Structure Determination Pipeline Research Articles

Determining Protein Structures Using X-Ray Crystallography.

X-ray crystallography is a robust and widely used technique that facilitates the three-dimensional structure determination of proteins at an atomic scale. This methodology entails the growth of protein crystals under controlled conditions followed by their exposure to X-ray beams and the subsequent analysis of the resulting diffraction patterns via computational tools to determine the three-dimensional architecture of the protein. However, achieving high-resolution structures through X-ray crystallography can be quite challenging due to complexities associated with protein purity, crystallization efficiency, and crystal quality.In this chapter, we provide a detailed overview of the gene to structure determination pipeline used in X-ray crystallography, a crucial tool for understanding protein structures. The chapter covers the steps in protein crystallization, along with the processes of data collection, processing, structure determination, and refinement. The most commonly faced challenges throughout this procedure are also addressed. Finally, the importance of standardized protocols for reproducibility and accuracy is emphasized, as they are crucial for advancing the understanding of protein structure and function.

A fully automatic data processing and structure determination pipeline for X-ray biological macromolecule crystallography beamlines

A fully automatic data processing and structure determination pipeline for X-ray biological macromolecule crystallography beamlines

Design and Development of Hydrophobicity and Net charge Based Artificial Neural Network Model for IDP/IDPR Prediction

Intrinsically disordered proteins (IDPs) and proteins containing intrinsically disordered protein regions (IDPRs) often affect protein structure determination. IDP/IDPRs cause failures in the protein structure determination pipeline leading to enhanced experimental cost and time. Hence the primary sequence of proteins is often analyzed using disorder prediction servers so that manipulations can be made to the protein sequence to aid its expression, purification, and crystallization. Assessment of existing IDP/IDPR prediction methods with CASP 10 targets shows the scope of improvement of prediction. In this paper, an attempt has been made to develop a hydrophobicity and net charge based artificial neural network model for protein disorder prediction. Subsequently, a prediction tool has been developed using Java using the Neural Network Schema. The prediction tool has been tested with CASP 10 targets and the developed predictor is found to outperform other predictors i.e RONN, PONDR VLXT, DisEMBL, FOLDINDEX, and GLOBPLOT in Sensitivity (0.983), Precision (0.854), Accuracy (0.937), MCC (0.840), and AUC (0.937). DisEMBL and GLOBPLOT are found to have better specificity i.e 0.909 and 0.979 respectively than the proposed predictor i.e 0.890.

Polyelectrolyte coating of cryo-EM grids improves lateral distribution and prevents aggregation of macromolecules.

Cryo-electron microscopy (cryo-EM) is one of the primary methods used to determine the structures of macromolecules and their complexes. With the increased availability of cryo-electron microscopes, the preparation of high-quality samples has become a bottleneck in the cryo-EM structure-determination pipeline. Macromolecules can be damaged during the purification or preparation of vitrified samples for cryo-EM, making them prone to binding to the grid support, to aggregation or to the adoption of preferential orientations at the air-water interface. Here, it is shown that coating cryo-EM grids with a negatively charged polyelectrolyte, such as single-stranded DNA, before applying the sample reduces the aggregation of macromolecules and improves their distribution. The single-stranded DNA-coated grids enabled the determination of high-resolution structures from samples that aggregated on conventional grids. The polyelectrolyte coating reduces the diffusion of macromolecules and thus may limit the negative effects of the contact of macromolecules with the grid support and blotting paper, as well as of the shear forces on macromolecules during grid blotting. Coating grids with polyelectrolytes can readily be employed in any laboratory dealing with cryo-EM sample preparation, since it is fast, simple, inexpensive and does not require specialized equipment.

New tools for automated cryo-EM single-particle analysis in RELION-4.0.

