prediction of protein function

Prediction of protein function

Lars Juhl JensenEMBL Heidelberg

Overview

• Part 1– Homology-based transfer of annotation– Function prediction from protein domains

• Part 2– Prediction of functional motifs from sequence– Feature-based prediction of protein function

• Part 3– Prediction of functional interaction networks

Why do we need to predict function?

What do we mean by function?

• The concept “function” is not clearly defined– A structural biologist, a cell biologist, and a medical

doctor will have very different views

• Many levels of granularity– For the overall definition of “function”, the knowledge

and description can be more or less specific

• Functional categories are somewhat artificial– People like to put things in boxes …

Descriptions of protein function

• Controlled vocabularies– Gene Ontology– SwissProt keywords– KEGG pathways– EcoCyc pathways

• Interaction networks

• More accurate data models– Reactome– Systems Biology Markup Language (SBML)

Molecular function

• Molecular function describes activities, such as catalytic or binding activities, at the molecular level

• GO molecular function terms represent activities rather than the entities that perform the actions, and do not specify where or when, or in what context, the action takes place

• Examples of broad functional terms are catalytic activity or transporter activity; an example of a narrower term is adenylate cyclase activity

Biological process

• A biological process is series of events accomplished by one or more ordered assemblies of molecular functions

• An example of a broad GO biological process terms is signal transduction; examples of more specific terms are pyrimidine metabolism or alpha-glucoside transport

• It can be difficult to distinguish between a biological process and a molecular function

Cellular component

• A cellular component is just that, a component of a cell that is part of some larger object

• It may be an anatomical structure (for example, the rough endoplasmic reticulum or the nucleus) or a gene product group (for example, the ribosome, the proteasome or a protein dimer)

• The cellular component categories are probably the best defined categories since they correspond to actual entities

Homology-basedtransfer of annotation


Detection of homologs

• Pairwise sequence similarity searches– BLAST (fastest)– FASTA– Full Smith-Waterman (most sensitive)

• Profile-based similarity searches– PSI-BLAST– Hidden Markov Models (HMMs)

• Sequence similarity should always be evaluated at the protein level

Sequence similarity, sequence homology, and functional homology

• Sequence similarity means that the sequences are similar – no more, no less

• Sequence homology implies that the proteins are encoded by genes that share a common ancestry

• Functional homology means that two proteins from two organisms have the same function

• Sequence similarity or sequence homology does not guarantee functional homology

Orthologs vs. paralogs

Functional consequencesof gene duplication

• Neofunctionalization– One copy has retained the ancestral function and can

be treated as a 1–to–1 ortholog (functional homolog)– The other copy have changed their function and behave

much like paralogs

• Subfunctionalization– Each copy has taken on a part of the ancestral function– A functional homolog cannot be defined– Each ortholog typically has the same molecular function

in a different sub-process or location

1–to–1 orthology

• A single gene in one organism corresponds to a single gene in another organism

• These can generally be assumed to encode functionally equivalent proteins– Same molecular function– Same biological process– Same localization

• 1–to–1 orthology is fairly common in prokaryotes and among very closely related organisms

1–to–many orthology

• A single gene in one organism corresponds to multiple genes in another organism

• Any mixture of neo- and sub-functionalizations can have occurred– Typically same molecular function– Often different biological process or sub-process– Often different sub-cellular localization or tissue

• 1–to–many orthology is very common between simple model organisms and higher eukaryotes

Many–to–many orthology

• Many genes in each organism have arisen from a single gene in their last common ancestor

• Different neo- and sub-functionalizations have likely taken place in each lineage– Typically same molecular function– Often different biological process or sub-process– Often different sub-cellular localization or tissue

• Many–to–many orthology is common between higher eukaryotes that are distantly related

Detection of orthologs

• Reconstruction of phylogenetic trees– The theoretically most correct way– Works for analyzing particular genes of interest

• Methods based on reciprocal matches– What currently works at the genomic scale

• Manual curation– Detection of very remote orthologs may require that

knowledge on gene synteny and/or protein function is taken into account

Construction of gene trees

• Identify the relevant proteins– Sequence similarity and possibly additional information

• Construct a blocked multiple sequence alignment– Use, for example, Muscle and Gblocks

