1. Chapter 14 Protein Secondary Structure Prediction

2. Proteins have secondary structures These structures are essential to maintain the 3D structure of the protein Secondary structure can be either of  -helix  -strand Coil  -helix H-bond between C=O and N-H of every 4+ith residue 3.6 aa per turn 1.5 Å / aa (= 5.4 Å per turn) (fully extended peptide backbone = 3.5 Å / aa)  -strand H-bond between C=O and N-H of distant regions Parallel or anti-parallel Coiled coil Hydrophobic amino acids interact Refresher

3. Secondary Structure Predictions Prediction of conformation of each amino acid: H:  -helix E:  -strand C: Coil (no defined 2° structure) Used for classification of proteins Defining domains and motifs Intermediary step towards 3° structure prediction Globular and trans-membrane proteins are structurally very different Required different algorithms to predict these two classes of proteins

4. Problem is not trivial  -helix based on short distance (4+i interactions)  -strand based on long distance (5 – 50+ residues) Long range interaction predictions less accurate Accuracy about 75% Ab initio based Statistical calculation of residues in single query sequence Homology-based Common 2° structure patterns in homologous sequences

5. Ab initio Methods A.A. HelixSheet Desig natio n P P AlaH1.42i0.83 Cysi0.70h1.19 AspI1.01B0.54 GluH1.51B0.37 Pheh1.13h1.38 GlyB0.57b0.75 HisI1.00h0.87 Ileh1.08H1.60 Lysh1.16b0.74 LeuH1.21h1.30 MetH1.45h1.05 Asnb0.67b0.89 ProB0.57B0.55 Glnh1.11h1.10 Argi0.98i0.93 Seri0.77b0.75 Thri0.83h1.19 Valh1.06H1.70 Trph1.08h1.37 Tyrb0.69H1.47 Chou-Fasman Intrinsic property of residue to be in helix, strand or turn structure A, E, M common in  -helices N: residues in all protein structures M: residues in  -helices Y: Total Ala in protein structures X: Ala in  -helices Propensity Ala in  -helix: (X/Y)/(M/N) Value = 1: same distribution as average Value > 1: more often in  -helix than average Value < 1: less often in  -helix than average 6 residue window of which 4 is H   -helix Window extended bidirectionally until P < 1.0 5 residue window of which 3 is E   -strand

6. http://fasta.bioch.virginia.edu/fasta_www2/fasta_www.cgi?rm=misc1

7. ...... SRRSASHPTYSEMIAAAIRAEKSRGGSSRQSIQKYIKSHYKVGHNADLQIKLSIRRLLAA helix <----------------- sheet EEEEEEEEE EEEEEE EEEEEEEEEEEEE turns T T T T T... GVLKQTKGVGASGSFRLAKSDKAKRSPGKK helix -------> sheet EEEEEEEEE turns T T TT T Example Chou-Fasman 10 20 30 40 50 60 SRRSASHPTY SEMIAAAIRA EKSRGGSSRQ SIQKYIKSHY KVGHNADLQI KLSIRRLLAA 70 80 90 GVLKQTKGVG ASGSFRLAKS DKAKRSPGKK HELIX 1 HA1 SER A 29 ALA A 38 HELIX 2 HA2 ARG A 47 SER A 56 HELIX 3 HA3 ALA A 64 ALA A 78 SHEET 1 SA 3 SER A 45 SER A 46 SHEET 2 SA 3 GLY A 91 ARG A 94 SHEET 3 SA 3 LEU A 81 GLY A 86

8. Garnier-Osguthorpe-Robson (GOR) Makes use of distant influences on propensity Uses 17 residue window Adds propensity for four 2º structure states (H, E, T, C) Highest value defines 2º structure state of central residue in window. 10. 20. 30. 40. 50. 60 SRRSASHPTYSEMIAAAIRAEKSRGGSSRQSIQKYIKSHYKVGHNADLQIKLSIRRLLAA helix HHHHHHHHHHH HHHHHH HHHH sheet EEEEEEEE E EEEEEE turns TTTT TTTTT T TTTT coil C CCCCC CCC C. 70. 80. 90 GVLKQTKGVGASGSFRLAKSDKAKRSPGKK helix HHHH HHHHHHHHHHH sheet EEEEE E turns TTT coil CCCC C C Residue totals: H: 36 E: 21 T: 17 C: 16 percent: H: 48.6 E: 28.4 T: 23.0 C: 21.6

9. Algorithms based on a larger database of crystal structure information: GOR II, III and IV SOPM http://npsa-pbil.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_server.html SRRSASHPTYSEMIAAAIRAEKSRGGSSRQSIQKYIKSHYKVGHNADLQIKLSIRRLLAAGVLKQTKGVG cccccccchhhhhhhhhhhhtccttcccchhhhhhhhhtcccccccthhhhhhhhhhhhhhhhhttttcc ASGSFRLAKSDKAKRSPGKK cccceeeecccccccccccc Expansion using larger crustal structure databases

10. Homology based methods

11. Neural Network programs A neural net has an input layer, hidden layers composed of nodes given different weights, and an output layer Neural net trained with multiply aligned sequences Accuracy >75% PHD 1.BLASTP 2.MAXHOM (sequence alignment) 3.Neural Net Layer one : 13 residue window Layer two: 17 residue window Layer three: “Jury layer” – removes very short stretches PSIPRED 1.PSI-BLAST 2.Neural net SSpro PROTER PROF HMMSTR