We describe new tools for the processing of electron cryo-microscopy (cryo-EM) images in the fourth major release of the RELION software. In particular, we introduce VDAM, a variable-metric gradient descent algorithm with adaptive moments estimation, for image refinement; a convolutional neural network for unsupervised selection of 2D classes; and a flexible framework for the design and execution of multiple jobs in pre-defined workflows. In addition, we present a stand-alone utility called MDCatch that links the execution of jobs within this framework with metadata gathering during microscope data acquisition. The new tools are aimed at providing fast and robust procedures for unsupervised cryo-EM structure determination, with potential applications for on-the-fly processing and the development of flexible, high-throughput structure determination pipelines. We illustrate their potential on 12 publicly available cryo-EM data sets.

Simplified heavy-atom derivatization of protein structures via co-crystallization with the MAD tetragon tetrabromoterephthalic acid.

The phase problem is a persistent bottleneck that impedes the structure-determination pipeline and must be solved to obtain atomic resolution crystal structures of macromolecules. Although molecular replacement has become the predominant method of solving the phase problem, many scenarios still exist in which experimental phasing is needed. Here, a proof-of-concept study is presented that shows the efficacy of using tetrabromoterephthalic acid (B4C) as an experimental phasing compound. Incorporating B4C into the crystal lattice using co-crystallization, the crystal structure of hen egg-white lysozyme was solved using MAD phasing. The strong anomalous signal generated by its four Br atoms coupled with its compatibility with commonly used crystallization reagents render B4C an effective experimental phasing compound that can be used to overcome the phase problem.

State-of-the-Art Data Management: Improving the Reproducibility, Consistency, and Traceability of Structural Biology and in Vitro Biochemical Experiments.

Efficient and comprehensive data management is an indispensable component of modern scientific research and requires effective tools for all but the most trivial experiments. The LabDB system developed and used in our laboratory was originally designed to track the progress of a structure determination pipeline in several large National Institutes of Health (NIH) projects. While initially designed for structural biology experiments, its modular nature makes it easily applied in laboratories of various sizes in many experimental fields. Over many years, LabDB has transformed into a sophisticated system integrating a range of biochemical, biophysical, and crystallographic experimental data, which harvests data both directly from laboratory instruments and through human input via a web interface. The core module of the system handles many types of universal laboratory management data, such as laboratory personnel, chemical inventories, storage locations, and custom stock solutions. LabDB also tracks various biochemical experiments, including spectrophotometric and fluorescent assays, thermal shift assays, isothermal titration calorimetry experiments, and more. LabDB has been used to manage data for experiments that resulted in over 1200 deposits to the Protein Data Bank (PDB); the system is currently used by the Center for Structural Genomics of Infectious Diseases (CSGID) and several large laboratories. This chapter also provides examples of data mining analyses and warnings about incomplete and inconsistent experimental data. These features, together with its capabilities for detailed tracking, analysis, and auditing of experimental data, make the described system uniquely suited to inspect potential sources of irreproducibility in life sciences research.

Crystal structure of Nsp15 endoribonuclease NendoU from SARS-CoV-2.

Severe Acute Respiratory Syndrome coronavirus 2 (SARS‐CoV‐2) is rapidly spreading around the world. There is no existing vaccine or proven drug to prevent infections and stop virus proliferation. Although this virus is similar to human and animal SARS‐CoVs and Middle East Respiratory Syndrome coronavirus (MERS‐CoVs), the detailed information about SARS‐CoV‐2 proteins structures and functions is urgently needed to rapidly develop effective vaccines, antibodies, and antivirals. We applied high‐throughput protein production and structure determination pipeline at the Center for Structural Genomics of Infectious Diseases to produce SARS‐CoV‐2 proteins and structures. Here we report two high‐resolution crystal structures of endoribonuclease Nsp15/NendoU. We compare these structures with previously reported homologs from SARS and MERS coronaviruses.

High-Throughput Crystallization Pipeline at the Crystallography Core Facility of the Institut Pasteur.

