An Overview of Tools for the Validation of Protein NMR Structures
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A Web Server for Rapid NMR-Based Protein Structure Determination Berjanskii, M.; Tang, P.; Liang, J.; Cruz, J
NRC Publications Archive Archives des publications du CNRC GeNMR: a web server for rapid NMR-based protein structure determination Berjanskii, M.; Tang, P.; Liang, J.; Cruz, J. A.; Zhou, J.; Zhou, Y.; Bassett, E.; Macdonell, C.; Lu, P.; Lin, G.; Wishart, D. S. This publication could be one of several versions: author’s original, accepted manuscript or the publisher’s version. / La version de cette publication peut être l’une des suivantes : la version prépublication de l’auteur, la version acceptée du manuscrit ou la version de l’éditeur. For the publisher’s version, please access the DOI link below./ Pour consulter la version de l’éditeur, utilisez le lien DOI ci-dessous. Publisher’s version / Version de l'éditeur: https://doi.org/10.1093/nar/gkp280 Nucleic Acids Research, 37, Suppl. 2, pp. W670-W677, 2009-07-01 NRC Publications Record / Notice d'Archives des publications de CNRC: https://nrc-publications.canada.ca/eng/view/object/?id=8c24f92f-5a63-4bc9-9510-868e720b942e https://publications-cnrc.canada.ca/fra/voir/objet/?id=8c24f92f-5a63-4bc9-9510-868e720b942e Access and use of this website and the material on it are subject to the Terms and Conditions set forth at https://nrc-publications.canada.ca/eng/copyright READ THESE TERMS AND CONDITIONS CAREFULLY BEFORE USING THIS WEBSITE. L’accès à ce site Web et l’utilisation de son contenu sont assujettis aux conditions présentées dans le site https://publications-cnrc.canada.ca/fra/droits LISEZ CES CONDITIONS ATTENTIVEMENT AVANT D’UTILISER CE SITE WEB. Questions? Contact the NRC Publications Archive team at [email protected]. -
Quantitative Analysis of Biomolecular NMR Spectra: a Prerequisite for the Determination of the Structure and Dynamics of Biomolecules
Current Organic Chemistry, 2006, 10, 00-00 1 Quantitative Analysis of Biomolecular NMR Spectra: A Prerequisite for the Determination of the Structure and Dynamics of Biomolecules Thérèse E. Malliavin* Laboratoire de Biochimie Théorique, CNRS UPR 9080, Institut de Biologie PhysicoChimique, 13 rue P. et M. Curie, 75 005 Paris, France. Abstract: Nuclear Magnetic Resonance (NMR) became during the two last decades an important method for biomolecular structure determination. NMR permits to study biomolecules in solution and gives access to the molecular flexibility at atomic level on a complete structure: in that respect, it is occupying a unique place in structural biology. During the first years of its development, NMR was trying to meet the requirements previously defined in X-ray crystallography. But, NMR then started to determine its own criteria for the definition of a structure. Indeed, the atomic coordinates of an NMR structure are calculated using restraints on geometrical parameters (angles and distances) of the structure, which are only indirectly related to atom positions: in that respect, NMR and X-ray crystallography are very different. The indirect relation between the NMR measurements and the molecular structure and dynamics makes critical the precision and the interpretation of the NMR parameters and the development of quantitative analysis methods. The methods published since 1997 for liquid-NMR of proteins are reviewed here. First, methods for structure determination are presented, as well as methods for spectral assignment and for structure quality assessment. Second, the quantitative analysis of structure mobility is reviewed. 1. INTRODUCTION parameters is fuzzy, because each NMR parameter is Nuclear Magnetic Resonance (NMR) became at the depending not only on the structure, but also on the internal beginning of 90's, a method of choice for structural biology dynamics of the molecule, and these two aspects are difficult in solution. -
HASH: a Program to Accurately Predict Protein H Shifts from Neighboring Backbone Shifts
J Biomol NMR DOI 10.1007/s10858-012-9693-7 ARTICLE a HASH: a program to accurately predict protein H shifts from neighboring backbone shifts Jianyang Zeng • Pei Zhou • Bruce Randall Donald Received: 25 October 2012 / Accepted: 5 December 2012 Ó Springer Science+Business Media Dordrecht 2012 Abstract Chemical shifts provide not only peak identities atoms (i.e., adjacent atoms in the sequence), can remedy for analyzing nuclear magnetic resonance (NMR) data, but the above dilemmas, and hence advance NMR-based also an important source of conformational information for structural studies of proteins. By specifically exploiting the studying protein structures. Current structural studies dependencies on chemical shifts of nearby backbone requiring Ha chemical shifts suffer from the following atoms, we propose a novel machine learning algorithm, a a limitations. (1) For large proteins, the H chemical shifts called HASH, to predict H chemical shifts. HASH combines can be difficult to assign using conventional NMR triple- a new fragment-based chemical shift search approach with resonance experiments, mainly due to the fast transverse a non-parametric regression model, called the generalized relaxation rate of Ca that restricts the signal sensitivity. (2) additive model, to effectively solve the prediction problem. Previous chemical shift prediction approaches either We demonstrate that the chemical shifts of nearby back- require homologous models with high sequence similarity bone atoms provide a reliable source of information for or rely heavily on accurate backbone and side-chain predicting accurate Ha chemical shifts. Our testing results structural coordinates. When neither sequence homologues on different possible combinations of input data indicate nor structural coordinates are available, we must resort to that HASH has a wide rage of potential NMR applications in other information to predict Ha chemical shifts. -
