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Secondary Structure PredictionSummary

Ph.D. in Electronic and Computer Engineering

Dept. of Electrical and Electronic Engineering

University of Cagliari

Protein Secondary Structure Prediction:Novel Methods and Software Architectures

Filippo Giuseppe LeddaAdvisor : Prof. Giuliano Armano

Cagliari, March 2, 2011

Filippo Giuseppe Ledda Ph.D. defense talk, March 2, 2011

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Contribution

BioinformaticsProtein secondary structure prediction

SLB: a novel input encoding techniqueSSP2: A novel multiple-expert architectureHeterogeneous Output Combination

BCalign: new pairwise alignment algorithm based onbeta-contact prediction

Machine Learning

GAME: a general architecture and framework for real-worldprediction problems


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Outline

1 BackgroundSecondary Structure PredictionFacing Real-World Problems

2 GAMEIntroductionThe Framework

3 Secondary Structure PredictionExploiting Inter-Sequence CorrelationExploiting Intra-Structure Correlation


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Secondary Structure PredictionFacing Real-World Problems

Outline





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Protein Structure Prediction

Biological background

Characteristics of living things (the phenotype) aredetermined by their genetic code (the genotype)Proteins, the first realization of the phenotype, carry outmost of cell functionsCentral dogma: DNA→ RNA→ Protein SequenceProtein Sequence→ Structure→ Function

PredictionPredicting protein structure from sequence is one of the greatopen problems of biology


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Why predict structure?Structure determines functionNo general theory to obtain structure from sequenceSequences are much easier to collect than structuresSequences can be engineered

Possible applicationsDisease analysisAd-hoc drug synthesis“Engineering” life!


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Reliable tertiary structure prediction needs templatestructures (close homologues)3D ab initio prediction is very hard→ use secondarystructure prediction


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Protein Secondary Structure

Secondary structure consists of local conformations ofresidues, stabilized by hydrogen bonds


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Secondary Structure Prediction

ExampleERLCLKYLVYKDLRTRGYIVKTGLKYGADFRLYERGANI

↓CCHHHHHHHHHHHHHCCCEEEECHHHCCCEEEECCCCCC

IssuesStructured dataGreat input spaceLarge and biased training setLow input/output correlationLabelling noise


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Evaluation Measures

Q3. It is a measure of accuracy, defined as follows:

Q3 =100

3

∑i=h,e,c

tpiNi

(1)

SOV. The Segment OVerlap score (Zemla, 99) accountsfor the predictive ability of a system by considering theoverlapping between predicted and actual structuralsegments.Ch, Ce, Cc . The Matthews Correlation Coefficient(Matthews, 75) relies on the concept of confusion matrix.Definition:

Ci =tpi tni − fpi fni√

(tpi + fpi )(tpi + fni )(tni + fpi )(tni + fni ), (2)


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The Informative Sources

The success of a predictor is determined by its ability to exploitthe main correlations of the problem

Informative sources1 Sequence-to-structure (Anfinsen’s dogma)2 Intra-sequence (really low)3 Inter-sequence (homology)4 Intra-structure (structure interactions)5 Inter-structure (homology)


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Classical Approach

Machine learning techniques with specific architectures allow toexploit different information sources.PHD architecture (Rost, 1993):


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Outline





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Real-World Problems

Protein structure prediction is a real-world problem (RWP)

Common issues of RWPsDeal with structured data (e.g. sequences, texts, images)Importance of pre-processing/feature extractionNoisy and biased example dataUncertain labelling

Useful approachesHuman expertise in the fieldCustom architecturesWise balancing of datasetsTry many different solutions (test&select)


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Tools for Machine Learning

Acknowledged tools (e.g. WEKA, RAPIDMINER) allow toeasily set up and compare lots of ML techniquesUnfortunately, they help only with part of RWPs issues

Limits of the acknowledged toolsWork with atomic data instances

Need for extra software for feature extraction/decompositionMemory requirements explosion due to decomposedstructured dataNo performance measure for the structured data

No support for release in the real environmentStatic approach to datasets


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IntroductionThe Framework

Outline





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GAME

GAME[1, 4] is a framework and tool for the definition andassessment of predictors for real-world problems.

