11/07/05 databases, visualization - iowa state...

Protein Structure: Classification,Databases, Visualization

11/07/05

D Dobbs ISU - BCB 444/544X 1

11/07/05 D Dobbs ISU - BCB 444/544X: Protein Structure: Classification,Databases, Visualization

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11/7/05

Protein Structure:Classification, Databases,

Visualization


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AnnouncementsBCB 544 Projects - Important Dates:

Nov 2 Wed noon - Project proposals due to David/Drena

Nov 4 Fri PM - Approvals/responses & tentative presentation schedule to students

Dec 2 Fri noon - Written project reports due

Dec 5,7,8,9 class/lab - Oral Presentations (20')

(Dec 15 Thurs = Final Exam)


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Bioinformatics Seminars

Nov 7 Mon 12:10 IG Faculty Seminar in 101 Ind Ed IIInborn Errors of Metabolism in Humans &

Animal ModelsMatt Ellinwood, Animal Science, ISU

Nov 10 Thurs 3:40 Com S Seminar in 223 AtanasoffComputational EpidemiologyArmin R. Mikler, Univ. North Texashttp://www.cs.iastate.edu/~colloq/#t3


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Bioinformatics SeminarsCORRECTION:

Next week - Baker Center/BCB Seminars: (seminar abstracts available at above link)

Nov 14 Mon 1:10 PM Doug Brutlag, StanfordDiscovering transcription factor binding sites

Nov 15 Tues 1:10 PM Ilya Vakser, Univ KansasModeling protein-protein interactions both seminars will be in Howe Hall Auditorium


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Protein Structure & Function:Analysis & Prediction

Mon Protein structure: classification,databases, visualization

Wed Protein structure: prediction & modeling

Thurs Lab Protein structure prediction

Fri Protein-nucleic acid interactions Protein-ligand docking


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Reading Assignment (for Mon-Fri)

Mount Bioinformatics• Chp 10 Protein classification & structure prediction

http://www.bioinformaticsonline.org/ch/ch10/index.html

• pp. 409-491• Ck Errata: http://www.bioinformaticsonline.org/help/errata2.html

Other? Additional reading assignments for BCB 544


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Review last lecture:

RNA Structure PredictionAlgorithms


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RNA structure prediction strategies

1) Energy minimization(thermodynamics)

2) Comparative sequence analysis(co-variation)

3) Combined experimental & computational

Secondary structure prediction


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1) Energy minimization method

What are the assumptions?

Native tertiary structure or "fold" of an RNAmolecule is (one of) its lowest free energyconfiguration(s)

Gibbs free energy = ΔG in kcal/mol at 37°C= equilibrium stability of structure

lower values (negative) are more favorableIs this assumption valid?

in vivo? - this may not hold, but we don't really know


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Gibbs free energy: ΔGGibbs Free energy (G) is formally defined in terms ofstate functions enthalpy & entropy, & state variable,temperature

G = H - TSΔG = ΔH - TΔS (for constant temp)

Enthalpy (H) = amount of heat absorbed by a system atconstant pressure

Entropy (S) = measure of the amount of disorder or randomness in a system

Note = this is not the same as "entropy" in information theory, but isrelated, see: http://en.wikipedia.org/wiki/Information_theory


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Gibbs free energy: ΔGGibbs free energy for formation of an RNA or protein

structure = ΔG° = equilibrium stability of that

structure at a specific temperature (kcal/mol at 37°C)

ΔG° = -RT lnKeq

R = gas constant


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Nearest-neighbor parameters

Most methods for free energy minimizationuse nearest-neighbor parameters (derivedfrom experiment) for predicting stability of anRNA secondary structure (in terms of ΔG at 37°C)

& most available software packages usethe same set of parameters: Mathews, Sabina, Zuker & Turner, 1999


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Energy minimization - calculations:

Total free energy of a specificconformation for a specificRNA molecule = sum ofincremental energy terms for:

• helical stacking (sequence dependent)• loop initiation• unpaired stacking

(favorable "increments" are < 0)

Fig 6.3Baxevanis &Ouellette 2005 11/07/05 D Dobbs ISU - BCB 444/544X: Protein Structure: Classification,

Databases, Visualization14

But how many possible conformations for asingle RNA molecule?

