3d structure prediction

30-03-2006 Doctorado UAMAna Rojas

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3D STRUCTURE 3D STRUCTURE PREDICTIONPREDICTION


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A Long, Long Time Ago…A Long, Long Time Ago…Amino acids started to

make complex

structures and life has appeared...

INTRODUCTION


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3.5 billion years later…(Around today…)

• We know more proteins sequences than we knew at the beginning of life (and even in the 1970’s!).

• In some cases we know their function.

• And rarely do we know their structure

INTRODUCTION


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Current methods to predict protein structure

Schema

1D 2D 3D 4D

Additional info

Ab Initio

No Ab-Initio

-molecular dynamics-Energy minimization

correlatedmutations

2nd pred

2nd pred.-homology modeling-threading

AAVLYFGREDHTLLVY

AAVLYFGREDHTLLVYAA

VLY

FG

RED

HTLLV

Y

-docking

-filtered docking

Secondary ------ Tertiary QuaternaryStructural level


5

Secondary structure prediction is the first step in the proteinfolding prediction.

Why to Predict Secondary Why to Predict Secondary Structure?Structure?

Introduction

Predicted secondary structure can be used to help identifying protein function - by searching for similar secondary structural motifs.

Good secondary structure prediction is also useful in fold detection. The best fold recognition methods use a combination of sequence profiles and prediction secondary structure.


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WHAT IS PREDICTING WHAT IS PREDICTING SECONDARY STRUCTURE ???SECONDARY STRUCTURE ???

To predict the alpha-beta-loop arrangementarrangement of a protein from aa sequence

Introduction


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Why Such a Why Such a shift?shift?Sequencing DNA is easy= 1-2 days

Experimental determination of a protein is difficult= 1-3 years

Small targets


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PDB


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PDB file 1CRN.txt

C:\Documents and Settings\arojas-user\Desktop\rw32b2a.exe


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PDB RECORD DISSECTION (OVERVIEW OF DESCRIPTORS)Protein Data Bank: PDB format

Section Record Name

Title (Summary descriptive remarks)

HEADER, TITLE, COMPND,SOURCE, KEYWDS, EXPDTA, AUTHOR,JRNL

Remark (Bibliography, annotations)

REMARKS 1, 2, 3, and others

Primary structure (sequence, databases)

DBREF, SEQRES, MODRES

Heterogen (non-standard groups) HET, HETNAM, FORMUL

Connectivity annotation SSBOND, LINK, HYDBND, SLTBRG, CISPEP

Miscellaneaous features, Crystalographic

SITE, CRYST1

Coordinate transformation ORIGXn, SCALEn, MTRIXn, TVECT

Coordinate (atomic coordinate data)

MODEL, ATOM, TER, HETATM, ENDMDL

Connectivity CONECT

Book keeping (Summary information)

MASTER. END

For a complete descritpion see: ftp:/ftp.rcsb.org/pub/pdb/doc/format_descritpions/Contests_Guide_21.txt


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HEADER PLANT SEED PROTEIN 30-APR-81 1CRN 1CRND 1

COMPND CRAMBIN 1CRN 4

SOURCE ABYSSINIAN CABBAGE (CRAMBE ABYSSINICA) SEED 1CRN 5AUTHOR W.A.HENDRICKSON,M.M.TEETER 1CRN 6

REVDAT 5 16-APR-87 1CRND 1 HEADER 1CRND 2REVDAT 4 04-MAR-85 1CRNC 1 REMARK 1CRNC 1REVDAT 3 30-SEP-83 1CRNB 1 REVDAT 1CRNB 1REVDAT 2 03-DEC-81 1CRNA 1 SHEET 1CRNB 2REVDAT 1 28-JUL-81 1CRN 0 1CRNB 3

Filename=accession number=PDB code1)Filename is 4 positions (often 1 digit & 3 letters, i.e.: 1CRN)2)Be aware: 0HKY means entry HKY does not contain coordinates

PDB RECORD (1)

Header: Describes molecule & gives deposition date

CMPND: Name of the molecule

Source: organism

Revision Date


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HELIX 1 H1 ILE 7 PRO 19 1 3/10 CONFORMATION RES 17,19 1CRN 55HELIX 2 H2 GLU 23 THR 30 1 DISTORTED 3/10 AT RES 30 1CRN 56SHEET 1 S1 2 THR 1 CYS 4 0 1CRNA 4SHEET 2 S1 2 CYS 32 ILE 35 -1 1CRN 58TURN 1 T1 PRO 41 TYR 44 1CRN 59