• Reconstruct the most likely phylogenetic tree– Use, for example, PhyML

• Orthologs and paralogs can be trivially extracted based on a gene tree

Reciprocal matches

• Simple “best reciprocal match” is a bad choice– Can only deal with one-to-one orthology

• Detection of in-paralogs– Similarity higher with species than between species

• Orthologs can now be detected based on best reciprocal matches between in-paralogous groups

• One or more out-group organisms can optionally be used to improve the definition of orthologs

Orthologous groups

• Orthologs and paralogs are in principle always defined with respect to two organisms

• Orthologous groups instead try to encompass an entire set of organisms

• The “inclusiveness” of the orthologous groups depends on how broad a set of organisms the groups cover

Definition of orthologous groups

COGs, KOGs, and NOGs

• The COGs and KOGs were manually curated– These were automatically expanded to more species

• Tri-clustering– Detection of in-paralogs– Identification of triangles of best reciprocal matches– Merging of triangles that share an edge

• Broad phylogenetics coverage– COGs and NOGs cover all three domains of life– KOGs cover all eukaryotes

Clustering based on similarity

• All-against-all sequence similarity is calculated

• A standard clustering method is applied to define groups of homologous genes– TribeMCL– Hierarchical clustering

• These methods generally detect groups of homologous genes, but are not good for distinguishing between orthologs and paralogs

Meta-servers

• Since numerous methods exist for identifying groups of orthologous proteins, meta-servers have begun to emerge

• These can be very useful for “fishing expeditions” where one is looking for a remote ortholog of a particular protein of interest

• However, such meta-servers do not attempt to unify the different orthologous groups and are thus not useful for genome-wide studies

Function predictionfrom protein domains


When homology searches fail

• Sometimes no orthologs or even paralogs can be identified by sequence similarity searches, or they are all of unknown function

• No functional information can thus be transferred based on simple sequence homology

• By instead analyzing the various parts that make up the complete protein, it is nonetheless often possible to predict the protein function

Protein domains

• Many eukaryotic proteins consist of multiple globular domains that can fold independently

• These domains have been mixed and matched through evolution

• Each type of domain contributes towards the molecular function of the complete protein

• Numerous resources are able to identify such domains from sequence alone using HMMs

Which domain resource should I use?

• SMART is focused on signal transduction domains

• Pfam is very actively developed and thus tends to have the most up-to-date domain collection

• InterPro is useful for genome annotation since the domains are annotated with GO terms

• CDD is conveniently integrated with the NCBI BLAST web interface

Predicting globular domains and intrinsically disordered regions

• Not all globular domains have been discovered and the databases are thus not comprehensive

• Methods exist for predicting from sequence which regions are globular and which are disordered– GlobPlot uses a simple propensity scale– DisEMBL, DISOPRED, and PONDR all use ensembles

of artificial neural networks

• Many disordered regions are important for protein function and they should thus not be ignored

Summary

• Functional annotation– Molecular function vs. biological process– Inference of molecular function by sequence similarity– Biological process only transferable between orthologs

• Detection of orthologs– In-depth studies: phylogenetic trees– Automated analysis: InParanoid and COG/KOG/NOG

• Profile searches for protein domains– Each domains contributes a different molecular function

Acknowledgments

Christian von MeringChristopher Creevey

Ivica LetunicRune Linding

Tobias DoerksFrancesca Ciccarelli

Berend SnelMartijn HuynenToby GibsonRob RussellPeer Bork

Prediction of functionalmotifs from sequence


Proteins – more than justglobular domains

• Transmembrane helices

• Disordered regions

• Eukaryotic linear motifs (ELMs)– Modification sites, e.g. phosphorylation sites– Ligand peptides, e.g. SH3 binding sites– Targeting signals, e.g nuclear localization sequences

• The short functional motifs are as important as the globular domains

Insulin Receptor Substrate 1

Databases of functional motifs

• Fewer and smaller databases– General databases of motifs: ProSite and ELM– Phosphorylation sites: Phospho.ELM and PhosphoSite– These databases contain much fewer instances that

protein domain databases

• Curation is more difficult– Protein domain databases can be constructed based on

analysis of protein sequences alone– Short functional motifs must be curated based on

experimental evidence

Prediction of ELMs

• Most functional motifs are “information poor”– Weak/short consensus sequences for ELMs– The typical ELM only has three conserved residues– Some variance is often allowed even for these

