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Bioinformatics For MNW 2nd Year Jaap Heringa FEW/FALW Integrative Bioinformatics Institute VU (IBIVU) [email protected] Current Bioinformatics Unit • Jens Kleinjung (1/11/02) • Victor Simosis – PhD (1/12/02) • Radek Szklarczyk - PhD (1/01/03) • John Romein (1/12/02, Henri Bal) Bioinformatics course 2nd year MNW spring 2003 • Pattern recognition – – – – – – – Supervised/unsupervised learning Types of data, data normalisation, lacking data Search image Similarity tables Clustering Principal component analysis Discriminant analysis Bioinformatics course 2nd year MNW spring 2003 • Protein – – – – – – – – – Folding Structure and function Protein structure prediction Secondary structure Tertiary structure Function Post-translational modification Prot.-Prot. Interaction -- Docking algorithm Molecular dynamics/Monte Carlo Bioinformatics course 2nd year MNW spring 2003 • Sequence analysis – – – – – Pairwise alignment Dynamic programming (NW, SW, shortcuts) Multiple alignment Combining information Database/homology searching (Fasta, Blast, Statistical issues-E/P values) Bioinformatics course 2nd year MNW spring 2003 • Gene structure and gene finding algorithm • Omics – DNA makes RNA makes protein – Expression data, Nucleus to ribosome, translation, etc. – Metabolomics – Physiomics – Databases • DNA, EST • Protein sequence • Protein structure Bioinformatics course 2nd year MNW spring 2003 o Microarray data o Protein structure (PDB) o Proteomics o Mass spectrometry/NMR/X-ray? Bioinformatics course 2nd year MNW spring 2003 • • • • • Bioinformatics method development IPR issues Programming and scripting languages Web solutions Computational issues – NP-complete problems – CPU, memory, storage problems – Parallel computing • Bioinformatics method usage/application • Molecular viewers (RasMol, MolMol, etc.) Gathering knowledge • Anatomy, architecture Rembrandt, 1632 • Dynamics, mechanics Newton, 1726 • Informatics (Cybernetics – Wiener, 1948) (Cybernetics has been defined as the science of control in machines and animals, and hence it applies to technological, animal and environmental systems) • Genomics, bioinformatics Bioinformatics Chemistry Biology Molecular biology Mathematics Statistics Bioinformatics Computer Science Informatics Medicine Physics Bioinformatics “Studying informational processes in biological systems” (Hogeweg, early 1970s) • No computers necessary • Back of envelope OK “Information technology applied to the management and analysis of biological data” (Attwood and Parry-Smith) Applying algorithms with mathematical formalisms in biology (genomics) -- USA Bioinformatics in the olden days • Close to Molecular Biology: – (Statistical) analysis of protein and nucleotide structure – Protein folding problem – Protein-protein and protein-nucleotide interaction • Many essential methods were created early on (BG era) – Protein sequence analysis (pairwise and multiple alignment) – Protein structure prediction (secondary, tertiary structure) Bioinformatics in the olden days (Cont.) • Evolution was studied and methods created – Phylogenetic reconstruction (clustering – NJ method The Human Genome -- 26 June 2000 The Human Genome -- 26 June 2000 Dr. Craig Venter Sir John Sulston Celera Genomics Human Genome Project -- Shotgun method Human DNA • There are about 3bn (3 109) nucleotides in the nucleus of almost all of the trillions (3.5 1012 ) of cells of a human body (an exception is, for example, red blood cells which have no nucleus and therefore no DNA) – a total of ~1022 nucleotides! • Many DNA regions code for proteins, and are called genes (1 gene codes for 1 protein in principle) • Human DNA contains ~30,000 expressed genes • Deoxyribonucleic acid (DNA) comprises 4 different types of nucleotides: adenine (A), thiamine (T), cytosine (C) and guanine (G). These nucleotides are sometimes also called bases Human DNA (Cont.) • All people are different, but the DNA of different people only varies for 0.2% or less. So, only 2 letters in 1000 are expected to be different. Over the whole genome, this means that about 3 million letters would differ between individuals. • The structure of DNA is the so-called double helix, discovered by Watson and Crick in 1953, where the two helices are cross-linked by A-T and C-G base-pairs (nucleotide pairs – so-called Watson-Crick base pairing). Tot hier 3/2 – 10.45-12.30 DNA compositional biases • Base composition of genomes: • E. coli: 25% A, 25% C, 25% G, 25% T • P. falciparum (Malaria parasite): 82%A+T • Translation initiation: • ATG is the near universal motif indicating the start of translation in DNA coding sequence. Some facts about human genes • • • • • • Comprise about 3% of the genome Average gene length: ~ 8,000 bp Average of 5-6 exons/gene Average exon length: ~200 bp Average intron length: ~2,000 bp ~8% genes have a single exon • Some exons can be as small as 1 or 3 bp. • HUMFMR1S is not atypical: 17 exons 40-60 bp long, comprising 3% of a 67,000 bp gene Genetic diseases • Many diseases run in families and are a result of genes which predispose such family members to these illnesses • Examples are Alzheimer’s disease, cystic fibrosis (CF), breast or colon cancer, or heart diseases. • Some of these diseases can be caused by a problem within a single gene, such as with CF. Genetic diseases (Cont.) • For other illnesses, like heart disease, at least 20-30 genes are thought to play a part, and it is still unknown which combination of problems within which genes are responsible. • With a “problem” within a gene is meant that a single nucleotide or a combination of those within the gene are causing the disease (or make that the body is not sufficiently fighting the disease). • Persons with different combinations of these nucleotides could then be unaffected by these diseases. Genetic diseases (Cont.) Cystic Fibrosis • Known since very early on (“Celtic gene”) • Inherited autosomal recessive condition (Chr. 7) • Symptoms: – Clogging and infection of lungs (early death) – Intestinal obstruction – Reduced fertility and (male) anatomical anomalies • CF gene CFTR has 3-bp deletion leading to Del508 (Phe) in 1480 aa protein (epithelial Cl- channel) – protein degraded in ER instead of inserted into cell membrane Genomic Data Sources • DNA/protein sequence • Expression (microarray) • Proteome (xray, NMR, mass spectrometry) • Metabolome • Physiome (spatial, temporal) Integrative bioinformatics Genomic Data Sources Vertical Genomics genome transcriptome proteome metabolome physiome Dinner discussion: Integrative Bioinformatics & Genomics VU A gene codes for a protein DNA CCTGAGCCAACTATTGATGAA transcription mRNA CCUGAGCCAACUAUUGAUGAA translation Protein PEPTIDE Humans have spliced genes… DNA makes RNA makes Protein Remark • The problem of identifying (annotating) human genes is considerably harder than the early success story for ßglobin might suggest. • The human factor VIII gene (whose mutations cause hemophilia A) is spread over ~186,000 bp. It consists of 26 exons ranging in size from 69 to 3,106 bp, and its 25 introns range in size from 207 to 32,400 bp. The complete gene is thus ~9 kb of exon and ~177 kb of intron. • The biggest human gene yet is for dystrophin. It has > 30 exons and is spread over 