Homology Modeling Workshop GHIKLSYTVNEQNLKPERFFYTSAVAIL

Homology Modeling Workshop

GHIKLSYTVNEQNLKPERFFYTSAVAIL

Outline:

• Introduction to protein structure & databases

• Structure prediction approaches– Ab-initio– Threading– Homology modeling

• Hands ON

From Sequence to StructureProtein structure is hierarchic:

• Primary – sequence of covalently attached amino acid

• Secondary – local 3D patterns (helices, sheets, loops)

• Tertiary – overall 3D fold

• Quaternary – two or more protein chains

• All information about the native structure of a protein is encoded in the amino acid sequence + its native solution environment.

• Many possible conformation still only one or few native folds are exhibited for each protein (Levinthal’s paradox)

• Protein folding is driven by various forces:– Ionic forces– Hydrogen bonds– The hydrophobic affect– . . .

From Sequence to Structure

Protein 3D StructuresA protein’s structure has a critical effect on its function:

1. Binding pockets

PDB ID 1nw7


2. Areas of specific chemical\electrical properties


3. Importance of the global fold for function

Motivation to Acquire a Structure

• Identifying active and binding sites

• Characterization of the protein’s mechanism (catalysis & interactions)

• Searching for ligand of a given binding site

• Understanding the molecular basis of diseases

• Designing mutants

• Drug design

• And more...

Determining Structure

• NMR

• X-ray diffraction

• Electron Microscopy

Why predict protein structure if we can use experimental tools to

determine it?

• Experimental methods are slow and expensive

• Some structures were failed to be solved

• A representative family structure can suffice to

deduce structures of the entire family sequences

Protein databases

Protein Sequence& Structure Databases

Some of the available databases:

• RCSB- the Protein Data Bank- all deposited structures

• UniProt- main sequence database– SwissProt– Tremble

• NCBI- lots of databases, including sequence and structures

• PDBsum- combines structural & sequence data

UniProt- Protein Sequence Database

• UniProt is a collaboration between the European Bioinformatics Institute (EBI), the Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR).

• In 2002, the three institutes decided to pool their resources and expertise and formed the UniProt Consortium.

http://www.ebi.ac.uk/

http://www.isb-sib.ch/

http://pir.georgetown.edu/

• The world's most comprehensive catalog of information on proteins

• Sequence, function & more…

• Comprised mainly of the databases:

– SwissProt –516081 entries– high quality annotation, non-redundant & cross-referenced to many other databases.

– TrEMBL – 10618387 entries – computer translation of the genetic information from the EMBL Nucleotide Sequence Database many proteins are poorly annotated since only automatic annotation is generated




• The PDB archive contains information about experimentally-determined structures of proteins, nucleic acids, and complex assemblies.

• The structures in the archive range from tiny proteins and bits of DNA to complex molecular machines like the ribosome.

• There are currently 57013 structures deposited in the PDB. However, taking out redundant sequences (e.g. 90%) reduces the number of structures to 19988…

• Each structure receives a unique 4 letter ID

Protein Data Bank (PDB)

Protein Data Bank (PDB)http://www.rcsb.org/pdb/home/home.do

PDB ID: 3mht

http://www.rcsb.org/pdb/home/home.do

Download structure

Displaystructure

Data concerning the structure -

resolution, R-value.…

The paper describingthe structure

Protein Data Bank (PDB)http://www.rcsb.org/pdb/home/home.do


Year

Protein Data Bank (PDB)

PdbSum• A database providing an overview of all biological

macromolecular structures

• Connected to UniProt find the sequence accession of a known PDB ID

• Detailed description of many structure properties, e.g.:– EC number– Chains & ligands and their interactions– Clefts– Secondary structure– FASTA sequence of structure…– …

PDB ID

Free text

Search by sequence

http://www.ebi.ac.uk/thornton-srv/databases/pdbsum/

PdbSum

http://www.ebi.ac.uk/thornton-srv/databases/pdbsum/

Useful tabs

UniProtaccession

Chains & ligands

PdbSum

Protein tab

Secondary structure-from the PDB

PdbSum

More Sequences Than Structures

• Discrepancy between the number of known sequences and solved structures:

5,047,807 UniRef90 entries vs.