• ELMs are very hard to predict from sequence– Simply consensus sequences match everywhere– Even more advanced methods like PSSMs, ANNs, or

SVMs give poor specificity– The full information is not in the site itself

Construction of data sets

• Compiling an initial data set– Positive examples can be obtained from existing

databases or curated from the literature– Good negative examples are often harder to get

• Separate training and test sets– A method may be able to learn the training examples

but to generalize to new examples

• Homology reduction!– It is crucial that there is no significant sequence

similarity between examples in the training and test sets

Machine learning

• Numerous algorithms exist– Artificial neural networks– Support vector machines– Decision trees

• The choice of algorithm is not so important

• Providing the relevant input is important

• Having high-quality training data is crucial

Kinase-specific prediction of phosphorylation sites (NetPhosK)

• Artificial neural networks (ANNs) were trained several different kinases

• The sequence logos show only the positive examples

• Negative examples also provide information

• Also, ANNs and SVMs can capture correlations between positions

Prediction of signal peptidesfrom sequence (SignalP)

• Function– Eukaryotic proteins are

targeted to the ER– Prokaryotic proteins are

targeted for secretion

• Architecture– Positively charged N-

terminus– Hydrophobic core– Short, more polar region– Cleavage site

• Signal peptides can be accurately predicted

Machine learning can help identifyerrors in curated databases

• Some of the manually curated databases contain obvious errors that can be eliminated

• General “SIGNAL” errors– Wrong signal peptide cleavage site– The secreted protein is processed by proteases– Signal peptide include propeptide– Wrong start codon used

Signal peptide or propeptide

N–

Signal peptide

Propeptide

Mature protein

Signal peptide or propeptide

Propeptide cleavage

Signal peptide cleavage

Wrong start codon

Use of short linear motifsfor function prediction

• Only a few motifs (mostly localization signals) can be predicted with high accuracy– Even in these cases advanced machine learning

methods are typically needed– These can be treated in the same way as domains

• Most motifs are weak, and predictions should be approached with care– To tell if these sites are likely to be true, one needs to

consider the context– An experiment is needed to prove that it is functional

Feature-based predictionof protein function


Function prediction from post translational modifications

• Proteins with similar function may not be related in sequence

• Still they must perform their function in the context of the same cellular machinery

• Similarities in features such like PTMs and physical/chemical properties could be expected for proteinswith similar function

Henrik Nielsen, CBS, DTU Lyngby

The concept of ProtFun

Function prediction on thehuman prion sequence

############## ProtFun 1.1 predictions ##############

>PRIO_HUMAN# Functional category Prob Odds Amino_acid_biosynthesis 0.020 0.909 Biosynthesis_of_cofactors 0.032 0.444 Cell_envelope 0.146 2.393 Cellular_processes 0.053 0.726 Central_intermediary_metabolism 0.130 2.063 Energy_metabolism 0.029 0.322 Fatty_acid_metabolism 0.017 1.308 Purines_and_pyrimidines 0.528 2.173 Regulatory_functions 0.013 0.081 Replication_and_transcription 0.020 0.075 Translation 0.035 0.795 Transport_and_binding => 0.831 2.027

# Enzyme/nonenzyme Prob Odds Enzyme 0.250 0.873 Nonenzyme => 0.750 1.051

# Enzyme class Prob Odds Oxidoreductase (EC 1.-.-.-) 0.070 0.336 Transferase (EC 2.-.-.-) 0.031 0.090 Hydrolase (EC 3.-.-.-) 0.057 0.180 Isomerase (EC 4.-.-.-) 0.020 0.426 Ligase (EC 5.-.-.-) 0.010 0.313 Lyase (EC 6.-.-.-) 0.017 0.334

ProtFun data sets

• Labeling of training and test data– Cellular role categories: human SwissProt sequences

were categorizes using EUCLID– Enzyme categories: top-level enzyme classifications

were extract from human SwissProt description lines– Gene Ontology terms were transferred from InterPro

