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The TB structural genomics consortium crystallization facility: Towards automation from protein to electron density
Abstract
The crystallization facility of the TB (Tuberculosis) structural genomics consortium, one of nine NIH sponsored p50 structural genomic centres, provides TB consortium members with automated crystallization, data collection and basic molecular replacement (MR) structure solution up to bias minimized electron density maps. Crystallization setup of up to ten proteins per day follows the CRYSTOOL combinatorial screen protocol using a modular and affordable robotic design with an open architecture. Components include screen preparation, plate setup, automated image acquisition and analysis, and optimisation design. A new 96 well crystallization plate has been designed for optimal robotic handling while maintaining ease of manual crystal harvesting. Robotic crystal mounting, screening, and data collection are conducted in-house and at the Advanced Light Source (ALS) in Berkeley. A simple automated protocol based on MR and homology based structure prediction automatically solves modestly difficult problems. Multiple search models are evaluated in parallel MR and the best multi-segment rigid body refined MR solution is subjected to simulated annealing torsion angle molecular dynamics using CNS, bringing even marginal MR solutions within the convergence radius of the subsequent highly effective bias removal and map reconstruction protocol, Shake&wARP, used to generate electron density for initial rebuilding. Real space correlation plots allow rapid assessment of local structure quality. Modular design of robotics and automated scripts using publicly available programs for structure solution allow for efficient high throughput crystallography - at a reasonable cost.
... A Q-scale of 1-7 may be used [28], or only three levels, clear, precipitate, crystalline, may be assigned [22]. With proper training and crossvalidation, individuals as well as automated image recognition and scoring routines [42][43][44][45] can assign scores in a relatively consistent way within a given laboratory. Unfortunately, what the scores really mean varies, and no strong common scoring metric exist. ...
... In random sampling, coverage of the crystallization space is achieved by using each crystallization condition only once, and robotics are a necessity to practically implement the CRYSTOOL protocol [19,44,62]. Except for the selection of the basis set which also contains various detergents [63], no assumptions about success rate distributions or about factors speciWc for a particular protein are made. ...
... Such knowledge allows us to optimize the basis set by retaining good crystallizers and to replace weak ones with new reagents without increasing the dimensionality of the sampling space (albeit ignoring the possibility of synergistic reagent eVects). We show in Fig. 8 a limited analysis of 230,000 random crystallization trials [19,62] using a basis set of 55 reagents from the TB structural genomics consortium [38,44]. The protein sampling is implicitly biased towards small, highly soluble, prokaryotic proteins, from Mycobacterium tuberculosis. ...
Crystallization of proteins is a nontrivial task, and despite the substantial efforts in robotic automation, crystallization screening is still largely based on trial-and-error sampling of a limited subset of suitable reagents and experimental parameters. Funding of high throughput crystallography pilot projects through the NIH Protein Structure Initiative provides the opportunity to collect crystallization data in a comprehensive and statistically valid form. Data mining and machine learning algorithms thus have the potential to deliver predictive models for protein crystallization. However, the underlying complex physical reality of crystallization, combined with a generally ill-defined and sparsely populated sampling space, and inconsistent scoring and annotation make the development of predictive models non-trivial. We discuss the conceptual problems, and review strengths and limitations of current approaches towards crystallization prediction, emphasizing the importance of comprehensive and valid sampling protocols. In view of limited overlap in techniques and sampling parameters between the publicly funded high throughput crystallography initiatives, exchange of information and standardization should be encouraged, aiming to effectively integrate data mining and machine learning efforts into a comprehensive predictive framework for protein crystallization. Similar experimental design and knowledge discovery strategies should be applied to valid analysis and prediction of protein expression, solubilization, and purification, as well as crystal handling and cryo-protection.
... The crystallization success probability is a property of the protein and the crystallization approach taken, and these results suggest that after some number of crystallization trials one should look at modifications to the protein that may increase its probability of crystallization, rather than blindly carrying out more trials. Rupp et al. (2002), based upon the work of Segelke, estimate that for the most efficient use of time and materials, only 288 random crystallization trials need be performed before resorting to other approaches or modifications to the macromolecule. ...
... Automated production of macromolecular crystals is important, but it is not necessarily the only issue of concern. Rupp et al. (2002) Thaumatin and lysozyme crystals have been obtained after pre-heat treatment prior to crystallization for 10 min compared to those of controls. All protein crystals were obtained by the hanging drop vapor diffusion method, where 10 ml of a protein droplet was equilibrated against 0.5 ml of precipitant solution. ...
... In addition to the commercially available systems, many have been developed by businesses or institutions for internal use. Descriptions of a number of systems and approaches have been published in recent years (e.g., Oldfield et al., 1991;Soriano and Fontecilla-Camps, 1993;Sadaoui et al., 1994;Luft et al., 2000Luft et al., , 2001Mueller et al., 2001;Krupka et al., 2002;Rupp et al., 2002;Santarsiero et al., 2002;Sulzenbacher et al., 2002;Brown et al., 2003;DeLucas et al., 2003;Hosfield et al., 2003;Walter et al., 2003). Nanovolume crystallization services are now commercially available from Syrrx, who offer the setup of 1000 crystallization trials using only 100 ml of solution. ...
The common goal for structural genomic centers and consortiums is to decipher as quickly as possible the three-dimensional structures for a multitude of recombinant proteins derived from known genomic sequences. Since X-ray crystallography is the foremost method to acquire atomic resolution for macromolecules, the limiting step is obtaining protein crystals that can be useful of structure determination. High-throughput methods have been developed in recent years to clone, express, purify, crystallize and determine the three-dimensional structure of a protein gene product rapidly using automated devices, commercialized kits and consolidated protocols. However, the average number of protein structures obtained for most structural genomic groups has been very low compared to the total number of proteins purified. As more entire genomic sequences are obtained for different organisms from the three kingdoms of life, only the proteins that can be crystallized and whose structures can be obtained easily are studied. Consequently, an astonishing number of genomic proteins remain unexamined. In the era of high-throughput processes, traditional methods in molecular biology, protein chemistry and crystallization are eclipsed by automation and pipeline practices. The necessity for high-rate production of protein crystals and structures has prevented the usage of more intellectual strategies and creative approaches in experimental executions. Fundamental principles and personal experiences in protein chemistry and crystallization are minimally exploited only to obtain "low-hanging fruit" protein structures. We review the practical aspects of today's high-throughput manipulations and discuss the challenges in fast pace protein crystallization and tools for crystallography. Structural genomic pipelines can be improved with information gained from low-throughput tactics that may help us reach the higher-bearing fruits. Examples of recent developments in this area are reported from the efforts of the Southeast Collaboratory for Structural Genomics (SECSG).
