Presentation by Andreas Schleicher Tackling the School Absenteeism Crisis 30 ...
Mind map secondary structure prediction 2
1. • 1st LAYER – raw predictions based multiple seq.
align. –sliding 13 windows
• 2nd LAYER – refine by sliding 17 positions
• 3rd LAYER – jury network makes final filtering
(delete short helices)
Background 2nd generation
Definition 3rd generation
[𝒙/𝒎]/[𝒚/𝒏]
Protein Secondary Structure
Prediction
Secondary structure –
stable conformations,
important in maintaining
protein 3D structure
∝-helix
• spiral-like
• 3.6 amino acids/
turn
• Stabilized by H-
bond between
residues I and I+4
ß-sheet
• 2/ more ß strands
extended in zigzag
conformation
• Stabilized by H-bond
between residues of
adjacent strands
prediction of the conformational
state of each amino acid residue
of a protein sequence as one of
the 4 possible states
• Helices (H)
• Strands (E)
Applications
Propensity
Methods
AB-INITIO
• Based on single query
sequence only
• Measure relative
propensity between
amino acids
• 1st & 2nd generation
HOMOLOGY
• Based on multiple
homologous
sequence
• 3rd generation
1st generation
CHOU & FASMAN (1964 & 1978)
Classification of proteins
Separation of protein
domains
Functional motifs
𝒙 no. of selected a. acid residue in H/ E 𝒎 total in H/ E residues
𝒚 total no. of selected a. acid residue 𝒏 total all residues
prediction based on
observed f in protein
crystal structures
disregard coils
• P = 1 (equal chance)
• P < 1 (less chance)
• P > 1 (most likely)
WINDOW SIZE
∝-helix
•6 residues
•4 residues - P>1
•stop at P<1
ß-strand
•5 residues
•at least 3
residues - P>1
Accuracy prediction
Q3 =
𝑁 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑
𝑁 𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑
× 100
Q3
(50%-60%)
include ad. info (adjacent
residues, segmental statistics)
evaluate each residue + adjacent
8 N-terminal & 8 C-terminal
residues
Missed ß-strand region
GOR II, GOR III, SOPM
(advanced) (1980S & early 1990s )
& GOR
METHOD
Q3 (64% / + 10% from 1st generation)
Neural network models
• machines learning process
• adjust mathematical weighs between
internal connections
PHD (1990s)
combine multiple seq. align. &
neural network
perform Blastp (query homologous
seq), align MAXHOM, profile fed in
neural network
OTHER : PSIPRED/JPRED/NNPRED
Q3 (70%- 80%)
Qiestiena Aliya
(1812516)
(SBT 2324)
• Turns (T)
• Coils (C)
![• 1st LAYER – raw predictions based multiple seq.
align. –sliding 13 windows
• 2nd LAYER – refine by sliding 17 positions
• 3rd LAYER – jury network makes final filtering
(delete short helices)
Background 2nd generation
Definition 3rd generation
[𝒙/𝒎]/[𝒚/𝒏]
Protein Secondary Structure
Prediction
Secondary structure –
stable conformations,
important in maintaining
protein 3D structure
∝-helix
• spiral-like
• 3.6 amino acids/
turn
• Stabilized by H-
bond between
residues I and I+4
ß-sheet
• 2/ more ß strands
extended in zigzag
conformation
• Stabilized by H-bond
between residues of
adjacent strands
prediction of the conformational
state of each amino acid residue
of a protein sequence as one of
the 4 possible states
• Helices (H)
• Strands (E)
Applications
Propensity
Methods
AB-INITIO
• Based on single query
sequence only
• Measure relative
propensity between
amino acids
• 1st & 2nd generation
HOMOLOGY
• Based on multiple
homologous
sequence
• 3rd generation
1st generation
CHOU & FASMAN (1964 & 1978)
Classification of proteins
Separation of protein
domains
Functional motifs
𝒙 no. of selected a. acid residue in H/ E 𝒎 total in H/ E residues
𝒚 total no. of selected a. acid residue 𝒏 total all residues
prediction based on
observed f in protein
crystal structures
disregard coils
• P = 1 (equal chance)
• P < 1 (less chance)
• P > 1 (most likely)
WINDOW SIZE
∝-helix
•6 residues
•4 residues - P>1
•stop at P<1
ß-strand
•5 residues
•at least 3
residues - P>1
Accuracy prediction
Q3 =
𝑁 𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑
𝑁 𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑
× 100
Q3
(50%-60%)
include ad. info (adjacent
residues, segmental statistics)
evaluate each residue + adjacent
8 N-terminal & 8 C-terminal
residues
Missed ß-strand region
GOR II, GOR III, SOPM
(advanced) (1980S & early 1990s )
& GOR
METHOD
Q3 (64% / + 10% from 1st generation)
Neural network models
• machines learning process
• adjust mathematical weighs between
internal connections
PHD (1990s)
combine multiple seq. align. &
neural network
perform Blastp (query homologous
seq), align MAXHOM, profile fed in
neural network
OTHER : PSIPRED/JPRED/NNPRED
Q3 (70%- 80%)
Qiestiena Aliya
(1812516)
(SBT 2324)
• Turns (T)
• Coils (C)](https://image.slidesharecdn.com/mindmapsecondarystructureprediction2-200804071129/85/Mind-map-secondary-structure-prediction-2-1-320.jpg)
