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Computational Structural Biology group focusing on dissecting, understanding and predicting biomolecular interactions at the molecular level.

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HADDOCKing of the p53 N-terminal peptide to MDM2

General Overview

This tutorial introduces protein-protein docking using the HADDOCK web server. It also introduces the CPORT web server for interface prediction, based on evolutionary conservation and other biophysical properties. By the end of this tutorial, you should know how to setup a HADDOCK run and interpret its results in terms of biological insights.

A bite of theory

Protein-protein interactions mediate most cellular processes in the cell, such as differentiation, proliferation, signal transduction, and cell death. Their structural characterization is however not always trivial, even with the constant developments in x-ray crystallography and nuclear magnetic resonance spectroscopy. The culprits widely vary, ranging from the native environment of the complexes, which might make them hard to purify or crystallize, to the size of the system being too large for current methodologies to grasp. More importantly, homeostasis often depends on very tightly regulated transient interactions, yet another hindrance to the purification and characterization of these complexes. Nevertheless, three decades of spectacular progress in structural biology have shown that the protein fold space is finite and the rules of the folding game are well defined. This prompted the development of several computational methods aimed at complementing experimental techniques in their quest for a full 3D view of the proteome. A widely used computational method for the prediction of the protein-protein complexes is molecular docking, which aims at generating the structure of such a complex starting from the structures (or models) of its native constituents.

This tutorial will introduce HADDOCK (High Ambiguity Driven DOCKing) as a method to predict the three-dimensional structure of protein-protein complexes in silico using a variety of sources of information to guide the docking process and score the predicted models. HADDOCK is a collection of python scripts derived from ARIA that harness the power of CNS (Crystallography and NMR System) for structure calculation of molecular complexes. What distinguishes HADDOCK from other docking software is its ability, inherited from CNS, to incorporate experimental data as restraints and use these to guide the docking process alongside traditional energetics and shape complementarity. Moreover, the intimate coupling with CNS endows HADDOCK with the ability to actually produce models of sufficient quality to be archived in the Protein Data Bank.

A central aspect to HADDOCK is the definition of ambiguous interaction restraints or AIRs. These allow the translation of raw data such as NMR chemical shift perturbation or mutagenesis experiments into distance restraints that are incorporated in the energy function used in the calculations. AIRs are defined through a list of residues that fall under two categories: active and passive. Generally, active residues are those of central importance for the interaction, such as residues whose knockouts abolish the interaction or those where the chemical shift perturbation is higher. Throughout the simulation, these active residues are restrained to be part of the interface, if possible, otherwise incurring in a scoring penalty. Passive residues are those that contribute for the interaction, but are deemed of less importance. If such a residue does not belong in the interface there is no scoring penalty. Hence, a careful selection of which residues are active and which are passive is critical for the success of the docking.

The docking protocol of HADDOCK was designed so that the molecules experience varying degrees of flexibility and different chemical environments, and it can be divided in three different stages, each with a defined goal and characteristics:

  1. Randomization of orientations and rigid-body minimization (it0). In this initial stage, the interacting partners are treated as rigid bodies, meaning that all geometrical parameters such as bonds lengths, bond angles, and dihedral angles are frozen. The partners are separated in space and rotated randomly about their centers of mass. This is followed by a rigid body energy minimization step, where the partners are allowed to rotate and translate to optimize the interaction. The role of AIRs in this stage is of particular importance. Since they are included in the energy function being minimized, the resulting complexes will be biased towards them. For example, defining a very strict set of AIRs leads to a very narrow sampling of the conformational space, meaning that the generated poses will be very similar. Conversely, very sparse restraints (e.g. the entire surface of a partner) will result in very different solutions, displaying greater variability in the region of binding.

  2. Semi-flexible simulated annealing in torsion angle space (it1) The second stage of the docking protocol introduces flexibility to the interacting partners through a three-step molecular dynamics-based refinement in order to optimize interface packing. It is worth noting that flexibility in torsion angle space means that bond lengths and angles are still frozen. The interacting partners are first kept rigid and only their orientations are optimized. Flexibility is then introduced in the interface, which is automatically defined based on an analysis of intermolecular contacts within a 5Å cut-off. This allows different binding poses coming from it0 to have different flexible regions defined. Residues belonging to this interface region are then allowed to move their side-chains in a second refinement step. Finally, both backbone and side-chains of the flexible interface are granted freedom. The AIRs again play an important role at this stage since they might drive conformational changes.