... Although the automated production of macromolecular crystals is not necessarily the only issue of concern, it could integrate solutions to other problems like protein availability. Rupp et al [58] pointed out that protein availability is the true limitation given the current status of automation, and we therefore must, at the very least, focus on the use of the smallest possible quantities of protein to obtain crystals. This restriction on protein quantity does not only pertains to the volume of crystallization drops but also to the equipment and processes used to set them up either through automation or by hand [22]. ...
... Over the last several years, a number of specialized crystallization robots have appeared on the market. Many of them use innovative approaches to address these difficulties and some have achieved admirable success [34,[58][59][60][61][62][63][64][65][66][67][68][69][70]. Nanovolume crystallization services are now commercially available from Syrrx, who offer the setup of 1000 crystallization trials using only 100 ml of solution. ...
X-ray crystallography is the most powerful technique to determine the three-dimensional structure of proteins. Several steps are required to solve the structure of proteins including cloning and expression of the gene, protein purification, crystallization, X-rays diffraction, data collection and structure determination. The vast amount of data generated by genomic studies has accelerated the development of new methodologies to increase the number of three-dimensional structures solved. This review focuses on recent advances in the field of protein crystallography, highlighting the high-throughput crystallization technologies.
... tion , which is reliably obtained by X-ray crystallography (Shapiro and Harris, 2000). The rate-limiting step in this technique is the production of high-quality crystals suitable for high-resolution X-ray analysis. Automated production of macromolecular crystals is therefore of great importance, but it is not necessarily the only issue of concern. Rupp et al. (2002) point out in their recent work that the availability of protein is the true limitation given the current status of automation, and we therefore must at the very least focus on the use of the smallest possible quantities of protein to obtain crystals. This restriction on protein quantity does not only pertain to the volume of crystalliza ...
... Intersections of channels allow injection of fluid from one stream into another by rapid modulation of the fluidsÕ respective driving forces, thereby allowing dispensing in amounts roughly two orders of magnitude smaller than stated above, i.e., in the range of a single picoliter (Fu et al., 2002). These volumes are very small compared to the regular laboratory crystallization setup in which we typically deliver liquids in microliter increments and are even small in contrast to existing high-throughput systems which use total dispensed volumes of 500 to 100 nl (Krupka et al., 2002; Luft et al., 2001; Rupp et al., 2002). To appreciate the next property of microfluidic systems , we perform the following thought experiment: suppose we take a filled channel 1 cm in length and with 10 lm internal radius and then empty the channel contents into a droplet in 10 s. ...
Microfluidics, or lab-on-a-chip technology, is proving to be a powerful, rapid, and efficient approach to a wide variety of bioanalytical and microscale biopreparative needs. The low materials consumption, combined with the potential for packing a large number of experiments in a few cubic centimeters, makes it an attractive technique for both initial screening and subsequent optimization of macromolecular crystallization conditions. Screening operations, which require a macromolecule solution with a standard set of premixed solutions, are relatively straightforward and have been successfully demonstrated in a microfluidics platform. Optimization methods, in which crystallization solutions are independently formulated from a range of stock solutions, are considerably more complex and have yet to be demonstrated. To be competitive with either approach, a microfluidics system must offer ease of operation, be able to maintain a sealed environment over several weeks to months, and give ready access for the observation and harvesting of crystals as they are grown.
... An overview of our underlying philosophy and the challenges faced in the first 2 years of the TBSG crystallization facility is provided in a separate review (Rupp, 2003). Technical implementation details, including crystallization robotics, crystal recognition , data collection, and structure solution, are provided in the special literature (Rupp et al., 2002). ...
... As a consequence of de novo cocktail design for each protein construct, a large number of crystallization cocktails need to be prepared for random screening and optimization. We thus implemented customizable random screen generation in the computer program CRY- STOOL (Segelke and Rupp, 1998) and interfaced it with a liquid-handling robot to automatically produce crystallization cocktails in 96-well format (Rupp et al., 2002). Production of de novo random screens is time consuming (20–40 min per 96-well cocktail block) and thus rate limiting in our high-throughput crystallization process. ...
The crystallization facility of the TB Structural Genomics Consortium, one of nine NIH-sponsored structural genomics pilot projects, employs a combinatorial random sampling technique in high-throughput crystallization screening. Although data are still sparse and a comprehensive analysis cannot be performed at this stage, preliminary results appear to validate the random-screening concept. A discussion of statistical crystallization data analysis aims to draw attention to the need for comprehensive and valid sampling protocols. In view of limited overlap in techniques and sampling parameters between the publicly funded high-throughput crystallography initiatives, exchange of information should be encouraged, aiming to effectively integrate data mining efforts into a comprehensive predictive framework for protein crystallization.
... The limited availability of many proteins is often the key impediment in crystallographic research [137], emphasizing the need for systems that require minimal amounts of protein for crystallization. This is easy when working on the scale of liters or milliliters, but the process gets complicated as we lower the scale by 5 or 6 orders of magnitude [138]. ...
This chapter provides a review of different advanced methods that help to increase the success rate of a crystallization project, by producing larger and higher quality single crystals for determination of macromolecular structures by crystallographic methods. For this purpose, the chapter is divided into three parts. The first part deals with the fundamentals for understanding the crystallization process through different strategies based on physical and chemical approaches. The second part presents new approaches involved in more sophisticated methods not only for growing protein crystals but also for controlling the size and orientation of crystals through utilization of electromagnetic fields and other advanced techniques. The last section deals with three different aspects: the importance of microgravity, the use of ligands to stabilize proteins, and the use of microfluidics to obtain protein crystals. All these advanced methods will allow the readers to obtain suitable crystalline samples for high-resolution X-ray and neutron crystallography.
... Parameters screened generally include protein concentration, pH (generally a pH range from 3.0 to 9.0 is evaluated in steps of 0.3 pH units), buffer type, precipitating agent type and concentration, temperature, additive type and concentration. This approach, combined with technological developments such as high-throughput protein-expression/purification and automated crystallographic determination protocols, the use of protein engineering to enhance crystallization lattice contacts and robotic liquid-dispensing/crystallization systems, have accelerated the crystallization and structure determination of thousands of new proteins (Blundell & Mizuguchi, 2000;Burley et al., 1999;Mittl & Grü tter, 2001;Montelione & Anderson, 1999;Teichmann et al., 1999;Christendat et al., 2000;Waldo et al., 1999;Terwilliger, 2000;Abola et al., 2000;Hendrickson, 2000;Derewenda, 2004a,b;Rupp et al., 2002;Krupka et al., 2002;Stevens, 2000). However, in spite of the plethora of new protein structures that have resulted from these technological advances, it is clear that robotics alone is not sufficient for the successful crystallization of a significant number of important proteins. ...
