BCB 444/544 Fall 07 Dobbs 1

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1 BCB 444/544 Lecture 19 A bit of: Protein Structure - Basics Protein Structure Visualization, & Comparison #19_Oct5 Required Reading (before lecture) Mon Oct 1 - Lecture 17 Protein Motifs & Domain Prediction Chp 7 - pp Wed Oct 3 - Lecture 18 Protein Structure: Basics (Note chg in Lecture Schedule online) Chp 12 - pp Thurs Oct 4 & Fri Oct 5 - Lab 6 & Lecture 19 Protein Structure: Basics, Databases, Visualization, & Comparison Chp 13 - pp BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 1 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 2 BCB Extra Required Reading BCB 544 Projects (Optional for BCB 444) Assigned Mon Sept 24 BCB 544 Extra Required Reading Assignment: for 544 Extra HW#1 Tas 2 Pollard KS,., Haussler D. (26) An RNA gene expressed during cortical development evolved rapidly in humans. Nature 443: doi:1.138/nature5113 PDF available on class website - under Required Reading Lin For a better idea about what's involved in the Team Projects, please loo over last year's expectations for projects: Please note: wrong URL (instead of that shown above) was included in originally posted 544ExtraHW#1; corrected version is posted now Criteria for evaluation of projects (oral presentations) are summarized here: BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 3 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 4 Assignments & Announcements - #1 Assignments & Announcements - #2 Students registered for BCB 444: Two Grading Options 1) Tae Final Exam per original Grading Policies 2) Instead of taing Final Exam - you may participate in a Team Research Project If you choose #2, please do 3 things: 1) Contact Drena (in person) 2) Send to Michael Terribilini (terrible@iastate.edu) 3) Complete 544 Extra HW#1 - Tas 1.1 by noon on Mon Oct 1 BCB 444s (Standard): 2 pts Midterm Exams = 1 points each 2 Homewor & Laboratory assignments = 2 points 1 Final Exam 5 pts Total for BCB 444 BCB 444p (Project): 2 pts Midterm Exams = 1 points each 2 Homewor & Laboratory assignments = 2 points 19 Team Research Project 59 pts Total for BCB 444p BCB 544: 2 pts Midterm Exams = 1 points each 2 Homewor & Laboratory assignments 1 Final Exam 2 Discussion Questions & Team Research Projects 7 pts Total for BCB 544 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 5 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 6 BCB 444/544 Fall 7 Dobbs 1

2 Assignments & Announcements #3 QUESTIONS re: HW#3? Due Mon ALL: HomeWor #3 Due: Mon Oct 8 by 5 PM HW544: HW544Extra #1 Due: Tas Mon Oct 1 by noon Due: Tas 1.2 & Tas 2 - Fri Oct 12 by 5 PM (not Monday) 444 "Project-instead-of-Final" students should also submit: HW544Extra #1 Due: Tas Mon Oct 8 by noon Due: Tas Fri Oct 12 by 5 PM (not Monday) Tas 2 NOT required! BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 7 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 8 This is a new slide HMM example from Eddy HMM paper: Toy HMM for Splice Site Prediction An HMM for Occasionally Dishonest Casino Transition probabilities Prob(Fair Loaded) =.1 Prob(Loaded Fair) =.2 But, where do you start? "Begin" state not shown BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 9 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 1 Occasionally Dishonest Casino - HW#3 Calculating Different Paths to an Observed Sequence This slide has been changed Calculations such as those shown below are used to fill a matrix with probability values for every state at every position x = x1, x2, x3 = (1)! = FFF transition probability 6,2,6 emission probability Pr(x," (1) ) = a F e F (6)a FF e F (2)a FF e F (6) =.5 # 1 6 #.99 # 1 6 #.99 # 1 6 $.227 "Begin" state? 5:5 chance of starting with F vs L die BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 11 (2)! = LLL (2) Pr( x," ) = alel(6) allel(2) allel(6) =.5!.5!.8!.1!.8!.5 =.8 (3)! = LFL (3) Pr( x,# ) = alel(6) alfef (2) aflel(6) al 1 =.5 ".5 ".2 " ".1 ".5 6!.417 BCB 444/544 F7 #19- Protein Structure Basics & ISU Dobbs 12 BCB 444/544 Fall 7 Dobbs 2