The availability of whole-genome sequence data, made possible by significant advances in DNA sequencing technology, led to the emergence of structural genomics projects in the late 1990s. These projects not only significantly increased the number of 3D structures deposited in the Protein Data Bank in the last two decades, but also influenced present crystallographic strategies by introducing automation and high-throughput approaches in the structure-determination pipeline. Today, dedicated crystallization facilities, many of which are open to the general user community, routinely set up and track thousands of crystallization screening trials per day. Here, we review the current methods for high-throughput crystallization and procedures to obtain crystals suitable for X-ray diffraction studies, and we describe the crystallization pipeline implemented in the medium-scale crystallography platform at the Institut Pasteur (Paris) as an example.

Development of “Parameter space screening”-based single-wavelength anomalous diffraction phasing and structure determination pipeline

In this paper, we present a highly efficient structure determination pipeline software suite (X2DF) that is based on the “Parameter space screening” method, by combining the popular crystallographic structure determination programs and high-performance parallel computing. The phasing method employed in X2DF is based on the single-wavelength anomalous diffraction (SAD) theory. In the X2DF, the choice of crystallographic software, the input parameters to this software and the results display layout, are all parameters which users can select and screen automatically. Users may submit multiple structure determination jobs each time, and each job uses a slightly different set of input parameters or programs. Upon completion, the results of the calculation performed can be displayed, harvested, and analyzed by using the graphical user interface (GUI) of the system. We have applied the X2DF successfully to many cases including the cases that the structure solutions fail to be yielded by using manual approaches.

CS-ROSETTA.

Chemical Shift-Rosetta (CS-Rosetta) is an automated method that employs NMR chemical shifts to model protein structures de novo. In this chapter, we introduce the terminology and central concepts of CS-Rosetta. We describe the architecture and functionality of automatic NOESY assignment (AutoNOE) and structure determination protocols (Abrelax and RASREC) within the CS-Rosetta framework. We further demonstrate how CS-Rosetta can discriminate near-native structures against a large conformational search space using restraints obtained from NMR data, and/or sequence and structure homology. We highlight how CS-Rosetta can be combined with alternative automated approaches to (i) model oligomeric systems and (ii) create NMR-based structure determination pipeline. To show its practical applicability, we emphasize on the computational requirements and performance of CS-Rosetta for protein targets of varying molecular weight and complexity. Finally, we discuss the current Python interface, which enables easy execution of protocols for rapid and accurate high-resolution structure determination.

FDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization

BackgroundDevelopment of predictors of propensity of protein sequences for successful crystallization has been actively pursued for over a decade. A few novel methods that expanded the scope of these predictions to address additional steps of protein production and structure determination pipelines were released in recent years. The predictive performance of the current methods is modest. This is because the only input that they use is the protein sequence and since the experimental annotations of these data might be inconsistent given that they were collected across many laboratories and centers. However, even these modest levels of predictive quality are still practical compared to the reported low success rates of crystallization, which are below 10%. We focus on another important aspect related to a high computational cost of running the predictors that offer the expanded scope.ResultsWe introduce a novel fDETECT webserver that provides very fast and modestly accurate predictions of the success of protein production, purification, crystallization, and structure determination. Empirical tests on two datasets demonstrate that fDETECT is more accurate than the only other similarly fast method, and similarly accurate and three orders of magnitude faster than the currently most accurate predictors. Our method predicts a single protein in about 120 milliseconds and needs less than an hour to generate the four predictions for an entire human proteome. Moreover, we empirically show that fDETECT secures similar levels of predictive performance when compared with four representative methods that only predict success of crystallization, while it also provides the other three predictions. A webserver that implements fDETECT is available at http://biomine.cs.vcu.edu/servers/fDETECT/.ConclusionsfDETECT is a computational tool that supports target selection for protein production and X-ray crystallography-based structure determination. It offers predictive quality that matches or exceeds other state-of-the-art tools and is especially suitable for the analysis of large protein sets.

