Division of Molecular Biosciences
Department of Life Sciences
Faculty of Natural Sciences

Research Overview

Contents

Biographical background (Professor Sternberg)

Professor Sternberg's research focusses on protein bioinformatics. He entered this area after obtaining a first degree in Physics (Cambridge) and a Masters in Computing (Imperial College). He then hopped discipline to undertake a PhD in Biophysics (Oxford).Starting with his thesis research, he has worked in protein bioinformatics contributing to the elucidation of new principles of form and function and the development of algorithms for prediction of protein structure, function and interactions. Recently these approaches have been extended to study protein systems and logic-based drug discovery. He worked at Oxford, Birkbeck College, Cancer Research UK and established the Structural Bioinformatics Group at Imperial in 2001.Professor Sternberg is also the Director of the Centre for Bioinformatics that has the dual roles of co-ordinating research and training across all Faculties in the College together with providing bioinformatics support for the entire College (under the management of Dr Sarah Butcher).

Research objectives

The current objectives of the Structural Bioinformatics Group are:


  • The development of computer algorithms:
    - to predict protein structure from sequence
    - to suggest protein function from sequence or structure
    - to predict the structure of a protein complex starting from the unbound components
  • The analysis of protein structure and function with the aim of deriving evolutionary insights
  • The modelling and comparison of biology networks to provide insights into Systems Biology
  • The modelling of the activity and toxicity of small molecules as an aid to the design of novel drugs.

The group's web page (http://www.sbg.bio.ic.ac.uk) provides access to web servers for several areas of protein modelling including protein structure prediction (3D-PSSM / PHYRE), protein-protein docking (3D-GARDEN) and protein function prediction (CONFUNC). Of particular note is the use by the community of the programs 3D-PSSM and Phyre. The 3D-PSSM paper has over 1282 citations (ISI Web of Science) and the server web page has had over 250,000 visits. The more recent program Phyre has approximately 5000 requests per month. Recent work has extended these concepts to study biology networks and to identify novel drugs.

Professor Sternberg's group interacts closely with the groups of Professor Michael Stumpf (Theoretical Systems Biology Group) and Professor Stephen Muggleton (in the use of advanced machine learning) at Imperial. In addition, there are collaborations with members of the Centre for Integrative Systems Biology at Imperial College (CISBIC) and the Institute of Systems and Synthetic Biology.

Current research projects

Protein Structure Prediction - Phyre

The Protein Homology/Analogy Recognition Engine (Phyre) is a user-friendly and widely used server for automated 3D protein modelling, averaging 150 submissions per day. A user may paste their protein sequence into the browser and will receive a notification email when modelling is complete (usually within approx. 30mins-1hr). This email contains the top scoring 3D model of the user's protein and a link to a web page containing a wide range of predicted structural features, including secondary structure, disorder, potential binding sites, SCOP fold and superfamily annotations, and 3D models with confidence estimates for the top 10 matching potential homologous structures. Phyre is capable of detecting remote homology to known structures significantly beyond the range of the popular PSI-Blast. By using advanced profile-profile matching techniques, loop modelling and sidechain placement algorithms, accurate full-atom models can be built based on homology to known protein structures with sequence identities <15%.

A new and more powerful version of Phyre (Phyre de novo) has recently been tested in the international CASP8 structure prediction competition and was ranked amongst the best performers. This new system contains advanced homology detection features, multi-domain modelling and ab initio components. We hope to release this new version of the server in the autumn of 2009. The Phyre server is available at http://www.sbg.bio.ic.ac.uk/phyre/.

    Protein structure prediction on the web: a case study using the Phyre server Kelley LA and Sternberg MJE. Nature Protocols 4, 363 - 371 (2009)

A simplified dynamic model of protein folding - Poing

Poing is a model for protein folding based upon Langevin dynamics, with the primary aim of predicting the structure of a protein from its sequence, without making use of template structures with homologous sequence. The current focus of this work is upon using general statistical and geometric featu res of known natural protein str uctures to identify good and bad structures from the ensemble generated by the model. It is simple to add features of the cellular environment to the model, which can be used to investigate their effects upon protein folding. This picture shows a model of a protein being synthesized into a crowded cell (grey translucent spheres) by a ribosome (blue, on the right). This work is funded by the BBSRC.

