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Databases, Protein

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Exploiting protein language models for the precise classification of ion channels and ion transporters.

Proteins
This study introduces TooT-PLM-ionCT, a comprehensive framework that consolidates three distinct systems, each meticulously tailored for one of the following tasks: distinguishing ion channels (ICs) from membrane proteins (MPs), segregating ion trans...

ProtTrans and multi-window scanning convolutional neural networks for the prediction of protein-peptide interaction sites.

Journal of molecular graphics & modelling
This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-vi...

DeepPI: Alignment-Free Analysis of Flexible Length Proteins Based on Deep Learning and Image Generator.

Interdisciplinary sciences, computational life sciences
With the rapid development of NGS technology, the number of protein sequences has increased exponentially. Computational methods have been introduced in protein functional studies because the analysis of large numbers of proteins through biological e...

RNA3DB: A structurally-dissimilar dataset split for training and benchmarking deep learning models for RNA structure prediction.

Journal of molecular biology
With advances in protein structure prediction thanks to deep learning models like AlphaFold, RNA structure prediction has recently received increased attention from deep learning researchers. RNAs introduce substantial challenges due to the sparser a...

Prediction of protein N-terminal acetylation modification sites based on CNN-BiLSTM-attention model.

Computers in biology and medicine
N-terminal acetylation is one of the most common and important post-translational modifications (PTM) of eukaryotic proteins. PTM plays a crucial role in various cellular processes and disease pathogenesis. Thus, the accurate identification of N-term...

Flattening the curve-How to get better results with small deep-mutational-scanning datasets.

Proteins
Proteins are used in various biotechnological applications, often requiring the optimization of protein properties by introducing specific amino-acid exchanges. Deep mutational scanning (DMS) is an effective high-throughput method for evaluating the ...

PMSPcnn: Predicting protein stability changes upon single point mutations with convolutional neural network.

Structure (London, England : 1993)
Protein missense mutations and resulting protein stability changes are important causes for many human genetic diseases. However, the accurate prediction of stability changes due to mutations remains a challenging problem. To address this problem, we...

EPDRNA: A Model for Identifying DNA-RNA Binding Sites in Disease-Related Proteins.

The protein journal
Protein-DNA and protein-RNA interactions are involved in many biological processes and regulate many cellular functions. Moreover, they are related to many human diseases. To understand the molecular mechanism of protein-DNA binding and protein-RNA b...

Identification of Protein-Protein Interaction Associated Functions Based on Gene Ontology.

The protein journal
Protein-protein interactions (PPIs) involve the physical or functional contact between two or more proteins. Generally, proteins that can interact with each other always have special relationships. Some previous studies have reported that gene ontolo...

A Comprehensive Review on Machine Learning Techniques for Protein Family Prediction.

The protein journal
Proteomics is a field dedicated to the analysis of proteins in cells, tissues, and organisms, aiming to gain insights into their structures, functions, and interactions. A crucial aspect within proteomics is protein family prediction, which involves ...