Protein Domain Analysis Using Hidden Markov Models

1. PLPTH 890 Introduction to Genomic Bioinformatics Lecture 17 Protein Domain Analysis Using Hidden Markov Models Liangjiang (LJ) Wang ljwang@ksu.edu March 10, 2005

2. Outline • Basic concepts and biological problems. • Search for protein domains: • The Pfam database, • Other domain/motif databases. • Protein domain modeling: • Hidden Markov Models (HMM), • Construction of the Pfam protein domain models using HMMER.

3. Biological Problem #1 You identified a new gene, which might be involved in a very interesting biological process. BLAST search in GenBank resulted in a few homologous sequences with unknown function. What else can you do to understand the function of the gene product and/or to localize the possible conserved domain in the protein?

4. Biological Problem #2 Suppose there is a novel gene identified in mammals, C. elegans and Drosophila, but not yet in plants. This gene is involved in an interesting biological process (e.g., apoptosis). You are interested in finding the orthologous gene in Arabidopsis. However, BLAST search using each of the known sequences failed to identify an Arabidopsis homologue. What else can you try?

5. Orthologs, Paralogs and Homologs Ancestral organism X Y Speciation Duplication B A Y Y X X B A X1 X2 Ya Yb X1 and X2 are orthologs with same function. Paralogs Ya and Yb may have different but related functions. Homologs

6. Protein Domains Domains represent evolutionarily conserved amino acid sequences carrying functional and structural information of a protein. Domain analysis helps understand the biological function of a gene product. bZIP

7. Protein Domain Analysis Using HMM >TC50726AIKLNDVKSCQGTAFWMAPEVVRGKVKGYGLPADIWSLGCTVLEMLTGQVPYAPMECISAMFRIGKGELPPVPDTLSRDARDFILQCLKVNPDDRPTAAQLLDHKFVQRSFSQSSGSASPHIPRRS >UFO_ARATH MDSTVFINNPSLTLPFSYTFTSSSNSSTTTSTTTDSSSGQWMDGRIWSKLPPPLLDRVIAFLPPPAFFRTRC Search HMMER Multiple Sequence Alignment Hidden Markov Models Your Sequence Set

8. Comparison of Search Approaches Threading BLAST HMM Sensitivity Speed Low Very Fast High Fast Very High Very Slow

9. The Pfam Database • Pfam is a database of multiple alignments and hidden Markov models (HMMs) of common conserved protein domains. • The alignments use a non-redundant protein set composed of SWISS-PROT and TrEMBL. • Pfam consists of parts A and B. Pfam-A contains curated domain families with high-quality alignments. Pfam-B contains families that were generated automatically by clustering the remaining sequences after removal of Pfam-A domains. • Pfam is available at http://pfam.wustl.edu/.

10. Other Domain/Motif Databases • ProDom: http://www.toulouse.inra.fr/prodom.html; contains domain families automatically generated from the SWISS-PROT and TrEMBL (Pfam-B). • SMART: Simple Modular Architecture Research Tool; available athttp://smart.embl-heidelberg.de/; contains domain families that are widely represented among nuclear, signaling and extracellular proteins. • TIGRFAMs: http://www.tigr.org/TIGRFAMs; is a collection of manually curated protein families of hidden Markov models; contains models of full-length proteins and shorter protein regions.

11. More Domain/Motif Databases • PROSITE: http://www.expasy.org/prosite/; consists of biologically significant sites, patterns and profiles; uses regular expression to represent most patterns. • PRINTS: http://www.bioinf.man.ac.uk/dbbrowser/PRINTS/; a collection of protein fingerprints (conserved motifs, ungapped alignments), which may be used to assign new sequences to known protein families. • Blocks: http://blocks.fhcrc.org/; consists of short ungapped alignments corresponding to the most highly conserved regions of proteins.

12. Even More Domain/Motif Databases • InterPro: http://www.ebi.ac.uk/interpro; an integrated and curated collection of protein families, domains and motifs from PROSITE, Pfam, PRINTS, ProDom, SMART and TIGRFAMs. • CDD:http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=cdd; contains domains derived from Pfam, SMART and models curated at NCBI. • 3Dee: http://www.compbio.dundee.ac.uk/3Dee/; contains structural domain definitions for all protein chains in the Protein Databank (PDB); clustered by both sequence and structural similarity.

