AIMC Topic: Proteins

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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...

MIC: A deep learning tool for assigning ions and waters in cryo-EM and crystal structures.

Nature communications
At sufficiently high resolution, x-ray crystallography and cryogenic electron microscopy are capable of resolving small spherical map features corresponding to either water or ions. Correct classification of these sites provides crucial insight for u...

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...

Exploring Protein-Protein Docking Tools: Comprehensive Insights into Traditional and Deep-Learning Approaches.

Journal of chemical information and modeling
Protein-protein interactions are crucial for numerous biological activities such as signaling, enzyme catalysis, gene expression regulation, cell adhesion, immune response, and drug action. Structural characterization of these interactions can elucid...

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...

Medium-sized protein language models perform well at transfer learning on realistic datasets.

Scientific reports
Protein language models (pLMs) can offer deep insights into evolutionary and structural properties of proteins. While larger models, such as the 15 billion parameter model ESM-2, promise to capture more complex patterns in sequence space, they also p...

SE(3)-equivariant ternary complex prediction towards target protein degradation.

Nature communications
Targeted protein degradation (TPD) has rapidly emerged as a powerful modality for drugging previously "undruggable" proteins. TPD employs small molecules like PROTACs and molecular glue degraders (MGD) to induce target protein degradation via the for...

A fast (CNN + MCWS-transformer) based architecture for protein function prediction.

Statistical applications in genetics and molecular biology
The transformer model for sequence mining has brought a paradigmatic shift to many domains, including biological sequence mining. However, transformers suffer from quadratic complexity, i.e., O( ), where is the sequence length, which affects the tra...

Sequence and Structure-based Prediction of Allosteric Sites.

Journal of molecular biology
Allosteric regulation in proteins is a critical aspect of cellular function, influencing various biological processes through conformational or dynamic changes induced by effector molecules. Allosteric drugs possess significant therapeutic value due ...

Advancing active compound discovery for novel drug targets: insights from AI-driven approaches.

Acta pharmacologica Sinica
The discovery of active compounds for novel, underexplored targets is essential for advancing innovative therapeutics across a wide range of diseases. Recent advancements in artificial intelligence (AI) are revolutionizing active compound discovery b...