CRYST1 40.960 18.650 22.520 90.00 90.77 90.00 P 21 2 1CRN 63ORIGX1 1.000000 0.000000 0.000000 0.00000 1CRN 64ORIGX2 0.000000 1.000000 0.000000 0.00000 1CRN 65ORIGX3 0.000000 0.000000 1.000000 0.00000 1CRN 66SCALE1 .024414 0.000000 -.000328 0.00000 1CRN 67SCALE2 0.000000 .053619 0.000000 0.00000 1CRN 68SCALE3 0.000000 0.000000 .044409 0.00000 1CRN 69

SSBOND 1 CYS 3 CYS 40 1CRN 60SSBOND 2 CYS 4 CYS 32 1CRN 61SSBOND 3 CYS 16 CYS 26 1CRN 62

PDB RECORD (2)

HELIX/SHEET/TURN: Secondary structure elements as provided by

crystallographer (subjective)

Disulfide- bridges

CRYST1, ORIGX1, ORIGX2, ORIGX3, SCALE1, SCALE2, SCALE3 : crystallographic parameters!


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ATOM 1 N THR 1 17.047 14.099 3.625 1.00 13.79 1CRN 70ATOM 2 CA THR 1 16.967 12.784 4.338 1.00 10.80 1CRN 71ATOM 3 C THR 1 15.685 12.755 5.133 1.00 9.19 1CRN 72ATOM 4 O THR 1 15.268 13.825 5.594 1.00 9.85 1CRN 73ATOM 5 CB THR 1 18.170 12.703 5.337 1.00 13.02 1CRN 74ATOM 6 OG1 THR 1 19.334 12.829 4.463 1.00 15.06 1CRN 75ATOM 7 CG2 THR 1 18.150 11.546 6.304 1.00 14.23 1CRN 76ATOM 8 N THR 2 15.115 11.555 5.265 1.00 7.81 1CRN 77ATOM 9 CA THR 2 13.856 11.469 6.066 1.00 8.31 1CRN 78ATOM 10 C THR 2 14.164 10.785 7.379 1.00 5.80 1CRN 79ATOM 11 O THR 2 14.993 9.862 7.443 1.00 6.94 1CRN 80ATOM 12 CB THR 2 12.732 10.711 5.261 1.00 10.32 1CRN 81ATOM 13 OG1 THR 2 13.308 9.439 4.926 1.00 12.81 1CRN 82ATOM 14 CG2 THR 2 12.484 11.442 3.895 1.00 11.90 1CRN 83ATOM 15 N CYS 3 13.488 11.241 8.417 1.00 5.24 1CRN 84ATOM 16 CA CYS 3 13.660 10.707 9.787 1.00 5.39 1CRN 85

ATOM 324 CG ASN 46 12.538 4.304 14.922 1.00 7.98 1CRN 393ATOM 325 OD1 ASN 46 11.982 4.849 15.886 1.00 11.00 1CRN 394ATOM 326 ND2 ASN 46 13.407 3.298 15.015 1.00 10.32 1CRN 395ATOM 327 OXT ASN 46 12.703 4.973 10.746 1.00 7.86 1CRN 396TER 328 ASN 46 1CRN 397

ATOM: one line for each atom with its unique name and its, x, y, z, coordinates

The TERM record terminates the amino acid chain

PDB RECORD (3)


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TODAY:

Programs that take coordinate files (mostly PDB-format) and process those toa graphical display output