3 Calculate optimal path? Construct a matrix of probability values for every state at every residue How: one way = Viterbi Algorithm Initialization (i = ) Recursion (i = 1,..., L): Termination: v () = 1, v () = for > i For each state r ( v ( i 1 a ) v ( i ) = e ( x )max! ) Pr( * x,! ) = max r ( v ( L) a ) To find π *, use trace-bac, as in dynamic programming r π ε 1 Viterbi for Calculating Most Probable Path* i r ( v ( i 1 a ) v ( i ) = e ( x )max! ) r r x * Path within HMM that matches query sequence with highest probability B F L (1/6) (1/2) = 1/12 (1/2) (1/2) = 1/4 (1/6) max{(1/12).99, (1/4).2} =.1375 (1/1) max{(1/12).1, (1/4).8} =.2 (1/6) max{ ,.2.2} = (1/2) max{ ,.2.8} =.8 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 13 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 14 Total Probability Calculating the Total Probability: This slide has bee changed Several different paths can result in observation x Probability that our model will emit x is: ε Note: This not the same as matrix on previous slide! Here, last column contains sums for each row x Pr( x ) =! " Pr( x," ) π B F 1 (1/6) (1/2) = 1/12 (1/6) sum{(1/12).99, (1/4).2} =.2283 (1/6) sum{ ,.283.2} =.4313 L (1/2) (1/2) = 1/4 (1/1) sum{(1/12).1, (1/4).8} =.283 (1/2) sum{ ,.283.8} =.8144 Total probability =! " Pr(x," ) = =.12 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 15 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 16 A few more Details re: Profiles & HMMs Chp 7 - Protein Motifs & Domain Prediction Smoothing or "Regularization" - method used to avoid "over-fitting" Common problem in machine learning (data-driven) approaches Limited training sample size causes over-representation of observed characters while "ignoring" unobserved characters Result? Miss members of family not yet sampled (too many false negative hits) Pseudocounts - adding artificial values for 'extra' amino acid(s) not observed in the training set Treated as a 'real' values in calculating probabilities Improve predictive power of profiles & HMMs Dirichlet mixture - commonly used mathematical model to simulate the aa distribution in a sequence alignment To "correct" problems in an observed alignment based on limited number of sequences SECTION II Xiong: Chp 7 SEQUENCE ALIGNMENT Protein Motifs and Domain Prediction Identification of Motifs & Domains in MSAs Motif & Domain Databases Using Regular Expressions Motif & Domain Databases Using Statistical Models Protein Family Databases Motif Discovery in Unaligned Sequences Sequence Logos BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 17 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 18 BCB 444/544 Fall 7 Dobbs 3

4 Motifs & Domains Motif - short conserved sequence pattern Associated with distinct function in protein or DNA Avg = 1 residues (usually 6-2 residues) e.g., zinc finger motif - in protein e.g., TATA box - in DNA Domain - "longer" conserved sequence pattern, defined as a independent functional and/or structural unit Avg = 1 residues (range from 4-7 in proteins) e.g., inase domain or transmembrane domain - in protein Domains may (or may not) include motifs 2 Approaches for Representing "Consensus" Information in Motifs & Domains Regular expression - symbolic representation of information from MSA e.g., protein phosphorylation site motif: [S,T]- X- [R,K] Symbols represent specific or unspecified residues, spaces, etc. 2 mechanisms for matching: Exact "Fuzzy" (inexact, approximate) - flexible, more permissive to detect "near matches" Statistical model - includes probability information derived from MSA e.g., PSSM, Profile, or HMM BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 19 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 2 Motif & Domain Databases Protein Family Databases Based on regular expressions: Prosite (Interpro includes Prosite, PRINTS, etc) Emofit Limitation: these don't tae probability info into account Based on statistical models: PRINTS BLOCKS ProDom Pfam SMART CDART Reverse PsiBLAST READ your textboo & try some of these at home; there are distinct advantages/disadvantages associated with each TAKE HOME LESSON: Always try several methods! (not just one!) In addition to databases of "related" protein sequences, based on shared motifs or domains (Pfam, BLOCKS, CDART), some databases "cluster" sequences into families based on near full-length sequence comparisons COGs - Clusters of Orthologous Groups (at NCBI) Mostly Proaryotic sequences KOG = newer Euaryotic version COGnitor - softwared to search database ProtoNet - also clusters of homologous protein sequences Advantages: tree-lie hierarchical structure Provide GO (gene ontology) annotations Provides InterPro eywords BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 21 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 22 Motif Discovery in Unaligned Sequences Expectation Maximization - generate"random" alignment of all sequences, derive PSSM, iteratively match individual sequences to PSSM to edit & improve it Problems? Can hit a local optimum (premature convergence) Sensitive to initial alignment MEME - Multiple EM for Motif Elicitation - modified EM, avoids local optimum issues; two step procedure Gibbs Sampling - generate "trial" PSSM from random alignment first, as in EM, but leave one sequence out of initial alignment, then iteratively match PSSM to left-out sequences Gibbs Sampler - web-based motif search via Gibbs sampling Not mentioned in textboo: Stochastic context-free grammers Other "state of the art"pproaches in recent literature, but not available in web-based servers (yet) BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 23 SECTION V Chp 12 - Protein Structure Basics Xiong: Chp 12 STRUCTURAL BIOINFORMATICS Protein Structure Basics LAB 6 Introduction to Protein DataBan - PDB PyMol Cn3D? BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 24 BCB 444/544 Fall 7 Dobbs 4

