Andrey V. Ilatovskiy

Pocketome: an encyclopedia of small-molecule binding sites in 4D

The importance of binding site plasticity in protein–ligand interactions is well-recognized, and so are the difficulties in predicting the nature and the degree of this plasticity by computational means. To assist in understanding the flexible protein–ligand interactions, we constructed the Pocketome, an encyclopedia of about one thousand experimentally solved conformational ensembles of druggable binding sites in proteins, grouped by location and consistent chain/cofactor composition. The multiplicity of pockets within the ensembles adds an extra, fourth dimension to the Pocketome entry data. Within each ensemble, the pockets were carefully classified by the degree of their pairwise similarity and compatibility with different ligands. The core of the Pocketome is derived regularly and automatically from the current releases of the Protein Data Bank and the Uniprot Knowledgebase; this core is complemented by entries built from manually provided seed ligand locations. The Pocketome website (www.pocketome.org) allows searching for the sites of interest, analysis of conformational clusters, important residues, binding compatibility matrices and interactive visualization of the ensembles using the ActiveICM web browser plugin. The Pocketome collection can be used to build multi-conformational docking and 3D activity models as well as to design cross-docking and virtual ligand screening benchmarks.

Compound activity prediction using models of binding pockets or ligand properties in 3D.

Transient interactions of endogenous and exogenous small molecules with flexible binding sites in proteins or macromolecular assemblies play a critical role in all biological processes. Current advances in high-resolution protein structure determination, database development, and docking methodology make it possible to design three-dimensional models for prediction of such interactions with increasing accuracy and specificity. Using the data collected in the Pocketome encyclopedia, we here provide an overview of two types of the three-dimensional ligand activity models, pocketbased and ligand property-based, for two important classes of proteins, nuclear and G-protein coupled receptors. For half the targets, the pocket models discriminate actives from property matched decoys with acceptable accuracy (the area under ROC curve, AUC, exceeding 84%) and for about one fifth of the targets with high accuracy (AUC > 95%). The 3D ligand property field models performed better than 95% in half of the cases. The high performance models can already become a basis of activity predictions for new chemicals. Family-wide benchmarking of the models highlights strengths of both approaches and helps identify their inherent bottlenecks and challenges.

Orphan receptor ligand discovery by pickpocketing pharmacological neighbors

Understanding the pharmacological similarity of G protein-coupled receptors (GPCRs) is paramount for predicting ligand off-target effects, drug repurposing, and ligand discovery for orphan receptors. Phylogenetic relationships do not always correctly capture pharmacological similarity. Previous family-wide attempts to define pharmacological relationships were based on three-dimensional structures and/or known receptor-ligand pairings, both unavailable for orphan GPCRs. Here, we present GPCR-CoINPocket, a novel contact-informed neighboring pocket metric of GPCR binding-site similarity that is informed by patterns of ligand-residue interactions observed in crystallographically characterized GPCRs. GPCR-CoINPocket is applicable to receptors with unknown structure or ligands and accurately captures known pharmacological relationships between GPCRs, even those undetected by phylogeny. When applied to orphan receptor GPR37L1, GPCR-CoINPocket identified its pharmacological neighbors, and transfer of their pharmacology aided in discovery of the first surrogate ligands for this orphan with a 30% success rate. Although primarily designed for GPCRs, the method is easily transferable to other protein families.

PeptiSite: a structural database of peptide binding sites in 4D.

We developed PeptiSite, a comprehensive and reliable database of biologically and structurally characterized peptide-binding sites, in which each site is represented by an ensemble of its complexes with protein, peptide and small molecule partners. The unique features of the database include: (1) the ensemble site representation that provides a fourth dimension to the otherwise three dimensional data, (2) comprehensive characterization of the binding site architecture that may consist of a multimeric protein assembly with cofactors and metal ions and (3) analysis of consensus interaction motifs within the ensembles and identification of conserved determinants of these interactions. Currently the database contains 585 proteins with 650 peptide-binding sites. http://peptisite.ucsd.edu/ link allows searching for the sites of interest and interactive visualization of the ensembles using the ActiveICM web-browser plugin. This structural database for protein-peptide interactions enables understanding of structural principles of these interactions and may assist the development of an efficient peptide docking benchmark.

