I have spent months as a student working on trying to form a tricky protein crystal. But I have never actually had explained to me why the structure will be useful. Once elucidated, what can we potentially learn from the structure in terms of biological significance?
Are there examples of drugs, or treatments that have only been made possible because of a known crystal structure?
Generally speaking, why do people spend all the money on synchrotrons, laboratories, robots and so on, for a crystal structure?
Protein structures, which can be obtained from protein crystals or from concentrated solutions of pure protein via NMR, are arguably the primary source of knowledge that we have about how genes perform their function on the molecular level.
I've added a link to RCSB.org above - they write up a story on an important protein structure monthly(?) - its a great way to pick up some fascinating stories.
The precise atomic positions from a protein structure are indispensible for several reasons. Since biological processes are all fundamentally chemical ones. The specific positions of the atoms reveal how proteins, nucleotides, lipids, drugs and other biological molecules specifically interact.
- Protein structures broke the ground in biological inheritance (Watson / Crick / Franklin's DNA structure)
- Protein structures of lysozyme and proteases were the first to show exactly how proteins bind their substrates and enzymatically catalyze chemical reactions.
- Protein structure of hemoglobin in both oxy- and deoxy- forms (i.e. with and without oxygen bound). showed that proteins change their spatial arrangement to modulate their function.
This list goes on to more recent breakthroughs in how signals and molecules interact with the cell through the membrane. There is literally no topic in cellular biology which has not substantially benefitted from having a protein structure revealed.
The advantages of working with protein crystals are that they can be larger proteins and certain kinds of difference experiments an be more easily performed when a protein crystal is obtained. For instance if you have a crystal of a 100 kDa (~900 amino acid) protein, you can often find the binding pocket of the enzyme without a tremendous amount of work.
The disadvantage of working with protein crystals, as you probably know well is that getting crystals is often a quixotic effort soaking up months or years, often with little or no results. Pretty demoralizing until you hit the target.
If you're astounded to think that proteins, which are often hundreds or thousands of times larger than a salt or mineral you usually find in crystals… you are dead on. The crystals are often very tiny - a decent sized crystal measures about a millimeter on one side, but often are only a fraction of that size. That's why protein crystals often are taken to synchrotrons, linear accellerators or other free electron X-ray sources which are millions (?) of times more intense than a dental x-ray. I would guess that most protein crystal structures are obtained using special beamlines created especially for biological crystallography.
That sort of tells you the funding priority science is willing to put into x-ray protein crystal structures. Simply the idea that protein crystals might be easier to grow in microgravity (along with the low weight of the experiment) justified over a decade of protein crystal growth experiments on the shuttle and ISS.
Interesting note: The Guardian (which is based in the UK where crystallography, protein or otherwise started out) has posted a short video outlining highlights of crystallography's contributions over the past 100 years. If you watch assiduously you will see protein crystallography cropping up with a litany of nobel prizes.
New thoughts: There are signs that crystal structures are coming to an end. Using an electron micrograph to simply take a picture of many thousands of individual proteins laying about can be digitally averaged to create low resolution structures they are gradually becoming higher resolution and are a lot less work than preparing and making a protein crystal - and it works well on larger proteins and protein complexes!
I always wondered this myself, but the structure of a protein can end up being quite important for a number of reasons. Most relate to the fact that protein function often depends on specific domains, and while a protein may have multiple functional domains it is important for all domains to be properly aligned and constructed in three-dimensional space. Misfolded proteins often have negative phenotypes, so being able to visualize the improperly folded areas can be enlightening. Perhaps more commonly, these techniques can be used to validate an artificially produced protein before approving it for therapy.
Additionally, while we can know the sequence of amino acids in a protein very easily we don't necessarily know which ones are useful. A region oriented toward the outside of a protein may partake in a biological function. If a specific protein domain is imbedded inside the center of the protein, it is not very available for function, either by the protein or for other targets such as antibodies. For example, one of the big projects in HIV research now is to get very good and very fine structure of viral proteins on the envelope of the virus; knowing which regions are readily accessible would make for potential good drug/cell/antibody targets.