Some have stereo view options and support animation

ViewersHEADER LIGAND BINDING PROTEIN 02-MAR-00 1EJE TITLE CRYSTAL STRUCTURE OF AN FMN-BINDING PROTEIN COMPND MOL_ID: 1; COMPND 2 MOLECULE: FMN-BINDING PROTEIN; COMPND 3 CHAIN: A; COMPND 4 ENGINEERED: YES SOURCE MOL_ID: 1; SOURCE 2 ORGANISM_SCIENTIFIC: METHANOBACTERIUM THERMOAUTOTROPHICUM; SOURCE 5 EXPRESSION_SYSTEM_PLASMID: PET15B KEYWDS FMN-BINDING PROTEIN, STRUCTURAL GENOMICS EXPDTA X-RAY DIFFRACTION AUTHOR D.CHRISTENDAT,V.SARIDAKIS,A.BOCHKAREV,C.ARROWSMITH, AUTHOR 2 A.M.EDWARDS REVDAT 2 15-AUG-01 1EJE 1 HEADER KEYWDS REVDAT 1 11-OCT-00 1EJE 0 JRNL AUTH D.CHRISTENDAT,A.YEE,A.DHARAMSI,Y.KLUGER, JRNL AUTH 2 A.SAVCHENKO,J.R.CORT,V.BOOTH,C.D.MACKERETH, JRNL AUTH 3 V.SARIDAKIS,I.EKIEL,G.KOZLOV,K.L.MAXWELL,N.WU, JRNL AUTH 4 L.P.MCINTOSH,K.GEHRING,M.A.KENNEDY,A.R.DAVIDSON, JRNL AUTH 5 E.F.PAI,M.GERSTEIN,A.M.EDWARDS,C.H.ARROWSMITH JRNL TITL STRUCTURAL PROTEOMICS OF AN ARCHAEON JRNL REF NAT.STRUCT.BIOL. V. 7 903 2000 JRNL REFN ASTM NSBIEW US ISSN 1072-8368 REMARK 1 REMARK 2 REMARK 2 RESOLUTION. 2.2 ANGSTROMS. REMARK 3 REMARK 3 REFINEMENT. ATOM 1 N GLY A 1 54.915 15.553 3.252 1.00 26.12 N ATOM 2 CA GLY A 1 54.219 16.804 3.668 1.00 23.30 C ATOM 3 C GLY A 1 54.870 18.009 3.019 1.00 25.07 C ATOM 4 O GLY A 1 55.848 17.853 2.295 1.00 26.88 O ATOM 5 N SER A 2 54.330 19.202 3.252 1.00 22.48 N ATOM 6 CA SER A 2 54.918 20.404 2.680 1.00 25.55 C ATOM 7 C SER A 2 56.202 20.683 3.460 1.00 26.03 C ATOM 8 O SER A 2 56.308 20.321 4.632 1.00 22.51 O ATOM 9 CB SER A 2 53.973 21.594 2.828 1.00 27.30 C …. etc etc


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Main 3D structure based databases I

:mostly manual, uses CE structure similarity program to decide whether two structures are similar.

CATH: uses structure similarity program SSAP

DALI: uses structure similarity program FSSP

PDB

SCOP


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The rate of new sequences is growing exponentially relative to the rate of proteinstructures being solved!


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WHERE ARE THE WHERE ARE THE STRUCTURES ???STRUCTURES ???


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How could we fill the gap How could we fill the gap between the number of known between the number of known

sequences and known sequences and known structures?structures?

Structural Genomics Structural Genomics Initiative: JCSGInitiative: JCSG


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20


21


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How could we fill the gap between the How could we fill the gap between the number of known sequences and number of known sequences and

known structures?known structures?

oror

Structural Genomics Structural Genomics Initiative: JCSGInitiative: JCSG

Predicting MethodsPredicting Methods

Gaeta, Italy


23Russell:http://speedy.embl-heidelberg.de/gtsp/flowchart2.html

Structural predictionflowchart


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Relationship between sequence and structural similarity

Chotia & Lesk, 1986

%id seq. => same 3D (for sure) %id seq. => sometimes same str.

sometimes not }depends on the length of thealigned region.


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SIMPLIFIED PROTEIN STRUCTURE PREDICTIONSIMPLIFIED PROTEIN STRUCTURE PREDICTIONFLOW CHARTFLOW CHART

EXPERIMENTALSEQUENCE

DATABASESEARCHING

STRUCTUREHOMOLOG

YESHOMOLOGYMODELING

SECONDARYSTRUCTUREPREDICTION

NO

FOLD PREDICTION“THREADING”

FINAL STRUCTURE???


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A target sequence (without a known structure)

Looking for a template *

WHY HOMOLOGY MODELING? Useful to infer function

Structure changes less than sequence in evolution!

Comparative modeling can generate models with <2A r.s.m.d


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Sometimes it’s not so easy to find a template…


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…or to make a good alignment…

?

Template

Target

Template

Target

A GOOD ALIGNMENT IS THE CRITICAL STEP!


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SOME HISTORYSOME HISTORY

First model : LACTALBUMIN.

TEMPLATE: lysozyme. (structure will come in 1989)

1990’s expansion of modeling

Nowadays: if >40% of seq. identity it is possible to make modelscomparable to X-ray level low resolution!.

How many sequences can be modeled? Mostly up to a quarter of all availablesequences!


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MODELLING STEPSMODELLING STEPS

1.- identify a suitable structural template

2.-Align and select the templates for modeling

3.- Build the model

4.- Evaluation of the model

N iterations to improvethe model


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STEP 1: IDENTIFYING THE STRUCTURAL STEP 1: IDENTIFYING THE STRUCTURAL TEMPLATETEMPLATE

Database searches using BLAST or similar algorithms(more than one template is recommended)

When similarity is between 25-30% identity additionaldetections methods are required


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?

Template

Target

Template

Target

A GOOD ALIGNMENT IS THE CRITICAL STEP!

