AIMC Topic: Amino Acids

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Evidential deep learning for trustworthy prediction of enzyme commission number.

Briefings in bioinformatics
The rapid growth of uncharacterized enzymes and their functional diversity urge accurate and trustworthy computational functional annotation tools. However, current state-of-the-art models lack trustworthiness on the prediction of the multilabel clas...

Struct2GO: protein function prediction based on graph pooling algorithm and AlphaFold2 structure information.

Bioinformatics (Oxford, England)
MOTIVATION: In recent years, there has been a breakthrough in protein structure prediction, and the AlphaFold2 model of the DeepMind team has improved the accuracy of protein structure prediction to the atomic level. Currently, deep learning-based pr...

Efficient prediction of peptide self-assembly through sequential and graphical encoding.

Briefings in bioinformatics
In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the effi...

PredLLPS_PSSM: a novel predictor for liquid-liquid protein separation identification based on evolutionary information and a deep neural network.

Briefings in bioinformatics
The formation of biomolecular condensates by liquid-liquid phase separation (LLPS) has become a universal mechanism for spatiotemporal coordination of biological activities in cells and has been widely observed to directly regulate the key cellular p...

ETLD: an encoder-transformation layer-decoder architecture for protein contact and mutation effects prediction.

Briefings in bioinformatics
The latent features extracted from the multiple sequence alignments (MSAs) of homologous protein families are useful for identifying residue-residue contacts, predicting mutation effects, shaping protein evolution, etc. Over the past three decades, a...

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...

Masked Language Modeling for Resource Constrained Biological Natural Language Processing.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Recent advances in Natural Language Processing (NLP) have produced state of the art results on several sequence to sequence (seq2seq) tasks. Enhancements in embedders and their training methodologies have shown significant improvement on downstream t...

CProMG: controllable protein-oriented molecule generation with desired binding affinity and drug-like properties.

Bioinformatics (Oxford, England)
MOTIVATION: Deep learning-based molecule generation becomes a new paradigm of de novo molecule design since it enables fast and directional exploration in the vast chemical space. However, it is still an open issue to generate molecules, which bind t...

High-accuracy protein model quality assessment using attention graph neural networks.

Briefings in bioinformatics
Great improvement has been brought to protein tertiary structure prediction through deep learning. It is important but very challenging to accurately rank and score decoy structures predicted by different models. CASP14 results show that existing qua...

ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides.

Bioinformatics (Oxford, England)
MOTIVATION: Plant Small Secreted Peptides (SSPs) play an important role in plant growth, development, and plant-microbe interactions. Therefore, the identification of SSPs is essential for revealing the functional mechanisms. Over the last few decade...