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Hidden Markov Models • Probabilistic model of a Multiple sequence alignment. • No indel penalties are needed • Experimentally derived information can be incorporated • Parameters are adjusted to represent observed variation. • Requires at least 20 sequences The Evolution of a Sequence • Over long periods of time a sequence will acquire random mutations. – These mutations may result in a new amino acid at a given position, the deletion of an amino acid, or the introduction of a new one. – Over VERY long periods of time two sequences may diverge so much that their relationship can not see seen through the direct comparison of their sequences. Hidden Markov Models • Pair-wise methods rely on direct comparisons between two sequences. • In order to over come the differences in the sequences, a third sequence is introduced, which serves as an intermediate. • A high hit between the first and third sequences as well as a high hit between the second and third sequence, implies a relationship between the first and second sequences. Transitive relationship Introducing the HMM • The intermediate sequence is kind of like a missing link. • The intermediate sequence does not have to be a real sequence. • The intermediate sequence becomes the HMM. Introducing the HMM • The HMM is a mix of all the sequences that went into its making. • The score of a sequence against the HMM shows how well the HMM serves as an intermediate of the sequence. – How likely it is to be related to all the other sequences, which the HMM represents. Match State with no Indels MSGL MTNL B M1 M2 M3 M4 Arrow indicates transition probability. In this case 1 for each step E Match State with no Indels MSGL MTNL B M=1 S=0.5 T=0.5 M1 M2 M3 M4 E Also have probability of Residue at each positon Typically want to incorporate small probability for all other amino acids. MSGL MTNL B M=1 S=0.5 T=0.5 M1 M2 M3 M4 E Permit insertion states MS.GL MT.NL MSANI I0 I1 I2 I3 I4 B M1 M2 M3 M4 Transition probabilities may not be 1 E Permit insertion states MS..GL MT..NL MSA.NI MTARNL I0 I1 I2 I3 I4 B M1 M2 M3 M4 E MS..GL-MT..NLAG MSA.NIAG MTARNLAG DELETE PERMITS INCORPORATION OF LAST TWO SITES OF SEQ1 D1 D2 D3 D4 D5 D6 D7 I4 I5 I6 I7 I0 I1 I2 I3 B M1 M2 M3 M4 S T A A G N M M5 M6 I L A M7 G E The bottom line of states are the main states (M) •These model the columns of the alignment The second row of diamond shaped states are called the insert states (I) •These are used to model the highly variable regions in the alignment. The top row or circles are delete states (D) •These are silent or null states because they do not match any residues, they simply allow the skipping over of main states. D1 D2 D3 D4 D5 D6 I4 I5 I6 I0 I1 I2 I3 B M1 M2 M3 M4 M5 M6 E Dirichlet Mixtures • Additional information to expand potential amino acids in individual sites. • Observed frequency of amino acids seen in certain chemical environments – aromatic – acidic – basic – neutral – polar STRUCTURES a helix b sheet coils turns Structures are used to build domains.-Legos of evolution Rotation around the peptide bond Ramachandran plot for Glycine Areas not permitted for other amino acids Psi Angles Phi angles Introduction to Protein Structure, Branden and Tooze Garland Publishing Co.1991 p.13 From: http://bioweb.ncsa.uiuc.edu/~bioph254/Class-slides/Lect12/figure13.html Longitudinal and Transverse image of alpha helix From: http://bioweb.ncsa.uiuc.edu/~bioph254/Class-slides/Lect12/figure14.html Turn connecting two helices Introduction to Protein Structure, Branden and Tooze Garland Publishing Co.1991 p. 17 Hemoglobin - ribbon representation Proline • Because of its structure, proline is typically excluded from a helices except in the first three positions at the amino end. b Structure b strand - single run of amino acids in b conformation b sheet- multiple b strands which are hydrogen bonded to yield a sheet like structure. b bulge - disruption of normal hydrogen bonding in a b sheet by amino acid(s) that will not fit into the sheet -for example: proline b sheets- Parallel Introduction to Protein Structure, Branden and Tooze Garland Publishing Co.1991 p.17. b sheet - longitudinal and transverse view. Side chains stick “out” http://bioweb.ncsa.uiuc.edu/~bioph254/Class-slides/Lect12/figure22.html Superoxide dismutase - b sheet Superoxide dismutase - b sheet Six classes of structure • Class a- bundled a helices connected by loops. • Class b- sandwich or barrel comprised entirely of b sheets typically anti-parallel. • Class a / b mainly parallel b sheets with intervening a helices. • Class a + b - segregated a helices and anti-parallel b sheets • Multi-domain • Membrane proteins CD8 -all b Thioredoxin a / b Endonuclease Class a + b Rhodopsin 7TM proten Common Hairpin Loop between two b Strands Introduction to Protein Structure, Branden and Tooze Garland Publishing Co.1991 p. 17 • Turn - short, regular loop. – Difference in frequency of amino acids at positions 1-4 of the turn. • Coils (not coiled coil) – Random turns or irregular structure. Disulfide bridges • Crosslink of two cysteine residues. • Distance between sulfur = 3 Angstroms. Coiled coil -two a helices bundled side by side From: http://catt.poly.edu/~jps/coilcoil.html a,d are internal, remaining amino acids are solvent exposed From: http://catt.poly.edu/~jps/coilcoil.html Coiled Coil • Two or more adjacent a helices Prediction of potential Coiled coil domain in Groucho Potential Residues involved in Coiled Coil MMFPQSRHSGSSHLPQQLKFTTSDSCDRIKDEFQLLQAQYHSL KLECDKLASEKSEMQRHYVMYYEMSYGLNIEMHKQAEIVKR LNGICAQVLPYLSQEHQQQVLGAIERAKQVTAPELNSIIRQQL QAHQLSQLQALALPLTPLPVGLQPPSLPAVSAGTGLLSLSALG SQTHLSKEDKNGHDGDTHQEDDGEKSD Triple helix coiled coil - built from a helices Backbone of triple coiled coil E. coli Nucleotide exchange factor Domains • Single domain proteins • Epidermal growth factor • Serine Proteases - Trypsin • Multi domain proteins -Factor IX -one Ca2+ binding, two EGF/ one protease domain. • Permit building of novel functions by swapping of domains Factor IX Domain Structure Ca EGF EGF Ca - Calcium binding domain EGF - Epidermal growth factor domain CT - Chymotrypsin domain CT Chou - Fasman Prediction of Secondary Structure • Based upon analysis of known structures (1974). • Frequency of occurrence of each amino acid in: a helix b strand – turn Chou - Fasman Prediction • List is then analyzed for stretches of amino acids that have a common tendency to form a given secondary structure. • Extend until a region of high probability for either a turn or region with a low probability of both a or b is encountered. • Window is typically <10 GOR prediction • Similar to Chou - Fassman – More recent (1988) tabulation of amino acid preferences. – Uses a larger window -17 More Recent Prediction Programs • Make use of library of 3d structures to predict structure. • Most use a Neural Net approach for prediction. • Examples – Nnpredict – PredictProtein Neural Net • Programs “trained” on structures. • Window -within the window each position is predicted based upon knowledge. • Rules also applied (alpha helix 4 AA long) window a b coil Input Hidden Output PredictProtein • Uses an alignment approach. • Submitted sequence is compared to database and alignment is generated • Profile is generated for further database searching. • Alignment is then used for prediction of secondary structure. • Confidence predicted - based upon number of residues of given type at a given position in the alignment Kyte and Doolittle Hydropathy • Average of hydropathy index for each residue. • Examle of Hydropathy index: • F +2.8 • R -4.5 Transmembrane Domain • Characteristics make them easier to predict: – – – a helix structure Hydrophobic amino acids 19 or more amino acids long charged residue will typically have an opposing charge for neutralization. • Difficulty in predicted ends of transmembrane domains. Caveat • Local secondary structure can be influenced by tertiary structure. • Identical string of residues can be an a helix in one protein but a b strand in another protein. 3D structural prediction >gi|14769656|ref|XP_010270.4| coagulation factor IX [Homo sapiens] MQRVNMIMAESPGLITICLLGYLLSAECTVFLDHENANKILNRPKRYNSGKLEEFVQGNLERECMEEKCSFEEAREVFEN TERTTEFWKQYVDGDQCESNPCLNGGSCKDDINSYECWCPFGFEGKNCELDVTCNIKNGRCEQFCKNSADNKVVCSCTEG YRLAENQKSCEPAVPFPCGRVSVSQTSKLTRAETVFPDVDYVNSTEAETILDNITQSTQSFNDFTRVVGGEDAKPGQFPW QVVLNGKVDAFCGGSIVNEKWIVTAAHCVETGVKITVVAGEHNIEETEHTEQKRNVIRIIPHHNYNAAINKYNHDIALLE LDEPLVLNSYVTPICIADKEYTNIFLKFGSGYVSGWGRVFHKGRSALVLQYLRVPLVDRATCLRSTKFTIYNNMFCAGFH EGGRDSCQGDSGGPHVTEVEGTSFLTGIISWGEECAMKGKYGIYTKVSRYVNWIKEKTKLT Pfam Protein Information Resource KFHU Tertiary Structure • Still challenging • Focus upon core structure for prediction Hydrophobic interactions that stabilize structure. Approach • Determine “fit”of a query sequence to library of known structures. – Threading- examine compatibility of amino acid side groups with known structures – Two approaches: • Environmental template • Contact potential Environmental Template • Each amino acid in known core evaluated for: – secondary structure – area of side chain buried – types of nearby AA side chains Arginine - basic Aa Isoleucine Different propensity to be in a hydrophobic environment. Might accommodate charge by opposite charge Environmental • Query sequence is submitted to previously analyzed database of structures – How well does your sequence fit these protein cores? Contact Potential • Number and closeness between each AA pair determined. • Query sequence examined to determine if potential AA interactions match those of known cores. Structural Profile • Structural position specific scoring matrix • Identify which amino acid fit into a specific position in the core of each known structure – each position is assigned to one of the 18 classes of structural environment – scores reflect suitability of AA for that position – log odds matrix • Use profile to examine query sequence Z score • Many return an E value or a Z score • Z score the number of standard deviations from the mean score for all sequences. • The higher the Z score, the more significant the model -typical good score >5.