Sequence search methods are biased towards seq. evolutiontherefore are not always optimal for modeling purposes

STEP2: ALIGNMENTSTEP2: ALIGNMENT


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DVSHCIQETVESVGF---------NVIRDYVDVGEAIQEVMESYEVEIDNVIYQVKPIRNLN

DVSHCIQETVESVGF---NVI------RDYVDVGEAIQEVMESYEVEIDNVIYQVKPIRNLN

another example…….

if looks good in structure it should be like:

STEP2: ALIGNMENT (I)STEP2: ALIGNMENT (I)


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PHE ASP ILE CYS ARG LEU PRO GLY SER ALA GLU ALA VAL CYS TEMPLATE PHE ASN VAL CYS ARG THR PRO --- --- --- GLU ALA ILE CYS TARGET (ALIGNMENT 1) PHE ASN VAL CYS ARG --- --- --- THR PRO GLU ALA ILE CYS TARGET (ALIGNMENT 2)

"Alignment 1" is chosen because of the PROs at position 7. But 10 Angstrom gap is too big to close.

?

STEP2: ALIGNMENT (II)STEP2: ALIGNMENT (II)


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STEP 3: MODEL BUILDINGSTEP 3: MODEL BUILDING

•RIGID BODY ASSEMBLY:

Fit the query seq into this frame.

Align template structures and create a“consensus” frame (average of Ca in core regions)

Needs high sequence similarities

Caveats: with dissimilar sequences models areusually wrong (espe. deletion and insertion regions)


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STEP 3: MODEL BUILDING (I)STEP 3: MODEL BUILDING (I)

•SEGMENT MATCHING:

Calculates conservation of positions in templates.Then calculates coordinates based on those.

•SATISFACTION OF SPATIAL RESTRAINS:

Satisfies spatial restrains between templates and query using:

distance geometryoptimisation


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Backbone generation EASY

Gap filling: if <3 residues is easy to fix (this size allows few configurations)

Canonical Loop generation: common loops, can be modeled form libraries.

Side Chain generation

Ab Initio loop building

Model optimisation

STEP 3: MODEL BUILDING (II)STEP 3: MODEL BUILDING (II)


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WHAT ABOUT THE SIDE CHAINS?WHAT ABOUT THE SIDE CHAINS?

Difficult! Several possible conformations!

Those are restricted to certainrotamers

What is known about rotamers?

Side chain rotamers of conserved residues are themselves conserved

Side chain replacements then focus on non-conserved regions

There are extensive databases and libraries of rotamers

STEP 3: MODEL BUILDING (III)STEP 3: MODEL BUILDING (III)


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HOW TO MODEL ROTAMERS?HOW TO MODEL ROTAMERS?

Caveats: when many side chains need to be replaced… how can I chose the first?

Take a model and don’t use the conserved regions (no Gly or PRO) and replace others with Alanine.

Side chains are then replaced in order of decreasing rotameric entropy

Residues with very narrow rotamer distribution are built first. Otherwise are replaced when there are less degrees of freedom!

STEP 3: MODEL BUILDING (IV)STEP 3: MODEL BUILDING (IV)


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STEP 4: MODEL EVALUATIONSTEP 4: MODEL EVALUATION

This step is crucial in the whole process!

Several programs evaluate the models:

Solv_Pref: Computes solvent exposure of the model. Negative values indicate structural stability

ProSA (Sippl): Based on potentials extracted from databases. Good models have low energies

PSQS: http://www1.jcsg.org/psqs/psqs.cgi)

An energy like measurement. It is calculated on the statistical potentials of mean force describing interactions between residue pairs and between single residues and solvent. Values approaching -0.2 are ok.

GLU71GLU70

GLU67

LYS64ASP32

LYS76


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Energy-like measure

1. Contacts between amino acids

2. Burial status of amino acid

3. Secondary structure

Some structural features of proteins are overrepresented or underrepresented in known protein structures:

STEP 4: MODEL EVALUATION (I) STEP 4: MODEL EVALUATION (I)


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Evaluate models built on alternative alignments with energy-like measures??

Modeling program

Good or bad?

-0.212 [d. a. f. u.]

GOOD MODEL! *

STEP 4: MODEL EVALUATION (III) STEP 4: MODEL EVALUATION (III)


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Energy-like measure again

An energy like measure is based on statistics and, in fact, gives a hint if your structure is similar to A typical protein structure or not.

In the previous example:

Value of -0.212 is OK if the average in PDB is -0.278. But it’s still statistics….