25566 90% Non-redundant structures

Computational methods are needed to obtain more

structures

Structure prediction approaches

Structure Prediction Approaches

1. Homology (Comparative) Modeling

Based on sequence similarity with a protein for

which a structure has been solved.

2. Threading (Fold Recognition)

Requires a structure similar to a known structure

3. Ab-initio fold prediction

Not based on similarity to a sequence\structure

Ab-initioStructure prediction from “first principals”:

Given only the sequence, try to predict the structure

based on physico-chemical properties

(energy, hydrophobicity etc.)

• When all else fails works for novel folds

• Shows that we understand the process

The Force Field(energy function)

A group of mathematical expressions describing the

potential energy of a molecular system

• Each expression describes a different type of physico-

chemical interaction between atoms in the system:

• Van der Waals forces

• Covalent bonds

• Hydrogen bonds

• Charges

• Hydrophobic effects

Non-bonded terms

Approaches to Ab-initio Prediction1. Molecular Dynamics

• Simulates the forces that governs the protein within water.• Since proteins usually naturally fold, this would lead to the

native protein structure.

Problems:• Thousands of atoms• Huge number of time steps to reach folded protein

feasible only for very small proteins

Approaches to Ab-initio Prediction

2. Minimal Energy

Assumption: the folded form is the minimal energy conformation of a protein

Main principals:• Define an energy function.• Search for 3D conformation that minimize energy.

• Current methods (e.g. Rosetta) primarily utilize the fact that although we are far from observing all protein folds, we probably have seen nearly all sub-structures:

Ab-initio

Moult J. Philos. Trans. R. Soc. B. 361:453–458 (2006)

• A library of known sub-structures (fragments less than 10 residues) is created.

• A range of possible conformations for each fragment in the query protein are selected.

Ab-initio - Example

Moult J. Philos. Trans. R. Soc. B. 361:453–458 (2006)

Given a sequence and a library of folds, thread the sequence

through each fold. Take the one with the highest score.

• Method will fail if new protein does not belong to any fold in

the library.

• Score of the threading is computed based on known

physical chemistry properties & statistics of amino acids.

• In practice, fold recognition methods are often mixtures

of sequence matching and threading.

Fold Recognition (Threading):Sequence to structure matching

Input:1. sequence

H bond donorH bond acceptor

GlycinHydrophobic

2. Library of folds of known proteins

Threading: example

Structure Prediction Approaches

S=20S=5S=-2Z=5Z=1.5Z= -1

H bond donorH bond acceptorGlycineHydrophobic

Threading: example

MAHFPGFGQSLLFGYPVYVFGD...

Potential fold

...

1) ... 56) ... n)

...

-10 ... -123 ... 20.5

Fold recognition (threading)Find best fold for a protein sequence:

We need a scoring (energy) function to distinguish native structure from misfolded structures.

Ideally, each misfolded structure should have an energy higher than the native energy, i.e. :Emisfolded-Enative> 0

Fold recognition: FFAS03

•The FFAS03 server provides an interface to the third generation of the profile-profile alignment and fold recognition algorithm FFAS.

• Profile-profile alignments utilize information present in sequences of homologous proteins to amplify the sequence conservation pattern defining the family

•The result: detection of remote homologies beyond the reach of other sequence comparison methods.

Jaroszewski, L., Rychlewski, L., Li, Z., Li, W. & Godzik, A. (2005) FFAS03: a server for profile-profile sequence alignments. Nucl. Acids Res. 33, W284-W288

Fold recognition: HHPRED

0.1

0.4

0.5

0.3

0.7

0.4

0.6

0.7

0.1

0.2

0.6

Emit Amino acid

Profiles are based on Hidden Markov Models:

Söding J. (2005) Protein homology detection by HMM-HMM comparison. Bioinformatics 21, 951-960.

Fold recognition: HHPRED

• Profile Hidden Markov Models (HMMs) are similar to sequence profiles, but in addition to the amino acid frequencies they contain information about the frequency of inserts and deletions.

