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Proteins

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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
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 ...

Naive Prediction of Protein Backbone Phi and Psi Dihedral Angles Using Deep Learning.

Molecules (Basel, Switzerland)
Protein structure prediction represents a significant challenge in the field of bioinformatics, with the prediction of protein structures using backbone dihedral angles recently achieving significant progress due to the rise of deep neural network re...

ResBiGAAT: Residual Bi-GRU with attention for protein-ligand binding affinity prediction.

Computational biology and chemistry
Protein-ligand interaction plays a crucial role in drug discovery, facilitating efficient drug development and enabling drug repurposing. Several computational algorithms, such as Graph Neural Networks and Convolutional Neural Networks, have been pro...

PointDE: Protein Docking Evaluation Using 3D Point Cloud Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
Protein-protein interactions (PPIs) play essential roles in many vital movements and the determination of protein complex structure is helpful to discover the mechanism of PPI. Protein-protein docking is being developed to model the structure of the ...

Predicting Protein-Protein Interactions Using Sequence and Network Information via Variational Graph Autoencoder.

IEEE/ACM transactions on computational biology and bioinformatics
Protein-protein interactions (PPIs) play a critical role in the proteomics study, and a variety of computational algorithms have been developed to predict PPIs. Though effective, their performance is constrained by high false-positive and false-negat...

Prediction of Protein-Protein Interactions Using Vision Transformer and Language Model.

IEEE/ACM transactions on computational biology and bioinformatics
The knowledge of protein-protein interaction (PPI) helps us to understand proteins' functions, the causes and growth of several diseases, and can aid in designing new drugs. The majority of existing PPI research has relied mainly on sequence-based ap...

E2EDA: Protein Domain Assembly Based on End-to-End Deep Learning.

Journal of chemical information and modeling
With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and fur...

SeqPredNN: a neural network that generates protein sequences that fold into specified tertiary structures.

BMC bioinformatics
BACKGROUND: The relationship between the sequence of a protein, its structure, and the resulting connection between its structure and function, is a foundational principle in biological science. Only recently has the computational prediction of prote...

DensePPI: A Novel Image-Based Deep Learning Method for Prediction of Protein-Protein Interactions.

IEEE transactions on nanobioscience
Protein-protein interactions (PPI) are crucial for understanding the behaviour of living organisms and identifying disease associations. This paper proposes DensePPI, a novel deep convolution strategy applied to the 2D image map generated from the in...

Folding and functions of knotted proteins.

Current opinion in structural biology
Topologically knotted proteins have entangled structural elements within their native structures that cannot be disentangled simply by pulling from the N- and C-termini. Systematic surveys have identified different types of knotted protein structures...