STEP 4: MODEL EVALUATION (IV) STEP 4: MODEL EVALUATION (IV)


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… … HOWEVERHOWEVER

Backbones might have conformational changes: see below backbone bending

2bb2and 1amm


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PDB is a mess !PDB is a mess !

PDB files have some missing atoms, unsolved parts of structures, do not start from AA 1, several atoms with the same number, several structures with the same chain ID, are not consecutively numbered...

For example, to automate things in the case of Modeller, it is needed to have correct a alignment... otherwise:

“Alignment sequence not found in PDB file”

… … ANDAND


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SWISS-MODEL - www.expasy.ch/swissmod/SWISS-MODEL.html

An automated comparative modelling server (ExPASy, CH)

CPHmodels - www.cbs.dtu.dk/services/CPHmodels/

Server using homology modelling (BioCentrum, Denmark)

SDSC1 - cl.sdsc.edu/hm.html

Protein structure homology modeling server (San Diego, USA)

3D-JIGSAW - www.bmm.icnet.uk/servers/3djigsaw/

Automated system for 3D models for proteins (Cancer Research UK)

WHATIF - www.cmbi.kun.nl/gv/servers/WIWWWI/

WHAT IF Web interface: homology modelling, drug docking, electrostatics calculations, structure validation and visualisation.

HOMOLOGY MODELING SERVERSHOMOLOGY MODELING SERVERS


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BIOTECH Validation Suite - biotech.ebi.ac.uk:8400/

An evaluation suite that uses three widely available validation programs (PROCHECK, PROVE and WHAT IF)

Verify3D - www.doe-mbi.ucla.edu/Services/Verify_3D/

A tool designed to help in the refinement of crystallographic structures. It also provides a visual analysis of model quality.

Loops Database - www.bmm.icnet.uk/loop/

A table of five protein loop classes. (Cancer Research UK)

HOMOLOGY MODELING SERVERSHOMOLOGY MODELING SERVERS


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SIMPLIFIED PROTEIN STRUCTURE PREDICTIONSIMPLIFIED PROTEIN STRUCTURE PREDICTIONFLOW CHARTFLOW CHART

EXPERIMENTALSEQUENCE

FINAL STRUCTURE???

DATABASESEARCHING

STRUCTUREHOMOLOGSECONDARY

STRUCTUREPREDICTION

NO YESHOMOLOGYMODELING

FOLD PREDICTION“THREADING”


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Homology Modelling vs Fold Detection

Fold Detection Homology Modelling

% seq. ID

0 30 100

Approach

Model Quality

Any Sequence?? >= 30-50% IDwith template

Fold Level Atomic Level

The best method of determining 3D structure is to base the model you make on a known structure.

If your sequence is sufficiently similar (>30-50% identity) you could generate an all atom model by homology modelling.

Target Sequence

25%: “twilight zone”


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THREADING

eg. FFAS03GenThreader

FOLD RECOGNITION

eg HMM

FOLD DETECTION

BLAST, FASTA

Fold recognition: distant/no clearhomology

Alignment of sequences tostructures asin THREADER(Jones et al. 1992)

CAPABLE TO DETECT VERY DISTANT HOMOLOGY(WHEN SEQUENCE-BASED METHODS FAIL)


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CAPABLE TO DETECT VERY DISTANT HOMOLOGY (WHEN SEQUENCE-BASED METHODS FAIL)

FOLD RECOGNITION

FFAS03example


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Find out the real structure with prediction methods

FIT SEQUENCES INTO STRUCTURES AND FIND THE BEST MATCH

FOR GOOD MATCH: HYDROPHOBIC BURIED AND POLARS EXPOSED

WE ASSUME THAT THE NATIVE PROTEIN CONFORMATION REALLY ISA FREE ENERGY MINIMUM!

However, some native conformations may only be of low energy becausepf prosthetic groups or unusual interactions.


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FOLD RECOGNITION

BIOLOGIST’s APPROACH:

If seq 1 is similar to seq2 then structure 1 is similar to structure2and there is probably an evolutionary explanation!

PHYSICIST’s APPROACH:

Proteins form structures according to fundamental rules that we calle energies or free energies!

Quoted from: Protein Structure Prediction, Huber & Torda.


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WHAT IS THREADING?

To fit a structure into a sequence!To fit a structure into a sequence!


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Suboptimal alignmentsOptimal alignments

S1

S2

S3S4S5

Sheet helix

QUERY TO STRUCTURE ALIGNMENT


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QUERY TO STRUCTURE ALIGNMENT I

query sequence

Structure template

ALIGNMENT (threading): covering of segments of the query sequence by template blocks!

