Imperial College London
Alternate

ProfessorMichaelSternberg

Faculty of Natural Sciences, Department of Life Sciences

Director, Systems Biology and Bioinformatics Centre
 
 
 
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Contact

 

+44 (0)20 7594 5212m.sternberg Website

 
 
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Location

 

306Sir Ernst Chain BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

368 results found

Bang J-W, Crockford DJ, Holmes E, Pazos F, Sternberg MJE, Muggleton SH, Nicholson JKet al., 2008, Integrative top-down system metabolic modeling in experimental disease states via data-driven Bayesian methods., J Proteome Res, Vol: 7, Pages: 497-503, ISSN: 1535-3893

Multivariate metabolic profiles from biofluids such as urine and plasma are highly indicative of the biological fitness of complex organisms and can be captured analytically in order to derive top-down systems biology models. The application of currently available modeling approaches to human and animal metabolic pathway modeling is problematic because of multicompartmental cellular and tissue exchange of metabolites operating on many time scales. Hence, novel approaches are needed to analyze metabolic data obtained using minimally invasive sampling methods in order to reconstruct the patho-physiological modulations of metabolic interactions that are representative of whole system dynamics. Here, we show that spectroscopically derived metabolic data in experimental liver injury studies (induced by hydrazine and alpha-napthylisothiocyanate treatment) can be used to derive insightful probabilistic graphical models of metabolite dependencies, which we refer to as metabolic interactome maps. Using these, system level mechanistic information on homeostasis can be inferred, and the degree of reversibility of induced lesions can be related to variations in the metabolic network patterns. This approach has wider application in assessment of system level dysfunction in animal or human studies from noninvasive measurements.

Journal article

Amini A, Shrimpton PJ, Muggleton SH, Sternberg MJEet al., 2007, A general approach for developing system-specific functions to score protein-ligand docked complexes using support vector inductive logic programming, PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, Vol: 69, Pages: 823-831, ISSN: 0887-3585

Journal article

Gherardini PF, Wass MN, Helmer-Citterich M, Sternberg MJEet al., 2007, Convergent evolution of enzyme active sites is not a rare phenomenon, JOURNAL OF MOLECULAR BIOLOGY, Vol: 372, Pages: 817-845, ISSN: 0022-2836

Journal article

Cannon EO, Amini A, Bender A, Sternberg MJE, Muggleton SH, Glen RC, Mitchell JBOet al., 2007, Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds, JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, Vol: 21, Pages: 269-280, ISSN: 0920-654X

Journal article

Amini A, Muggleton SH, Lodhi H, Sternberg MJEet al., 2007, A novel logic-based approach for quantitative toxicology prediction, JOURNAL OF CHEMICAL INFORMATION AND MODELING, Vol: 47, Pages: 998-1006, ISSN: 1549-9596

Journal article

Tamaddoni-Nezhad A, Chaleil R, Kakas AC, Sternberg M, Nicholson J, Muggleton Set al., 2007, Modeling the effects of toxins in metabolic networks - Abductive and inductive reasoning for learning models of inhibition in biological networks, IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE, Vol: 26, Pages: 37-46, ISSN: 0739-5175

Journal article

Hermoso A, Espadaler J, Enrique Querol E, Aviles FX, Sternberg MJE, Oliva B, Fernandez-Fuentes Net al., 2007, Including Functional Annotations and Extending the Collection of Structural Classifications of Protein Loops (ArchDB), Bioinformatics and Biology Insights, Vol: 1, Pages: 117793220700100-117793220700100, ISSN: 1177-9322

<jats:p>Loops represent an important part of protein structures. The study of loop is critical for two main reasons: First, loops are often involved in protein function, stability and folding. Second, despite improvements in experimental and computational structure prediction methods, modeling the conformation of loops remains problematic. Here, we present a structural classification of loops, ArchDB, a mine of information with application in both mentioned fields: loop structure prediction and function prediction. ArchDB ( http://sbi.imim.es/archdb ) is a database of classified protein loop motifs. The current database provides four different classification sets tailored for different purposes. ArchDB-40, a loop classification derived from SCOP40, well suited for modeling common loop motifs. Since features relevant to loop structure or function can be more easily determined on well-populated clusters, we have developed ArchDB-95, a loop classification derived from SCOP95. This new classification set shows a ~40% increase in the number of subclasses, and a large 7-fold increase in the number of putative structure/function-related subclasses. We also present ArchDB-EC, a classification of loop motifs from enzymes, and ArchDB-KI, a manually annotated classification of loop motifs from kinases. Information about ligand contacts and PDB sites has been included in all classification sets. Improvements in our classification scheme are described, as well as several new database features, such as the ability to query by conserved annotations, sequence similarity, or uploading 3D coordinates of a protein. The lengths of classified loops range between 0 and 36 residues long. ArchDB offers an exhaustive sampling of loop structures. Functional information about loops and links with related biological databases are also provided. All this information and the possibility to browse/query the database through a web-server outline an useful tool with application in the compara

