AIMC Topic: Proteins

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Accuracy and data efficiency in deep learning models of protein expression.

Nature communications
Synthetic biology often involves engineering microbial strains to express high-value proteins. Thanks to progress in rapid DNA synthesis and sequencing, deep learning has emerged as a promising approach to build sequence-to-expression models for stra...

Novel machine learning approaches revolutionize protein knowledge.

Trends in biochemical sciences
Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no l...

Multi-state modeling of antibody-antigen complexes with SAXS profiles and deep-learning models.

Methods in enzymology
Antibodies are an established class of human therapeutics. Epitope characterization is an important part of therapeutic antibody discovery. However, structural characterization of antibody-antigen complexes remains challenging. On the one hand, X-ray...

Protein-DNA Binding Residue Prediction via Bagging Strategy and Sequence-Based Cube-Format Feature.

IEEE/ACM transactions on computational biology and bioinformatics
Protein-DNA interactions play an important role in diverse biological processes. Accurately identifying protein-DNA binding residues is a critical but challenging task for protein function annotations and drug design. Although wet-lab experimental me...

Accurate Prediction of Human Essential Proteins Using Ensemble Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Essential proteins are considered the foundation of life as they are indispensable for the survival of living organisms. Computational methods for essential protein discovery provide a fast way to identify essential proteins. But most of them heavily...

New Labeling Methods for Deep Learning Real-Valued Inter-Residue Distance Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
BACKGROUND: Much of the recent success in protein structure prediction has been a result of accurate protein contact prediction-a binary classification problem. Dozens of methods, built from various types of machine learning and deep learning algorit...

TripletProt: Deep Representation Learning of Proteins Based On Siamese Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Pretrained representations have recently gained attention in various machine learning applications. Nonetheless, the high computational costs associated with training these models have motivated alternative approaches for representation learning. Her...

Multi-dimensional feature recognition model based on capsule network for ubiquitination site prediction.

PeerJ
Ubiquitination is an important post-translational modification of proteins that regulates many cellular activities. Traditional experimental methods for identification are costly and time-consuming, so many researchers have proposed computational met...

Convolutional ProteinUnetLM competitive with long short-term memory-based protein secondary structure predictors.

Proteins
The protein secondary structure (SS) prediction plays an important role in the characterization of general protein structure and function. In recent years, a new generation of algorithms for SS prediction based on embeddings from protein language mod...

Long-distance dependency combined multi-hop graph neural networks for protein-protein interactions prediction.

BMC bioinformatics
BACKGROUND: Protein-protein interactions are widespread in biological systems and play an important role in cell biology. Since traditional laboratory-based methods have some drawbacks, such as time-consuming, money-consuming, etc., a large number of...