2.4 million bp. DNA makes RNA makes Protein: Expression data • More copies of mRNA for a gene leads to more protein • mRNA can now be measured for all the genes in a cell at ones through microarray technology • Can have 60,000 spots (genes) on a single gene chip • Colour change gives intensity of gene expression (over- or under-expression) Metabolic networks Glycolysis and Gluconeogenesis Kegg database (Japan) High-throughput Biological Data • Enormous amounts of biological data are being generated by high-throughput capabilities; even more are coming – – – – – – genomic sequences gene expression data mass spec. data protein-protein interaction protein structures ...... Protein structural data explosion Protein Data Bank (PDB): 14500 Structures (6 March 2001) 10900 x-ray crystallography, 1810 NMR, 278 theoretical models, others... Dickerson’s formula: equivalent to Moore’s law n = e0.19(y-1960) with y the year. On 27 March 2001 there were 12,123 3D protein structures in the PDB: Dickerson’s formula predicts 12,066 (within 0.5%)! Sequence versus structural data • Despite structural genomics efforts, growth of PDB slowed down in 2001-2002 (i.e did not keep up with Dickerson’s formula) • More than 100 completely sequenced genomes Increasing gap between structural and sequence data Bioinformatics Large - external (integrative) Science Planetary Science Population Biology Sociobiology Systems Biology Biology Human Cultural Anthropology Sociology Psychology Medicine Molecular Biology Chemistry Physics Small – internal (individual) Bioinformatics • Offers an ever more essential input to – – – – – – – – Molecular Biology Pharmacology (drug design) Agriculture Biotechnology Clinical medicine Anthropology Forensic science Chemical industries (detergent industries, etc.) High-throughput Biological Data The data deluge • Hidden in these data is information that reflects – existence, organization, activity, functionality …… of biological machineries at different levels in living organisms Most effectively utilising this information will prove to be essential for Integrative Bioinformatics Data Issues …… • Data collection: getting the data • Data representation: data standards, data normalisation ….. • Data organisation and storage: database issues ….. • Data analysis and data mining: discovering “knowledge”, patterns/signals, from data, establishing associations among data patterns • Data utilisation and application: from data patterns/signals to models for bio-machineries • Data visualization: viewing complex data …… • Data transmission: data collection, retrieval, ….. • …… Tot hier 5/2 Bioinformatics “Nothing in Biology makes sense except in the light of evolution” (Theodosius Dobzhansky (1900-1975)) “Nothing in bioinformatics makes sense except in the light of Biology” Pair-wise alignment T D W V T A L K T D W L - - I K Combinatorial explosion - 1 gap in 1 sequence: n+1 possibilities - 2 gaps in 1 sequence: (n+1)n - 3 gaps in 1 sequence: (n+1)n(n-1), etc. 2n ~ = n 22n (2n)! (n!)2 n 2 sequences of 300 a.a.: ~1088 alignments 2 sequences of 1000 a.a.: ~10600 alignments! Dynamic programming Scoring alignments Sa,b = l s(ai, b )+ j gp(k) = pi + kpe k Nk gp(k ) affine gap penalties pi and pe are the penalties for gap initialisation and extension, respectively Dynamic programming Scoring alignments T D W V T A L K T D W L - - I K 2020 10 Amino Acid Exchange Matrix 1 Gap penalties (open, extension) Score: s(T,T)+s(D,D)+s(W,W)+s(V,L)+Po+2Px + +s(L,I)+s(K,K) Pairwise sequence alignment Global dynamic programming MDAGSTVILCFVG M D A A S T I L C G S Evolution Amino Acid Exchange Matrix Search matrix MDAGSTVILCFVGMDAAST-ILC--GS Gap penalties (open,extension) Global dynamic programming j-1 i-1 Si,j = si,j + Max Max{S0<x<i-1, j-1 - Pi - (i-x-1)Px} Si-1,j-1 Max{Si-1, 0<y<j-1 - Pi - (j-y-1)Px} Global dynamic programming Global dynamic programming Tot hier 17/02/03 Local dynamic programming (Smith & Waterman, 1981) LCFVMLAGSTVIVGTR E D A S T I L C G S Negative numbers Amino Acid Exchange Matrix Search matrix AGSTVIVG A-STILCG Gap penalties (open, extension) Local dynamic programming (Smith & Waterman, 1981) j-1 i-1 Si,j = Max Si,j + Max{S0<x<i-1,j-1 - Pi - (i-x-1)Px} Si,j + Si-1,j-1 Si,j + Max {Si-1,0<y<j-1 - Pi - (j-y-1)Px} 0 Local dynamic programming Sequence database searching – Homology searching DP too slow for repeated database searches • FASTA • BLAST and PSI-BLAST • QUEST • HMMER • SAM-T98 Fast heuristics Hidden Markov modelling FASTA • Compares a given query sequence with a library of sequences and calculates for each pair the highest scoring local alignment • Speed is obtained by delaying application of the dynamic programming technique to the moment where the most similar segments are already identified by faster and less sensitive techniques • FASTA routine operates in four steps: FASTA Operates in four steps: 1. Rapid searches for identical words of a user specified length occurring in query and database sequence(s) (Wilbur and Lipman, 1983, 1984). For each target sequence the 10 regions with the highest density of ungapped common words are determined. 2. These 10 regions are rescored using Dayhoff PAM-250 residue exchange matrix (Dayhoff et al., 1983) and the best scoring region of the 10 is reported under init1 in the FASTA output. 3. Regions scoring higher than a threshold value and being sufficiently near each other in the sequence are joined, now allowing gaps. The highest score of these new fragments can be found under initn in the FASTA output. 4. full dynamic programming alignment (Chao et al., 1992) over the final region which is widened by 32 residues at either side, of which the score is written under opt in the FASTA output. FASTA output example DE METAL RESISTANCE PROTEIN YCF1 (YEAST CADMIUM FACTOR 1). . . . SCORES Init1: 161 Initn: 161 Opt: 162 z-score: 229.5 E(): 3.4e-06 Smith-Waterman score: 162; 35.1% identity in 57 aa overlap test.seq YCFI_YEAST 10 20 30 MQRSPLEKASVVSKLFFSWTRPILRKGYRQRLE :| :|::| |:::||:|||::|: | CASILLLEALPKKPLMPHQHIHQTLTRRKPNPYDSANIFSRITFSWMSGLMKTGYEKYLV 180 test.seq YCFI_YEAST 190 200 210 220 230 40 50 60 LSDIYQIPSVDSADNLSEKLEREWDRE :|:|::| |:::||:|||::|: | EADLYKLPRNFSSEELSQKLEKNWENELKQKSNPSLSWAICRTFGSKMLLAAFFKAIHDV 240 250 260 270 280 290 FASTA (1) Rapid identical word searches: • Searching for k-tuples of a certain size within a specified bandwidth along search matrix diagonals. • For not-too-distant sequences (> 35% residue identity), little sensitivity is lost while speed is greatly increased. • Technique employed is known as hash coding or hashing: a lookup table is constructed for all words in the query sequence, which is then used to compare all encountered words in each database sequence. FASTA • The k-tuple length is user-defined and is usually 1 or 2 for protein sequences (i.e. either the positions of each of the individual 20 amino acids or the positions of each of the 400 possible dipeptides are located). • For nucleic acid sequences, the k-tuple is 5-20, and should be longer because short k-tuples are much more common due to the 4 letter alphabet of nucleic acids. The larger the k-tuple chosen, the more rapid but less thorough, a database search. BLAST • blastp compares an amino acid query sequence against a protein sequence database • blastn