Victor Farutin

Edge‐count probabilities for the identification of local protein communities and their organization

We present a computational approach based on a local search strategy that discovers sets of proteins that preferentially interact with each other. Such sets are referred to as protein communities and are likely to represent functional modules. Preferential interaction between module members is quantified via an analytical framework based on a network null model known as the random graph with given expected degrees. Based on this framework, the concept of local protein community is generalized to that of community of communities. Protein communities and higher‐level structures are extracted from two yeast protein interaction data sets and a network of published interactions between human proteins. The high level structures obtained with the human network correspond to broad biological concepts such as signal transduction, regulation of gene expression, and intercellular communication. Many of the obtained human communities are enriched, in a statistically significant way, for proteins having no clear orthologs in lower organisms. This indicates that the extracted modules are quite coherent in terms of function. Proteins 2006.

Detection of Activity Centers in Cellular Pathways Using Transcript Profiling

Abstract We present a new computational method for identifying regulated pathway components in transcript profiling (TP) experiments by evaluating transcriptional activity in the context of known biological pathways. We construct a graph representing thousands of protein functional relationships by integrating knowledge from public databases and review articles. We use the notion of distance in a graph to define pathway neighborhoods. The pathways perturbed in an experiment are then identified as the subgraph induced by the genes, referred to as activity centers, having significant density of transcriptional activity in their functional neighborhoods. We illustrate the predictive power of this approach by performing and analyzing an experiment of TP53 overexpression in NCI-H125 cells. The detected activity centers are in agreement with the known TP53 activation effects and our independent experimental results. We also apply the method to a serum starvation experiment using HEY cells and investigate the predicted activity of the transcription factor MYC. Finally, we discuss interesting properties of the activity center approach and its possible applications beyond the comparison of two experiments.

An integrated approach using orthogonal analytical techniques to characterize heparan sulfate structure

Heparan sulfate (HS), a glycosaminoglycan present on the surface of cells, has been postulated to have important roles in driving both normal and pathological physiologies. The chemical structure and sulfation pattern (domain structure) of HS is believed to determine its biological function, to vary across tissue types, and to be modified in the context of disease. Characterization of HS requires isolation and purification of cell surface HS as a complex mixture. This process may introduce additional chemical modification of the native residues. In this study, we describe an approach towards thorough characterization of bovine kidney heparan sulfate (BKHS) that utilizes a variety of orthogonal analytical techniques (e.g. NMR, IP-RPHPLC, LC-MS). These techniques are applied to characterize this mixture at various levels including composition, fragment level, and overall chain properties. The combination of these techniques in many instances provides orthogonal views into the fine structure of HS, and in other instances provides overlapping / confirmatory information from different perspectives. Specifically, this approach enables quantitative determination of natural and modified saccharide residues in the HS chains, and identifies unusual structures. Analysis of partially digested HS chains allows for a better understanding of the domain structures within this mixture, and yields specific insights into the non-reducing end and reducing end structures of the chains. This approach outlines a useful framework that can be applied to elucidate HS structure and thereby provides means to advance understanding of its biological role and potential involvement in disease progression. In addition, the techniques described here can be applied to characterization of heparin from different sources.

Combining measurements to estimate properties and characterization extent of complex biochemical mixtures; applications to Heparan Sulfate

Complex mixtures of molecular species, such as glycoproteins and glycosaminoglycans, have important biological and therapeutic functions. Characterization of these mixtures with analytical chemistry measurements is an important step when developing generic drugs such as biosimilars. Recent developments have focused on analytical methods and statistical approaches to test similarity between mixtures. The question of how much uncertainty on mixture composition is reduced by combining several measurements still remains mostly unexplored. Mathematical frameworks to combine measurements, estimate mixture properties, and quantify remaining uncertainty, i.e. a characterization extent, are introduced here. Constrained optimization and mathematical modeling are applied to a set of twenty-three experimental measurements on heparan sulfate, a mixture of linear chains of disaccharides having different levels of sulfation. While this mixture has potentially over two million molecular species, mathematical modeling and the small set of measurements establish the existence of nonhomogeneity of sulfate level along chains and the presence of abundant sulfate repeats. Constrained optimization yields not only estimations of sulfate repeats and sulfate level at each position in the chains but also bounds on these levels, thereby estimating the extent of characterization of the sulfation pattern which is achieved by the set of measurements.

Connectedness profiles in protein networks for the analysis of gene expression data

Knowledge about protein function is often encoded in the form of large and sparse undirected graphs where vertices are proteins and edges represent their functional relationships. One elementary task in the computational utilization of these networks is that of quantifying the density of edges, referred to as connectedness, inside a prescribed protein set. For instance, many functional modules can be identified because of their high connectedness. Since individual proteins can have very different numbers of interactions, a connectedness measure should be well-normalized for vertex degree. Namely, its distribution across random sets of vertices should not be affected when these sets are biased for hubs. We show that such degree-robustness can be achieved via an analytical framework based on a model of random graph with given expected degrees. We also introduce the concept of connectedness profile, which characterizes the relation between adjacency in a graph and a prescribed order of its vertices. A straightforward application to gene expression data and protein networks is the identification of tissue-specific functional modules or cellular processes perturbed in an experiment. The strength of the mapping between gene-expression score and interaction in the network is measured by the area of the connectedness profile. Deriving the distribution of this area under the random graph enables us to define degree-robust statistics that can be computed in O(M), M being the network size. These statistics can identify groups of microarray experiments that are pathway-coherent, and more generally, vertex attributes that relate to adjacency in a graph.

Data-independent oxonium ion profiling of multi-glycosylated biotherapeutics

ABSTRACT The characterization of glycosylation is required for many protein therapeutics. The emergence of antibody and antibody-like molecules with multiple glycan attachment sites has rendered glycan analysis increasingly more complicated. Reliance on site-specific glycopeptide analysis is therefore necessary to fully analyze multi-glycosylated biotherapeutics. Established glycopeptide methodologies have generally utilized a priori knowledge of the glycosylation states of the investigated protein(s), database searching of results generated from data-dependent liquid chromatography–tandem mass spectrometry workflows, and extracted ion quantitation of the individual identified species. However, the inherent complexity of glycosylation makes predicting all glycoforms on all glycosylation sites extremely challenging, if not impossible. That is, only the “knowns” are assessed. Here, we describe an agnostic methodology to qualitatively and quantitatively assess both “known” and “unknown” site-specific glycosylation for biotherapeutics that contain multiple glycosylation sites. The workflow uses data-independent, all ion fragmentation to generate glycan oxonium ions, which are then extracted across the entirety of the chromatographic timeline to produce a glycan-specific “fingerprint” of the glycoprotein sample. We utilized both HexNAc and sialic acid oxonium ion profiles to quickly assess the presence of Fab glycosylation in a therapeutic monoclonal antibody, as well as for high-throughput comparisons of multi-glycosylated protein drugs derived from different clones to a reference product. An automated method was created to rapidly assess oxonium profiles between samples, and to provide a quantitative assessment of similarity.