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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.
B M1 M2 M3 M4 E
Match State with no Indels
MSGLMTNL
Arrow indicates transition probability.In this case 1 for each step
B M1 M2 M3 M4 E
Match State with no Indels
MSGLMTNL
Also have probability of Residue at each positon
M=1S=0.5T=0.5
B M1 M2 M3 M4 E
MSGLMTNL
M=1S=0.5T=0.5
Typically want to incorporate small probability for all other amino acids.
B M1 M2 M3 M4 E
I1 I2 I3 I4
MS.GLMT.NLMSANI
Permit insertion states
Transition probabilities may not be 1
I0
B M1 M2 M3 M4 E
I1 I2 I3 I4
MS..GLMT..NLMSA.NIMTARNL
Permit insertion states
I0
MS..GL--MT..NLAGMSA.NIAGMTARNLAG
DELETE PERMITS INCORPORATION OFLAST TWO SITES OF SEQ1
D1 D2 D3 D4 D5 D6
I1 I2 I3 I4 I5 I6I0
B M1 M2 M3 M4 EM5 M6
M ST
AA
GN
IL
A
M7
I7
D7
G
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.
B M1 M2 M3 M4 E
I1 I2 I3 I4
D1 D2 D3 D4
M5 M6
D5 D6
I5 I6I0
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 helix sheet coils turnsStructures are used to build domains.-Legos of evolution
Rotation around the peptide bond
Ramachandran plot for Glycine
Areas not permittedfor other amino acids
Phi angles
Psi Angles
Introduction to Protein Structure, Branden and ToozeGarland Publishing Co.1991 p.13
From: http://bioweb.ncsa.uiuc.edu/~bioph254/Class-slides/Lect12/figure13.html
From: http://bioweb.ncsa.uiuc.edu/~bioph254/Class-slides/Lect12/figure14.html
Longitudinal and Transverse image of alpha helix
Turn connecting two helices
Introduction to Protein Structure, Branden and ToozeGarland Publishing Co.1991 p. 17
Hemoglobin - ribbon representation
Proline
• Because of its structure, proline is typically excluded from helices except in the first three positions at the amino end.
Structure
strand - single run of amino acids in conformation
sheet- multiple strands which are hydrogen bonded to yield a sheet like structure.
bulge - disruption of normal hydrogen bonding in a sheet by amino acid(s) that will not fit into the sheet -for example: proline
Introduction to Protein Structure, Branden and ToozeGarland Publishing Co.1991 p.17.
sheets- Parallel
sheet - longitudinal and transverse view.Side chains stick “out”
http://bioweb.ncsa.uiuc.edu/~bioph254/Class-slides/Lect12/figure22.html
Superoxide dismutase - sheet
Superoxide dismutase - sheet
Six classes of structure• Class - bundled a helices connected by
loops.
• Class - sandwich or barrel comprised entirely of sheets typically anti-parallel.
• Class / mainly parallel sheets with intervening a helices.
• Class + - segregated a helices and anti-parallel sheets
• Multi-domain
• Membrane proteins
CD8 -all
Thioredoxin /
Endonuclease Class +
Rhodopsin7TM proten
Common Hairpin Loop between two Strands
Introduction to Protein Structure, Branden and ToozeGarland 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 helices
Prediction of potential Coiled coil domain in Groucho
MMFPQSRHSGSSHLPQQLKFTTSDSCDRIKDEFQLLQAQYHSLKLECDKLASEKSEMQRHYVMYYEMSYGLNIEMHKQAEIVKRLNGICAQVLPYLSQEHQQQVLGAIERAKQVTAPELNSIIRQQLQAHQLSQLQALALPLTPLPVGLQPPSLPAVSAGTGLLSLSALGSQTHLSKEDKNGHDGDTHQEDDGEKSD
Potential Residues involved in Coiled Coil
Triple helix coiled coil - built from 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
Ca EGF EGF CT
Factor IX Domain Structure
Ca - Calcium binding domainEGF - Epidermal growth factor domainCT - Chymotrypsin domain
Chou - Fasman Prediction of Secondary Structure
• Based upon analysis of known structures (1974).
• Frequency of occurrence of each amino acid in: helix 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 or 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)
coil
Input Hidden Output
win
dow
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:
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 helix in one protein but a strand in another protein.
3D structural prediction
>gi|14769656|ref|XP_010270.4| coagulation factor IX [Homo sapiens]MQRVNMIMAESPGLITICLLGYLLSAECTVFLDHENANKILNRPKRYNSGKLEEFVQGNLERECMEEKCSFEEAREVFENTERTTEFWKQYVDGDQCESNPCLNGGSCKDDINSYECWCPFGFEGKNCELDVTCNIKNGRCEQFCKNSADNKVVCSCTEGYRLAENQKSCEPAVPFPCGRVSVSQTSKLTRAETVFPDVDYVNSTEAETILDNITQSTQSFNDFTRVVGGEDAKPGQFPWQVVLNGKVDAFCGGSIVNEKWIVTAAHCVETGVKITVVAGEHNIEETEHTEQKRNVIRIIPHHNYNAAINKYNHDIALLELDEPLVLNSYVTPICIADKEYTNIFLKFGSGYVSGWGRVFHKGRSALVLQYLRVPLVDRATCLRSTKFTIYNNMFCAGFHEGGRDSCQGDSGGPHVTEVEGTSFLTGIISWGEECAMKGKYGIYTKVSRYVNWIKEKTKLT
Pfam
Protein Information Resource
KFHU
Tertiary Structure
• Still challenging
• Focus upon core structure for prediction
Hydrophobic interactionsthat 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.