  3. Refinement in Cartesian space with explicit solvent (water) The final stage of the docking protocol immerses the complex in a solvent shell so as to improve the energetics of the interaction. HADDOCK currently supports water (TIP3P model) and DMSO environments. The latter can be used as a membrane mimic. In this short explicit solvent refinement the models are subjected to a short molecular dynamics simulation at 300K, with position restraints on the non-interface heavy atoms. These restraints are later relaxed to allow all side chains to be optimized.

The performance of this protocol of course depends on the number of models generated at each step. Few models are less probable to capture the correct binding pose, while an exaggerated number will become computationally unreasonable. The standard HADDOCK protocol generates 1000 models in the rigid body minimization stage, and then refines the best 200 – regarding the energy function - in both it1 and water. Note, however, that while 1000 models are generated by default in it0, they are the result of five minimization trials and for each of these the 180º symmetrical solution is also sampled. Effectively, the 1000 models written to disk are thus the results of the sampling of 10.000 docking solutions.

The final models are automatically clustered based on a specific similarity measure – positional interface ligand RMSD (iL-RMSD) – that captures conformational changes about the interface by fitting on the interface of the receptor (the first molecule) and calculating the RMSDs on the interface of the smaller partner. The interface used in this calculation is automatically defined based on an analysis of all contacts made in all models.

Predicting the interface of p53 on Mdm2

HADDOCK excels at predicting the structure of the protein complexes given there is some sort of information to guide the docking. In the absence of experimental information, it is possible to use features such as sequence conservation and biophysical characteristics of surface residues to infer putative interfaces on a protein surface. Since the homology modeling module created a list of homologues of mouse MDM2, it is possible to assess which residues are more conserved.

The assess sequence conservation, the homologous sequences have first to be aligned using a multiple sequence alignment algorithm, such as Clustal. HMMER calculates pairwise sequence alignments between the query and all the hits. In the homology modeling module, these alignments were converted to a FASTA format and stored in the psa.fasta file. All it takes to build a multiple sequence alignment is then to combine the original MDM2_MOUSE.fasta file with this other FASTA file and submit it to Clustal Omega. To visualize the alignment, and which positions are more conserved, the easiest way is to generate a sequence logo. For each position in the sequence, the logo identifies the most frequently occurring residues and scales its one-letter code according to a conservation score. We will be using the weblogo server, in order the generate the sequence logo for the alignment produced by clustalo.

Generate a multiple sequence alignment of the MDM2 homologous sequences and create a web logo to inspect which residues are highly conserved.

cat MDM2_MOUSE.fasta psa.fasta > MDM2_family.fasta
clustalo -i MDM2_family.fasta –dealign -o MDM2_family.aln

Create the sequence logo by submitting the alignment file to the weblogo server.

Which regions of the sequence are highly conserved? And which are less conserved?

Visualize the homology model of mouse MDM2 in Pymol.

Based on the conservation analysis and on the model, is there a region of the structure you could discard? Can you draw a conclusion about which region of the protein can be important for binding, based solely on the evolutionary conservation analysis?

Besides sequence conservation, other features can be used to predict possible interfaces on protein structures. For example, certain residues tend to be overrepresented at protein-protein interfaces. This information, combined with evolutionary conservation and with a surface clustering algorithm that finds groups of surface residues meeting both the previous criteria results in reasonably accurate predictions. This is the basis of the WHISCY algorithm. A more advanced predictor, the CPORT web server, judiciously combines (up to) 6 different predictors to provide a consensus prediction that is more robust and more reliable than any of the individual predictors alone. CPORT was designed to provide predictions for HADDOCK. The server also returns a PDB file of the original structure loaded with the predictions in the temperature factor column. This is extremely helpful to visualize the predictions in Pymol.

Submit the homology model of mouse MDM2 to the CPORT web server and load the resulting PDB file in Pymol. spectrum b, cyan_red, cport Do the predictions highlight a particular region of the homology model?

Note down the list of residues predicted by CPORT to be part of an interface.

