AIMC Topic: Protein Structure, Secondary

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Constructive Prediction of Potential RNA Aptamers for a Protein Target.

IEEE/ACM transactions on computational biology and bioinformatics
Aptamers are short single-stranded nucleic acids that bind to target molecules with high affinity and selectivity. Aptamers are generally identified in vitro by performing SELEX (systematic evolution of ligands by exponential enrichment). Complementi...

Deeper Profiles and Cascaded Recurrent and Convolutional Neural Networks for state-of-the-art Protein Secondary Structure Prediction.

Scientific reports
Protein Secondary Structure prediction has been a central topic of research in Bioinformatics for decades. In spite of this, even the most sophisticated ab initio SS predictors are not able to reach the theoretical limit of three-state prediction acc...

Protein secondary structure prediction using neural networks and deep learning: A review.

Computational biology and chemistry
Literature contains over fifty years of accumulated methods proposed by researchers for predicting the secondary structures of proteins in silico. A large part of this collection is comprised of artificial neural network-based approaches, a field of ...

PaleAle 5.0: prediction of protein relative solvent accessibility by deep learning.

Amino acids
Predicting the three-dimensional structure of proteins is a long-standing challenge of computational biology, as the structure (or lack of a rigid structure) is well known to determine a protein's function. Predicting relative solvent accessibility (...

Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning.

Nature methods
Although structures determined at near-atomic resolution are now routinely reported by cryo-electron microscopy (cryo-EM), many density maps are determined at an intermediate resolution, and extracting structure information from these maps is still a...

DeepACLSTM: deep asymmetric convolutional long short-term memory neural models for protein secondary structure prediction.

BMC bioinformatics
BACKGROUND: Protein secondary structure (PSS) is critical to further predict the tertiary structure, understand protein function and design drugs. However, experimental techniques of PSS are time consuming and expensive, and thus it's very urgent to ...

Analysis and prediction of human acetylation using a cascade classifier based on support vector machine.

BMC bioinformatics
BACKGROUND: Acetylation on lysine is a widespread post-translational modification which is reversible and plays a crucial role in some biological activities. To better understand the mechanism, it is necessary to identify acetylation sites in protein...

Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index.

Nature communications
Chemical shifts (CS) are determined from NMR experiments and represent the resonance frequency of the spin of atoms in a magnetic field. They contain a mixture of information, encompassing the in-solution conformations a protein adopts, as well as th...

MCP: A multi-component learning machine to predict protein secondary structure.

Computers in biology and medicine
The Gene or DNA sequence in every cell does not control genetic properties on its own; Rather, this is done through the translation of DNA into protein and subsequent formation of a certain 3D structure. The biological function of a protein is tightl...

NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning.

Proteins
The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unraveling its function in absence of experimental structural information. Two main factors affect the utility of potential predict...