Information Technology Reference
In-Depth Information
Table 2
List of sequence pro
le-based web servers and programmes for secondary structure
prediction along with the webpage URL and the programme description
S. no.
Name of the web server/group (URL)
Description of the web server/group
1
A simple and accurate secondary struc-
ture prediction server, incorporating two
feed-forward neural networks which
perform an analysis on output obtained
from PSI-BLAST
2
PORTER (Pollastri and McLysaght
2005
)[
http://distill.ucd.ie/porter/
]
A server which relies on bidirectional
recurrent neural networks with shortcut
connections, accurate coding of input
pro
les obtained from multiple sequence
alignments, second stage
filtering by
recurrent neural networks
3
PHD (Rost et al.
1994
)[
http://npsapbil.
An automated server which uses evolu-
tionary information from multiple
sequence alignment to predict the sec-
ondary structure prediction of proteins
4
JPRED3 (Cole et al.
2008
)[
http://www.
Jpred incorporates the Jnet algorithm in
order to make more accurate predictions.
In addition to protein secondary struc-
ture Jpred also makes predictions on
solvent accessibility and coiled-coil
regions
5
STRIDE (Heinig and Frishman
2004
)
This server implements a knowledge-
based algorithm that makes combined
use of hydrogen bond energy and
statistically derived backbone torsional
angle information
6
SPARROW (Bettella et al.
2012
)[
http://
This server uses a hierarchical scheme of
scoring functions and a neural network
to predict the secondary structure
7
SOPMA (Geourjon and Del
é
age
1995
)
A web-server which improved their
prediction accuracy when combined
with PHD secondary structure prediction
method
3.1 Ab Initio Folding/Modeling
This method is simply based on elementary fundamentals of energy and geometry
(Moult and Melamud
2000
). Ab initio structure prediction seeks to predict the
native conformation of a protein from the amino acid sequence alone. Ab initio
prediction of protein structures makes no use of information available in databases
mainly PDB (Nanias et al.
2005
). The goal of this method is to predict the structure
of a protein based entirely on the laws of physics and chemistry. It is assumed that
the actual native state of a protein sequence has the lowest free energy. It means that
the protein native state conformation is basically a model at the global minima of
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