Cell of origin classification of diffuse large B-cell lymphoma (DLBCL) identifies subsets with biological and clinical significance. Despite the established nature of the classification existing studies display variability in classifier implementation, and a comparative analysis across multiple data sets is lacking. Here we describe the validation of a cell of origin classifier for DLBCL, based on balanced voting between 4 machine-learning tools: the DLBCL automatic classifier (DAC). This shows superior survival separation for assigned Activated B-cell (ABC) and Germinal Center B-cell (GCB) DLBCL classes relative to a range of other classifiers. DAC is effective on data derived from multiple microarray platforms and formalinfixed paraffin embedded samples and is parsimonious, using 20 classifier genes. We use DAC to perform a comparative analysis of gene expression in 10 data sets (2030 cases). We generate ranked meta-profiles of genes showing consistent class-association using≥6 data sets as a cut-off: ABC (414 genes) and GCB (415 genes). The transcription factor ZBTB32 emerges as the most consistent and differentially expressed gene in ABC-DLBCL while other transcription factors such as ARID3A, BATF, and TCF4 are also amongst the 24 genes associated with this class inall datasets. Analysis of enrichment of 12323 gene signatures against meta-profiles and all data sets individually confirms consistent associations with signatures of molecular pathways, chromosomal cytobands, and transcription factor binding sites. We provide DAC as an open access Windows application, and the accompanying meta-analyses as a resource.
hide

The t(8;21) translocation fuses the DNA-binding domain of the hematopoietic master regulator RUNX1 to the ETO protein. The resultant RUNX1/ETO fusion protein is a leukemia-initiating transcription factor that interferes with RUNX1 function. The result of this interference is a block in differentiation and, finally, the development of acute myeloid leukemia (AML). To obtain insights into RUNX1/ETO-dependant alterations of the epigenetic landscape, we measured genome-wide RUNX1- and RUNX1/ETO-bound regions in t(8;21) cells and assessed to what extent the effects of RUNX1/ETO on the epigenome depend on its continued expression in established leukemic cells. To this end, we determined dynamic alterations of histone acetylation, RNA Polymerase II binding and RUNX1 occupancy in the presence or absence of RUNX1/ETO using a knockdown approach. Combined global assessments of chromatin accessibility and kinetic gene expression data show that RUNX1/ETO controls the expression of important regulators of hematopoietic differentiation and self-renewal. We show that selective removal of RUNX1/ETO leads to a widespread reversal of epigenetic reprogramming and a genome-wide redistribution of RUNX1 binding, resulting in the inhibition of leukemic proliferation and self-renewal, and the induction of differentiation. This demonstrates that RUNX1/ETO represents a pivotal therapeutic target in AML.
hide

The t(8;21) translocation fuses the DNA-binding domain of the hematopoietic master regulator RUNX1 to the ETO protein. The resultant RUNX1/ETO fusion protein is a leukemia-initiating transcription factor that interferes with RUNX1 function. The result of this interference is a block in differentiation and, finally, the development of acute myeloid leukemia (AML). To obtain insights into RUNX1/ETO-dependant alterations of the epigenetic landscape, we measured genome-wide RUNX1-and RUNX1/ETO-bound regions in t(8;21) cells and assessed to what extent the effects of RUNX1/ETO on the epigenome depend on its continued expression in established leukemic cells. To this end, we determined dynamic alterations of histone acetylation, RNA Polymerase II binding and RUNX1 occupancy in the presence or absence of RUNX1/ETO using a knockdown approach. Combined global assessments of chromatin accessibility and kinetic gene expression data show that RUNX1/ETO controls the expression of important regulators of hematopoietic differentiation and self-renewal. We show that selective removal of RUNX1/ETO leads to a widespread reversal of epigenetic reprogramming and a genome-wide redistribution of RUNX1 binding, resulting in the inhibition of leukemic proliferation and self-renewal, and the induction of differentiation. This demonstrates that RUNX1/ETO represents a pivotal therapeutic target in AML.© 2012 Macmillan Publishers Limited.
hide

