Bioinformatics Methods for NMR Chemical Shift Data Dissertation an der Fakult¨at fur¨ Mathematik, Informatik und Statistik der Ludwig-Maximilians-Universit¨at Munchen¨ Simon W. Ginzinger vorgelegt am 24.10.2007 Erster Gutachter: Prof. Dr. Volker Heun, Ludwig-Maximilians-Universit¨at Munchen¨ Zweiter Gutachter: Prof. Dr. Robert Konrat, Universit¨at Wien Rigorosum: 8. Februar 2008 Kurzfassung Die nukleare Magnetresonanz-Spektroskopie (NMR) ist eine der wichtigsten Meth- oden, um die drei-dimensionale Struktur von Biomolekulen¨ zu bestimmen. Trotz großer Fortschritte in der Methodik der NMR ist die Aufl¨osung einer Protein- struktur immer noch eine komplizierte und zeitraubende Aufgabe. Das Ziel dieser Doktorarbeit ist es, Bioinformatik-Methoden zu entwickeln, die den Prozess der Strukturaufkl¨arung durch NMR erheblich beschleunigen k¨onnen. Zu diesem Zweck konzentriert sich diese Arbeit auf bestimmte Messdaten aus der NMR, die so genannten chemischen Verschiebungen. Chemische Verschiebungen werden standardm¨aßig zu Beginn einer Struktur- aufl¨osung bestimmt. Wie alle Labordaten k¨onnen chemische Verschiebungen Fehler enthalten, die die Analyse erschweren, wenn nicht sogar unm¨oglich machen. Als erstes Resultat dieser Arbeit wird darum CheckShift pr¨asentiert, eine Meth- ode, die es erm¨oglich einen weit verbreiteten Fehler in chemischen Verschiebungs- daten automatisch zu korrigieren. Das Hauptziel dieser Doktorarbeit ist es jedoch, strukturelle Informationen aus chemischen Verschiebungen zu extrahieren. Als erster Schritt in diese Richtung wurde SimShift entwickelt. SimShift erm¨oglicht es zum ersten Mal, strukturelle Ahnlichkeiten¨ zwischen Proteinen basierend auf chemischen Verschiebungen zu identifizieren. Der Vergleich zu Methoden, die nur auf der Aminos¨aurensequenz basieren, zeigt die Uberlegenheit¨ des verschiebungsbasierten Ansatzes. Als eine naturliche¨ Erweiterung des paarweisen Vergleichs von Proteinen wird darauf fol- gend SimShiftDB vorgestellt. Gegeben ein Protein, durchsucht SimShiftDB eine Datenbank bekannter Proteinstrukturen nach strukturell homologen Eintr¨agen. Die Suche basiert hierbei nur auf der Aminos¨auresequenz und den chemischen Verschiebungen des Proteins. Die detektierten Ahnlichkeiten¨ werden zus¨atzlich nach statistischer Signifikanz bewertet. Mit der Chemical Shift Pipeline wird schließlich das Hauptresultat der Dis- sertation vorgestellt. Durch die Kombination der automatischen Fehlerkorrektur (CheckShift) mit dem Datenbank-Suchalgorithmus (SimShiftDB), wird in 70% bis 80% der vorhergesagten strukturellen Ahnlichkeiten¨ eine sehr hohe Qualit¨at erreicht. Der Anteil der fehlerhaften Vorhersagen betr¨agt nur etwa 10%. iii Summary Nuclear magnetic resonance spectroscopy (NMR) is one of the most important methods for measuring the three-dimensional structure of biomolecules. Despite major progress in the NMR methodology, the solution of a protein structure is still a tedious and time-consuming task. The goal of this thesis is to develop bioinformatics methods which may strongly accelerate the NMR process. This work concentrates on a special type of measurements, the so-called chemical shifts. Chemical shifts are routinely measured at the beginning of a structure resolu- tion process. As all data from the laboratory, chemical shifts may be error-prone, which might complicate or even circumvent the use of this data. Therefore, as the first result of the thesis, we present CheckShift, a method which automatically corrects a frequent error in NMR chemical shift data. However, the main goal of this thesis is the extraction of structural information hidden in chemical shifts. SimShift was developed as a first step in this direc- tion. SimShift is the first approach to identify structural similarities between proteins based on chemical shifts. Compared to methods based on the amino acid sequence alone, SimShift shows its strength in detecting distant structural relationships. As a natural further development of the pairwise comparison of proteins, the SimShift algorithm is adapted for database searching. Given a pro- tein, the improved algorithm, named SimShiftDB, searches a database of solved proteins for structurally homologue entries. The search is based only on the amino acid sequence and the associated chemical shifts. The detected similarities are additionally ranked based on calculations of statistical significance. Finally, the Chemical Shift Pipeline, the main result of this work, is presented. By combining automatic chemical shift error correction (CheckShift) and the database search algorithm (SimShiftDB), it is possible to achieve very high quality in 70% to 80% of the similarities identified. Thereby, only about 10% of the predictions are in error. v Contents 1 Introduction 1 1.1 Overview ............................... 