Finally, the structure itself can give some insight as to function. Proteins may not have homology by amino acid sequence, but similarly-structured proteins can have similar functions. People smarter than me can recognize those features and can learn quite a bit about a protein just from its shape.
Here's a fun little anecdote I conveniently just came upon. It's a quote from Your Inner Fish by Neil Shubin (© 2008) about two researchers, Linda Buck and Richard Axel, who in 1991 discovered a family of genes that allow us to smell.
Experiments showed that odor receptors have a characteristic structure with a number of molecular loops that help them convey information across a cell. This was a big clue, because Buck and Axel could then search the genome of a mouse for every gene that makes this structure.
Expanding on something Amory said:
They are very beneficial in drug discovery. This is because it is absolutely essential that you have a structure to do any sort of molecular dynamic simulation. In the early phases of drug discovery it is cheap and easy to do these types of experiments on a computer, rather than setting up an assay for different potential therapeutics. You just take the crystal stucture of the protein, and a structure of your compound, and a program will simulate how they will interact with each other. From this you can see if the compound is able to enter the active site of the protein, the orientation it would be in, etc. Using a supercomputer you can do this with hundreds of compounds in a matter of hours. Molecular dynamics has other useful purposes, drug discovery is just one example.
What are the benefits of elucidating the crystal structure of a protein? - Biology
Single-molecule Förster resonance energy transfer experiments have elucidated key details of the SecA-SecY translocation mechanism.
Structural details of both post- and co-translational mechanisms have been resolved by cryogenic electron microscopy (cryo-EM), including the conformation of the co-translational quaternary complex and a post-translational translocation intermediate.
Human disease mutants of Sec61 have been structurally resolved by cryo-EM, providing a foundation for understanding the role of the translocon-associated complex in some disorders.
Detailed structural analysis of high-resolution cryo-EM structures of Tom40, a mitochondrial translocation channel, has led to an updated mechanistic model for preprotein entry and exit.
Single-particle tracking has revealed the dynamics of plastid translocon components in vivo.
Translocons are protein assemblies that facilitate the targeting and transport of proteins into and across biological membranes. Our understanding of these systems has been advanced using genetics, biochemistry, and structural biology. Despite these classic advances, until recently we have still largely lacked a detailed understanding of how translocons recognize and facilitate protein translocation. With the advent and improvements of cryogenic electron microscopy (cryo-EM) single-particle analysis and single-molecule fluorescence microscopy, the details of how translocons function are finally emerging. Here, we introduce these methods and evaluate their importance in understanding translocon structure, function, and dynamics.
What could DeepMind 'solving' the protein folding problem mean for cancer research?
3-D model of the crystal structure of the tumour marker protein. Credit: Sergunt
Earlier this month, biologist Mohammed AlQuraishi was driven to excitedly exclaim that a new set of findings constituted "a seismic and unprecedented shift so profound it has literally turned a field upside down over night." You don't read that every day.
What had led him to make such a bold claim? It was the news that Google-owned British artificial intelligence company, DeepMind, had 'cracked' the decades-old conundrum of how proteins fold with a new version of their deep learning system, AlphaFold.
Proteins are fundamental to all life and the mysterious way they fold into elaborate 3-D shapes has dramatic implications for how our cells and tissues operate, so this was certainly big news and the headlines echoed as much. But what did the findings tell us, what could they mean for cancer research and was the hyperbole justified? We asked two of our expert protein specialists, who are trying to understand more about how the way proteins fold affect cancer outcomes, to give their verdict on the news.
What problem has DeepMind solved?
The 'protein folding problem' has remained a headscratcher since it was first posed around 50 years ago. In a nutshell, being able to predict how a protein folds is key to understanding how it will function, which in turn could unlock answers to big questions, including how to treat diseases like cancer.
Researchers have put a great deal of time, effort and resource into trying to scrutinize the way proteins fold, which have led to the emergence of innovative but expensive experimental techniques to study protein structures such as X-ray crystallography, which creates a 3-D structure of a protein using, you guessed it, X-ray beams. There are other techniques too, such as using an electron microscope to beam electrons onto a protein to magnify its image.