Automatic 13C Chemical Shift Reference Correction for Unassigned Protein NMR Spectra
Journal of Biomolecular NMR https://doi.org/10.1007/s10858-018-0202-5 ARTICLE Automatic 13C chemical shift reference correction for unassigned protein NMR spectra Xi Chen1,2 · Andrey Smelter1,3,4 · Hunter N. B. Moseley1,2,3,4,5 Received: 10 April 2018 / Accepted: 1 August 2018 © The Author(s) 2018 Abstract Poor chemical shift referencing, especially for 13C in protein Nuclear Magnetic Resonance (NMR) experiments, fundamen- tally limits and even prevents effective study of biomacromolecules via NMR, including protein structure determination and analysis of protein dynamics. To solve this problem, we constructed a Bayesian probabilistic framework that circumvents the limitations of previous reference correction methods that required protein resonance assignment and/or three-dimensional protein structure. Our algorithm named Bayesian Model Optimized Reference Correction (BaMORC) can detect and correct 13C chemical shift referencing errors before the protein resonance assignment step of analysis and without three-dimensional structure. By combining the BaMORC methodology with a new intra-peaklist grouping algorithm, we created a combined method called Unassigned BaMORC that utilizes only unassigned experimental peak lists and the amino acid sequence. Unassigned BaMORC kept all experimental three-dimensional HN(CO)CACB-type peak lists tested within ± 0.4 ppm of the correct 13C reference value. On a much larger unassigned chemical shift test set, the base method kept 13C chemical shift ref- erencing errors to within ± 0.45 ppm at a 90% confidence interval. With chemical shift assignments, Assigned BaMORC can detect and correct 13C chemical shift referencing errors to within ± 0.22 at a 90% confidence interval. Therefore, Unassigned BaMORC can correct 13C chemical shift referencing errors when it will have the most impact, right before protein resonance assignment and other downstream analyses are started. -
Noor E Hafsa
Protein Structure Characterization From NMR Chemical Shifts by Noor E Hafsa A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Computing Science University of Alberta © Noor E Hafsa, 2016 Abstract In order to understand the complex biological functions of proteins, highly detailed, atomic resolution protein structures are needed. Experimental methods such as X-ray crystallography and NMR spectroscopy provide standard platforms for determining the atomic-resolution structures of proteins. However, a continuing bottleneck in conventional NOE-based NMR structure determination lies in the difficulty of measuring NOEs for medium-to-large proteins and the resulting time-costs and the corresponding reduction in structure accuracy and precision. This has led to an increased interest in using other easily identifiable NMR parameters, such as chemical shifts, to facilitate protein structure determination by NMR. Chemical shifts, often considered as mileposts of NMR, have long been used to decipher the structures of small molecules. However, chemical shifts are much less frequently utilized for structural interpretation of larger macromolecules such as peptides and proteins. Most existing macromolecular methods use chemical shifts and various heuristic, rule-based algorithms to identify and determine a small number of structural parameters (such as secondary structure). Other methods, such as CS-Rosetta and CS23D, which attempt to determine 3D structures from chemical shifts alone, are only modestly successful (~50% success). So while good progress has been made, I believe that there is still substantial room for improvement and that the “Shift-to- Structure” problem has not yet been fully solved. My PhD project involves investigating innovative computational and machine- learning approaches to develop chemical-shift based prediction models to determine protein structures with high efficiency and high accuracy (>90%). -
A Modest Improvement in Empirical NMR Chemical Shift Prediction by Means of an Artificial Neural Network
