CSSI-PRO: a method for secondary structure type editing, assignment and estimation in proteins using linear combination of backbone chemical shifts

Unraveling the meaning of chemical shifts in protein NMR

Chemical shifts are among the most informative parameters in protein NMR. They provide wealth of information about protein secondary and tertiary structure, protein flexibility, and protein-ligand binding. In this report, we review the progress in interpreting and utilizing protein chemical shifts that has occurred over the past 25years, with a particular focus on the large body of work arising from our group and other Canadian NMR laboratories. More specifically, this review focuses on describing, assessing, and providing some historical context for various chemical shift-based methods to: (1) determine protein secondary and super-secondary structure; (2) derive protein torsion angles; (3) assess protein flexibility; (4) predict residue accessible surface area; (5) refine 3D protein structures; (6) determine 3D protein structures and (7) characterize intrinsically disordered proteins. This review also briefly covers some of the methods that we previously developed to predict chemical shifts from 3D protein structures and/or protein sequence data. It is hoped that this review will help to increase awareness of the considerable utility of NMR chemical shifts in structural biology and facilitate more widespread adoption of chemical-shift based methods by the NMR spectroscopists, structural biologists, protein biophysicists, and biochemists worldwide. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.

Application of data mining tools for classification of protein structural class from residue based averaged NMR chemical shifts

The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for protein structural information that is directly related to function. Nuclear magnetic resonance (NMR) provides powerful means to determine three-dimensional structures of proteins in the solution state. However, translation of the NMR spectral parameters to even low-resolution structural information such as protein class requires multiple time consuming steps. In this paper, we present an unorthodox method to predict the protein structural class directly by using the residue's averaged chemical shifts (ACS) based on machine learning algorithms. Experimental chemical shift information from 1491 proteins obtained from Biological Magnetic Resonance Bank (BMRB) and their respective protein structural classes derived from structural classification of proteins (SCOP) were used to construct a data set with 119 attributes and 5 different classes. Twenty four different classification schemes were evaluated using several performance measures. Overall the residue based ACS values can predict the protein structural classes with 80% accuracy measured by Matthew correlation coefficient. Specifically protein classes defined by mixed αβ or small proteins are classified with >90% correlation. Our results indicate that this NMR-based method can be utilized as a low-resolution tool for protein structural class identification without any prior chemical shift assignments.

High-yield bacterial expression and structural characterization of recombinant human insulin-like growth factor binding protein-2

The diverse biological activities of the insulin-like growth factors (IGF-1 and IGF-2) are mediated by the IGF-1 receptor (IGF-1R). These actions are modulated by a family of six IGF-binding proteins (IGFBP-1-6; 22-31 kDa) that via high affinity binding to the IGFs (K(D) approximately 300-700 pM) both protect the IGFs in the circulation and attenuate IGF action by blocking their receptor access. In recent years, IGFBPs have been implicated in a variety of cancers. However, the structural basis of their interaction with IGFs and/or other proteins is not completely understood. A critical challenge in the structural characterization of full-length IGFBPs has been the difficulty in expressing these proteins at levels suitable for NMR/X-ray crystallography analysis. Here we describe the high-yield expression of full-length recombinant human IGFBP-2 (rhIGFBP-2) in Escherichia coli. Using a single step purification protocol, rhIGFBP-2 was obtained with >95% purity and structurally characterized using NMR spectroscopy. The protein was found to exist as a monomer at the high concentrations required for structural studies and to exist in a single conformation exhibiting a unique intra-molecular disulfide-bonding pattern. The protein retained full biologic activity. This study represents the first high-yield expression of wild-type recombinant human IGFBP-2 in E. coli and first structural characterization of a full-length IGFBP.

Spontaneous and reversible self-assembly of a polypeptide fragment of insulin-like growth factor binding protein-2 into fluorescent nanotubular structures

In this communication, we report the spontaneous and reversible in vitro self-assembly of a polypeptide fragment derived from the C-terminal domain of Insulin-like Growth Factor Binding Protein (IGFBP-2) into soluble nanotubular structures several micrometres long via a mechanism involving inter-molecular disulfide bonds and exhibiting enhanced fluorescence.