## Warning: package 'knitr' was built under R version 3.5.2

Introduction

From the MODELLER web site1 :

MODELLER is used for homology or comparative modeling of protein three-dimensional structures (Webb and Sali 2016, Marti-Renom et al. (2000))

The user provides an alignment of a sequence to be modeled with known related structures and MODELLER automatically calculates a model containing all non-hydrogen atoms.

MODELLER implements comparative protein structure modeling by satisfaction of spatial restraints (Sali and Blundell 1993, Fiser, Do, and Sali (2000)), and can perform many additional tasks, including de novo modeling of loops in protein structures, optimization of various models of protein structure […]

MODELLER process flow.

Figure 1.

Modeller is 9.18 is intalled on all the iMacs. However, each user should register with the web site to obtain the install keyword at https://salilab.org/modeller/registration.html

Acknowledgments

Part of this tutorial is from “Comparative Protein Structure Prediction MODELLER tutorial” by Marc A. Marti-Renom ( PDF )2

Set-up

We will use MODELLER on a Macintosh system but it would work exactly the same on other platforms.

MODELLER is made of a collection of python scripts, that the user just has to modify to reflect the name of the target sequence(s) and the template structure(s).

It is always good practise to create a directory for a specific project. Let’s create a directory on the desktop called MOD1 where we will save the necessary files.


Create a folder/directory on your desktop called MOD1 or any name you wish.

Terminal

Then MODELLER is invoked on the line command with the name of the current version. The current release is 9.18 and is invoked on the line command as mod9.18 followed by the name of the script to run.


Open a text Terminal.

It is necessary to open a text Terminal to run MODELLER. On Mac Terminal is found as /Applications/Utilities/Terminal but can easily be launched by typing Terminal within the “Spotlight Search” on the top-right corner of the Mac screen (magnifying glass icon.)

(On a Windows computer you would need to open a command line by searching for the cmd program with Cortna or the Start button.)

Next it is necessary to change where the Terminal is “looking” with the “change directory” cd command:

cd Desktop
cd MOD1

You can check which directory Terminal is looking into with the command:

pwd

In the next section we will add files and scripts to this folder.

Text editing

Script and/or plain text files can be edited on a Macintosh with the built-in text editor TextEdit. However, it is necessary to verify that the format is plain text by engaging the menu Format > Make Plain Text if the program opens in Rich Text format as it is often the default behavior.

Within Terminal the full screen word processor nano could also be used (and is also available on Linux systems.)

Windows users can use Notepad or Wordpad to easily create plain text files.

To create the necessary text files simply Copy/Paste the information from this page into a text document on your computer using one of the text editors mentioned above.

Using MODELLER

To run MODELLER we need input data: sequence(s) and 3D template(s) in the proper format as well as python scripts. The later are found on the MODELLER web site as example files to be modified.

The output will consist of 1 or more (if requested in the script) 3D PDB format models, an alignment of sequence(s), a log file and other ancilary output.

INPUT:

  • sequence(s) target(s): FASTA/PIR format
  • structure(s) template(s): PDB format
  • Python command file(s): plain text format

OUTPUT:

  • Target-Template Alignment
  • Model(s) in PDB format
  • Other data

Simple example

This simple example assumes that some prior study work has been done on the sequence to be modeled to find a suitable 3D template (e.g. with BLAST.)

The purpose of the exercise is to create a 3D model from the sequence of the “brain lipid-binding protein” (blbp) of a mouse sequence based on one existing 3D structure with a different sequence that has been solved and published on the Protein Data Bank (PDB) (Berman et al. 2000).

The sequence in FASTA format looks like this, and has accession code NP_067247.1.

>NP_067247.1 fatty acid-binding protein, brain [Mus musculus]
MVDAFCATWKLTDSQNFDEYMKALGVGFATRQVGNVTKPTVIISQEGGKVVIRTQCTFKNTEINFQLGEE
FEETSIDDRNCKSVVRLDGDKLIHVQKWDGKETNCTREIKDGKMVVTLTFGDIVAVRCYEKA

Prior analysis (e.g. BLAST) reveals that the sequence of the “brain lipid-binding protein” is closely related of that of “human muscle fatty acid binding protein” that has been solved by X-ray crystallography with accession code 1HMS 1hms.pdb (Young et al. 1994).

