A statistical analytical approach to predict the secondary structure of proteins from amino acid sequence information

1999 ◽  
Vol 101 (1-3) ◽  
pp. 41-45
Author(s):  
Shrish Tiwari ◽  
Boojala V. B. Reddy
Keyword(s):  
Amino Acid ◽  
Secondary Structure ◽  
Amino Acid Sequence ◽  
Analytical Approach ◽  
Sequence Information ◽  
Secondary Structure Of Proteins ◽  
Structure Of Proteins ◽  
Amino Acid Sequence Information

scholarly journals Molecular Identification of Family 38 α-Mannosidase of Bacillus sp. Strain GL1, Responsible for Complete Depolymerization of Xanthan

2002 ◽  
Vol 68 (6) ◽  
pp. 2731-2736 ◽  
Author(s):  
Hirokazu Nankai ◽  
Wataru Hashimoto ◽  
Kousaku Murata
Keyword(s):  
Amino Acid ◽  
Amino Acid Sequence ◽  
Cell Extract ◽  
Amino Acid Sequences ◽  
Glycoside Hydrolase Family ◽  
Sequence Information ◽  
Reading Frame ◽  
A Cell ◽  
Terminal Amino ◽  
Amino Acid Sequence Information

ABSTRACT When cells of Bacillus sp. strain GL1 were grown in a medium containing xanthan as a carbon source, α-mannosidase exhibiting activity toward p-nitrophenyl-α-d-mannopyranoside (pNP-α-d-Man) was produced intracellularly. The 350-kDa α-mannosidase purified from a cell extract of the bacterium was a trimer comprising three identical subunits, each with a molecular mass of 110 kDa. The enzyme hydrolyzed pNP-α-d-Man (Km = 0.49 mM) and d-mannosyl-(α-1,3)-d-glucose most efficiently at pH 7.5 to 9.0, indicating that the enzyme catalyzes the last step of the xanthan depolymerization pathway of Bacillus sp. strain GL1. The gene for α-mannosidase cloned most by using N-terminal amino acid sequence information contained an open reading frame (3,144 bp) capable of coding for a polypeptide with a molecular weight of 119,239. The deduced amino acid sequence showed homology with the amino acid sequences of α-mannosidases belonging to glycoside hydrolase family 38.

scholarly journals CoRNeA: A Pipeline to Decrypt the Inter-Protein Interfaces from Amino Acid Sequence Information

Biomolecules ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 938
Author(s):  
Kriti Chopra ◽  
Bhawna Burdak ◽  
Kaushal Sharma ◽  
Ajit Kembhavi ◽  
Shekhar C. Mande ◽  
...  
Keyword(s):  
Amino Acid ◽  
Random Forest ◽  
Amino Acid Sequence ◽  
Protein Complexes ◽  
Sequence Information ◽  
Contact Potential ◽  
Prokaryotic Protein ◽  
The Individual ◽  
Amino Acid Sequence Information ◽  
Interface Residues

Decrypting the interface residues of the protein complexes provides insight into the functions of the proteins and, hence, the overall cellular machinery. Computational methods have been devised in the past to predict the interface residues using amino acid sequence information, but all these methods have been majorly applied to predict for prokaryotic protein complexes. Since the composition and rate of evolution of the primary sequence is different between prokaryotes and eukaryotes, it is important to develop a method specifically for eukaryotic complexes. Here, we report a new hybrid pipeline for predicting the protein-protein interaction interfaces in a pairwise manner from the amino acid sequence information of the interacting proteins. It is based on the framework of Co-evolution, machine learning (Random Forest), and Network Analysis named CoRNeA trained specifically on eukaryotic protein complexes. We use Co-evolution, physicochemical properties, and contact potential as major group of features to train the Random Forest classifier. We also incorporate the intra-contact information of the individual proteins to eliminate false positives from the predictions keeping in mind that the amino acid sequence of a protein also holds information for its own folding and not only the interface propensities. Our prediction on example datasets shows that CoRNeA not only enhances the prediction of true interface residues but also reduces false positive rates significantly.

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