• Using profile HMMs in place of simple sequence profiles should therefore further improve sensitivity.

• The first to employ HMM-HMM comparison, based on a novel statistical method.

• Using HMMs both on the query and the database side greatly enhances the sensitivity and selectivity over sequence-profile based.

Söding J. (2005) Protein homology detection by HMM-HMM comparison. Bioinformatics 21, 951-960.

I-TASSER- Hybrid Approach

• In a recent wide blind experiment, I-TASSER generated the best 3D structure predictions among all automated servers.

• Based on the secondary-structure threading and the iterative implementation of the Threading ASSEmbly Refinement (TASSER) program.

I-TASSER

Homology Modeling

Homology Modeling – Basic Idea

Triophospate ismoerases44.7% sequence identity0.95 RMSD

1. A protein structure is defined by its amino acid sequence.

2. Closely related sequences adopt highly similar structures, distantly related sequences may still fold into similar structures.

3. Three-dimensional structure of

proteins from the same family is

more conserved than their

primary sequences.

Homology modeling requires handling structures & sequences

• Query- only the protein sequence is available- usually found at the UniProt database

• Template- after identification, both structural and sequence-related data should be found- UniPort (or NCBI databases), RCSB and PDBsum

Query proteinsequence

Homology modeling-

widespread technique

e.g. Fiser et al., 2004; Petrey et al., 2005; Zhang, 2008

Homologous protein-structural template

Align query & templateprotein sequences

Build model

Evaluate model

Identify Homologous protein-structural template

Align query & templateprotein sequences

General Scheme

1. Searching for structures related to the query sequence

2. Selecting templates

3. Aligning query sequence with template structures

4. Building a model for the query using information from the template structures

5. Evaluating the model

Modeller

Fiser A et al. Methods in Enzymology 374: 461-491(2004)

General Scheme

1. Searching For Structures

• Sequence search against the PDB sequences

• Sequence-profile search

• Threading: sequence-structure fitness function

1. Searching For StructuresIf BLAST search against the PDB fail to recognize adequate templates, turn to fold recognition (threading) servers:

• FFAS03- http://ffas.ljcrf.edu/ffas-cgi/cgi/ffas.pl

• HHPRED- http://toolkit.tuebingen.mpg.de/hhpred

• HMAP (available through the FUDGE pipeline)- http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Software:PUDGE

• I-TASSER- http://zhang.bioinformatics.ku.edu/I-TASSER/

These servers not only find optional templates, but also suggest a pairwise alignment and in some cases even construct the 3D model.

http://ffas.ljcrf.edu/ffas-cgi/cgi/ffas.pl

http://toolkit.tuebingen.mpg.de/hhpred

http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Software:PUDGE

http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Software:PUDGE

http://zhang.bioinformatics.ku.edu/I-TASSER/

2. Selecting TemplatesHow to select the right template?

• Higher sequence similarity - %ID

• Close subfamily - phylogenetic tree

• “Environment” similarity - solvent, pH, ligand, quaternary interactions

• The quality of the experimentally determined

structure

• Purpose of modeling - e.g. protein-ligand model vs. geometry of active site

Seq. 2

Seq. 1

Seq. 3

Seq. 4

Seq. 5Seq. 6

2. Selecting Templates

More than one template

• Two ways to combine multiple templates:

– Global model – alignment with different domain of the target with little overlap between them

– Local model – alignment with the same part of the target

More than one template

The more the merrier -

multiple structures with

the same fold:



Trial and error

• Generate a model for each candidate template and/or their combination.

• Evaluate the models by an energy or any other scoring function.(will be discussed later…)

3. Aligning query and template sequences

• All comparative modeling programs depend on a target-template alignment.

• When the sequence similarity between the template and target proteins is high, simple pairwise alignments are usually fine (e.g. Needleman-Wunsch global alignment).

• Gaps or low/medium sequence similarity indicate that we should improve the alignment...

Guidelines:

1. Create a multiple sequence alignment and extract thetemplate-query pairwise alignment.


Pairwise alignments – not enough!