This article begins by highlighting some of the ground-based studies emanating from NASA's Microgravity Protein Crystal Growth (PCG) program. This is followed by a more detailed discussion of the history of and the progress made in one of the NASA-funded PCG investigations involving the use of measured second virial coefficients (
B
values) as a diagnostic indicator of solution conditions conducive to protein crystallization. A second application of measured
B
values involves the determination of solution conditions that improve or maximize the solubility of aqueous and membrane proteins. These two important applications have led to several technological improvements that simplify the experimental expertise required, enable the measurement of membrane proteins and improve the diagnostic capability and measurement throughput.
... SGPP data collection was done at two synchrotron sources: the Advanced Light Source (ALS) at the Lawrence Berkeley National Laboratory and the Stanford Synchrotron Radiation Laboratory (SSRL). Both synchrotron labs developed robotic equipment for crystal handling and loading including automated crystal annealing, streamlining procedures for users including remote data collection, and software for control of data collection, processing, and analysis (13)(14)(15). Strategies for improving crystal quality included crystal annealing techniques (16,17) and cocrystallization with ligands. SGPP developed a special database (XRAYDB; Bosch et al., unpublished), which was used to track mounted crystals, cryoprotection procedures, results of screens at synchrotrons, and other early data in the structure determination process. ...
SGPP) Consortium aimed to determine crystal structures of proteins from trypanosomatid and malaria parasites in a high throughput manner. The pipeline of target selection, protein production, crystallization, and structure determination, is sketched. Special emphasis is given to a number of technology developments including domain prediction, the use of "co-crystallants," and capillary crystallization. "Fragment cocktail crystallography" for medical structural genomics is also described.
... The Perrin equation describes the case for a spherical rotator (1) where r 0 is the fundamental anisotropy of the fluorescing species, r is the measured anisotropy, τ is the fluorescence lifetime, θ is the rotational correlation time, and D the rotational diffusion coefficient. The rotational correlation time is related to the molecular mass (M) by (2) where is η the viscosity, V the volume of the rotating species of mass M, R the gas constant, T the temperature, the specific volume of the rotator and h the hydration, grams of H 2 O per gram of protein. The anisotropy is classically measured by exciting the sample with vertically polarized light, then measuring the intensities of the vertically polarized (I VV ) and horizontally polarized (I VH ) emission light. ...
Current macromolecule crystallization screening methods rely on the random testing of crystallization conditions, in the hope that one or more will yield positive results, crystals. Most plate outcomes are either clear or precipitated solutions, which results are routinely discarded by the experimenter. However, many of these may in fact be close to crystallization conditions, which fact is obscured by the nature of the apparent outcome. We are developing a fluorescence-based approach to the determination of crystallization conditions, which approach can also be used to assess conditions that may be close to those that would give crystals. The method uses measurements of fluorescence anisotropy and intensity. The method was first tested using model proteins, with likely outcomes as determined by fluorescence measurements where the plate data showed either clear or precipitated solutions being subjected to optimization screening. The results showed a ~83% increase in the number of crystallization conditions. The method was then tried as the sole screening method with a number of test proteins. In every case at least one or more crystallization conditions were found, and it is estimated that ~53% of these would not have been found using a plate screen.
... WebTB.org is provided by the TB structural genomics consortium [60][61][62]. It contains tools to search and browse the TB genome as well as structure summary pages on all known TB proteins, the MTBreg database of proteins up-or downregulated in TB, top 100 persistence targets in TB and many more tools. ...
We are witnessing the growing menace of both increasing cases of drug-sensitive and drug-resistant Mycobacterium tuberculosis strains and the challenge to produce the first new tuberculosis (TB) drug in well over 40 years. The TB community, having invested in extensive high-throughput screening efforts, is faced with the question of how to optimally leverage these data to move from a hit to a lead to a clinical candidate and potentially, a new drug. Complementing this approach, yet conducted on a much smaller scale, cheminformatic techniques have been leveraged and are examined in this review. We suggest that these computational approaches should be optimally integrated within a workflow with experimental approaches to accelerate TB drug discovery.
... Down-scaling, parallelization and automation are new trends in the field of recombinant protein expression in the post genomic era123. During the past years many companies and academic institutions have heavily invested in process and automation technologies. ...
who generously supported the meeting. Meeting
... This would avoid passage of the air gap through the valve and should enable us to reduce the dead volume to a few percent. Since we designed and started our high-throughput crystallisation facility, many other crystallisation facilities were launched (e.g. Brown et al., 2003; Heinemann et al., 2003; Rupp et al., 2002; Stevens, 2000; Sulzenbacher et al., 2002; Walter et al., 2003). In general, researchers have become very reluctant to set up crystallisation trials manually. ...
We have set up high-throughput robotic systems to screen and optimise crystallisation conditions of biological macromolecules with the aim to make difficult structural biology projects easier. The initial screening involves two robots. A Tecan Genesis liquid handler is used to transfer commercially available crystallisation reagents from 15 ml test tubes into the reservoirs of 96-well crystallisation plates. This step is fully automated and includes a carousel for intermediate plate storage, a Beckman plate sealer and a robotic arm, which transfers plates in between steps. For adding the sample, we use a second robot, a 17-tip Cartesian Technologies PixSys 4200 SynQuad liquid handler, which uses a syringe/solenoid valve combination to dispense small quantities of liquid (typically 100 nl) without touching the surface of the plate. Sixteen of the tips are used to transfer the reservoir solution to the crystallisation wells, while the 17th tip is used to dispense the protein. The screening of our standard set of 1440 conditions takes about 3 h and requires 300 microl of protein solution. Once crystallisation conditions have been found, they are optimised using a second Tecan Genesis liquid handler, which is programmed to pipette gradients from four different corner solutions into a wide range of crystallisation plate formats. For 96-well plates, the Cartesian robot can be used to add the sample. The methods described are now used almost exclusively for obtaining diffraction quality crystals in our laboratory with a throughput of several thousand plates per year. Our set-up has been copied in many institutions worldwide.
Modern chemotherapy has significantly improved patient outcomes against drug-sensitive tuberculosis. However, the rapid emergence of drug-resistant tuberculosis, together with the bacterium’s ability to persist and remain latent present a major public health challenge. To overcome this problem, research into novel anti-tuberculosis targets and drug candidates is thus of paramount importance. This review article provides an overview of tuberculosis highlighting the recent advances and tools that are employed in the field of anti-tuberculosis drug discovery. The predominant focus is on anti-tuberculosis agents that are currently in the pipeline, i.e. clinical trials.
