Proteogenomic insights suggest druggable pathways in endometrial carcinoma.

We characterized a prospective endometrial carcinoma (EC) cohort containing 138 tumors and 20 enriched normal tissues using 10 different omics platforms. Targeted quantitation of two peptides can predict antigen processing and presentation machinery activity, and may inform patient selection for immunotherapy. Association analysis between MYC activity and metformin treatment in both patients and cell lines suggests a potential role for metformin treatment in non-diabetic patients with elevated MYC activity.

Proteogenomic and metabolomic characterization of human glioblastoma.

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology.

Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer.

We report a comprehensive proteogenomics analysis, including whole-genome sequencing, RNA sequencing, and proteomics and phosphoproteomics profiling, of 218 tumors across 7 histological types of childhood brain cancer: low-grade glioma (n = 93), ependymoma (32), high-grade glioma (25), medulloblastoma (22), ganglioglioma (18), craniopharyngioma (16), and atypical teratoid rhabdoid tumor (12). Proteomics data identify common biological themes that span histological boundaries, suggesting that treatments used for one histological type may be applied effectively to other tumors sharing similar proteomics features. Immune landscape characterization reveals diverse tumor microenvironments across and within diagnoses.

Proteogenomic Characterization of Endometrial Carcinoma.

We undertook a comprehensive proteogenomic characterization of 95 prospectively collected endometrial carcinomas, comprising 83 endometrioid and 12 serous tumors. This analysis revealed possible new consequences of perturbations to the p53 and Wnt/β-catenin pathways, identified a potential role for circRNAs in the epithelial-mesenchymal transition, and provided new information about proteomic markers of clinical and genomic tumor subgroups, including relationships to known druggable pathways. An extensive genome-wide acetylation survey yielded insights into regulatory mechanisms linking Wnt signaling and histone acetylation.

Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma.

To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules.

Genomic Analysis of Uterine Lavage Fluid Detects Early Endometrial Cancers and Reveals a Prevalent Landscape of Driver Mutations in Women without Histopathologic Evidence of Cancer: A Prospective Cross-Sectional Study.

Background: Endometrial cancer is the most common gynecologic malignancy, and its incidence and associated mortality are increasing. Despite the immediate need to detect these cancers at an earlier stage, there is no effective screening methodology or protocol for endometrial cancer. The comprehensive, genomics-based analysis of endometrial cancer by The Cancer Genome Atlas (TCGA) revealed many of the molecular defects that define this cancer.

A new molecular signature method for prediction of driver cancer pathways from transcriptional data.

Assigning cancer patients to the most effective treatments requires an understanding of the molecular basis of their disease. While DNA-based molecular profiling approaches have flourished over the past several years to transform our understanding of driver pathways across a broad range of tumors, a systematic characterization of key driver pathways based on RNA data has not been undertaken. Here we introduce a new approach for predicting the status of driver cancer pathways based on signature functions derived from RNA sequencing data.

New statistical potential for quality assessment of protein models and a survey of energy functions.

Background: Scoring functions, such as molecular mechanic forcefields and statistical potentials are fundamentally important tools in protein structure modeling and quality assessment.

Results: The performances of a number of publicly available scoring functions are compared with a statistical rigor, with an emphasis on knowledge-based potentials. We explored the effect on accuracy of alternative choices for representing interaction center types and other features of scoring functions, such as using information on solvent accessibility, on torsion angles, accounting for secondary structure preferences and side chain orientation.

Acetylsalicylic acid (aspirin) reduces damage to reconstituted human tissues infected with Candida species by inhibiting extracellular fungal lipases.

A reconstituted human tissue model was used to mimic Candida albicans and Candida parapsilosis infection in order to investigate the protective effects of acetylsalicylic acid (aspirin, ASA). We found that therapeutic concentrations of ASA reduced tissue damage in the in vitro infection model. We further evaluated the lipase inhibitory effects of ASA by investigating the growth of C.

Nonprotein based enrichment method to analyze peptide cross-linking in protein complexes.

Cross-linking analysis of protein complexes and structures by tandem mass spectrometry (MS/MS) has advantages in speed, sensitivity, specificity, and the capability of handling complicated protein assemblies. However, detection and accurate assignment of the cross-linked peptides are often challenging due to their low abundance and complicated fragmentation behavior in collision-induced dissociation (CID). To simplify the MS analysis and improve the signal-to-noise ratio of the cross-linked peptides, we developed a novel peptide enrichment strategy that utilizes a cross-linker with a cryptic thiol group and using beads modified with a photocleavable cross-linker.