Protein Function Prediction - Confunc

The thousands of sequenced genomes and millions of sequences identified by metagenomics projects make the prediction of protein function an important problem. While function prediction can be relatively simple when sequences share high levels of similarity, it is cases where sequences only have more remote homologues that current function prediction methods are ineffective. This has led to the development of ConFunc, a sequence based protein function method that complement existing tools by performing well for these more difficult cases. ConFunc identifies GO annotated sequences present in PSI-BLAST searches and uses these to identify conserved residues associated with each individual function, which in turn are used to infer the function of query sequences. ConFunc is available to the academic community via a webserver at http://www.sbg.bio.ic.ac.uk/confunc/.

    Wass, M. N. & Sternberg, M. J. E. (2008). ConFunc--functional annotation in the twilight zone. Bioinformatics 24, 798-806.
    Pazos, F. & Sternberg, M. J. E. (2004) Automated prediction of protein function and detection of functional sites from structure. Proc Natl Acad Sci U SA 101, 14754-14759.

Macromolecular Docking - 3D-Garden

Macromolecular docking problems involve predicting the molecular geometry of the complex formed when two macromolecules associate. 3DGarden is an integrated software suite for performing protein-protein and protein-polynucleotide docking. For any pair of biomolecules structures specified by the user, 3DGarden's primary function is to generate an ensemble of putative complexed structures and rank them. The highest-ranking candidates constitute predictions for the structure of the complex. 3DGarden cannot be used to decide whether or not a particular pair of biomolecules interacts. Complexes of protein and nucleic acid chains can also be specified as individual interactors for docking purposes.3D-Garden is available to the academic community via a webserver at http://www.sbg.bio.ic.ac.uk/3dgarden/.

    Lesk, V. I. & Sternberg, M. J. (2008). 3D-Garden: a system for modelling protein-protein complexes based on conformational refinement of ensembles generated with the marching cubes algorithm. Bioinformatics 24, 1137-44
    Gabb, H. A., Jackson, R. M. & Sternberg, M. J. E. (1997). Modelling protein docking using shape complementarity, electrostatics and biochemical information. J. Mol. Biol. 272, 106-120.
    Jackson, R. M., Gabb, H. A. & Sternberg, M. J. E. (1998). Rapid refinement of protein interfaces incorporating solvation: application to the docking problem. J. Mol. Biol. 276, 265-285.

Insights into protein mobility from normal mode analysis

Understanding protein interactions has broad implications for the mechanism of recognition, protein design, and assigning putative functions to uncharacterised proteins. Studying protein flexibility is a key component in the challenge of describing protein interactions. We have characterised the observed conformational change for a set of 20 proteins that undergo large conformational change upon association (> 2 Ca RMSD) and ask what features of the motion are successfully reproduced by the normal modes of the system. We demonstrated that normal modes can be used to identify mobile regions and in some proteins to reproduce the direction of conformational change. In 35% of the proteins studied a single low frequency normal mode was found that describes the direction of the observed conformational change well. Finally, we find that for a set of 138 proteins from a docking benchmark that the characteristic frequencies of normal modes can be used to predict reliably the extent of conformational change. This study has implications for the mechanics of protein recognition.

    Dobbins, S. E., Lesk, V. I. & Sternberg, M. J. E. (2008). Insights into protein flexibility: The relationship between normal modes and conformational change upon protein-protein docking. Proc Natl Acad Sci U S A 105, 10390-5.

Predicting protein-ligand binding interactions using Machine Learning

We are applying cutting edge machine learning technology to problems of protein function and structure. Coupling the statistical power of Support Vector Machines (SVMs) with the logical relational learning approach Inductive Logic Programming (ILP), we are learning rules that determine the small molecules that bind to a protein active site.