12. Predictions with Multiple Methods No single prediction program is correct, and it is generally good practice to use the output from several programs Some web servers do this: JPred PHD, PREDATOR, DSC, NNSSP, Inet and ZPred First submitted to PSI-BLAST Multiple alignment Submitted to above 6 programs Consensus returned No consensus, uses PHD SRRSASHPTYSEMIAAAIRAEKSRGGSSRQSIQKYIKSHYKVGHNADLQIKLSIRRLLAAGVLKQTKGVGASGSFRLAKSDKAKRSPGKK ---------HHHHHHHHHHH--------HHHHHHHHHH-------HHHHHHHHHHHHH---EEEEE------EEEE--------------

13. How accurate?

14. Trans-membrane proteins Two types of trans-membrane proteins  -helix  -barrel Many consists solely of  -helix and are found in the cytoplasmic membrane  -barrel normally found in outer- membrane of gram negative bacteria Difficult to get X-ray or NMR structure

15.  -helix perpendicular to membrane 17-25 residues Hydrophobic residues separated by hydrophilic loops (<60 residues) Residues bordering hydrophobic module is generally charged Inner cytosolic region most often highly charged (orientation info) Positive inside rule Scan window 17-25 residues calculate hydrophobicity score Many false positives Signal peptide sequences confuse algorithm

16. TMHMM Trained with 160 known TM sequences Probability of having an  -helix is given Orientation of  -helix based on positive inside rule Phobius Incorporates distinct HMM models for signal peptides and TM helices Signal peptide sequence ignored Can use sequence homologs and multiply aligned sequences

17. Prediction of  -barrel proteins  -strand forming trans-membrane section is amphipatic 10-22 residues Alternating hydrophobic and hydrophilic sequence arrangement  -helix TM prediction programs thus not applicable to  -barrel proteins TBBpred Neural net trained with  -barrel protein sequences

18. Coiled coil prediction Two or more  -helices winding around each other For every 7 residues, 1 and 4 are hydrophobic, facing central core Coils Scan window of 14, 21 or 28 residues Compares residues to probability matrix based on known coiled coils Accurate for left-handed coil, but not right-handed coil Multicoil Scoring matrix based on 2-strand and 3-strand coils Used in several genome-wide studies Leucine zippers sub-class of coiled coils L-X 6 -L-X 6 -L- Found in transcription factors Anti-parallel  -helices stabilized by leucine core

19. Chapter 13 Protein Tertiary Structure Prediction

20. The need for predicting 3D structures X-ray crystallography is extremely tedious DNA sequences and therefore protein sequences are rapidly generated A gap between sequence and structure is widening Protein structure often provides insight info function Thee main methods for 3D prediction 1.Homology modeling 2.Threading 3.Ab initio

21. Homology Modeling

22. Search PDB for homologous sequences with BLAST or FASTA Should have >30% sequence identity (20% at a stretch) In case of multiple hits, choose Highest identity Highest resolution Most appropriate co-factors Template Selection Sequence Alignment Critical Incorrectly aligned residues will give an incorrect model Use Praline or T-Coffee for alignment Inspect visually to confirm alignment of key residues

23. Backbone Model Building Copy the backbone atoms of the query sequence to that of the corresponding aligned residue If the residues are identical, the coordinates of the whole residue can be copied If the residues are different, only the  C are copied The remaining atoms of the residue are modeled later Loop Modeling It often happens that there are “gaps” in the aligned sequences Two techniques to connect the protein on either side of the gap: Database Search database for fragments that fit the gap Measure coordinates and orientation of backbone on either side of gap Search for fragments that can fit Best loop gives no steric clash with structure Ab Initio Generate random loop No clash with nearby side-chains  And  angles in acceptable region of Ramachandran plot

24. Side Chain Refinement Need to model side-chains where these differ from aligned template sequence Search database for all occurrences of given side-chain in backbone conformation and minimal clash with neighbouring residues Computationally prohibitive Library of rotamers Collection of conformations for each residue that is most often observed in structure database Select rotamer with conformation that best fits backbone Minimal interference with neighbouring side-chains SCWRL

25. Model Refinement using Energy Function After loop modeling and side-chain refinement the follwing remain Unfavourable torsion angles Unacceptable proximity of atoms Use energy minimization to alleviate such problems Limit number of iteration (<100) to ensure that the entire model does not change form the template Molecular Dynamic can be used to search for a global minimum Model Evaluation Check consistency in  -  angles Bond lengths Close contacts Flag regions below acceptability threshold Procheck WHATIF ANOLEA Verify3D

26. Comprehensive Modeling Programs Modeler Swiss-Model 3D-Jigsaw

27. Threading and Fold Recognition Pairwise Energy Method Fit sequence to each fold in database Use local alignment to improve fit Calculate energies Pairwise residue interaction Solvation Hydrophobic Profile Method Fit sequence to fold Calculate propensity of each amino acid to be present at each profile position Secondary structure types Solvent exposure Hydrophobicity Use structure fold that best fits profile of parameters

28. Ab Initio Prediction Protein fold into a native, low-energy native state The mechanism driving this process is poorly understood Computationally untenable to explore all possible states and calculate energies A 40 residue peptide will require 10 20 years to calculate all states using a 1×10 12 FLOPS computer Not realistic approach currently