Currently, macromolecular crystallography projects often require the use of highly automated facilities for crystallization and X-ray data collection. However, crystal harvesting and processing largely depend on manual operations. Here, a series of new methods are presented based on the use of a low X-ray-background film as a crystallization support and a photoablation laser that enable the automation of major operations required for the preparation of crystals for X-ray diffraction experiments. In this approach, the controlled removal of the mother liquor before crystal mounting simplifies the cryocooling process, in many cases eliminating the use of cryoprotectant agents, while crystal-soaking experiments are performed through diffusion, precluding the need for repeated sample-recovery and transfer operations. Moreover, the high-precision laser enables new mounting strategies that are not accessible through other methods. This approach bridges an important gap in automation and can contribute to expanding the capabilities of modern macromolecular crystallography facilities.
Although protein crystallization is generally considered more art than science and remains significantly trial-and-error, large-scale data sets hold the promise of providing general learning. Observations are presented here from retrospective analyses of the strategies actively deployed for the extensive crystallization experiments at the Oxford site of the Structural Genomics Consortium (SGC), where comprehensive annotations by SGC scientists were recorded on a customized database infrastructure. The results point to the importance of using redundancy in crystallizing conditions, specifically by varying the mixing ratios of protein sample and precipitant, as well as incubation temperatures. No meaningful difference in performance could be identified between the four most widely used sparse-matrix screens, judged by the yield of crystals leading to deposited structures; this suggests that in general any comparison of screens will be meaningless without extensive cross-testing. Where protein sample is limiting, exploring more conditions has a higher likelihood of being informative by yielding hits than does redundancy of either mixing ratio or temperature. Finally, on the logistical question of how long experiments should be stored, 98% of all crystals that led to deposited structures appeared within 30 days. Overall, these analyses serve as practical guidelines for the design of initial screening experiments for new crystallization targets.
In the context of structural genomics projects, there are two main routes to consider for solving efficiently and rapidly the three-dimensional (3D) structure of the target gene products by crystallography. The first is the Multiple-wavelength Anomalous Diffraction (MAD) technique, which necessitates growing SeMet-substituted protein crystals. The second is molecular replacement (MR), which requires X-ray data for the native protein as well as the structure of a related homolog. This chapter describes some of the newest developments in MR to get the best possible set of phases to initialize refinement and reconstruction in the best possible conditions.
Structure determination of biological macromolecules using x-ray crystallography has been greatly improved in recent years through the development of a number of key technologies and better integration with sample preparation steps. This has enabled the crystallization of large numbers of proteins, including integral membrane proteins. This chapter discusses the crystallization process as well as the development of automation that has dramatically increased the likelihood of success. Most noteworthy has been the introduction of crystallization protocols that use nanoliter protein solutions and the insight that production of high-quality crystals requires high protein sample quality. The latter underscores the importance of the development of powerful biophysical characterization techniques.
Nouvelles approches en génomique comparative
et bio-informatique structurale :
à la recherche de
relations séquence-structure-fonction.
High-throughput crystallographic approaches require integrated software solutions to minimize the need for manual effort. REdiii is a system that allows fully automated crystallographic structure solution by integrating existing crystallographic software into an adaptive and partly autonomous workflow engine. The program can be initiated after collecting the first frame of diffraction data and is able to perform processing, molecular-replacement phasing, chain tracing, ligand fitting and refinement without further user intervention. Preset values for each software component allow efficient progress with high-quality data and known parameters. The adaptive workflow engine can determine whether some parameters require modifications and choose alternative software strategies in case the preconfigured solution is inadequate. This integrated pipeline is targeted at providing a comprehensive and efficient approach to screening for ligand-bound co-crystal structures while minimizing repetitiveness and allowing a high-throughput scientific discovery process.
The last years have seen a major development in automation for protein production, crystallization, and X-ray diffraction data collection, which has contributed to accelerate the pace of structure solution and to facilitate the study of ever more challenging targets through macromolecular crystallography. This has led to a considerable increase in the numbers of crystals produced and analyzed. However the process of recovering crystals from crystallization supports and mounting them in X-ray data collection pins remains a manual and delicate operation. Here we present a novel approach enabling full automation of the crystal mounting process and describe the operation of the first-automated CrystalDirect harvesting unit. Implications for crystallography applications and for the future operational integration of automated crystallization and data collection resources are discussed.
An effective process for screening, imaging, and optimizing crystallization trials using a combination of external and internal hardware and software has been deployed. The combination of this infrastructure with a vast annotated crystallization database enables the creation of custom crystallization screening strategies. Because of the strong chemotype-dependent crystallization observed with HCV NS3 protease (HCVPr), this strategy was applied to a chemotype resistant to all prior crystallization efforts. The crystallization database was mined for ingredients used to generate earlier HCVPr/inhibitor co-crystals. A random screen was created from the most prolific ingredients. A previously untested combination of proven ingredients was identified that led to a successful crystallization condition for the resistant chemotype.
CATS allows users to mount and dismount their crystal samples remotely on the diffractometer, without entering the experimental hutch. CATS has been integrated into the automated control of FIP, allowing users to choose the wavelengths, optimize the beam intensity, mount and screen their crystal sample automatically and finally record diffraction data on the best sample(s).
Downloadable manuscript in PDF format: http://igs-server.cnrs-mrs.fr/suhre/HDR.pdf
Introduction:
X-ray crystallography is the main tool for macromolecular structure solution at atomic resolution. It provides key information for the understanding of protein function, opening opportunities for the modulation of enzymatic mechanisms, and protein-ligand interactions. As a consequence, macromolecular crystallography plays an essential role in drug design, as well as in the a posteriori validation of drug mechanisms.
Areas covered:
The demand for method developments and also tools for macromolecular crystallography has significantly increased over the past 10 years. As a consequence, access to the facilities required for these investigations, such as synchrotron beamlines, became more difficult and significant efforts were dedicated to the automation of the experimental setup in laboratories. In this article, the authors describe how this was accomplished and how robot-based systems contribute to the enhancement of the macromolecular structure solution pipeline.
Expert opinion:
The evolution in robot technology, together with progress in X-ray beam performance and software developments, contributes to a new era in macromolecular X-ray crystallography. Highly integrated experimental environments open new possibilities for crystallography experiments. It is likely that it will also change the way this technique will be used in the future, opening the field to a larger community.