Protein interaction networks - protein functions

High-throughput technologies have generated large amounts of data on the interactions of macromolecules. However the usage of these interaction data is still limited. The aim of the project is to develop a method to use protein interaction data to infer functions of proteins. The idea is to detect similarities in protein interaction networks which thus helps inferring functions of correlated proteins by function transfer. This project extends previous research in developing a method to compare biological networls PHUNKEE (Cootes et al, 2007).

    Cootes, A.P., Muggelton, S.H. & Sternberg, M.J.E. (2007). The identification of similarities between biological networks: Application to the Metabolome and the Interactome. J. Mol. Biol. 369, 1126-1139.

Homology detection in a changing sequence landscape

This project focuses on discovering what effect the massive increase in available protein sequences has had on our ability to identify remote homology. The use of conventional methods shows that we are already facing diminishing returns and the aim of this project is to investe methods to improve homology detection based upon these findings.

Network modelling for Systems Biology

The role of modelling in systems biology is to assimilate background and experimental knowledge and identify new hypotheses which are interesting and cost-effective to test. Here we use inductive logic programming, a machine reasoning framework, in a systems biology project investigating the glycomics behind interactions between pathogenic bacteria and their host. Cell surface glycans in the form of polysaccharides, glycoproteins and glycolipids are critical in cell-cell communication and cell signalling. In the context of host-pathogen interactions, microbial glycans are frequent targets of the pattern recognition receptors involved in triggering innate immune responses, and influence the uptake of microbes by host phagocytes. The aim of this part of the project is to develop a artificial reasoning model that will allow us to infer the surface glycome from genetic data. Transcriptome and metabolome data are used to build the models. This project is part of the research studying host/pathogen interactions in the BBSRC/EPSRC Centre of Intergrative Systems Biology at Imperial College (CISBIC). The work using inductive logic programming in collaboration with Professor Stephen Muggleton. This project extends the methodology develop to model the effects of specfic toxins in metabolic networks. Tamaddoni-Nezhad, A., Chaeleil, R. A. G., Kakas, A., Sternberg, M. J. E., Nicholson, J. & Muggelton, S. H. (2007). Modeling the effects of toxins in metabolic networks. IEEE Engineering in Medicine and Biology 26, 37-46.
Bang, J. W., Crockford, D. J., Holmes, E., Pazos, F., Sternberg, M. J. E, Muggleton, S. H. & Nicholson, J. K. (2008). Integrative top-down system metabolic modeling in experimental disease states via data-driven Bayesian methods. J Proteome Res 7, 497-503.

Inductive Logic-Based Drug Design

This project builds upon the paper by Amini, Shrimpton, Muggleton, and Sternberg (2007) "A general approach for developing system-specific functions to score protein-ligand docked complexes using support vector inductive logic programming" Proteins, 69(4): 823-831. This project will attempt to use a machine learning algorithm based on logical rules to design new, original, and more effective pharmaceutical drugs. The project will use knowledge of chemical synthesis to try and automate initial hit to lead searching. The overall aims will be to produce a tool that can contribute to future drug design, and to identify at least one small molecule with an improved docking affinity to existing drugs with the same target. This project is in collaboration with Professor Stephen Muggleton.

This technology has been commercialised by a spin-out company Equinox Pharma Ltd and further details can be found on the companies web pages at http://www.equinoxpharma.com

    Amin i, A., Muggleton, S. H. Lodhi, H. & Sternberg, M. J.E. (2007). A Novel Logic-Based Approach for Quantitative Toxicology Prediction. J Chem Inf Model, 2007.47, 998-1006.
    Amini, A., Shrimpton, P. J., Muggelton, S. H. & Sternberg, M. J. E. (2007). A general approach for developing system-specfic scoring functions to score protein-liganddocked complexes using support vector inductive logic programming. Proteins 2007, 69, 823 - 831

Highlights of previous research

Analysis and prediction of protein topology - Comparative analyses of the known protein structures identified fundamental features of their folding topology. The connection between two parallel strands in the same sheet including the beta-alpha-beta unit was found almost invariably to be right-handed.

