Results 71 to 80 of about 418,398 (323)
DSC: public domain protein secondary structure prediction [PDF]
1. DSC has high accuracy. It has a prediction accuracy of 70.1% (per residue) on a standard set of 126 proteins. This percentage was confirmed by the recent CASP2 blind prediction challenge (see below). 2. DSC is based on simple linear statistics. Existing highaccuracy prediction methods are 'black-box' predictors based on complex non-linear statistics
King, Ross D. +3 more
openaire +3 more sources
Cell wall target fragment discovery using a low‐cost, minimal fragment library
LoCoFrag100 is a fragment library made up of 100 different compounds. Similarity between the fragments is minimized and 10 different fragments are mixed into a single cocktail, which is soaked to protein crystals. These crystals are analysed by X‐ray crystallography, revealing the binding modes of the bound fragment ligands.
Kaizhou Yan +5 more
wiley +1 more source
A dynamic Bayesian network approach to protein secondary structure prediction
Background Protein secondary structure prediction method based on probabilistic models such as hidden Markov model (HMM) appeals to many because it provides meaningful information relevant to sequence-structure relationship.
Zhu Huaiqiu, Yao Xin-Qiu, She Zhen-Su
doaj +1 more source
Secondary Protein Structure Prediction Using Neural Networks [PDF]
Sidharth Malhotra, Robin Walters
openalex +1 more source
Predicting protein properties such as solvent accessibility and secondary structure from its primary amino acid sequence is an important task in bioinformatics.
Lanchantin, Jack +2 more
core +1 more source
Abundance of intrinsic disorder in SV-IV, a multifunctional androgen-dependent protein secreted from rat seminal vesicle [PDF]
The potent immunomodulatory, anti-inflammatory and procoagulant properties of the protein no. 4 secreted from the rat seminal vesicle epithelium (SV-IV) have been previously found to be modulated by a supramolecular monomer-trimer ...
Ambrosone +66 more
core +3 more sources
Prediction of protein secondary structures from conformational biases [PDF]
AbstractWe use LINUS (the “Local Independently Nucleated Units of Structure”), a procedure developed by Srinivasan and Rose, to provide a physical interpretation of and predict the secondary structures of proteins. The secondary structure type at a given site is identified by the largest conformational bias during short simulations. We examine the rate
T. X. HOANG +3 more
openaire +3 more sources
A‐to‐I editing of miRNAs, particularly miR‐200b‐3p, contributes to HGSOC progression by enhancing cancer cell proliferation, migration and 3D growth. The edited form is linked to poorer patient survival and the identification of novel molecular targets.
Magdalena Niemira +14 more
wiley +1 more source
Prediction of 8-state protein secondary structures by a novel deep learning architecture
Background Protein secondary structure can be regarded as an information bridge that links the primary sequence and tertiary structure. Accurate 8-state secondary structure prediction can significantly give more precise and high resolution on structure ...
Buzhong Zhang, Jinyan Li, Qiang Lü
doaj +1 more source
Following high dose rate brachytherapy (HDR‐BT) for hepatocellular carcinoma (HCC), patients were classified as responders and nonresponders. Post‐therapy serum induced increased BrdU incorporation and Cyclin E expression of Huh7 and HepG2 cells in nonresponders, but decreased levels in responders.
Lukas Salvermoser +14 more
wiley +1 more source

