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Sequence Analysis, Protein

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iQDeep: an integrated web server for protein scoring using multiscale deep learning models.

Journal of molecular biology
The remarkable recent advances in protein structure prediction have enabled computational modeling of protein structures with considerably higher accuracy than ever before. While state-of-the-art structure prediction methods provide self-assessment c...

Enzyme function prediction using contrastive learning.

Science (New York, N.Y.)
Enzyme function annotation is a fundamental challenge, and numerous computational tools have been developed. However, most of these tools cannot accurately predict functional annotations, such as enzyme commission (EC) number, for less-studied protei...

MMSMAPlus: a multi-view multi-scale multi-attention embedding model for protein function prediction.

Briefings in bioinformatics
Protein is the most important component in organisms and plays an indispensable role in life activities. In recent years, a large number of intelligent methods have been proposed to predict protein function. These methods obtain different types of pr...

Protein remote homology detection and structural alignment using deep learning.

Nature biotechnology
Exploiting sequence-structure-function relationships in biotechnology requires improved methods for aligning proteins that have low sequence similarity to previously annotated proteins. We develop two deep learning methods to address this gap, TM-Vec...

ResCNNT-fold: Combining residual convolutional neural network and Transformer for protein fold recognition from language model embeddings.

Computers in biology and medicine
A comprehensive understanding of protein functions holds significant promise for disease research and drug development, and proteins with analogous tertiary structures tend to exhibit similar functions. Protein fold recognition stands as a classical ...

Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing.

Nature communications
Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins are lacking, hindering the utility of proteomics in applications where the sequences are unknown including variant calling, neoepitope identification, and metaproteo...

Effect of tokenization on transformers for biological sequences.

Bioinformatics (Oxford, England)
MOTIVATION: Deep-learning models are transforming biological research, including many bioinformatics and comparative genomics algorithms, such as sequence alignments, phylogenetic tree inference, and automatic classification of protein functions. Amo...

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...

PTransIPs: Identification of Phosphorylation Sites Enhanced by Protein PLM Embeddings.

IEEE journal of biomedical and health informatics
Phosphorylation is pivotal in numerous fundamental cellular processes and plays a significant role in the onset and progression of various diseases. The accurate identification of these phosphorylation sites is crucial for unraveling the molecular me...

SeqNovo: De Novo Peptide Sequencing Prediction in IoMT via Seq2Seq.

IEEE journal of biomedical and health informatics
In the Internet of Medical Things (IoMT), de novo peptide sequencing prediction is one of the most important techniques for the fields of disease prediction, diagnosis, and treatment. Recently, deep-learning-based peptide sequencing prediction has be...