chapter 14 protein secondary structure prediction
DESCRIPTION
Chapter 14 Protein Secondary Structure Prediction. 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 - PowerPoint PPT PresentationTRANSCRIPT
Chapter 14
Protein Secondary Structure Prediction
Proteins have secondary structuresThese structures are essential to maintain the 3D structure of the proteinSecondary structure can be either of•-helix•-strand•Coil
-helix H-bond between C=O and N-H of every 4+ith residue3.6 aa per turn1.5 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 regionsParallel or anti-parallel
Coiled coilHydrophobic amino acids interact
Refresher
Secondary Structure Predictions
Prediction of conformation of each amino acid: •H: -helix•E: -strand•C: Coil (no defined 2° structure)
Used for classification of proteinsDefining domains and motifsIntermediary step towards 3° structure predictionGlobular and trans-membrane proteins are structurally very differentRequired different algorithms to predict these two classes of proteins
• 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 basedStatistical calculation of residues in single query sequence
Homology-basedCommon 2° structure patterns in homologous sequences
Ab initio Methods
A.A.
Helix Sheet
Designatio
nP
Designatio
nP
Ala H 1.42 i 0.83
Cys i 0.70 h 1.19
Asp I 1.01 B 0.54
Glu H 1.51 B 0.37
Phe h 1.13 h 1.38
Gly B 0.57 b 0.75
His I 1.00 h 0.87
Ile h 1.08 H 1.60
Lys h 1.16 b 0.74
Leu H 1.21 h 1.30
Met H 1.45 h 1.05
Asn b 0.67 b 0.89
Pro B 0.57 B 0.55
Gln h 1.11 h 1.10
Arg i 0.98 i 0.93
Ser i 0.77 b 0.75
Thr i 0.83 h 1.19
Val h 1.06 H 1.70
Trp h 1.08 h 1.37
Tyr b 0.69 H 1.47
Chou-FasmanIntrinsic property of residue to be in helix, strand or turn structureA, E, M common in -helices
N: residues in all protein structuresM: residues in -helicesY: Total Ala in protein structuresX: Ala in -helices
Propensity Ala in -helix: (X/Y)/(M/N)
Value = 1: same distribution as averageValue > 1: more often in -helix than averageValue < 1: less often in -helix than average
6 residue window of which 4 is H -helix Window extended bidirectionally until P < 1.05 residue window of which 3 is E -strand
http://fasta.bioch.virginia.edu/fasta_www2/fasta_www.cgi?rm=misc1
. . . . . .
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 60SRRSASHPTY SEMIAAAIRA EKSRGGSSRQ SIQKYIKSHY KVGHNADLQI KLSIRRLLAA
70 80 90GVLKQTKGVG ASGSFRLAKS DKAKRSPGKK
HELIX 1 HA1 SER A 29 ALA A 38HELIX 2 HA2 ARG A 47 SER A 56HELIX 3 HA3 ALA A 64 ALA A 78SHEET 1 SA 3 SER A 45 SER A 46SHEET 2 SA 3 GLY A 91 ARG A 94SHEET 3 SA 3 LEU A 81 GLY A 86
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
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
SRRSASHPTYSEMIAAAIRAEKSRGGSSRQSIQKYIKSHYKVGHNADLQIKLSIRRLLAAGVLKQTKGVGcccccccchhhhhhhhhhhhtccttcccchhhhhhhhhtcccccccthhhhhhhhhhhhhhhhhttttcc
ASGSFRLAKSDKAKRSPGKKcccceeeecccccccccccc
Expansion using larger crustal structure databases
Homology based methods
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%
PHD1. BLASTP2. MAXHOM (sequence alignment)3. Neural Net
Layer one : 13 residue windowLayer two: 17 residue windowLayer three: “Jury layer” – removes very short stretches
PSIPRED1. PSI-BLAST2. Neural net
SSproPROTERPROFHMMSTR
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--------------
How accurate?
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
-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
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
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
Coiled coil prediction
Two or more -helices winding around each otherFor 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
Chapter 13
Protein Tertiary Structure Prediction
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 modeling2. Threading3. Ab initio
Homology Modeling
•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
CriticalIncorrectly aligned residues will give an incorrect modelUse Praline or T-Coffee for alignmentInspect visually to confirm alignment of key residues
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 sequencesTwo 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 structureAb Initio•Generate random loop No clash with nearby side-chains And angles in acceptable region of Ramachandran plot
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
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
Comprehensive Modeling Programs
•Modeler•Swiss-Model•3D-Jigsaw
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
Ab Initio Prediction
Protein fold into a native, low-energy native stateThe mechanism driving this process is poorly understoodComputationally untenable to explore all possible states and calculate energiesA 40 residue peptide will require 1020 years to calculate all states using a 1×1012 FLOPS computerNot realistic approach currently