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      Novel structural features drive DNA binding properties of Cmr, a CRP family protein in TB complex mycobacteria

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          Abstract

          Mycobacterium tuberculosis (Mtb) encodes two CRP/FNR family transcription factors (TF) that contribute to virulence, Cmr (Rv1675c) and CRP Mt (Rv3676). Prior studies identified distinct chromosomal binding profiles for each TF despite their recognizing overlapping DNA motifs. The present study shows that Cmr binding specificity is determined by discriminator nucleotides at motif positions 4 and 13. X-ray crystallography and targeted mutational analyses identified an arginine-rich loop that expands Cmr’s DNA interactions beyond the classical helix-turn-helix contacts common to all CRP/FNR family members and facilitates binding to imperfect DNA sequences. Cmr binding to DNA results in a pronounced asymmetric bending of the DNA and its high level of cooperativity is consistent with DNA-facilitated dimerization. A unique N-terminal extension inserts between the DNA binding and dimerization domains, partially occluding the site where the canonical cAMP binding pocket is found. However, an unstructured region of this N-terminus may help modulate Cmr activity in response to cellular signals. Cmr’s multiple levels of DNA interaction likely enhance its ability to integrate diverse gene regulatory signals, while its novel structural features establish Cmr as an atypical CRP/FNR family member.

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          Most cited references62

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          The role of DNA shape in protein-DNA recognition

          The recognition of specific DNA sequences by proteins is thought to depend on two types of mechanisms: one that involves the formation of hydrogen bonds with specific bases, primarily in the major groove, and one involving sequence-dependent deformations of the DNA helix. By comprehensively analyzing the three dimensional structures of protein-DNA complexes, we show that the binding of arginines to narrow minor grooves is a widely used mode for protein-DNA recognition. This readout mechanism exploits the phenomenon that narrow minor grooves strongly enhance the negative electrostatic potential of the DNA. The nucleosome core particle offers a striking example of this effect. Minor groove narrowing is often associated with the presence of A-tracts, AT-rich sequences that exclude the flexible TpA step. These findings suggest that the ability to detect local variations in DNA shape and electrostatic potential is a general mechanism that enables proteins to use information in the minor groove, which otherwise offers few opportunities for the formation of base-specific hydrogen bonds, to achieve DNA binding specificity.
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            Variation in homeodomain DNA binding revealed by high-resolution analysis of sequence preferences.

            Most homeodomains are unique within a genome, yet many are highly conserved across vast evolutionary distances, implying strong selection on their precise DNA-binding specificities. We determined the binding preferences of the majority (168) of mouse homeodomains to all possible 8-base sequences, revealing rich and complex patterns of sequence specificity and showing that there are at least 65 distinct homeodomain DNA-binding activities. We developed a computational system that successfully predicts binding sites for homeodomain proteins as distant from mouse as Drosophila and C. elegans, and we infer full 8-mer binding profiles for the majority of known animal homeodomains. Our results provide an unprecedented level of resolution in the analysis of this simple domain structure and suggest that variation in sequence recognition may be a factor in its functional diversity and evolutionary success.
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              RONN: the bio-basis function neural network technique applied to the detection of natively disordered regions in proteins.

              Recent studies have found many proteins containing regions that do not form well-defined three-dimensional structures in their native states. The study and detection of such disordered regions is important both for understanding protein function and for facilitating structural analysis since disordered regions may affect solubility and/or crystallizability. We have developed the regional order neural network (RONN) software as an application of our recently developed 'bio-basis function neural network' pattern recognition algorithm to the detection of natively disordered regions in proteins. The results of blind-testing a panel of nine disorder prediction tools (including RONN) against 80 protein sequences derived from the Protein Data Bank shows that, based on the probability excess measure, RONN performed the best.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                09 January 2018
                20 November 2017
                20 November 2017
                : 46
                : 1
                : 403-420
                Affiliations
                Department of Biomedical Sciences, School of Public Health, University at Albany, SUNY, Albany, NY 12201, USA
                New York Structural Biology Center, New York, NY 10027, USA
                Wadsworth Center, New York State Department of Health, 120 New Scotland Avenue, PO Box 22002, Albany, NY 12201-2002, USA
                Author notes
                To whom correspondence should be addressed. Tel: +1 518 486 4253; Fax: +1 518 473 1326; Email: kathleen.mcdonough@ 123456health.ny.gov . Correspondence may also be addressed to Jonah Cheung. Tel: +1 212 939 0660 (Ext. 375); Fax: +1 212 939 0863; Email: jcheung@ 123456nysbc.org
                Article
                gkx1148
                10.1093/nar/gkx1148
                5758884
                29165665
                1f531b76-d6aa-4da6-859f-7dd110b17787
                © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 13 November 2017
                : 26 October 2017
                : 01 July 2017
                Page count
                Pages: 18
                Categories
                Structural Biology

                Genetics
                Genetics

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