BIM_2010_20_Bioinformatics_Project
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Transcript of BIM_2010_20_Bioinformatics_Project
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Validation of Time Series Technique for Prediction of Conformational States of Amino Acids
Dr. Sangeeta Sawant , Bioinformatics Centre, UoP, Pune (Guide)
Dr. Mohan Kale, Dept. of Statistics, UoP, Pune (co-guide)
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Concepts Used
Ramachandran Plot
Time series
AR,ARMA,ARIMA models
AIC criteria
Euclidean distance
Potential values for AA residues
Feynman Problem Solving Algorithm
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Ramachandran Plot
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Time Series
a sequence of data points or set of observations, measured typically at successive time instants spaced at uniform time intervals.
Patterns, variations
forecasting
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Autoregressive (AR) models
Autoregressive-moving average (ARMA)
Autoregressive integrated moving average (ARIMA) models
- depend linearly on previous data points
Time Series Models (probability model)
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Materials & Methods
R
R-Studio, Tinn-R
bio3d,itsmr,forecast,tseries,timsac,wordcloud
ITSM_2000- Standalone
R Nabble
BioStars
stats.stackexchange
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Methods
A) Calculation of Potential values for AA residues
B)Forecasting of AA states
C) Clustering
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Calculation of Potential values for AA residues
Dataset-I
Assignment of Conformational state 1, 2, or 3 - to regions I, II, or III of the Rama. Plot, to each amino-acid residue (Phi_psi values)
Phi-Psi values –torsion.pdb() of “bio3d” & verified via PDBGoodies (IISC, Bangalore) & Protein Angle Descriptor utility (IIT, Delhi )
Chain breaks, only CA atoms
Expt. method-X-ray, R-factor: - 0-0.25 (for best resolved structures)
3829 proteins selected from PDB (Protein Data Bank) –PDBSelect dataset list(25 % seq. similarity)
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Figure No- 2 Ramachandran plot showing three conformational regions I ,II and III
I- closely/tightly packed conformations, Phi-140 to 0,Psi -100 to 0 II-extended conformations, Phi -180 to 0, Psi 80 to 180 III- all remaining confirmations
ᵠ
ᶲ
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Frequencies of single residues in three states calculated & normalized using (Kolaskar, A.S. & Sawant, S.V. -1996 )
ikik
kik
nn
Nni=P
Nik –no. of times the AA of type (i) occurs in state k=1-3;
N -total no. of residues
Pik -potential values of AA of type (i) in state k
Potential values in pdf
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Potential values
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Time Series
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ACF Plot
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ACF –Stat Vs. Non-stationary
Non-stationary
Stationary
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Time Series
Stationary
Non-stationary
Stationary
ACF plot
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Stationary TS
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TS model building…..
AR (p)
ARMA(p,q)
ARIMA (p,q)
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Best model Selection
AR (p)
ARMA (p, q)
ARIMA (p, q)
AIC
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Forecasting of AA states for best models
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Forecasting of AA states for best models….
e.g. for AR(1) process,
X t = φ X (t-1) + Z (t), t=0,± 1,….
Where {Z t}~ WN (0, s2) & | φ | <1
1st observed potential for AA with index given as data points & t respectively, prediction starts from 2nd position up to last index
using forecast() “itsmr”
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Similarly for ARMA (1,1) /ARIMA (1,1)
X t = φ X (t-1) + Z (t) + θ Z (t-1), θ + φ
Forecasting Quality by coefficient of determination (R2) using formula
2
2
2 1)Y(Y
)F(Y=R
i
ii
Yi =True value /Observed value Fi = Forecasted/predicted value
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Clustering
Dataset-II
SCOP Domain specific PDB-style files(ATOM & HETATM records ) downloaded from
ASTRAL Compendium for Sequence and Structure Analysis -release 1.75 (June 2009)
Scan for chain breaks & presence of CA atoms only, breaked files kept aside
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Length of AA residues(100-110) e.g. 10gsa1_a_133_pot.txt
File
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Potential values (Time series),each domain divided into stationary (506) & non-stationary process (1692)
Non-stationary data kept aside for further transformations
AR,ARMA & ARIMA models
Best model (minimum AIC criteria)
Best-AR(22),ARMA(484),ARIMA(No model)
AR(p), ARMA(p,q) -distance matrix (Euclidean distance )
Dendrogram-Neighbour-joing ( Phylip packages)
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Dendrogram_TS –AR models-22
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Results & Discussion
For each AA of all the proteins, 3D- Cartesian co-ordinates were transformed into 2D info. i.e. conformational states of AA and potential values were computed and used to build time-distance (index of AA) dependent statistical model as time series for forecasting purposes.
