BIM_2010_20_Bioinformatics_Project
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The document discusses the validation of time series techniques for predicting the conformational states of amino acids using various statistical models like AR, ARMA, and ARIMA. It details the materials, methods, and datasets used, including potential values for amino acid residues derived from protein data and clustering techniques. The findings highlight improved prediction accuracy for amino acid conformational states through time series approaches compared to traditional methods.
BIM_2010_20_Bioinformatics_Project
- 1. 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)
- 2. Concepts Used Ramachandran Plot Time series AR,ARMA,ARIMA models AIC criteria Euclidean distance Potential values for AA residues Feynman Problem Solving Algorithm
- 4. 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
- 5. Time Series Models (probability model) Autoregressive (AR) models Autoregressive-moving average (ARMA) Autoregressive integrated moving average (ARIMA) models - depend linearly on previous data points
- 6. Materials & Methods R R-Studio, Tinn-R bio3d,itsmr,forecast,tseries,timsac,wordcloud ITSM_2000- Standalone R Nabble BioStars stats.stackexchange
- 7. Methods A) Calculation of Potential values for AA residues B)Forecasting of AA states C) Clustering
- 8. Calculation of Potential values for AA residues Dataset-I 3829 proteins selected from PDB (Protein Data Bank) –PDBSelect dataset list(25 % seq. similarity) Expt. method-X-ray, R-factor: - 0-0.25 (for best resolved structures) Chain breaks, only CA atoms Phi-Psi values –torsion.pdb() of “bio3d” & verified via PDBGoodies (IISC, Bangalore) & Protein Angle Descriptor utility (IIT, Delhi ) 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)
- 9. ᵠ ᶲ 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
- 10. Frequencies of single residues in three states calculated & normalized using (Kolaskar, A.S. & Sawant, S.V. -1996 ) nik N Pik = nik nik 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
- 11. Potential values
- 13. Time Series
- 14. ACF Plot
- 15. ACF –Stat Vs. Non-stationary Stationary Non-stationary
- 16. Time Series ACF plot Stationary Non- stationary Stationary
- 17. Stationary TS
- 18. TS model building….. AR (p) ARMA(p,q) ARIMA (p,q)
- 19. Best model Selection AR (p) ARMA (p, q) ARIMA (p, q) AIC
- 20. Forecasting of AA states for best models
- 21. 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”
- 22. 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 R =1 2 (Yi Fi )2 (Yi Y )2 Yi =True value /Observed value Fi = Forecasted/predicted value
- 23. 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
- 24. Length of AA residues(100-110) e.g. 10gsa1_a_133_pot.txt File
- 25. 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)
- 27. Dendrogram_TS –ARMA models-484 • Phylowidget link
- 28. 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.
- 29. AR values Autoregressive order (p) 1-18 range Short & long range dependence variations in protein structural arrangements Variations proves diversity exhibits through structural components
- 30. 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 All (a)-12 All (b)-5 / (c)-9 + (d)-13 Small Coiled-coil Designed proteins (h)-3 proteins (g)-1 (k)-1 Max Min Max Min Max Min Max Min Max Min AA 26.82 2.41 16.30 8.88 27.77 1.47 28.57 7.04 19.51 22.5 5.88 29.03 seq (%) States 55.68 21.77 51.11 44.76 54.76 30.64 51.70 19.04 48.78 26 15 26.88 (%) Conformational states accuracy > AA residues accuracy due to low resolution of potential values(forecasted values)
- 31. 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 All (a)-123 All (b)-146 / (c)-120 + (d)-127 Multi domains Membrane & Small proteins (e)-13 cell surface proteins(g)- (f)-3 17 Max Min Max Min Max Min Max Min Max Min Max Min Max Min AA 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 seq (%) 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 (%) 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
- 32. 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)
- 33. CONCLUSIONS 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
- 34. FUTURE WORK 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
- 35. 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
- 36. Questions
- 37. Thank You !