Hesam Dashti

Metabolic Evidence of Diminished Lipid Oxidation in Women With Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS), a common female endocrinopathy, is a complex metabolic syndrome of enhanced weight gain. The goal of this pilot study was to evaluate metabolic differences between normal (n=10) and PCOS (n=10) women via breath carbon isotope ratio, urinary nitrogen and nuclear magnetic resonance (NMR)-determined serum metabolites. Breath carbon stable isotopes measured by cavity ring down spectroscopy (CRDS) indicated diminished (p<0.030) lipid use as a metabolic substrate during overnight fasting in PCOS compared to normal women. Accompanying urinary analyses showed a trending correlation (p<0.057) between overnight total nitrogen and circulating testosterone in PCOS women, alone. Serum analyzed by NMR spectroscopy following overnight, fast and at 2 h following an oral glucose tolerance test showed that a transient elevation in blood glucose levels decreased circulating levels of lipid, glucose and amino acid metabolic intermediates (acetone, 2-oxocaporate, 2-aminobutyrate, pyruvate, formate, and sarcosine) in PCOS women, whereas the 2 h glucose challenge led to increases in the same intermediates in normal women. These pilot data suggest that PCOS-related inflexibility in fasting-related switching between lipid and carbohydrate/protein utilization for carbon metabolism may contribute to enhanced weight gain.

Defining a Two-pronged Structural Model for PB1 (Phox/Bem1p) Domain Interaction in Plant Auxin Responses

Background: Phox/Bem1p domains are universal domains that organize cellular signaling scaffolds. Results: Biophysical analyses reveal driving forces and core residues involved in PB1 interaction. Conclusion: Electrostatic interactions focused around two complementary prongs. Significance: These results provide the first in-depth analysis of the factors driving self-interaction of a type I/II PB1 domain. Phox/Bem1p (PB1) domains are universal structural modules that use surfaces of different charge for protein-protein association. In plants, PB1-mediated interactions of auxin response factors (ARF) and auxin/indole 3-acetic acid inducible proteins regulate transcriptional events modulated by the phytohormone auxin. Here we investigate the thermodynamic and structural basis for Arabidopsis thaliana ARF7 PB1 domain self-interaction. Isothermal titration calorimetry and NMR experiments indicate that key residues on both the basic and acidic faces of the PB1 domain contribute to and organize coordinately to stabilize protein-protein interactions. Calorimetric analysis of ARF7PB1 site-directed mutants defines a two-pronged electrostatic interaction. The canonical PB1 interaction between a lysine and a cluster of acidic residues provides one prong with an arginine and a second cluster of acidic residues defining the other prong. Evolutionary conservation of this core recognition feature and other co-varying interface sequences allows for versatile PB1-mediated interactions in auxin signaling.

Integrative NMR for biomolecular research

NMR spectroscopy is a powerful technique for determining structural and functional features of biomolecules in physiological solution as well as for observing their intermolecular interactions in real-time. However, complex steps associated with its practice have made the approach daunting for non-specialists. We introduce an NMR platform that makes biomolecular NMR spectroscopy much more accessible by integrating tools, databases, web services, and video tutorials that can be launched by simple installation of NMRFAM software packages or using a cross-platform virtual machine that can be run on any standard laptop or desktop computer. The software package can be downloaded freely from the NMRFAM software download page (http://pine.nmrfam.wisc.edu/download_packages.html), and detailed instructions are available from the Integrative NMR Video Tutorial page (http://pine.nmrfam.wisc.edu/integrative.html).

Unique identifiers for small molecules enable rigorous labeling of their atoms

Rigorous characterization of small organic molecules in terms of their structural and biological properties is vital to biomedical research. The three-dimensional structure of a molecule, its ‘photo ID’, is inefficient for searching and matching tasks. Instead, identifiers play a key role in accessing compound data. Unique and reproducible molecule and atom identifiers are required to ensure the correct cross-referencing of properties associated with compounds archived in databases. The best approach to this requirement is the International Chemical Identifier (InChI). However, the current implementation of InChI fails to provide a complete standard for atom nomenclature, and incorrect use of the InChI standard has resulted in the proliferation of non-unique identifiers. We propose a methodology and associated software tools, named ALATIS, that overcomes these shortcomings. ALATIS is an adaptation of InChI, which operates fully within the InChI convention to provide unique and reproducible molecule and all atom identifiers. ALATIS includes an InChI extension for unique atom labeling of symmetric molecules. ALATIS forms the basis for improving reproducibility and unifying cross-referencing across databases.

Probabilistic validation of protein NMR chemical shift assignments

AbstractData validation plays an important role in ensuring the reliability and reproducibility of studies. NMR investigations of the functional properties, dynamics, chemical kinetics, and structures of proteins depend critically on the correctness of chemical shift assignments. We present a novel probabilistic method named ARECA for validating chemical shift assignments that relies on the nuclear Overhauser effect data . ARECA has been evaluated through its application to 26 case studies and has been shown to be complementary to, and usually more reliable than, approaches based on chemical shift databases. ARECA is available online at http://areca.nmrfam.wisc.edu/.