J Biomol NMR (2010) 48:13–22 DOI 10.1007/s10858-010-9433-9 ARTICLE SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network Yang Shen • Ad Bax Received: 4 June 2010 / Accepted: 30 June 2010 / Published online: 14 July 2010 Ó US Government 2010 Abstract NMR chemical shifts provide important local Introduction structural information for proteins and are key in recently described protein structure generation protocols. We describe NMR chemical shifts have long been recognized as a new chemical shift prediction program, SPARTA?,which important sources of protein structural information (Saito is based on artificial neural networking. The neural network is 1986; Spera and Bax 1991; Wishart et al. 1991; Iwadate trained on a large carefully pruned database, containing 580 et al. 1999; Wishart and Case 2001). During protein proteins for which high-resolution X-ray structures and nearly structure calculations, chemical shift derived backbone //w complete backbone and 13Cb chemical shifts are available. torsion angles (Luginbu¨hl et al. 1995; Cornilescu et al. The neural network is trained to establish quantitative rela- 1999; Shen et al. 2009) are often used as empirical tions between chemical shifts and protein structures, including restraints, complementing the more traditional restraints backbone and side-chain conformation, H-bonding, electric derived from NOEs, J couplings and RDCs. More recently, fields and ring-current effects. The trained neural network several approaches for generating protein structures have yields rapid chemical shift prediction for backbone and 13Cb been developed which rely on backbone chemical shifts as atoms, with standard deviations of 2.45, 1.09, 0.94, 1.14, 0.25 the only source of experimental input information (Cavalli and 0.49 ppm for d15N, d13C’, d13Ca, d13Cb, d1Ha and d1HN, et al. -
Bioinformatics Methods for NMR Chemical Shift Data
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. -
David Wishart University of Alberta [email protected] NOE-Based NMR
Calculating Protein Structures from Chemical Shifts & Vice Versa 9th CCPN Meeting, July 24, 2009 University of Cumbria, Ambleside UK David Wishart University of Alberta [email protected] NOE-based NMR 3-6 months Chemical Shift Assignments NOE Intensities J-Couplings Distance Geometry Simulated Annealing Conventional NMR Advantages Disadvantages • Robust and well • NOE measurement is tested tedious & error prone • Yields structure • Limited to smaller almost every time (<30 kD) proteins • Large body of • Time consuming and software available slow • Structure reflects • Ignores other expt. dynamics of system information • Lower structure quality than X-ray Unconventional NMR • Conventional NMR is still slow and very manually intensive • Structural proteomics initiatives are looking for better/faster/cheaper routes to protein structure determination • Use of chemical shifts would skip the NOE assignment problem and allow larger structures to be determined, to higher accuracy and with greater speed Chemical Shifts & Structure Easy(ier) More information Less information Hard Less information More information Featured Tools SHIFTX http://redpoll.pharmacy.ualberta.ca/shiftx/ CS23D http://www.cs23d.ca GeNMR http://www.genmr.ca Why Shifts from Structure? • Can be used for chemical shift refinement • Can be used for structure generation, fold identification or structure evaluation • Allows identification of possible assignment errors or spectral folding problems • Allows correction of chemical shift mis- referencing • Useful for guiding assignments (if X-ray structure known) • Useful for ligand or interaction mapping Shift Calculation Methods • SHIFTS (D. Case) – QM calculated hypersurfaces + ring current effects • SPARTA (A. Bax) – Matching triplets for sequence + phi/psi/chi1 • PROSHIFT (J. Meiler) – ANNs from 3D coords + torsion angles • SHIFTX (D. -
Predicting Chemical Shifts with Graph Neural Networks APREPRINT
bioRxiv preprint doi: https://doi.org/10.1101/2020.08.26.267971; this version posted August 27, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. PREDICTING CHEMICAL SHIFTS WITH GRAPH NEURAL NETWORKS APREPRINT Ziyue Yang Maghesree Chakraborty Department of Chemical Engineering Department of Chemical Engineering University of Rochester University of Rochester Andrew D White∗ Department of Chemical Engineering University of Rochester August 26, 2020 ABSTRACT Inferring molecular structure from NMR measurements requires an accurate forward model that can predict chemical shifts from 3D structure. Current forward models are limited to specific molecules like proteins and state of the art models are not differentiable. Thus they cannot be used with gradient methods like biased molecular dynamics. Here we use graph neural networks (GNNs) for NMR chemical shift prediction. Our GNN can model chemical shifts accurately and capture important phenomena like hydrogen bonding induced downfield shift between multiple proteins, secondary structure effects, and predict shifts of organic molecules. Previous empirical NMR models of protein NMR have relied on careful feature engineering with domain expertise. These GNNs are trained from data alone with no feature engineering yet are as accurate and can work on arbitrary molecular structures. The models are also efficient, able to compute one million chemical shifts in about 5 seconds. This work enables a new category of NMR models that have multiple interacting types of macromolecules. -
Using Chemical Shift Perturbation to Characterise Ligand Binding ⇑ Mike P
