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1、the 7 bridges in knigsberg and compositional representation of protein sequencesbailin hao (郝柏林)(itp & bgi, cas )huimin xie (谢惠民)(math dept. suzhou u)shuyu zhang (张淑誉)(ip. acad. sinica)compositional approach inprokaryote phylogeny justification of using k-tuples instead of primary protein sequen

2、ces. problem of uniqueness of reconstruction of protein sequence from its constituent k-tuples. picking up a special class of proteins without biological knowledge.7 bridges in knigsbergabdca ab bc cd d euler (1736) 4 odd nodes: no! dnes knig, theory of finite and infinite graphs 1st ed.(1932). birk

3、haser (1990)“ from knigsberg to knigs book, so runs the graphic talebasic notions a graph g=(v, e), where v is a set of nodes (vertices), e is a set of edges (bonds) edges: undirected (u,v)=(v,u), uand v adjacent; directed (u,v) differs from (v,u), u incident to v. a weight may be associated with (u

4、,v): cost, distance, transfer function, reaction rate, etc. eulerian graph: each edge appears once and only once in a path hamiltonian graph: each vertex appears once and only once in a path. hamiltonian cycle of minimal weight - travel salesman problem (tsp)an euler path:an euler loop:euler grahp:

5、loopsemi-euler praph: path, no loopproblem of eulerian loop: simple, known solutionsproblem of hamiltonian paths: much harder 1 12 23 34 46 65 57 71 12 23 34 46 65 57 7hamiltonian loops: much harder10 nodes15 arcs di=3 nodestraveling salesman problemnp-hard problemsyes!no!graph = nodes + arcsdirecte

6、d , labeled arcs and nodessimple graph: no rings at nodes: no repeated arcs:i ij jiijindegree din(i) outdegree dout(i)euler graph: din(i) = dout(i) di isimple euler graphdiagonal matrix: m=diag( d1, d2, dn )adjacent matrix: a=aij aij= aii= 0 kirchhoff matrix: c=m-a cij= cij=0 det(c)=0all minors of c

7、 are equal. denote this common minor by ni,j=010ijnumber of euler loops in simple euler graphn g de bruijnt van aardenrie ehrenfestc a b smithw t tuitebest theoreme (g) = (di-1)! inumber of eulerian loops in general eular g. some aii0 rings some aij1 parallel arcsputting auxiliary nodes on these rin

8、gs and parallel arcs makes the graph simple.i ij jino need to work with bigger a matrix.just let some aii0, aij1 in original a.eliminate redundancy caused by unlebeled arcs.modified best theorem: e(g) = (di-1)! aij!iijanpa_pseam 82aamalslftvgqliflfwtmriteaspdpaakaapaaaaapaaaapdtasdaaaaaaltaanakaaael

9、taanaaaaaaatargmalsalsllslfslftftvgtvgqvgqlakaak=5lftvltaa 7aaaa 5paaa 4apaa 3tann 8aana 9sspa 2aaap 6akaa 1auxiliary arc6 ringsfrom pdb.seq-a special selection of swissprot2821-1=2820 proteins ( may 2000 )rnumber of reconstructed aa sequences from a given protein decompositionr rk k1 12-102-101 11-

10、1001-1001 101-100001-10001 1001-001-1000010000 10 10 4 45 56 67 78 89 91 10 01 11 121642164(76.7%)40440490904545212193932 2651651(94%)7777292910104 4494927322732(96.9%)323216163 32 2444427402740(97.1%)232310103 30 044442762763 3(97.9%)1 13 37 71 10 0363627932793(99%)11117 72 21 16 627982798(99.2%)12

11、122 21 11 16 6compositional representation of proteinsthe collection w or w ,n j may be used as an equivalent representation of the original protein sequence.a seemingly trivial result upon further reflection: random aa sequences have unique reconstruction as well.compositional representation works

12、equally for random aa sequences and most of protein sequences.a given realization of a short random aa sequence is as specific as a real protein sequence. j=1mjki=1l= -k+1iknucleotide correlations in dna/rna much studied k=2 correlation functions 16 9 6 see wentian li, computer chem. 21(1997) 257-27

13、1.amino acid correlations in proteins almost no study hard to comprehend 400 correlation functions at k=2 proteins too short to define correlation functions one should approach the problem from a more deterministic point of viewrepeated aa segments in proteins are strong manifestation of correlation

14、s!on-going study: the other extremequit a few proteins have an enormous number of reconstructions. transmembrane antifreeze fibrous: collagenscoarse-graining: closer to biology by reducing the number of aasproteinproteinaaaar(1r(11)1)k kfor r(k)=1for r(k)=1maga_xenlamaga_xenlaspa2_staauspa2_staaus sr rtx_atrentx_atrenebn1_ebvebn1_ebvi icen_psesycen_psesyxpin_xenlaxpin_xenlacaih_mousecaih_mousec cipa_clotmipa_clotm3 303031 150508 84 45 508088640864028285 54 43 39.9732*109.9732*105 510110164641 15.407*105.407*1016162828120012001.55675*101.5

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