The sequence of that protein in FASTA format looks like this:

>1HMS:A|PDBID|CHAIN|SEQUENCE
VDAFLGTWKLVDSKNFDDYMKSLGVGFATRQVASMTKPTTIIEKNGDILTLKTHSTFKNTEISFKLGVEFDETTADDRKV
KSIVTLDGGKLVHLQKWDGQETTLVRELIDGKLILTLTHGTAVCTRTYEKEA

A simple two-sequence BLAST alignment reveals that the protein sequences are 62% identical and 78% similar with no sequence gaps (see below.)

Therefore these are a perfect subject for homology modeling.

Score Expect Method Identities Positives Gaps
177 bits(450) 8e-64 Compositional matrix adjust. 81/130(62%) 102/130(78%) 0/130(0%) 
Query  1    VDAFLGTWKLVDSKNFDDYMKSLGVGFATRQVASMTKPTTIIEKNGDILTLKTHSTFKNT  60
            VDAF  TWKL DS+NFD+YMK+LGVGFATRQV ++TKPT II + G  + ++T  TFKNT
Sbjct  1    VDAFCATWKLTDSQNFDEYMKALGVGFATRQVGNVTKPTVIISQEGGKVVIRTQCTFKNT  60

Query  61   EISFKLGVEFDETTADDRKVKSIVTLDGGKLVHLQKWDGQETTLVRELIDGKLILTLTHG  120
            EI+F+LG EF+ET+ DDR  KS+V LDG KL+H+QKWDG+ET   RE+ DGK+++TLT G
Sbjct  61   EINFQLGEEFEETSIDDRNCKSVVRLDGDKLIHVQKWDGKETNCTREIKDGKMVVTLTFG  120

Query  121  TAVCTRTYEK  130
              V  R YEK
Sbjct  121  DIVAVRCYEK  130

INPUT: Target sequence


Create a text file called blbp.seq containing the sequence sequence in the MOD1 directory.

You can copy/paste the sequence below. The format starts with >P1 which is an original annotation form from the early PIR protein database .

The : colon separators are part of the MODELLER format and will make more sense later when you see the PDB sequence transformed in this format automatically below. For now simply copy/paste te following sequence into a plain text file

Example Target: Brain lipid-binding protein (BLBP).

BLBP sequence in PIR (MODELLER) format:

>P1;blbp
sequence:blbp::::::::
VDAFCATWKLTDSQNFDEYMKALGVGFATRQVGNVTKPTVIISQEGGKVVIRTQCTFKNTEINFQLGEEFEETSIDDRNCKSVV
RLDGDKLIHVQKWDGKETNCTREIKDGKMVVTLTFGDIVAVRCYEKA*

INPUT: download PDB structure

The input structure has accession code 1HMS.

The downloaded file will appear in your Downloads directory as 1HMS.pdb.


Download and then move downloaded file 1HMS.pdb to the MOD1 directory.

INPUT: Align sequences

The target sequence and 3D structure sequence need to be aligned and saved in a file with the proper format.

To accomplish this we need to edit a python script listing the name of the files containing the sequences. The sequence will be extracted from the PDB file itself by MODELLER from the script instructions.


Create a text file called align.py with the following content and save it in folder MOD1:

# Example for: alignment.align()
# This will read two sequences, align them, and write the alignment
# to a file:

log.verbose()
env = environ()
aln = alignment(env)
mdl = model(env, file='1hms')
aln.append_model(mdl, align_codes='1hms')
aln.append(file='blbp.seq', align_codes=('blbp'))
# The as1.sim.mat similarity matrix is used by default:
aln.align(gap_penalties_1d=(-600, -400))
aln.write(file='blbp-1hms.ali', alignment_format='PIR')
aln.write(file='blbp-1hms.pap', alignment_format='PAP')

Note: Since these are python functions, they need parentheses () even if there is nothing inside them. The meaning of the commands can be found under MODELLER online manual https://salilab.org/modeller/manual/ and described succintly below.

