AIMC Topic: Sequence Analysis, Protein

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Deep Learning Approaches for the Prediction of Protein Functional Sites.

Molecules (Basel, Switzerland) Jan 7, 2025
Knowing which residues of a protein are important for its function is of paramount importance for understanding the molecular basis of this function and devising ways of modifying it for medical or biotechnological applications. Due to the difficulty...
Proteins Databases, Protein Binding Sites Humans Deep Learning Computational Biology Sequence Analysis, Protein
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π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing.

Nature communications Jan 2, 2025
Peptide sequencing via tandem mass spectrometry (MS/MS) is essential in proteomics. Unlike traditional database searches, deep learning excels at de novo peptide sequencing, even for peptides missing from existing databases. Current deep learning mod...
Deep Learning Proteomics Protein Processing, Post-Translational Databases, Protein Tandem Mass Spectrometry Software Sequence Analysis, Protein Algorithms Humans Phosphopeptides Peptides
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NovoRank: Refinement for Peptide Sequencing Based on Spectral Clustering and Deep Learning.

Journal of proteome research Dec 31, 2024
peptide sequencing is a valuable technique in mass-spectrometry-based proteomics, as it deduces peptide sequences directly from tandem mass spectra without relying on sequence databases. This database-independent method, however, relies solely on im...
Databases, Protein Tandem Mass Spectrometry Sequence Analysis, Protein Proteomics Cluster Analysis Software Peptides Deep Learning
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Improving Antifreeze Proteins Prediction With Protein Language Models and Hybrid Feature Extraction Networks.

IEEE/ACM transactions on computational biology and bioinformatics Dec 10, 2024
Accurate identification of antifreeze proteins (AFPs) is crucial in developing biomimetic synthetic anti-icing materials and low-temperature organ preservation materials. Although numerous machine learning-based methods have been proposed for AFPs pr...
Algorithms Neural Networks, Computer Databases, Protein Deep Learning Sequence Analysis, Protein Computational Biology Antifreeze Proteins
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Bi-SeqCNN: A Novel Light-Weight Bi-Directional CNN Architecture for Protein Function Prediction.

IEEE/ACM transactions on computational biology and bioinformatics Dec 10, 2024
Deep learning approaches, such as convolution neural networks (CNNs) and deep recurrent neural networks (RNNs), have been the backbone for predicting protein function, with promising state-of-the-art (SOTA) results. RNNs with an in-built ability (i) ...
Databases, Protein Sequence Analysis, Protein Deep Learning Computational Biology Proteins Neural Networks, Computer Algorithms
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Generative AI Models for the Protein Scaffold Filling Problem.

Journal of computational biology : a journal of computational molecular cell biology Oct 23, 2024
De novo protein sequencing is an important problem in proteomics, playing a crucial role in understanding protein functions, drug discovery, design and evolutionary studies, etc. Top-down and bottom-up tandem mass spectrometry are popular approaches ...
Humans Artificial Intelligence Algorithms Computational Biology Databases, Protein Proteomics Amino Acid Sequence Proteins Sequence Analysis, Protein
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DeepDBS: Identification of DNA-binding sites in protein sequences by using deep representations and random forest.

Methods (San Diego, Calif.) Sep 11, 2024
Interactions of biological molecules in organisms are considered to be primary factors for the lifecycle of that organism. Various important biological functions are dependent on such interactions and among different kinds of interactions, the protei...
Sequence Analysis, Protein DNA-Binding Proteins Protein Binding Amino Acid Sequence Neural Networks, Computer DNA Binding Sites Algorithms Computational Biology Random Forest Deep Learning Databases, Protein
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PCP-GC-LM: single-sequence-based protein contact prediction using dual graph convolutional neural network and convolutional neural network.

BMC bioinformatics Sep 2, 2024
BACKGROUND: Recently, the process of evolution information and the deep learning network has promoted the improvement of protein contact prediction methods. Nevertheless, still remain some bottleneck: (1) One of the bottlenecks is the prediction of o...
Neural Networks, Computer Algorithms Deep Learning Computational Biology Protein Conformation Proteins Sequence Analysis, Protein Databases, Protein
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Optimizing protein sequence classification: integrating deep learning models with Bayesian optimization for enhanced biological analysis.

BMC medical informatics and decision making Aug 27, 2024
Efforts to enhance the accuracy of protein sequence classification are of utmost importance in driving forward biological analyses and facilitating significant medical advancements. This study presents a cutting-edge model called ProtICNN-BiLSTM, whi...
Sequence Analysis, Protein Computational Biology Proteins Humans Bayes Theorem Deep Learning
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An end-to-end framework for the prediction of protein structure and fitness from single sequence.

Nature communications Aug 27, 2024
Significant research progress has been made in the field of protein structure and fitness prediction. Particularly, single-sequence-based structure prediction methods like ESMFold and OmegaFold achieve a balance between inference speed and prediction...
Protein Stability Neural Networks, Computer Protein Conformation Algorithms Models, Molecular Sequence Analysis, Protein Computational Biology Mutation Proteins
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