AIMC Topic: Sequence Analysis, Protein

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

IEEE journal of biomedical and health informatics Jun 6, 2024
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...
Humans Deep Learning A549 Cells Amino Acid Sequence Sequence Analysis, Protein Proteins Phosphorylation
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Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences.

Proteins May 29, 2024
We apply methods of Artificial Intelligence and Machine Learning to protein dynamic bioinformatics. We rewrite the sequences of a large protein data set, containing both folded and intrinsically disordered molecules, using a representation developed ...
Sequence Analysis, Protein Fourier Analysis Protein Folding Computational Biology Proteins Amino Acid Sequence Intrinsically Disordered Proteins Databases, Protein Machine Learning Algorithms Artificial Intelligence
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Sa-TTCA: An SVM-based approach for tumor T-cell antigen classification using features extracted from biological sequencing and natural language processing.

Computers in biology and medicine Apr 4, 2024
Accurately predicting tumor T-cell antigen (TTCA) sequences is a crucial task in the development of cancer vaccines and immunotherapies. TTCAs derived from tumor cells, are presented to immune cells (T cells) through major histocompatibility complex ...
Humans Antigens, Neoplasm Support Vector Machine Natural Language Processing Neoplasms Computational Biology Sequence Analysis, Protein
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Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing.

Nature communications Jan 2, 2024
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...
Sequence Analysis, Protein Deep Learning Amino Acid Sequence Algorithms Peptides
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ResCNNT-fold: Combining residual convolutional neural network and Transformer for protein fold recognition from language model embeddings.

Computers in biology and medicine Oct 17, 2023
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 ...
Databases, Protein Protein Folding Sequence Analysis, Protein Computational Biology Proteins Humans Neural Networks, Computer
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Protein remote homology detection and structural alignment using deep learning.

Nature biotechnology Sep 7, 2023
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...
Proteins Databases, Protein Algorithms Protein Conformation Sequence Alignment Deep Learning Sequence Analysis, Protein Computational Biology
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Enzyme function prediction using contrastive learning.

Science (New York, N.Y.) Mar 30, 2023
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...
Sequence Analysis, Protein Molecular Sequence Annotation Enzymes Proteins Biocatalysis Reproducibility of Results Machine Learning Genomics Algorithms Computational Biology
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iQDeep: an integrated web server for protein scoring using multiscale deep learning models.

Journal of molecular biology Mar 23, 2023
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...
Protein Conformation Sequence Analysis, Protein Software Computational Biology Deep Learning Proteins
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Evolutionary-scale prediction of atomic-level protein structure with a language model.

Science (New York, N.Y.) Mar 16, 2023
Recent advances in machine learning have leveraged evolutionary information in multiple sequence alignments to predict protein structure. We demonstrate direct inference of full atomic-level protein structure from primary sequence using a large langu...
Machine Learning Proteins Amino Acid Sequence Evolution, Molecular Protein Conformation Sequence Analysis, Protein
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Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics.

PLoS computational biology Jan 20, 2023
Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains ...
Tandem Mass Spectrometry Peptide Hydrolases Proteomics Sequence Analysis, Protein Peptides Antibodies, Monoclonal Deep Learning
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