AI Medical Compendium Journal:
Protein science : a publication of the Protein Society

Showing 21 to 30 of 35 articles

MA-PEP: A novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism.

Protein science : a publication of the Protein Society
AntiCancer Peptides (ACPs) have emerged as promising therapeutic agents for cancer treatment. The time-consuming and costly nature of wet-lab discriminatory methods has spurred the development of various machine learning and deep learning-based ACP c...

Protein structure accuracy estimation using geometry-complete perceptron networks.

Protein science : a publication of the Protein Society
Estimating the accuracy of protein structural models is a critical task in protein bioinformatics. The need for robust methods in the estimation of protein model accuracy (EMA) is prevalent in the field of protein structure prediction, where computat...

TEPCAM: Prediction of T-cell receptor-epitope binding specificity via interpretable deep learning.

Protein science : a publication of the Protein Society
The recognition of T-cell receptor (TCR) on the surface of T cell to specific epitope presented by the major histocompatibility complex is the key to trigger the immune response. Identifying the binding rules of TCR-epitope pair is crucial for develo...

DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning.

Protein science : a publication of the Protein Society
Fungal infections have become a significant global health issue, affecting millions worldwide. Antifungal peptides (AFPs) have emerged as a promising alternative to conventional antifungal drugs due to their low toxicity and low propensity for induci...

QuoteTarget: A sequence-based transformer protein language model to identify potentially druggable protein targets.

Protein science : a publication of the Protein Society
The development of efficient computational methods for drug target protein identification can compensate for the high cost of experiments and is therefore of great significance for drug development. However, existing structure-based drug target prote...

Neural network-derived Potts models for structure-based protein design using backbone atomic coordinates and tertiary motifs.

Protein science : a publication of the Protein Society
Designing novel proteins to perform desired functions, such as binding or catalysis, is a major goal in synthetic biology. A variety of computational approaches can aid in this task. An energy-based framework rooted in the sequence-structure statisti...

AMP-BERT: Prediction of antimicrobial peptide function based on a BERT model.

Protein science : a publication of the Protein Society
Antimicrobial resistance is a growing health concern. Antimicrobial peptides (AMPs) disrupt harmful microorganisms by nonspecific mechanisms, making it difficult for microbes to develop resistance. Accordingly, they are promising alternatives to trad...

LambdaPP: Fast and accessible protein-specific phenotype predictions.

Protein science : a publication of the Protein Society
The availability of accurate and fast artificial intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first in...

GeoPacker: A novel deep learning framework for protein side-chain modeling.

Protein science : a publication of the Protein Society
Atomic interactions play essential roles in protein folding, structure stabilization, and function performance. Recent advances in deep learning-based methods have achieved impressive success not only in protein structure prediction, but also in prot...

BindWeb: A web server for ligand binding residue and pocket prediction from protein structures.

Protein science : a publication of the Protein Society
Knowledge of protein-ligand interactions is beneficial for biological process analysis and drug design. Given the complexity of the interactions and the inadequacy of experimental data, accurate ligand binding residue and pocket prediction remains ch...