But while these are considered optimal techniques, they each have their limitations. X-ray crystallography only really works when studying stable proteins that can form the neat crystals required for the process. And even then, it's a laborious and costly task. With flexible or 'unstable' proteins, which have less structure and rigidity, it's a whole other ball game.
But luckily, back in 1963, American biochemist Christian Anfinsen proposed that a protein's one dimensional sequence of amino acids—something far easier to obtain—should give away its full 3-D structure. This work earned him a Nobel Prize in 1972. And since that time, scientists have been exploring this route using less expensive and more accessible computational methods. But therein lies another problem. There are almost infinite numbers of ways that a protein could fold and identifying them all can take a lifetime. Not so good for tackling important challenges like cancer.
This is where DeepMind comes in. Their AlphaFold technology uses deep learning to take what we already know about the structures of known proteins held on a global database and 'learn' about and therefore predict the structures of other proteins. The team tested the predictions made by AlphaFold against structures that had been determined experimentally, using methods like X-ray crystallography. And the results looked extremely promising. AlphaFold managed to predict the structures of proteins with a never-before-seen level of accuracy at low cost and within days, not years or decades. And this is what got the scientific community just a tiny bit excited.
So what do our protein experts think?
Professor Richard Bayliss at the University of Leeds uses crystallography to determine the shape of proteins and how they fold. This knowledge is vital to uncovering their function in cancer cells and importantly, how they can be targeted and treated. His particular focus is on the Myc protein, which is associated with many different cancers, including aggressive forms of prostate and breast cancers.
Dr. Patricia Muller at our Cancer Research UK Manchester Institute is researching a protein that plays an important role in stopping cancer developing, the p53 protein. She's particularly interested in how p53 functions in both its unfolded and folded states and believes that the latter has an impact on how it operates in cancers.
What did you make of the news? Is it as monumental as the headlines would have us believe?
Richard: It's certainly an impressive and exciting advance, but in a more limited and specific sense than some of the headlines would lead you to believe. Being able to accurately predict the structure of a protein from its amino acid sequence has been something of a holy grail for structural biologists. The work from DeepMind is the first time that this has been achieved with a level of reliability that can compete with the experimental methods. It's enough to give you the confidence to make substantial investment in the predictions that arise from the structure.
It's limited in several ways, but perhaps the most important is that it cannot accurately predict the structure of proteins that work intimately with other proteins, and whose structure is therefore dependent on the presence of these other proteins. This sort of problem is a major focus of structural biologists, and the structures predicted by DeepMind will be useful in helping us to interpret and use experimental data.
Patricia: Our understanding of protein folding mainly comes from studies using techniques that take a long time and a lot of patience. If AlphaFold is as accurate as the articles suggest, it would really make a big difference and speed up many different lines of research.
What could this mean for cancer research?
Richard: Despite the recent technological advances in experimental techniques, such as cryogenic electron microscopy and high-throughput crystallography, determining a protein structure is a challenge that can take many years to solve. Some projects are held up at the very first step because the protein of interest cannot be made in sufficient quantity or purity for the methods to work, or they turn out to be unstable proteins. This is where having a reliable computational method could provide enough information to advance a project. For example, if we want to map the location of cancer mutations onto the structure of the protein to predict how they might affect its function, we could do this much more quickly with the DeepMind computational method than experimental methods.
It's difficult to say how useful it will be for drug discovery, which is an important application of structural biology in cancer research, because we depend on accurate models generated by X-ray crystallography. But sometimes we have to use computational models for proteins with unknown structures and having more reliable models will help us to develop drugs more efficiently.
Patricia: It could help in many ways, such as developing drugs or simply understanding more about how proteins work. As an example, some drugs work by preventing proteins from binding to other proteins. If we know their structure, we can predict how they will bind to each other and we can then design drugs that would prevent the binding between these proteins. For the proteins that we do know the structure of, this has been shown to be a successful strategy. However, the techniques that we have for elucidating protein structure don't work for every protein. Predictions from AlphaFold could be very useful for determining the structures of proteins that other techniques have not yet worked for.
