protein secondary structure

1. • PROTEIN SECONDRY STRUCTURE
AND ITS PREDICTING ELEMENT

2. CONTENTS
• INTRODUCTION
• PROTIEN STRUCTURE
• SECONDRY STRUCTURE
• ALPHA-HELIX
• TYPES OF HELIXS
• BETA PLEATED SHEATS
• SECONDRY PROTEIN STRUCTURE PREDICTION
• PURPOSE OF SECONDRY STRUCTURE PREDICTION
• OBJECTIVES
• ALPHA HELIX
• BETA SHEATS
• METHODS OF SECONDRY STRUCTURE PREDICTION
• CHOU FASMAN METHOD
• NEAREST NEIGHBOUR METHOD
• REFRENCES

4. • PROTEIN STRUCTURE

5. SECONDRY STRUCTURE

21. HIDDEN MARKOV METHOD
HIDDEN MARKOV means the state is directly invisible
to be observer. It provide a basic description of what
pfam provides. Each HMM is trained with the sequences
of the protein in that structural class. The models used are
with a query sequence to predict both the class and
secondary structure.

23. Pfam
Pfam is a database of protein families that includes their annotations and
multiple sequence alignments generated using hidden Markov models
Features:
Family Clan Sequence Structure Complete Proteone For Searching and
visualizing data in the Pfam sequence Browse Search Sequence search
Submit In Pfam families: We can enter the domain, motif repeat eg. Piwi In
Pfam Structures: We can choose the viewing structures open Astex Viewer

24. NEURAL NETWORKS
Most effective structure prediction tool for pattern recognition and classification.
the protein sequence is translated into patterns by shifting a window of n adjacent
residues (n= 13-21) through the protein. Methods to predict,
Hierchical Neural Network
nnPredict
PSA
PSIPRED
Gen THREADER
MEMSAT
PSI BLAST Version 2.0

26. MULTIPLE ALIGNMENTS BASED SELF- OPTIMIZATION
METHOD
SOPMA(Self-Optimized Prediction Method with Alignment)
correctly predicts 69.5% of amino acids for a three state description
of the secondary structure in a whole database containing 126
chains of non- homologous proteins.
Joint prediction with SOPMA and PHD correctly predicts 82.2%
of residues for 74% of co- predicted amino acids.

27. REFRENCES
1. Alberts B. et al. Molecular biology of the cell, 5th ed. New York: Garland Science
(2008).
2. Yang, Y. et al. Sixty-five years of the long march in protein secondary structure
prediction: the final stretch? Briefings in Bioinformatics (2016).
3. Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of
hydrogen-bonded and geometrical features. Biopolymers. 1983;22:2577–2637. doi:
10.1002/bip.360221211.
4. Fasman GD, Chou PY. Prediction of protein conformation: consequences and
aspirations. 5. Chou PY, Fasman GD. Conformational parameters for amino acids in
helical, beta-sheet, and random coil regions calculated from proteins.