Planning combinatorial disulfide cross-links for protein fold determination

<p>Abstract</p> <p>Background</p> <p>Fold recognition techniques take advantage of the limited number of overall structural organizations, and have become increasingly effective at identifying the fold of a given target sequence. However, in the absence of sufficient sequence identity, it remains difficult for fold recognition methods to always select the correct model. While a native-like model is often among a pool of highly ranked models, it is not necessarily the highest-ranked one, and the model rankings depend sensitively on the scoring function used. <it>Structure elucidation</it> methods can then be employed to decide among the models based on relatively rapid biochemical/biophysical experiments.</p> <p>Results</p> <p>This paper presents an integrated computational-experimental method to determine the fold of a target protein by probing it with a set of planned disulfide cross-links. We start with predicted structural models obtained by standard fold recognition techniques. In a first stage, we characterize the fold-level differences between the models in terms of topological (contact) patterns of secondary structure elements (SSEs), and select a small set of SSE pairs that differentiate the folds. In a second stage, we determine a set of residue-level cross-links to probe the selected SSE pairs. Each stage employs an information-theoretic planning algorithm to maximize information gain while minimizing experimental complexity, along with a Bayes error plan assessment framework to characterize the probability of making a correct decision once data for the plan are collected. By focusing on overall topological differences and planning cross-linking experiments to probe them, our <it>fold determination</it> approach is robust to noise and uncertainty in the models (e.g., threading misalignment) and in the actual structure (e.g., flexibility). We demonstrate the effectiveness of our approach in case studies for a number of CASP targets, showing that the optimized plans have low risk of error while testing only a small portion of the quadratic number of possible cross-link candidates. Simulation studies with these plans further show that they do a very good job of selecting the correct model, according to cross-links simulated from the actual crystal structures.</p> <p>Conclusions</p> <p>Fold determination can overcome scoring limitations in purely computational fold recognition methods, while requiring less experimental effort than traditional protein structure determination approaches.</p

Graph algorithms for NMR resonance assignment and cross-link experiment planning

The study of three-dimensional protein structures produces insights into protein function at the molecular level. Graphs provide a natural representation of protein structures and associated experimental data, and enable the development of graph algorithms to analyze the structures and data. This thesis develops such graph representations and algorithms for two novel applications: structure-based NMR resonance assignment and disulfide cross-link experiment planning for protein fold determination. The first application seeks to identify correspondences between spectral peaks in NMR data and backbone atoms in a structure (from x-ray crystallography or homology modeling), by computing correspondences between a contact graph representing the structure and an analogous but very noisy and ambiguous graph representing the data. The assignment then supports further NMR studies of protein dynamics and protein-ligand interactions. A hierarchical grow-and-match algorithm was developed for smaller assignment problems, ensuring completeness of assignment, while a random graph approach was developed for larger problems, provably determining unique matches in polynomial time with high probability. Test results show that our algorithms are robust to typical levels of structural variation, noise, and missings, and achieve very good overall assignment accuracy. The second application aims to rapidly determine the overall organization of secondary structure elements of a target protein by probing it with a set of planned disulfide cross-links. A set of informative pairs of secondary structure elements is selected from graphs representing topologies of predicted structure models. For each pair in this ``fingerprint\u27\u27, a set of informative disulfide probes is selected from graphs representing residue proximity in the models. Information-theoretic planning algorithms were developed to maximize information gain while minimizing experimental complexity, and Bayes error plan assessment frameworks were developed to characterize the probability of making correct decisions given experimental data. Evaluation of the approach on a number of structure prediction case studies shows that the optimized plans have low risk of error while testing only a very small portion of the quadratic number of possible cross-link candidates

Experiment Planning for Protein Structure Elucidation and Site-Directed Protein Recombination