Journal article

Chen J, Kelley L, Muggleton S, Sternberg Met al., 2007, Multi-class prediction using stochastic logic programs, 16th International Conference on Inductive Logic Programming, Publisher: SPRINGER-VERLAG BERLIN, Pages: 109-+, ISSN: 0302-9743

Conference paper

Cootes AP, Muggleton SH, Sternberg MJE, 2007, The identification of similarities between biological networks: Application to the metabolome and interactome prediction, Molecular Biology, Vol: 369, Pages: 1126-1139

The increasing interest in systems biology has resulted in extensive experimental data describing networks of interactions (or associations) between molecules in metabolism, protein-protein interactions and gene regulation. Comparative analysis of these networks is central to understanding biological systems. We report a novel method (PHUNKEE Pairing subgrapHs Using NetworK Environment Equivalence) by which similar subgraphs in a pair of networks can be identified. Like other methods, PHUNKEE explicitly considers the graphical form of the data and allows for gaps. However, it is novel in that it includes information about the context of the subgraph within the adjacent network. We also explore a new approach to quantifying the statistical significance of matching subgraphs. We report similar subgraphs in metabolic pathways and in protein-protein interaction networks. The most similar metabolic subgraphs were generally found to occur in processes central to life, such as purine, pyrimidine and amino acid metabolism. The similar pairs of subgraphs found in the protein-protein interaction networks of Drosophila melanogaster and Saccharomyces cerevisiae also include central processes such as cell division but, interestly, also include protein sub-networks involved in pre-mRNA processing. The inclusion of network context information in the comparison of protein interaction networks increased the number of similar subgraphs found consisting of proteins involved in the same functional process. This could have implications for the prediction of protein function.

Journal article

Muggleton SH, Lodhi H, Amini A, Sternberg MJEet al., 2006, Support vector inductive logic programming, Studies in Fuzziness and Soft Computing, Vol: 194, Pages: 113-135, ISSN: 1434-9922

In this paper we explore a topic which is at the intersection of two areas of Machine Learning: namely Support Vector Machines (SVMs) and Inductive Logic Programming (ILP). We propose a general method for constructing kernels for Support Vector Inductive Logic Programming (SVILP). The kernel not only captures the semantic and syntactic relational information contained in the data but also provides the flexibility of using arbitrary forms of structured and non-structured data coded in a relational way. While specialised kernels have been developed for strings, trees and graphs our approach uses declarative background knowledge to provide the learning bias. The use of explicitly encoded background knowledge distinguishes SVILP from existing relational kernels which in ILP-terms work purely at the atomic generalisation level. The SVILP approach is a form of generalisation relative to background knowledge, though the final combining function for the ILP-learned clauses is an SVM rather than a logical conjunction. We evaluate SVILP empirically against related approaches, including an industry-standard toxin predictor called TOPKAT. Evaluation is conducted on a new broad-ranging toxicity dataset (DSSTox). The experimental results demonstrate that our approach significantly outperforms all other approaches in the study. © Springer-Verlag Berlin Heidelberg 2006.

Journal article

Paszkiewicz KH, Sternberg MJE, Lappe M, 2006, Prediction of viable circular permutants using a graph theoretic approach, BIOINFORMATICS, Vol: 22, Pages: 1353-1358, ISSN: 1367-4803

Journal article

Jefferys BR, Kelley LA, Sergot MJ, Fox J, Sternberg MJEet al., 2006, Capturing expert knowledge with argumentation: a case study in bioinformatics, BIOINFORMATICS, Vol: 22, Pages: 924-933, ISSN: 1367-4803

Journal article

Fleming K, Kelley LA, Islam SA, MacCallum RM, Muller A, Pazos F, Sternberg MJEet al., 2006, The proteome: structure, function and evolution, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 361, Pages: 441-451, ISSN: 0962-8436

Journal article

Jones DT, Sternberg MJE, Thornton JM, 2006, Introduction. Bioinformatics: from molecules to systems, PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 361, Pages: 389-391, ISSN: 0962-8436

Journal article

, 2006, Bioinformatics: from molecules to systems. Proceedings of a discussion meeting. April 4-5, 2005. London, United Kingdom., Philos Trans R Soc Lond B Biol Sci, Vol: 361, Pages: 389-527, ISSN: 0962-8436

Journal article

Fernandez-Fuentes N, Querol E, Aviles FX, Sternberg MJE, Oliva Bet al., 2005, Prediction of the conformation and geometry of loops in globular proteins: Testing ArchDB, a structural classification of loops, PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, Vol: 60, Pages: 746-757, ISSN: 0887-3585

Journal article

Carter P, Lesk VI, Islam SA, Sternberg MJEet al., 2005, Protein-protein docking using 3D-dock in rounds 3, 4, and 5 of CAPRI, PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS, Vol: 60, Pages: 281-288, ISSN: 0887-3585

Journal article

Smith GR, Sternberg MJE, Bates PA, 2005, The relationship between the flexibility of proteins and their conformational states on forming protein-protein complexes with an application to protein-protein docking, JOURNAL OF MOLECULAR BIOLOGY, Vol: 347, Pages: 1077-1101, ISSN: 0022-2836

