Chemical crosslinking and mass spectrometry studies of the structure and dynamics of membrane proteins and receptors.

Membrane proteins make up a diverse and important subset of proteins for which structural information is limited. In this study, chemical cross-linking and mass spectrometry were used to explore the structure of the G-protein-coupled photoreceptor bovine rhodopsin in the dark-state conformation. All experiments were performed in rod outer segment membranes using amino acid 'handles' in the native protein sequence and thus minimizing perturbations to the native protein structure. Cysteine and lysine residues were covalently cross-linked using commercially available reagents with a range of linker arm lengths. Following chemical digestion of cross-linked protein, cross-linked peptides were identified by accurate mass measurement using liquid chromatography-fourier transform mass spectrometry and an automated data analysis pipeline. Assignments were confirmed and, if necessary, resolved, by tandem MS. The relative reactivity of lysine residues participating in cross-links was evaluated by labeling with NHS-esters. A distinct pattern of cross-link formation within the C-terminal domain, and between loop I and the C-terminal domain, emerged. Theoretical distances based on cross-linking were compared to inter-atomic distances determined from the energy-minimized X-ray crystal structure and Monte Carlo conformational search procedures. In general, the observed cross-links can be explained by re-positioning participating side-chains without significantly altering backbone structure. One exception, between C3 16 and K325, requires backbone motion to bring the reactive atoms into sufficient proximity for cross-linking. Evidence from other studies suggests that residues around K325 for a region of high backbone mobility. These findings show that cross-linking studies can provide insight into the structural dynamics of membrane proteins in their native environment

Combining protein structure prediction with experiments and functional information

Proteins are key players in all cells of living organisms. In particular, knowledge of the spatial protein structure may give fundamental insights into protein function and disease processes. For many years, the successful prediction of the structural and functional properties of proteins has been a major research field in bioinformatics. This field is also addressed in this work, which comprises an applied biomedical and a methodological part. Comprehensive application studies of bioinformatics approaches were performed, which primarily targeted autoinflammatory and neurodegenerative diseases. A variety of computational tools was used to analyze medically relevant proteins and to evaluate experimental data. Many bioinformatics methods were applied to predict the molecular structure and function of proteins. The results provided a rationale for the design, prioritization, and interpretation of experiments performed by cooperation partners. Some of the generated biological hypotheses were tested and confirmed by experiments. In addition, the application studies revealed limitations of current bioinformatics techniques, which led to suggestions for novel approaches. Three new computational methods were developed to support the prediction of the secondary and tertiary structure of proteins and the investigation of their interaction networks. First, consensus formation between three different methods for secondary structure prediction was shown to considerably improve the prediction quality and reliability. Second, in order to utilize experimental measurements in tertiary structure prediction, scoring functions were implemented that incorporate distance constraints into the alignment evaluation, thus increasing the fold recognition rate. Third, an automatic procedure for decomposing protein networks into interacting domains was designed to obtain a more detailed molecular view of protein-protein interactions, facilitating further functional and structural analyses.Proteinen kommt in allen Zellen lebender Organismen eine Schlüsselrolle zu. Insbesondere die Kenntnis der Raumstruktur von Proteinen kann fundamentale Einsichten in ihre Funktion und in Krankheitsprozesse liefern. Seit vielen Jahren ist die erfolgreiche Vorhersage struktureller und funktioneller Eigenschaften von Proteinen ein wichtiges Forschungsgebiet in der Bioinformatik. Dieses Gebiet ist auch Gegenstand der vorliegenden Arbeit, welche einen angewandten biomedizinischen und einen methodischen Teil umfasst. Es wurden umfangreiche Applikationsstudien von bioinformatischen Verfahren durchgeführt, die sich vornehmlich mit autoinflammatorischen und neurodegenerativen Erkrankungen befassten. Verschiedene Computerwerkzeuge wurden verwendet, um medizinisch relevante Proteine zu analysieren und experimentelle Daten auszuwerten. Es kamen viele Bioinformatikmethoden zur Anwendung, um die molekulare Struktur und Funktion von Proteinen vorherzusagen. Die Ergebnisse dienten als Grundlage für die Planung, Priorisierung und Interpretation von Experimenten, die von Kooperationspartnern durchgeführt wurden. Einige der generierten biologischen Hypothesen wurden durch Experimente überprüft und bestätigt. Zusätzlich deckten die Applikationsstudien Grenzen von Bioinformatikmethoden auf, was zu Vorschlägen für neuartige Verfahren führte. So wurden drei neue rechnerbasierte Methoden entwickelt, um die Vorhersage der Sekundär- und Tertiärstruktur von Proteinen sowie die Untersuchung ihrer Interaktionsnetzwerke zu unterstützen. Erstens wurde gezeigt, dass die Bildung eines Konsensus zwischen drei verschiedenen Methoden der Sekundärstrukturvorhersage die Vorhersagequalität und -verlässlichkeit erheblich verbessert. Zweitens wurden zur Nutzung experimenteller Messungen in der Tertiärstrukturvorhersage Bewertungsfunktionen implementiert, die Distanzbeschränkungen in die Alignmentevaluation einbinden, um die Faltungserkennungsrate zu erhöhen. Drittens wurde eine automatische Prozedur zur Dekomposition von Proteinnetzwerken in interagierende Domänen entworfen, um eine detailliertere molekulare Sicht von Interaktionen zwischen Proteinen zu erhalten. Hierdurch werden weitere Analysen zu Funktion und Struktur erleichtert

The interfacial bioscience grand challenge.

This report is broken down into the following 3 sections: (1) Chemical Cross-linking and Mass Spectrometry Applied to Determination of Protein Structure and Dynamics; (2) Computational Modeling of Membrane Protein Structure and Dynamics; and (3) Studies of Toxin-Membrane Interactions using Single Molecule Biophysical Methods

Confidence measures for protein fold recognition

Motivation: We present an extensive evaluation of different methods and criteria to detect remote homologs of a given protein sequence. We investigate two associated problems: first, to develop a sensitive searching method to identify possible candidates and, second, to assign a confidence to the putative candidates in order to select the best one. For searching methods where the score distributions are known, p-values are used as confidence measure with great success. For the cases where such theoretical backing is absent, we propose empirical approximations to p-values for searching procedures. Results: As a baseline, we review the performances of different methods for detecting remote protein folds (sequence alignment and threading, with and without sequence profiles, global and local). The analysis is performed on a large representative set of protein structures. For fold recognition, we find that methods using sequence profiles generally perform better than methods using plain sequences, and that threading methods perform better than sequence alignment methods. In order to assess the quality of the predictions made, we establish and compare several confidence measures, including raw scores, Z-scores, raw score gaps, z-score gaps, and different methods of p-value estimation. We work our way from the theoretically well backed local scores towards more explorative global and threading scores. The methods for assessing the statistical significance of predictions are compared using specificity-sensitivity plots. For local alignment techniques we find that p-value methods work best, albeit computationally cheaper methods such as those based on score gaps achieve similar performance. For global methods where no theory is available methods based on score gaps work best. By using the score gap functions as the measure of confidence we improve the more powerful fold recognition methods for which p-values are unavailable