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Amino Acid Sequence

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Interpretable pairwise distillations for generative protein sequence models.

PLoS computational biology
Many different types of generative models for protein sequences have been proposed in literature. Their uses include the prediction of mutational effects, protein design and the prediction of structural properties. Neural network (NN) architectures h...

Sequence-based drug-target affinity prediction using weighted graph neural networks.

BMC genomics
BACKGROUND: Affinity prediction between molecule and protein is an important step of virtual screening, which is usually called drug-target affinity (DTA) prediction. Its accuracy directly influences the progress of drug development. Sequence-based d...

Research on DNA-Binding Protein Identification Method Based on LSTM-CNN Feature Fusion.

Computational and mathematical methods in medicine
Protein is closely related to life activities. As a kind of protein, DNA-binding protein plays an irreplaceable role in life activities. Therefore, it is very important to study DNA-binding protein, which is a subject worthy of study. Although tradit...

BepFAMN: A Method for Linear B-Cell Epitope Predictions Based on Fuzzy-ARTMAP Artificial Neural Network.

Sensors (Basel, Switzerland)
The public health system is extremely dependent on the use of vaccines to immunize the population from a series of infectious and dangerous diseases, preventing the system from collapsing and millions of people dying every year. However, to develop t...

A Deep Learning Model for Accurate Diagnosis of Infection Using Antibody Repertoires.

Journal of immunology (Baltimore, Md. : 1950)
The adaptive immune receptor repertoire consists of the entire set of an individual's BCRs and TCRs and is believed to contain a record of prior immune responses and the potential for future immunity. Analyses of TCR repertoires via deep learning (DL...

Prediction of protein-protein interaction using graph neural networks.

Scientific reports
Proteins are the essential biological macromolecules required to perform nearly all biological processes, and cellular functions. Proteins rarely carry out their tasks in isolation but interact with other proteins (known as protein-protein interactio...

DLSSAffinity: protein-ligand binding affinity prediction a deep learning model.

Physical chemistry chemical physics : PCCP
Evaluating the protein-ligand binding affinity is a substantial part of the computer-aided drug discovery process. Most of the proposed computational methods predict protein-ligand binding affinity using either limited full-length protein 3D structur...

LM-GVP: an extensible sequence and structure informed deep learning framework for protein property prediction.

Scientific reports
Proteins perform many essential functions in biological systems and can be successfully developed as bio-therapeutics. It is invaluable to be able to predict their properties based on a proposed sequence and structure. In this study, we developed a n...

Performing protein fold recognition by exploiting a stack convolutional neural network with the attention mechanism.

Analytical biochemistry
Protein fold recognition is a critical step in protein structure and function prediction, and aims to ascertain the most likely fold type of the query protein. As a typical pattern recognition problem, designing a powerful feature extractor and metri...

Prediction of Disordered Regions in Proteins with Recurrent Neural Networks and Protein Dynamics.

Journal of molecular biology
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 ...