Guidelines:


• Visual inspection of alignments - difficult to teach… a matter of experience…

TemplateQuery


Guidelines:


2. Use secondary structure information to improve pairwise alignment- avoid gaps in these regions!

QueryTemplate


Guidelines:

1. Create a multiple sequence alignment and extract thetemplate-query pairwise alignment

2. Use secondary structure information to improve pairwise alignment- avoid gaps in these regions!

3. Biochemical and structural previous data


• Where? (to find homologues)

• Structural templates- search against the PDB

• Sequence homologues- search against SwissProt or Uniprot (recommended!)- usually using BLAST

• How many?

• As many as possible, as long as the MSA looks good (next week…)


Tips for MSA building

• How long? (length of homologues)

• Fragments- short homologues (less than 50,60% the query’s length) = bad alignment

• Ensure your sequences exhibit the wanted domain(s)

• N/C terminal tend to vary in length between homologues

• How close? (distance from query sequence)

• All too close- no information

• Too many too far- bad alignment

• Ensure that you have a balanced collection!



• From who? (which species the sequence belongs to)

• Don’t care, all homologues are welcome

• Orthologues/paralogues may be helpful

• Sequences from distant/close species provide different types of information

• Which alignment method?

• The best today are MUSCLE, T-Coffee and MAFFT. All available at





• Most importantly, make sure that both the query and the selected template are included in the MSA.

• Sequences which are more distant than the template are not needed to be included in the alignment.


Query-template alignment via a profile-to-profile approach:

1. Construct an MSA for the query, serving as profiles depicting the protein family properties.

2. Align the profile to profiles of all proteins of the PDB, using, e.g., FFAS03 or HHpred.

3. Compare pairwise alignments constructed via the different methods – hope to get a consensus prediction…


Different levels of similarity between the template & query initiate various computational approaches:

4. Building a model

Once you have an improved pairwise alignment between your query & template

Use Modeller to build your model!

A. Sali & T.L. Blundell. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779-815, 1993.

Generation and Refinement Using satisfaction of spatial restrains Can perform additional tasks:

de novo modeling of loops Optimization of models – using an objective

function Multiple alignment Comparison of protein structures

Modeller

4. Building a model

• Other spatial features, such as hydrogen bonds, and dihedral angles, are transferred from the templates to the target.

• Thus, a number of spatial restraints on its structure are obtained.

• The 3D model is obtained by satisfying all the restraints as well as possible.

Modeller

4. Building a model

• Distance and dihedral angle restraints on the target are calculated from its alignment with template.

• Restraints were obtained also from a statistical analysis of the relationships from a large database of pairs of homologous structures.

• Various correlations were obtained, e.g. correlations between Ca-Ca distances. These relationships can be used directly as spatial restraints.

• Restraints and CHARMM energy terms are then combined into an objective function, which is optimized in 3D space.

Modeller

4. Building a model

5 .Model Evaluation

• The accuracy of the model depends on its sequence identity with the template:

5 .Model EvaluationThe model can be assessed in two levels:

• Global- reliability of the model as a whole.*Useful when several models are generated and one should be chosen as the best one.*When different models were based on various templates, may help choose the best one.

• Local- assessing the reliability of the different regions, even specific residues, of the model. *Useful to detect local mistakes, that may originate in many time from alignment errors.

5 .Model EvaluationExamples of assessment approaches:

1. Assessment of the model’s stereochemistry

2. Prediction of unreliable regions of the model - “pseudo energy” profile: peaks errors

3. Consistence with experimental observations

4. Consistence with evolutionary conservation rates

Summary :

5 Basic Steps

Hands ON

The Query ProteinName: Dihydrodipicolinate reductase

Enzyme reaction:

Molecular process: Lysine biosynthesis (early stages)

