AIMC Topic: Protein Domains

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Evotuning protocols for Transformer-based variant effect prediction on multi-domain proteins.

Briefings in bioinformatics
Accurate variant effect prediction has broad impacts on protein engineering. Recent machine learning approaches toward this end are based on representation learning, by which feature vectors are learned and generated from unlabeled sequences. However...

dSPRINT: predicting DNA, RNA, ion, peptide and small molecule interaction sites within protein domains.

Nucleic acids research
Domains are instrumental in facilitating protein interactions with DNA, RNA, small molecules, ions and peptides. Identifying ligand-binding domains within sequences is a critical step in protein function annotation, and the ligand-binding properties ...

Deep representation learning improves prediction of LacI-mediated transcriptional repression.

Proceedings of the National Academy of Sciences of the United States of America
Recent progress in DNA synthesis and sequencing technology has enabled systematic studies of protein function at a massive scale. We explore a deep mutational scanning study that measured the transcriptional repression function of 43,669 variants of ...

Structures of the β-barrel assembly machine recognizing outer membrane protein substrates.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
β-barrel outer membrane proteins (β-OMPs) play critical roles in nutrition acquisition, protein import/export, and other fundamental biological processes. The assembly of β-OMPs in Gram-negative bacteria is mediated by the β-barrel assembly machinery...

FUpred: detecting protein domains through deep-learning-based contact map prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Protein domains are subunits that can fold and function independently. Correct domain boundary assignment is thus a critical step toward accurate protein structure and function analyses. There is, however, no efficient algorithm available...

DNN-Dom: predicting protein domain boundary from sequence alone by deep neural network.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate delineation of protein domain boundary plays an important role for protein engineering and structure prediction. Although machine-learning methods are widely used to predict domain boundary, these approaches often ignore long-ran...

ConDo: protein domain boundary prediction using coevolutionary information.

Bioinformatics (Oxford, England)
MOTIVATION: Domain boundary prediction is one of the most important problems in the study of protein structure and function. Many sequence-based domain boundary prediction methods are either template-based or machine learning (ML) based. ML-based met...

BIPSPI: a method for the prediction of partner-specific protein-protein interfaces.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-Protein Interactions (PPI) are essentials for most cellular processes and thus, unveiling how proteins interact is a crucial question that can be better understood by identifying which residues are responsible for the interaction....

DeepDom: Predicting protein domain boundary from sequence alone using stacked bidirectional LSTM.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Protein domain boundary prediction is usually an early step to understand protein function and structure. Most of the current computational domain boundary prediction methods suffer from low accuracy and limitation in handling multi-domain types, or ...

Structure-based prediction of protein- peptide binding regions using Random Forest.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-peptide interactions are one of the most important biological interactions and play crucial role in many diseases including cancer. Therefore, knowledge of these interactions provides invaluable insights into all cellular processe...