Preparing the structures for the docking calculation

In order to perform a docking calculation with HADDOCK, the initial structures of both MDM2 and p53 must fulfill a few requirements. First, the PDB files must have an END statement as a last line. The files can also not contain atoms with multiple occupancies. It is however possible to submit an ensemble of structures, which is useful for the representatives of p53 extracted from the molecular dynamics simulation. When submitting such an ensemble, all members must contain exactly the same atoms. Luckily, both the files produced by MODELLER and by GROMACS respect the END statement and multiple occupancy requirement, so no action is necessary here.

Then, when performing a docking calculation, it is wise to remove any unstructured or flexible termini that might obstruct the binding of the partner, particularly if there is no flexibility allowed and little information on the role of these termini in the binding is available. These long unstructured or flexible regions probably flop about in space, making their conformation in the model very likely artificial. The previous conservation analysis showed that the first 16 residues are not conserved in the MDM2 alignment, which contains 169 members. In the structure of the homology model, these 16 residues correspond to the N-terminal region that was modeled using the loopmodel protocol, and which scored badly at the validation stage. It seems safe then, to remove this region of the homology model before the docking calculations.

Edit the MDM2_MOUSE structure in Pymol to remove the first 16 residues of the unstructured N-terminal. remove resi 1-16 and MDM2_MOUSE.BL00070001

The structures of the p53 peptide originating from the molecular dynamics simulation can be submitted as a single ensemble to HADDOCK. The pdb_join.py utility of the pdb-tools set provides a quick way of building such an ensemble structure from isolated PDB files. It also adds the proper END statement to the PDB file. Finally, it has a built-in check for the integrity of the ensemble, i.e. that all members have exactly the same atomic constitution.

Concatenate different representatives of the MD simulation of the p53 peptide into an ensemble structure. pdb_join.py p53_cluster_1.pdb p53_cluster_2.pdb p53_cluster_3.pdb > p53_ensemble.pdb

Setting up the docking calculation using the HADDOCK web server

If you are following the Molecular Modeling course, ask the instructors for the web server credentials. Otherwise, please register for an account (free for academics).

HADDOCK registration page

Having prepared the initial structures and constructed a list of putative interface residues, it is time to submit the docking calculation using the HADDOCK web server interface. Under the header HADDOCK Webserver there are links to all the interfaces: Easy, Expert, Guru, Prediction, Multibody, and File Upload. It also lists both the default settings of the webserver, which are important to understand how restraints for example are handled, as well as a listing of supported modified amino acids. Proceed to the Guru interface by clicking on the appropriate link.

The Guru interface presents several foldable tabs and text input fields for a custom run name, the registered username and password.

Note: The red or blue bars on the server can be folded/unfolded by clicking on the arrow on the right. In the following we will only describe the fields/parameters that needs to be filled/changed.

Step 1: Define a name for your docking run

Enter a meaningful name for your run e.g. MDM2-p53.

Step 2: Input the protein PDB file

For this unfold the First molecule menu.

First molecule: where is the structure provided? -> “I am submitting it” Which chain to be used? -> All (our PDB only contains one chain) PDB structure to submit -> Browse and select the homology model you prepared before Active residues (directly involved in the interaction) -> Input here the list of active residues returned by CPORT for MDM2 Passive residues (surrounding surface residues) -> Leave blank (passive should only be defined if active residues are defined for the second molecule) Segment ID to use during docking -> A The N-terminus of your protein is positively charged -> uncheck the box if needed The C-terminus of your protein is negatively charged -> uncheck the box if needed

Since our homology model does not correspond to the full sequence it is better to have uncharged termini

Step 3: Input the peptide PDB file

For this unfold the Second molecule menu.

First molecule: where is the structure provided? -> “I am submitting it” Which chain to be used? -> All (our PDB only contains one chain) PDB structure to submit -> Browse and select the PDB file containing the ensemble of models you prepared before Active residues (directly involved in the interaction) -> Leave blank (no active for the peptide in this case) Passive residues (surrounding surface residues) -> Enter here all residues of the peptide as a comma-separated list Segment ID to use during docking -> B The N-terminus of your protein is positively charged -> uncheck the box if needed The C-terminus of your protein is negatively charged -> uncheck the box if needed

Again, the peptide is only a fragment of the full p53 and as such its termini should be uncharged.