1 1.2 Synopsis................................ 2 2 Preliminaries 5 2.1 Introduction.............................. 5 2.2 NMRBasics.............................. 5 2.3 SHIFTX................................ 9 2.3.1 RingCurrentEffects . .. .. .. .. 9 2.3.2 ElectricFieldEffects . 10 2.3.3 HydrogenBondEffects. 11 2.3.4 Empirical Chemical Shift Hypersurfaces . 12 2.4 TALOS ................................ 12 2.5 PSIPRED ............................... 15 2.6 Chemical Shift Index (CSI) . 15 2.7 Structural Identification (STRIDE) . 16 2.8 HHsearch ............................... 16 2.9 Secondary Structure Element Alignment (SSEA) . .. 17 2.10 CombinatorialExtension(CE). 17 2.10.1 DistanceScores . 18 2.11MaxSub ................................ 19 2.12Databases ............................... 19 3 CheckShift 21 3.1 Introduction.............................. 21 3.2 TheCheckShiftAlgorithm . 22 3.2.1 Preparation of Reference Density Functions . 22 3.2.2 CalculationofSimilarity . 23 3.2.3 Re-ReferencingofDataSets . 26 3.3 Results................................. 26 3.4 Discussion............................... 29 3.5 Availability .............................. 29 vii viii Contents 4 SimShift 31 4.1 Introduction.............................. 31 4.2 SelectionoftheBenchmarkSet . 33 4.2.1 DatabasesUsed........................ 34 4.2.2 Evaluating the Structural Correctness of Alignments ... 34 4.2.3 Defining a Benchmark Set . 35 4.3 The Shift-Alignment Algorithm . 36 4.3.1 Phase 1: Calculation of the Shift-Difference Matrix . .. 36 4.3.2 Phase2:FindGoodBlocks . 36 4.3.3 Phase3: ConcatenationofBlocks . 37 4.3.4 ParameterOptimization . 39 4.4 Results................................. 40 4.4.1 Comparison to SSEA and HHsearch . 40 4.4.2 ComparisontoTALOS. 45 4.5 Discussion............................... 45 5 SimShiftDB 47 5.1 Introduction.............................. 47 5.2 TheTemplateDatabase . 48 5.3 SubstitutionMatricesforShiftData . 48 5.4 E-Values for Chemical Shift Alignments . 49 5.5 The Shift Alignment Algorithm . 51 5.5.1 Step 1: Calculate local alignments . 51 5.5.2 Step 2: Identify the best legal combination . 52 5.6 Results................................. 55 5.6.1 Evaluation of the Modeling Performance . 55 5.6.2 Evaluation of the P-Value Correctness . 56 5.7 Discussion............................... 56 5.8 Availability .............................. 59 6 The Chemical Shift Pipeline 61 6.1 Introduction.............................. 61 6.2 Coping with Missing Chemical Shift Data . 61 6.3 Chemical Shift Substitution Matrices . 62 6.4 TheBenchmarkSet.......................... 65 6.5 Results................................. 65 6.6 Discussion............................... 69 6.7 Availability .............................. 69 7 Outlook 71 A SHIFTX Supplementary Material 81 Contents ix B Empirical vs. Theoretical P-Values for Set S1 85 C Empirical vs. Theoretical P-Values for Set S2 89 D The Benchmark Set 93 E Chemical Shift Pipeline Results 99 1 Introduction 1.1 Overview The main goal of Bioinformatics is to assist people working on biological prob- lems with the solution of tasks that require a thorough understanding of computer science. One of the most prominent methods, which has nowadays become a stan- dard tool for people working in molecular biology, is sequence comparison. With the size of the publicly available databases increasing, it became unfeasible to compare a newly derived sequence to a database of already solved and possibly functionally annotated sequences by hand. Therefore, tools were developed to search databases of millions of sequences automatically. To date, sequence com- parison has a strong research background and many problems arising in this area have been solved. Is the time for computational biology now over? Of course not, because there is more than just the protein sequence one might like to compare and investigate. What is strongly important in understanding the function of a protein is the protein’s three-dimensional structure. It has been shown that there exist proteins with very low sequence similarity sharing a strongly similar three-dimensional structure (see for example [Pastore and Lesk, 1990]). This underlines the need for structural data as often sequence comparison alone is not sufficient. To date, it is not possible to calculate the protein structure reliably from the protein sequence alone. NMR spectroscopy is one of the most important methods for resolving a protein structure in the laboratory. However, solving a protein as a whole is a complicated task, requiring a series of complex experiments. The work presented in this dissertation focuses on data which may be acquired fairly easily in a standard NMR experiment. This data are the so-called chemical shifts,