FeaturesPlug-in architectureSupport for structured dataSupport for comparative experimentsSupport for expert combinationJust-in-time dataset iterationSupport for final releasePortability


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GAME Approach to Prediction

Data are managed in their natural formatSuitable encoding modules interface with underlying(generic) prediction algorithms

InputData

Instancedata

OutputData

Prediction/Classification

Machine learning system

Formatting/extraction

InputEncoding

OutputEncodingLabelling

LearningAlgorithmSource


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Generic Architectures with Multiple Experts

Three kinds of Expert allow to build tree architectures

Experts1 Ground2 Refiner (to build pipelines)3 Combiner (for parallel combination)


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Configuring Experts: Graphical Interface


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Outline





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The Framework

GAME is written in Java 6.0 following a plug-in architecture(actually at version 2.0: 354 classes and 2173 methods)

Usage1 Define the problem (implement data description modules)2 Define the encoding modules3 Graphically configure and run experiments

Applied toSecondary structure predictionOptical character recognitionAntibody packing angles predictionProtein beta contact prediction


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Configuring Experiments: Architecture

AlgorithmModules

EncoderModules

ExpertModules

Expert

DatasetIteratorModules Experiment

ControllerModules

DataModules

InstanceData

TrainingDataset

DatasetIterator

TestDataset

InstanceData

ModulesModule manager

ExpertGUI/XML/

Serialization

ExperimentGUI/XML/

Serialization

GAME Experiment

Net/FileSystem

Setting ManagerGeneralSettings

GUI/XML/Serialization

Predictor

Decoder

DecoderModules


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Configuring Experiments: Graphical Interface


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Defining Modules – Code

Defining modules which integrate with the graphical interface isvery simple.Example:


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Defining Modules – Interface

With the given example code, configuration and documentationwindows are generated automatically


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GAME for Secondary Structure Prediction

GAME has been used to perform all the secondary structureprediction experiments

Actually~15 input encoding methods~5 output encoding methodsCustom post processingStandard measures: Q3, SOV , Matthews correlationcoefficientReleased two web servers


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Exploiting Inter-Sequence CorrelationExploiting Intra-Structure Correlation

Outline





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Inter-Sequence Correlation

A strong correlation can be observed between sequences ofevolutionary related proteins (homologues)

Background

Sequences diverge during evolution from a commonancestorStructure is much more conserved than sequenceHence, similar sequences have similar structure

How to use this information for prediction?Including information about similar sequences in the inputrepresentation. About +10% accuracy.


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Input Encoding for Secondary Structure Prediction

The prediction algorithm operates on slices extracted with asliding window

The protein’s sequence is represented as a profile before slicesare extracted


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Input Encoding

Naive ApproachStatically associate a vector to each amino acid(position-independent encoding)Relies only on the low local sequence-structure correlation

Modern ApproachExploit inter-sequence correlation with position-specificrepresentationsUse multiple alignmentsThree phases:

1 Similarity Search (find homologues)2 Multiple alignment (align them)3 Encoding (generate profile)


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Multiple Sequence Alignment

Proteins are evolutionary related to other proteins. Multiplealignments put in evidence the changes occurred during theevolution in a protein family

Substitutions observed in a multiple alignment are expected tobarely affect structure


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Frequency-Based Encoding (FR)

Frequency-based encoding

Introduced for prediction by PHD (Rost, 1993)Purely position-specificRepresents each column of the multiple alignment with theobserved frequency for each amino acid

WeaknessThe multiple alignment not always permits to reliably estimatethe substitution frequencies


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PSSM Encoding

PSI-BLAST PSSM is the most acknowledged way to encodemultiple alignment information for similarity searches andprediction (firstly used with PSIpred (Jones, 1999))

Algorithm

PSSM (Henikoff&Henikoff, 1996) improves frequencies addingpseudo-counts where the estimation is weak

Pseudo-counts are obtained by standard amino acidsubstitution matrices-search hits, +pseudo-countsMain issue: how much pseudo counts, exactly?


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Sum-Linear Blosum Encoding

Algorithm

Like PSSM, SLB[2] uses substitution matrices to enhancefrequency counts.

A linear combination of BLOSUM62 matrix columns isused to encode the i-th position in the protein sequenceMatrix columns are weighted with the frequency counts

AdvantagesNaturally includes position-independent withposition-specific informationSimple implementationBetter performances?


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Experimental Results

Encoding techniques were compared with the PHD architecture


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Experimental Results


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Outline





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Intra-Structure Correlation

Secondary structure elements are not independent. Mutualinteractions holding between the amino-acids along the protein,including the hydrogen bonds involved in the formation ofsecondary structure

Residue interactionsWithin α-helices and β-strandsBetween β-strands in the same sheet

IssueResidues should not be predicted independently


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Exploiting Inter-Structure Correlation

Common ApproachModern predictors include a structure-to-structure layer, whichrefine the point-by-point predictions of a firstsequence-to-structure layer

Our ProposalAlso include intra-structure information in the outputrepresentation [3, 6]


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The SSP2 Architecture

IssueAdding information to the output representation hardens thetraining process

SolutionAdd the information step-by-step along suitable pipelines

SSP2 Architecture1 Combination2 Pipelines3 Diversity


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GAMESSP2

GAMESSP2 is a realization of the SSP2 with GAMEUses variable output windows to produce scalablerepresentations