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AR values
Autoregressive order (p) 1-18 range
Short & long range dependence variations in protein structural arrangements
Variations proves diversity exhibits through structural components
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All (a)-12 All (b)-5 / (c)-9 + (d)-13 Small
proteins
(g)-1
Coiled-coil
(h)-3
Designed
proteins
(k)-1
Max Min Max Min Max Min Max Min Max Min
AA
seq
(%)
26.82 2.41 16.30 8.88 27.77 1.47 28.57 7.04 19.51 22.5 5.88 29.03
States
(%)
55.68 21.77 51.11 44.76 54.76 30.64 51.70 19.04 48.78 26 15 26.88
Table No. II – Forecasting results for AR models (44) out of best 90 models (Note- for 46 models, class information not found in SCOP database) All values are in % accuracy
Conformational states accuracy > AA residues accuracy due to low resolution of potential values(forecasted values)
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All (a)-123 All (b)-146 / (c)-120 + (d)-127 Multi domains
proteins (e)-13
Membrane &
cell surface
(f)-3
Small
proteins(g)-
17
Max Min Max Min Max Min Max Min Max Min Max Min Max Min
AA
seq
(%)
32.55 2.63 32.81 3.96 43.47 5 37.96 2.70 24.39 6.034 12.65 7.01 30.64 6.60
States
(%)
65.77 8.06 65.01 17.94 62.89 8.97 68.15 11.11 50 17.80 34.33 11.42 64.51 14.28
Table No. III– Forecasting results for ARMA models (557) out of best 1239 models (Note- for 682 models, class information not found in SCOP database) —All values are in % accuracy
Due to non-representative dataset & inadequate info. about class, can’t say that for any particular class i) pred. accuracy ↑ or ↓ & ii) follows mostly ARMA process
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Discussion
TS graphs opens new door in scientific visualization of proteins (no 3D str. info) i.e. specific AA can be visualized on line plot with its value proportional to frequency to occur into allowed regions of Ramachandran plot.
Potential value for each AA adds new feature of selection in machine learning techniques.
Order of AR model tells how current value linearly related to past p value
Intra-dependency of AA shown using models of TS e.g. AR(4),ARMA(1,3)
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Found new way of looking at protein structure prediction.
Application of TS technique for predicting conformational states based on the conformational state potentials instead of secondary str. has been attempted.
Accuracy of prediction of conformational states for AA, using time series is higher than that for prediction of AA residues.
To increase accuracy for prediction, multivariate time series concept may be useful instead of uni-variate time series
Intra-fluctuations inside proteins, due to AA arrangement can be traced out by stationary & non-stationary groups
CONCLUSIONS
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AR and MA order of TS models -as point of genetic information (distances) to predict evolutionary relationship between different proteins.
TS concept can be used to predict conformational states of missing residues in PDB data files
Hierarchical clustering/classification of TS of proteins -birth to new concept of time dependent clustering (pseudo-clustering) & pseudo-phylogeny.
Development of synthetic proteins to combat seasonal diseases & to tackle chemical warfare attacks.
TS fluctuations for specific class of proteins can be used as “Pattern” for data analysis and pattern-dependent classification of proteins
FUTURE WORK
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References
Blundell TL, Sibanda BL, Sternberg MJ, Thornton JM. Knowledge-based prediction of protein structures and the design of novel molecules. Nature. 1987 Mar 26-Apr 1;326(6111):347-52. Review
Kolaskar, A.S., Sawant, S.V. (1996). Prediction of conformational states of amino acids using a Ramachandran plot. Int.J.Peptide Protein Res.110-116
Alessandro G.,Romualdo B.,(2000). Nonlinear Methods in the Analysis of Protein Sequences:A Case Study in Rubredoxins. Biophysical Journal.136-148
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Questions
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Thank You !