We developed a new computational algorithm for the accurate identification of ligand binding envelopes rather than surface binding sites. We performed a large scale classification of the identified envelopes according to their shape and physicochemical properties. The predicting algorithm, called PocketFinder, uses a transformation of the Lennard-Jones potential calculated from a threedimensional protein structure and does not require any knowledge about a potential ligand molecule. We validated this algorithm using two systematically collected data sets of ligand binding pockets from complexed (bound) and uncomplexed (apo) structures from the Protein Data Bank, 5616 and 11,510, respectively. As many as 96.8% of experimental binding sites were predicted at better than 50% overlap level. Furthermore 95.0% of the asserted sites from the apo receptors were predicted at the same level. We demonstrate that conformational differences between the apo and bound pockets do not dramatically affect the prediction results. Prediction of ligand binding sites is a fundamental step in the investigation of the molecular recognition mechanism and function of a protein. An increasing number of protein structures are becoming available from high throughput structural genomic projects prior to biological and functional characterization. Therefore, computational methods to predict ligand binding sites are becoming increasingly important.There are three independent sources of information that can be used to infer the location of possible ligand binding sites on the surface of a protein: (i) protein structure, (ii) evolutionary information (sequence alignments), and (iii) ligand/substrate information. A number of sophisticated algorithms using evolutionary information or algorithms predicting locations of binding sites for specific substrates have been published (1-3). Here we attempted to develop an algorithm that is based solely on the protein structure and without any prior knowledge about the nature of the substrate. We hypothesized that the structure itself is sufficiently informative, whereas the evolutionary conservation and the nature of the ligand can only be used as optional contributions.Proteins are involved in several kinds of molecular interactions: with other proteins, DNA, RNA, peptides, and small molecules. In this study we present an algorithm to predict the binding envelopes near potential small ligand binding sites or areas that could be targeted with small "druglike" compounds. Once the ligand binding pocket is predicted, a high throughput ligand docking procedure or structure-based drug design (4 -9) can be used to generate a list of the lead molecules. The properties of druglike molecules are well studied (10, 11) and cover a certain range of sizes, typically with molecular mass between 300 and 700 daltons. Therefore, we excluded from consideration very small ligands, such as metals and small solvent molecules, as well as very large substrates. However we wanted to develop an algorithm that within this size rang...

Oligodendroglioma is characterized by unique clinical, pathological, and genetic features. Recurrent losses of chromosomes 1p and 19q are strongly associated with this brain cancer but knowledge of the identity and function of the genes affected by these alterations is limited. We performed exome sequencing on a discovery set of 16 oligodendrogliomas with 1p/19q co-deletion to identify new molecular features at base-pair resolution. As anticipated, there was a high rate of IDH mutations: all cases had mutations in either IDH1 (14/16) or IDH2 (2/16). In addition, we discovered somatic mutations and insertions/deletions in the CIC gene on chromosome 19q13.2 in 13/16 tumours. These discovery set mutations were validated by deep sequencing of 13 additional tumours, which revealed 7 others with CIC mutations, thus bringing the overall mutation rate in oligodendrogliomas in this study to 20/29 (69%). In contrast, deep sequencing of astrocytomas and oligoastrocytomas without 1p/19q loss revealed that CIC alterations were otherwise rare (1/60; 2%). Of the 21 non-synonymous somatic mutations in 20 CIC-mutant oligodendrogliomas, 9 were in exon 5 within an annotated DNA interacting domain and 3 were in exon 20 within an annotated protein interacting domain. The remaining 9 were found in other exons and frequently included truncations. CIC mutations were highly associated with oligodendroglioma histology, 1p/19q co-deletion and IDH1/2 mutation (p<0.001). Although we observed no differences in the clinical outcomes of CIC mutant versus wild-type tumors, in a background of 1p/19q co-deletion, hemizygous CIC mutations are likely important. We hypothesize that the mutant CIC on the single retained 19q allele is linked to the pathogenesis of oligodendrogliomas with IDH mutation. Our detailed study of genetic aberrations in oligodendroglioma suggests a functional interaction between CIC mutation, IDH1/2 mutation and 1p/19q co-deletion.

We used trio-based whole-exome sequencing to analyze two families affected by Weaver syndrome, including one of the original families reported in 1974. Filtering of rare variants in the affected probands against the parental variants identified two different de novo mutations in the enhancer of zeste homolog 2 (EZH2). Sanger sequencing of EZH2 in a third classically-affected proband identified a third de novo mutation in this gene. These data show that mutations in EZH2 cause Weaver syndrome.