compares a nucleotide query sequence against a nucleotide sequence database • blastx compares the six-frame conceptual protein translation products of a nucleotide query sequence against a protein sequence database • tblastn compares a protein query sequence against a nucleotide sequence database translated in six reading frames • tblastx compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database. BLAST • Generates all tripeptides from a query sequence and for each of those the derivation of a table of similar tripeptides: number is only fraction of total number possible. • Quickly scans a database of protein sequences for ungapped regions showing high similarity, which are called high-scoring segment pairs (HSP), using the tables of similar peptides. The initial search is done for a word of length W that scores at least the threshold value T when compared to the query using a substitution matrix. • Word hits are then extended in either direction in an attempt to generate an alignment with a score exceeding the threshold of S, and as far as the cumulative alignment score can be increased. BLAST Extension of the word hits in each direction are halted • when the cumulative alignment score falls off by the quantity X from its maximum achieved value • the cumulative score goes to zero or below due to the accumulation of one or more negative-scoring residue alignments • upon reaching the end of either sequence • The T parameter is the most important for the speed and sensitivity of the search resulting in the high-scoring segment pairs • A Maximal-scoring Segment Pair (MSP) is defined as the highest scoring of all possible segment pairs produced from two sequences. PSI-BLAST • Query sequences are first scanned for the presence of so-called low-complexity regions (Wooton and Federhen, 1996), i.e. regions with a biased composition likely to lead to spurious hits; are excluded from alignment. • The program then initially operates on a single query sequence by performing a gapped BLAST search • Then, the program takes significant local alignments found, constructs a multiple alignment and abstracts a position specific scoring matrix (PSSM) from this alignment. • Rescan the database in a subsequent round to find more homologous sequences Iteration continues until user decides to stop or search has converged PSI-BLAST iteration Q xxxxxxxxxxxxxxxxx Query sequence Gapped BLAST search Q xxxxxxxxxxxxxxxxx Query sequence Database hits A C D . . Y PSSM Pi Px Gapped BLAST search A C D . . Y Pi Px PSSM Database hits PSI-BLAST output example Multiple alignment profiles Gribskov et al. 1987 i A C D W Y Gap penalties 1.0 0.3 0.1 0 0.3 0.3 0.5 Position dependent gap penalties Normalised sequence similarity The p-value is defined as the probability of seeing at least one unrelated score S greater than or equal to a given score x in a database search over n sequences. This probability follows the Poisson distribution (Waterman and Vingron, 1994): P(x, n) = 1 – e-nP(S x), where n is the number of sequences in the database Depending on x and n (fixed) Normalised sequence similarity Statistical significance The E-value is defined as the expected number of nonhomologous sequences with score greater than or equal to a score x in a database of n sequences: E(x, n) = nP(S x) if E-value = 0.01, then the expected number of random hits with score S x is 0.01, which means that this Evalue is expected by chance only once in 100 independent searches over the database. if the E-value of a hit is 5, then five fortuitous hits with S x are expected within a single database search, which renders the hit not significant. Normalised sequence similarity Statistical significance • Database searching is commonly performed using an E-value in between 0.1 and 0.001. • Low E-values decrease the number of false positives in a database search, but increase the number of false negatives, thereby lowering the sensitivity of the search. HMM-based homology searching • Most widely used HMM-based profile searching tools currently are SAM-T98 (Karplus et al., 1998) and HMMER2 (Eddy, 1998) • formal probabilistic basis and consistent theory behind gap and insertion scores • HMMs good for profile searches, bad for alignment • HMMs are slow The HMM algorithms Forward: t (i) = P(observed sequence, ending in state i at base t) Backward: ß t (i) = P(obs. after t | ending in state i at base t) Viterbi: t (i) = max P(obs. , ending in state i at base t) Questions: 1. What is the most likely die (predicted) sequence? Viterbi 2. What is the probability of the observed sequence? Forward 3. What is the probability that the 3rd state is B, given the observed sequence? Backward HMM-based homology searching Transition probabilities and Emission probabilities Gapped HMMs also have insertion and deletion states Profile HMM: m=match state, I-insert state, d=delete state; go from left to right. I and m states output amino acids; d states are ‘silent”. d1 d2 d3 d4 I0 I1 I2 I3 I4 m0 m1 m2 m3 m4 Start m5 End Homology-derived Secondary Structure of Proteins (HSSP) Sander & Schneider, 1991 Tot hier 17/02/03 Bio-Data Analysis and Data Mining • Existing/emerging bio-data analysis and mining tools for – – – – – – – – – DNA sequence assembly Genetic map construction Sequence comparison and database searching Gene finding …. Gene expression data analysis Phylogenetic tree analysis to infer horizontally-transferred genes Mass spec. data analysis for protein complex characterization …… • Current mode of work: Often enough: developing ad hoc tools for each individual application Bio-Data Analysis and Data Mining • As the amount and types of data and their cross connections increase rapidly • the number of analysis tools needed will go up “exponentially” – blast, blastp, blastx, blastn, … from BLAST family of tools – gene finding tools for human, mouse, fly, rice, cyanobacteria, ….. – tools for finding various signals in genomic sequences, protein-binding sites, splice junction sites, translation start sites, ….. Bio-Data Analysis and Data Mining Many of these data analysis problems are fundamentally the same problem(s) and can be solved using the same set of tools: e.g. clustering or optimal segmentation by Dynamic Programming Developing ad hoc tools for each application (by each group of individual researchers) may soon become inadequate as bio-data production capabilities further ramp up Bio-data Analysis, Data Mining and Integrative Bioinformatics To have analysis capabilities covering wide range of problems, we need to discover the common fundamental structures of these problems; HOWEVER in biology one size does NOT fit all… Goal is development of a data analysis infrastructure in support of Genomics and beyond Algorithms in bioinformatics • string algorithms • dynamic programming • machine learning (NN, k-NN, SVM, GA, ..) • Markov chain models • hidden Markov models • Markov Chain Monte Carlo (MCMC) algorithms • stochastic context free grammars • EM algorithms • Gibbs sampling • clustering • tree algorithms • text analysis • hybrid/combinatorial techniques and more… Sequence analysis and homology searching Finding genes and regulatory elements Expression data Functional genomics • Monte Carlo Protein translation Example of algorithm reuse: Data clustering • Many biological data analysis problems can be formulated as clustering problems – microarray gene expression data analysis – identification of regulatory binding sites (similarly, splice junction sites, translation start sites, ......) – (yeast) two-hybrid data analysis (for inference of protein complexes) – phylogenetic tree clustering (for inference of horizontally transferred genes) – protein domain identification – identification of structural motifs – prediction reliability assessment of protein structures – NMR peak assignments – ...... Data Clustering Problems • Clustering: partition a data set into clusters so that data points of the same cluster are “similar” and points of different clusters are “dissimilar” • cluster identification -- identifying clusters with significantly different features than the background Application Examples • Regulatory binding site identification: CRP (CAP) binding site • Two hybrid data analysis Gene expression data analysis Are all solvable by the same algorithm! Other Application Examples • Phylogenetic tree clustering analysis • Protein sidechain packing prediction • Assessment of prediction reliability of protein structures • Protein secondary structures • Protein domain prediction • NMR peak assignments • …… Integrative bioinformatics @ VU Studying informational processes at biological system level • From gene sequence to intercellular processes • Computers necessary • We have biology, statistics, computational intelligence (AI), HTC, .. • VUMC: microarray facility • Enabling technology: new glue to integrate • New integrative algorithms • Goals: understanding cells in terms of genomes, fighting disease (VUMC) Bioinformatics @ VU Progression: • DNA: gene prediction, predicting regulatory elements • mRNA expression • Proteins: docking, domain prediction • Metabolic pathways: metabolic control • Cell-cell communication Protein structure and function can be complex… Pyruvate kinase Phosphotransferase b barrel regulatory domain /b barrel catalytic substrate binding domain /b nucleotide binding domain 1 continuous + 2 discontinuous domains Bioinformatics @ VU Qualitative challenges: • High quality alignments (alternative splicing) • In-silico structural genomics • In-silico functional genomics: reliable annotation • Protein-protein interactions. • Metabolic pathways: assign the edges in the networks • Cell-cell communication: find membrane associated components • New algorithms Bioinformatics @ VU Quantitative challenges: • Understanding mRNA expression levels • Understanding resulting protein activity • Time dependencies • Spatial constraints, compartmentalisation • Are classical differential equation models adequate or do we need more individual modeling (e.g macromolecular crowding and activity at oligomolecular level)? • Metabolic pathways: calculate fluxes through time • Cell-cell communication: tissues, hormones, innervations Need ‘complete’ experimental data for good biological model system to learn to integrate Bioinformatics @ VU VUMC • Neuropeptide – addiction • Oncogenes – disease patterns • Reumatic disease CNCR • From synapses to higher order behaviour • Addiction FPP • Genetic psychology – twin data bank Integrative bioinformatics • Integrate data sources • Integrate methods • Integrate data through method integration (biological model) Bioinformatics tool Algorithm Data tool Biological Interpretation (model) Bioinformatics “Nothing in Biology makes sense except in the light of evolution” (Theodosius Dobzhansky (1900-1975)) “Nothing in Bioinformatics makes sense except in the light of Biology” Pair-wise sequence alignment (more than just string matching) Global dynamic programming MDAGSTVILCFVG M D A A S T I L C G S Evolution Amino Acid Exchange Matrix Search matrix MDAGSTVILCFVGMDAAST-ILC--GS Gap penalties (open,extension) Pair-wise alignment search explosions T D W V T A L K T D W L - - I K Combinatorial explosion - 1 gap in 1 sequence: n+1 possibilities - 2 gaps in 1 sequence: (n+1)n - 3 gaps in 1 sequence: (n+1)n(n-1), etc. 2n ~ = n 22n (2n)! (n!)2 n 2 sequences of 300 a.a.: ~1088 alignments 2 sequences of 1000 a.a.: ~10600 alignments! Global dynamic programming This talk – own kitchen Three integrative methods to predict protein structural aspects: • Iterative multiple alignment + protein secondary structure (Praline) Intermezzo: 2½-D structure prediction of flavodoxin fold by hand • Protein domain delineation based on consistency of multiple ab initio model tertiary structures (SnapDRAGON) • Protein domain delineation based on combining homology searching with domain prediction (Domaination) Comparing sequences - Similarity Score Many properties can be used: • Nucleotide or amino acid composition • Isoelectric point • Molecular weight • Morphological characters Multivariate statistics – Cluster analysis 1 2 3 4 5 C1 C2 C3 C4 C5 C6 .. Raw table Similarity criterion Scores Similarity matrix 5×5 Cluster criterion Phylogenetic tree Human Evolution Comparing sequences - Similarity Score Many properties can be used: • Nucleotide or amino acid composition • Isoelectric point • Molecular weight • Morphological characters • But: molecular evolution through sequence alignment Multivariate statistics – Cluster analysis 1 2 3 4 5 Multiple alignment Similarity criterion Scores 5×5 Similarity matrix Phylogenetic tree Lactate dehydrogenase multiple alignment Human Chicken Dogfish Lamprey Barley Maizey casei Bacillus Lacto__ste Lacto_plant Therma_mari Bifido Thermus_aqua Mycoplasma -KITVVGVGAVGMACAISILMKDLADELALVDVIEDKLKGEMMDLQHGSLFLRTPKIVSGKDYNVTANSKLVIITAGARQ -KISVVGVGAVGMACAISILMKDLADELTLVDVVEDKLKGEMMDLQHGSLFLKTPKITSGKDYSVTAHSKLVIVTAGARQ –KITVVGVGAVGMACAISILMKDLADEVALVDVMEDKLKGEMMDLQHGSLFLHTAKIVSGKDYSVSAGSKLVVITAGARQ SKVTIVGVGQVGMAAAISVLLRDLADELALVDVVEDRLKGEMMDLLHGSLFLKTAKIVADKDYSVTAGSRLVVVTAGARQ TKISVIGAGNVGMAIAQTILTQNLADEIALVDALPDKLRGEALDLQHAAAFLPRVRI-SGTDAAVTKNSDLVIVTAGARQ -KVILVGDGAVGSSYAYAMVLQGIAQEIGIVDIFKDKTKGDAIDLSNALPFTSPKKIYSA-EYSDAKDADLVVITAGAPQ TKVSVIGAGNVGMAIAQTILTRDLADEIALVDAVPDKLRGEMLDLQHAAAFLPRTRLVSGTDMSVTRGSDLVIVTAGARQ -RVVVIGAGFVGASYVFALMNQGIADEIVLIDANESKAIGDAMDFNHGKVFAPKPVDIWHGDYDDCRDADLVVICAGANQ QKVVLVGDGAVGSSYAFAMAQQGIAEEFVIVDVVKDRTKGDALDLEDAQAFTAPKKIYSG-EYSDCKDADLVVITAGAPQ MKIGIVGLGRVGSSTAFALLMKGFAREMVLIDVDKKRAEGDALDLIHGTPFTRRANIYAG-DYADLKGSDVVIVAAGVPQ -KLAVIGAGAVGSTLAFAAAQRGIAREIVLEDIAKERVEAEVLDMQHGSSFYPTVSIDGSDDPEICRDADMVVITAGPRQ MKVGIVGSGFVGSATAYALVLQGVAREVVLVDLDRKLAQAHAEDILHATPFAHPVWVRSGW-YEDLEGARVVIVAAGVAQ -KIALIGAGNVGNSFLYAAMNQGLASEYGIIDINPDFADGNAFDFEDASASLPFPISVSRYEYKDLKDADFIVITAGRPQ Distance Matrix 1 2 3 4 5 6 7 8 9 10 11 12 13 Human Chicken Dogfish Lamprey Barley Maizey Lacto_casei Bacillus_stea Lacto_plant Therma_mari Bifido Thermus_aqua Mycoplasma 1 0.000 0.112 0.128 0.202 0.378 0.346 0.530 0.551 0.512 0.524 0.528 0.635 0.637 2 0.112 0.000 0.155 0.214 0.382 0.348 0.538 0.569 0.516 0.524 0.524 0.631 0.651 3 0.128 0.155 0.000 0.196 0.389 0.337 0.522 0.567 0.516 0.512 0.524 0.600 0.655 4 0.202 0.214 0.196 0.000 0.426 0.356 0.553 0.589 0.544 0.503 0.544 0.616 0.669 5 0.378 0.382 0.389 0.426 0.000 0.171 0.536 0.565 0.526 0.547 0.516 0.629 0.575 6 0.346 0.348 0.337 0.356 0.171 0.000 0.557 0.563 0.538 0.555 0.518 0.643 0.587 7 0.530 0.538 0.522 0.553 0.536 0.557 0.000 0.518 0.208 0.445 0.561 0.526 