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120 Gronwald and Kalbitzer

resolution (number of data points). This is the case with the

cosine ﬁlter as well as the Lorentzian-to-Gaussian ﬁlter (here

as an artifact resulting from the deﬁnitions provided by

TOPSPIN) (also see Subheading 3.3).

7. Application of an unshifted sine ﬁlter removes the volume

information (the volume of all cross peaks is zero) (also see

Subheading 3.3).

8. Do a careful baseline correction of all spectra before starting

with the data evaluation (also see Subheading 3.3.1).

9. Apply the different processing methods that your software

provides sequentially, e. g., a back prediction of the ﬁrst data

points in the time domain, followed by appropriate ﬁltering

and Fourier transform of the time domain data and a poly-

nomial baseline correction in the frequency domain (also see

Subheadings 3.3 and 3.3.1).

10. Be careful that some of the different processing func tions do

not work properly with oversampled data that have a different

time domain data structure (also see Subheadings 3.3

and 3.3.1).

11. Most carefully prepare your peak list since the quality

of the peak list is the most important single factor inﬂuen-

cing the outcome of any automated procedure (also see

Subheadings 3.3.2, 3.4, and 3.4.1).

12. Do not remove signiﬁcant weak peaks by a too high peak

picking threshold (in AUREMOL the threshold is set auto-

matically) (also see Subheadings 3.4 and 3.4.1).

13. Bayesian methods are rather powerful to discriminate between

true signals and noise and artifacts, but they can only decide

on the basis of the information available. Important

information is the number of expected cross peaks that

depends on the pulse sequence and the sample composition.

Choose the ﬁnal probability cutoff such that the number of

experimental cross-peaks corresponds to the expectation.

A factor of two is still acceptable by many routines (also see

Subheadings 3.4 and 3.4.1).

14. Try to understand the principles of the integration software

used (also see Subheading 3.4.2).

15. The iterative segmentation software that facilitates the

signal integration of AUREMOL requires the deﬁnition of a

maximum integration area. This has to be deﬁned large

enough, otherwise the integrals will be erroneous (also see

Subheading 3.4.2).

16. For an efﬁcient computing such a parameter is also (inherently)

present in many other routines (read the manual!) (also see

Subheading 3.4.2).

121

Automated Protein NMR Structure Determination in Solution

17. For all sequential assignment methods, the obtained result

strongly depends on the quality of the peak lists (see above),

since artifact peaks and missing peaks lead to wrong or

ambiguous connectivities. In most cases, connectivity infor-

mation solely based on Ca’s is not sufﬁcient for obtaining

unambiguous assignments. Therefore, it is highly recom-

mended to additionally provide information for the Cb ’s as

well. In case that a nonexhaustive routine like simulated

annealing is used, it is generally recommended to perform the

routines several times and to analyze the results for discrepan-

cies between the different runs (also see Subheading 3.5).

18. The method that has been implemented in AUREMOL for

side-chain assignment works directly on the NOESY spectra;

therefore, the preparation of peak lists is not critical (also see

Subheading 3.6).

19. Since for the side-chain assignments the backbone assign-

ments are a strong source of information (when available), be

careful that the chemical shifts given ﬁt optimally to the

NOESY spectra used. A typical effect that should be corrected

for is the shift introduced by using TROSY methods (also see

Subheading 3.6).

20. For automated NOE assignment, carefully prepare the

peak list. Be sure that the peak picking threshold is low

enough to retain the weak long range peaks. Set the proba-

bility cutoff at a value that the number of peaks is smaller

than twice the number of expected cross peaks (also see

Subheading 3.7).

21. Before starting the automated NOE assignment, be sure that

the chemical shift table corresponds closely to the conditions

used for the recording of the NOESY-spectrum. Apply the

chemical shift optimization routine AUREMOL-SHIFTOPT

(78) that adapts the shift table to the actual spectrum (also

see Subheading 3.7).

22. Adapt the other parameters (especially the search distance

range) to the iteration cycle (see also Subheadings

3.7.1–3.7.4).

23. For many of the parameters, default values are recommended

by the program. For an additional detailed description of the

various parameters, please refer to the AUREMOL manual

(see also Subheadings 3.7.1–3.7.4).

24. Note that upper and lower bounds are added to and subtracted

from the distance dist that was obtained from the individual

cross-peak volume. Relatively large upper bounds were selected

for the ﬁrst cycles of structure calculations to ensure that

the inﬂuence of erroneous assignments is limited. Upper

bound deﬁnition was taken from Linge et al., 2001 (79).

122 Gronwald and Kalbitzer

Lower bounds were selected in a way that the minimal distance

between two protons corresponds to the sum of their van der

Waals radii (see also Subheadings 3.7.1–3.7.4).

25. Please note that for the last iteration (iteration 6) the option

“assign all peaks” has been switched on, which leads to an

increased number of NOE assignments (should only be done

in the last iteration), since still ambiguous NOEs are assigned

to the solution that correspond to the smallest distance in the

trial structure, since this contribution will explain most of the

crosspeak volume. It is clear that for truly overlapping signals

the minor signal component will be neglected, which, in turn,

will lead to an underestimation of the resulting restraint

distance of the major component. However, in most cases,

this deviation is rather small and within the measurement

error. For the relaxation matrix-based restraint generation

(see below), the error bounds were now calculated based

on the local noise levels plus an additional error of 0.05 nm.