High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE

SPHIRE (SPARX for High-Resolution Electron Microscopy) is a novel open-source, user-friendly software suite for the semi-automated processing of single particle electron cryo-microscopy (cryo-EM) data. The protocol presented here describes in detail how to obtain a near-atomic resolution structure starting from cryo-EM micrograph movies by guiding users through all steps of the single particle structure determination pipeline. These steps are controlled from the new SPHIRE graphical user interface and require minimum user intervention. Using this protocol, a 3.5 Å structure of TcdA1, a Tc toxin complex from Photorhabdus luminescens, was derived from only 9500 single particles. This streamlined approach will help novice users without extensive processing experience and a priori structural information, to obtain noise-free and unbiased atomic models of their purified macromolecular complexes in their native state.

High-resolution Single Particle Analysis from Electron Cryo-microscopy Images Using SPHIRE.

SPHIRE (SPARX for High-Resolution Electron Microscopy) is a novel open-source, user-friendly software suite for the semi-automated processing of single particle electron cryo-microscopy (cryo-EM) data. The protocol presented here describes in detail how to obtain a near-atomic resolution structure starting from cryo-EM micrograph movies by guiding users through all steps of the single particle structure determination pipeline. These steps are controlled from the new SPHIRE graphical user interface and require minimum user intervention. Using this protocol, a 3.5 Å structure of TcdA1, a Tc toxin complex from Photorhabdus luminescens, was derived from only 9500 single particles. This streamlined approach will help novice users without extensive processing experience and a priori structural information, to obtain noise-free and unbiased atomic models of their purified macromolecular complexes in their native state.

Ligand co-crystallization of aminoacyl-tRNA synthetases from infectious disease organisms

Aminoacyl-tRNA synthetases (aaRSs) charge tRNAs with their cognate amino acid, an essential precursor step to loading of charged tRNAs onto the ribosome and addition of the amino acid to the growing polypeptide chain during protein synthesis. Because of this important biological function, aminoacyl-tRNA synthetases have been the focus of anti-infective drug development efforts and two aaRS inhibitors have been approved as drugs. Several researchers in the scientific community requested aminoacyl-tRNA synthetases to be targeted in the Seattle Structural Genomics Center for Infectious Disease (SSGCID) structure determination pipeline. Here we investigate thirty-one aminoacyl-tRNA synthetases from infectious disease organisms by co-crystallization in the presence of their cognate amino acid, ATP, and/or inhibitors. Crystal structures were determined for a CysRS from Borrelia burgdorferi bound to AMP, GluRS from Borrelia burgdorferi and Burkholderia thailandensis bound to glutamic acid, a TrpRS from the eukaryotic pathogen Encephalitozoon cuniculi bound to tryptophan, a HisRS from Burkholderia thailandensis bound to histidine, and a LysRS from Burkholderia thailandensis bound to lysine. Thus, the presence of ligands may promote aaRS crystallization and structure determination. Comparison with homologous structures shows conformational flexibility that appears to be a recurring theme with this enzyme class.

NMR in structural genomics to increase structural coverage of the protein universe: Delivered by Prof. Kurt Wüthrich on 7 July 2013 at the 38th FEBS Congress in St. Petersburg, Russia.

