Testing for Spatial Clustering of Amino Acid Replacements Within Protein Tertiary Structure

2006 ◽  
Vol 62 (6) ◽  
pp. 682-692 ◽  
Author(s):  
Jiaye Yu ◽  
Jeffrey L. Thorne
Keyword(s):  
Amino Acid ◽  
Tertiary Structure ◽  
Spatial Clustering ◽  
Protein Tertiary Structure

PROTEIN TERTIARY STRUCTURE PREDICTION BASED ON MAIN CHAIN ANGLE USING A HYBRID BEES COLONY OPTIMIZATION ALGORITHM

2012 ◽  
Vol 09 ◽  
pp. 143-156 ◽  
Author(s):  
ZAKARIA N. MAHMOOD ◽  
MASSUDI MAHMUDDIN ◽  
MOHAMMED NOORALDEEN MAHMOOD
Keyword(s):  
Amino Acid ◽  
Amino Acid Sequence ◽  
Optimization Algorithm ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Promising Result ◽  
3D Structure ◽  
Research Area ◽  
Prototype System ◽  
Protein Tertiary Structure

Encoding proteins of amino acid sequence to predict classified into their respective families and subfamilies is important research area. However for a given protein, knowing the exact action whether hormonal, enzymatic, transmembranal or nuclear receptors does not depend solely on amino acid sequence but on the way the amino acid thread folds as well. This study provides a prototype system that able to predict a protein tertiary structure. Several methods are used to develop and evaluate the system to produce better accuracy in protein 3D structure prediction. The Bees Optimization algorithm which inspired from the honey bees food foraging method, is used in the searching phase. In this study, the experiment is conducted on short sequence proteins that have been used by the previous researches using well-known tools. The proposed approach shows a promising result.

scholarly journals Template-based prediction of protein structure with deep learning

BMC Genomics ◽  
2020 ◽  
Vol 21 (S11) ◽  
Author(s):  
Haicang Zhang ◽  
Yufeng Shen
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Query Sequence ◽  
Dynamic Programming Algorithm ◽  
Tertiary Structure Prediction ◽  
Protein Tertiary Structure ◽  
Protein Threading ◽  
Protein Tertiary Structure Prediction

Abstract Background Accurate prediction of protein structure is fundamentally important to understand biological function of proteins. Template-based modeling, including protein threading and homology modeling, is a popular method for protein tertiary structure prediction. However, accurate template-query alignment and template selection are still very challenging, especially for the proteins with only distant homologs available. Results We propose a new template-based modelling method called ThreaderAI to improve protein tertiary structure prediction. ThreaderAI formulates the task of aligning query sequence with template as the classical pixel classification problem in computer vision and naturally applies deep residual neural network in prediction. ThreaderAI first employs deep learning to predict residue-residue aligning probability matrix by integrating sequence profile, predicted sequential structural features, and predicted residue-residue contacts, and then builds template-query alignment by applying a dynamic programming algorithm on the probability matrix. We evaluated our methods both in generating accurate template-query alignment and protein threading. Experimental results show that ThreaderAI outperforms currently popular template-based modelling methods HHpred, CNFpred, and the latest contact-assisted method CEthreader, especially on the proteins that do not have close homologs with known structures. In particular, in terms of alignment accuracy measured with TM-score, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 56, 13, and 11%, respectively, on template-query pairs at the similarity of fold level from SCOPe data. And on CASP13’s TBM-hard data, ThreaderAI outperforms HHpred, CNFpred, and CEthreader by 16, 9 and 8% in terms of TM-score, respectively. Conclusions These results demonstrate that with the help of deep learning, ThreaderAI can significantly improve the accuracy of template-based structure prediction, especially for distant-homology proteins.

scholarly journals Catalytic and Molecular Properties of the Quinohemoprotein Tetrahydrofurfuryl Alcohol Dehydrogenase from Ralstonia eutropha Strain Bo

2001 ◽  
Vol 183 (6) ◽  
pp. 1954-1960 ◽  
Author(s):  
Grit Zarnt ◽  
Thomas Schräder ◽  
Jan R. Andreesen
Keyword(s):  
Amino Acid ◽  
Amino Acid Sequence ◽  
Alcohol Dehydrogenase ◽  
Tertiary Structure ◽  
Ralstonia Eutropha ◽  
Signal Sequence ◽  
Substrate Inhibition ◽  
Catalytic Efficiency ◽  
Gene Encoding

ABSTRACT The quinohemoprotein tetrahydrofurfuryl alcohol dehydrogenase (THFA-DH) from Ralstonia eutropha strain Bo was investigated for its catalytic properties. The apparentk cat/Km andK i values for several substrates were determined using ferricyanide as an artificial electron acceptor. The highest catalytic efficiency was obtained with n-pentanol exhibiting a k cat/Km value of 788 × 104 M−1 s−1. The enzyme showed substrate inhibition kinetics for most of the alcohols and aldehydes investigated. A stereoselective oxidation of chiral alcohols with a varying enantiomeric preference was observed. Initial rate studies using ethanol and acetaldehyde as substrates revealed that a ping-pong mechanism can be assumed for in vitro catalysis of THFA-DH. The gene encoding THFA-DH from R. eutropha strain Bo (tfaA) has been cloned and sequenced. The derived amino acid sequence showed an identity of up to 67% to the sequence of various quinoprotein and quinohemoprotein dehydrogenases. A comparison of the deduced sequence with the N-terminal amino acid sequence previously determined by Edman degradation analysis suggested the presence of a signal sequence of 27 residues. The primary structure of TfaA indicated that the protein has a tertiary structure quite similar to those of other quinoprotein dehydrogenases.

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