This additional error was obtained by analyzing the

obtained distance variations due to an assumed uncertainty

0.15 in the main and side-chain order parameters. The

obtained structures were further reﬁned in explicit solvent

following the protocol by Linge et al., 2003 (80) (see also

Subheadings 3.7.1–3.7.4).

26. For the simulation of NOESY spectra, make sure that the

corresponding parameters are set correctly. Especially give

the correct mixing time, repetition time and

H-frequency

(see also Subheading 3.8).

In the last sections, we have seen that automated protein structure

determination in solution at least for smaller well-behaved

proteins is a feasible task. As a consequence, computer-automated

determination of these structures will continue to grow in impor-

tance. However, for difﬁcult test cases such as large biopolymers,

aid from human experts is still required. Typically, in macromo-

lecular NMR, the information content of the NMR spectra alone

is often not sufﬁcient for a complete three-dimensional structure

determination. Signal-to-noise ratios are necessarily limited

due to the limited solubility of most biopolymers. Furthermore,

superpositions of resonance lines often lead to interpretational

ambiguities. Therefore, it is still of importance that any computer

program used for automated structure determination permits

intervention at any step in the analysis to allow the inclusion of

structural and spectroscopic information from other sources and

5. Conclusion

123

Automated Protein NMR Structure Determination in Solution

to supervise various aspects of the validation process. Especially

for the structure improvement by the inclusion of data from

other sources, we have recently developed the AUREMOL-ISIC

(81) algorithm that allows the reliable combination of NMR and

X-ray data.

In addition, the constant development of new experimental

multidimensional NMR techniques requires that new strategies

for automated analysis need to be implemented in existing com-

puter programs. Within this chapter, we have summarized some

of the main points required for automated protein structure

determination in solution. It is clear, however, that due to space

limitations not all possible strategies could be discussed.

Acknowledgments

This work was supported by the Bavarian Genomic Network

BayGene (W. G.) and the European Union (H. R. K.).

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Chapter 8

Computational Tools in Protein Crystallography

Deepti Jain and Valerie Lamour

Abstract

Protein crystallography emerged in the early 1970s and is, to this day, one of the most powerful techniques

for the analysis of enzyme mechanisms and macromolecular interactions at the atomic level. It is also an

extremely powerful tool for drug design. This ﬁeld has evolved together with developments in computer

science and molecular biology, allowing faster three-dimensional structure determination of complex bio-

logical assemblies. In recent times, structural genomics initiatives have pushed the development of meth-

ods to further speed up this process. The algorithms initially deﬁned in the last decade for structure

determination are now more and more elaborate, but the computational tools have evolved toward simpler

and more user-friendly packages and web interfaces. We present here a modest overview of the popular

software packages that have been developed for solving protein structures, and give a few guidelines and

examples for structure determination using the two most popular methods, molecular replacement and

multiple anomalous dispersion.

Key words: X-ray crystallography, Protein structure determination, Crystallography software,

Molecular replacement, Multi-wavelength anomalous dispersion

The correct three-dimensional (3D) arrangement of constituent

atoms is central to the proper functioning of a majority of biologi-

cal molecules. This fact is especially true of proteins, and hence, it

is important to determine the structures of macromolecules and

their assemblies to understand their function in great detail. X-ray

crystallography is an experimental science that has beneﬁted tre-

mendously from all the software and hardware developments dur-

ing the past three decades. Structure determination through X-ray

crystallography is becoming more and more automated through

the development of user-friendly interfaces and new crystallogra-

phy packages. These developments have made the method more

accessible to the biochemist with little theoretical background in

1. Introduction

David Fenyö (ed.), Computational Biology, Methods in Molecular Biology, vol. 673,

130 Jain and Lamour

crystallography. Often behind every structure lies months or more

commonly years of bench work to clone, express, and purify mil-

ligram quantities of protein required to produce diffracting crystals,

a prerequisite for any structural work using X-ray crystallography.

Useful information regarding this aspect of the method can be

found in numerous reviews that summarize decades of research in

structural biology, spanning simple proteins to high-molecular

weight biological macromolecular assemblies (1–3). The present

chapter will focus on the major methods and tools available to

determine the 3D structure of a macromolecule once diffracting

crystals have been prepared. It provides a concise reference to a

beginner in protein crystallography.

Computational tools in the form of program packages are con-

stantly evolving to feed the need for faster and more convenient

structure determination, especially driven by structural genomics

initiatives. We provide here a brief overview of the most common

and most widely used crystallographic packages (see Fig. 1) and

guidelines for structure determination using the favored phasing

Data collection

Data processing and scaling

HKL2000, Mosflm , XDS, d*trek

Refinement

Integrated

packages :

CNS

CCP4

Phenix

MRBump

AutoRickshaw

HKL3000

Density modification

Model building

Phasing

Model validation

PDB submission

O, Coot

LIGPLOT, CNS, Grasp

MAD, SAD, MIR

Structure analysis

Dino, Rasmol, Pymol

Procheck, Sfcheck, WHAT_CHECK, Molprobity

Figures

Strategy, BEST

SnB, SHELX, SOLVE

Amore, Molrep, Phaser, EPMR

SHARP, MLPHARE,

SOLVE

DM, SOLOMON, RESOLVE

ARP/wARP, SOLVE/RESOLVE

Automated

Manual

Heavy atom search

Refinement & Phasing

Fig. 1. General scheme representing the different steps to solve a crystallographic structure. A selection of programs is

reported next to each step of X-ray structure determination. Program packages or pipelines (dash line box) cover several

steps of structure determination. HKL3000 also includes data processing in the pipeline.