In order to keep subscribers up-to-date with the latest developments in their field, this current awareness service is provided by John Wiley & Sons and contains newly-published material on comparative and functional genomics. Each bibliography is divided into 16 sections. 1 Reviews & symposia; 2 General; 3 Large-scale sequencing and mapping; 4 Genome evolution; 5 Comparative genomics; 6 Gene families and regulons; 7 Pharmacogenomics; 8 Large-scale mutagenesis programmes; 9 Functional complementation; 10 Transcriptomics; 11 Proteomics; 12 Protein structural genomics; 13 Metabolomics; 14 Genomic approaches to development; 15 Technological advances; 16 Bioinformatics. Within each section, articles are listed in alphabetical order with respect to author. If, in the preceding period, no publications are located relevant to any one of these headings, that section will be omitted
The series of 11 International Conferences on the Crystallization of Biological Macromolecules (ICCBM) took place over the period 1986–2006 in the USA (four times), Germany (two times), China, France, Japan, Spain, and lastly the 11th in Canada in Quebec City. Here we review the first 10 ICCBMs. Their focus was to bring rational approaches to the field of protein crystal growth and thus overcome the rate-limiting step in macromolecular X-ray crystallography. This survey summarizes how the ICCBM series contributed to the emergence of the science of biocrystallogenesis. This was achieved through the joint efforts of scientists from the small molecule crystal growth community and from biochemists, biophysicists, and protein crystallographers. Highlights from each conference are discussed, and scientific synergies are emphasized. While the first conferences focused on fundamentals, especially from the standpoint of physics and biochemical considerations, the more recent conferences stressed applications in structural biology, to advanced methods of crystallization, and of crystal quality improvement. Particular attention will be given to themes that were recurrent through all the ICCBMs: purity and impurities, solution properties of macromolecules under precrystallization conditions, microgravity and assessment of crystal quality, as well as specific trends of practical interest to structural biology.
A novel immunoaffinity separation material, cross-linked antibody crystals (CLAC), was developed by crystallization and cross-linking of a recombinant antibody Fab fragment ENA5His capable of enantiospecific separation of a chiral drug, finrozole. Crystallization conditions of the antibody fragment were screened by hanging drop vapor diffusion experiments. The small-scale vapor diffusion crystallization was easily scaled up to 10-mL batch crystallization with a 70% yield. Also, the low affinity mutant of ENA5His having one single amino acid mutation crystallized at the same conditions. Simple glutaraldehyde cross-linking of the crystals yielded CLAC, which were insoluble in water, 5−100% methanol, and 2% DMSO in PBS buffer. Batch experiments showed that the CLAC material was functional as it specifically bound the desired enantiomer from the drug racemate.
The use of automated systems for crystallization and X-ray data collection is now widespread. However, these two steps are separated by the need to transfer crystals from crystallization supports to X-ray data-collection supports, which is a difficult manual operation. Here, a new approach is proposed called CrystalDirect (CD) which enables full automation of the crystal-harvesting process. In this approach, crystals are grown on ultrathin films in a newly designed vapour-diffusion crystallization plate and are recovered by excision of the film through laser-induced photoablation. The film pieces containing crystals are then directly attached to a pin for X-ray data collection. This new method eliminates the delicate step of `crystal fishing', thereby enabling full automation of the crystal-mounting process. Additional advantages of this approach include the absence of mechanical stress and that it facilitates handling of microcrystals. The CD crystallization plates are also suitable for in situ crystal screening with minimal X-ray background. This method could enable the operational integration of highly automated crystallization and data-collection facilities, minimizing the delay between crystal identification and diffraction measurements. It can also contribute significantly to the advancement of challenging projects that require the systematic testing of large numbers of crystals.
IntroductionGene CloningProtein ExpressionHigh Throughput Protein PurificationValidation of the Pipeline and OutlookConclusion
References
X-ray diffraction experiments on protein crystals are at the core of the structure determination process. An overview of X-ray sources and data collection methods to support structure-based drug design (SBDD) efforts is presented in this chapter. First, methods of generating and manipulating X-rays for the purpose of protein crystallography, as well as the components of the diffraction experiment setup are discussed. SBDD requires the determination of numerous protein-ligand complex structures in a timely manner, and the second part of this chapter describes how to perform diffraction experiments efficiently on a large number of crystals, including crystal screening and data collection.
The crystallization experiment has one main objective: to obtain diffraction quality crystals. This can be achieved through myriad avenues; here the focus will be on crystallization in support of drug discovery. In drug discovery there are two main paradigms for crystallography: high-throughput, and by any means necessary. Each paradigm requires the investigator to formulate strategies based on different priorities. In the high-throughput environment, the emphasis is on rapid prosecution of a large number of protein targets. In the by any means necessary paradigm the target pool is generally smaller and structural information is absolutely necessary for success. The process of growing diffraction quality protein crystals involves deciding on a crystallization method, initial screening, cryoprotection, initial diffraction analysis, and growth optimization. Furthermore, in structure-based drug design it is necessary to obtain crystal structures of protein-ligand complexes.
Here we present a perspective on a range of practical uses of structural genomics for mutagen research. Structural genomics is an overloaded term and requires some definition to bound the discussion; we give a brief description of public and private structural genomics endeavors, along with some of their objectives, their activities, their capabilities, and their limitations. We discuss how structural genomics might impact mutagen research in three different scenarios: at a structural genomics center, at a lab with modest resources that also conducts structural biology research, and at a lab that is conducting mutagen research without in-house experimental structural biology. Applications span functional annotation of single genes or SNP, to constructing gene networks and pathways, to an integrated systems biology approach. Structural genomics centers can take advantage of systems biology models to target high value targets for structure determination and in turn extend systems models to better understand systems biology diseases or phenomenon. Individual investigator run structural biology laboratories can collaborate with structural genomics centers, but can also take advantage of technical advances and tools developed by structural genomics centers and can employ a structural genomics approach to advancing biological understanding. Individual investigator-run non-structural biology laboratories can also collaborate with structural genomics centers, possibly influencing targeting decisions, but can also use structure based annotation tools enabled by the growing coverage of protein fold space provided by structural genomics. Better functional annotation can inform pathway and systems biology models.
Structural genomics has become a powerful tool for studying microorganisms at the molecular level. Advances in technology have enabled the assembly of high-throughput pipelines that can be used to automate X-ray crystal structure determination for many proteins in the genome of a target organism. In this paper, we describe the methods used in the Tuberculosis Structural Genomics Consortium (TBSGC), ranging from protein production and crystallization to diffraction data collection and processing. The TBSGC is unique in that it uses biological importance as a primary criterion for target selection. The over-riding goal is to solve structures of proteins that may be potential drug targets, in order to support drug discovery efforts. We describe the crystal structures of several significant proteins in the M. tuberculosis genome that have been solved by the TBSGC over the past few years. We conclude by describing the high-throughput screening facilities and virtual screening facilities we have implemented for identifying small-molecule inhibitors of proteins whose structures have been solved.
