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Kaur P.,Case Western Reserve University | Kaur P.,Neoproteomics, Inc. | Tomechko S.E.,Case Western Reserve University | Kiselar J.,Case Western Reserve University | And 8 more authors.
mAbs | Year: 2015

Structural characterization of proteins and their antigen complexes is essential to the development of new biologicbased medicines. Amino acid-specific covalent labeling (CL) is well suited to probe such structures, especially for cases that are difficult to examine by alternative means due to size, complexity, or instability. We present here a detailed account of carboxyl group labeling (with glycine ethyl ester (GEE) tagging) applied to a glycosylated monoclonal antibody therapeutic (mAb). The experiments were optimized to preserve the structural integrity of the mAb, and experimental conditions were varied and replicated to establish the reproducibility of the technique. Homology-based models were generated and used to compare the solvent accessibility of the labeled residues, which include aspartic acid (D), glutamic acid (E), and the C-terminus (i.e., the target probes), with the experimental data in order to understand the accuracy of the approach. Data from the mAb were compared to reactivity measures of several model peptides to explain observed variations in reactivity. Attenuation of reactivity in otherwise solvent accessible probes is documented as arising from the effects of positive charge or bond formation between adjacent amine and carboxyl groups, the latter accompanied by observed water loss. A comparison of results with previously published data by Deperalta et al using hydroxyl radical footprinting showed that 55% (32/58) of target residues were GEE labeled in this study whereas the previous study reported 21% of the targets were labeled. Although the number of target residues in GEE labeling is fewer, the two approaches provide complementary information. The results highlight advantages of this approach, such as the ease of use at the bench top, the linearity of the dose response plots at high levels of labeling, reproducibility of replicate experiments (<2% variation in modification extent), the similar reactivity of the three target probes, and significant correlation of reactivity and solvent accessible surface area. © 2015, Taylor and Francis Group, LLC.


Kaur P.,Case Western Reserve University | Kaur P.,Neoproteomics, Inc. | Tomechko S.,Case Western Reserve University | Kiselar J.,Case Western Reserve University | And 7 more authors.
mAbs | Year: 2014

Amino acid-specific covalent labeling is well suited to probe protein structure and macromolecular interactions, especially for macromolecules and their complexes that are difficult to examine by alternative means, due to size, complexity, or instability. Here we present a detailed account of carbodiimide-based covalent labeling (with GEE tagging) applied to a glycosylated monoclonal antibody therapeutic, which represents an important class of biologic drugs. Characterization of such proteins and their antigen complexes is essential to development of new biologic-based medicines. In this study, the experiments were optimized to preserve the structural integrity of the protein, and experimental conditions were varied and replicated to establish the reproducibility and precision of the technique. Homology-based models were generated and used to compare the solvent accessibility of the labeled residues, which include D, E, and the C-terminus, against the experimental surface accessibility data in order to understand the accuracy of the approach in providing an unbiased assessment of structure. Data from the protein were also compared to reactivity measures of several model peptides to explain sequence or structure-based variations in reactivity. The results highlight several advantages of this approach. These include: the ease of use at the bench top, the linearity of the dose response plots at high levels of labeling (indicating that the label does not significantly perturb the structure of the protein), the high reproducibility of replicate experiments (<2% variation in modification extent), the similar reactivity of the 3 target probe residues (as suggested by analysis of model peptides), and the overall positive and significant correlation of reactivity and solvent accessible surface area (the latter values predicted by the homology modeling). Attenuation of reactivity, in otherwise solvent accessible probes, is documented as arising from the effects of positive charge or bond formation between adjacent amine and carboxyl groups, the latter accompanied by observed water loss. The results are also compared with data from hydroxyl radical-mediated oxidative footprinting on the same protein, showing that complementary information is gained from the 2 approaches, although the number of target residues in carbodiimide/GEE labeling is fewer. Overall, this approach is an accurate and precise method for assessing protein structure of biologic drugs. © 2014 Taylor & Francis Group, LLC.


Liu Y.,Case Western Reserve University | Maxwell S.,Case Western Reserve University | Maxwell S.,Neoproteomics, Inc. | Feng T.,Case Western Reserve University | And 5 more authors.
BMC Systems Biology | Year: 2012