Huge number:Zuker estimates (1.8)N possible secondary structures for a sequence of N nucleotides

for 100 nts (small RNA…) =3 X 1025 structures!

Solution? Not exhaustive enumeration… Dynamic programming

O(N3) in timeO(N2) in space/storage

iff pseudoknots excluded, otherwise:O(N6 ), timeO(N4 ), space


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Algorithms based on energy minimization

For outline of algorithm used in Mfold, includingdescription of dynamic programming recursion, pleasevisit Michael Zuker's lecture:http://www.bioinfo.rpi.edu/~zukerm/lectures/RNAfold-html

From this site, you may also download Zuker's lectureas either PDF or PS file.


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2) Comparative sequence analysis(co-variation)

Two basic approaches:

• Algorithms constrained by initial alignmentMuch faster, but not as robust as unconstrained

Base-pairing probabilities determined by a partition function

• Algorithms not constrained by initial alignmentGenetic algorithms often used for finding analignment & set of structures


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RNA structure prediction strategies

Requires "craft" & significant user input & insight1) Extensive comparative sequence analysis to predict

tertiary contacts (co-variation)e.g., MANIP - Westhof

2) Use experimental data to constrain model buildinge.g., MC-CYM - Major

3) Homology modeling using sequence alignment & reference tertiary structure (not many of these!)

4) Low resolution molecular mechanicse.g., yammp - Harvey

Tertiary structure prediction


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New Last Time:

Protein Structure & Function


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Protein Structure & FunctionProtein structure - primarily determined by sequence

Protein function - primarily determined by structure

• Globular proteins: compact hydrophobic core & hydrophilic surface

• Membrane proteins: special hydrophobic surfaces• Folded proteins are only marginally stable• Some proteins do not assume a stable "fold" until they

bind to something = Intrinsically disordered Predicting protein structure and function can be very

hard -- & fun!


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4 Basic Levels of Protein Structure


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Primary & Secondary Structure

Primary• Linear sequence of amino acids• Description of covalent bonds linking aa’s

Secondary• Local spatial arrangement of amino acids• Description of short-range non-covalent

interactions• Periodic structural patterns: α-helix, β-sheet


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Tertiary & Quaternary Structure

Tertiary• Overall 3-D "fold" of a single polypeptide chain• Spatial arrangement of 2’ structural elements;

packing of these into compact "domains"• Description of long-range non-covalent interactions

(plus disulfide bonds)

Quaternary• In proteins with > 1 polypeptide chain, spatial

arrangement of subunits


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"Additional" Structural Levels

• Super-secondary elements• Motifs• Domains• Foldons


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New Today:

Protein Structure & FunctionAmino acids characteristicsStructural classes & motifsProtein functions & functional families

not much - more on this laterClassificationDatabasesVisualization


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Amino Acids

Each of 20 different amino acids has different"R-Group," side chain attached to Cα


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Peptide bond is rigid and planar


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Hydrophobic Amino Acids


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Charged Amino Acids


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Polar Amino Acids


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Certain side-chain configurations areenergetically favored (rotamers)

Ramachandran plot:"Allowable" psi & phi angles


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Glycine is smallest amino acidR group = H atom

• Glycine residues increasebackbone flexibility becausethey have no R group


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Proline is cyclic• Proline residuesreduce flexibility ofpolypeptide chain

• Proline cis-transisomerization is oftena rate-limiting step inprotein folding• Recent worksuggests it also mayalso regulate ligandbinding in nativeproteins -Andreotti


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Cysteines can form disulfide bonds

• Disulfide bonds(covalent) stabilize3-D structures

• In eukaryotes,disulfide bonds arefound only in secretedproteins orextracellular domains


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Globular proteins have a compacthydrophobic core

Packing of hydrophobic side chains into interior is maindriving force for folding

Problem? Polypeptide backbone is highly polar(hydrophilic) due to polar -NH and C=O in eachpeptide unit; these polar groups must be neutralized

Solution? Form regular secondary structures,e.g., α-helix, β-sheet, stabilized by H-bonds


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Exterior surface of globular proteinsis generally hydrophilic

Hydrophobic core formed by packed secondarystructural elements provides compact, stable core

"Functional groups" of protein are attached to thisframework; exterior has more flexible regions(loops) and polar/charged residues