This study tested the validity of whole-genome expression profiling (GEP) using RNA from formalin-fixed, paraffin-embedded (FFPE) tissue to sub-classify Diffuse Large B-cell Lymphoma (DLBCL), in a population based cohort of 172 patients. GEP was performed using Illumina Whole Genome cDNA-mediated Annealing, Selection, extension&Ligation, and tumours were classified into germinal centre (GCB), activated B-cell (ABC) and Type-III subtypes. The method was highly reproducible and reliably classified cell lines of known phenotype. GCB and ABC subtypes were each characterized by unique gene expression signatures consistent with previously published data. A significant relationship between subtype and survival was observed, with ABC having the worst clinical outcome and in a multivariate survival model only age and GEP class remained significant. This effect was not seen when tumours were classified by immunohistochemistry. There was a significant association between age and subtype (mean ages ABC - 72·8 years, GC - 68·4 years, Type-III - 64·5 years). Older patients with ABC subtype were also over-represented in patients who died soon after diagnosis. The relationship between prognosis and subtype improved when only patients assigned to the three categories with the highest level of confidencewere analysed. This study demonstrates that GEP-based classification of DLBCL can be applied to RNA extracted from routine FFPE samples and has potential for use in stratified medicine trials and clinical practice.
hide

Cell fate decisions during haematopoiesis are governed by lineage-specific transcription factors, such as RUNX1, SCL/TAL1, FLI1 and C/EBP family members. To gain insight into how these transcription factors regulate the activation of haematopoietic genes during embryonic development, we measured the genome-wide dynamics of transcription factor assembly on their target genes during the RUNX1-dependent transition from haemogenic endothelium (HE) to haematopoietic progenitors. Using a Runx1-/- embryonic stem cell differentiation model expressing an inducible Runx1 gene, we show that in the absence of RUNX1, haematopoietic genes bind SCL/TAL1, FLI1 and C/EBPβ and that this early priming is required for correct temporal expression of the myeloid master regulator PU.1 and its downstream targets. After induction, RUNX1 binds to numerous de novo sites, initiating a local increase in histone acetylation and rapid global alterations in the binding patternsof SCL/TAL1 and FLI1. The acquisition of haematopoietic fate controlled by Runx1 therefore does not represent the establishment of a new regulatory layer on top of a pre-existing HE program but instead entails global reorganization of lineage-specific transcription factor assemblies.
hide

This study tested the validity of whole-genome expression profiling (GEP) using RNA from formalin-fixed, paraffin-embedded (FFPE) tissue to sub-classify Diffuse Large B-cell Lymphoma (DLBCL), in a population based cohort of 172 patients. GEP was performed using Illumina Whole Genome cDNA-mediated Annealing, Selection, extension&Ligation, and tumours were classified into germinal centre (GCB), activated B-cell (ABC) and Type-III subtypes. The method was highly reproducible and reliably classified cell lines of known phenotype. GCB and ABC subtypes were each characterized by unique gene expression signatures consistent with previously published data. A significant relationship between subtype and survival was observed, with ABC having the worst clinical outcome and in a multivariate survival model only age and GEP class remained significant. This effect was not seen when tumours were classified by immunohistochemistry. There was a significant association between age and subtype (mean ages ABC - 72·8 years, GC - 68·4 years, Type-III - 64·5 years). Older patients with ABC subtype were also over-represented in patients who died soon after diagnosis. The relationship between prognosis and subtype improved when only patients assigned to the three categories with the highest level of confidencewere analysed. This study demonstrates that GEP-based classification of DLBCL can be applied to RNA extracted from routine FFPE samples and has potential for use in stratified medicine trials and clinical practice. © 2012 Blackwell Publishing Ltd.
hide