A threading is completely determined by the starting positions of the blocks


57

QUERY TO STRUCTURE ALIGNMENT RULES

query sequence

Structure template

The blocks preserve their order

The blocks DO NOT OVERLAP

There is NO GAPS in the blocks!


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The General Principle I

1. Library of protein structures (fold library) • all known structures• representative subset (seq. similarity

filters) • structural cores with loops removed

2. Binary alignment algorithm with Scoring functioncontact potentialenvironmentsOthers…..

Instead of aligning a sequence to a sequence, align strings of descriptors that represent 3D structural features.Usual Dynamic Programming: score matrix relates two amino acids

Threading Dynamic Programming: relates amino acids to environments in 3D structure

3. Method for generating models via alignments

ALMVWTGH.........

....

....

....

....

The General Principle I


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S T

Blocki=1 i=2 i=3 i=4 i=5 i=6

j=1

j=2

j=3

j=4

Position

Each possible threading corresponds to a path from S to T in thegraph and vice-versa

The RED path corresponds to the threading (1,2,2,3,4,4)

THE KEY IS TO FIND THE SHORTEST PATH FROM S TO T=dynamic programming!!!

The GREEN path corresponds to the threading (1,4,1,4,1,4)


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FOLD RECOGNITION

ji i + 1

LC i,j,i+1,l

F(i,j)

F(i +1,L)

F(i + 1, L)=min { F(i,j) + Ci,j,i+1,l } j=1,…,L

DYNAMIC PROGRAMMING:


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FOLD RECOGNITION

ST

Blocki=1 i=2 i=3 i=4 i=5 i=6

j=1

j=2

j=3

j=4

Position

C1122

C2232

C3243

C4354

C5464


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What are the scoring functionsWhat are the scoring functions in Fold recognition?in Fold recognition?

•Pair potentialsPair potentials

•Solvation energySolvation energy

•Consistency between real and predicted Consistency between real and predicted secondarysecondarystructure and accessibilitystructure and accessibility

•Structural environmentsStructural environments

•Sequence profilesSequence profiles


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Threading 1D predictions (and accessibility)-Threading 1D predictions (and accessibility)-into 3D structures: compatibility based on into 3D structures: compatibility based on dynamic programmingdynamic programming

Approach 0: Approach 0:

F.R. by Threading: essential componentsF.R. by Threading: essential components

i.e.:TOPITS


64Rost, 1995Threading

Predicted 1D structure profile isaligned by dynamic programming( MaXHOM) to 1D assigned structures by DSSP.

INPUT

Secondary structure pred


65Kelley et al., 2000http://www.bmm.icnet.uk/~3dpssm


66


67

what are the scoring functions in Fold recognition?what are the scoring functions in Fold recognition?







68

Sequence-structure compatibility Sequence-structure compatibility function based on pairwise potentialsfunction based on pairwise potentials

e.g.Sipple.g.Sippl

Approach I:Approach I:

F.R. by Threading:essential componentsF.R. by Threading:essential components


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Count pairs of each residue type at different separations

Energy of interaction = -KT ln (frequency of interactions) Boltzmann principle

d

Eco

unts

d

Jones, 1992; Sippl, 1995

This is transformed into energies:

Caveat: energy depends on inter-residue interactions:How do you know the position of the residues?


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Threading: Essential componentsThreading: Essential componentsThreading: Essential componentsThreading: Essential components

EEabab A C D E …..

A -3 -1 0 0 ..C -1 -4 1 2 ..D 0 1 5 6 ..E 0 2 6 7 ... . . . .

ACCECADAAC -3-1-4-4-1-4-3-3=-23

E = Eaibjaibj positions i,j

• structural templatestructural template

• neighbor definitionneighbor definition

• energy functionenergy function

11

22

33

44

55

66

77

1010

88

99

AA

CC

CC

EE

CC

AA

DDAA

AA

CC

FOLD RECOGNITION


71

What are the scoring functions in Fold recognition?What are the scoring functions in Fold recognition?







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SCORING FUNCTIONS : STRUCTURAL ENVIRONMENTS

There are 18 environments

•An environment is:

- area of the buried side chain- fraction of side chain exposed to polar atoms- local secondary structure

•Scoring matrix: probabilities of aa/environment class

•3D profile matrix is created for each fold in a bench mark

•Target sequence is aligned with the 3D profile.


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SOLVATION ENERGY

How buried-like is a certain amino acid?

