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AMPLE – Using de novo or ab initio protein structure modelling techniques to create and enhance search models for use in Molecular Replacement Jaclyn Bibby,

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Presentation on theme: "AMPLE – Using de novo or ab initio protein structure modelling techniques to create and enhance search models for use in Molecular Replacement Jaclyn Bibby,"— Presentation transcript:

1 AMPLE – Using de novo or ab initio protein structure modelling techniques to create and enhance search models for use in Molecular Replacement Jaclyn Bibby, Jens Thomas, Olga Mayans and Daniel Rigden Institute of Integrative Biology Ronan Keegan and Martyn Winn Collaborative Computational Project 4 (CCP4)

2 AMPLE ab initio modelling of proteins for molecular replacement Joint development by CCP4 and the University of Liverpool AMPLE is a comprehensive project to assess the suitability of using cheaply obtained ab initio models in molecular replacement An additional goal of the project is to make AMPLE into an automated software tool that can be made generally available to potential users through CCP4

3 Ab initio structure prediction Ab initio (or de novo) structure prediction is the prediction of a target structure fold based purely on its sequence information Methods have greatly improved in recent years with the aid of the CASP experiments (Critical Assessment of Protein structure prediction) Some examples are: – Rosetta – I-TASSER – QUARK

4 Ab initio structure prediction s of Decoys assembled of fragments from PDB structures 2.Decoys are clustered and centroid representatives of largest cluster are considered candidate fold predictions 3.Side chains added to selected decoys 4.Refinement under a more realistic physics-based force field Initial fragment assembly stage requires relatively modest computing power Refinement stage can require supercomputing resources

5 Ab initio structure prediction s of Decoys assembled of fragments from PDB structures 2.Decoys are clustered and centroid representatives of largest cluster are considered candidate fold predictions 3.Side chains added to selected decoys 4.Refinement under a more realistic physics-based force field Initial fragment assembly stage requires relatively modest computing power Refinement stage can require supercomputing resources

6 Ab initio modelling and Molecular Replacement Combining this method with molecular replacement can be a powerful technique for solving the phase problem in cases where there are no obvious homologous structures available Two approaches have been taken 1.All-atom modelling to produce single search models of maximum completeness and accuracy (Qian et. al, 2007) Solved 1/3 of test set of 30 targets (Das et. al, 2009) Computationally expensive 2.Taking cheaply obtained decoys from the initial fragment assembly step and preparing them has been shown to produce successful MR search models (Rigden et al. 2008, Caliandor 2009)

7 Synergies ab initio modellingMolecular Replacement Produces clusters of similar model structures Works effectively with superposed ensembles approximating the target Within and between clusters, similarity indicates accuracy can trim inaccurate regions leaving more reliable core May only require a partial model Fast modelling is polyAla onlySide chains are often (partially) removed for MR

8 The AMPLE Pipeline Uses Rosetta to perform ab initio modelling and the generation of decoy models Can also accept models generated externally Currently designed for < 120 residues and resolution better than 2.2 Å (but may work outside these restrictions e.g. transmembrane, coiled-coil proteins)

9 Decoys generated first with Rosetta Quark has also been used during development Typical number of decoys required is 1000 but this can be varied In easier cases as few as 50 decoys can be sufficient Decoy Generation

10 Decoy Clustering Clustering using SPICKER to identify the most likely fold for the target A large top cluster is usually indicative of a correct prediction A subset of decoys (max. 200) closest to the centroid of each of the largest 3 clusters are selected for further processing

11 Decoy Clustering Each cluster is then structurally aligned using the maximum likelihood algorithm implemented in Theseus (Theobold & Wuttke, 2006) – This helps to identify structurally conserved regions – Gives a variance score which can later be used to guide truncation

12 Ensemble Truncation Weve found that success or failure in the molecular replacement step is highly sensitive to the accuracy of the search model Sampling many degrees of truncation with different levels of side chain inclusion is essential High variance regions are cut away in steps to give a set of ensemble models

13 These truncated clusters are further processed and side chains are added to give a large set of search models for molecular replacement Further processing

14 Molecular replacement Molecular replacement is performed using MrBUMP from the CCP4 suite which automates the procedure – Search models are processed by both Phaser and Molrep – Post molecular replacement, positioned search models are refined using Refmac5 to get an initial indication of success or failure – C-alpha tracing with SHELXE, model building with Buccaneer, ARP/wARP

