Detecting DNA-binding helix-turn-helix structural motifs using sequence and structure information

Pellegrini-Calace, Marialuisa

doi:10.1093/nar/gki349

Cited by 22 publications

(9 citation statements)

References 34 publications

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“…40 Likewise, a hidden Markov model using structural information has been employed to identify helix-turnhelix DNA-binding motifs achieving about 71% accuracy. 35 A neural network combined with composition, sequence and structure was used to identify general DNA-binding proteins achieving 79% accuracy;…”

Section: Introductionmentioning

confidence: 99%

Learning to Translate Sequence and Structure to Function: Identifying DNA Binding and Membrane Binding Proteins

et al. 2007

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Abstract-A proteinÕs function depends in a large part on interactions with other molecules. With an increasing number of protein structures becoming available every year, a corresponding structural annotation approach identifying such interactions grows more expedient. At the same time, machine learning has gained popularity in bioinformatics providing robust annotation of genes and proteins without sequence homology. Here we have developed a general machine learning protocol to identify proteins that bind DNA and membrane. In general, there is no theory or even rule of thumb to pick the best machine learning algorithm. Thus, a systematic comparison of several classification algorithms known to perform well is investigated. Indeed, the boosted tree classifier is found to give the best performance, achieving 93% and 88% accuracy to discriminate non-homologous proteins that bind membrane and DNA, respectively, significantly outperforming all previously published works. We also attempted to address the importance of the attributes in function prediction and the relationships between relevant attributes. A graphical model based on boosted trees is applied to study the important features in discriminating DNA-binding proteins. In summary, the current protocol identified physical features important in DNA and membrane binding, rather than annotating function through sequence similarity.

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Section: Introductionmentioning

confidence: 99%

Learning to Translate Sequence and Structure to Function: Identifying DNA Binding and Membrane Binding Proteins

et al. 2007

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“…They show little sequence similarity to each other, and their relationships are only visible through structures. It has even been proposed that some HTH motifs had arisen independently in distantly related proteins during evolution (Rosinski & Atchley, ; Pellegrini‐Calace & Thornton, ). In addition to the HTH motif, the fungal HOP1CTD sequences also harbor a zinc‐finger motif, which was not detected in plant or animal HOP1CTD sequences.…”

Section: Discussionmentioning

confidence: 99%

Structural and functional diversification of HORMA domain‐containing proteins

Zhang

Zhu

Yang

2015

J of Sytematics Evolution

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HORMA domain-containing proteins play important roles in cell cycle regulation and DNA repair, acting as adaptors to recruit other proteins. Although containing the conserved HORMA domain in structure, different types of HORMA proteins have diverged significantly in function. The mechanisms underlying the evolution and functional diversification of HORMA proteins remain unclear. Here we conduct an integrative approach, combining sequence, structural, gene coexpression and protein-protein interaction data, to trace the structural and functional evolution of HORMA proteins. Comparative sequence and structure analysis revealed that variations in both amino acid sequence and domain composition contributed to the functional diversification of different HORMA domaincontaining proteins. Multiple amino acid substitutions at the C-terminal region promoted the functional divergence between MAD2 and REV7 by facilitating interaction with different partners. The emergence of the HOP1CTD domain contributed to the function of HOP1 as a meiosis-specific structural component of the lateral elements of the synaptonemal complex. Additionally, different types of HORMA proteins were recruited into different functional modules in the genetic network while functioning distinctly.Key words: Gene coexpression, HORMA proteins, protein-protein interaction, structure-function relationship.The HORMA domain was named after the proteins HOP1, REV7, and MAD2, which were first identified in the yeast Saccharomyces cerevisiae (Lawrence et al., 1985;Hollingsworth & Byers, 1989;Boguski et al., 1992). The typical HORMA domain consists of 180-240 amino acid residues and forms a b-sheet complex with associated a-helices (Aravind & Koonin, 1998). HORMA domain-containing proteins play important roles in cell cycle regulation and DNA repair, acting as adaptors to recruit other proteins. HOP1 is a meiosis-specific protein involved in synaptonemal complex assembly that is required for chiasma formation in meiotic recombination (Hollingsworth et al., 1990;Latypov et al., 2010). REV7 is a subunit of the DNA polymerase z that is involved in translesion DNA synthesis, and is required for DNA double-strand break (DSB) repair (Murakumo et al., 2000). MAD2 is a key component of the mitotic spindle assembly checkpoint complex, which prevents separation of the duplicated chromosomes until each chromosome is properly attached to the spindle apparatus (Kabeche & Compton, 2012;Zich et al., 2012).HORMA domain-containing proteins have been identified in a wide range of eukaryotes (Aravind & Koonin, 1998;Muniyappa et al., 2014). Based on amino acid sequence homology and domain architectures, HORMA domain-containing proteins can be divided into three distinct types: HOP1, REV7, and MAD2 types (Muniyappa et al., 2014). Sequences of the same type protein from different species may have different names (e.g., the HOP1 homologues from Arabidopsis ASY1, ASY2, and the mammalian HOP1 homologues HORMAD1, HORMAD2), but share similar structures. The MAD2-and REV7-type prot...

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“…HTH domains are present in the most prevalent transcription factors of all prokaryotic genomes. The HTH domain helps in interaction with a cognate σ factor for transcription initiation, specifically in this context the σ 54 (Pellegrini‐Calace & Thornton 2005). A domain wise search on the DIP1512 protein also resulted into a σ 54 interacting domain at its C‐terminus starting from residue 205–360 overlapping partially with that of the AAA+ domain.…”

Section: Discussionmentioning

confidence: 99%

Interplay between DtxR and nitric oxide reductase activities: a functional genomics approach indicating involvement of homologous protein domains in bacterial pathogenesis

Gupta

Bansal

Deb

et al. 2007

Int J Experimental Path

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Corynebacterium diphtheriae pathogenesis depends on the production of toxin (Dtx), which in turn depends on a micromolar concentration of nitric oxide (NO)-mediated deactivation of DtxR (an iron-dependent regulator). Inside a host, the pathogen often encounters excess of NO that acts as an oxidative toxicant. Therefore a critical level of NO needs to be maintained by the pathogen. This necessitates reduction of excess NO by the presence of a reductase, namely nitric oxide reductase (NOR). Similar to the expression of toxin, the expression of NOR is possibly regulated by another regulator NorR, as has been found in other gram positive and gram-negative bacteria. Therefore, a correlation between concentration of NO on the deactivation of DtxR and transactivation of NorR becomes apparent. However, unlike many other pathogens the presence of NOR and NorR in C. diphtheriae has not been established. We applied a combination of bioinformatics and comparative genomics approach on C. diphtheriae genome using Escherichia coli as a model organism to find some structural and functional homologoues for the two genes in question. The various domain characteristics for the two proteins (NOR and NorR) have been taken into account in this analysis. Through extensive genome and proteome search we have been able to identify key regulatory genes, which are possibly involved in coordination and control of NO stress in C. diphtheriae. Our finding will progress the understanding of the complete regulatory mechanism for evasion and maintenance of pathogenesis by this and other pathogenic organisms.

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Detecting DNA-binding helix-turn-helix structural motifs using sequence and structure information

Cited by 22 publications

References 34 publications

Learning to Translate Sequence and Structure to Function: Identifying DNA Binding and Membrane Binding Proteins

Learning to Translate Sequence and Structure to Function: Identifying DNA Binding and Membrane Binding Proteins

Structural and functional diversification of HORMA domain‐containing proteins

Interplay between DtxR and nitric oxide reductase activities: a functional genomics approach indicating involvement of homologous protein domains in bacterial pathogenesis

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