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1.  X-linked Charcot-Marie-Tooth disease, Arts syndrome, and prelingual non-syndromic deafness form a disease continuum: evidence from a family with a novel PRPS1 mutation 
Background
X-linked Charcot-Marie-Tooth disease type 5 (CMTX5), Arts syndrome, and non-syndromic sensorineural deafness (DFN2) are allelic syndromes, caused by reduced activity of phosphoribosylpyrophosphate synthetase 1 (PRS-I) due to loss-of-function mutations in PRPS1. As only few families have been described, knowledge about the relation between these syndromes, the phenotypic spectrum in patients and female carriers, and the relation to underlying PRS-I activity is limited.
Methods
We investigated a family with a novel PRPS1 mutation (c.830A > C, p.Gln277Pro) by extensive phenotyping, MRI, and genetic and enzymatic tests.
Results
The male index subject presented with an overlap of CMTX5 and Arts syndrome features, whereas his sister presented with prelingual DFN2. Both showed mild parietal and cerebellar atrophy on MRI. Enzymatically, PRS-I activity was undetectable in the index subject, reduced in his less affected sister, and normal in his unaffected mother.
Conclusions
Our findings demonstrate that CMTX5, Arts syndrome and DFN2 are phenotypic clusters on an intrafamilial continuum, including overlapping phenotypes even within individuals. The respective phenotypic presentation seems to be determined by the exact PRPS1 mutation and the residual enzyme activity, the latter being largely influenced by the degree of skewed X-inactivation. Finally, our findings show that brain atrophy might be more common in PRPS1-disorders than previously thought.
doi:10.1186/1750-1172-9-24
PMCID: PMC3931488  PMID: 24528855
Ataxia; Early onset ataxia; Genetics; Hearing loss; Optic atrophy; Behr syndrome
2.  CING: an integrated residue-based structure validation program suite 
Journal of Biomolecular Nmr  2012;54(3):267-283.
We present a suite of programs, named CING for Common Interface for NMR Structure Generation that provides for a residue-based, integrated validation of the structural NMR ensemble in conjunction with the experimental restraints and other input data. External validation programs and new internal validation routines compare the NMR-derived models with empirical data, measured chemical shifts, distance- and dihedral restraints and the results are visualized in a dynamic Web 2.0 report. A red–orange–green score is used for residues and restraints to direct the user to those critiques that warrant further investigation. Overall green scores below ~20 % accompanied by red scores over ~50 % are strongly indicative of poorly modelled structures. The publically accessible, secure iCing webserver (https://nmr.le.ac.uk) allows individual users to upload the NMR data and run a CING validation analysis.
Electronic supplementary material
The online version of this article (doi:10.1007/s10858-012-9669-7) contains supplementary material, which is available to authorized users.
doi:10.1007/s10858-012-9669-7
PMCID: PMC3483101  PMID: 22986687
NMR; Structure validation; PDB; Errors; Quality; Protein structure
3.  Evolution and Diversification of the Organellar Release Factor Family 
Molecular Biology and Evolution  2012;29(11):3497-3512.
Translation termination is accomplished by proteins of the Class I release factor family (RF) that recognize stop codons and catalyze the ribosomal release of the newly synthesized peptide. Bacteria have two canonical RFs: RF1 recognizes UAA and UAG, RF2 recognizes UAA and UGA. Despite that these two release factor proteins are sufficient for de facto translation termination, the eukaryotic organellar RF protein family, which has evolved from bacterial release factors, has expanded considerably, comprising multiple subfamilies, most of which have not been functionally characterized or formally classified. Here, we integrate multiple sources of information to analyze the remarkable differentiation of the RF family among organelles. We document the origin, phylogenetic distribution and sequence structure features of the mitochondrial and plastidial release factors: mtRF1a, mtRF1, mtRF2a, mtRF2b, mtRF2c, ICT1, C12orf65, pRF1, and pRF2, and review published relevant experimental data. The canonical release factors (mtRF1a, mtRF2a, pRF1, and pRF2) and ICT1 are derived from bacterial ancestors, whereas the others have resulted from gene duplications of another release factor. These new RF family members have all lost one or more specific motifs relevant for bona fide release factor function but are mostly targeted to the same organelle as their ancestor. We also characterize the subset of canonical release factor proteins that bear nonclassical PxT/SPF tripeptide motifs and provide a molecular-model-based rationale for their retained ability to recognize stop codons. Finally, we analyze the coevolution of canonical RFs with the organellar genetic code. Although the RF presence in an organelle and its stop codon usage tend to coevolve, we find three taxa that encode an RF2 without using UGA stop codons, and one reverse scenario, where mamiellales green algae use UGA stop codons in their mitochondria without having a mitochondrial type RF2. For the latter, we put forward a “stop-codon reinvention” hypothesis that involves the retargeting of the plastid release factor to the mitochondrion.
doi:10.1093/molbev/mss157
PMCID: PMC3472500  PMID: 22688947
release factor; translation termination; mitochondrion; plastid; evolution; genetic code
4.  Structure based hypothesis of a mitochondrial ribosome rescue mechanism 
Biology Direct  2012;7:14.
Background
mtRF1 is a vertebrate mitochondrial protein with an unknown function that arose from a duplication of the mitochondrial release factor mtRF1a. To elucidate the function of mtRF1, we determined the positions that are conserved among mtRF1 sequences but that are different in their mtRF1a paralogs. We subsequently modeled the 3D structure of mtRF1a and mtRF1 bound to the ribosome, highlighting the structural implications of these differences to derive a hypothesis for the function of mtRF1.