Calculated: frequency of ocurrence at a specific degree or residue burialto the frequency of occurrence of all other aa types with this degree ofburial

Degree of burial: ratio between solvent accessible surface areaand its overall surface area


75

Combination of sequence-sequenceCombination of sequence-sequenceand sequence-structure comparisonsand sequence-structure comparisons

e.g. Jonese.g. Jones

Approach II:Approach II:



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GenTHREADER(Jones , 1999, JMB 287:797-815)

- for each template provide MSA- align the query sequence with the MSA* assess the alignment by sequence alignment score* assess the alignment by pairwise potentials* assess the alignment by solvation function* record lengths of: alignment, query, template


77

Essentials of GenTHREADEREssentials of GenTHREADER

Trained 383 pairs: in each pair the fold is shared but the sequence similarity is low.


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79

PROFILE METHODSPROFILE METHODS

e.g. FFAS03 (Godzik, A)e.g. FFAS03 (Godzik, A)

Approach III:Approach III:



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Differences between profile-based methods (Rychlewski, et al, 2000)

PSI-BLAST

Multiple alignments: 5 iterations with 10-3 evalue tresholdProfile: Preclustering with 98% cutoff, pseudocount based onvariability estimation-background aminoacid frequenciesDatabase: NR

PDB-BLAST Multiple alignment: same as PSI-BlastProfile: same as PSI-BlastDatabase: PDB database

BASIC Multiple alignment: 2 PSI-Blast it. with 0.1 e-value thresholdProfile: preclustering with 97% id cutoff;

amino-acid composition filter, distant homologues have smaller weights

Database: profiles of proteins from PDB

FFAS/FFAS03 Multiple alignment: same as PSI-BlastProfile: preclustering with 97% id cutoff; amino-acid composition

filter, sequence diversity based weightDatabase: profiles of proteins from PDB


81

Homology Modelling vs Fold Detection

Fold Detection Homology Modelling

% seq. ID

0 30 100

Approach

Model Quality

Any Sequence?? >= 30-50% IDwith template

Fold Level Atomic Level

The best method of determining 3D structure is to base the model you make on a known structure.

If your sequence is sufficiently similar (>30-50% identity) you could generate an all atom model by homology modelling.

Target Sequence

25%: “twilight zone”


82

Baker & Sali,Science 2001.


83

OKAY! I ‘VE GOT A FOLD, NOW WHAT?


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COMBINING ADDITIONAL INFORMATION


85

2004

1996

2000

2002

1998

Critical Assessment of Techniques for Protein Structure Prediction

ASILOMAR, USA GAETA, ITALY


86


87http://maple.bioc.columbia.edu/eva/

LARGE SCALE BENCHMARKING PROJECTS

EVA/LiveBench


88


89

LIVEBENCH


90


91

QUERY

locate prot: BLAST

any similarity with a known protein?BLAST against PDB

YES

NO

search and align core loops ....

homology modeling serversSWISSMODEL/WHATIF

dom1 dom2 ...

model3D

¿Any domains?- experimental- PFAM/ProDom/InterPro- BLAST^^^

Model Evaluation- ProSa- Biotech suite

Full 3D model

Threadingservers:3DPSSMSAMT99...

model 1model 2model 3model 4.....

1D predictions:secondary struct/acc., hydrophobicity, trasnmemb.

2D: contacts......

Biology, MedLine, Swissprot, ...active sites, Mutants, functional domains,cofactors ....

Multiple alignment BLAST+ClustalW+ T-COFFEEconserved positions, correlated mutations, ...

model 1model 2

Visualization and model comparisons: Threadlizestructural classifications: FSSP, SCOP

model 3D (only C)

Canonic side chain generation MaxSprout

3D PREDICTIONS: STEPS


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WHAT ABOUT PREDICTING INTERACTIONS?


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Ras

Ral

Rho

RasRalRho

ranrcc1

by J.A. G-Ranea


94

Azuma et al., J,Mol. Biol. 1999


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Complex(Model on Vomplex superposition)

Model

GDP

Mg++

D44

H78

D128

E157

R206

H270

H304

H78

H78

H410

D44GDPMg++

H270

R206

H304

E157

D128H78

H410

Green: Km, red: Kcat.

Mapping of mutants (side view)


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SOME EXAMPLES:

PAAD DOMAIN (Rojas et al, 2003 Protein Science)

SPOC DOMAIN (Sanchez-Pulido et al, 2004, BMC bioinformatics)


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PAAD/DAPIN/PYRIN DOMAIN:

Prediction of binding sites

Pyrin, Aim (absent in melanoma), Asc (apoptosis associated speck-likeprotein containing a Caspase recrutiment domain) and a Death domain-like (DD)


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Nacht family: PAN/NALPs/DEFCAP/PYCARD,CATERPILLER(Tschopp et al, Nature, 2003)

PAAD family: MEFV/PYRIN (Pawlowski, et.al., 2001 , others)

WHERE IS THE PAAD DOMAIN?