Organism: E. coli

Sequence length: 273 aa



Get your sequence

<DAPB_ECOLIMHDANIRVAIAGAGGRMGRQLIQAALALEGVQLGAALEREGSSLLGSDAGELAGAGKTGVTVQSSLDAVKDDFDVFIDFTRPEGTLNHLAFCRQHGKGMVIGTTGFDEAGKQAIRDAAADIAIVFAANFSVGVNVMLKLLEKAAKVMGDYTDIEIIEAHHRHKVDAPSGTALAMGEAIAHALDKDLKDCAVYSREGHTGERVPGTIGFATVRAGDIVGEHTAMFADIGERLEITHKASSRMTFANGAVRSALWLSGKESGLFDMRDVLDLNNL

http://www.uniprot.org/

http://www.uniprot.org/

1. Searching For StructuresFind templates with significant homology:

• BLAST against the sequences in the PDB

Find also more distant templates, using profile-to-profile approach:

• FFAS03 server• HHPRED server

1. Searching For StructuresBlast against the PDB

http://www.ncbi.nlm.nih.gov/BLAST/


1 .Paste sequence

2. Select the PDB database

3.




1. Searching For StructuresUse fold recognition - FFAS03

Select the PDB database

1 .Paste sequence

Run

1. Searching For StructuresUse fold recognition - HHPRED

Select the PDB database

1 .Paste sequence

Run

http://toolkit.tuebingen.mpg.de/hhpred

2. Selecting templates

2. Selecting templatesBlast against the PDB

The real structureof our protein

Closest homologousstructure

2. Selecting templatesBlast against the PDB


The selected template:

1VM6, chain A

2. Selecting templatesUse fold recognition - FFAS03

http://ffas.ljcrf.edu/ffas-cgi/cgi/get_mu.pl?ses=&qdb=public&tdb=PDB0408&type=re&key=221830166.3750.0000000

2. Selecting templatesUse fold recognition - FFAS03

Scores below -9.5 significant

2. Selecting templatesUse fold recognition - HHPRED

http://toolkit.tuebingen.mpg.de/hhpred/histograms/8455009

2. Selecting templatesUse fold recognition - HHPRED

2. Selecting templatesWho is our template?

www.ebi.ac.uk/thornton-srv/databases/pdbsum

PDB ID 1VM6 is UniProt entry

‘DAPB_THEMA’

3. Alignment

http://consurftest.tau.ac.il/

3. Alignment

No model yet…

We will use ConSurf to get homologues and

build and MSA

3. Alignment

Alignment method

Database;Swissprot/

uniprot/uniref90/NR

Set to max- 500

Redundancy

Min. identity

3. Alignment

Email

Job name

3. Alignment

3. Alignment

MSA- download the file- right click on the mouse

Filtered sequences

PSIBLAST result

3. Alignment

• http://www.mbio.ncsu.edu/BioEdit/BioEdit.html

• Easy-to-use sequence alignment editor

• View and manipulate alignments up to 20,000 sequences. •Four modes of manual alignment: select and slide, dynamic grab and drag, gap insert and delete by mouse click, and on-screen typing which behaves like a text editor.

•Reads and writes Genbank, Fasta, Phylip 3.2, Phylip 4, and NBRF/PIR formats. Also reads GCG and Clustal formats

Easiest Using Bioedit


http://www.mbio.ncsu.edu/BioEdit/bioedit.html


http://www.mbio.ncsu.edu/BioEdit/bioedit.html

• Find a specific sequence: “Edit-> search -> in titles”

• Erase\add sequences: “Edit-> cut\paste\delete sequence”

• “Sequence Identity matrix” under “Alignment”- useful for a rough evaluation of distances within the alignment.

• After taking out sequences, “Minimize Alignment” under “Alignment” takes out unessential gaps.

• Can save an image using: “File -> Graphic View” & then “Edit -> Copy page as BITMAP”

1. Open: Start Phylogeny BioEdit

2. Open the alignment: file open ‘query.aln’

2. Select the template:Edit Search Find in Titles “DAPB_THEMA”

3. AlignmentExtract query-template pairwise alignment


“DAPB_THEMA”

4. Add the query to the template selection: ctrl + ‘query’

5. Invert selection: Edit invert title selection

6. Delete other sequences: Edit Cut Sequences(s)

7. Minimize gaps: Alignment Minimize Alignment

8. Save the pairwise alignment:File Save as (Fasta format) “DAPB_ECOLI_1VM6.fas”



Save as “fasta” format!!!!!!!