• The sequences were divided into training and test sets without significant sequence similarity

• Binary predictors were for each category

Prediction performance oncellular role categories

Prediction performance onenzyme categories

Predictive performance onGene Ontology categories

Non-classical secretion

• Some proteins without N-terminal signal peptides are secreted via alternative secretion pathways– Several growth factors, i.e. FGF1 and FGF2– Interleukine 1 beta– HIV-1 tat

• No consensus sequence motif is known

• Maybe they have some features in common with other secreted proteins …

SecretomeP data sets

• Training and test set– Positive examples: 3321 extracellular mammalian

proteins with their signal peptides removed– Negative examples: 3654 mammalian proteins from

cytoplasm or nucleus

• Validation set– 14 known non-classically secreted proteins

Secreted proteins are typically small

ROC plot for SecretomeP

Similar properties of classically and non-classically secreted proteins

A look into the black box

• Neural networks are often criticized for being a “black box” method

• However, there are several ways to investigate what a neural network ensemble has learned– Which fraction of the ensemble use a certain feature?– How good performance can be attained using each of

the features individually?– How much does performance decrease if the neural

networks are retrained without a certain feature (or combination of features)?

SecretomeP feature usage

ProtFun performance forother organisms

• Our predictors work in general for eukaryotes– Best performance on

metazoan proteins

• Some categories work quite well for prokaryotes– Most metabolism categories– Transport and binding

• While other categories fail– Energy metabolism– Regulatory functions

Mapping category performancesonto input features

Performance contribution of sequence derived features

• The correlations between features and function is conserved for eukaryotes

• Some correlations extend to archaea and bacteria– Physical/chemical properties– Secondary structure and

transmembrane helices

• Other correlations only hold for eukaryotes– PTMs and subcellular

localization features

Evolution conserves proteinfeatures and function

• Protein features are more conserved between orthologs than paralogs

• This leads to ProtFun predicting orthologs to be more likely to share function than paralogs

• That prediction is fully consistent with the notion that it is best to infer function from orthologous proteins

Conclusions

• Short linear motifs are likely equally important for protein function as the large well-studied domains

• These are much harder to predict from sequence– Reasonable accuracy can be obtained by applying

machine learning methods on high-quality datasets

• Many classes of proteins can be predicted based on such sequence derived-protein features– These methods a not nearly as reliable as homology– However, often they are the only option

AcknowledgmentsRamneek Gupta

Can KesmirJannick Dyrløv Bendtsen

Henrik NielsenNikolaj Blom

Francesca DiellaRune Linding

Damien DevosAlfonso Valencia

Søren BrunakToby Gibson

Prediction of functionalinteraction networks


What is an interaction?

• Physical protein interactions– Proteins that physically touch each other– Members of the same stable complex– Transient interactions, e.g. a kinase and its substrate

• The pragmatic definition – whatever the assay in question can measure

• Functional interactions– Neighbors in metabolic networks– Members of the same pathway

The use of interaction networksfor function prediction

• A functional interaction implies that two proteins are involved in the same biological process

• However, the networks do not divide proteins into a predefined set of functional classes such as the Gene Ontology terms

• Functional associations do not require homology to proteins of know function, and can complement the predictions even when homology is present

Functional interaction networks

Evidence types

• Genomic context methods– Phylogenetic profiles, gene neighborhood, and fusion

• Primary experimental data– Physical protein interactions and gene expression data

• Manually curated databases– Pathways and protein complexes

• Automatic literature mining– Co-ocurrence and Natural Language Processing

Phylogenetic profiles

Cell

Cellulosomes

Cellulose

Formalizing the phylogeneticprofile method

Align all proteins against all

Calculate best-hit profile

Join similar species by PCA

Calculate PC profile distances

Calibrate against KEGG maps

Gene neighbourhood

Gene neighborhood

Identify runs of adjacent geneswith the same direction

Score each gene pair based onintergenic distances


Infer associationsin other species

Gene fusion

Gene fusion

Find in A genes that matcha the same gene in B

Exclude overlappingalignments

Calibrate againstKEGG maps

Calculate all-against-allpairwise alignments

Calibration of quality scores

• Different pieces of evidence are not directly comparable

– A different raw quality score is used for each evidence type

– Quality differences exist among data sets of the same type

• Solved by calibrating all scores against a common reference

– The accuracy relative to a “gold standard” is calculated within score intervals