0.501 8 0.551 0.569 0.567 0.589 0.565 0.563 0.518 0.000 0.477 0.536 0.536 0.598 0.495 9 0.512 0.516 0.516 0.544 0.526 0.538 0.208 0.477 0.000 0.433 0.489 0.563 0.485 10 0.524 0.524 0.512 0.503 0.547 0.555 0.445 0.536 0.433 0.000 0.532 0.405 0.598 11 0.528 0.524 0.524 0.544 0.516 0.518 0.561 0.536 0.489 0.532 0.000 0.604 0.614 12 0.635 0.631 0.600 0.616 0.629 0.643 0.526 0.598 0.563 0.405 0.604 0.000 0.641 13 0.637 0.651 0.655 0.669 0.575 0.587 0.501 0.495 0.485 0.598 0.614 0.641 0.000 Multiple sequence alignment Why? • It is the most important means to assess relatedness of a set of sequences • Gain information about the structure/function of a query sequence (conservation patterns) • Construct a phylogenetic tree • Putting together a set of sequenced fragments (Fragment assembly) • Comparing a segment sequenced by two different labs • Many bioinformatics methods depend on it (e.g. secondary/tertiary structure prediction) Flavodoxin fold: aligning 13 Flavodoxins + cheY 5(b) fold Flavodoxin-cheY multiple alignment Praline with pre-processing 1fx1 FLAV_DESDE FLAV_DESVH FLAV_DESSA FLAV_DESGI 2fcr FLAV_AZOVI FLAV_ENTAG FLAV_ANASP FLAV_ECOLI 4fxn FLAV_MEGEL FLAV_CLOAB 3chy -PKALIVYGSTTGNT-EYTAETIARQLANAG-YEVDSRDAASVEAGGLFEGFDLVLLGCSTWGDDSI------ELQDDFIPLF-DSLEETGAQGRKVACF MSKVLIVFGSSTGNT-ESIaQKLEELIAAGG-HEVTLLNAADASAENLADGYDAVLFgCSAWGMEDL------EMQDDFLSLF-EEFNRFGLAGRKVAAf MPKALIVYGSTTGNT-EYTaETIARELADAG-YEVDSRDAASVEAGGLFEGFDLVLLgCSTWGDDSI------ELQDDFIPLF-DSLEETGAQGRKVACf MSKSLIVYGSTTGNT-ETAaEYVAEAFENKE-IDVELKNVTDVSVADLGNGYDIVLFgCSTWGEEEI------ELQDDFIPLY-DSLENADLKGKKVSVf MPKALIVYGSTTGNT-EGVaEAIAKTLNSEG-METTVVNVADVTAPGLAEGYDVVLLgCSTWGDDEI------ELQEDFVPLY-EDLDRAGLKDKKVGVf --KIGIFFSTSTGNT-TEVADFIGKTLGA---KADAPIDVDDVTDPQALKDYDLLFLGAPTWNTG----ADTERSGTSWDEFLYDKLPEVDMKDLPVAIF -AKIGLFFGSNTGKT-RKVaKSIKKRFDDET-MSDA-LNVNRVS-AEDFAQYQFLILgTPTLGEGELPGLSSDCENESWEEFL-PKIEGLDFSGKTVALf MATIGIFFGSDTGQT-RKVaKLIHQKLDG---IADAPLDVRRAT-REQFLSYPVLLLgTPTLGDGELPGVEAGSQYDSWQEFT-NTLSEADLTGKTVALf SKKIGLFYGTQTGKT-ESVaEIIRDEFGN---DVVTLHDVSQAE-VTDLNDYQYLIIgCPTWNIGEL--------QSDWEGLY-SELDDVDFNGKLVAYf -AITGIFFGSDTGNT-ENIaKMIQKQLGK---DVADVHDIAKSS-KEDLEAYDILLLgIPTWYYGE--------AQCDWDDFF-PTLEEIDFNGKLVALf -MK--IVYWSGTGNT-EKMAELIAKGIIESG-KDVNTINVSDVNIDELL-NEDILILGCSAMGDEVL-------EESEFEPFI-EEIS-TKISGKKVALF MVE--IVYWSGTGNT-EAMaNEIEAAVKAAG-ADVESVRFEDTNVDDVA-SKDVILLgCPAMGSEEL-------EDSVVEPFF-TDLA-PKLKGKKVGLf -MKISILYSSKTGKT-ERVaKLIEEGVKRSGNIEVKTMNLDAVD-KKFLQESEGIIFgTPTYYAN---------ISWEMKKWI-DESSEFNLEGKLGAAf ADKELKFLVVDDFSTMRRIVRNLLKELGFN--NVEEAEDGVDALNKLQAGGYGFVI---SDWNMPNM----------DGLELL-KTIRADGAMSALPVLM 1fx1 FLAV_DESDE FLAV_DESVH FLAV_DESSA FLAV_DESGI 2fcr FLAV_AZOVI FLAV_ENTAG FLAV_ANASP FLAV_ECOLI 4fxn FLAV_MEGEL FLAV_CLOAB 3chy GCGDS-SY-EYFCGA-VDAIEEKLKNLGAEIVQD---------------------GLRIDGD--PRAARDDIVGWAHDVRGAI-------ASGDQ-EY-EHFCGA-VPAIEERAKELgATIIAE---------------------GLKMEGD--ASNDPEAVASfAEDVLKQL-------GCGDS-SY-EYFCGA-VDAIEEKLKNLgAEIVQD---------------------GLRIDGD--PRAARDDIVGwAHDVRGAI-------GCGDS-DY-TYFCGA-VDAIEEKLEKMgAVVIGD---------------------SLKIDGD--PE--RDEIVSwGSGIADKI-------GCGDS-SY-TYFCGA-VDVIEKKAEELgATLVAS---------------------SLKIDGE--PD--SAEVLDwAREVLARV-------GLGDAEGYPDNFCDA-IEEIHDCFAKQGAKPVGFSNPDDYDYEESKS-VRDGKFLGLPLDMVNDQIPMEKRVAGWVEAVVSETGV-----GLGDQVGYPENYLDA-LGELYSFFKDRgAKIVGSWSTDGYEFESSEA-VVDGKFVGLALDLDNQSGKTDERVAAwLAQIAPEFGLS--L-GLGDQLNYSKNFVSA-MRILYDLVIARgACVVGNWPREGYKFSFSAALLENNEFVGLPLDQENQYDLTEERIDSwLEKLKPAV-L-----GTGDQIGYADNFQDA-IGILEEKISQRgGKTVGYWSTDGYDFNDSKA-LRNGKFVGLALDEDNQSDLTDDRIKSwVAQLKSEFGL-----GCGDQEDYAEYFCDA-LGTIRDIIEPRgATIVGHWPTAGYHFEASKGLADDDHFVGLAIDEDRQPELTAERVEKwVKQISEELHLDEILNA G-----SY-GWGDGKWMRDFEERMNGYGCVVVET---------------------PLIVQNE--PDEAEQDCIEFGKKIANI--------G-----SY-GWGSGEWMDAWKQRTEDTgATVIGT----------------------AIVNEM--PDNA-PECKElGEAAAKA--------STANSIAGGSDIA---LLTILNHLMVKgMLVYSG----GVAFGKPKTHLGYVHINEIQENEDENARIfGERiANkVKQIF----------VTAEAKK--ENIIAA---------AQAGAS-------------------------GYVV-----KPFTAATLEEKLNKIFEKLGM------ Iteration 0 T G SP= 136944.00 AvSP= 10.675 SId= 4009 AvSId= 0.313 Flavodoxin-cheY NJ tree Integrating secondary structure prediction in multiple alignment Victor Simossis Praline multiple alignment method (Heringa, Comp. Chem. 23, 341-364;1999, Comp. Chem., 26, 459-477;2002; Kleinjung, Douglas & Heringa, Bioinformatics, in press;2002) • Combining sequence data and secondary structure prediction (Heringa, Curr. Prot. Pept. Sci., 1 (3), 273-301;2000) • Secondary structure methods: PhD, Predator, PSIPred, Jpred, SSPRED,... Using secondary structure in multiple alignment “Structure more conserved than sequence” Protein structure hierarchical levels PRIMARY STRUCTURE (amino acid sequence) SECONDARY STRUCTURE (helices, strands) VHLTPEEKSAVTALWGKVNVDE VGGEALGRLLVVYPWTQRFFE SFGDLSTPDAVMGNPKVKAHG KKVLGAFSDGLAHLDNLKGTFA TLSELHCDKLHVDPENFRLLGN VLVCVLAHHFGKEFTPPVQAAY QKVVAGVANALAHKYH QUATERNARY STRUCTURE (oligomers) TERTIARY STRUCTURE (fold) Protein structure hierarchical levels PRIMARY STRUCTURE (amino acid sequence) SECONDARY STRUCTURE (helices, strands) VHLTPEEKSAVTALWGKVNVDE VGGEALGRLLVVYPWTQRFFE SFGDLSTPDAVMGNPKVKAHG KKVLGAFSDGLAHLDNLKGTFA TLSELHCDKLHVDPENFRLLGN VLVCVLAHHFGKEFTPPVQAAY QKVVAGVANALAHKYH QUATERNARY STRUCTURE (oligomers) TERTIARY STRUCTURE (fold) Secondary structure-induced alignment Using secondary structure in multiple alignment Dynamic programming search matrix M D A A S T I L C G S Amino acid exchange weights matrices MDAGSTVILCFV HHHCCCEEEEEE H H H H H C C E E E C C H C C E E Default Flavodoxin-cheY predicted secondary structure (PREDATOR) 1fx1 FLAV_DESVH FLAV_DESGI FLAV_DESSA FLAV_DESDE 2fcr FLAV_ANASP FLAV_ECOLI FLAV_AZOVI FLAV_ENTAG 4fxn FLAV_MEGEL FLAV_CLOAB 3chy 1fx1 FLAV_DESVH FLAV_DESGI FLAV_DESSA FLAV_DESDE 2fcr FLAV_ANASP FLAV_ECOLI FLAV_AZOVI FLAV_ENTAG 4fxn FLAV_MEGEL FLAV_CLOAB 3chy -PK-ALIVYGSTTGNTEYTAETIARQLANAG-YEVDSRDAASVEAGGLFEGFDLVLLGCSTWGDDSI------ELQDDFIPLFDS-LEETGAQGRKVACF e eeee b ssshhhhhhhhhhhhhhttt eeeee stt tttttt seeee b ee sss ee ttthhhhtt ttss tt eeeee MPK-ALIVYGSTTGNTEYTaETIARELADAG-YEVDSRDAASVEAGGLFEGFDLVLLgCSTWGDDSI------ELQDDFIPLFDS-LEETGAQGRKVACf e eeeeee hhhhhhhhhhhhhhh eeeeee eeeeee hhhhhh eeeee MPK-ALIVYGSTTGNTEGVaEAIAKTLNSEG-METTVVNVADVTAPGLAEGYDVVLLgCSTWGDDEI------ELQEDFVPLYED-LDRAGLKDKKVGVf e eeeeee hhhhhhhhhhhhhh eeeeee hhhhhh eeeeeee hhhhhh eeeeee MSK-SLIVYGSTTGNTETAaEYVAEAFENKE-IDVELKNVTDVSVADLGNGYDIVLFgCSTWGEEEI------ELQDDFIPLYDS-LENADLKGKKVSVf eeeeee hhhhhhhhhhhhhh eeeee eeeee hhhhhhh h eeeee MSK-VLIVFGSSTGNTESIaQKLEELIAAGG-HEVTLLNAADASAENLADGYDAVLFgCSAWGMEDL------EMQDDFLSLFEE-FNRFGLAGRKVAAf eeee hhhhhhhhhhhhhh eeeee hhhhhhhhhhheeeee hhhhhhh hh eeeee --K-IGIFFSTSTGNTTEVADFIGKTLGAK---ADAPIDVDDVTDPQALKDYDLLFLGAPTWNTGAD----TERSGTSWDEFLYDKLPEVDMKDLPVAIF eeeee ssshhhhhhhhhhhhhggg b eeggg s gggggg seeeeeee stt s s s sthhhhhhhtggg tt eeeee SKK-IGLFYGTQTGKTESVaEIIRDEFGND--VVTL-HDVSQAE-VTDLNDYQYLIIgCPTWNIGEL--------QSDWEGLYSE-LDDVDFNGKLVAYf eeeee hhhhhhhhhhhh eee hhh hhhhhhheeeeee hhhhhhhhh eeeeee -AI-TGIFFGSDTGNTENIaKMIQKQLGKD--VADV-HDIAKSS-KEDLEAYDILLLgIPTWYYGEA--------QCDWDDFFPT-LEEIDFNGKLVALf eee hhhhhhhhhhhh eee hhh hhhhhhheeeee hhhhh eeeeee -AK-IGLFFGSNTGKTRKVaKSIKKRFDDET-MSDA-LNVNRVS-AEDFAQYQFLILgTPTLGEGELPGLSSDCENESWEEFLPK-IEGLDFSGKTVALf eee hhhhhhhhhhhhh hhh hhhhhhheeeee hhhhhhhhh eeeeee