AIMC Topic: Amino Acid Sequence

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EFG-CS: Predicting chemical shifts from amino acid sequences with protein structure prediction using machine learning and deep learning models.

Protein science : a publication of the Protein Society
Nuclear magnetic resonance (NMR) crystallography is one of the main methods in structural biology for analyzing protein stereochemistry and structure. The chemical shift of the resonance frequency reflects the effect of the protons in a molecule prod...

Knot or not? Identifying unknotted proteins in knotted families with sequence-based Machine Learning model.

Protein science : a publication of the Protein Society
Knotted proteins, although scarce, are crucial structural components of certain protein families, and their roles continue to be a topic of intense research. Capitalizing on the vast collection of protein structure predictions offered by AlphaFold (A...

DEAttentionDTA: protein-ligand binding affinity prediction based on dynamic embedding and self-attention.

Bioinformatics (Oxford, England)
MOTIVATION: Predicting protein-ligand binding affinity is crucial in new drug discovery and development. However, most existing models rely on acquiring 3D structures of elusive proteins. Combining amino acid sequences with ligand sequences and bette...

AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences.

Briefings in bioinformatics
The optimization of therapeutic antibodies through traditional techniques, such as candidate screening via hybridoma or phage display, is resource-intensive and time-consuming. In recent years, computational and artificial intelligence-based methods ...

TemStaPro: protein thermostability prediction using sequence representations from protein language models.

Bioinformatics (Oxford, England)
MOTIVATION: Reliable prediction of protein thermostability from its sequence is valuable for both academic and industrial research. This prediction problem can be tackled using machine learning and by taking advantage of the recent blossoming of deep...

Enhancing cryo-EM structure prediction with DeepTracer and AlphaFold2 integration.

Briefings in bioinformatics
Understanding the protein structures is invaluable in various biomedical applications, such as vaccine development. Protein structure model building from experimental electron density maps is a time-consuming and labor-intensive task. To address the ...

SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition.

Briefings in bioinformatics
Protein sequence design can provide valuable insights into biopharmaceuticals and disease treatments. Currently, most protein sequence design methods based on deep learning focus on network architecture optimization, while ignoring protein-specific p...

EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks.

Nucleic acids research
Protein language models (pLMs) trained on a large corpus of protein sequences have shown unprecedented scalability and broad generalizability in a wide range of predictive modeling tasks, but their power has not yet been harnessed for predicting prot...

Multi-indicator comparative evaluation for deep learning-based protein sequence design methods.

Bioinformatics (Oxford, England)
MOTIVATION: Proteins found in nature represent only a fraction of the vast space of possible proteins. Protein design presents an opportunity to explore and expand this protein landscape. Within protein design, protein sequence design plays a crucial...

TIMED-Design: flexible and accessible protein sequence design with convolutional neural networks.

Protein engineering, design & selection : PEDS
Sequence design is a crucial step in the process of designing or engineering proteins. Traditionally, physics-based methods have been used to solve for optimal sequences, with the main disadvantages being that they are computationally intensive for t...