– The resulting points are approximated by a sigmoid

Data integration

Protein-protein interaction databases

• Imported databases– BIND, Biomolecular Interaction Network Database– DIP, Database of Interacting Proteins– GRID, General Repository for Interaction Datasets– HPRD, Human Protein Reference Database– MINT, Molecular Interactions Database

• Databases to be added– IntAct– PDB

Physical protein interactionsMake binary

representationof complexes

Yeast two-hybriddata sets are

inherently binary

Calculate scorefrom number of

(co-)occurrences

Calculate scorefrom non-shared

partners


Infer associations in other species

Combine evidence from experiments

Binary representationsof purification data

Topology based quality scores

• Scoring scheme for yeast two-hybrid data:– S1 = -log((N1+1)·(N2+1))

– N1 and N2 are the numbers of non-shared interaction partners– Similar scoring schemes have been published by Saito et al.

• Scoring scheme for complex pull-down data:– S2 = log[(N12·N)/((N1+1)·(N2+1))]

– N12 is the number of purifications containing both proteins

– N1 is the number containing protein 1, N2 is defined similarly– N is the total number of purifications

• Both schemes aim at identifying ubiquitous interactors

Mining microarrayexpression databases

Re-normalize arraysby modern methodto remove biases

Buildexpression

matrix

Combinesimilar arrays

by PCA

Construct predictorby Gaussian kerneldensity estimation

Calibrateagainst

KEGG maps

Inferassociations inother species

Databases of curated knowledge

• Pathway databases– BioCarta– KEGG, Kyoto Encyclopedia of Genes and Genomes– Reactome– STKE, Signal Transduction Knowledge Environment

• Curated protein complexes– MIPS, Munich Information center for Protein Sequences

• Databases to be added– Gene Ontology annotation

Co-occurrence in the scientific textsAssociate abstracts with species

Identify gene names in title/abstract

Count (co-)occurrences of genes

Test significance of associations


Infer associations in other species

Databases used for text mining

• Corpora– Medline– OMIM, Online Mendelian

Inheritance in Man– SGD, Saccharomyces

Genome Database– The Interactive Fly

• These text sources are all parsed and converted into a unified format

• Gene synonyms– Ensembl– SwissProt– HUGO– LocusLink– SGD– TAIR

• Cross references and sequence comparison is used for merging

Gene and protein namesCue words for entity recognitionVerbs for relation extraction

[nxgene The GAL4 gene]

[nxexpr The expression of [nxgene the cytochrome genes [nxpg CYC1 and CYC7]]]is controlled by[nxpg HAP1]

Natural Language Processing

Multiple types of interactions

Transfer of evidence

• STRING “red” – COG mode– Each node in the network represents a COG– For each pair of COGs, the highest confidence score for

each evidence type counts from each clade– The scores are combined using naïve Bayes

• STRING “blue” – protein mode– Each node in the network represents a single locus– Evidence from other organisms are transferred based

on fuzzy orthology– The scores are combined using naïve Bayes

?

Source species

Target species

Evidence transfer basedon “fuzzy orthology”

• Orthology transfer is tricky– Correct assignment of

orthology is difficult for distant species

– Functional equivalence is not guaranteed for paralogs

• These problems are addressed by our “fuzzy orthology” scheme– Functional equivalence

scores are calculated from all-against-all alignment

– Evidence is distributed across possible pairs

The power of cross-species transferand evidence integration

The big challenge

Prediction of “mode of action”

Summary

• Functional interaction networks are useful for predicting the biological role of a protein

• Many algorithms and types of data can be used for predicting functional interactions– Each method must be benchmarked– The different types of evidence should be integrated in

a probabilistic scoring scheme

• To make the most of the available data, evidence should also be transferred between organisms

AcknowledgmentsChristian von Mering

Jasmin SaricBerend SnelSean Hooper

Rossitza OuzounovaSamuel ChaffronJulien Lagarde

Mathilde FoglieriniIsabel Rojas

Martijn HuynenPeer Bork

prediction of protein function

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