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A Similar Fragments Merging Approach to Learn Automata on Proteins Goulven KERBELLEC & François COSTE IRISA / INRIA Rennes

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Outline of the talk Protein families signatures Similar Fragment Merging Approach (Protomata-L) Characterization Similar Fragment Pairs (SFPs) Ordering the SFPs Generalization Merging of SFP in an automaton Gap generalization Identification of Physico-chemical properties Experiments

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Protein families Amino acid alphabet : Protein sequence : Protein data set : >AQP1_BOVIN MASEFKKKLFWRAVVAEFLAMILFIFISIGSALGFHY PIKSNQTTGAVQDNVKVSLAFGLSI… >AQP1_BOVIN MASEFKKKLFWRAVVAEFLAMILFIFISIGSALGFHY PIKSNQTTGAVQDNVKVSLAFGLSI… >AQP2_RAT MWELRSIAFSRAVLAEFLATLLFVFFGLGSALQWAS SPPSVLQIAVAFGLGIGILVQALGH… >AQP3_MOUSE MGRQKELMNRCGEMLHIRYRLLRQALAECLGTLIL VMFGCGSVAQVVLSRGTHGGFLT… {A,R,N,D,C,Q,E,G,H,I,L,K,M,F,P,S,T,W,Y,V} Common function & Common topology (3D structure)

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Characterization of a protein family x x x x x x C H x \ / x x Zn x x / \ x C H x x C-x(2,4)-C-x(3)-[LIVMFYWC]-x(8)-H-x(3,5)-H ZBT11...Csi..CgrtLpklyslriHmlk..H... ZBT10...Cdi..CgklFtrrehvkrHslv..H... ZBT34...Ckf..CgkkYtrkdqleyHirg..H... Zinc Finger Pattern

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Expressivity classes of patterns PROSITE PRATT TEIRESIAS PROTOMATA-L

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Characterization

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Similar Fragment Pairs Significantly similar fragment pairs (SFPs) Natural selection Important area characterization Data set D:

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Ordering the SFPs Problem : Solution : ordering the SFPs by scoring each SFP S(f1,f2)= ? 3 different scoring functions : dialign Sd support Ss implication Si

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Dialign Score Sd ( f1, f2 ) = - log P ( L, Sim ) L = |f1| = |f2| Sim = Sum of the individual similarity values P = Probability that a random SFP of the same L has the same S Blossum62 similarity

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Support Score Taking into account the representativeness of SFP Ss (f1,f2,D) = Number of sequences supporting f1 f2 f is supported by f with respect the triangular inequality : Sd(f,f1) + Sd(f,f2) Sd(f1,f2)

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Implication Score Taking into account a counter-example set N Discriminative fragments Lerman index: Si(f1,f2,D,N) = avec P(X) = -P( Ss(f1,f2,N) ) + P( Ss(f1,f2,D) ) x P(N) P( Ss(f1,f2,D) ) x |N| |X| |D| + |N|

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Generalization

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From protein data sets to automata MASEIKLFW M A S E I K L F W

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From protein data sets to automata MASEIKLFW MGYEVKYRV M G Y E V K Y R V M A S E I K L F W

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Merging SFPs MASEIKLFW MGYEVKYRV M G Y E V K Y R V M A S E I K L F W

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Merging SFPs MASEIKLFW MGYEVKYRV M G Y E K Y R V M A S L F W [I, V]

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Merging SFPs MASEIKLFW MGYEVKYRV M G Y E [I, V] K Y R V M A S L F W MASEVKLFM MGYEIKYRV MASEIKYRV MGYEVKLFW MASEVKYRV MGYEIKLFW

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Protein Sequence Data Set List of SFPs MCA Automaton / Regular Expression Ordered List of SFPs MERGING

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Gap Generalization Merging on themself non-representative transitions Treat them as "gaps"

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Identification of Physico-chemical properties Similar Fragments ~ potential function area Amino acids share out the same position Physicochemical property at play => Generalization from a group (of amino acids) to a Taylor group I,V I,Q,W,P aliphatic x I,L,V no information C C [I,V] [I,L,V] C C [I,Q,W,P] X {A,R,N,D,C,Q,E,G,H,I,L,K,M,F,P,S,T,W,Y,V}

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Likelihood ratio test To decide if the multi-set A has been generated according to a physico-chemical group G or not by a likelihood ratio test: Given a threshold, we test the expansion of A to G and reject it when LR G/A <

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Experiments

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MIP : the Major Intrinsic Protein Family Family MIP Subfamilies AQP, Glpf, Gla

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Data sets UNIPROT MIP in SWISS-PROT Set « T » (159 seq) Set « M» (44 seq) identity<90% Set « W+» (24 seq) Set « W-» (16 seq) Set « C» (49 seq) Blast(1

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Experiments First Common Fragment on a Family MIP family Positive set Comparison with pattern discovery tools Teiresias Pratt Protomata-L (short pattern) Water-specific Characterization MIP sub-families Positive and negative sets Leave-one-out cross-validation Protomata-L (short to long pattern)

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First Common Fragment Automaton Results of 4 patterns scanned on Swiss-Prot protein Database Set « M» (44 seq) Learning Set Learning set Set « T » (159 seq) Target set

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From short automata to long automata Previous experiment only the first SFPs of the ordered list of SFPs short automaton first common fragment automaton Next experiment larger cut-offs in the list of SFPs Protomat-L is able to create longer automata with more common subparts Long patterns are closed of the topoly (3D-structure) of the family

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Water-specific characterization Leave-one-out cross-validation Learning set W+ \ Si : Positive learning set W- \ Sj : Negative learning set Test set { Si U Sj } Control set Set T Implication score Set « W+» (24 seq) Set « W-» (16 seq) Set « C» (49 seq)

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Leave-one-out cross-validation

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Error Correcting Cost The error correcting cost of a sequence S represents the distance (blossum similarity) between S and the closest sequence given by the automaton A. Distibution of sequences with long automata (size Approx. 100)

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Leave-one-out cross-validation With Error Correcting Cost

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Leave-one-out cross-validation

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Conclusion & Perspective Good characterization of protein family using automata (-> hmm structure) No need of a multiple alignment greedy data-driven algorithm Important subparts localization Physico-chemical identification and generalization Counter example sets Bringing of knowledge is possible in automata (-> 2D structure)

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Questions ? ? ? ? ? ? ? ? ? ? ? ? ? ? ??

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Demo

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Protomata-L ’s Approach First Common Fragment

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Protomata-L ’s Approach To get a more precise automaton

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IDENTIFICATION OF PHYSICOCHEMICAL GROUPS Data set (Protein sequences) Pairs of fragments SORT EXTRACTION Initial Automaton(MCA) MERGING IDENTIFICATION OF « GAPS »

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Structural discrimination

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Aromatique Hydrophobe Non Informatif Generalization of an Aquaporins automaton

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Physico-chemical properties identification Ratio likelihood test Aliphatic Small x

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