NMRFAM-SDF: a protein structure determination framework

The computationally demanding nature of automated NMR structure determination necessitates a delicate balancing of factors that include the time complexity of data collection, the computational complexity of chemical shift assignments, and selection of proper optimization steps. During the past two decades the computational and algorithmic aspects of several discrete steps of the process have been addressed. Although no single comprehensive solution has emerged, the incorporation of a validation protocol has gained recognition as a necessary step for a robust automated approach. The need for validation becomes even more pronounced in cases of proteins with higher structural complexity, where potentially larger errors generated at each step can propagate and accumulate in the process of structure calculation, thereby significantly degrading the efficacy of any software framework. This paper introduces a complete framework for protein structure determination with NMR—from data acquisition to the structure determination. The aim is twofold: to simplify the structure determination process for non-NMR experts whenever feasible, while maintaining flexibility by providing a set of modules that validate each step, and to enable the assessment of error propagations. This framework, called NMRFAM-SDF (NMRFAM-Structure Determination Framework), and its various components are available for download from the NMRFAM website (http://nmrfam.wisc.edu/software.htm).

NMReDATA, a standard to report the NMR assignment and parameters of organic compounds

Even though NMR has found countless applications in the field of small molecule characterization, there is no standard file format available for the NMR data relevant to structure characterization of small molecules. A new format is therefore introduced to associate the NMR parameters extracted from 1D and 2D spectra of organic compounds to the proposed chemical structure. These NMR parameters, which we shall call NMReDATA (for nuclear magnetic resonance extracted data), include chemical shift values, signal integrals, intensities, multiplicities, scalar coupling constants, lists of 2D correlations, relaxation times, and diffusion rates. The file format is an extension of the existing Structure Data Format, which is compatible with the commonly used MOL format. The association of an NMReDATA file with the raw and spectral data from which it originates constitutes an NMR record. This format is easily readable by humans and computers and provides a simple and efficient way for disseminating results of structural chemistry investigations, allowing automatic verification of published results, and for assisting the constitution of highly needed open‐source structural databases.

Spin System Modeling of Nuclear Magnetic Resonance Spectra for Applications in Metabolomics and Small Molecule Screening

The exceptionally rich information content of nuclear magnetic resonance (NMR) spectra is routinely used to identify and characterize molecules and molecular interactions in a wide range of applications, including clinical biomarker discovery, drug discovery, environmental chemistry, and metabolomics. The set of peak positions and intensities from a reference NMR spectrum generally serves as the identifying signature for a compound. Reference spectra normally are collected under specific conditions of pH, temperature, and magnetic field strength, because changes in conditions can distort the identifying signatures of compounds. A spin system matrix that parametrizes chemical shifts and coupling constants among spins provides a much richer feature set for a compound than a spectral signature based on peak positions and intensities. Spin system matrices expand the applicability of NMR spectral libraries beyond the specific conditions under which data were collected. In addition to being able to simulate spectra at any field strength, spin parameters can be adjusted to systematically explore alterations in chemical shift patterns due to variations in other experimental conditions, such as compound concentration, pH, or temperature. We present methodology and software for efficient interactive optimization of spin parameters against experimental 1D-1H NMR spectra of small molecules. We have used the software to generate spin system matrices for a set of key mammalian metabolites and are also using the software to parametrize spectra of small molecules used in NMR-based ligand screening. The software, along with optimized spin system matrix data for a growing number of compounds, is available from http://gissmo.nmrfam.wisc.edu/ .

Architectural Features of Human Mitochondrial Cysteine Desulfurase Complexes from Crosslinking Mass Spectrometry and Small-Angle X-Ray Scattering

Cysteine desulfurase plays a central role in mitochondrial iron-sulfur cluster biogenesis by generating sulfur through the conversion of L-cysteine to L-alanine and by serving as the platform for assembling other components of the biosynthetic machinery, including ISCU, frataxin, and ferredoxin. The human mitochondrial cysteine desulfurase complex consists of two copies each of NFS1, ISD11, and acyl carrier protein. We describe results from chemical crosslinking coupled with tandem mass spectrometry and small-angle X-ray scattering studies that are consistent with a closed NFS1 dimer rather than an open one for both the cysteine desulfurase-ISCU and cysteine desulfurase-ISCU-frataxin complexes. We present a structural model for the cysteine desulfurase-ISCU-frataxin complex derived from chemical crosslinking restraints in conjunction with the recent crystal structure of the cysteine desulfurase-ISCU-zinc complex and distance constraints from nuclear magnetic resonance.

MK-means - Modified K-means clustering algorithm

This paper discusses a density based clustering approach for a guided kernel based clustering algorithm, named MK-means (Modified K-means). Our idea is to improve the guided K-Means clustering algorithm and discuss the benefits of using MK-Means algorithm for clustering algorithm in astrophysics data bases. The improvements made allow handling clustering without apriori knowledge and also include the flexibility of merging classes when similarities are detected.