An upgraded version of the sample changer ;CATS' (Cryogenic Automated Transfer System) that was developed on the FIP-BM30A beamline at the ESRF is presented. At present, CATS is installed at SLS (three systems), BESSY (one system), DLS (two systems) and APS (four systems for the LSCAT beamline). It consists mainly of an automated Dewar with an assortment of specific grippers designed to obtain a fast and reliable mounting/dismounting rate without jeopardizing the flexibility of the system. The upgraded system has the ability to manage any sample standard stored in any kind of puck.
The unprecedented increase in the number of new protein sequences arising from genomics and proteomics highlights directly the need for methods to rapidly and reliably determine the molecular and cellular functions of these proteins. One such approach, structural genomics, aims to delineate the total repertoire of protein folds, thereby providing three-dimensional portraits for all proteins in a living organism and to infer molecular functions of the proteins. The goal of obtaining protein structures on a genomic scale has motivated the development of high-throughput technologies for macromolecular structure determination, which have begun to produce structures at a greater rate than previously possible. These new structures have revealed many unexpected functional and evolution relationships that were hidden at the sequence level.
Structure-based drug discovery in the pharmaceutical industry benefits from cost-efficient methodologies that quickly assess the feasibility of specific, often refractory, protein targets to form well-diffracting crystals. By tightly coupling construct and purification diversity with nanovolume crystallization, the Structural Biology Group at Syrrx has developed such a platform to support its small-molecule drug-discovery program. During the past 18 months of operation at Syrrx, the Structural Biology Group has executed several million crystallization and imaging trials on over 400 unique drug-discovery targets. Here, key components of the platform, as well as an analysis of some experimental results that allowed for platform optimization, will be described.
Molecular replacement (MR) is the method of choice for X-ray crystallographic data phasing when structural data of suitable homologues are available. However, MR may fail even in cases of high sequence homology when conformational changes arising for example from ligand binding or different crystallogenic conditions come into play. In this work, the potential of normal-mode analysis as an extension to MR to allow recovery from such drawbacks is demonstrated. Three examples are presented in which screening for MR solutions with templates perturbed in the direction of one or two normal modes allows a valid MR solution to be found where MR using the original template failed to yield a model that could ultimately be refined. It has been shown recently that half of the known protein movements can be modelled by displacing the studied structure using at most two low-frequency normal modes. This suggests that normal-mode analysis has the potential to break tough MR problems in up to 50% of cases. Moreover, even in cases where an MR solution is available, this method can be used to further improve the starting model prior to refinement, eventually reducing the time spent on manual model construction (in particular for low-resolution data sets).
High-throughput data collection for macromolecular crystallography requires an automated sample mounting and alignment system for cryo-protected crystals that functions reliably when integrated into protein-crystallography beamlines at synchrotrons. Rapid mounting and dismounting of the samples increases the efficiency of the crystal screening and data collection processes, where many crystals can be tested for the quality of diffraction. The sample-mounting subsystem has random access to 112 samples, stored under liquid nitrogen. Results of extensive tests regarding the performance and reliability of the system are presented. To further increase throughput, we have also developed a sample transport/storage system based on "puck-shaped" cassettes, which can hold sixteen samples each. Seven cassettes fit into a standard dry shipping Dewar. The capabilities of a robotic crystal mounting and alignment system with instrumentation control software and a relational database allows for automated screening and data collection to be developed.
The Mycobacterium tuberculosis rmlC gene encodes dTDP-4-keto-6-deoxyglucose epimerase, the third enzyme in the M. tuberculosis dTDP-L-rhamnose pathway which is essential for mycobacterial cell-wall synthesis. Because it is structurally unique, highly substrate-specific and does not require a cofactor, RmlC is considered to be the most promising drug target in the pathway, and the M. tuberculosis rmlC gene was selected in the initial round of TB Structural Genomics Consortium targets for structure determination. The 1.7 A native structure determined by the consortium facilities is reported and implications for in silico screening of ligands for structure-guided drug design are discussed.
There are five broad areas where noteworthy advances have occurred in the field of macromolecular crystallization in the past 10 years, though some areas have seen the major part of those advances in only the last two years. This is largely a consequence of the international structural genomics initiative and its early results. The five areas are: (1) Physical studies and characterization of the protein crystallization process; (2) Development of new practical approaches and procedures; (3) The implementation of protein engineering by genetic means to enhance both purification and crystallization; (4) The creation of new screening conditions based on information and databases emerging from structural genomics; and (5) Development and implementation of automation, robotics, and mass screening of crystallization conditions using very small amounts of protein. A brief summary is provided here of the progress in the past few years and the influence of the structural genomics project.
Growth of high quality crystals is often the most difficult step in the determination of protein structures by X-ray diffraction. Automation can improve the success of this process both by reducing the amount of protein required for each screen and by relieving the tedium of setting up crystallization experiments by hand. We have been using an automated system for the design and execution of hanging drop crystallization experiments for the last two years. The system includes robots for the preparation of solutions, setup of hanging drops, and automated imaging, as well as a new software package (RoCKS) for managing all phases of the crystallization process. Here, we review the fundamentals of automated protein crystallization and present results from our comparisons of various approaches to screening.
The Mycobacterium tuberculosis pyrR gene (Rv1379) encodes a protein that regulates the expression of pyrimidine-nucleotide biosynthesis (pyr) genes in a UMP-dependent manner. Because pyrimidine biosynthesis is an essential step in the progression of TB, the gene product pyrR is an attractive antitubercular drug target. The 1.9 A native structure of Mtb pyrR determined by the TB Structural Genomics Consortium facilities in trigonal space group P3(1)21 is reported, with unit-cell parameters a = 66.64, c = 154.72 A at 120 K and two molecules in the asymmetric unit. The three-dimensional structure and residual uracil phosphoribosyltransferase activity point to a common PRTase ancestor for pyrR. However, while PRPP- and UMP-binding sites have been retained in Mtb pyrR, a distinct dimer interaction among subunits creates a deep positively charged cleft capable of binding pyr mRNA. In silico screening of pyrimidine-nucleoside analogs has revealed a number of potential lead compounds that, if bound to Mtb pyrR, could facilitate transcriptional attenuation, particularly cyclopentenyl nucleosides.
The production of three-dimensional crystallographic structural information of macromolecules can now be thought of as a pipeline which is being streamlined at every stage from protein cloning, expression and purification, through crystallisation to data collection and structure solution. Synchrotron X-ray beamlines are a key section of this pipeline as it is at these that the X-ray diffraction data that ultimately leads to the elucidation of macromolecular structures are collected. The burgeoning number of macromolecular crystallography (MX) beamlines available worldwide may be enhanced significantly with the automation of both their operation and of the experiments carried out on them. This paper reviews the current situation and provides a glimpse of how a MX beamline may look in the not too distant future.