For more than a decade, the Joint Center for Structural Genomics (JCSG; www.jcsg.org) worked toward increased three-dimensional structure coverage of the protein universe. This coordinated quest was one of the main goals of the four high-throughput (HT) structure determination centers of the Protein Structure Initiative (PSI; www.nigms.nih.gov/Research/specificareas/PSI). To achieve the goals of the PSI, the JCSG made use of the complementarity of structure determination by X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy to increase and diversify the range of targets entering the HT structure determination pipeline. The overall strategy, for both techniques, was to determine atomic resolution structures for representatives of large protein families, as defined by the Pfam database, which had no structural coverage and could make significant contributions to biological and biomedical research. Furthermore, the experimental structures could be leveraged by homology modeling to further expand the structural coverage of the protein universe and increase biological insights. Here, we describe what could be achieved by this structural genomics approach, using as an illustration the contributions from 20 NMR structure determinations out of a total of 98 JCSG NMR structures, which were selected because they are the first three-dimensional structure representations of the respective Pfam protein families. The information from this small sample is representative for the overall results from crystal and NMR structure determination in the JCSG. There are five new folds, which were classified as domains of unknown functions (DUF), three of the proteins could be functionally annotated based on three-dimensional structure similarity with previously characterized proteins, and 12 proteins showed only limited similarity with previous deposits in the Protein Data Bank (PDB) and were classified as DUFs.

Exploiting Microbeams for Membrane Protein Structure Determination.

A reproducible, and sample independent means of predictably obtaining large, well-ordered crystals has proven elusive in macromolecular crystallography. In the structure determination pipeline, crystallisation often proves to be a rate-limiting step, and the process of obtaining even small or badly ordered crystals can prove time-consuming and laborious. This is particularly true in the field of membrane protein crystallography and this is reflected in the limited number of unique membrane protein structures deposited in the protein data bank (less than 650 by June 2016 – http://blanco.biomol.uci.edu/mpstruc). Over recent years the requirement for, and time and cost associated with obtaining, large crystals has been partially alleviated through the development of beamline instrumentation allowing data collection, and structure solution, from ever-smaller crystals. Advances in several areas have led to a step change in what might be considered achievable during a synchrotron trip over the last decade. This chapter will briefly review the current status of the field, the tools available to ease data collection and processing, and give some examples of exploitation of these for membrane protein microfocus macromolecular crystallography.

Nonlinear Optical Characterization of Membrane Protein Microcrystals and Nanocrystals.

Nonlinear optical methods such as second harmonic generation (SHG) and two-photon excited UV fluorescence (TPE-UVF)imaging are promising approaches to address bottlenecks in the membrane protein structure determination pipeline. The general principles of SHG and TPE-UVF are discussed here along with instrument design considerations. Comparisons to conventional methods in high throughput crystallization condition screening and crystal quality assessment prior to X-ray diffraction are also discussed.

Structural Genomics Support for Infectious Disease Drug Design.

Structural Genomics Support for Infectious Disease Drug Design.

Fluorescence Recovery After Photobleaching in Lipidic Cubic Phase (LCP-FRAP): A Precrystallization Assay for Membrane Proteins.

Crystallization of integral membrane proteins (MPs) is notoriously difficult, given their poor stability outside native membrane environment and due to the interference of detergent micelles with crystallization process. MP crystallization in a membrane mimetic matrix, known as lipidic cubic phase (LCP), has recently started to gain popularity, following successes in structure determination of G protein-coupled receptors (GPCRs), transporters, and enzymes. Unlike crystallization trials in aqueous solutions where protein molecules are free to move, diffusion of MPs in LCP is restricted, and, thus, a high level of protein mobility can serve as an early indication for subsequent crystallization success. Prompted by our initial observations that precipitant conditions can dramatically affect diffusion of GPCRs in LCP, we have developed a simple precrystallization assay, based on measuring protein diffusion at a number of different conditions by fluorescence recovery after photobleaching (LCP-FRAP). Over the last few years, the LCP-FRAP assay was incorporated in our GPCR structure determination pipeline and proved as a powerful technique allowing for a faster identification of crystallization conditions for many different receptors. The assay is used to screen for the best protein constructs, ligands, LCP host lipids, precipitants, and additives, thereby focusing subsequent crystallization trials on the most promising parts of the multidimensional crystallization phase diagram, substantially increasing the likelihood of finding the right crystallization condition. Here, we describe our LCP-FRAP protocols for guiding GPCR crystallization, which can be adapted to any other MP, and discuss some of the critical considerations related to application of this assay.