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J Liermann

Publications and source records attributed to J Liermann.

2 recordsLinked to original sources

The solution structure of VAT-N reveals a 'missing link' in the evolution of complex enzymes from a simple betaalphabetabeta element.

BACKGROUND: The VAT protein of the archaebacterium Thermoplasma acidophilum, like all other members of the Cdc48/p97 family of AAA ATPases, has two ATPase domains and a 185-residue amino-terminal substrate-recognition domain, VAT-N. VAT shows activity in protein folding and unfolding and thus shares the common function of these ATPases in disassembly and/or degradation of protein complexes. RESULTS: Using nuclear magnetic resonance (NMR) spectroscopy, we found that VAT-N is composed of two equally sized subdomains. The amino-terminal subdomain VAT-Nn (comprising residues Met1-Thr92) forms a double-psi beta-barrel whose pseudo-twofold symmetry is mirrored by an internal sequence repeat of 42 residues. The carboxy-terminal subdomain VAT-Nc (comprising residues Glu93-Gly185) forms a novel six-stranded beta-clam fold. Together, VAT-Nn and VAT-Nc form a kidney-shaped structure, in close agreement with results from electron microscopy. Sequence and structure analyses showed that VAT-Nn is related to numerous proteins including prokaryotic transcription factors, metabolic enzymes, the protease cofactors UFD1 and PrlF, and aspartic proteinases. These proteins map out an evolutionary path from simple homodimeric transcription factors containing a single copy of the VAT-Nn repeat to complex enzymes containing four copies. CONCLUSIONS: Our results suggest that VAT-N is a precursor of the aspartic proteinases that has acquired peptide-binding activity while remaining proteolytically incompetent. We propose that the binding site of the protein is similar to that of aspartic proteinases, in that it lies between the psi-loops of the amino-terminal beta-barrel and that it coincides with a crescent-shaped band of positive charge extending across the upper face of the molecule.

Adenosine Triphosphatases↗

Automated backbone assignment of labeled proteins using the threshold accepting algorithm.

The sequential assignment of backbone resonances is the first step in the structure determination of proteins by heteronuclear NMR. For larger proteins, an assignment strategy based on proton side-chain information is no longer suitable for the use in an automated procedure. Our program PASTA (Protein ASsignment by Threshold Accepting) is therefore designed to partially or fully automate the sequential assignment of proteins, based on the analysis of NMR backbone resonances plus C beta information. In order to overcome the problems caused by peak overlap and missing signals in an automated assignment process, PASTA uses threshold accepting, a combinatorial optimization strategy, which is superior to simulated annealing due to generally faster convergence and better solutions. The reliability of this algorithm is shown by reproducing the complete sequential backbone assignment of several proteins from published NMR data. The robustness of the algorithm against misassigned signals, noise, spectral overlap and missing peaks is shown by repeating the assignment with reduced sequential information and increased chemical shift tolerances. The performance of the program on real data is finally demonstrated with automatically picked peak lists of human nonpancreatic synovial phospholipase A2, a protein with 124 residues.

Algorithms↗