5 Chp 12 - Protein Structure Basics Protein Structure & Function SECTION V Xiong: Chp 12 STRUCTURAL BIOINFORMATICS Protein Structure Basics Amino Acids Peptide Bond Formation Dihedral Angles Hierarchy Secondary Structures Tertiary Structures Determination of Protein 3-Dimensional Structure Protein Structure DataBan (PDB) Protein 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! BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 25 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 26 4 Basic Levels of Protein Structure Primary & Secondary Structure Primary Linear sequence of amino acids Description of covalent bonds lining aa s Secondary Local spatial arrangement of amino acids Description of short-range non-covalent interactions Periodic structural patterns: α-helix, β-sheet BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 27 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 28 Tertiary & Quaternary Structure "Additional" Structural Levels Tertiary Overall 3-D "fold" of a single polypeptide chain Spatial arrangement of 2 structural elements; pacing 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 Super-secondary elements Motifs Domains Foldons BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 29 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 3 BCB 444/544 Fall 7 Dobbs 5

6 Amino Acids Peptide Bond is Rigid and Planar Each of 2 different amino acids has different "R-Group" or side chain attached to Cα BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 31 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 32 Hydrophobic Amino Acids Charged Amino Acids BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 33 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 34 Polar Amino Acids Certain Side-chain Configurations are Energetically Favored (Rotamers) Ramachandran plot: "Allowable" psi & phi angles BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 35 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 36 BCB 444/544 Fall 7 Dobbs 6

7 Glycine is Smallest Amino Acid R group = H atom Glycine residues increase bacbone flexibility because they have no R group Proline is Cyclic Proline residues reduce flexibility of polypeptide chain Proline cis-trans isomerization is often a rate-limiting step in protein folding Recent wor suggests it also may also regulate ligand binding in native proteins Andreotti (BBMB) BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 37 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 38 Cysteines can Form Disulfide (S-S) Bonds Disulfide bonds (covalent) stabilize 3-D structures In euaryotes, disulfide bonds are often found in secreted proteins or extracellular domains Globular Proteins Have a Compact Hydrophobic Core Pacing of hydrophobic side chains into interior is main driving force for folding Problem? Polypeptide bacbone is highly polar (hydrophilic) due to polar -NH and C=O in each peptide unit (which are charged at neutral ph=7, found in biological systems); these polar groups must be neutralized Solution? Form regular secondary structures, e.g., α-helix, β-sheet, stabilized by H-bonds BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 39 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 4 Exterior Surface of Globular Proteins is Generally Hydrophilic Hydrophobic core formed by paced secondary structural elements provides compact, stable core "Functional groups" of protein are attached to this framewor; exterior has more flexible regions (loops) and polar/charged residues Hydrophobic "patches" on protein surface are often involved in protein-protein interactions Protein Secondary Structures α Helices β Sheets Loops Coils BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 41 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 42 BCB 444/544 Fall 7 Dobbs 7

8 α Helix: Stabilized by H-bonds between every ~ 4th residue in Bacbone C = blac O = red N = blue H = white 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 Loo! - Charges on bacbone are "neutralized" by hydrogen bonds (H-bonds) - red fuzzy vertical bonds BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 43 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 44 β-sheets - also Stabilized by H-bonds Between Bacbone Atoms Loops Anti-parallel Parallel 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) BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 45 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 46 Coils Chp 13 - Protein Structure Basics Regions of 2' structure that are not helices, sheets, or recognizable turns Intrinsically disordered regions appear to play important functional roles SECTION V Xiong: Chp 13 STRUCTURAL BIOINFORMATICS Protein Structure Visualization, Comparison & Classfication Protein Structural Visualization Protein Structure Comparison Protein Structure BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 47 BCB 444/544 F7 ISU Dobbs #19- Protein Structure Basics & 48 BCB 444/544 Fall 7 Dobbs 8