AIMC Topic: Proteins

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DEMO2: Assemble multi-domain protein structures by coupling analogous template alignments with deep-learning inter-domain restraint prediction.

Nucleic acids research
Most proteins in nature contain multiple folding units (or domains). The revolutionary success of AlphaFold2 in single-domain structure prediction showed potential to extend deep-learning techniques for multi-domain structure modeling. This work pres...

GRaSP-web: a machine learning strategy to predict binding sites based on residue neighborhood graphs.

Nucleic acids research
Proteins are essential macromolecules for the maintenance of living systems. Many of them perform their function by interacting with other molecules in regions called binding sites. The identification and characterization of these regions are of fund...

LOMETS3: integrating deep learning and profile alignment for advanced protein template recognition and function annotation.

Nucleic acids research
Deep learning techniques have significantly advanced the field of protein structure prediction. LOMETS3 (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is a new generation meta-server approach to template-based protein structure prediction and function...

CycleDNN - A Novel Deep Neural Network Model for CETSA Feature Prediction cross Cell Lines.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Cellular Thermal Shift Assay (CETSA) has been widely used in drug discovery, cancer cell biology, immunology, etc. One of the barriers for CETSA applications is that CETSA experiments have to be conducted on various cell lines, which is extremely tim...

AlphaFold accurately predicts distinct conformations based on the oligomeric state of a de novo designed protein.

Protein science : a publication of the Protein Society
Using the molecular modeling program Rosetta, we designed a de novo protein, called SEWN0.1, which binds the heterotrimeric G protein Gα The design is helical, well-folded, and primarily monomeric in solution at a concentration of 10 μM. However, whe...

Conditional generative modeling for de novo protein design with hierarchical functions.

Bioinformatics (Oxford, England)
MOTIVATION: Protein design has become increasingly important for medical and biotechnological applications. Because of the complex mechanisms underlying protein formation, the creation of a novel protein requires tedious and time-consuming computatio...

Predicting protein-peptide binding residues via interpretable deep learning.

Bioinformatics (Oxford, England)
SUMMARY: Identifying the protein-peptide binding residues is fundamentally important to understand the mechanisms of protein functions and explore drug discovery. Although several computational methods have been developed, most of them highly rely on...

InterPepScore: a deep learning score for improving the FlexPepDock refinement protocol.

Bioinformatics (Oxford, England)
MOTIVATION: Interactions between peptide fragments and protein receptors are vital to cell function yet difficult to experimentally determine in structural details of. As such, many computational methods have been developed to aid in peptide-protein ...

Complementing machine learning-based structure predictions with native mass spectrometry.

Protein science : a publication of the Protein Society
The advent of machine learning-based structure prediction algorithms such as AlphaFold2 (AF2) and RoseTTa Fold have moved the generation of accurate structural models for the entire cellular protein machinery into the reach of the scientific communit...