Mutations in RIT1 cause Noonan syndrome - additional functional evidence and expanding the clinical phenotype.

RASopathies are a clinically heterogeneous group of conditions caused by mutations in 1 of 16 proteins in the RAS-mitogen activated protein kinase (RAS-MAPK) pathway. Recently, mutations in RIT1 were identified as a novel cause for Noonan syndrome. Here we provide additional functional evidence for a causal role of RIT1 mutations and expand the associated phenotypic spectrum. We identified two de novo missense variants p.Met90Ile and p.Ala57Gly. Both variants resulted in increased MEK-ERK signaling compared to wild-type, underscoring gain-of-function as the primary functional mechanism. Introduction of p.Met90Ile and p.Ala57Gly into zebrafish embryos reproduced not only aspects of the human phenotype but also revealed abnormalities of eye development, emphasizing the importance of RIT1 for spatial and temporal organization of the growing organism. In addition, we observed severe lymphedema of the lower extremity and genitalia in one patient. We provide additional evidence for a causal relationship between pathogenic mutations in RIT1, increased RAS-MAPK/MEK-ERK signaling and the clinical phenotype. The mutant RIT1 protein may possess reduced GTPase activity or a diminished ability to interact with cellular GTPase activating proteins; however the precise mechanism remains unknown. The phenotypic spectrum is likely to expand and includes lymphedema of the lower extremities in addition to nuchal hygroma.

Background frequencies for residue variability estimates: BLOSUM revisited.

BACKGROUND: Shannon entropy applied to columns of multiple sequence alignments as a score of residue conservation has proven one of the most fruitful ideas in bioinformatics. This straightforward and intuitively appealing measure clearly shows the regions of a protein under increased evolutionary pressure, highlighting their functional importance. The inability of the column entropy to differentiate between residue types, however, limits its resolution power. RESULTS: In this work we suggest generalizing Shannon's expression to a function with similar mathematical properties, that, at the same time, includes observed propensities of residue types to mutate to each other. To do that, we revisit the original construction of BLOSUM matrices, and re-interpret them as mutation probability matrices. These probabilities are then used as background frequencies in the revised residue conservation measure. CONCLUSION: We show that joint entropy with BLOSUM-proportional probabilities as a reference distribution enables detection of protein functional sites comparable in quality to a time-costly maximum-likelihood evolution simulation method (rate4site), and offers greater resolution than the Shannon entropy alone, in particular in the cases when the available sequences are of narrow evolutionary scope.

On itinerant water molecules and detectability of protein-protein interfaces through comparative analysis of homologues.

We discuss the question of which residues are sufficiently important for protein-protein interaction to be under notable evolutionary pressure. Its interest stems from the applicability of this knowledge in the reverse direction, to detect a protein-protein interface on a single protomer, starting from the rate of mutation of participating residues. Using the analysis of trajectories produced by the molecular dynamics simulations, we suggest that, in the case of water soluble proteins, a large fraction of evolutionarily privileged residues can be found by considering the dynamic behavior of the protein interface and by looking for residues which exchange water molecules with the bulk of the solvent outstandingly slowly (tentatively termed "dry residues"). We show that the dry interface residues are better conserved across homologues than the generic "geometric footprint" and can be quite reliably detected through comparative analysis of protein homologues, without strong dependence on the choice of method. Furthermore, we show that dry residues distinguish themselves through a set of biophysical properties consistent with the known mechanisms of protein oligomerization: their compositional shift toward nonpolar, overlap, and co-location with residues exhibiting low mobility, their two- to threefold increased propensity over the rest of the geometric footprint to form hydrogen bonds, and four- to almost tenfold increased likelihood to participate in formation of salt bridges. These properties, consistently, help understand the observed increase in the evolutionary pressure that dry residues experience.

Evolutionary trace report_maker: a new type of service for comparative analysis of proteins.

: Evolutionary trace report_maker offers a new type of service for researchers investigating the function of novel proteins. It pools, from different sources, information about protein sequence, structure and elementary annotation, and to that background superimposes inference about the evolutionary behavior of individual residues, using real-valued evolutionary trace method. As its only input it takes a Protein Data Bank identifier or UniProt accession number, and returns a human-readable document in PDF format, supplemented by the original data needed to reproduce the results quoted in the report.