Modeling and small-angle neutron scattering spectra of chromatin supernucleosomal structures at genome scale

Eukaryotic genome is a highly compacted nucleoprotein complex organized in a hierarchical structure based on nucleosomes. Detailed organization of this structure remains unknown. In the present work we developed algorithms for geometry modeling of the supernucleosomal chromatin structure and for computing distance distribution functions and small-angle neutron scattering (SANS) spectra of the genome-scale (∼106 nucleosomes) chromatin structure at residue resolution. Our physical nucleosome model was based on the mononucleosome crystal structure. A nucleosome was assumed to be rigid within a local coordinate system. Interface parameters between nucleosomes can be set for each nucleosome independently. Pair distance distributions were computed with Monte Carlo simulation. SANS spectra were calculated with Fourier transformation of weighted distance distribution; the concentration of heavy water in solvent and probability of H/D exchange were taken into account. Two main modes of supernucleosomal structure generation were used. In a free generation mode all interface parameters were chosen randomly, whereas nucleosome self-intersections were not allowed. The second generation mode (generation in volume) enabled spherical or cubical wall restrictions. It was shown that calculated SANS spectra for a number of our models were in general agreement with available experimental data.Eukaryotic genome is a highly compacted nucleoprotein complex organized in a hierarchical structure based on nucleosomes. Detailed organization of this structure remains unknown. In the present work we developed algorithms for geometry modeling of the supernucleosomal chromatin structure and for computing distance distribution functions and small-angle neutron scattering (SANS) spectra of the genome-scale (∼106 nucleosomes) chromatin structure at residue resolution. Our physical nucleosome model was based on the mononucleosome crystal structure. A nucleosome was assumed to be rigid within a local coordinate system. Interface parameters between nucleosomes can be set for each nucleosome independently. Pair distance distributions were computed with Monte Carlo simulation. SANS spectra were calculated with Fourier transformation of weighted distance distribution; the concentration of heavy water in solvent and probability of H/D exchange were taken into account. Two main modes of supernucleosomal structure g...

Genome-Wide Search for Local DNA Segments with Anomalous GC-Content

An anomalous (i.e., significantly different from genome-average) GC-content is often used as one of the markers to reveal the events of horizontal gene transfer (HGT). Unfortunately, results obtained by the traditional fixed-length window analysis strongly depend on an arbitrary selection of DNA window length. Here we present a new method for genome-wide statistical analysis of GC-content without that drawback. The method is based on a set of nonparametric statistical tests and is capable of providing reliable estimations of both a local and global GC-content, and thus can identify small local areas (as short as 30 bp) with anomalous GC-content in a bacterial genome. The tests, applied to a well-studied bacterial genome of Escherichia coli K-12, show that approximately 21% of the genome belongs to the anomalous GC-content areas. Among top 23 anomalous GC-content areas, seven correspond to the annotated prophages, four to Rhs elements, and two to IS elements. A remaining 10 areas contain putative horizontally transferred DNA and genes with still unknown functions. Software is available at http://mml.spbstu.ru/gcstat.

SANS spectra of the fractal supernucleosomal chromatin structure models

The eukaryotic genome consists of chromatin—a nucleoprotein complex with hierarchical architecture based on nucleosomes, the organization of higher-order chromatin structures still remains unknown. Available experimental data, including SANS spectra we had obtained for whole nuclei, suggested fractal nature of chromatin. Previously we had built random-walk supernucleosomal models (up to 106 nucleosomes) to interpret our SANS spectra. Here we report a new method to build fractal supernucleosomal structure of a given fractal dimension or two different dimensions. Agreement between calculated and experimental SANS spectra was significantly improved, especially for model with two fractal dimensions—3 and 2.