Literature DB >> 15736949

Quantitative analysis of substrate specificity of haloalkane dehalogenase LinB from Sphingomonas paucimobilis UT26.

Jan Kmunícek1, Kamila Hynková, Tomás Jedlicka, Yuji Nagata, Ana Negri, Federico Gago, Rebecca C Wade, Jirí Damborský.   

Abstract

Haloalkane dehalogenases are microbial enzymes that cleave a carbon-halogen bond in halogenated compounds. The haloalkane dehalogenase LinB, isolated from Sphingomonas paucimobilis UT26, is a broad-specificity enzyme. Fifty-five halogenated aliphatic and cyclic hydrocarbons were tested for dehalogenation with the LinB enzyme. The compounds for testing were systematically selected using a statistical experimental design. Steady-state kinetic constants K(m) and k(cat) were determined for 25 substrates that showed detectable cleavage by the enzyme and low abiotic hydrolysis. Classical quantitative structure-activity relationships (QSARs) were used to correlate the kinetic constants with molecular descriptors and resulted in a model that explained 94% of the experimental data variability. The binding affinity of the tested substrates for this haloalkane dehalogenase correlated with hydrophobicity, molecular surface, dipole moment, and volume:surface ratio. Binding of the substrate molecules in the active site pocket of LinB depends nonlinearly on the size of the molecules. Binding affinity increases with increasing substrate size up to a chain length of six carbon atoms and then decreases. Comparative binding energy (COMBINE) analysis was then used to identify amino acid residues in LinB that modulate its substrate specificity. A model with three statistically significant principal components explained 95% of the experimental data variability. van der Waals interactions between substrate molecules and the enzyme dominated the COMBINE model, in agreement with the importance of substrate size in the classical QSAR model. Only a limited number of protein residues (6-8%) contribute significantly to the explanation of variability in binding affinities. The amino acid residues important for explaining variability in binding affinities are as follows: (i) first-shell residues Asn38, Asp108, Trp109, Glu132, Ile134, Phe143, Phe151, Phe169, Val173, Trp207, Pro208, Ile211, Leu248, and His272, (ii) tunnel residues Pro144, Asp147, Leu177, and Ala247, and (iii) second-shell residues Pro39 and Phe273. The tunnel and the second-shell residues represent the best targets for modulating specificity since their replacement does not lead to loss of functionality by disruption of the active site architecture. The mechanism of molecular adaptation toward a different specificity is discussed on the basis of quantitative comparison of models derived for two protein family members.

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Year:  2005        PMID: 15736949     DOI: 10.1021/bi047912o

Source DB:  PubMed          Journal:  Biochemistry        ISSN: 0006-2960            Impact factor:   3.162


  12 in total

1.  Weak activity of haloalkane dehalogenase LinB with 1,2,3-trichloropropane revealed by X-Ray crystallography and microcalorimetry.

Authors:  Marta Monincová; Zbynek Prokop; Jitka Vévodová; Yuji Nagata; Jirí Damborsky
Journal:  Appl Environ Microbiol       Date:  2007-01-26       Impact factor: 4.792

2.  Haloalkane dehalogenase LinB is responsible for beta- and delta-hexachlorocyclohexane transformation in Sphingobium indicum B90A.

Authors:  Poonam Sharma; Vishakha Raina; Rekha Kumari; Shweta Malhotra; Charu Dogra; Hansi Kumari; Hans-Peter E Kohler; Hans-Rudolf Buser; Christof Holliger; Rup Lal
Journal:  Appl Environ Microbiol       Date:  2006-09       Impact factor: 4.792

Review 3.  Biochemistry of microbial degradation of hexachlorocyclohexane and prospects for bioremediation.

Authors:  Rup Lal; Gunjan Pandey; Pooja Sharma; Kirti Kumari; Shweta Malhotra; Rinku Pandey; Vishakha Raina; Hans-Peter E Kohler; Christof Holliger; Colin Jackson; John G Oakeshott
Journal:  Microbiol Mol Biol Rev       Date:  2010-03       Impact factor: 11.056

4.  The evolution of new enzyme function: lessons from xenobiotic metabolizing bacteria versus insecticide-resistant insects.

Authors:  Robyn J Russell; Colin Scott; Colin J Jackson; Rinku Pandey; Gunjan Pandey; Matthew C Taylor; Christopher W Coppin; Jian-Wei Liu; John G Oakeshott
Journal:  Evol Appl       Date:  2011-03       Impact factor: 5.183

5.  Sequence- and activity-based screening of microbial genomes for novel dehalogenases.

Authors:  Wing Yiu Chan; Max Wong; Jennifer Guthrie; Alexei V Savchenko; Alexander F Yakunin; Emil F Pai; Elizabeth A Edwards
Journal:  Microb Biotechnol       Date:  2009-11-12       Impact factor: 5.813

6.  Sensitive operation of enzyme-based biodevices by advanced signal processing.

Authors:  Stanislav Mazurenko; Sarka Bidmanova; Marketa Kotlanova; Jiri Damborsky; Zbynek Prokop
Journal:  PLoS One       Date:  2018-06-18       Impact factor: 3.240

Review 7.  Dehalogenases: From Improved Performance to Potential Microbial Dehalogenation Applications.

Authors:  Thiau-Fu Ang; Jonathan Maiangwa; Abu Bakar Salleh; Yahaya M Normi; Thean Chor Leow
Journal:  Molecules       Date:  2018-05-07       Impact factor: 4.411

8.  qPIPSA: relating enzymatic kinetic parameters and interaction fields.

Authors:  Razif R Gabdoulline; Matthias Stein; Rebecca C Wade
Journal:  BMC Bioinformatics       Date:  2007-10-05       Impact factor: 3.169

9.  Genomic Analysis of γ-Hexachlorocyclohexane-Degrading Sphingopyxis lindanitolerans WS5A3p Strain in the Context of the Pangenome of Sphingopyxis.

Authors:  Michal A Kaminski; Adam Sobczak; Andrzej Dziembowski; Leszek Lipinski
Journal:  Genes (Basel)       Date:  2019-09-06       Impact factor: 4.096

Review 10.  Insights Into the Biodegradation of Lindane (γ-Hexachlorocyclohexane) Using a Microbial System.

Authors:  Wenping Zhang; Ziqiu Lin; Shimei Pang; Pankaj Bhatt; Shaohua Chen
Journal:  Front Microbiol       Date:  2020-03-27       Impact factor: 5.640

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