Cell fate decisions during haematopoiesis are governed by lineage-specific transcription factors, such as RUNX1, SCL/TAL1, FLI1 and C/EBP family members. To gain insight into how these transcription factors regulate the activation of haematopoietic genes during embryonic development, we measured the genome-wide dynamics of transcription factor assembly on their target genes during the RUNX1-dependent transition from haemogenic endothelium (HE) to haematopoietic progenitors. Using a Runx1-/- embryonic stem cell differentiation model expressing an inducible Runx1 gene, we show that in the absence of RUNX1, haematopoietic genes bind SCL/TAL1, FLI1 and C/EBPβ and that this early priming is required for correct temporal expression of the myeloid master regulator PU.1 and its downstream targets. After induction, RUNX1 binds to numerous de novo sites, initiating a local increase in histone acetylation and rapid global alterations in the binding patternsof SCL/TAL1 and FLI1. The acquisition of haematopoietic fate controlled by Runx1 therefore does not represent the establishment of a new regulatory layer on top of a pre-existing HE program but instead entails global reorganization of lineage-specific transcription factor assemblies. © 2012 European Molecular Biology Organization.
hide

It is known that the pH dependence of conductance for the rat potassium channel Kv1.4 is susbstantially reduced upon mutation of either H508 or K532. These residues lie in the extracellular mouth of the channel pore. We have used continuum electrostatics to investigate their interactions with K(+) sites in the pore. The predicted scale of interactions between H508/K532 and potassium sites is sufficient to significantly alter potassium occupancy and thus channel function. We interpret the effect of K532 mutation as indicating that the pH-dependent effect requires not only an ionisable group with a suitable pK(a) value (i.e. histidine), but also that other charged groups set the potential profile at a threshold level. This hypothesis is examined in the context of pH dependence for other members of the Kv1 family, and may represent a general tool with which to study potassium channels.
hide

Overlapping transcripts in antisense orientation have the potential to form double-stranded RNA (dsRNA), a substrate for a number of different RNA-modification pathways. One prominent route for dsRNA is its breakdown by Dicer enzyme complexes into small RNAs, a pathway that is widely exploited by RNA interference technology to inactivate defined genes in transgenic lines. The significance of this pathway for endogenous gene regulation remains unclear.
hide

We describe a fold level fast protein comparison and motif matching facility based on the TOPS representation of structure. This provides an update to a previous service at the EBI, with a better graph matching with faster results and visualization of both the structures being compared against and the common pattern of each with the target domain.
hide

Background: Beta-barrel transmembrane (bbtm) proteins are a functionally important and diverse group of proteins expressed in the outer membranes of bacteria ( both gram negative and acid fast gram positive), mitochondria and chloroplasts. Despite recent publications describing reasonable levels of accuracy for discriminating between bbtm proteins and other proteins, screening of entire genomes remains troublesome as these molecules only constitute a small fraction of the sequences screened. Therefore, novel methods are still required capable of detecting new families of bbtm protein in diverse genomes. Results: We present TMB-Hunt, a program that uses a k-Nearest Neighbour (k-NN) algorithm to discriminate between bbtm and non-bbtm proteins on the basis of their amino acid composition. By including differentially weighted amino acids, evolutionary information and by calibrating the scoring, an accuracy of 92.5% was achieved, with 91% sensitivity and 93.8% positive predictive value (PPV), using a rigorous cross-validation procedure. A major advantage of this approach is that because it does not rely on beta-strand detection, it does not require resolved structures and thus larger, more representative, training sets could be used. It is therefore believed that this approach will be invaluable in complementing other, physicochemical and homology based methods. This was demonstrated by the correct reassignment of a number of proteins which other predictors failed to classify. We have used the algorithm to screen several genomes and have discussed our findings. Conclusion: TMB-Hunt achieves a prediction accuracy level better than other approaches published to date. Results were significantly enhanced by use of evolutionary information and a system for calibrating k-NN scoring. Because the program uses a distinct approach to that of other discriminators and thus suffers different liabilities, we believe it will make a significant contribution to the development of a consensus approach for bbtm protein detection.
hide

The large volume of single nucleotide polymorphism data now available motivates the development of methods for distinguishing neutral changes from those which have real biological effects. Here, two different machine-learning methods, decision trees and support vector machines (SVMs), are applied for the first time to this problem. In common with most other methods, only non-synonymous changes in protein coding regions of the genome are considered.
hide

