Accurate de novo design of hyperstable constrained peptides.

Naturally occurring, pharmacologically active peptides constrained with covalent crosslinks generally have shapes that have evolved to fit precisely into binding pockets on their targets. Such peptides can have excellent pharmaceutical properties, combining the stability and tissue penetration of small-molecule drugs with the specificity of much larger protein therapeutics. The ability to design constrained peptides with precisely specified tertiary structures would enable the design of shape-complementary inhibitors of arbitrary targets. Here we describe the development of computational methods for accurate de novo design of conformationally restricted peptides, and the use of these methods to design 18-47 residue, disulfide-crosslinked peptides, a subset of which are heterochiral and/or N-C backbone-cyclized. Both genetically encodable and non-canonical peptides are exceptionally stable to thermal and chemical denaturation, and 12 experimentally determined X-ray and NMR structures are nearly identical to the computational design models. The computational design methods and stable scaffolds presented here provide the basis for development of a new generation of peptide-based drugs.

Assessing Protein Sequence Database Suitability Using De Novo Sequencing.

The analysis of samples from unsequenced and/or understudied species as well as samples where the proteome is derived from multiple organisms poses two key questions. The first is whether the proteomic data obtained from an unusual sample type even contains peptide tandem mass spectra. The second question is whether an appropriate protein sequence database is available for proteomic searches. We describe the use of automated de novo sequencing for evaluating both the quality of a collection of tandem mass spectra and the suitability of a given protein sequence database for searching that data. Applications of this method include the proteome analysis of closely related species, metaproteomics, and proteomics of extinct organisms.

SILAC surrogates: rescue of quantitative information for orphan analytes in spike-in SILAC experiments.

Super-stable isotope labeling by amino acids in cell culture (Super-SILAC) enables the sensitive and accurate analysis of complex biological tissue and tumor samples by comparison of light peptides observed in biological samples to heavy peptides from SILAC cell culture spike-ins. However, despite the use of multiple cell lines for Super-SILAC spike-in standards, the full protein and peptide profiles of biological samples are not completely represented in these internal standards, leading to orphan analytes for which sample to standard ratios cannot be calculated. This problem is exacerbated in some biological systems, such as muscle tissue, which lack adequate cell culture lines to reflect their complex and idiosyncratic protein profiles, resulting in up to 40% of peptide analytes without heavy cognates. Furthermore, these unquantified orphan analytes may be among the most biologically interesting and significant species, since their presence is not common to cell lines cultured in vitro. Here, we report on the development of a surrogate analysis strategy to interpolate quantitative relationships between peptide species, observed across multiple biological samples, which lack representation within the spike-in standards. The precision and accuracy of this method was assessed by replicate experiments in which surrogate-derived ratios from defined mixtures of spike-in SILAC standard and tissue lysate were compared against traditional SILAC ratios for species where both light and heavy peptide cognates were observed. We demonstrate the robustness of our SILAC surrogates strategy across a variety of murine tissues, including liver, spleen, brain, and muscle. Our approach increases the quantitative coverage and precision within a biological sample by rescuing previously intractable peptide species and applying additional evidence to improve the precision of existing quantifications.

Increasing phosphoproteomic coverage through sequential digestion by complementary proteases.

Protein phosphorylation is a reversible post-translational modification known to regulate protein function, subcellular localization, complex formation, and protein degradation. Detailed phosphoproteomic information is critical to kinomic studies of signal transduction and for elucidation of cancer biomarkers, such as in non-small-cell lung adenocarcinoma, where phosphorylation is commonly dysregulated. However, the collection and analysis of phosphorylation data remains a difficult problem. The low concentrations of phosphopeptides in complex biological mixtures as well as challenges inherent in their chemical nature have limited phosphoproteomic characterization and some phosphorylation sites are inaccessible by traditional workflows. We developed a sequential digestion method using complementary proteases, Glu-C and trypsin, to increase phosphoproteomic coverage and supplement traditional approaches. The sequential digestion method is more productive than workflows utilizing only Glu-C and we evaluated the orthogonality of the sequential digestion method relative to replicate trypsin-based analyses. Finally, we demonstrate the ability of the sequential digestion method to access new regions of the phosphoproteome by comparison to existing public phosphoproteomic databases. Our approach increases coverage of the human lung cancer phosphoproteome by accessing both new phosphoproteins and novel phosphorylation site information.

A Bayesian estimator of protein-protein association probabilities.

UNLABELLED: The Bayesian Estimator of Protein-Protein Association Probabilities (BEPro aff3) is a software tool for estimating probabilities of protein-protein association between bait and prey protein pairs using data from multiple-bait, multiple-replicate, protein liquid chromatography tandem mass spectrometry LC-MS/MS affinity isolation experiments. AVAILABILITY: BEPro (3) is public domain software, has been tested on WIndows XP, Linux and Mac OS, and is freely available from http://www.pnl.gov/statistics/BEPro3. SUPPLEMENTARY INFORMATION: A user guide, example dataset with analysis and additional documentation are included with the BEPro (3) download.

De novo design of protein homo-oligomers with modular hydrogen-bond network-mediated specificity.

In nature, structural specificity in DNA and proteins is encoded differently: In DNA, specificity arises from modular hydrogen bonds in the core of the double helix, whereas in proteins, specificity arises largely from buried hydrophobic packing complemented by irregular peripheral polar interactions. Here, we describe a general approach for designing a wide range of protein homo-oligomers with specificity determined by modular arrays of central hydrogen-bond networks. We use the approach to design dimers, trimers, and tetramers consisting of two concentric rings of helices, including previously not seen triangular, square, and supercoiled topologies. X-ray crystallography confirms that the structures overall, and the hydrogen-bond networks in particular, are nearly identical to the design models, and the networks confer interaction specificity in vivo. The ability to design extensive hydrogen-bond networks with atomic accuracy enables the programming of protein interaction specificity for a broad range of synthetic biology applications; more generally, our results demonstrate that, even with the tremendous diversity observed in nature, there are fundamentally new modes of interaction to be discovered in proteins.

A general system for studying protein-protein interactions in Gram-negative bacteria.

One of the most promising methods for large-scale studies of protein interactions is isolation of an affinity-tagged protein with its in vivo interaction partners, followed by mass spectrometric identification of the copurified proteins. Previous studies have generated affinity-tagged proteins using genetic tools or cloning systems that are specific to a particular organism. To enable protein-protein interaction studies across a wider range of Gram-negative bacteria, we have developed a methodology based on expression of affinity-tagged "bait" proteins from a medium copy-number plasmid. This construct is based on a broad-host-range vector backbone (pBBR1MCS5). The vector has been modified to incorporate the Gateway DEST vector recombination region, to facilitate cloning and expression of fusion proteins bearing a variety of affinity, fluorescent, or other tags. We demonstrate this methodology by characterizing interactions among subunits of the DNA-dependent RNA polymerase complex in two metabolically versatile Gram-negative microbial species of environmental interest, Rhodopseudomonas palustris CGA010 and Shewanella oneidensis MR-1. Results compared favorably with those for both plasmid and chromosomally encoded affinity-tagged fusion proteins expressed in a model organism, Escherichia coli.