queryDAPB_THEMA

File name

Use fold recognition - FFAS03

Scores below -9.5 significant

3. Alignment

Use fold recognition - FFAS03

3. Alignment

http://ffas.ljcrf.edu/ffas-cgi/cgi/get_mu.pl?ses=&qdb=public&tdb=PDB0408&type=re&key=221830166.3750.0000000

Use fold recognition - HHPRED

http://toolkit.tuebingen.mpg.de/hhpred/histograms/8455009

3. Alignment

Use fold recognition - HHPRED3. Alignment

• Generally speaking, in this step we would compare the pairwise alignments computed by the three approaches:• MSA-derived• FFAS03• HHPRED

• We don’t have the time/patience for that now….

• Thus, we will now edit the pairwise from the MSA- Modeller requires a specific format, which we have to manually adjust

3 .AlignmentInspect query-template pairwise alignment

>P1; DAPB_ECOLIsequence:DAPB_ECOLI:1:A:274:A ::::MHDANIRVAIAGAGGRMGRQLIQAALALEGVQLGAALEREGSSLLGSDAGELAGAGKTGVTVQSSLDAVKDDFDVFIDFTRPEGTLNHLAFCRQHGKGMVIGTTGFDEAGKQAIRDAAADIAIVFAANFSVGVNVMLKLLEKAAKVMGDYTDIEIIEAHHRHKVDAPSGTALAMGEAIAHALDKDLKDCAVYSREGHTGERVPGTIGFATVRAGDIVGEHTAMFADIGERLEITHKASSRMTFANGAVRSALWLSGKESGLFDMRDVLDLNNL*

>P1;1VM6structureX:1VM6:1:A:212:A ::::-----MKYGIVGYSGRMGQEIQKVFSE-KGHELVLKVDV------------------------NGVEEL-DSPDVVIDFSSPEALPKTVDLCKKYRAGLVLGTTALKEEHLQMLRELSKEVPVVQAYNFSIGINVLKRFLSELVKVLE-DWDVEIVETHHRFKKDAPSGTAILLESAL--------------------GK----SVPIHSLRVGGVPGDHVVVFGNIGETIEIKHRAISRTVFAIGALKAAEFLVGKDPGMYSFEEVI-----*

3. AlignmentEdit query-template pairwise alignment

The PDB file of the template (rename DAPB_THEMA)

The name of the query protein (this will be the name of the modeled PDB file)

Save as “dapb_ecoli_1vm6.pir”

Start, end and chain

4 .Model Building

from modeller import * from modeller.automodel import *

log.verbose ()env = environ ()

a = automodel(env, alnfile = 'dapb_ecoli_1vm6.pir ,'

knowns = ('1VM6') , sequence = 'DAPB_ECOLI ('

a.starting_model= 1 a.ending_model = 1

a.make ()

A script for Modeller- copy to a text file.…

4. Model Building

4. Model BuildingGet the template structure

1 .Paste the template’s PDB ID “1VM6”

2 .


Get the template structure: 1vm6 chain A

Save as: “1VM6.pdb”

4. Model Building

Notice:case

sensitive!

4 .Model BuildingRunning modeller:

1. Put the PDB file, PIR alignment and modeller script in a specific directory, e.g. c:\test

2. Desktop Modeller:


3. “cd c:\test”4. “mod9v7 [modeller script name]


5. The run completed successfully:


6. Output files:• Model, e.g. “P2RX1_HUMAN.B99990001.pdb”• Log file- very important- specifies the problems of

the run• Other, not important, files

7. Open pymol and look at your model….

8. Evaluate it- tomorrow!

Edit query-template pairwise alignment

Watch out! Modeller can fail owing to:

1.Non-matching start and end points of the template at the PIR alignment and PDB template file

2. Small discrepancies between the sequence of the template and in the PIR alignment… may have to manually edit the alignment a little…

This, and more, will be reported in the log file

4 .Model Building

Documents

Homology Modeling Workshop GHIKLSYTVNEQNLKPERFFYTSAVAIL