa...

Graph with Residue-Based Cross-Modal Framework Enhances Cell Function-Related Protein Properties Prediction.

Journal of chemical information and modeling
Accurate prediction of protein properties that influence cellular functions is crucial for drug design, disease research, and guiding biological wet-lab experiments. Previous methods primarily relied on physicochemical property analysis and homologou...

EquiCPI: SE(3)-Equivariant Geometric Deep Learning for Structure-Aware Prediction of Compound-Protein Interactions.

Journal of chemical information and modeling
Accurate prediction of compound-protein interactions (CPI) remains a cornerstone challenge in computational drug discovery. While existing sequence-based approaches leverage molecular fingerprints or graph representations, they critically overlook th...

'Intelligent' proteins.

Cellular and molecular life sciences : CMLS
We present an idea of protein molecules that challenges the traditional view of proteins as simple molecular machines and suggests instead that they exhibit a basic form of "intelligence". The idea stems from suggestions coming from Integrated Inform...

Molecular insights into the unique activation and allosteric modulation mechanisms of the human mas-related G-protein-coupled receptor X1.

International journal of biological macromolecules
MRGPRX1 plays dual roles in mediating nociception and pruritus, making it a promising target for alleviating itch and inhibiting pain. However, the mechanisms underlying MRGPRX1 activation and allosteric modulation remain poorly understood, posing si...