Explanations for the commands contained within this script:

  • log.verbose() : display all log output
  • env = environ() : create a short name for environ()
  • environ() : contains most information about the MODELLER environment, such as the energy function and parameter and topology libraries […].
  • aln = alignment(env) : This creates a new alignment object; by default, this contains no sequences. aln is the short name for this object.
  • mdl = model(env, file='1hms') : create a new 3D model. Here we pass on the information about the PDB file and atom information will be read. mdl is the short name for this object.
  • aln.append_model(mdl, align_codes='1hms') : append the sequence of 1hms to the alignment. In more complex analyzes there could be multiple PDB codes passed on.
  • aln.append(file='blbp.seq', align_codes=('blbp')) : append the target sequence to the alignment.
  • # The as1.sim.mat similarity matrix is used by default: This is a comment line
  • aln.align(gap_penalties_1d=(-600, -400)) the command aln.align create the alignment based on the indicated gap penalties.
  • aln.write(file='blbp-1hms.ali', alignment_format='PIR') the alignment is written in PIR format.
  • aln.write(file='blbp-1hms.pap', alignment_format='PAP') the alignment is written in PAP format.

It is worth noting the following point:

  • the PDB codes are within single quotes, for example '1hms'
  • If there are multiple arguments passed to a function, there is a space after the comma , for example before the word alignment_format= in the lines above.

Run script to create alignment files


Run alignment script align.py within MOD1.

Verify that you are within the MOD1 directory:

pwd

The answer should be something like:

/Users/yourname/Desktop/MOD1

Now run the alignment script by typing:

mod9.18 align.py

This will create the files: blbp-1hms.ali, blbp-1hms.pap, and align.log.

To see the content of the alignment files we can use the simple cat command on the Terminal (or use the graphical interface with TextEdit for example.)

Note the use of the : colon separator in the PDB sequence file.

cat blbp-1hms.ali
>P1;1hms
structureX:1hms:   1 :A:+131 :A:MOL_ID  1; MOLECULE  MUSCLE FATTY ACID BINDING PROTEIN; CHAIN  A; ENGINEERED  YES:MOL_ID  1; ORGANISM_SCIENTIFIC  HOMO SAPIENS; ORGANISM_COMMON  HUMAN; ORGANISM_TAXID  9606; EXPRESSION_SYSTEM  ESCHERICHIA COLI; EXPRESSION_SYSTEM_TAXID  562: 1.40: 0.12
VDAFLGTWKLVDSKNFDDYMKSLGVGFATRQVASMTKPTTIIEKNGDILTLKTHSTFKNTEISFKLGVEFDETTA
DDRKVKSIVTLDGGKLVHLQKWDGQETTLVRELIDGKLILTLTHGTAVCTRTYEKE*

>P1;blbp
sequence:blbp:     : :     : :::-1.00:-1.00
VDAFCATWKLTDSQNFDEYMKALGVGFATRQVGNVTKPTVIISQEGGKVVIRTQCTFKNTEINFQLGEEFEETSI
DDRNCKSVVRLDGDKLIHVQKWDGKETNCTREIKDGKMVVTLTFGDIVAVRCYEKA*

This alignment extracted sequence information from the PDB file for 1HMS including header information about the content that is placed within the header of structureX:1hms.

The .ali formatted alignment file is used later by MODELLER to create the 3D model(s).

The .pap formatted alignment is easier for human eyes to evaluate the alignment with the marked conserved (identity) regions.

cat blbp-1hms.pap
 _aln.pos         10        20        30        40        50        60
1hms      VDAFLGTWKLVDSKNFDDYMKSLGVGFATRQVASMTKPTTIIEKNGDILTLKTHSTFKNTEISFKLGV 
blbp      VDAFCATWKLTDSQNFDEYMKALGVGFATRQVGNVTKPTVIISQEGGKVVIRTQCTFKNTEINFQLGE 
 _consrvd ****  **** ** *** *** **********   **** **   *      *  ******* * **

 _aln.p   70        80        90       100       110       120       130
1hms      EFDETTADDRKVKSIVTLDGGKLVHLQKWDGQETTLVRELIDGKLILTLTHGTAVCTRTYEKE 
blbp      EFEETSIDDRNCKSVVRLDGDKLIHVQKWDGKETNCTREIKDGKMVVTLTFGDIVAVRCYEKA 
 _consrvd ** **  ***  ** * *** ** * ***** **   **  ***   *** *  *  * ***

Model building

We now have the necessary “ingredients” to create the 3D model:

  • aligned sequences
  • 3D original template

We now need to create/edit the MODELLER python script that will list these ingredients and call the MODELLER functions to build the model.


Create a text file called model.py with the following content and save it in folder MOD1. Note that the comments noted with # do not need to be re-typed if not creating the file with a copy/paste method. The blank lines are only for text clarity and can also be omitted if desired.