13. Why So Many Domain/Motif Databases? • Different representations of patterns: • PROSITE: regular expression. • ProDom: multiple alignment and consensus. • Pfam: multiple alignment and HMM. • Different approaches or focuses: • SMART: focused on signaling proteins. • PRINTS and Blocks: highly conserved segments. • 3Dee: structural domain definitions. • “Meta-sites” (databases of databases): • InterPro: an integrated collection, derived from several domain/motif databases.

14. Protein Domain Modeling • Machine learning concepts. • Hidden Markov Models (HMM). • HMMER (a software tool for constructing and searching HMM). • Construction of the Pfam protein domain models.

15. Machine Learning • The study of computer algorithms that automatically improve performance through experience. • In practice, this means: we have a set of examples from which we want to extract some rules (regularities) using computers. • Two types of machine learning: • Supervised:learn with a teacher (using a set of input-output training examples). • Unsupervised: let the machine explore the data space and find some interesting patterns.

16. Learning from Examples • Learning refers to the process in which a model is generalized (induced) from given examples (training dataset). • Error-correction learning: for each of the given examples, a computer program • makes a prediction based on what was already learned (i.e., model parameters). • compares the prediction with the given output to calculate the error. • adjusts the model parameters in some way (learning algorithm) to minimize the error.

17. Common Pitfalls - Training Dataset Data space Data instances sampled Too few examples (overfitting) Sampling problem Good (“Garbage in, garbage out”)

18. Hidden Markov Model (HMM) • A class of probabilistic models that are generally applicable to time series or linear sequences. • Widely used in speech recognition since early 1970s. David Haussler’s group at UC Santa Cruz introduced HMMs for biological sequence profiles in 1994. • HMM turns a multiple alignment into a position-specific scoring system that can be used to search for remotely homologous sequences.

19. The Occasionally Dishonest Casino Problem The casino has two dies: a fair and a loaded die. They use the fair die most of the time, but occasionally (P = 0.05) switch to the loaded die and may switch back to a fair die with probability 0.1. The loaded die has probability 0.5 of a six and probability 0.1 for the numbers one to five. The fair die has probability 0.167 for each number. HMM Rolls 521462536316562646465251 Symbol Die FFFFFFFFLLLLLLLLLLFFFFFF State/Path • The state sequence or path is hidden (HMM). • Transition probabilities: P(L|F) = 0.05;P(F|F) = 0.95. • Emission probabilities: P(6|L) = 0.5;P(6|F) = 0.167.

20. Fair Loaded 0.95 0.9 An HMM for the Casino Problem 1: 1/6 2: 1/6 3: 1/6 4: 1/6 5: 1/6 6: 1/6 1: 1/10 2: 1/10 3: 1/10 4: 1/10 5: 1/10 6: 1/2 Emission Probability 0.05 0.1 Transition Probability

21. States: E – Exon 5 – 5’ splice site I – Intron An HMM for 5’ Splice Site Recognition (Eddy, 2004) An observation (nucleotide sequence) corresponds to a state path (or paths) through the HMM.

22. Finding the Best Hidden State Path (Eddy, 2004) The probability P of a state path, given the model and an observation (sequence), is the product of all the emission and transition probabilities along the path.

23. Calculating the Probability of a State Path

24. How to Model a Protein Domain? Consider a two-state HMM: Is there a domain X (Yes/No)? A.A. EDQILIKARNTEAARRSRVIANYL Symbol DomX? NNNNNNNNYYYYYYYYYYNNNNNN State/Path No Is this sufficient for modeling a protein domain? How to represent position-dependent amino acid distribution? What about insertions and deletions? Seq1 KGIQEF--GADWYKVAK--NVGNKSPEQCILRFLQ Seq2 ALVKKHGQG-EWKTIAS--NLNNRTEQQCQHRWLR Seq3 SGVRKYGEG-NWSKILLHYKFNNRTSVMLKDRWRT

25. An HMM for Protein Domain Recognition (Eddy, 1996) States: M - match D - delete I - insert

26. HMM Parameterization (Training) • HMM parameters are estimated from the multiple sequence alignment. • Basic: maximum likelihood estimation. • Advanced: the MAP construction algorithm. • (See Durbin et al., Biological sequence analysis, p.107-124) • A High-quality alignment is essential for the model construction. This includes selection of sequences and manual editing of the multiple sequence alignment generated by the ClustalW program.