MAT-IGIFFGSDTGQTRKVaKLIHQKLDG---IADAPLDVRRAT-REQFLSYPVLLLgTPTLGDGELPGVEAGSQYDSWQEFTNT-LSEADLTGKTVALf eeee hhhhhhhhhhhh hhh hhhhhhheeeee hhhhh eeeee ----MKIVYWSGTGNTEKMAELIAKGIIESG-KDVNTINVSDVNIDELLNE-DILILGCSAMGDEVL------E-ESEFEPFIEE-IST-KISGKKVALF eeeee ssshhhhhhhhhhhhhhhtt eeeettt sttttt seeeeee btttb ttthhhhhhh hst t tt eeeee M---VEIVYWSGTGNTEAMaNEIEAAVKAAG-ADVESVRFEDTNVDDVASK-DVILLgCPAMGSEEL------E-DSVVEPFFTD-LAP-KLKGKKVGLf hhhhhhhhhhhhhh eeeee hhhhhhhh eeeee eeeee M-K-ISILYSSKTGKTERVaKLIEEGVKRSGNIEVKTMNL-DAVDKKFLQESEGIIFgTPTY-YANI--------SWEMKKWIDE-SSEFNLEGKLGAAf eee hhhhhhhhhhhhhh eeeeee hhhhhhhhhh eeee hhhhhhhhh eeeee ADKELKFLVVDDFSTMRRIVRNLLKELGFNN-VEEAEDGV-DALNKLQAGGYGFVISD---WNMPNM----------DGLELLKTIRADGAMSALPVLMV tt eeee s hhhhhhhhhhhhhht eeeesshh hhhhhhhh eeeee s sss hhhhhhhhhh ttttt eeee GCGDS-SY-EYFCGAVDAIEEKLKNLGAEIVQD---------------------GLRIDGD--PRAARDDIVGWAHDVRGAI-------eee s ss sstthhhhhhhhhhhttt ee s eeees gggghhhhhhhhhhhhhh GCGDS-SY-EYFCGAVDAIEEKLKNLgAEIVQD---------------------GLRIDGD--PRAARDDIVGwAHDVRGAI-------eee hhhhhhhhhhhh eeeee eeeee hhhhhhhhhhhhhh GCGDS-SY-TYFCGAVDVIEKKAEELgATLVAS---------------------SLKIDGE--P--DSAEVLDwAREVLARV-------eee hhhhhhhhhhhh eeeee hhhhhhhhhhh GCGDS-DY-TYFCGAVDAIEEKLEKMgAVVIGD---------------------SLKIDGD--P--ERDEIVSwGSGIADKI-------hhhhhhhhhhhh eeeee e eee ASGDQ-EY-EHFCGAVPAIEERAKELgATIIAE---------------------GLKMEGD--ASNDPEAVASfAEDVLKQL-------e hhhhhhhhhhhhhh eeeee ee hhhhhhhhhhh GLGDAEGYPDNFCDAIEEIHDCFAKQGAKPVGFSNPDDYDYEESKSVRD-GKFLGLPLDMVNDQIPMEKRVAGWVEAVVSETGV-----eee ttt ttsttthhhhhhhhhhhtt eee b gggs s tteet teesseeeettt ss hhhhhhhhhhhhhhhht GTGDQIGYADNFQDAIGILEEKISQRgGKTVGYWSTDGYDFNDSKALR-NGKFVGLALDEDNQSDLTDDRIKSwVAQLKSEFGL-----hhhhhhhhhhhhhh eeee hhhhhhhhhhhhhhhh GCGDQEDYAEYFCDALGTIRDIIEPRgATIVGHWPTAGYHFEASKGLADDDHFVGLAIDEDRQPELTAERVEKwVKQISEELHLDEILNA hhhhhhhhhhhhhh eeee hhhhhhhhhhhhhhhhhh GLGDQVGYPENYLDALGELYSFFKDRgAKIVGSWSTDGYEFESSEAVVD-GKFVGLALDLDNQSGKTDERVAAwLAQIAPEFGLS--L-e hhhhhhhhhhhhhh eeeee hhhhhhhhhhh GLGDQLNYSKNFVSAMRILYDLVIARgACVVGNWPREGYKFSFSAALLENNEFVGLPLDQENQYDLTEERIDSwLEKLKPAV-L-----hhhhhhhhhhhhhhh eeee hhhhhhh hhhhhhhhhhhh G-----SYGWGDGKWMRDFEERMNGYGCVVVET---------------------PLIVQNE--PDEAEQDCIEFGKKIANI--------e eesss shhhhhhhhhhhhtt ee s eeees ggghhhhhhhhhhhht G-----SYGWGSGEWMDAWKQRTEDTgATVIGT----------------------AIVNEM--PDNAPE-CKElGEAAAKA--------hhhhhhhhhhh eeeee eeee h hhhhhhhh STANSIA-GGSDIALLTILNHLMVK-gMLVYSG----GVAFGKPKTHLG-----YVHINEI--QENEDENARIfGERiANkV--KQIF-hhhhhhhhhhhhhh eeeee hhhh hhh hhhhhhhhhhhh h -----------TAEAKKENIIAAAQAGASGY-------------------------VVK----P-FTAATLEEKLNKIFEKLGM-----ess hhhhhhhhhtt see ees s hhhhhhhhhhhhhhht G Enough to predict 5(b) topology Secondary structure-induced alignment Flavodoxin-cheY multiple alignment/ secondary structure iteration cheY SSEs 3chy-AA SEQUENCE|| 3chy-ITERATION-0|| 3chy-ITERATION-1|| 3chy-ITERATION-2|| 3chy-ITERATION-3|| 3chy-ITERATION-4|| 3chy-ITERATION-5|| 3chy-ITERATION-6|| 3chy-ITERATION-7|| 3chy-ITERATION-8|| 3chy-ITERATION-9|| AA PHD PHD PHD PHD PHD PHD PHD PHD PHD PHD |ADKELKFLVVDDFSTMRRIVRNLLKELGFNNVEEAEDGVDALNKLQAGGYGFVISDWNMP| | EEEEEEE HHHHHHHHHHHHHHHHH E HHHHHHHHHH HHHEEE | | EEEEEEEE HHHHHHHHHHHHHHH HHHHHHHH EEEEEE | | EEEEEEEE HHHHHHHHHHHHHH HHHHHHHHH EEEEEE | | EEEEEEEE HHHHHHHHHHHHHH EEE HHHHHH EEEEE | | EEEEEEEE HHHHHHHHHHHHHH HHHHHHH EEEEE | | EEEEEEEE HHHHHHHHHHHHHH EEE HHHHHH EEEEE | | EEEEEEEE HHHHHHHHHHHHHH HHHHHHHH EEEEEE | | EEEEEEEE HHHHHHHHHHHHHH EEE HHHHHH EEEEE | | EEEEEEEE HHHHHHHHHHHHHH HHHHHHH EEEEEE | | EEEEEEEE HHHHHHHHHHHHHH HHHHHHHHHH EEEEE | 3chy-AA SEQUENCE|| 3chy-ITERATION-0|| 3chy-ITERATION-1|| 3chy-ITERATION-2|| 3chy-ITERATION-3|| 3chy-ITERATION-4|| 3chy-ITERATION-5|| 3chy-ITERATION-6|| 3chy-ITERATION-7|| 3chy-ITERATION-8|| 3chy-ITERATION-9|| AA PHD PHD PHD PHD PHD PHD PHD PHD PHD PHD |NMDGLELLKTIRADGAMSALPVLMVTAEAKKENIIAAAQAGASGYVVKPFTAATLEEKLNKIFEKLGM| | HHHHHHEEEEEE HHHHHHHHHHHHHHHHH HHHHHHHHHHHHHH | | HHHHHHEEEEEE HHH HHHHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH | | HHHHHHEEEEEE HHHHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH | | HHHHHHHHHHHH HHHHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH | | HHHHH EEEEE HHHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH | | HHHHHHHH EEEEE HHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH | | HHHHHHHH EEEEE HHHHHHHHHHHHHHHH EEEE HHHHHHHHHHHHHH | | HHHHHHHH EEEEEE HHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH | | HHHHHHHH EEEEE HHHHHHHHHHHHHHHH EEE HHHHHHHHHHHHHH | | HHHHHHHH EEEEE HHHHHHHHHHHHHHH EEEE HHHHHHHHHHHHHH | 4fxn-AA SEQUENCE|| 4fxn-ITERATION-0|| 4fxn-ITERATION-1|| 4fxn-ITERATION-2|| 4fxn-ITERATION-3|| 4fxn-ITERATION-4|| 4fxn-ITERATION-5|| 4fxn-ITERATION-6|| 4fxn-ITERATION-7|| 4fxn-ITERATION-8|| 4fxn-ITERATION-9|| AA PHD PHD PHD PHD PHD PHD PHD PHD PHD PHD |MKIVYWSGTGNTEKMAELIAKGIIESGKDVNTINVSDVNIDELLNEDILILGCSAMGDEV| | EEEEE HHHHHHHHHHHHHHH EEE EEEEE | | EEEEE HHHHHHHHHHHHHHH EEEE EEEEE | | EEEEE HHHHHHHHHHHHHHH EEEE EEEEE | | EEEEE HHHHHHHHHHHHHHH E EEEEE | | EEEEEE HHHHHHHHHHHHHHH EEEE EEEEE | | EEEEEE HHHHHHHHHHHHHHH EE EEEEE | | EEEEEE HHHHHHHHHHHHHHH EEEE EEEEE | | EEEEEE HHHHHHHHHHHHHHH EE EEEEE | | EEEEEE HHHHHHHHHHHHHHH EEE EEEEE | | EEEEE HHHHHHHHHHHHHHH EEE EEEEE | 4fxn-AA SEQUENCE|| 4fxn-ITERATION-0|| 4fxn-ITERATION-1|| 4fxn-ITERATION-2|| 4fxn-ITERATION-3|| 4fxn-ITERATION-4|| 4fxn-ITERATION-5|| 4fxn-ITERATION-6|| 4fxn-ITERATION-7|| 4fxn-ITERATION-8|| 4fxn-ITERATION-9|| AA PHD PHD PHD PHD PHD PHD PHD PHD PHD PHD |LEESEFEPFIEEISTKISGKKVALFGSYGWGDGKWMRDFEERMNGYGCVVVETPLIVQNE| | EEEEE HHHHHHHHHHHHHHHHH EEE EEE | | HHHH EEEEE HHHHHHHHHHHHHHH EEE EE | | HHHHHHHHHHHH EEEEEE HHHHHHHHHHHHHHH EEE EE | | HHHHHHHHHHHH EEEEE HHHHHHHHHHHHHHH EEE EE | | HHHHHHHHHHHH EEEEE HHHHHHHHHHHHHHHHH EEE E | | HHHHHHHHHHHH EEEEE HHHHHHHHHHHHHHHHH EEE E | | HHHHHHHHHHHH EEEEEE HHHHHHHHHHHHHHHH EEE E | | HHHHHHHHHHHH EEEEE HHHHHHHHHHHHHHHHH EEE E | | HHHHHHHHHHHH EEEEE HHHHHHHHHHHHHHHHH EEE E | | HHHHHHHHHHHH EEEEEE HHHHHHHHHHHHHHHH EEE E | 4fxn-AA SEQUENCE|| 4fxn-ITERATION-0|| 4fxn-ITERATION-1|| 4fxn-ITERATION-2|| 4fxn-ITERATION-3|| 4fxn-ITERATION-4|| 4fxn-ITERATION-5|| 4fxn-ITERATION-6|| 4fxn-ITERATION-7|| 4fxn-ITERATION-8|| 4fxn-ITERATION-9|| AA PHD PHD PHD PHD PHD PHD PHD PHD PHD PHD |PDEAEQDCIEFGKKIANI| | HHHHHHHHHHHHH | | HHHHHHHHHHHHH | | HHHHHHHHHHHHH | | HHHHHHHHHHHHH | | HHHHHHHHHHHH | | HHHHHHHHHHHHH | | HHHHHHHHHHHH | | HHHHHHHHHHHHH | | HHHHHHHHHHHHH | | HHHHHHHHHHHH | Optimal segmentation of predicted secondary structures by Dynamic Programming H score E score C score ? score Region The recorded values are used in a weighted function according to their secondary structure type, that gives each position a window-specific score. The more probable the secondary structure element, the higher the score. window size Restrictions: H only if ws>=4 E only if ws>=2 Segmentation score (Total score of each path) 2 sequence position Max score Offset Label 5 H 6 Example of an optimally segmented secondary structure prediction library for sequence 3chy 3chy 3chy 3chy 3chy 3chy 3chy 3chy 3chy 3chy 3chy 3chy 3chy 3chy 3chy 3chy <<<<<<<<<<<<<<- 1fx1 FLAV_DESDE FLAV_DESVH FLAV_DESGI FLAV_DESSA 4fxn FLAV_MEGEL 2fcr FLAV_ANASP FLAV_ECOLI FLAV_AZOVI FLAV_ENTAG FLAV_CLOAB 3chy ---------------GYVV-----KPFTAATLEEKLNKIFEKLGM-----??????????????? ee ?? hhhhhhhhhhhhhh ???????? ??????????????? ee ?? hhhhhhhhhhhhhhh ???????? ??????????????? ee ?? hhhhhhhhhhhhhh ???????? ??????????????? eee ?? ??hhhhhhhhhhhhh ???????? ??????????????? eee ?? ??hhhhhhhhhhhhh ???????? ??????????????? eee ?? hhhhhhhhhhhhh ????????? ????????????????eee ?? hh?hhhhhhhhhhh ????????? e ? eeeeeee hhhhhhhhhhhhhhh ?????? ? eeeeeee hhhhhhhhhhhhhhh ?????? eeeeeee hhhhhhhhhhhhhhh hhhhh ? eeeeeee hhhhhhhhhhhhhhh ???? e eeeeeeee hhhhhhhhhhhhhhhh? ?????? eeeeeee hhhhhhhhhh ??????????? ------------------hhhhhhhhhhhhhh ------ Consensus Consensus-DSSP ---------------EEEE----HHHHHHHHHHHHH -----...............