P152R Mutation Within MeCP2 Can Cause Loss of DNA-Binding Selectivity.

MeCP2 is a protein highly expressed in the brain that participates in the genetic expression and RNA splicing regulation. MeCP2 binds preferably to methylated DNA and other nuclear corepressors to alter chromatin. MECP2 gene mutations can cause rett syndrome (RTT), a severe neurological disorder that affects around one in ten thousand girls. In this paper, Molecular Dynamics (MD) simulations were performed to scrutinize how the MeCP2 P152R mutation influences the protein binding to DNA. Also, the Umbrella Sampling technique was used to obtain the potential mean forces (PMFs) of both wild-type and mutated MeCP2 Methyl-CpG-binding domain (MBD) binding to both non-methylated and methylated DNA. P152R is a common missense mutation in MBD associated with RTT; however, there are no studies that explain how it causes protein dysfunction. The results from this study hypothesize that P152R mutation leads to MBD binding more strongly to DNA, while selectively decreasing binding affinity to methylated DNA. These provide an explanation for previous not conclusive experimental results regarding the mechanism of how this mutation affects the binding of the protein to DNA, and subsequently its effects on RTT. Furthermore, the results of this research-in-progress can be used as the basis for further investigations into the molecular basis of RTT and to potentially reveal a target for therapy in the future.

Detecting atypical examples of known domain types by sequence similarity searching: the SBASE domain library approach.

SBASE is a project initiated to detect known domain types and predicting domain architectures using sequence similarity searching (Simon et al., Protein Seq Data Anal, 5: 39-42, 1992, Pongor et al, Nucl. Acids. Res. 21:3111-3115, 1992). The current approach uses a curated collection of domain sequences - the SBASE domain library - and standard similarity search algorithms, followed by postprocessing which is based on a simple statistics of the domain similarity network (http://hydra.icgeb.trieste.it/sbase/). It is especially useful in detecting rare, atypical examples of known domain types which are sometimes missed even by more sophisticated methodologies. This approach does not require multiple alignment or machine learning techniques, and can be a useful complement to other domain detection methodologies. This article gives an overview of the project history as well as of the concepts and principles developed within this the project.

Probing dynamic protein ensembles with atomic proximity measures.

The emerging role of internal dynamics in protein fold and function requires new avenues of structure analysis. We analyzed the dynamically restrained conformational ensemble of ubiquitin generated from residual dipolar coupling data, in terms of protruding and buried atoms as well as interatomic distances, using four proximity-based algorithms, CX, DPX, PRIDE and PRIDE-NMR (http://hydra.icgeb.trieste.it/protein/). We found that Ubiquitin, this relatively rigid molecule has a highly diverse dynamic ensemble. The environment of protruding atoms is highly variable across conformers, on the other hand, only a part of buried atoms tends to fluctuate. The variability of the ensemble cautions against the use of single conformers when explaining functional phenomena. We also give a detailed evaluation of PRIDE-NMR on a wide dataset and discuss its usage in the light of the features of available NMR distance restraint sets in public databases.

Application of a simple likelihood ratio approximant to protein sequence classification.

MOTIVATION: Likelihood ratio approximants (LRA) have been widely used for model comparison in statistics. The present study was undertaken in order to explore their utility as a scoring (ranking) function in the classification of protein sequences. RESULTS: We used a simple LRA-based on the maximal similarity (or minimal distance) scores of the two top ranking sequence classes. The scoring methods (Smith-Waterman, BLAST, local alignment kernel and compression based distances) were compared on datasets designed to test sequence similarities between proteins distantly related in terms of structure or evolution. It was found that LRA-based scoring can significantly outperform simple scoring methods.