A robotic system has been developed to be used for macromolecular crystallization and observation in typical university laboratories with a research focus on protein crystallography. The system consists of three major parts: a dispenser unit, a storage unit and an observation unit. This system is designed to automatically perform all of the processes involved in crystallization and observation without requiring any manual operations. The dispenser and observation units can carry out both sitting-drop vapor-diffusion procedures and microbatch procedures. With this system, the procedures are controlled by a personal computer running GUI-based software. After the dispensing of protein solution into the crystallization plates, they are automatically transferred to the storage units, followed by automatic observation according to a required schedule with arbitrary intervals. At each stage of crystallization, droplets in the crystallization plates are examined by original image-processing software in order to evaluate the appearance of the crystals.
High-throughput, automated or semiautomated methodologies implemented by companies and structural genomics initiatives have accelerated the process of acquiring structural information for proteins via x-ray crystallography. This has enabled the application of structure-based drug design technologies to a variety of new structures that have potential pharmacologic relevance. Although there remain major challenges to applying these approaches more broadly to all classes of drug discovery targets, clearly the continued development and implementation of these structure-based drug design methodologies by the scientific community at large will help to address and provide solutions to these hurdles. The result will be a growing number of protein structures of important pharmacologic targets that will help to streamline the process of identification and optimization of lead compounds for drug development. These lead agonist and antagonist pharmacophores should, in turn, help to alleviate one of the current critical bottlenecks in the drug discovery process; that is, defining the functional relevance of potential novel targets to disease modification. The prospect of generating an increasing number of potential drug candidates will serve to highlight perhaps the most significant future bottleneck for drug development, the cost and complexity of the drug approval process.
Modeling by homology is the most accurate computational method for translating an amino acid sequence into a protein structure. Homology modeling can be divided into two sub-problems, placing the polypeptide backbone and adding side-chains. We present a method for rapidly predicting the conformations of protein side-chains, starting from main-chain coordinates alone. The method involves using fewer than ten rotamers per residue from a backbone-dependent rotamer library and a search to remove steric conflicts. The method is initially tested on 299 high resolution crystal structures by rebuilding side-chains onto the experimentally determined backbone structures. A total of 77% of χ1 and 66% of χ1+2 dihedral angles are predicted within 40° of their crystal structure values. We then tested the method on the entire database of known structures in the Protein Data Bank. The predictive accuracy of the algorithm was strongly correlated with the resolution of the structures. In an effort to simulate a realistic homology modeling problem, 9424 homology models were created using three different modeling strategies. For prediction purposes, pairs of structures were identified which shared between 30% and 90% sequence identity. One strategy results in 82% of χ1 and 72% χ1+2 dihedral angles predicted within 40 degrees of the target crystal structure values, suggesting that movements of the backbone associated with this degree of sequence identity are not large enough to disrupt the predictive ability of our method for non-native backbones. These results compared favorably with existing methods over a comprehensive data set.
A new software suite, called Crystallography & NMR System (CNS), has been developed for macromolecular structure determination by X-ray crystallography or solution nuclear magnetic resonance (NMR) spectroscopy. In contrast to existing structure-determination programs, the architecture of CNS is highly flexible, allowing for extension to other structure-determination methods, such as electron microscopy and solid-state NMR spectroscopy. CNS has a hierarchical structure: a high-level hypertext markup language (HTML) user interface, task-oriented user input files, module files, a symbolic structure-determination language (CNS language), and low-level source code. Each layer is accessible to the user. The novice user may just use the HTML interface, while the more advanced user may use any of the other layers. The source code will be distributed, thus source-code modification is possible. The CNS language is sufficiently powerful and flexible that many new algorithms can be easily implemented in the CNS language without changes to the source code. The CNS language allows the user to perform operations on data structures, such as structure factors, electron-density maps, and atomic properties. The power of the CNS language has been demonstrated by the implementation of a comprehensive set of crystallographic procedures for phasing, density modification and refinement. User-friendly task-oriented input files are available for nearly all aspects of macromolecular structure determination by X-ray crystallography and solution NMR.
Phases determined by the molecular-replacement method often suffer from model bias. In extreme cases, the refinement of the atomic model can stall at high free R values when the resulting electron-density maps provide little indication of how to correct the model, sometimes rendering even a correct solution unusable. Here, it is shown that several recent advances in refinement methodology allow productive refinement, even in cases where the molecular-replacement-phased electron-density maps do not allow manual rebuilding. In test calculations performed with a series of homologous models of penicillopepsin using either backbone atoms, or backbone atoms plus conserved core residues, model bias is reduced and refinement can proceed efficiently, even if the initial model is far from the correct one. These new methods combine cross-validation, torsion-angle dynamics simulated annealing and maximum-likelihood target functions. It is also shown that the free R value is an excellent indicator of model quality after refinement, potentially discriminating between correct and incorrect molecular-replacement solutions. The use of phase information, even in the form of bimodal single-isomorphous-replacement phase distributions, greatly improves the radius of convergence of refinement and hence the quality of the electron-density maps, further extending the limits of molecular replacement.
The aim of ARP/wARP is improved automation of model building and refinement in macromolecular crystallography. Once a molecular-replacement solution has been obtained, it is often tedious to refine and rebuild the initial (search) model. ARP/wARP offers three options to automate that task to varying extents: (i) autobuilding of a completely new model based on phases calculated from the molecular-replacement solution, (ii) updating of the initial model by atom addition and deletion to obtain an improved map and (iii) docking of a structure onto a new (or mutated) sequence, followed by rebuilding and refining the side chains in real space. A few examples are presented where ARP/wARP made a considerable difference in the speed of structure solution and/or made possible refinement of otherwise difficult or uninterpretable maps. The resolution range allowing complete autobuilding of protein structures is currently 2.0 A, but for map improvement considerable advances over more conventional refinement techniques are evident even at 3.2 A spacing.
Here, the proposal is investigated that protein tertiary structure prediction methods and threading methods in particular might be applied to the problem of solving a protein structure by X-ray crystallography, thus reducing the need for the more traditional experimental intensity methods of data phasing, such as heavy-metal isomorphous replacement and anomalous scattering methods, and without reference to a very closely related protein of known structure. If this kind of approach were to become successful and reliable, this would represent a significant advance in protein structure determination, offering an easy and accessible method for the initial data phasing for proteins' crystal structures, utilizing the vast amount of structural data, deposited in the Brookhaven PDB, that has been accumulated over the past 30 years of crystallographic structural studies. In the light of the ongoing structural genomics initiatives, the successful development of this kind of approach would be of enormous benefit.