Evolutionary and structural feedback on selection of sequences for comparative analysis of proteins.

It has been noted that slowly evolving protein residues have two properties: (a) they tend to cluster in the native fold, and (b) they delineate functional surfaces-parts of the surface through which the protein interacts with other proteins or small ligands. Herein, we demonstrate that the two are coupled sufficiently strongly that one effect, when observed, statistically implies the other. Detection of both can be accomplished in multiple sequence alignment related methods by the careful selection of relevant sequences. For the demonstration, we use two sets of protein families: a small set of diverse proteins with diverse functional surfaces, and a large set of homodimerizing enzymes. A practical outcome of our considerations is a simple prescriptive rule for the selection of homologous sequences for the comparative analysis of proteins: in order to optimize the detection of (potentially unknown) functional surfaces, it is sufficient to select sequences in such a way that the residues observed at any level of evolutionary divergence, as implied by the alignment, cluster on the folded protein.

A structure and evolution-guided Monte Carlo sequence selection strategy for multiple alignment-based analysis of proteins.

MOTIVATION: Various multiple sequence alignment-based methods have been proposed to detect functional surfaces in proteins, such as active sites or protein interfaces. The effect that the choice of sequences has on the conclusions of such analysis has seldom been discussed. In particular, no method has been discussed in terms of its ability to optimize the sequence selection for the reliable detection of functional surfaces. RESULTS: Here we propose, for the case of proteins with known structure, a heuristic Metropolis Monte Carlo strategy to select sequences from a large set of homologues, in order to improve detection of functional surfaces. The quantity guiding the optimization is the clustering of residues which are under increased evolutionary pressure, according to the sample of sequences under consideration. We show that we can either improve the overlap of our prediction with known functional surfaces in comparison with the sequence similarity criteria of selection or match the quality of prediction obtained through more elaborate non-structure based-methods of sequence selection. For the purpose of demonstration we use a set of 50 homodimerizing enzymes which were co-crystallized with their substrates and cofactors.

An evolution based classifier for prediction of protein interfaces without using protein structures.

MOTIVATION: The number of available protein structures still lags far behind the number of known protein sequences. This makes it important to predict which residues participate in protein-protein interactions using only sequence information. Few studies have tackled this problem until now. RESULTS: We applied support vector machines to sequences in order to generate a classification of all protein residues into those that are part of a protein interface and those that are not. For the first time evolutionary information was used as one of the attributes and this inclusion of evolutionary importance rankings improves the classification. Leave-one-out cross-validation experiments show that prediction accuracy reaches 64%.

A family of evolution-entropy hybrid methods for ranking protein residues by importance.

In order to identify the amino acids that determine protein structure and function it is useful to rank them by their relative importance. Previous approaches belong to two groups; those that rely on statistical inference, and those that focus on phylogenetic analysis. Here, we introduce a class of hybrid methods that combine evolutionary and entropic information from multiple sequence alignments. A detailed analysis in insulin receptor kinase domain and tests on proteins that are well-characterized experimentally show the hybrids' greater robustness with respect to the input choice of sequences, as well as improved sensitivity and specificity of prediction. This is a further step toward proteome scale analysis of protein structure and function.

Combining inference from evolution and geometric probability in protein structure evaluation.

Starting from the hypothesis that evolutionarily important residues form a spatially limited cluster in a protein's native fold, we discuss the possibility of detecting a non-native structure based on the absence of such clustering. The relevant residues are determined using the Evolutionary Trace method. We propose a quantity to measure clustering of the selected residues on the structure and show that the exact values for its average and variance over several ensembles of interest can be found. This enables us to study the behavior of the associated z-scores. Since our approach rests on an analytic result, it proves to be general, customizable, and computationally fast. We find that clustering is indeed detectable in a large representative protein set. Furthermore, we show that non-native structures tend to achieve lower residue-clustering z-scores than those attained by the native folds. The most important conclusion that we draw from this work is that consistency between structural and evolutionary information, manifested in clustering of key residues, imposes powerful constraints on the conformational space of a protein.