To create the file you can use TextEdit or nano for example.

# Homology modelling by the automodel class
from modeller.automodel import *  # Load the automodel class
log.verbose()                     # request verbose output
env = environ()                   # create a new MODELLER environment

a = automodel(env,
   alnfile = 'blbp-1hms.ali',     # alignment filename
   knowns = '1hms',               # codes of the templates
   sequence = 'blbp')             # code of the target

a.starting_model= 1               # index of the first model
a.ending_model = 1                # index of the last model
                                  # (determines how many models to calculate)

a.make()                          # do the actual homology modelling

Remarks: The automodel function is renamed a and the “dot notation” is used to call on sub function appended to a as it is the usual writing mode in python.

In this simple file we create only one model, but to obtain e.g. 5 models the a.ending_model argument would be set to 5.

Run model building script


Run model.py within MOD1 in the same manner as we ran the align.py script:

mod9.18 model.py

This will create the following files:

blbp.B99990001.pdb
blbp.D00000001
blbp.V99990001
model.log
blbp.ini
blbp.rsr
blbp.sch

The final 3D model is called blbp.B99990001.pdb and that is the “end product” that was desired.

In real life, multiple models would be calculated (e.g. 5) and various evaluation methods could be applied to decide which are “best.”

You can explore the content of the remaining file (all text files) with the less -S command that will display the file content to the screen without wrapping long lines.

Compare model and template graphically

Now that we have a model we can compare the structure onbtained with the original template.

For this you can use Chimera or PyMOL or any other molecular graphics software that can read PDB files.

PyMOL

To open and compare files in PyMOL open the PyMOL program first.

  • At the line command type: fetch 1hms to load the original template file.

  • Using the menu cascade File > Open... navigate to the MOD1 directory to open file blbp.B99990001.pdb.

  • Use left mouse button to rotate structure.

Note: the 2 structures will not be superimposed at first and it will be necessary to align them in 3D.

  • Align the structures: on the Names panel at right, click on A (action) button next to the line that reads blbp.B99990001.pdb 1 for the model. Following further down on this pull-down menu follow the menu cascade: A > align > to molecule (*/CA) > 1hms
Align structures menu.


“Align structures menu.”

Open and align structures in PyMOL.


“Open and align structures in PyMOL.”

  • To hide or show either structure simply click once on the name of the structure on the list at the right hand side Names panel.

  • In order to highlight the bound lipid use the following menu casade next to the all line on the right hand side: all > S > organic > sticks

  • To hide the red dot water molecules: all > H > waters

Note: only the protein is modeled, the ligands are not modeled by MODELLER. These are typically written as HETATM within the PDB file.

Chimera

If you prefer using Chimera:

  • Open Chimera
  • Open template structure: File > Fetch by ID… and enter 1HMS in the Fetch Structure by ID in the text space next to the PDB button. This will open the structure in “first view” mode as a cartoon ribbon diagram.
  • Open the model: **File > Open... and navigate to the MOD1 directory to open file blbp.B99990001.pdb. The default view will also be as a cartoon ribbon.

Note: the 2 structures will not be superimposed at first and it will be necessary to align them in 3D.

Open and align structures in Chimera.


“Open and align structures in Chimera.”

  • Tools > Sequence Comparison > MatchMaker will open the MatchMaker window. Keep everything the the current default and click 1HMS (#0) for the “Reference structure” and blbp.B99990001.pdb (#1) for the “Structure(s) to match”
  • Click Apply and the 2 structures will be aligned.
  • Use left mouse button to rotate structure.

Comparing the model(s) with solved strcutures.

It happens that since this exercise was written many actual structures were solved.

A BLAST restricted to the Protein Data Bank will give some PDB codes of solved structures. For example:

Description Max score Total score Query cover E value Ident Accession
Chain A, Enantiomer-specific Binding Of The Potent Antinociceptive Agent Sbfi- 26 To Anandamide Transporters Fabp7 240 240 100% 5e-84 87% 5URA_A
Chain A, Crystal Structure Of Human Brain Fatty Acid Binding Protein 238 238 99% 4e-83 87% 1FDQ_A
Chain A, Human Fabp3 In Complex With 6-chloro-2-methyl-4-phenyl-quinoline-3- Carboxylic Acid 180 180 99% 3e-60 63% 5HZ9_A
Chain A, Serial Femtosecond X-ray Structure Of Human Fatty Acid-binding Protein Type-3 (fabp3) In Complex With Stearic Acid (c18:0) Determined Using X-ray Free-electron Laser At Sacla 180 180 99% 3e-60 63% 3WXQ_A

The table is much longer!

