Essentiality, protein-protein interactions and evolutionary properties are key predictors for identifying cancer-associated genes using machine learning.

The distinctive nature of cancer as a disease prompts an exploration of the special characteristics the genes implicated in cancer exhibit. The identification of cancer-associated genes and their characteristics is crucial to further our understanding of this disease and enhanced likelihood of therapeutic drug targets success. However, the rate at which cancer genes are being identified experimentally is slow. Applying predictive analysis techniques, through the building of accurate machine learning models, is potentially a useful approach in enhancing the identification rate of these genes and their characteristics. Here, we investigated gene essentiality scores and found that they tend to be higher for cancer-associated genes compared to other protein-coding human genes. We built a dataset of extended gene properties linked to essentiality and used it to train a machine-learning model; this model reached 89% accuracy and > 0.85 for the Area Under Curve (AUC). The model showed that essentiality, evolutionary-related properties, and properties arising from protein-protein interaction networks are particularly effective in predicting cancer-associated genes. We were able to use the model to identify potential candidate genes that have not been previously linked to cancer. Prioritising genes that score highly by our methods could aid scientists in their cancer genes research.

EVI1 phosphorylation at S436 regulates interactions with CtBP1 and DNMT3A and promotes self-renewal.

The transcriptional regulator EVI1 has an essential role in early development and haematopoiesis. However, acute myeloid leukaemia (AML) driven by aberrantly high EVI1 expression has very poor prognosis. To investigate the effects of post-translational modifications on EVI1 function, we carried out a mass spectrometry (MS) analysis of EVI1 in AML and detected dynamic phosphorylation at serine 436 (S436). Wild-type EVI1 (EVI1-WT) with S436 available for phosphorylation, but not non-phosphorylatable EVI1-S436A, conferred haematopoietic progenitor cell self-renewal and was associated with significantly higher organised transcriptional patterns. In silico modelling of EVI1-S436 phosphorylation showed reduced affinity to CtBP1, and CtBP1 showed reduced interaction with EVI1-WT compared with EVI1-S436A. The motif harbouring S436 is a target of CDK2 and CDK3 kinases, which interacted with EVI1-WT. The methyltransferase DNMT3A bound preferentially to EVI1-WT compared with EVI1-S436A, and a hypomethylated cell population associated by EVI1-WT expression in murine haematopoietic progenitors is not maintained with EVI1-S436A. These data point to EVI1-S436 phosphorylation directing functional protein interactions for haematopoietic self-renewal. Targeting EVI1-S436 phosphorylation may be of therapeutic benefit when treating EVI1-driven leukaemia.

Erbb2 is required for cardiac atrial electrical activity during development.

The heart is the first organ required to function during embryonic development and is absolutely necessary for embryo survival. Cardiac activity is dependent on both the sinoatrial node (SAN), which is the pacemaker of heart's electrical activity, and the cardiac conduction system which transduces the electrical signal though the heart tissue, leading to heart muscle contractions. Defects in the development of cardiac electrical function may lead to severe heart disorders. The Erbb2 (Epidermal Growth Factor Receptor 2) gene encodes a member of the EGF receptor family of receptor tyrosine kinases. The Erbb2 receptor lacks ligand-binding activity but forms heterodimers with other EGF receptors, stabilising their ligand binding and enhancing kinase-mediated activation of downstream signalling pathways. Erbb2 is absolutely necessary in normal embryonic development and homozygous mouse knock-out Erbb2 embryos die at embryonic day (E)10.5 due to severe cardiac defects. We have isolated a mouse line, l11Jus8, from a random chemical mutagenesis screen, which carries a hypomorphic missense mutation in the Erbb2 gene. Homozygous mutant embryos exhibit embryonic lethality by E12.5-13. The l11Jus8 mutants display cardiac haemorrhage and a failure of atrial function due to defects in atrial electrical signal propagation, leading to an atrial-specific conduction block, which does not affect ventricular conduction. The l11Jus8 mutant phenotype is distinct from those reported for Erbb2 knockout mouse mutants. Thus, the l11Jus8 mouse reveals a novel function of Erbb2 during atrial conduction system development, which when disrupted causes death at mid-gestation.

Tartrate-resistant acid phosphatase deficiency causes a bone dysplasia with autoimmunity and a type I interferon expression signature.