How will you use this new information to advance your work?
Richard: I study the Myc protein, which is associated with many human cancers but is regarded as "undruggable" by most researchers because it doesn't have a fixed structure. The DeepMind approach will not be much help for Myc itself because it only adopts a structure when it binds to other proteins.
But it could be very useful to study the binding proteins that partner with Myc, some of which are very challenging proteins to work with. We don't expect Myc to affect the structures of its binding partners, and so predictions made by AlphaFold about these other proteins are likely to be accurate and useful.
These structures, together with our experimental data, would enable us to generate models of the interactions between Myc and these partner proteins. And this information could be used to develop molecules that block the interactions, which we see as a route to developing new cancer treatments.
Patricia: My work is on the p53 protein. We don't yet know the full structure of p53 and it's very difficult to determine its shape with conventional techniques. Currently, we rely on antibodies to help us detect the structure, but they can only detect two states: folded and unfolded. But I believe there's more to the story than that and that there are actually several different states of folded p53. I would love to see if AlphaFold could help me uncover these states because in cancer, there are hundreds of different ways that p53 mutates. I believe the technology could help me reveal why this is the case and shed light on how these mutants are different from each other.
So what have we learned?
It seems that while AlphaFold's breakthrough may take some time to directly benefit our researchers in their work to tackle cancer, there are some clear advantages to the news. Has DeepMind fully 'solved' the protein folding? The jury is still out according to our experts, but we're certainly encouraged by its potential to more quickly, more accurately and more cheaply determine the structure of proteins in order to understand more about how they function and can be targeted in human cancers.
Materials and methods
Cloning, protein expression, purification, and crystallization
The MTH1491 gene (GenBank accession number <"type":"entrez-nucleotide","attrs":<"text":"G69065","term_id":"14327330","term_text":"G69065">> G69065) was cloned from genomic MTH DNA and its gene product was recombinantly expressed, selenomethionine labeled, and purified as described elsewhere for other MTH proteins (Christendat et al. 2000b). Screening for crystallization conditions was also performed as described elsewhere for other MTH proteins (Christendat et al. 2000b). The final crystallization condition consists of methyl-pentanediol (MPD) as precipitant. The crystals chosen for X-ray data collection were flash-frozen in this buffer, which also acted as cryoprotectant.
The single crystals grow as rods with maximum dimensions of 0.30 mm by 0.10 mm by 0.4 mm. Crystals selected for MAD data collection were grown in 30% MPD and 100 mM HEPES at pH 7.5 at 20ଌ. The crystals belonged to the hexagonal space group P63, with the following unit cell parameters: a = b = 82.4, and c = 37.0 Å. The Matthew's coefficient, VM, was determined to be 2.9 Å 3 Da 𢄡 implying a solvent content of 57.3% with a single molecule of MTH1491 in each asymmetric unit (Matthews 1968 Westbrook 1985). These values are within the range normally found for protein crystals. The diffraction data from the remote wavelength have an Rsym of 7.1% and a completeness of 99.4% to 2.3 Å. Data collection statistics are summarized in Table 1 1 .
Summary of data collection and refinement statistics
|X-ray data||Peak||Edge||Remote||Refinement data|
|Space group||P63||P63||P63||Rcryst e||20.2|
|Unit cell (Å 3 )||82.5 × 82.5 × 37.1||82.5 × 82.5 × 37.1||82.5 × 82.5 × 37.1||Rfree f||22.6|
|Resolution (Å)||50𠄲.30||50𠄲.30||50𠄲.30||protein atoms (no.)||862|
|Wavelength (Å)||0.97948||0.97964||0.93927||water molecules (no.)||19|
|No. of Se sites||3||3||3||r.m.s.d. bond lengths (Å)||0.007|
|No. of observations||77,390||77,281||77,199||r.m.s.d. bond angles (°)||1.1|
|No. of unique reflections||6492||6500||6566||r.m.s.d. dihedrals (°)||23.2|
|Intensity (I/σ≤I>)||34 (12.3) a||38 (11.1)||32 (7.4)||average B factor (°)||26.4|
|Completeness (%)||98.4 (99.8)||97.9 (88.6)||99.4 (99.8)|
|Rsym b||0.071 (0.183)||0.064 (0.192)||0.071 (0.313)|
|FOMMAD c||0.77||FOMSF d||0.87|
a Numbers in parentheses represent values in the highest resolution shell 2.38𠄲.30 Å.