****.....****xx***************...... PHD PHD-DSSP ------------------HHHHHHHHHHHHHH -----...............xxxx.....******************x**...... DSSP LumpDSSP ...............EEEE.....SS ...............EEEE..... HHHHHHHHHHHHHHHT ...... HHHHHHHHHHHHHHH ...... What to do with a multiple alignment? • Use it to eyeball and detect structural/functional features • Use it to make a profile and search a database for homologs • Give it to other bioinformatics methods and predict secondary structure, functional residues, correlated mutations, phylogenetic trees, etc. Rules of thumb when looking at a multiple alignment (MA) • • • • Hydrophobic residues are internal Gly (Thr, Ser) in loops MA: hydrophobic block -> internal b-strand MA: alternating (1-1) hydrophobic/hydrophilic => edge b-strand • MA: alternating 2-2 (or 3-1) periodicity => -helix • MA: gaps in loops • MA: Conserved column => functional? => active site Rules of thumb when looking at a multiple alignment (MA) • Active site residues are together in 3D structure • Helices often cover up core of strands • Helices less extended than strands => more residues to cross protein • b--b motif is right-handed in >95% of cases (with parallel strands) • MA: ‘inconsistent’ alignment columns and match errors! • Secondary structures have local anomalies, e.g. b-bulges Rules of thumb when looking at a multiple alignment (MA) • Active site residues are together in 3D structure • Helices often cover up core of strands • Helices less extended than strands => more residues to cross protein • b--b motif is right-handed in >95% of cases (with parallel strands) • MA: ‘inconsistent’ alignment columns and match errors! • Secondary structures have local anomalies, e.g. b-bulges Periodicity patterns Burried b-strand Edge b-strand -helix Burried and Edge strands Parallel b-sheet Anti-parallel b-sheet b--b motif is right-handed in >95% of cases RH LH Flavodoxin-cheY example: 5(b) 1fx1 FLAV_DESDE FLAV_DESVH FLAV_DESSA FLAV_DESGI 2fcr FLAV_AZOVI FLAV_ENTAG FLAV_ANASP FLAV_ECOLI 4fxn FLAV_MEGEL FLAV_CLOAB 3chy -PKALIVYGSTTGNT-EYTAETIARQLANAG-YEVDSRDAASVEAGGLFEGFDLVLLGCSTWGDDSI------ELQDDFIPLF-DSLEETGAQGRKVACF MSKVLIVFGSSTGNT-ESIaQKLEELIAAGG-HEVTLLNAADASAENLADGYDAVLFgCSAWGMEDL------EMQDDFLSLF-EEFNRFGLAGRKVAAf MPKALIVYGSTTGNT-EYTaETIARELADAG-YEVDSRDAASVEAGGLFEGFDLVLLgCSTWGDDSI------ELQDDFIPLF-DSLEETGAQGRKVACf MSKSLIVYGSTTGNT-ETAaEYVAEAFENKE-IDVELKNVTDVSVADLGNGYDIVLFgCSTWGEEEI------ELQDDFIPLY-DSLENADLKGKKVSVf MPKALIVYGSTTGNT-EGVaEAIAKTLNSEG-METTVVNVADVTAPGLAEGYDVVLLgCSTWGDDEI------ELQEDFVPLY-EDLDRAGLKDKKVGVf --KIGIFFSTSTGNT-TEVADFIGKTLGA---KADAPIDVDDVTDPQALKDYDLLFLGAPTWNTG----ADTERSGTSWDEFLYDKLPEVDMKDLPVAIF -AKIGLFFGSNTGKT-RKVaKSIKKRFDDET-MSDA-LNVNRVS-AEDFAQYQFLILgTPTLGEGELPGLSSDCENESWEEFL-PKIEGLDFSGKTVALf MATIGIFFGSDTGQT-RKVaKLIHQKLDG---IADAPLDVRRAT-REQFLSYPVLLLgTPTLGDGELPGVEAGSQYDSWQEFT-NTLSEADLTGKTVALf SKKIGLFYGTQTGKT-ESVaEIIRDEFGN---DVVTLHDVSQAE-VTDLNDYQYLIIgCPTWNIGEL--------QSDWEGLY-SELDDVDFNGKLVAYf -AITGIFFGSDTGNT-ENIaKMIQKQLGK---DVADVHDIAKSS-KEDLEAYDILLLgIPTWYYGE--------AQCDWDDFF-PTLEEIDFNGKLVALf -MK--IVYWSGTGNT-EKMAELIAKGIIESG-KDVNTINVSDVNIDELL-NEDILILGCSAMGDEVL-------EESEFEPFI-EEIS-TKISGKKVALF MVE--IVYWSGTGNT-EAMaNEIEAAVKAAG-ADVESVRFEDTNVDDVA-SKDVILLgCPAMGSEEL-------EDSVVEPFF-TDLA-PKLKGKKVGLf -MKISILYSSKTGKT-ERVaKLIEEGVKRSGNIEVKTMNLDAVD-KKFLQESEGIIFgTPTYYAN---------ISWEMKKWI-DESSEFNLEGKLGAAf ADKELKFLVVDDFSTMRRIVRNLLKELGFN--NVEEAEDGVDALNKLQAGGYGFVI---SDWNMPNM----------DGLELL-KTIRADGAMSALPVLM 1fx1 FLAV_DESDE FLAV_DESVH FLAV_DESSA FLAV_DESGI 2fcr FLAV_AZOVI FLAV_ENTAG FLAV_ANASP FLAV_ECOLI 4fxn FLAV_MEGEL FLAV_CLOAB 3chy GCGDS-SY-EYFCGA-VDAIEEKLKNLGAEIVQD---------------------GLRIDGD--PRAARDDIVGWAHDVRGAI-------ASGDQ-EY-EHFCGA-VPAIEERAKELgATIIAE---------------------GLKMEGD--ASNDPEAVASfAEDVLKQL-------GCGDS-SY-EYFCGA-VDAIEEKLKNLgAEIVQD---------------------GLRIDGD--PRAARDDIVGwAHDVRGAI-------GCGDS-DY-TYFCGA-VDAIEEKLEKMgAVVIGD---------------------SLKIDGD--PE--RDEIVSwGSGIADKI-------GCGDS-SY-TYFCGA-VDVIEKKAEELgATLVAS---------------------SLKIDGE--PD--SAEVLDwAREVLARV-------GLGDAEGYPDNFCDA-IEEIHDCFAKQGAKPVGFSNPDDYDYEESKS-VRDGKFLGLPLDMVNDQIPMEKRVAGWVEAVVSETGV-----GLGDQVGYPENYLDA-LGELYSFFKDRgAKIVGSWSTDGYEFESSEA-VVDGKFVGLALDLDNQSGKTDERVAAwLAQIAPEFGLS--L-GLGDQLNYSKNFVSA-MRILYDLVIARgACVVGNWPREGYKFSFSAALLENNEFVGLPLDQENQYDLTEERIDSwLEKLKPAV-L-----GTGDQIGYADNFQDA-IGILEEKISQRgGKTVGYWSTDGYDFNDSKA-LRNGKFVGLALDEDNQSDLTDDRIKSwVAQLKSEFGL-----GCGDQEDYAEYFCDA-LGTIRDIIEPRgATIVGHWPTAGYHFEASKGLADDDHFVGLAIDEDRQPELTAERVEKwVKQISEELHLDEILNA G-----SY-GWGDGKWMRDFEERMNGYGCVVVET---------------------PLIVQNE--PDEAEQDCIEFGKKIANI--------G-----SY-GWGSGEWMDAWKQRTEDTgATVIGT----------------------AIVNEM--PDNA-PECKElGEAAAKA--------STANSIAGGSDIA---LLTILNHLMVKgMLVYSG----GVAFGKPKTHLGYVHINEIQENEDENARIfGERiANkVKQIF----------VTAEAKK--ENIIAA---------AQAGAS-------------------------GYVV-----KPFTAATLEEKLNKIFEKLGM------ Iteration 0 T G SP= 136944.00 AvSP= 10.675 SId= 4009 AvSId= 0.313 Building flavodoxin 1 2 RH 3 4 5 Building flavodoxin 1 2 RH 3 4 5 Building flavodoxin 1 2 RH 3 4 5 Building flavodoxin 1 2 RH 3 4 5 Building flavodoxin 1 2 RH 3 4 5 Building flavodoxin 1 2 RH 3 4 5 Building flavodoxin try again 2 1 RH 3 4 5 Building flavodoxin 2 1 RH 3 4 5 Building flavodoxin 2 1 RH 3 4 5 Building flavodoxin 2 1 RH 3 4 5 Building flavodoxin 2 1 RH 3 4 5 Building flavodoxin 2 1 RH 3 4 5 Flavodoxin family - TOPS diagrams (Flores et al., 1994) 4 5 4 5 3 2 3 1 1 2 Protein structure evolution Insertion/deletion of secondary structural elements can ‘easily’ be done at loop sites Protein structure evolution Insertion/deletion of structural domains can ‘easily’ be done at loop sites N C Integrating protein multiple alignment, secondary and tertiary structure prediction to predict structural domains in sequence data SnapDRAGON Richard A. George George R.A. and Heringa, J. (2002) J. Mol. Biol., 316, 839-851. A domain is a: • Compact, semi-independent unit (Richardson, 1981). • Stable unit of a protein structure that can fold autonomously (Wetlaufer, 1973). • Recurring functional and evolutionary module (Bork, 1992). “Nature is a ‘tinkerer’ and not an inventor” (Jacob, 1977). The DEATH Domain http://www.mshri.on.ca/pawson • Present in a variety of Eukaryotic proteins involved with cell death. • Six helices enclose a tightly packed hydrophobic core. • Some DEATH domains form homotypic and heterotypic dimers. Delineating domains is essential for: • • • • • • • • Obtaining high resolution structures (x-ray, NMR) Sequence analysis Multiple sequence alignment methods Prediction algorithms (SS, Class, secondary/tertiary structure) Fold recognition and threading Elucidating the evolution, structure and function