It is well established that recognition between exposed edges of beta-sheets is an important mode of protein-protein interaction and can have pathological consequences; for instance, it has been linked to the aggregation of proteins into a fibrillar structure, which is associated with a number of predominantly neurodegenerative disorders. A number of protective mechanisms have evolved in the edge strands of beta-sheets, preventing the aggregation and insolubility of most natural beta-sheet proteins. Such mechanisms are unfavorable in the interior of a beta-sheet. The problem of distinguishing edge strands from central strands based on sequence information alone is important in predicting residues and mutations likely to be involved in aggregation, and is also a first step in predicting folding topology. Here we report support vector machine (SVM) and decision tree methods developed to classify edge strands from central strands in a representative set of protein domains. Interestingly, rules generated by the decision tree method are in close agreement with our knowledge of protein structure and are potentially useful in a number of different biological applications. When trained on strands from proteins of known structure, using structure-based (Dictionary of Secondary Structure in Proteins) strand assignments, both methods achieved mean cross-validated, prediction accuracies of approximately 78%. These accuracies were reduced when strand assignments from secondary structure prediction were used. Further investigation of this effect revealed that it could be explained by a significant reduction in the accuracy of standard secondary structure prediction methods for edge strands, in comparison with central strands.
hide

Topsalign is a method that will structurally align diverse protein structures, for example, structural alignment of protein superfolds. All proteins within a superfold share the same fold but often have very low sequence identity and different biological and biochemical functions. There is often significant structural diversity around the common scaffold of secondary structure elements of the fold. Topsalign uses topological descriptions of proteins. A pattern discovery algorithm identifies equivalent secondary structure elements between a set of proteins and these are used to produce an initial multiple structure alignment. Simulated annealing is used to optimize the alignment. The output of Topsalign is a multiple structure-based sequence alignment and a 3D superposition of the structures. This method has been tested on three superfolds: the beta jelly roll, TIM (alpha/beta) barrel and the OB fold. Topsalign outperforms established methods on very diverse structures. Despite the pattern discovery working only on beta strand secondary structure elements, Topsalign is shown to align TIM (alpha/beta) barrel superfamilies, which contain both alpha helices and beta strands.
hide

Computational methods for the detection and characterisation of protein ligand-binding sites have increasingly become an area of interest now that large amounts of protein structural information are becoming available prior to any knowledge of protein function. There have been particularly interesting recent developments in the following areas: first, functional site detection, whereby protein evolutionary information has been used to locate binding sites on the protein surface; second, functional site similarity, whereby structural similarity and three-dimensional templates can be used to compare and classify and potentially locate new binding sites; and third, ligand docking, which is being used to find and validate functional sites, in addition to having more conventional uses in small-molecule lead discovery.
hide

Software is presented for the calculation of packing angles and geometry of helical secondary structure elements in protein structures. AVAILABILITY: C language source code and documentation is available from http://www.bioinformatics.leeds.ac.uk.
hide

It is well established that recognition between exposed edges ofβ-sheets is an important mode of protein-protein interaction and can have pathological consequences; for instance, it has been linked to the aggregation of proteins into a fibrillar structure, which is associated with a number of predominantly neurodegenerative disorders. A number of protective mechanisms have evolved in the edge strands of β-sheets, preventing the aggregation and insolubility of most natural β-sheet proteins. Such mechanisms are unfavorable in the interior of a β-sheet. The problem of distinguishing edge strands from central strands based on sequence information alone isimportant in predicting residues and mutations likely to be involved in aggregation, and is also a first step in predicting folding topology. Here we report support vector machine (SVM) and decision tree methods developed to classify edge strands from central strands in a representative set of protein domains. Interestingly, rules generated by the decision tree method are in close agreement with our knowledge of protein structure and are potentially useful in a number of different biological applications. When trained on strands from proteins of known structure, using structure-based (Dictionary of Secondary Structure in Proteins) strand assignments, both methods achieved mean cross-validated, prediction accuracies of -78%. These accuracies were reduced when strand assignments from secondary structure prediction were used. Further investigation of this effect revealed that it could be explained by a significant reduction in the accuracy of standard secondary structure prediction methods for edge strands, in comparison with central strands.
hide