15 Testing Test set of 295 small proteins ( residues) from the PDB Structure factor data also available Resolution of 2.2 Å or better Single molecule in the asymmetric unit Mixture of all-α, all-β and mixed α-β secondary structure 1000 decoys generated for each case using Rosetta Information from any homologues was excluded from the fragment generation step

16 Assessing Solutions Initially we used Reforigin to compare solutions with the deposited structures More stringent method: attempt to rebuild the structures – SHELXE: partial CC of >25% & average fragment length of 10 or more – Further confirmation provided through building with ARP/wARP and Buccaneer.

17 Using these guidelines, 126 successes out of 295 (~43%) were achieved These are solutions that could be successfully traced in SHELXE Other well positioned solutions existed but could not be traced. These may be possible to solve through manual model building

18 Results based on secondary structure type Overall success rate: all-α = 80%; all-β = 2%; mixed α-β = 37%

19 Variance and Truncation Variability between decoys in each cluster corresponds to their deviation from the deposited native structure 2P5K example: C-terminal region predicted as least reliably modelled portion by Theseus alignment variance score

20 Search model ensemble size/truncation

21 Running Times Average times for complete run (decoy generation, preparation, MR and chain tracing) was 2 CPU days A parallelised version of the code making use of Sun Grid Engine for batch farming of model generation and molecular replacement significantly speeds up the process. Results can be achieved in less than 1 hour

22 a. Clustered decoy models, b. Truncated ensemble, c. Positioned MR solution, d. Shelxe c-alpha trace, e. Completed structure Exploiting distant homologues

23 Remodelling related NMR structures or distant homologues Can provide AMPLE with a template for the target which could be a related NMR structure or a distant homologue AMPLE will use Rosetta to re- model this template to something that should in theory be closer to the target

24 Experimentally very difficult to work with/crystallise Represent ~30% of all proteins Make up < 3% of structures in the Protein Data Bank Presence in the membrane constrains their shape, so they can be easier to model MR with ab initio transmembrane models hasn't been tried yet extra cellular (aqueous) transmembrane region (hydrophobic) intra cellular (aqueous) Image: Transmembrane Proteins

25 Selected 18 transmembrane proteins: 23 – 249 residues 1.45 – 2.5A resolution 7 clear successes 5 possible successes 223 residue structure (3GD8) could be largest ever solved with ab initio modelling.

26 Selected 18 transmembrane proteins: 23 – 249 residues 1.45 – 2.5A resolution 7 clear successes 5 possible successes 223 residue structure (3GD8) could be largest ever solved with ab initio modelling.

27 Coiled-coil targets Difficult to solve in MR even with good homologues α-helical nature makes them suitable targets for AMPLE Initial testing has been very promising with 80% success rate Some novel structures have also been solved

28 AMPLE Availability Beta version available as part of CCP Improved and more robust version to be released as part of CCP Requires installation of several non-ccp4 packages: – Rosetta, SHELXE, Theseus, SPICKER, Maxcluster Future versions will have a reduced number of dependencies

29 Documentation available from

30 Summary AMPLE is a pipeline designed to prepare cheaply obtained decoy models from ab initio modelling for use as search models in molecular replacement Results show that the method works well for smaller proteins particularly those containing α-helical secondary structure Tests were limited to structures of 120 residues in length but has worked for cases up to 250 residues New avenues – NMR, Homolgue remodelling Several real successes Currently available as a beta-version in CCP

31 Acknowledgements Jaclyn Bibby, Daniel Rigden, Jens Thomas, University of Liverpool Olga Mayans, University of Liverpool Martyn Winn, Daresbury Laboratory Andrea Thorn, Tim Gruene & George Sheldrick (SHELX) Developers of Rosetta and Quark Refmac: Garib Mushudov, LMB-MRC Cambridge Molrep: Alexei Vagin & Andrey Lebedev Phaser: Randy Read, Airlie McCoy & Gabor Bunkozci Thanks to authors of all underlying programs Funding: BBSRC Support from CCP4 & the Research Complex at Harwell Poster: MS04-12 (Rootes Building)


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