In order to most effectively investigate protein structure and improve protein function, it is necessary to carefully plan appropriate experiments. The combinatorial number of possible experiment plans demands effective criteria and efficient algorithms to choose the one that is in some sense optimal. This thesis addresses experiment planning challenges in two significant applications. The first part of this thesis develops an integrated computational-experimental approach for rapid discrimination of predicted protein structure models by quantifying their consistency with relatively cheap and easy experiments (cross-linking and site-directed mutagenesis followed by stability measurement). In order to obtain the most information from noisy and sparse experimental data, rigorous Bayesian frameworks have been developed to analyze the information content. Efficient algorithms have been developed to choose the most informative, least expensive, and most robust experiments. The effectiveness of this approach has been demonstrated using existing experimental data as well as simulations, and it has been applied to discriminate predicted structure models of the pTfa chaperone protein from bacteriophage lambda. The second part of this thesis seeks to choose optimal breakpoint locations for protein engineering by site-directed recombination. In order to increase the possibility of obtaining folded and functional hybrids in protein recombination, it is necessary to retain the evolutionary relationships among amino acids that determine protein stability and functionality. A probabilistic hypergraph model has been developed to model these relationships, with edge weights representing their statistical significance derived from database and a protein family. The effectiveness of this model has been validated by showing its ability to distinguish functional hybrids from non-functional ones in existing experimental data. It has been proved to be NP-hard in general to choose the optimal breakpoint locations for recombination that minimize the total perturbation to these relationships, but exact and approximate algorithms have been developed for a number of important cases

New applications of dynamic combinatorial chemistry to medicinal chemistry

Die Verwendung dynamisch kombinatorischer Chemie (DCC) in medizinisch-chemischen Projekten kann eine sehr hilfreiche Strategie sein, um Anknüpfungspunkte für die Wirkstoffentdeckung zu finden. 14-3-3 Proteine spielen eine Rolle in verschiedenen Krankheiten und vielen biologischen Prozessen. Proteine dieser Familie beteiligen sich an Protein-Protein-Interaktionen (PPIs) und können die Aktivität der Bindungspartner sowohl hoch- als auch herabregulieren. Eine andere Familie relevanter Targets sind die Glukansucrasen, welche wichtige Enzyme in der Initiierung und Entwicklung von kariogenen dentalen Biofilmen, allgemein bekannt als Plaque, sind. In den letzten beiden Kapiteln wurde Endothiapepsin für Protein-vermittelte DCC (ptDCC) verwendet. Endothiapepsin gehört zur Familie der Aspartylproteasen, welche zum Beispiel an der Reifung des HIV Viruspartikels beteiligt sind. Im Verlauf dieser Arbeit fokussieren wir uns auf die Anwendung von DCC in verschiedenen Projekten. Die Hauptleistungen sind: 1) die Beschreibung des hausinternen DCC-Protokolls, in welchem Aspekte wie Löslichkeit von Bausteinen und Produkten, Proteinstabilität und weiteres wichtige zu beachten sind, 2) die Anwendung von Acylhydrazon-basierter DCC auf zwei Targets, eine Glukansucrase und ein PPI-Target, 3) die Identifikation kleiner Moleküle, die PPIs von 14-3-3/ Synaptopodin stabilisieren, 4) die Erweiterung des Reaktionsspielraums der ptDCC durch zwei zusätzliche Reaktionen: Nitron- und Thiazolidinbildung.Applying dynamic combinatorial chemistry (DCC) to medicinal chemistry projects can be a helpful strategy for finding starting points in the drug-discovery process. As relevant drug target, 14-3-3 proteins play a role in several diseases and many biological processes. Proteins of this family engage in protein-protein interactions (PPIs), and can up-or down-regulate their binding partner’s activity. Another family of relevant targets are glucansucrases, which are important enzymes in the initiation and development of cariogenic dental biofilms, commonly known as dental plaque. In the last two chapters, endothiapepsin was used for protein-templated DCC (ptDCC). Endothiapepsin belongs to the family of the aspartic proteases, which are involved in for example the maturation of the HIV virus particle. Throughout this thesis, we focus on applying DCC to various projects. The main achievements are: 1) the description of the in-house protocol of DCC, in which aspects like solubility of building blocks and products, protein stability and more need to be taken in to account, 2) the application of acylhydrazone-based DCC to two targets, a (PPI)-target and a glucansucrase, 3) the identification of small-molecules, which stabilise PPIs of 14-3-3/ synaptopodin, 4) expanding the reaction toolbox of ptDCC by two additional reactions: nitrone and thiazolidine formation