Hydrophobic "patches" on protein surface are ofteninvolved in protein-protein interactions


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Protein Secondary Structures

α−Helixβ− SheetsLoopsCoils


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α - Helix

Most abundant 2' structure in proteinsAverage length = 10 aa's (~10 Angstroms)

• Length varies from 5-40 aa's• Alignment of H-bonds creates dipole moment

(positive charge at NH end)• Often at surface of core, with hydrophobic residues

on inner-facing side, hydrophilic on other side


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α−helix is stabilized by H-bondsbetween ~ every 4th residue

C = blackO = redN = blue


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R-groups are on outside of α−helix


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Types of α−helices"Standard" α−helix: 3.6 residues per turn

H-bonds between C=0 of residue n and NH of residue n + 4

Helix ends are polar; almost always on surface of protein

Other types of helices?n + 5 = π helixn + 3 = 310 helix


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Certain amino acids are "preferred" &others are rare in α−helices

• Ala, Glu, Leu, Met = good helix formers• Pro, Gly Tyr, Ser = very poor• Amino acid composition & distribution varies, depending

on on location of helix in 3-D structure


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β-Strands & Sheets

H-bonds formed between 5-10 consecutiveresidues in one portion of chain with anotherset of 5-10 residues farther down chain

Interacting regions may be adjacent (with shortloop between) or far apart

β-sheets usually have all strands either parallelor antiparallel


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Antiparallel β-sheet


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Antiparallel β-sheet


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Parallel β-sheet


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Mixed β-Sheets also occur


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Loops

• Connect helices and sheets• Vary in length and 3-D configurations• Are located on surface of structure• Are more "tolerant" of mutations• Are more flexible and can adopt multiple

conformations• Tend to have charged and polar amino acids• Are frequently components of active sites• Some fall into distinct structural families

(e.g., hairpin loops, reverse turns)


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Coils

• Regions of 2' structure that are nothelices, sheets, or recognizable turns

• Intrinsically disordered regions appear toplay important functional roles


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Globular proteins are built fromrecurring structural patterns

Motifs or supersecondary structures =combinations of 2' structural elements

Domains = combinations of motifs• Independently folding unit (foldon)• Functional unit


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A few common structural motifs

Helix-turn-helix e.g., DNA binding

Helix-loop-helix e.g., Calcium binding

β-hairpin 2 adjacent antiparallel strandsconnected by short loop

Greek key 4 adjacent antiparallel strands

β−α−β 2 parallel strands connected by helix


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H-T-H H-L-H


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β-hairpin


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Greek key


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Beta-alpha-beta


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Simple motifs combine to form domains


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Large polypeptide chains fold intoseveral domains


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6 main classes of protein structure1) α Domains

• Bundles of helices connected by loops

2) β Domains• Mainly antiparallel sheets, usually with 2 sheets forming

sandwich

3) α/β Domains• Mainly parallel sheets with intervening helices, also

mixed sheets

4) α+β Domains• Mainly segregated helices and sheets

5) Multidomain (α & β)• Containing domains from more than one class

6) Membrane & cell-surface proteins


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α-domain structures:coiled-coils


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α-domain structures:4-helix bundles


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All-α proteins: Globins


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β-domain structures

Anti-parallel β structuresFunctionally most diverseIncludes:

• Up-and-down sheets or barrels• Propeller-like structures• Jelly roll barrels (from Greek key motifs)


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Up-and-down sheets and barrel


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Up-and-down sheets can formpropeller-like structures


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Greek key motifs can formjelly roll barrels


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α/β-domain structures

3 main classesTIM barrel = Core of twisted parallel strands close

togetherRossman fold = open twisted sheet surrounded by

helices on both sidesLeucine-rich motif = specific pattern of Leu residues,

strands form a curved sheet with helices on outside


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TIM barrel Rossman fold


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Leucine rich motifs can formα/β horseshoes


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Protein structure databases, structuralclassification & visualization

PDB = Protein Data Bank http://www.rcsb.org/pdb/

(RISC) - several different structure viewers

MMDB = Molecular Modeling Databasehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=Structure

(NCBI Entrez) - Cn3D viewer

SCOP = Structural Classification of ProteinsLevels reflect both evolutionary and structural relationships

CATH = Classification by Class, Architecture, Topologyand Homology

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