27. Scoring a Sequence with an HMM • The task is to find the hidden state path with the highest probability, given the model and an observation (sequence). • The Viterbi algorithm (dynamic programming). • The forward algorithm. • The backward algorithm. • (See Durbin et al., Biological Sequence Analysis, p.55-61)

28. HMM versus PWM • Advantages: • A HMM has position-dependent amino acid distributions, which are represented as emission probabilities at each match state. (also PWM) • Insertion/deletion gap penalties are handled using transition probabilities. (Usually not with PWM) • The possible dependence of an amino acid on its preceding neighbor can be represented using the transition probabilities. (Not with PWM) • Problems: • Long-range interactions between amino acids. • Requirement of multiple sequence alignments.

29. HMMER • A software package for constructing and searching HMMs. • Source code and binary distribution for various platforms (UNIX, Linux and Macintosh PowerPC) are available at http://hmmer.wustl.edu/. Follow the detailed User’s Guide for software installation. • Multiple sequence alignment: ClustalW or ClustalX (with Windows interface), available at ftp://ftp-igbmc.u-strasbg.fr/pub/ClustalX/. • Sequences in FASTA format.

30. HMMER Programs • hmmbuild: build a model from a multiple sequence alignment. • hmmalign: align multiple sequences to a HMM. • hmmcalibrate: determine appropriate statistical significance parameters for an HMM prior to database searches. • hmmsearch: search a sequence database with an HMM. • hmmpfam: search an HMM database with one or more sequences. • hmmconvert and hmmindex.

31. Construction of the Pfam HMMs PROSITE, literature Family definition ClustalW, editing If the HMM doesn’t find all members Seed alignment (representative, stable) hmmbuild HMM profile hmmalign Full alignment (complete, volatile)

32. A Solution to Problem #2 Collect known sequences in literature Do multiple alignment (ClustalX, editing) Create an HMM profile using hmmbuild Search an Arabidopsis sequence dataset using the HMM and hmmsearch

33. Other Tools for Protein Pattern Analysis • SignalP: • For predicting signal peptide and cleavage site. • Available at http://www.cbs.dtu.dk/services/SignalP/. • PSORT: • For predicting protein localization sites in cells. • Available at http://psort.nibb.ac.jp/. • TMHMM: • For predicting transmembrane segments. • Available at http://www.cbs.dtu.dk/services/TMHMM/.

34. Summary • Hidden Markov Model (HMM) is well suited to represent protein domains. • Since HMMs are constructed from aligned sequence families, HMM search is often more sensitive than BLAST for detecting remotely related homologues. • Resources are available for modeling and searching for protein domains/motifs.

35. PROSITE vs. Perl RegExp PDOC00269 (Heat shock hsp70 signature) PROSITE: [IV]-D-L-G-T-[ST]-x-[SC] Perl: [IV]DLGT[ST]\w[SC] PDOC50884 (Part of Zinc finger Dof-type signature) PROSITE: C-x(2)-C-x(7)-[CS]-x(13)-C-x(2)-C Perl: C\w{2}C\w{7}[CS]\w{13}C\w{2}C PDOC00081 (Part of Cytochrome P450 signature) PROSITE: [FW]-[SGNH]-x-[GD]-{F}-[RKHPT]-{P}-C Perl: [FW][SGNH]\w[GD][^F][RKHPT][^P]C PDOC00036 (Part of bZIP domain signature) PROSITE: [KR]-x(1,3)-[RKSAQ]-N-{VL}-x-[SAQ](2)-{L} Perl: [KR]\w{1,3}[RKSAQ]N[^VL]\w[SAQ]{2}[^L]