Test configuration

GROUND(ANN)

REFINER(ANN)IN OUT

ENC = PSSMWout = 1..11

ENC = SLB / noneWout = 1..9

REFINER(ANN)

ENC = noneWout = 1


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Finding the Best Configuration

ENC =< PSSM,none,none >, Wout =< j , k ,1 >

System Q3 SOV Ch Ce Cc

j = 1, k = 1 79.70 77.31 0.74 0.67 0.61j = 1, k = 3 79.71 77.46 0.75 0.67 0.61j = 1, k = 5 79.79 78.00 0.75 0.67 0.61j = 1, k = 7 79.80 78.14 0.75 0.67 0.61j = 1, k = 9 79.78 77.70 0.75 0.67 0.61j = 3, k = 5 80.17 78.37 0.75 0.67 0.61j = 3, k = 7 80.14 78.87 0.75 0.67 0.62j = 3, k = 9 80.02 78.21 0.75 0.67 0.61j = 5, k = 7 80.38 78.45 0.76 0.68 0.62j = 5, k = 9 80.13 78.55 0.76 0.67 0.62j = 7, k = 9 80.23 78.65 0.76 0.67 0.62


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Finding the Best Configuration

ENC =< PSSM,SLB,none >, Wout =< j , k ,1 >


j = 1, k = 1 79.70 77.31 0.74 0.67 0.61j = 1, k = 3 79.66 77.43 0.74 0.66 0.61j = 1, k = 5 79.95 77.67 0.75 0.67 0.61j = 1, k = 7 80.09 77.79 0.75 0.67 0.62j = 1, k = 9 79.97 77.97 0.75 0.67 0.62j = 3, k = 5 80.11 78.00 0.75 0.67 0.62j = 3, k = 7 80.34 78.29 0.76 0.67 0.62j = 3, k = 9 80.12 78.24 0.75 0.67 0.61j = 5, k = 7 80.17 78.78 0.75 0.67 0.62j = 5, k = 9 80.14 78.54 0.76 0.67 0.62j = 7, k = 9 80.06 78.47 0.75 0.67 0.62


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Benchmarking: EVA common set 6

The four best configurations have been combined to obtain thefinal GAMESSP2 system on the EVA common 6 dataset (212proteins)


PHDpsi(Rost ,1993) 74.99 70.87 0.66 0.69 0.53PSIpred(Jones,1999) 77.76 75.36 0.69 0.74 0.56PROFsec(Rost ,unp.) 76.70 74.76 0.68 0.72 0.56DBNN (Yao et al. , 2008) 77.8 72.4 0.71 0.65 0.58GAMESSP2 78.34 76.17 0.70 0.76 0.59


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Summary

Five different kinds of correlation were identified for thesecondary structure prediction problemWe realized GAME, a general framework for real-worldprediction problems, and used it for secondary structurepredictionWe proposed a novel input encoding to take into accountinter-sequence correlationsWe proposed an original use of output encoding alongsuitable pipelines to exploit intra-structure correlations

OutlookApply the framework to further problemsExploit inter-structure correlations


Appendix Publications

Publications I

F. LEDDA, L. MILANESI, AND E. VARGIU.GAME: A Generic Architecture based on MultipleExperts for Predicting Protein Structures.International Journal Communications of SIWN,3:107–112, 2008.

G. ARMANO, F. LEDDA, AND E. VARGIU.Sum-Linear Blosum: A Novel Protein Encoding Methodfor Secondary Structure Prediction.International Journal Communications of SIWN, 6:71–77,2009.



Publications II

G. ARMANO, F. LEDDA, AND E. VARGIU.SSP2: A Novel Software Architecture for Predicting ProteinSecondary Structure.Sequence and Genome Analysis: Methods andApplication, in press.

G. ARMANO, F. LEDDA, AND E. VARGIU.GAME: a Generic Architecture based on MultipleExperts for bioinformatics applications.In BITS Annual Meeting 2009, Genova (Italy), 2009



Publications III

F. LEDDA AND E. VARGIU.Experimenting Heterogeneous Output Combination toImprove Secondary Structure Predictions.In Workshop on Data Mining and Bioinformatics, Cagliari(Italy), 2008

G. ARMANO AND F. LEDDA.Exploiting Intra-Structure Information for SecondaryStructure Prediction with Multifaceted Pipelines.submitted



Publications IV

FILIPPO LEDDA, GIULIANO ARMANO, AND ANDREW

C.R. MARTIN.Using a Beta Contact Predictor to Guide PairwiseSequence Alignments for Comparative Modelling.submitted


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