The molecular-replacement method works well with good models and simple unit cells, but often fails with more difficult problems. Experience with likelihood in other areas of crystallography suggests that it would improve performance significantly. For molecular replacement, the form of the required likelihood function depends on whether there is ambiguity in the relative phases of the contributions from symmetry-related molecules (e.g. rotation versus translation searches). Likelihood functions used in structure refinement are appropriate only for translation (or six-dimensional) searches, where the correct translation will place all of the atoms in the model approximately correctly. A new likelihood function that allows for unknown relative phases is suitable for rotation searches. It is shown that correlations between sequence identity and coordinate error can be used to calibrate parameters for model quality in the likelihood functions. Multiple models of a molecule can be combined in a statistically valid way by setting up the joint probability distribution of the true and model structure factors as a multivariate complex normal distribution, from which the conditional distribution of the true structure factor given the models can be derived. Tests in a new molecular-replacement program, Beast, show that the likelihood-based targets are more sensitive and more accurate than previous targets. The new multiple-model likelihood function has a dramatic impact on success.
An automated high-throughput dispenser has been developed for the setup of protein crystallization trials by vapor diffusion or Microbatch methods. The Hydra-Plus-One is composed of a Hydra-PP system equipped with a motorized XYZ-platform, 96 precision glass syringes and a single-channel microsolenoid dispenser, which transfers 100 nl-50 micro l of protein solution with an accuracy of > 90% at a speed of 60s per 96 wells. Up to 300 micro l of premixed cocktails can be aspirated with the 96-syringe-assembly and dispensed into reservoir and droplet wells within 60s. The Hydra-Plus-One combines high precision, reliability and speed in a cost-effective high-throughput system ideally suited for protein crystallization
Macromolecular crystallization efforts are frequently divided into a search phase, during which approximate conditions are sought, and an optimization phase, when the approximate conditions are optimized to yield crystals of sufficient quality for diffraction work. Faced with the possibility that, on a yearly basis, many hundreds of proteins might be generated, both in our laboratories and at the laboratories of our collaborators, we have recently designed and commissioned a high throughput robotics lab designed for the search phase. The lab is capable of setting up and photographically evaluating over 60,000 microbatch crystallization experiments per week. In the first four months of operation we have set up crystallization experiments for more than one hundred proteins.
In an effort to objectively compare the efficiency of protein crystallization screening techniques, a probability model of sampling efficiency is developed and used to calculate sampling efficiencies from experimental data. Three typical sampling protocols (grid screening, footprint screening, and random screening) are used to crystallize each of five proteins (Phospholipase A2, Thaumatin, Catalase, Lysozyme, and Ribonuclease B). For each of the three sampling protocols, experiments are chosen from a large set of possible experiments generated by systematic combination of a number of parameters common in crystallization screens. Software has been developed to generate and select from the combinations with each of the three sampling protocols examined in this study. The protocols differ only in the order samples are chosen from the set of possible combinations. Random sampling is motivated by the “Incomplete Factorial” screen (Carter and Carter, J. Biol. Chem. 254 (1979) 12 219); sampling with subsets of four is motivated by the “Footprint” screen (Stura et al., J. Crystal Growth 122 (1992) 273) and sampling with subsets of twenty-four is motivated by the “Grid” screen (McPherson, Prepartion and Analysis of Protein Crystals, Wiley, New York, 1982). For the five proteins examined, random sampling has the greatest average efficiency. Additional benefits of random sampling are discussed.
A new procedure for molecular replacement is presented in which an efficient six-dimensional search is carried out using an evolutionary optimization algorithm. In this procedure, a population of initially random molecular-replacement solutions is iteratively optimized with respect to the correlation coefficient between observed and calculated structure factors. The sensitivity and reliability of the method is enhanced by uniform sampling of the rotational-search space and the use of continuously variable rotational and translational parameters. The process is several orders of magnitude faster than a systematic six-dimensional search, and comparisons show that it can identify solutions using significantly less accurate or less complete search models than is possible with two existing molecular-replacement methods. A program incorporating the method, EPMR, allows the rapid and highly automated solution of molecular-replacement problems involving single or multiple molecules in the asymmetric unit. EPMR has been used to solve a number of difficult molecular-replacement problems.
Formation of the chromophore of green fluorescent protein (GFP) depends on the correct folding of the protein. We constructed a "folding reporter" vector, in which a test protein is expressed as an N-terminal fusion with GFP. Using a test panel of 20 proteins, we demonstrated that the fluorescence of Escherichia coli cells expressing such GFP fusions is related to the productive folding of the upstream protein domains expressed alone. We used this fluorescent indicator of protein folding to evolve proteins that are normally prone to aggregation during expression in E. coli into closely related proteins that fold robustly and are fully soluble and functional. This approach to improving protein folding does not require functional assays for the protein of interest and provides a simple route to improving protein folding and expression by directed evolution.
The results of genome sequencing projects and recent advances in structure determination have ushered in structural genomics, a new field focused on the large-scale analysis of protein structures and functions based on gene sequences. In response, the National Institute of General Medical Sciences (NIGMS) announced its "Protein Structure Initiative", which is designed to organize a large, cooperative effort in structural genomics. NIGMS is a component of the US National Institutes of Health that supports basic biomedical research, including a major program in structural biology.
wARP is a procedure that substantially improves crystallographic phases (and subsequently electron-density maps) as an additional step after density-modification methods such as solvent flattening and averaging. The initial phase set is used to create a number of dummy atom models which are subjected to least-squares or maximum-likelihood refinement and iterative model updating in an automated refinement procedure (ARP). Averaging of the phase sets calculated from the refined output models and weighting of structure factors by their similarity to an average vector results in a phase set that improves and extends the initial phases substantially. An important requirement is that the native data have a maximum resolution beyond approximately 2.4 A. The wARP procedure shortens the time-consuming step of model building in crystallographic structure determination and helps to prevent the introduction of errors.
Model refinement has been a personalized affair for which laboratories have their preferred strategies, programs, etc. This has resulted in models with distinctive features of both the groups concerned and the software used. This chapter discusses the way a macromolecule should be refined and argues that the present practices in the community are often far from optimal, especially when only low-resolution data are available. All refinement programs nowadays use empirical restraints or constraints to ensure that a reasonable structure ensues during the refinement steps. This can result in a model with good stereochemical properties and also in a model in which molecules related by non-crystallographic symmetry (NCS) are forced to have similar (restrained) or identical (constrained) conformations. The aim of model building and refinement should be to construct a model that adequately explains the experimental observations, while making physical, chemical, and biological sense. It is a fact that low-resolution data can yield only low-resolution models. The refinement process, in particular, should always be tailored for each problem individually, keeping in mind the amount, resolution, and quality of the data.
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