Literature DB >> 9155030

Linker length and composition influence the flexibility of Oct-1 DNA binding.

H C van Leeuwen1, M J Strating, M Rensen, W de Laat, P C van der Vliet.   

Abstract

POU domain transcription factors have two separate helix-turn-helix DNA-binding subdomains, the POU homeodomain (POUhd) and the POU-specific domain (POUs). Each subdomain recognizes a specific subsite of 4 or 5 bp in the octamer recognition sequence. The Oct-1 POU subdomains are connected by a 23 amino acid unstructured linker region. To investigate the requirements for the linker and its role in DNA recognition, we constructed POU domains in which the subdomains are connected with linkers varying in length between 2 and 37 amino acids. Binding to the natural octamer site required a minimal linker length of between 10 and 14 amino acids. A POU domain with an eight amino acid linker, however, had a high affinity for a site in which the POUs recognition sequence was inverted. Computer modelling shows that inversion of the POUs subdomain shortens the distance between the subdomains sufficiently to enable an eight amino acid linker to bridge the distance. DNase I footprinting as well as mutation of the POUs-binding site confirms the inverted orientation of the POUs domain. Switching of the POUs and POUhd subdomains and separation by 3 bp leads to a large distance which could only be bridged effectively by a long 37 amino acid linker. In addition to linker length, mutation of a conserved glutamate residue in the linker affected binding. As shown by surface plasmon resonance measurements, this was caused by a decrease in the on-rate. Our data indicate that there are both length and sequence requirements in the linker region which allow flexibility leading to selective binding to differently spaced and oriented subsites.

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Year:  1997        PMID: 9155030      PMCID: PMC1169807          DOI: 10.1093/emboj/16.8.2043

Source DB:  PubMed          Journal:  EMBO J        ISSN: 0261-4189            Impact factor:   11.598


  34 in total

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10.  Ab initio and homology based prediction of protein domains by recursive neural networks.

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