Since peptides are highly flexible we will give more flexibility to the peptide to allow for larger conformational changes. For this unfold the Fully flexible segments tab and in First segment enter:

First number -> 1 (the first residue of your peptide) Last number -> 15 (the last residue of your peptide)

This will cause HADDOCK to consider the peptide residues as fully flexible during all stages of the simulated annealing refinement stage and therefore increase sampling.

The definition of restraints does require some thoughts. Active residues in HADDOCK are those that are required to be at the interface. Passive residues, on the other hand, are those that might be at the interface. Ambiguous Interaction Restraints, or AIRs, are created between each active residue of a partner and the combination of active and passive residues of the other partner. An active residue which is not at the interface will cause an energy penalty while this is not the case for passive residues. For the docking of MDM2 and p53, active residues on MDM2 are taken from CPORT predictions, while the peptide is only defined as passive. This follows the recipe published in our Structure 2013 paper In that way the active residues of the protein will attract the peptide, while peptide residues do not have all to make contacts per se.

Step 4: Increase the fraction of randomly deleted restraints

For this unfold the Distance restraints menu.

Since we have used CPORT to define the putative interface on MDM2 it is recommended to increase the fraction of restraints randomly deleted for each docking trial. By default this is 50%. In CPORT, however, we rather overpredict than underpredict to make sure that the binding site is covered by our predictions. Because of this overprediction it is therefore recommended to increase the fraction of randomly deleted restraints to 87.5% (as described in our CPORT paper). For this change the value of the number of partitions:

Number of partitions for random exclusion (%excluded=100/number of partitions) -> 1.1429

Step 5: Change the sampling parameters to increase the number of models generated

For this unfold the Sampling parameter menu.

The use of an ensemble of structures translates into a worse sampling per conformation at the rigid-body stage. Each starting conformation will be sampled a limited number of times as defined by the total number of models sampled at the rigid-body docking stage divided by the number of models in the ensemble. With the default values of 1000 rigid-body models, an ensemble of 10 starting conformations translates to 100 models generated per member of the ensemble. This reduction in sampling per model might deteriorate the accuracy of the docking calculations, particularly if the restraints are fuzzy, as is the case when using bioinformatics predictions. For this reason it is recommended to increase the number of structures generated at the various stages of the docking protocol. As a rule of thumb, 1000 rigid-body models per member of the ensemble is a good number. The number of models selected to it1 and water can simply be doubled. The computational cost of these refinement stages does not allow a proportional increase. These numbers can be edited in the Sampling parameters tab.

Number of structures for rigid body docking -> Increase this number from 1000 to N*1000 (with a max of 10000), where N is the number of conformers in the ensemble. Number of structures for semi-flexible refinement -> Increase this number to 400. Number of structures for the explicit solvent refinement -> Increase this number to 400.

Note: Because of the decreased sampling per model in the case of an ensemble of starting structures, it is recommended to limit the number of conformations in the starting ensembles. This is even more important if ensembles of conformations are used for each molecule to dock. For example, 10 models per molecule will generate 100 combinations in the case of two molecule docking. With a sampling of 10000 at the rigid body docking stage, each combination will only be sampled 100 times. Note that the server limits the number of it0 models to a maximum of 10000.

Step 6: Adjust the clustering parameters

For this unfold the Parameters for clustering menu.

HADDOCK offers two different clustering algorithms. Refer to the online manual for more details. For peptide and small molecules we recommend the use of RMSD clustering. The clustering algorithm must also be adjusted to accommodate the small size of the peptide. The default cutoff of 7.5Å (interface-ligand RMSD) was optimized for protein-protein docking and is very likely too large in the case of protein-peptide complexes. Clustering with this value would very likely generate very large and diverse clusters. We should therefore reduce the clustering cutoff:

Clustering method (RMSD or Fraction of Common Contacts (FCC)) -> Select RMSD RMSD Cutoff for clustering (Recommended: 7.5A for RMSD, 0.75 for FCC) -> 5.0

Step 7: Turn on automatic restraining of secondary structure elements

For this unfold the Restraint energy contstants menu.

HADDOCK offers an option to automatically define dihedral angle restraints based on the input structure. This can be applied either to the entire sequence, or only to alpha helical segments or to alpha and beta segments. These are automatically detected based on the measured dihedral angles. For flexible peptides, since we are treating them as fully flexible, it is recommended to turn on this option.

Automatically define backbone dihedral restraints from structure? -> Select Only for alpha helix

Step 8: Adjust the number of flexible refinement steps to increase the sampling of peptide conformations

For this unfold the Advanced sampling parameter menu.