Journal article

Pazos F, Ranea JAG, Juan D, Sternberg MJEet al., 2005, Assessing protein co-evolution in the context of the tree of life assists in the prediction of the interactome, J MOL BIOL, Vol: 352, Pages: 1002-1015, ISSN: 0022-2836

Journal article

Madhusudan S, Smart F, Shrimpton P, Parsons JL, Gardiner L, Houlbrook S, Talbot DC, Hammonds T, Freemont PA, Sternberg MJE, Dianov GL, Hickson IDet al., 2005, Isolation of a small molecule inhibitor of DNA base excision repair, NUCLEIC ACIDS RESEARCH, Vol: 33, Pages: 4711-4724, ISSN: 0305-1048

Journal article

Muggleton S, Lodhi H, Amini A, Sternberg MJEet al., 2005, Support vector inductive logic programming, Berlin, 8th International Conference on Discovery Science, 8 - 11 October 2005, Singapore, SINGAPORE, Publisher: Springer-Verlag, Pages: 163-175

Conference paper

Pazos F, Sternberg MJE, 2004, Automated prediction of protein function and detection of functional sites from structure, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 101, Pages: 14754-14759, ISSN: 0027-8424

Journal article

Mayor LR, Fleming KP, Müller A, Balding DJ, Sternberg MJEet al., 2004, Clustering of protein domains in the human genome, JOURNAL OF MOLECULAR BIOLOGY, Vol: 340, Pages: 991-1004, ISSN: 0022-2836

Journal article

Aguilar D, Aviles FX, Querol E, Sternberg MJEet al., 2004, Analysis of phenetic trees based on metabolic capabilites across the three domains of life, JOURNAL OF MOLECULAR BIOLOGY, Vol: 340, Pages: 491-512, ISSN: 0022-2836

Journal article

Fleming K, Müller A, MacCallum RM, Sternberg MJEet al., 2004, 3D-GENOMICS: A database to compare structural and functional annotations of proteins between sequenced genomes, Nucleic Acids Research, Vol: 32, ISSN: 0305-1048

The 3D-GENOMICS database (http://www.sbg.bio.ic.ac.uk/3dgenomics/) provides structural annotations for proteins from sequenced genomes. In August 2003 the database included data for 93 proteomes. The annotations stored in the database include homologous sequences from various sequence databases, domains from SCOP and Pfam, patterns from Prosite and other predicted sequence features such as transmembrane regions and coiled coils. In addition to annotations at the sequence level, several precomputed crossproteome comparative analyses are available based on SCOP domain superfamily composition. Annotations are available to the user via a web interface to the database. Multiple points of entry are available so that a user is able to: (i) directly access annotations for a single protein sequence via keywords or accession codes, (ii) examine a sequence of interest chosen from a summary of annotations for a particular proteome, or (iii) access precomputed frequency-based cross-proteome comparative analyses.

Journal article

Espadaler J, Fernandez-Fuentes N, Hermoso A, Querol E, Aviles FX, Sternberg MJE, Oliva Bet al., 2004, ArchDB: Automated protein loop classification as a tool for structural genomics, Nucleic Acids Research, Vol: 32, ISSN: 0305-1048

The annotation of protein function has become a crucial problem with the advent of sequence and structural genomics initiatives. A large body of evidence suggests that protein structural information is frequently encoded in local sequences, and that folds are mainly made up of a number of simple local units of super-secondary structural motifs, consisting of a few secondary structures and their connecting loops. Moreover, protein loops play an important role in protein function. Here we present ArchDB, a classification database of structural motifs, consisting of one loop plus its bracing secondary structures. ArchDB currently contains 12 665 super-secondary elements classified into 1496 motif subclasses. The database provides an easy way to retrieve functional information from protein structures sharing a common motif, to search motifs found in a given SCOP family, superfamily or fold, or to search by keywords on proteins with classified loops. The ArchDB database of loops is located at http://sbi.imim.es/archdb.

Journal article

Fleming K, Müller A, MacCallum RM, Sternberg MJEet al., 2004, 3D-GENOMICS:: a database to compare structural and functional annotations of proteins between sequenced genomes, NUCLEIC ACIDS RESEARCH, Vol: 32, Pages: D245-D250, ISSN: 0305-1048

Journal article

Espadaler J, Fernandez-Fuentes N, Hermoso A, Querol E, Aviles FX, Sternberg MJE, Oliva Bet al., 2004, ArchDB: automated protein loop classification as a tool for structural genomics, NUCLEIC ACIDS RESEARCH, Vol: 32, Pages: D185-D188, ISSN: 0305-1048

Journal article

Smith GR, Sternberg MJE, Bates PA, 2004, Molecular dynamics study of the flexibility of complex-forming proteins, 48th Annual Meeting of the Biophysical Society, Publisher: BIOPHYSICAL SOCIETY, Pages: 413A-413A, ISSN: 0006-3495

Conference paper

Cootes AP, Muggleton SH, Sternberg MJE, 2003, The automatic discovery of structural principles describing protein fold space, JOURNAL OF MOLECULAR BIOLOGY, Vol: 330, Pages: 839-850, ISSN: 0022-2836

Journal article

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