Chapter 14 Protein Secondary Structure Prediction

1. Chapter 14 Protein Secondary Structure Prediction

2. Refresher • 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

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 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=misc1http://fasta.bioch.virginia.edu/fasta_www2/fasta_www.cgi?rm=misc1

7. 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 . . . . . . SRRSASHPTYSEMIAAAIRAEKSRGGSSRQSIQKYIKSHYKVGHNADLQIKLSIRRLLAA helix <--------> <-----> <----------------- sheet EEEEEEEEE EEEEEE EEEEEEEEEEEEE turns T T T T T . . . GVLKQTKGVGASGSFRLAKSDKAKRSPGKK helix -------> <-------> sheet EEEEEEEEE turns T T TT T

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. Expansion using larger crustal structure databases • 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

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 • BLASTP • MAXHOM (sequence alignment) • Neural Net • Layer one : 13 residue window • Layer two: 17 residue window • Layer three: “Jury layer” – removes very short stretches • PSIPRED • PSI-BLAST • 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-X6-L-X6-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 • Homology modeling • Threading • Ab initio

21. Homology Modeling

22. Template Selection • 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 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 1020 years to calculate all states using a 1×1012 FLOPS computer Not realistic approach currently