Double the number of steps for all four stages of the semi-flexible refinement:

number of MD steps for rigid body high temperature TAD -> 1000 number of MD steps during first rigid body cooling stage -> 1000 number of MD steps during second cooling stage with flexible side-chains at interface -> 2000 number of MD steps during third cooling stage with fully flexible interface –> 2000

Step 9: Adjust the number of models considered for the analysis

For this unfold the Analysis parameters menu.

Make the number of models equal to the number of models generated at the water refinement stage:

Number of structures to analyze -> 400

Step 10: Submit your docking run

Having filled all the necessary fields, running the docking simulation is one click away. Type in the webserver credentials – username and password – and click Submit.

After submission, the webserver redirects to a confirmation page that contains two links.

The first prompts the download of the docking parameter file – haddockparam.web. This file contains all the settings and data necessary to reproduce the simulation. It is therefore recommended to save it! If necessary a run with the exact same setting can be submitted by simply submitting this file at the File Upload interface of the webserver.

The second link points to the results page of the simulation. Since a regular docking simulation lasts, on average, a couple of hours, this page displays its current status while not complete as PROCESSING, QUEUED, or RUNNING. In case a critical error prevents the simulation from continuing, whether because of problems with the input data, or problems during the simulation itself, the webpage displays an ERROR message. Most of these status changes are accompanied by an e-mail that is sent to the address linked to the user account. In case of errors, this e-mail also offers additional details on the cause(s). For students, since all accounts are pre-configured, the email notification is turned off.

Analyzing the docking calculation results

After the simulation is complete, the results page is generated and a notification email sent to the user. This results page includes an overview of the top ten clusters, ranked by average HADDOCK score of their four best structures, including statistics of energetic terms and other structural measures for each cluster. This allows a quick assessment of the quality of the generated models. Furthermore, at the bottom of the page, several plots of different quality-control measures of the models also give the opportunity of checking the quality of the docking simulation at a glance.

How many of the final water-refined models cluster? How many clusters did the docking produce? Can you provide an explanation for these numbers?

Focus on the il-RMSD vs. HADDOCK score plot: does the scoring of HADDOCK provide an unambiguous answer of which conformation is more likely to be realistic?

Visual inspection of the cluster representatives

Any molecular simulation, docking included, lacks the accuracy to produce one single good model. However, with sufficient attempts, reasonable models are likely to populate the results. HADDOCK in particular, given its data-driven character, produces much higher quality models if the quality of the data is good enough. At the end of the simulation, all the models are clustered so as to filter out the isolated structures that resemble few others in the pool of models. The models are then analyzed on a cluster basis and the best models of the best clusters are considered the best representative models of the simulation. Nevertheless, a careful visual inspection is of paramount importance and should always be the first step of any simulation analysis. To better visualize the differences between each cluster, superimpose all models onto the backbone of the largest partner, in this case the MDM2 molecule. For this we have to select a reference model, which we arbitrarily define as that of cluster1_1.

If your simulation produced several clusters, download the top-ranking model of each cluster and open them in Pymol. If there is only one cluster, open the top four best models of that cluster. select refe, cluster1_1 and chain A
align cluster2_1, refe
align cluster3_1, refe

Do the models have any unreasonable feature, such as atomic clashes or unphysical interactions? Based on the best scoring clusters, can you advance a putative binding interface for p53 on MDM2? Can you identify key residues that might be “hotspots” of this interaction?

Congratulations!

The docking calculation of MDM2 and the p53 N-terminal transactivation peptide was the culmination of a three-stage computational exercise that involved the three major methods in the repertoire of a Computational Structural Biologist. As you have seen, in modeling, there are rarely any certainties and you must always operate with extreme care and a constant sense of (self-)criticism. Nevertheless, you started with only two sequences and have now three-dimensional models of interactions that can be put to the test in the lab. Who knows? Maybe one of your models is actually correct and it will help researchers to spare the life of a few of our mice friends!

Thank you for following this tutorial. We welcome any feedback to improve it. For the students following the course Molecular Modeling and Simulation, please feel free to voice any criticism and/or suggestions to the instructors. You won’t be negatively judged for it :) Others may send comments to the email on the right side of the page, under the head of Captain HADDOCK!