Literature DB >> 16337654

Amino acid pairing preferences in parallel beta-sheets in proteins.

H M Fooks1, A C R Martin, D N Woolfson, R B Sessions, E G Hutchinson.   

Abstract

Statistical approaches have been applied to examine amino acid pairing preferences within parallel beta-sheets. The main chain hydrogen bonding pattern in parallel beta-sheets means that, for each residue pair, only one of the residues is involved in main chain hydrogen bonding with the strand containing the partner residue. We call this the hydrogen bonded (HB) residue and the partner residue the non-hydrogen bonded (nHB) residue, and differentiate between the favorability of a pair and that of its reverse pair, e.g. Asn(HB)-Thr(nHB)versus Thr(HB)-Asn(nHB). Significantly (p < or = 0.000001) favoured pairings were rationalised using stereochemical arguments. For instance, Asn(HB)-Thr(nHB) and Arg(HB)-Thr(nHB) were favoured pairs, where the residues adopted favoured chi1 rotamer positions that allowed side-chain interactions to occur. In contrast, Thr(HB)-Asn(nHB) and Thr(HB)-Arg(nHB) were not significantly favoured, and could only form side-chain interactions if the residues involved adopted less favourable chi1 conformations. The favourability of hydrophobic pairs e.g. Ile(HB)-Ile(nHB), Val(HB)-Val(nHB) and Leu(HB)-Ile(nHB) was explained by the residues adopting their most preferred chi1 and chi2 conformations, which enabled them to form nested arrangements. Cysteine-cysteine pairs are significantly favoured, although these do not form intrasheet disulphide bridges. Interactions between positively and negatively charged residues were asymmetrically preferred: those with the negatively charged residue at the HB position were more favoured. This trend was accounted for by the presence of general electrostatic interactions, which, based on analysis of distances between charged atoms, were likely to be stronger when the negatively charged residue is the HB partner. The Arg(HB)-Asp(nHB) interaction was an exception to this trend and its favorability was rationalised by the formation of specific side-chain interactions. This research provides rules that could be applied to protein structure prediction, comparative modelling and protein engineering and design. The methods used to analyse the pairing preferences are automated and detailed results are available (http://www.rubic.rdg.ac.uk/betapairprefsparallel/).

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Year:  2005        PMID: 16337654     DOI: 10.1016/j.jmb.2005.11.008

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  33 in total

1.  Parallel β-sheet secondary structure is stabilized and terminated by interstrand disulfide cross-linking.

Authors:  Aaron M Almeida; Rebecca Li; Samuel H Gellman
Journal:  J Am Chem Soc       Date:  2011-12-13       Impact factor: 15.419

2.  Protein beta-sheet nucleation is driven by local modular formation.

Authors:  Brent Wathen; Zongchao Jia
Journal:  J Biol Chem       Date:  2010-04-10       Impact factor: 5.157

3.  A tightly packed hydrophobic cluster directs the formation of an off-pathway sub-millisecond folding intermediate in the alpha subunit of tryptophan synthase, a TIM barrel protein.

Authors:  Ying Wu; Ramakrishna Vadrevu; Sagar Kathuria; Xiaoyan Yang; C Robert Matthews
Journal:  J Mol Biol       Date:  2006-12-15       Impact factor: 5.469

4.  Beta-strand flipping and slipping triggered by turn replacement reveal the opportunistic nature of beta-strand pairing.

Authors:  Koki Makabe; Shude Yan; Valentina Tereshko; Grzegorz Gawlak; Shohei Koide
Journal:  J Am Chem Soc       Date:  2007-11-07       Impact factor: 15.419

5.  Impact of strand length on the stability of parallel-β-sheet secondary structure.

Authors:  Felix Freire; Aaron M Almeida; John D Fisk; Jay D Steinkruger; Samuel H Gellman
Journal:  Angew Chem Int Ed Engl       Date:  2011-08-02       Impact factor: 15.336

6.  Exploring beta-sheet structure and interactions with chemical model systems.

Authors:  James S Nowick
Journal:  Acc Chem Res       Date:  2008-09-18       Impact factor: 22.384

7.  An amino acid packing code for α-helical structure and protein design.

Authors:  Hyun Joo; Archana G Chavan; Jamie Phan; Ryan Day; Jerry Tsai
Journal:  J Mol Biol       Date:  2012-03-15       Impact factor: 5.469

8.  Solvent-induced tuning of internal structure in a protein amyloid protofibril.

Authors:  Anjali Jha; Satya Narayan; Jayant B Udgaonkar; G Krishnamoorthy
Journal:  Biophys J       Date:  2012-08-22       Impact factor: 4.033

9.  Position-specific propensities of amino acids in the β-strand.

Authors:  Nicholus Bhattacharjee; Parbati Biswas
Journal:  BMC Struct Biol       Date:  2010-09-28

Review 10.  Folding by numbers: primary sequence statistics and their use in studying protein folding.

Authors:  Brent Wathen; Zongchao Jia
Journal:  Int J Mol Sci       Date:  2009-04-08       Impact factor: 6.208

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