We studied ten individuals from eight families showing features consistent with the immuno-osseous dysplasia spondyloenchondrodysplasia. Of particular note was the diverse spectrum of autoimmune phenotypes observed in these individuals (cases), including systemic lupus erythematosus, Sjögren's syndrome, hemolytic anemia, thrombocytopenia, hypothyroidism, inflammatory myositis, Raynaud's disease and vitiligo. Haplotype data indicated the disease gene to be on chromosome 19p13, and linkage analysis yielded a combined multipoint log(10) odds (LOD) score of 3.6. Sequencing of ACP5, encoding tartrate-resistant acid phosphatase, identified biallelic mutations in each of the cases studied, and in vivo testing confirmed a loss of expressed protein. All eight cases assayed showed elevated serum interferon alpha activity, and gene expression profiling in whole blood defined a type I interferon signature. Our findings reveal a previously unrecognized link between tartrate-resistant acid phosphatase activity and interferon metabolism and highlight the importance of type I interferon in the genesis of autoimmunity.

Interactions between Kar2p and its nucleotide exchange factors Sil1p and Lhs1p are mechanistically distinct.

Kar2p, an essential Hsp70 chaperone in the endoplasmic reticulum of Saccharomyces cerevisiae, facilitates the transport and folding of nascent polypeptides within the endoplasmic reticulum lumen. The chaperone activity of Kar2p is regulated by its intrinsic ATPase activity that can be stimulated by two different nucleotide exchange factors, namely Sil1p and Lhs1p. Here, we demonstrate that the binding requirements for Lhs1p are complex, requiring both the nucleotide binding domain plus the linker domain of Kar2p. In contrast, the IIB domain of Kar2p is sufficient for binding of Sil1p, and point mutations within IIB specifically blocked Sil1p-dependent activation while remaining competent for activation by Lhs1p. Taken together, these results demonstrate that the interactions between Kar2p and its two nucleotide exchange factors can be functionally resolved and are thus mechanistically distinct.

Secondary structure assignment that accurately reflects physical and evolutionary characteristics.

BACKGROUND: Secondary structure is used in hierarchical classification of protein structures, identification of protein features, such as helix caps and loops, for fold recognition, and as a precursor to ab initio structure prediction. There are several methods available for assigning secondary structure if the three-dimensional structure of the protein is known. Unfortunately they differ in their definitions, particularly in the exact positions of the termini. Additionally, most existing methods rely on hydrogen bonding, which means that important secondary structural classes, such as isolated beta-strands and poly-proline helices cannot be identified as they do not have characteristic hydrogen-bonding patterns. For this reason we have developed a more accurate method for assigning secondary structure based on main chain geometry, which also allows a more comprehensive assignment of secondary structure. RESULTS: We define secondary structure based on a number of geometric parameters. Helices are defined based on whether they fit inside an imaginary cylinder: residues must be within the correct radius of a central axis. Different types of helices (alpha, 3(10) or pi) are assigned on the basis of the angle between successive peptide bonds. beta-strands are assigned based on backbone dihedrals and with alternating peptide bonds. Thus hydrogen bonding is not required and beta-strands can be within a parallel sheet, antiparallel sheet, or can be isolated. Poly-proline helices are defined similarly, although with three-fold symmetry. CONCLUSION: We find that our method better assigns secondary structure than existing methods. Specifically, we find that comparing our methods with those of others, amino-acid trends at helix caps are stronger, secondary structural elements less likely to be concatenated together and secondary structure guided sequence alignment is improved. We conclude, therefore, that secondary structure assignments using our method better reflects physical and evolutionary characteristics of proteins. The program is available from http://www.bioinf.man.ac.uk/~lovell/segno.shtml.

Ab initio construction of polypeptide fragments: efficient generation of accurate, representative ensembles.

We describe a novel method to generate ensembles of conformations of the main-chain atoms [N, C(alpha), C, O, Cbeta] for a sequence of amino acids within the context of a fixed protein framework. Each conformation satisfies fundamental stereo-chemical restraints such as idealized geometry, favorable phi/psi angles, and excluded volume. The ensembles include conformations both near and far from the native structure. Algorithms for effective conformational sampling and constant time overlap detection permit the generation of thousands of distinct conformations in minutes. Unlike previous approaches, our method samples dihedral angles from fine-grained phi/psi state sets, which we demonstrate is superior to exhaustive enumeration from coarse phi/psi sets. Applied to a large set of loop structures, our method samples consistently near-native conformations, averaging 0.4, 1.1, and 2.2 A main-chain root-mean-square deviations for four, eight, and twelve residue long loops, respectively. The ensembles make ideal decoy sets to assess the discriminatory power of a selection method. Using these decoy sets, we conclude that quality of anchor geometry cannot reliably identify near-native conformations, though the selection results are comparable to previous loop prediction methods. In a subsequent study (de Bakker et al.: Proteins 2003;51:21-40), we demonstrate that the AMBER forcefield with the Generalized Born solvation model identifies near-native conformations significantly better than previous methods.