AIMC Topic: 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...
Deep Learning Amino Acid Sequence Sequence Analysis, Protein 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 ...
Protein Folding Sequence Analysis, Protein Proteins Databases, Protein Neural Networks, Computer Computational Biology Humans
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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...
Algorithms Deep Learning Sequence Alignment Sequence Analysis, Protein Databases, Protein Computational Biology Protein Conformation Proteins
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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 Computational Biology Proteins Molecular Sequence Annotation Enzymes Biocatalysis Machine Learning Reproducibility of Results Algorithms Genomics
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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...
Deep Learning Computational Biology Software Proteins Protein Conformation Sequence Analysis, Protein
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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...
Evolution, Molecular Proteins Machine Learning Sequence Analysis, Protein Amino Acid Sequence Protein Conformation
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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 ...
Peptides Proteomics Antibodies, Monoclonal Deep Learning Sequence Analysis, Protein Tandem Mass Spectrometry Peptide Hydrolases
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DeepRHD: An efficient hybrid feature extraction technique for protein remote homology detection using deep learning strategies.

Computational biology and chemistry Aug 3, 2022
In computational biology, the Protein Remote homology Detection technique (PRHD) has got undeniable significance. It is mostly important for structure and function identification of a protein sequence. The previous years have seen a challenge that la...
Sequence Analysis, Protein Algorithms Amino Acid Sequence Deep Learning Sequence Homology, Amino Acid Proteins
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Prediction of Disordered Regions in Proteins with Recurrent Neural Networks and Protein Dynamics.

Journal of molecular biology Apr 22, 2022
The role of intrinsically disordered protein regions (IDRs) in cellular processes has become increasingly evident over the last years. These IDRs continue to challenge structural biology experiments because they lack a well-defined conformation, and ...
Software Computational Biology Neural Networks, Computer Protein Structure, Secondary Sequence Analysis, Protein Amino Acid Sequence Intrinsically Disordered Proteins
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Positional SHAP (PoSHAP) for Interpretation of machine learning models trained from biological sequences.

PLoS computational biology Jan 28, 2022
Machine learning with multi-layered artificial neural networks, also known as "deep learning," is effective for making biological predictions. However, model interpretation is challenging, especially for sequential input data used with recurrent neur...
Macaca mulatta Computational Biology Peptides Humans Models, Biological Amino Acid Sequence Binding Sites Animals Deep Learning Sequence Analysis, Protein
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