    Sternberg, M. J. E. & Thornton, J. M. (1976). On the conformation of proteins: the handedness of β-stand / α-helix / β-strand unit J. Mol. Biol. 105, 367-382. Sternberg, M. J. E. & Thornton, J. M. (1977). On the conformation of proteins: an analysis of β-pleated sheets. J. Mol. Biol. 110, 285-296.

Knowledge-based protein structure prediction - Analysis of the packing in beta/alpha and beta/beta proteins identified rules which were used for a knowledge-based approach for structure prediction.

    Sternberg, M. J. E. & Thornton, J. M. (1977). On the conformation of proteins: towards the prediction of strand arrangements in β-pleated sheets. J. Mol. Biol. 113, 401-418. Cohen, F. E., Sternberg, M. J. E. & Taylor, W. R. (1980). Analysis and prediction of protein β-sheet structures by a combinatorial approach. Nature, 285, 378-382.

Protein mobility - Analysis of crystallographic B-values (temperature factors) derived from refinement of lysozyme provided insight into protein mobility.

    Sternberg, M. J. E., Grace, D. E. & Phillips, D. C. (1979). Dynamic information from protein crystallography. An analysis of temperature factors from refinement of the hen egg-white lysozyme structure. J. Mol. Biol. 130, 231-252. Artymiuk, P. J., Blake, C. C., Grace, D. E., Oatley, S. J., Phillips, D. C. & Sternberg, M. J. E. (1979). Crystallographic studies of the dynamic properties of lysozyme. Nature, 280, 563-568.

Electrostatic effects in proteins - Modelling electrostatic effects in protein was shown to yield successful blind predictions of experimentally observed pKa shifts.

    Sternberg, M. J. E., Hayes, F. R., Russell, A. J., Thomas, P. G. & Fersht, A. R. (1987). Prediction of electrostatic effects of engineering of protein charges. Nature, 330, 86-88

Novel algorithms for protein sequence alignment - Algorithms were developed to perform multiple alignment of protein sequences and to identify remote homologues using profile scanning.

    Barton, G. J. & Sternberg, M. J. E. (1987). A strategy for the rapid multiple alignment of protein sequences. Confidence levels from tertiary structure comparisons. J. Mol. Biol. 198, 327-337

Relational database of protein structure - A relational database encoding features of protein structure was established. This facilitated rapid analyses to derive novel principles of protein conformation.

    McGregor, M. J., Islam, S. A. & Sternberg, M. J. E. (1987). Analysis of the relationship between side-chain conformation and secondary structure in globular proteins. J. Mol. Biol. 198, 295-310.

Poly-proline helices - A comparative analysis of protein structures showed that the poly-proline helix commonly occur in globular proteins

    Adzhubei, A. A. & Sternberg, M. J. E. (1993). Left-handed poly-proline II helices commonly occur in globular proteins. J. Mol. Biol. 229, 472-493.

Application of machine learning to drug design - A logic-based approach to model the structure-activity relationship of small molecules was developed. The approach yields rules which can readily be understood by chemists and can provide predictions of activity (or toxicity) which can be superior to established methods.

    King, R. D., Muggleton, S. H., Srinivasan, A. & Sternberg, M. J. E. (1996). Representing molecular structure activity relationships: the use of atoms and their bond connectivities to predict mutagenicity using inductive logic programming. Proc. Nat. Acad. Sci. USA, 93, 438-442
 

Funding

BBSRC                                      BBSRC

DTI                                              DTI

EPSRC                                        epsrc

European Commision                    EU

MRC (Medical Research Council)  MRC

Syngenta                                      Syngenta

Wellcome Trust                            Wellcome Trust