Progress in Nuclear Magnetic Resonance Spectroscopy 73 (2013) 1–16 Contents lists available at SciVerse ScienceDirect Progress in Nuclear Magnetic Resonance Spectroscopy journal homepage: www.elsevier.com/locate/pnmrs Using chemical shift perturbation to characterise ligand binding ⇑ Mike P. Williamson Department of Molecular Biology and Biotechnology, University of Sheffield, Firth Court, Western Bank, Sheffield S10 2TN, UK Edited by David Neuhaus and Gareth Morris article info abstract Article history: Chemical shift perturbation (CSP, chemical shift mapping or complexation-induced changes in chemical Received 22 January 2013 shift, CIS) follows changes in the chemical shifts of a protein when a ligand is added, and uses these to Accepted 18 February 2013 determine the location of the binding site, the affinity of the ligand, and/or possibly the structure of Available online 21 March 2013 the complex. A key factor in determining the appearance of spectra during a titration is the exchange rate between free and bound, or more specifically the off-rate koff. When koff is greater than the chemical shift difference between free and bound, which typically equates to an affinity Kd weaker than about 3 lM, Keywords: then exchange is fast on the chemical shift timescale. Under these circumstances, the observed shift is Chemical shift the population-weighted average of free and bound, which allows Kd to be determined from measure- Protein ment of peak positions, provided the measurements are made appropriately. 1H shifts are influenced Exchange rate to a large extent by through-space interactions, whereas 13C and 13Cb shifts are influenced more by Dissociation constant a 15 13 0 Docking through-bond effects. -
Arxiv:1305.2164V2 [Physics.Chem-Ph] 24 Nov 2013
1 Protein structure validation and refinement using amide proton chemical shifts derived from quantum mechanics Anders S. Christensen1;∗, Troels E. Linnet2, Mikael Borg3 Wouter Boomsma2 Kresten Lindorff-Larsen2 Thomas Hamelryck3 Jan H. Jensen1 1 Department of Chemistry, University of Copenhagen, Copenhagen, Denmark 2 Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark 3 Structural Bioinformatics group, Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, Copenhagen, Denmark ∗ E-mail: Corresponding [email protected] Abstract We present the ProCS method for the rapid and accurate prediction of protein backbone amide proton chemical shifts - sensitive probes of the geometry of key hydrogen bonds that determine protein structure. ProCS is parameterized against quantum mechanical (QM) calculations and reproduces high level QM results obtained for a small protein with an RMSD of 0.25 ppm (r = 0.94). ProCS is interfaced with the PHAISTOS protein simulation program and is used to infer statistical protein ensembles that reflect experimentally measured amide proton chemical shift values. Such chemical shift-based structural refine- ments, starting from high-resolution X-ray structures of Protein G, ubiquitin, and SMN Tudor Domain, h3 result in average chemical shifts, hydrogen bond geometries, and trans-hydrogen bond ( J NC0 ) spin-spin coupling constants that are in excellent agreement with experiment. We show that the structural sensi- tivity of the QM-based amide proton chemical shift predictions is needed to obtain this agreement. The ProCS method thus offers a powerful new tool for refining the structures of hydrogen bonding networks to high accuracy with many potential applications such as protein flexibility in ligand binding. -
HASH: a Program to Accurately Predict Protein H Shifts from Neighboring Backbone Shifts
J Biomol NMR (2013) 55:105–118 DOI 10.1007/s10858-012-9693-7 ARTICLE a HASH: a program to accurately predict protein H shifts from neighboring backbone shifts Jianyang Zeng • Pei Zhou • Bruce Randall Donald Received: 25 October 2012 / Accepted: 5 December 2012 / Published online: 16 December 2012 Ó Springer Science+Business Media Dordrecht 2012 Abstract Chemical shifts provide not only peak identities atoms (i.e., adjacent atoms in the sequence), can remedy for analyzing nuclear magnetic resonance (NMR) data, but the above dilemmas, and hence advance NMR-based also an important source of conformational information for structural studies of proteins. By specifically exploiting the studying protein structures. Current structural studies dependencies on chemical shifts of nearby backbone requiring Ha chemical shifts suffer from the following atoms, we propose a novel machine learning algorithm, a a limitations. (1) For large proteins, the H chemical shifts called HASH, to predict H chemical shifts. HASH combines can be difficult to assign using conventional NMR triple- a new fragment-based chemical shift search approach with resonance experiments, mainly due to the fast transverse a non-parametric regression model, called the generalized relaxation rate of Ca that restricts the signal sensitivity. (2) additive model, to effectively solve the prediction problem. Previous chemical shift prediction approaches either We demonstrate that the chemical shifts of nearby back- require homologous models with high sequence similarity bone atoms provide a reliable source of information for or rely heavily on accurate backbone and side-chain predicting accurate Ha chemical shifts. Our testing results structural coordinates. When neither sequence homologues on different possible combinations of input data indicate nor structural coordinates are available, we must resort to that HASH has a wide rage of potential NMR applications in other information to predict Ha chemical shifts.