Predicting protein structure and function

Protein function Genome/DNA Transcription factors Transcriptome/mRNA Proteome Metabolome Physiome Transcription factors Ribosomal proteins Chaperonins Enzymes

Protein function Not all proteins are enzymes: -crystallin: eye lens protein – needs to stay stable and transparent for a lifetime (very little turnover in the eye lens)

What can happen to protein function through evolution Proteins can have multiple functions (and sometimes many -- Ig). Enzyme function is defined by specificity and activity Through evolution: Function and specificity can stay the same Function stays same but specificity changes Change to some similar function (e.g. somewhere else in metabolic system) Change to completely new function

How to arrive at a given function Divergent evolution – homologous proteins –proteins have same structure and “same-ish” function Convergent evolution – analogous proteins – different structure but same function Question: can homologous proteins change structure (and function)?

How to evolve Important distinction: Orthologues: homologous proteins in different species (all deriving from same ancestor) Paralogues: homologous proteins in same species (internal gene duplication) In practice: to recognise orthology, bi-directional best hit is used in conjunction with database search program

How to evolve By addition of domains (at either end of protein sequence) – Lesk book page 108 Often through gene duplication followed by divergence

Structural domain organisation can be nasty… Pyruvate kinase Phosphotransferase b barrel regulatory domain a/b barrel catalytic substrate binding domain a/b nucleotide binding domain 1 continuous + 2 discontinuous domains

The DEATH Domain 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. http://www.mshri.on.ca/pawson

Flavodoxin fold 5() fold

Rules of thumb when looking at a multiple alignment (MA) Hydrophobic residues are internal Gly (Thr, Ser) in loops MA: hydrophobic block -> internal -strand MA: alternating (1-1) hydrophobic/hydrophilic => edge -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 -- 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. -bulges

Burried and Edge strands Parallel -sheet Anti-parallel -sheet

Periodicity patterns Burried -strand Edge -strand -helix

Genome Science (Genomics) Aims: To assemble physical and genetic maps of the genome To generate and order genomic and expressed sequences To identify and annotate the complete set of genes encoded within a genome To compile atlases of gene expression To accumulate functional data, including biochemical and phenotypic properties of genes To characterise DNA sequence diversity To provide resources for comparison with other genomes (bioinformatics) To establish an integrated web-based database and research interface (bioinformatics)

Assembling physical and genetic maps of the genome Genetic map gives relative order of genetic markers in linkage groups Distance is expressed as units of recombination Genetic markers are mostly physical DNA attributes; including sequence tags, simple repeats, and restriction enzyme polymorphisms Can also be phenotypes associated with Mendelian loci

Assembling physical and genetic maps of the genome In diploid organisms, co-segregation is used in pedigrees or progeny of controlled crosses Standard unit of genetic distance is centiMorgan (cM) 1 cM denotes recombination frequency of 0.01 In human, 1 cM ~= 1000 kB (kilo Base), in Drosophila 1cM ~= 500 kB Recombination frequency of 0.5 (50 cM) is threshold for assigning markers to same linkage groups

M1 M2 M3 M4 M5 M6 m1 m2 m3 m4 m5 m6 ×

Sequence database searching – Homology searching Dynamic Programming (DP) too slow for repeated database searches. Therefore fast heuristic methods: 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: 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. 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. 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. 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 10 20 30 test.seq MQRSPLEKASVVSKLFFSWTRPILRKGYRQRLE :| :|::| |:::||:|||::|: | YCFI_YEAST CASILLLEALPKKPLMPHQHIHQTLTRRKPNPYDSANIFSRITFSWMSGLMKTGYEKYLV 180 190 200 210 220 230 40 50 60 test.seq LSDIYQIPSVDSADNLSEKLEREWDRE :|:|::| |:::||:|||::|: | YCFI_YEAST 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 Q Database hits PSSM PSSM Database hits Query sequence xxxxxxxxxxxxxxxxx Gapped BLAST search Q Query sequence xxxxxxxxxxxxxxxxx Database hits A C D . Y PSSM Pi Px Gapped BLAST search A C D . Y PSSM Pi Px Database hits

PSI-BLAST output example

Multiple alignment profiles Gribskov et al Multiple alignment profiles Gribskov et al. 1987 A way to represent multiple alignment consensus i A C D  W Y 0.3 0.1  Gap penalties 1.0 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-nP(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 non-homologous sequences with score greater than or equal to a score x in a database of n sequences: E(x, n) = nP(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 E-value 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: (i) = P(observed sequence, ending in state i at base t) Backward: ß (i) = P(obs. after t | ending in state i at base t) Viterbi:  (i) = max P(obs. , ending in state i at base t) t Questions: What is the most likely die (predicted) sequence? Viterbi What is the probability of the observed sequence? Forward 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 I2 I3 I4 I1 m0 m1 m2 m3 m4 m5 Start End

Homology-derived Secondary Structure of Proteins (HSSP) Sander & Schneider, 1991