Many current methods for protein analysis depend on the detection of similarity in either the primary sequence, or the overall tertiary structure (the C-alpha atoms of the protein backbone). These common sequences or structures may imply similar functional characteristics or active properties. Active sites and ligand binding sites usually occur on or near the surface of the protein; so similarly shaped surface regions could imply similar functions. We investigate various methods for describing the shape properties of protein surfaces and for comparing them. Our current work uses algorithms from computer vision to describe the protein surfaces. and methods from graph theory to compare the surface regions. Early results indicate that we can successfully match a family of related ligand binding sites, and find their similarly shaped surface regions. This method of surface analysis could be extended to help identify unknown surface regions for possible ligand binding or active sites. (C) 2001 Elsevier Science Ltd. All rights reserved.
hide

Motivation: TOPS cartoons are a schematic abstraction of protein three-dimensional structures in two dimensions, and are used for understanding and manual comparison of protein folds. Recently, an algorithm that produces the cartoons automatically from protein structures has been devised and cartoons have been generated to represent all the structures in the structural databank. There is now a need to be able to define target topological patterns and to search the database for matching domains. Results: We have devised a formal language for describing TOPS diagrams and patterns, and have designed an efficient algorithm to match a pattern to a set of diagrams. A pattern-matching system has been implemented, and tested on a database derived from all the current entries in the Protein Data Bank (15000 domains). Users can search on patterns selected from a library of motifs or; alternatively, they can define their own search patterns.
hide

This article describes the implementation of a new docking approach. The method uses a Tabu search methodology to dock flexibly ligand molecules into rigid receptor structures. It uses an empirical objective function with a small number of physically based terms derived from fitting experimental binding affinities for crystallographic complexes. This means that docking energies produced by the searching algorithm provide direct estimates of the binding affinities of the ligands, The method has been tested on 50 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known, All water molecules are removed from the structures and ligand molecules are minimized in vacuo before docking. The lowest energy geometry produced by the docking protocol is within 1.5 Angstrom root-mean square of the experimental binding mode for 86% of the complexes. The lowest energies produced by the docking are in fair agreement with the known free energies of binding for the ligands. Proteins 33:367-382, 1998, (C) 1998 Wiley-Liss, Inc.
hide

Genome sequencing projects continue to provide a flood of new protein sequences, and prediction methods remain an important means of adding structural information. Recently, there have been advances in secondary structure prediction, which feed, in turn, into improved fold recognition algorithms. Finally, there have been technical improvements in comparative modelling, and studies of the expected accuracy of three-dimensional structural models built by this method.
hide

The determination of protein structures has furthered our understanding of how various proteins perform their functions. With the large number of structures currently available in the PDB, it is necessary to be able to easily study these proteins in detail. Here new software tools are presented which aim to facilitate this analysis; these include the PDBsum WWW site which provides a summary description of all PDB entries, the programs TOPS and NUCPLOT to plot schematic diagrams representing protein topology and DNA-binding interactions, SAS a WWW-based sequence-analysis tool incorporating structural data, and WWW servers for the analysis of protein-protein interfaces and analyses of over 300 haem-binding proteins.
hide

This paper describes a novel methodology, PRO_SELECT, which combines elements of structure-based drug design and combinatorial chemistry to create a new paradigm for accelerated lead discovery. Starting with a synthetically accessible template positioned in the active site of the target of interest, PRO_SELECT employs database searching to generate lists of potential substituents for each substituent position on the template. These substituents are selected on the basis of their being able to couple to the template using known synthetic routes and their possession of the correct functionality to interact with specified residues in the active site. The lists of potential substituents are then screened computationally against the active site using rapid algorithms. An empirical scoring function, correlated to binding free energy, is used to rank the substituents at each position. The highest scoring substituents at each position can then be examined using a variety of techniques and a final selection is made. Combinatorial enumeration of the final lists generates a library of synthetically accessible molecules, which may then be prioritised for synthesis and assay. The results obtained using PRO_SELECT to design thrombin inhibitors are briefly discussed.
hide