Disulfide Bond and Topological Isomerization of the Conopeptide PNID: Disulfide Bonds with a Twist.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Application of PI-deconvolution to the screening of protein ligand combinatorial libraries using the yeast-two-hybrid assay

Reagents that bind proteins are applicable in biology for detection of molecules, perturbation of signaling pathways and development of small-molecule pharmaceuticals. Protein ligands interact with proteins, inhibiting or altering their function. They are isolated from combinatorial libraries to interact with a specific target, using selection techniques such as phage display or yeast-two-hybrid assay. For the latter, one inconvenience is the detection of false positives, which can be solved by screening pools containing the samples to be tested, instead of individual samples. Samples are distributed in the pools following a pooling design. The PI-deconvolution pooling design was developed to screen cDNA libraries using the yeast-two-hybrid assay, which are smaller in size than protein ligand combinatorial libraries. Modifications to the PI-deconvolution screening technique were developed to adapt it to the screening of protein ligand combinatorial libraries using the yeast-two-hybrid assay. Every spot of the array containing the combinatorial library was randomly pooled. However, the yeast-two-hybrid assay loses sensitivity when strains are pooled. As PI-deconvolution requires detecting every interaction, we determined the optimal amount of library members that can be pooled in a spot, and the optimal number of replicates to ensure the detection of an interaction. The yeast-two-hybrid assay was used to perform a screening of a combinatorial library with seven domains of BCR-ABL, which were pooled according to PI-deconvolution. BCR-ABL is a chimeric protein with unregulated kinase activity that is responsible for chronic myelogenous leukemia. The scaffold used in the combinatorial library was an engineered intein that forms lariat peptides. After a screening of this library was performed, positive interactions were detected in 775 spots of the arrays that contained 1432 positive hits. Only 53 spots were deconvoluted. The coding sequences of the lariat peptides were determined for 23 lariat peptides interacted with the GEF domain of BCR, and for ABL, two with the FABD domain, one with the SH1 domain, and one with the SH3 domain. Finally, a β-galactosidase assay was performed to assess the affinity of the lariat peptides for their target. The isolated lariat peptides are potential inhibitors of BCR-ABL that can have therapeutic potential. This study will improve other screenings of combinatorial libraries with the yeast-two-hybrid assay

Inferring structures, free energy differences, and kinetic rates of biological macromolecular assemblies by integrative modeling

Biological macromolecular assemblies play crucial roles in most cellular processes. The determination of their structures, thermodynamics, and kinetics is essential to understand their function, evolution, modulation, and design. Determining such models, however, remains challenging. One particularly powerful approach to constructing models in general is integrative modeling. Integrative modeling aims to maximize the accuracy, precision, and completeness of models, by simultaneously utilizing all available information, including experimental data, physical principles, statistical analyses, and other prior models. The goal of this thesis is to expand the scope of integrative modeling to the inference of spatial, thermodynamic, and kinetic aspects of macromolecular assemblies. In Chapter I, I introduce the integrative modeling framework for spatiotemporal modeling of biological macromolecular assemblies. In Chapter II, I demonstrate how the synergy between multi-chemistry cross-linking mass spectrometry and integrative modeling can map the structural dynamics of macromolecular assemblies, by application to the human Cop9 signalosome complex. In Chapter III, I present a method for determining structures, free energy differences, and kinetic rates of macromolecular assemblies along their functional cycle, mainly from negative stain electron microscopy (EM). We apply the method to the yeast Hsp90 to estimate the free energy differences and kinetic parameters along its nucleotide hydrolysis cycle, which includes open and closed states of Hsp90. In Chapter IV, I describe a validation of stochastic sampling in integrative modeling. The remaining chapters describe applications of integrative modeling to assemblies of various sizes and scales, using various sources of information, thus illustrating the flexibility of the integrative modeling approach. Specifically, I apply integrative modeling to the human ECM29-Proteasome assembly under oxidative stress (Chapter V), the yeast nuclear pore complex (NPC) cytoplasmic mRNA export platform (Chapter VI), the major membrane ring component of the yeast NPC (Chapter VII), the entire yeast NPC (Chapter VIII), and the reconstruction of 3D structures of MET antibodies (Chapter IX)