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Amino Acid Sequence

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AbAgIntPre: A deep learning method for predicting antibody-antigen interactions based on sequence information.

Frontiers in immunology
INTRODUCTION: Antibody-mediated immunity is an essential part of the immune system in vertebrates. The ability to specifically bind to antigens allows antibodies to be widely used in the therapy of cancers and other critical diseases. A key step in a...

Improved compound-protein interaction site and binding affinity prediction using self-supervised protein embeddings.

BMC bioinformatics
BACKGROUND: Compound-protein interaction site and binding affinity predictions are crucial for drug discovery and drug design. In recent years, many deep learning-based methods have been proposed for predications related to compound-protein interacti...

Feedback-AVPGAN: Feedback-guided generative adversarial network for generating antiviral peptides.

Journal of bioinformatics and computational biology
In this study, we propose , a system that aims to computationally generate novel antiviral peptides (AVPs). This system relies on the key premise of the Generative Adversarial Network (GAN) model and the Feedback method. GAN, a generative modeling ap...

Accurate Prediction of Human Essential Proteins Using Ensemble Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Essential proteins are considered the foundation of life as they are indispensable for the survival of living organisms. Computational methods for essential protein discovery provide a fast way to identify essential proteins. But most of them heavily...

Long-distance dependency combined multi-hop graph neural networks for protein-protein interactions prediction.

BMC bioinformatics
BACKGROUND: Protein-protein interactions are widespread in biological systems and play an important role in cell biology. Since traditional laboratory-based methods have some drawbacks, such as time-consuming, money-consuming, etc., a large number of...

Ten quick tips for sequence-based prediction of protein properties using machine learning.

PLoS computational biology
The ubiquitous availability of genome sequencing data explains the popularity of machine learning-based methods for the prediction of protein properties from their amino acid sequences. Over the years, while revising our own work, reading submitted m...

AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics.

Nature communications
Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid ...

End-to-End Protein Normal Mode Frequency Predictions Using Language and Graph Models and Application to Sonification.

ACS nano
The prediction of mechanical and dynamical properties of proteins is an important frontier, especially given the greater availability of proteins structures. Here we report a series of models that provide end-to-end predictions of nanodynamical prope...

Entropy and Variability: A Second Opinion by Deep Learning.

Biomolecules
BACKGROUND: Analysis of the distribution of amino acid types found at equivalent positions in multiple sequence alignments has found applications in human genetics, protein engineering, drug design, protein structure prediction, and many other fields...

SENSDeep: An Ensemble Deep Learning Method for Protein-Protein Interaction Sites Prediction.

Interdisciplinary sciences, computational life sciences
PURPOSE: The determination of which amino acid in a protein interacts with other proteins is important in understanding the functional mechanism of that protein. Although there are experimental methods to detect protein-protein interaction sites (PPI...