BACKGROUND


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PAAD ?

PAAD

? CARD

CARD

CARD CARD

NAC

NALP2

MATER

CARD4

NOD2

NAIP

COS1.5

CLAN

NACHT LRR’S

LRR’S

LRR’S

LRR’S

LRR’S NACHT

NACHT

NACHT

NACHT

?

CARD

LRR’S

LRR’S

LRR’S

NACHT

NACHT

NACHT

?

BIR

BIR

BIR

BIR

IF120X

PAAD

PAAD CASPASE

PAAD B-BOX Zn FINGER SPRY

IF120X PAAD IF120X

CARD

PAAD

PAAD

ASC

CASPASE ZF

PYRIN

IF16

MNDA,AIM2

ASC2

DOMAIN ARCHITECTURES

Sensors!

BACKGROUND

They connectdifferent pathways!


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PAAD OF MEFV

Psi-Blast FFAS Saturatedblast

MALN=T-coffee

Trees (Bayes, NJME)

HITS

*Removal of redundancy(splicing variants)40 sequences

2nd struct. Pred(metaserver)

Pairwise-FFAS

Structural neighbours(SCOP)

JACKAL = MODELS

Minimized=CHARM

PSQS EvaluationConserved patchesIn the surface: CONSURF

Phylogeny

Modeling

METHODS


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CARDDD DED

PAAD

ANCESTORAL DOMAIN


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1 2 3 4 5 6

Sec.StructurePrediction

Hydrophobic core(sol. acc. area <10%maximum solv. area)

HELIX 3does not have coreresidues. In DD, and othershelix3 doesn’t pack too well


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N

C

N

C

Hydrophobic core

Homology modeling of PAAD domain (MEFV from mouse)

H3 H3


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ILE40

PRO41

VAL51

MET45

LYS35

LYS39

ARG49

ARG42

LYS52

180

pyrin

Charged patch

Hydrophobic patch

Pan2/NALP4

LYS48

ILE42VAL47

ALA50

PRO43

TRP44


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GLU71GLU70

GLU67

LYS64ASP32

LYS76

GLU53

GLU54

LYS55

90o

+CHARGED

GLU20

ASP19

LYS23

AIM2

LYS71ARG67

LYS64180o

+ CHARGED (CONVEX)- CHARGED (CONCAVE)

IFI204


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Paad is a 6 alpha helical bundle

Helix 3 is disordered Real structure 1PN5

Septiembre 2003

Released October 2003

Helix 3 is disordered

Binding patches correctly predicted


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Needs partner interaction to fold properly .# Helix3 is disordered in DD/DED/CARD structures.

# PAAD_DAPIN is a vertebrate-specific domain

# PAAD from MEFV genes are the ancestral ones,sucesive duplications of the PAAD-PYR group yielded the mammalian pool

# Viral PAAD’s might mimic IFI/AIM family

# id, character and conserved patches are as divergent within PAAD, as PAAD with DED/DD/CARD=> suggest specialization for not “cross-talking”

SUMMARY

Confirmed later on by NMR (1UPC,1PN5)

# The binding interface contains at least 10 hydrophobic residues. By analogy withCARD domains, electrostatic forces are also important.


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SPOC DOMAINA NOVEL DOMAIN ASSOCIATED TO CANCER


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Blast tonr/uniprot90

Blast to EST’s &unfinished genomes

TO ENRICH PROFILE!

PROFILE BUILDING

Multiple alignmentT-COFFEE,MUSCLE, etc

HMMER/PSI-BLAST SEARCHES in Uniprot90

METHODS: Selecting regions first!

Query seq


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Known

Known!!!

METHODS: HMMER Strategy/Intermediate searches


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HMMER ANALYSES IIIMETHODS

PHD

Coiled-coil1183 aa

iso2

2256 aaiso3

614 aaiso1

NLS

SPOC: Protein-protein interaction (SanchezPulido et al, 2004)

0.083 0.05


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HMMER ANALYSES IIIMETHODS

SPOC: Protein-protein interaction

Homologymodeling

iso2

RBMF_HUMAN


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WHY THERE IS A FULLY R/Y CONSERVATION?

The co-activator of CREB-binding protein follows this structural schema(Xu et al, Science 2001) where a critical interaction occurs between R600 and Y640.The R is methylated causing a transcriptional switch!!

WHERE ELSE THIS IS FOUND?


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ACKNOWLEDGEMENTSACKNOWLEDGEMENTS

LUIS SANCHEZ,MICHAEL TRESSFLORENCIO PAZOS, … AND REST OF PDG

3d structure prediction

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