of a protein family (e.g. ‘Rosetta Stone’ method) Structural/functional genomics Cross genome comparative analysis Structural domain organisation can be nasty… Pyruvate kinase Phosphotransferase b barrel regulatory domain /b barrel catalytic substrate binding domain /b nucleotide binding domain 1 continuous + 2 discontinuous domains Protein structure hierarchical levels PRIMARY STRUCTURE (amino acid sequence) SECONDARY STRUCTURE (helices, strands) VHLTPEEKSAVTALWGKVNVDE VGGEALGRLLVVYPWTQRFFE SFGDLSTPDAVMGNPKVKAHG KKVLGAFSDGLAHLDNLKGTFA TLSELHCDKLHVDPENFRLLGN VLVCVLAHHFGKEFTPPVQAAY QKVVAGVANALAHKYH QUATERNARY STRUCTURE TERTIARY STRUCTURE (fold) Protein structure hierarchical levels PRIMARY STRUCTURE (amino acid sequence) SECONDARY STRUCTURE (helices, strands) VHLTPEEKSAVTALWGKVNVDE VGGEALGRLLVVYPWTQRFFE SFGDLSTPDAVMGNPKVKAHG KKVLGAFSDGLAHLDNLKGTFA TLSELHCDKLHVDPENFRLLGN VLVCVLAHHFGKEFTPPVQAAY QKVVAGVANALAHKYH QUATERNARY STRUCTURE TERTIARY STRUCTURE (fold) Protein structure hierarchical levels PRIMARY STRUCTURE (amino acid sequence) SECONDARY STRUCTURE (helices, strands) VHLTPEEKSAVTALWGKVNVDE VGGEALGRLLVVYPWTQRFFE SFGDLSTPDAVMGNPKVKAHG KKVLGAFSDGLAHLDNLKGTFA TLSELHCDKLHVDPENFRLLGN VLVCVLAHHFGKEFTPPVQAAY QKVVAGVANALAHKYH QUATERNARY STRUCTURE TERTIARY STRUCTURE (fold) Protein structure hierarchical levels PRIMARY STRUCTURE (amino acid sequence) SECONDARY STRUCTURE (helices, strands) VHLTPEEKSAVTALWGKVNVDE VGGEALGRLLVVYPWTQRFFE SFGDLSTPDAVMGNPKVKAHG KKVLGAFSDGLAHLDNLKGTFA TLSELHCDKLHVDPENFRLLGN VLVCVLAHHFGKEFTPPVQAAY QKVVAGVANALAHKYH QUATERNARY STRUCTURE TERTIARY STRUCTURE (fold) Domain prediction using DRAGON Distance Regularisation Algorithm for Geometry OptimisatioN (Aszodi & Taylor, 1994) •Folds proteins based on the requirement that (conserved) hydrophobic residues cluster together. •First constructs a random high dimensional C distance matrix. •Distance geometry is used to find the 3D conformation corresponding to a prescribed target matrix of desired distances between residues. The DRAGON target matrix is inferred from: • A multiple sequence alignment of a protein (old) – Conserved hydrophobicity • Secondary structure information (SnapDRAGON) – predicted by PREDATOR (Frishman & Argos, 1996). – strands are entered as distance constraints from the Nterminal C to the C-terminal C. Multiple alignment C distance matrix N Target matrix 3 N 100 randomised initial matrices 100 predictions N N Predicted secondary structure CCHHHCCEEE N Input data •The C distance matrix is divided into smaller clusters. •Seperately, each cluster is embedded into a local centroid. •The final predicted structure is generated from full embedding of the multiple centroids and their corresponding local structures. SnapDragon Multiple alignment Predicted secondary structure CCHHHCCEEE Generated folds by Dragon Boundary recognition Summed and Smoothed Boundaries SnapDRAGON 1 2 3 Domains in structures assigned using method by Taylor (1997) Domain boundary positions of each model against sequence Summed and Smoothed Boundaries (Biased window protocol) SnapDRAGON • Is very slow (can be hours for proteins>400 aa) – cluster computing implementation • Uses consistency in the absence of standard of truth • Goes from primary+secondary to tertiary structure to ‘just’ chop protein sequences • SnapDRAGON webserver is underway Integrating protein sequence database searching and on-the-fly domain recognition DOMAINATION Richard A. George Protein domain identification and improved sequence searching using PSI-BLAST (George & Heringa, Prot. Struct. Func. Genet., in press; 2002) Domaination • Current iterative homology search methods do not take into account that: – Domains may have different ‘rates of evolution’. – Common conserved domains, such as the tyrosine kinase domain, can obscure weak but relevant matches to other domain types – Premature convergence (false negatives) – Matrix migration / Profile wander (false positives). PSI-BLAST • Query sequence is first scanned for the presence of socalled low-complexity regions (Wooton and Federhen, 1996), i.e. regions with a biased composition (e.g. TM regions or coiled coils) likely to lead to spurious hits, which are excluded from alignment. • Initially operates on a single query sequence by performing a gapped BLAST search • Then takes significant local alignments found, constructs a ‘multiple alignment’ and abstracts a position specific scoring matrix (PSSM) from this alignment. • Rescans the database in a subsequent round to find more homologous sequences -- Iteration continues until user decides to stop or search converges PSI-BLAST iteration Q xxxxxxxxxxxxxxxxx Query sequence Gapped BLAST search Q xxxxxxxxxxxxxxxxx Query sequence Database hits A C D . . Y PSSM Pi Px Gapped BLAST search A C D . . Y Pi Px PSSM Database hits DOMAINATION Chop and Join Domains Identifying domain boundaries Sum N- and C-termini of gapped local alignments True N- and C- termini are counted twice (within 10 residues) Boundaries are smoothed using two windows (15 residues long) Combine scores using biased protocol: if Ni x Ci = 0 then Si = Ni+Ci else Si = Ni+Ci +(NixCi)/(Ni+Ci) Identifying domain deletions • Deletions in the query (or insertion in the DB sequences) are identified by – two adjacent segments in the query align to the same DB sequences (>70% overlap), which have a region of >35 residues not aligned to the query. (remove N- and C- termini) DB Query Identifying domain permutations • A domain shuffling event is declared – when two local alignments (>35 residues) within a single DB sequence match two separate segments in the query (>70% overlap), but have a different sequential order. b a a b DB Query Identifying continuous and discontinuous domains •Each segment is assigned an independence score (In). If In>10% the segment is assigned as a continuous domain. •An association score is calculated between non-adjacent fragments by assessing the shared sequence hits to the segments. If score > 50% then segments are considered as discontinuous domains and joined. Create domain profiles • A representative set of the database sequence fragments that overlap a putative domain are selected for alignment using OBSTRUCT (Heringa et al. 1992). > 20% and < 60% sequence identity (including the query seq). • A multiple sequence alignment is generated using PRALINE (Heringa 1999, 2002; Kleinjung et al., 2002). • Each domain multiple alignment is used as a profile in further database searches using PSI-BLAST (Altschul et al 1997). • The whole process is iterated until no new domains are identified. Significant sequences found in database searches At an E-value cut-off of 0.1 the performance of DOMAINATION searches with the full-length proteins is 15% better than PSI-BLAST Summary Algorithmic integration issues: • Integrating data categories • Integrating alternative methods (consensus) • Making an web-integrated genomics pipeline that combines it all Big task ahead @ VU Needs: • People • Teams with an interest in Integrative Bioinformatics • HTC/Dedicated cluster computing Acknowledgements VU CvB FEW FALW Victor Simossis – NIMR to VU (1 November 2002) Jens Kleinjung – NIMR to VU (1 December 2002) Hans Westerhoff – FALW, VU Henri Bal – CS, FEW, VU Hans van Beek – VUMC/FALW, VU View at NIMR (Mill Hill)