AIMC Topic: Databases, Protein

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Protein-Protein Interfaces: A Graph Neural Network Approach.

International journal of molecular sciences
Protein-protein interactions (PPIs) are fundamental processes governing cellular functions, crucial for understanding biological systems at the molecular level. Compared to experimental methods for PPI prediction and site identification, computationa...

MoRF_ESM: Prediction of MoRFs in disordered proteins based on a deep transformer protein language model.

Journal of bioinformatics and computational biology
Molecular recognition features (MoRFs) are particular functional segments of disordered proteins, which play crucial roles in regulating the phase transition of membrane-less organelles and frequently serve as central sites in cellular interaction ne...

Prediction of peptide hormones using an ensemble of machine learning and similarity-based methods.

Proteomics
Peptide hormones serve as genome-encoded signal transduction molecules that play essential roles in multicellular organisms, and their dysregulation can lead to various health problems. In this study, we propose a method for predicting hormonal pepti...

Prediction of Protein-Protein Interactions Based on Integrating Deep Learning and Feature Fusion.

International journal of molecular sciences
Understanding protein-protein interactions (PPIs) helps to identify protein functions and develop other important applications such as drug preparation and protein-disease relationship identification. Deep-learning-based approaches are being intensel...

PfgPDI: Pocket feature-enabled graph neural network for protein-drug interaction prediction.

Journal of bioinformatics and computational biology
Biomolecular interaction recognition between ligands and proteins is an essential task, which largely enhances the safety and efficacy in drug discovery and development stage. Studying the interaction between proteins and ligands can improve the unde...

Transferable deep generative modeling of intrinsically disordered protein conformations.

PLoS computational biology
Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. ...

SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks.

International journal of molecular sciences
The subcellular location of a protein provides valuable insights to bioinformaticians in terms of drug designs and discovery, genomics, and various other aspects of medical research. Experimental methods for protein subcellular localization determina...

Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF.

Nature communications
Immunopeptidomics is crucial for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible pro...

ProBAN: Neural network algorithm for predicting binding affinity in protein-protein complexes.

Proteins
Determining binding affinities in protein-protein and protein-peptide complexes is a challenging task that directly impacts the development of peptide and protein pharmaceuticals. Although several models have been proposed to predict the value of the...

CollaPPI: A Collaborative Learning Framework for Predicting Protein-Protein Interactions.

IEEE journal of biomedical and health informatics
Exploring protein-protein interaction (PPI) is of paramount importance for elucidating the intrinsic mechanism of various biological processes. Nevertheless, experimental determination of PPI can be both time-consuming and expensive, motivating the e...