PTG-PLM: Predicting Post-Translational Glycosylation and Glycation Sites Using Protein Language Models and Deep Learning

Alkuhlani, Alhasan; Gad, Walaa; Roushdy, Mohamed; Voskoglou, Michael Gr.; Salem, Abdel-Badeeh M.

doi:10.3390/axioms11090469

Cited by 6 publications

(4 citation statements)

References 57 publications

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“…RF is an ensemble-based algorithm that is based on decision trees and is commonly used in computational biology for its simplicity and suitability for highdimensional data. XGBoost is also an ensemble learning method that is based on tree boosting and uses gradient descent to deal with high dimensional data [25], [26]. Multiple hyperparameters are experimented with each classifier using training and validation datasets.…”

Section: ) Traditional ML Methodsmentioning

confidence: 99%

GNNGLY: Graph Neural Networks for Glycan Classification

et al. 2023

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Glycans are important biological molecules that can be found on their own or attached to other molecules. They have complex, branching structures that do not follow the linear structure. Glycans are crucial for many biological processes and they are involved in the development of several important diseases. Due to the complexity and the branched structure of glycans, most of the current studies have mainly focused on the other attached molecules instead of glycans themselves. This paper proposes, GNNGLY, a graph neural networks model for glycans classification. Firstly, Glycans are represented as molecular graphs, where atoms are represented as nodes and bonds are represented as edges. Graph convolutional networks (GCNs) are then used to make predictions on eight taxonomic classification levels and for the level of immunogenicity property. The performance results indicate that GNNGLY outperforms traditional machine learning methods and when compared to other existing tools for glycan classification, GNNGLY showed considerable performance results. GNNGLY could have a significant impact on the field of glycoinformatics and related research areas.

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Section: ) Traditional ML Methodsmentioning

confidence: 99%

GNNGLY: Graph Neural Networks for Glycan Classification

et al. 2023

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“…The sequence alignment is widely applied in computational biology (e.g., aligning protein sequences, cf. [15,16]), computational linguistics (e.g., spell correction, speech recognition, machine translation, information extraction, cf. [11]), and information security (e.g., impersonation attacks detection in cloud computing environments, cf.…”

Section: Background and Related Workmentioning

confidence: 99%

Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties

Gnjatović¹,

Maček

Saračević

et al. 2022

Axioms

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This paper introduces a heuristic for multiple sequence alignment aimed at improving real-time object recognition in short video streams with uncertainties. It builds upon the idea of the progressive alignment but is cognitively economical to the extent that the underlying edit distance approach is adapted to account for human working memory limitations. Thus, the proposed heuristic procedure has a reduced computational complexity compared to optimal multiple sequence alignment. On the other hand, its relevance was experimentally confirmed. An extrinsic evaluation conducted in real-life settings demonstrated a significant improvement in number recognition accuracy in short video streams under uncertainties caused by noise and incompleteness. The second line of evaluation demonstrated that the proposed heuristic outperforms humans in the post-processing of recognition hypotheses. This indicates that it may be combined with state-of-the-art machine learning approaches, which are typically not tailored to the task of object sequence recognition from a limited number of frames of incomplete data recorded in a dynamic scene situation.

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“…Similarly, protein language models (PLMs) based on the transformer architecture have found success in the field of proteomics. PLMs are trained on extensive datasets of protein sequences to capture underlying evolutionary patterns and extract semantic information embedded within the protein sequences [ 17 , 18 ]. One of the basic pre-processing steps in NLP is tokenization, the splitting of the protein amino acid sequences into individual units of atomic information called tokens.…”

Section: Introductionmentioning

confidence: 99%

ESMSec: Prediction of Secreted Proteins in Human Body Fluids Using Protein Language Models and Attention

Wang,

Sun,

Sheng

et al. 2024

IJMS

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The secreted proteins of human body fluid have the potential to be used as biomarkers for diseases. These biomarkers can be used for early diagnosis and risk prediction of diseases, so the study of secreted proteins of human body fluid has great application value. In recent years, the deep-learning-based transformer language model has transferred from the field of natural language processing (NLP) to the field of proteomics, leading to the development of protein language models (PLMs) for protein sequence representation. Here, we propose a deep learning framework called ESM Predict Secreted Proteins (ESMSec) to predict three types of proteins secreted in human body fluid. The ESMSec is based on the ESM2 model and attention architecture. Specifically, the protein sequence data are firstly put into the ESM2 model to extract the feature information from the last hidden layer, and all the input proteins are encoded into a fixed 1000 × 480 matrix. Secondly, multi-head attention with a fully connected neural network is employed as the classifier to perform binary classification according to whether they are secreted into each body fluid. Our experiment utilized three human body fluids that are important and ubiquitous markers. Experimental results show that ESMSec achieved average accuracy of 0.8486, 0.8358, and 0.8325 on the testing datasets for plasma, cerebrospinal fluid (CSF), and seminal fluid, which on average outperform the state-of-the-art (SOTA) methods. The outstanding performance results of ESMSec demonstrate that the ESM can improve the prediction performance of the model and has great potential to screen the secretion information of human body fluid proteins.

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PTG-PLM: Predicting Post-Translational Glycosylation and Glycation Sites Using Protein Language Models and Deep Learning

Cited by 6 publications

References 57 publications

GNNGLY: Graph Neural Networks for Glycan Classification

GNNGLY: Graph Neural Networks for Glycan Classification

Cognitively Economical Heuristic for Multiple Sequence Alignment under Uncertainties

ESMSec: Prediction of Secreted Proteins in Human Body Fluids Using Protein Language Models and Attention

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