Computational analysis and experimental validation of gene predictions in Toxoplasma gondii.

Background: Toxoplasma gondii is an obligate intracellular protozoan that infects 20 to 90% of the population. It can cause both acute and chronic infections, many of which are asymptomatic, and, in immunocompromised hosts, can cause fatal infection due to reactivation from an asymptomatic chronic infection. An essential step towards understanding molecular mechanisms controlling transitions between the various life stages and identifying candidate drug targets is to accurately characterize the T.

Improved scoring function for comparative modeling using the M4T method.

Improvements in comparative protein structure modeling for the remote target-template sequence similarity cases are possible through the optimal combination of multiple template structures and by improving the quality of target-template alignment. Recently developed MMM and M4T methods were designed to address these problems. Here we describe new developments in both the alignment generation and the template selection parts of the modeling algorithms.

In vitro evaluation, biodistribution and scintigraphic imaging in mice of radiolabeled anthrax toxins.

Introduction: There is a lot of interest towards creating therapies and vaccines for Bacillus anthracis, a bacterium which causes anthrax in humans and which spores can be made into potent biological weapons. Systemic injection of lethal factor (LF), edema factor (EF) and protective antigen (PA) in mice produces toxicity, and this protocol is commonly used to investigate the efficacy of specific antibodies in passive protection and vaccine studies. Availability of toxins labeled with imageable radioisotopes would allow to demonstrate their tissue distribution after intravenous injection at toxin concentration that are below pharmacologically significant to avoid masking by toxic effects.

Effects of amino acid composition, finite size of proteins, and sparse statistics on distance-dependent statistical pair potentials.

Statistical distance dependent pair potentials are frequently used in a variety of folding, threading, and modeling studies of proteins. The applicability of these types of potentials is tightly connected to the reliability of statistical observations. We explored the possible origin and extent of false positive signals in statistical potentials by analyzing their distance dependence in a variety of randomized protein-like models.

Search for the most stable folds of protein chains: III. Improvement in fold recognition by averaging over homologous sequences and 3D structures.

Three-dimensional (3D) protein fold recognition by query sequence can be improved using information of fold recognition yielded by the sequences homologous to the query one. This idea is now used more and more widely. Our paper presents its consequent development.

[When and how can homologs overcome errors in the energy estimates and make the 3D structure prediction possible].

One still cannot predict the 3D fold of a protein from its amino acid sequence, mainly because of errors in the energy estimates underlying the prediction. However, a recently developed theory [1] shows that having a set of homologs (i.e.

Optimization of protein structure on lattices using a self-consistent field approach.

Lattice modeling of proteins is commonly used to study the protein folding problem. The reduced number of possible conformations of lattice models enormously facilitates exploration of the conformational space. In this work, we suggest a method to search for the optimal lattice models that reproduced the off-lattice structures with minimal errors in geometry and energetics.

[Estimation of quality of approximation of atom-atom contacts of amino acid residues in proteins by the contacts of the residue force centers].

How precisely the atom-atom contacts of amino acid residues in proteins can be approximated by the contacts of amino acid residue "force centers"? To answer this question, we examined the force centers positioned in the C alpha-atom, the C beta-atom, in the optimal point of the C alpha-C beta axis, and in the geometrical center of residues. The maximal coefficient of correlation between the residue force center contacts and the presence of atom-atom contacts of the residues (85%) was obtained for the force centers positioned in geometrical centers of the residues. The correlation is 80% for the optimal position of the centers on the C alpha-C beta axes; a somewhat smaller value (78%) is obtained for the force centers positioned in C beta-atoms, and 71% only for the centers positioned in C alpha-atoms.

Building self-avoiding lattice models of proteins using a self-consistent field optimization.

We present an algorithm to build self-avoiding lattice models of chain molecules with low RMS deviation from their actual 3D structures. To find the optimal coordinates for the lattice chain model, we minimize a function that consists of three terms: (1) the sum of squared deviations of link coordinates on a lattice from their off-lattice values, (2) the sum of "short-range" terms, penalizing violation of chain connectivity, and (3) the sum of "long-range" repulsive terms, penalizing chain self-intersections. We treat this function as a chain molecule "energy" and minimize it using self-consistent field (SCF) theory to represent the pairwise link repulsions as 3D fields acting on the links.