Background: Interactions among genomic loci (also known as epistasis) have been suggested as one of the potential sources of missing heritability in single locus analysis of genome-wide association studies (GWAS). The computational burden of searching for interactions is compounded by the extremely low threshold for identifying significant p-values due to multiple hypothesis testing corrections. Utilizing prior biological knowledge to restrict the set of candidate SNP pairs to be tested can alleviate this problem, but systematic studies that investigate the relative merits of integrating different biological frameworks and GWAS data have not been conducted.Results: We developed four biologically based frameworks to identify pairwise interactions among candidate SNP pairs as follows: (1) for each human protein-coding gene, a set of SNPs associated with that gene was constructed providing a gene-based interaction model, (2) for each known biological pathway, a set of SNPs associated with the genes in the pathway was constructed providing a pathway-based interaction model, (3) a set of SNPs associated with genes in a disease-related subnetwork provides a network-based interaction model, and (4) a framework is based on the function of SNPs. The last approach uses expression SNPs (eSNPs or eQTLs), which are SNPs or loci that have defined effects on the abundance of transcripts of other genes. We constructed pairs of eSNPs and SNPs located in the target genes whose expression is regulated by eSNPs. For all four frameworks the SNP sets were exhaustively tested for pairwise interactions within the sets using a traditional logistic regression model after excluding genes that were previously identified to associate with the trait. Using previously published GWAS data for type 2 diabetes (T2D) and the biologically based pair-wise interaction modeling, we identify twelve genes not seen in the previous single locus analysis.Conclusion: We present four approaches to detect interactions associated with complex diseases. The results show our approaches outperform the traditional single locus approaches in detecting genes that previously did not reach significance; the results also provide novel drug targets and biomarkers relevant to the underlying mechanisms of disease. © 2012 Liu et al; licensee BioMed Central Ltd.


Padayatti P.S.,Polgenix, Inc. | Wang L.,Case Western Reserve University | Gupta S.,Case Western Reserve University | Orban T.,Case Western Reserve University | And 7 more authors.
Molecular and Cellular Proteomics | Year: 2013

Hybrid structural methods have been used in recent years to understand protein-protein or protein-ligand interactions where high resolution crystallography or NMR data on the protein of interest has been limited. For G protein-coupled receptors (GPCRs), high resolution structures of native structural forms other than rhodopsin have not yet been achieved; gaps in our knowledge have been filled by creative crystallography studies that have developed stable forms of receptors by multiple means. The neurotransmitter serotonin (5-hydroxytryptamine) is a key GPCR-based signaling molecule affecting many physiological manifestations in humans ranging from mood and anxiety to bowel function. However, a high resolution structure of any of the serotonin receptors has not yet been solved. Here, we used structural mass spectrometry along with theoretical computations, modeling, and other biochemical methods to develop a structured model for human serotonin receptor subtype 4(b) in the presence and absence of its antagonist GR125487. Our data confirmed the overall structure predicted by the model and revealed a highly conserved motif in the ligand-binding pocket of serotonin receptors as an important participant in ligand binding. In addition, identification of waters in the transmembrane region provided clues as to likely paths mediating intramolecular signaling. Overall, this study reveals the potential of hybrid structural methods, including mass spectrometry, to probe physiological and functional GPCR-ligand interactions with purified native protein. © 2013 by The American Society for Biochemistry and Molecular Biology, Inc.


Neoproteomics, Inc. | Entity website

NeoProteomics' mission is to accelerate the process of drug and diagnostics development by providing innovative informatics solutions allowing clients to achieve biological understanding of their genomics and proteomics data at the systems level. Our solutions to customer problems provide fast and intuitive data analysis for assessing the structures of biologics and a navigable road map of the cell for understanding pharmaceutical drug targets, drug efficacy, and drug safety


Neoproteomics, Inc. | Entity website


Neoproteomics, Inc. | Entity website

Our team has utilized hybrid structural methods to characterize protein-protein or protein-ligand interactions where high-resolution crystallography or NMR data are not fully available. The examples below demonstrate the power of covalent labeling methods for determining the structure of protein complexes in a variety of applications ...


Neoproteomics, Inc. | Entity website

John L.H ...


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 299.99K | Year: 2011

DESCRIPTION (provided by applicant): Many complex human diseases (e.g. cancer, diabetes, schizophrenia etc.) have correspondingly complex, polygenic genotypes that initiate and sustain disease progression. Despite significant progress over the past few decades identifying genes critical to mediating phenotype, our understanding of the functional basis of molecular phenotype for complex diseases is insufficient. Signaling pathways that consist of a few proteins interacting in a serial fashion oversimplify and provide inadequate models for the behavior mediated by multiple interacting gene products. Partly revealed by rigorous studies of increasingly well-annotated protein-protein interaction (PPI) networks, it has become clear that many of the proteins in these canonical signaling pathways engage in crosstalk with, and are modulated by, an ontologically diverse set of additional proteins, where this crosstalk is frequently mediated in a tissue and/or disease specific manner. We propose to develop and deliveran integrated suite of software tools to the academic and commercial research community to fulfill the unmet demand for quantitative PPI network analysis that can drive practical translational research and validation. The tool DiseaseNet Finder will search for and score candidate disease sub- networks within global PPI networks. It will permit integration of multiple high- dimensional -omic types (GWAS, SNP, CNV, proteomic, miRNA etc.) with PPI networks and include classification tools. Novel aspects of the software include: combinatorial scoring, multi data type integration, node and edge prediction tools, with end-point classification and quantitative scoring seamlessly implemented through graphical user interfaces. PUBLIC HEALTH RELEVANCE: Complex diseases include the contributions of many genes interacting with the environment. Enhanced computational research tools to discover biomarkers and understand complex disease mechanisms are needed to integrate the various types of genomic and proteomicsdata that are accumulating. This will permit a more rapid development of personalized medicine.


Neoproteomics, Inc. | Entity website

Mark R. Chance, Ph ...

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