Here is the alignment for the first in the table: 5URA chain A.

Range 1: 4 to 135
Alignment statistics for match #1
Score   Expect  Method  Identities  Positives   Gaps
240 bits(613)   5e-84   Compositional matrix adjust.    115/132(87%)    124/132(93%)    0/132(0%)
Query  1    MVDAFCATWKLTDSQNFDEYMKALGVGFATRQVGNVTKPTVIISQEGGKVVIRTQCTFKN  60
            MV+AFCATWKLT+SQNFDEYMKALGVGFATRQVGNVTKPTVIISQEG KVVIRT  TFKN
Sbjct  4    MVEAFCATWKLTNSQNFDEYMKALGVGFATRQVGNVTKPTVIISQEGDKVVIRTLSTFKN  63

Query  61   TEINFQLGEEFEETSIDDRNCKSVVRLDGDKLIHVQKWDGKETNCTREIKDGKMVVTLTF  120
            TEI+FQLGEEF+ET+ DDRNCKSVV LDGDKL+H+QKWDGKETN  REIKDGKMV+TLTF
Sbjct  64   TEISFQLGEEFDETTADDRNCKSVVSLDGDKLVHIQKWDGKETNFVREIKDGKMVMTLTF  123

Query  121  GDIVAVRCYEKA  132
            GD+VAVR YEKA
Sbjct  124  GDVVAVRHYEKA  135

OPTIONAL EXERCISE:

Load some of the solved structures and compare them to the model(s.)

MODELLER tutorials online

Official web site

The MODELLER web site offers tutorials with different levels of difficulty https://salilab.org/modeller/tutorial/ :

  1. Basic Modeling. Model a sequence with high identity to a template. This exercise introduces the use of MODELLER in a simple case where the template selection and target-template alignments are not a problem.
  2. Advanced Modeling. Model a sequence based on multiple templates and bound to a ligand. This exercise introduces the use of multiple templates, ligands and loop refinement in the process of model building with MODELLER.
  3. Iterative Modeling. Increase the accuracy of the modeling exercise by iterating the 4 step process. This exercise introduces the concept of MOULDING to improve the accuracy of comparative models.
  4. Difficult Modeling. Model a sequence based on a low identity to a template. This exercise uses resources external to MODELLER in order to select a template for a difficult case of protein structure prediction.
  5. Modeling with cryo-EM. Model a sequence using both template and cryo-EM data. This exercise assesses the quality of generated models and loops by rigid fitting into cryo-EM maps, and improves them with flexible EM fitting.

Other courses

Virtual Proteomics Laboratory - Experiment 10: Homology Modelling - http://iitb.vlab.co.in/?sub=41&brch=118&sim=657&cnt=2

REFERENCES

Berman, H. M., J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, and P. E. Bourne. 2000. “The Protein Data Bank.” Nucleic Acids Res. 28 (1): 235–42.

Fiser, A., R. K. Do, and A. Sali. 2000. “Modeling of loops in protein structures.” Protein Sci. 9 (9): 1753–73.

Marti-Renom, M. A., A. C. Stuart, A. Fiser, R. Sanchez, F. Melo, and A. Sali. 2000. “Comparative protein structure modeling of genes and genomes.” Annu Rev Biophys Biomol Struct 29: 291–325.

Sali, A., and T. L. Blundell. 1993. “Comparative protein modelling by satisfaction of spatial restraints.” J. Mol. Biol. 234 (3): 779–815.

Webb, B., and A. Sali. 2016. “Comparative Protein Structure Modeling Using MODELLER.” Curr Protoc Bioinformatics 54 (June): 1–5.

Young, A. C., G. Scapin, A. Kromminga, S. B. Patel, J. H. Veerkamp, and J. C. Sacchettini. 1994. “Structural studies on human muscle fatty acid binding protein at 1.4 A resolution: binding interactions with three C18 fatty acids.” Structure 2 (6): 523–34.

  1. https://salilab.org/modeller/↩

  2. http://sgt.cnag.cat/www/presentations/files/slides/20081104_MODELLER_Tutorial.pdf↩