AIMC Topic: Protein Structure, Secondary

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A deep attention network for predicting amino acid signals in the formation of [Formula: see text]-helices.

Journal of bioinformatics and computational biology
The secondary and tertiary structure of a protein has a primary role in determining its function. Even though many folding prediction algorithms have been developed in the past decades - mainly based on the assumption that folding instructions are en...

Using machine learning to improve ensemble docking for drug discovery.

Proteins
Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking for virtual screening. Unfortunately, as there is no rigorous theory to connect the docking scores from multiple structures to measured activ...

Haruspex: A Neural Network for the Automatic Identification of Oligonucleotides and Protein Secondary Structure in Cryo-Electron Microscopy Maps.

Angewandte Chemie (International ed. in English)
In recent years, three-dimensional density maps reconstructed from single particle images obtained by electron cryo-microscopy (cryo-EM) have reached unprecedented resolution. However, map interpretation can be challenging, in particular if the const...

Multifaceted analysis of training and testing convolutional neural networks for protein secondary structure prediction.

PloS one
Protein secondary structure prediction remains a vital topic with broad applications. Due to lack of a widely accepted standard in secondary structure predictor evaluation, a fair comparison of predictors is challenging. A detailed examination of fac...

Deep neural networks for human microRNA precursor detection.

BMC bioinformatics
BACKGROUND: MicroRNAs (miRNAs) play important roles in a variety of biological processes by regulating gene expression at the post-transcriptional level. So, the discovery of new miRNAs has become a popular task in biological research. Since the expe...

DeepECA: an end-to-end learning framework for protein contact prediction from a multiple sequence alignment.

BMC bioinformatics
BACKGROUND: Recently developed methods of protein contact prediction, a crucially important step for protein structure prediction, depend heavily on deep neural networks (DNNs) and multiple sequence alignments (MSAs) of target proteins. Protein seque...

ProDCoNN: Protein design using a convolutional neural network.

Proteins
Designing protein sequences that fold to a given three-dimensional (3D) structure has long been a challenging problem in computational structural biology with significant theoretical and practical implications. In this study, we first formulated this...

Integrated structural modeling and super-resolution imaging resolve GPCR oligomers.

Progress in molecular biology and translational science
Formation of G protein-coupled receptors (GPCRs) dimers and higher order oligomers represents a key mechanism in pleiotropic signaling, yet how individual protomers function within oligomers remains poorly understood. For the Class A/rhodopsin subfam...

RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning.

Nature communications
The majority of our human genome transcribes into noncoding RNAs with unknown structures and functions. Obtaining functional clues for noncoding RNAs requires accurate base-pairing or secondary-structure prediction. However, the performance of such p...

Sounds interesting: can sonification help us design new proteins?

Expert review of proteomics
: The practice of turning scientific data into music, a practice known as sonification, is a growing field. Driven by analogies between the hierarchical structures of proteins and many forms of music, multiple attempts of mapping proteins to music ha...