Results
Our model predicts, in agreement with the experimental data, that the 3D structure of mtRF1a allows it to recognize the stop codons UAA and UAG in the A-site of the ribosome. In contrast, we show that mtRF1 likely can only bind the ribosome when the A-site is devoid of mRNA. Furthermore, while mtRF1a will adopt its catalytic conformation, in which it functions as a peptidyl-tRNA hydrolase in the ribosome, only upon binding of a stop codon in the A-site, mtRF1 appears specifically adapted to assume this extended, peptidyl-tRNA hydrolyzing conformation in the absence of mRNA in the A-site.
Conclusions
We predict that mtRF1 specifically recognizes ribosomes with an empty A-site and is able to function as a peptidyl-tRNA hydrolase in those situations. Stalled ribosomes with empty A-sites that still contain a tRNA bound to a peptide chain can result from the translation of truncated, stop-codon less mRNAs. We hypothesize that mtRF1 recycles such stalled ribosomes, performing a function that is analogous to that of tmRNA in bacteria.
Reviewers
This article was reviewed by Dr. Eugene Koonin, Prof. Knud H. Nierhaus (nominated by Dr. Sarah Teichmann) and Dr. Shamil Sunyaev.
doi:10.1186/1745-6150-7-14
PMCID: PMC3418547  PMID: 22569235
Class I release factor; mtRF1; mtRF1a; Mitochondrial genetic code; Translation termination; Stalled ribosome
5.  In Silico Veritas: The Pitfalls and Challenges of Predicting GPCR-Ligand Interactions 
Pharmaceuticals  2011;4(9):1196-1215.
Recently the first community-wide assessments of the prediction of the structures of complexes between proteins and small molecule ligands have been reported in the so called GPCR Dock 2008 and 2010 assessments. In the current review we discuss the different steps along the protein-ligand modeling workflow by critically analyzing the modeling strategies we used to predict the structures of protein-ligand complexes we submitted to the recent GPCR Dock 2010 challenge. These representative test cases, focusing on the pharmaceutically relevant G Protein-Coupled Receptors, are used to demonstrate the strengths and challenges of the different modeling methods. Our analysis indicates that the proper performance of the sequence alignment, introduction of structural adjustments guided by experimental data, and the usage of experimental data to identify protein-ligand interactions are critical steps in the protein-ligand modeling protocol.
doi:10.3390/ph4091196
PMCID: PMC4058654
comparative modeling; ligand binding mode prediction; G protein-coupled receptor (GPCR); GPCR Dock 2010
6.  Enzyme-Specific Activation versus Leaving Group Ability 
Chembiochem  2012;13(12):1785-1790.
Enzyme-specific activation and the substrate mimetics strategy are effective ways to circumvent the limited substrate recognition often encountered in protease-catalyzed peptide synthesis. A key structural element in both approaches is the guanidinophenyl (OGp) ester, which enables important interactions for affinity and recognition by the enzyme—at least, this is usually the explanation given for its successful application. In this study we show that leaving group ability is of equal or even greater importance. To this end we used both experimental and computational methods: 1) synthesis of close analogues of OGp, and their evaluation in a dipeptide synthesis assay with trypsin, 2) molecular docking studies to provide insights into the binding mode, and 3) ab initio calculations to evaluate their electronic properties.
doi:10.1002/cbic.201200227
PMCID: PMC3569868  PMID: 22821810
enzyme catalysis; enzyme-specific activation; guanidinophenyl ester; papain; peptides
7.  Traditional Biomolecular Structure Determination by NMR Spectroscopy Allows for Major Errors 
One of the major goals of structural genomics projects is to determine the three-dimensional structure of representative members of as many different fold families as possible. Comparative modeling is expected to fill the remaining gaps by providing structural models of homologs of the experimentally determined proteins. However, for such an approach to be successful it is essential that the quality of the experimentally determined structures is adequate. In an attempt to build a homology model for the protein dynein light chain 2A (DLC2A) we found two potential templates, both experimentally determined nuclear magnetic resonance (NMR) structures originating from structural genomics efforts. Despite their high sequence identity (96%), the folds of the two structures are markedly different. This urged us to perform in-depth analyses of both structure ensembles and the deposited experimental data, the results of which clearly identify one of the two models as largely incorrect. Next, we analyzed the quality of a large set of recent NMR-derived structure ensembles originating from both structural genomics projects and individual structure determination groups. Unfortunately, a visual inspection of structures exhibiting lower quality scores than DLC2A reveals that the seriously flawed DLC2A structure is not an isolated incident. Overall, our results illustrate that the quality of NMR structures cannot be reliably evaluated using only traditional experimental input data and overall quality indicators as a reference and clearly demonstrate the urgent need for a tight integration of more sophisticated structure validation tools in NMR structure determination projects. In contrast to common methodologies where structures are typically evaluated as a whole, such tools should preferentially operate on a per-residue basis.
Synopsis
Three-dimensional biomolecular structures provide an invaluable source of biologically relevant information. To be able to learn the most of the wealth of information that these structures can provide us, it is of great importance that the quality and accuracy of the protein structure models deposited in the Protein Data Bank are as high as possible. In this work, the authors describe an analysis that illustrates that this is unfortunately not the case for many protein structures solved using nuclear magnetic resonance spectroscopy. They present an example in which two strikingly different models describing the same protein are analyzed using commonly available structure validation tools, and the results of this analysis show one of the two models to be incorrect. Subsequently, using a large set of recently determined structures, the authors demonstrate that unfortunately this example does not stand on its own. The analyses and examples clearly illustrate that relying solely on the experimental data to evaluate structural quality can provide a false sense of correctness and the combination of multiple sophisticated structure validation tools is required to detect the presence of errors in protein nuclear magnetic resonance structures.
doi:10.1371/journal.pcbi.0020009
PMCID: PMC1359070  PMID: 16462939

Results 1-7 (7)