NMR paper Application of Data Mining Tools for Classification of Protein Structural Class from Residue Based Averaged NMR Chemical Shifts.

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[NMR paper] Application of Data Mining Tools for Classification of Protein Structural Class from Residue Based Averaged NMR Chemical Shifts.

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NMR assignment:

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Side-chains:

NOEs:

Structure from NMR restraints:

Ab initio:

Fragment-based:

Rosetta-NMR (Robetta)

Template-based:

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I-TASSER

Refinement:

Amber

Structure from chemical shifts:

Fragment-based:

Homology-based:

Torsion angles from chemical shifts:

Preditor

TALOS

Promega- Proline

Secondary structure from chemical shifts:

CSI (via RCI server)

TALOS

MICS caps, β-turns

d2D

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RCI

Interactions from chemical shifts:

HADDOCK

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Backbone S2

Methyl S2

B-factor

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From structure:

Shiftx2

Sparta+

Camshift

CH3shift- Methyl

ArShift- Aromatic

ShiftS

Proshift

PPM

CheShift-2- Cα

From sequence:

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Camcoil

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Disordered proteins:

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Format conversion & validation:

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Protein disorder:

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Protein solubility:

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03-12-2015, 10:33 AM

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Application of Data Mining Tools for Classification of Protein Structural Class from Residue Based Averaged NMR Chemical Shifts.

Application of Data Mining Tools for Classification of Protein Structural Class from Residue Based Averaged NMR Chemical Shifts.

Application of Data Mining Tools for Classification of Protein Structural Class from Residue Based Averaged NMR Chemical Shifts.

Biochim Biophys Acta. 2015 Mar 7;

Authors: Kumar AV, Ali RF, Cao Y, Krishnan VV

Abstract
The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for protein structural information that is directly related to function. Nuclear magnetic resonance (NMR) provides powerful means to determine three-dimensional structures of proteins in the solution state. However, translation of the NMR spectral parameters to even low-resolution structural information such as protein class requires multiple time consuming steps. In this paper, we present an unorthodox method to predict the protein structural class directly by using the residue's averaged chemical shifts (ACS) based on machine learning algorithms. Experimental chemical shift information from 1491 proteins obtained from Biological Magnetic Resonance Bank (BMRB) and their respective protein structural classes derived from structural classification of proteins (SCOP) were used to construct a data set with 119 attributes and 5 different classes. Twenty four different classification schemes were evaluated using several performance measures. Overall the residue based ACS values can predict the protein structural classes with 80 % accuracy measured by Matthew Correlation coefficient. Specifically protein classes defined by mixed ?? or small proteins are classified with > 90% correlation. Our results indicate that this NMR-based method can be utilized as a low-resolution tool for protein structural class identification without any prior chemical shift assignments.

PMID: 25758094 [PubMed - as supplied by publisher]

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