b Rsym = ∑|I-〈I〉|/∑I, where I is the observed integrated intensity, 〈I〉 is the average integrated intensity obtained from multiple measurements, and the summation is over all observed reflections.
c FOMMAD = Figure of merit after MAD phasing.
d FOMSF = Figure of merit after solvent flipping.
f Rfree was calculated using randomly selected reflections (5%).
X-ray diffraction and structure determination
Making use of the anomalous scattering of Se atoms, three wavelength MAD data were collected at 100K at the 19ID beamline of the Structural Biology Center at the Advanced Photon Source, Argonne National Laboratory. Diffraction data were collected on the SBC-2, a 3 by 3 CCD detector built at APS, to 2.3 Å resolution from a single crystal containing SeMet-labeled protein at three different X-ray wavelengths near the Se edge (Table 1 1). ). The MAD data were processed using the HKL2000 suite of programs (Otwinowski and Minor 1997). Data collection statistics are presented in Table 1 1. . The MAD phasing component of CNS was used to locate the three selenium sites, calculate the phases, and modify the density (Brunger et al. 1998). Electron density visualization and model building were done with O (Jones et al. 1991). Rigid body and simulated annealing torsion angle refinement were followed by individual B-factor refinement and performed using CNS 1.0 (Brunger et al. 1998). Several rounds of refinement were combined with model rebuilding in O after inspection of both 2Fo𠄿c and Fo𠄿c maps. Water molecules were initially picked using CNS and then manually verified in O using the following criteria: a peak of at least 2.5 σ in an Fo𠄿c map, a peak of at least 1.0 σ in a 2Fo𠄿c map, and reasonable intermolecular interactions. Refinement statistics are found in Table 1 1. . The programs MOLSCRIPT (Kraulis 1991), RASTER 3D (Merrit and Murphy 1991), and SPOCK (Christopher 1998) were used in the production of the figures.
Atomic coordinates have been deposited into the Protein Data Bank as PDB ID 1L1S.
Protein Crystal lography Lab
Proteins are important biological macromolecules as they play key roles in almost every biological process of a living organism. Protein molecules function as enzymes, transporters, mechanical strength enhancers, protective immune systems, signal transducers, etc. Three dimensional structure of a protein dictates its functional properties. Hence, determination and understanding of three dimensional structure a protein is essential for deciphering the mechanistic details about how a protein performs its activities. Our research group at IIT Bombay is focused in elucidating the structure-function relationship of proteins, rational design of enzymes and structure based drug development. We determine crystal structures of proteins. We perform extensive biochemical and biophysical studies on proteins, which enables us to obtain information complementary to that obtained from structure analysis. Recently we have determined several crystal structures of proteins including a very high resolution (1.25 Å) crystal structure of a sugar binding protein complexed with glucose. The crystal structures on P. falciparum plasmepsins (PMs) complexed with KNI compounds solved in our group provide detailed molecular insights for antimalarial drug development. We also perform molecular dynamics simulations in order to capture different dynamic motions and interactions in proteins and their complexes.
We have performed extensive structural studies on glutamate dehydrogenase (GDH) to decipher the catalytic mechanism and for understanding the molecular basis of cofactor recognition of this enzyme. Following movie presents some of the exciting structural features of Aspergillus niger GDH (AnGDH) complexed with substrate alpha-ketoglutarate (AKG) and cofactor NADP.
Our structural and biophysical studies indicate a novel mechanism of activation of vacuolar plasmepsins. Following videos show the structural changes during the activation of histo-aspartic protease (HAP), a vacuolar PM.
A review article presenting advancements in the field of protein crystallography or macromolecular crystallography (MX). The paper provides a brief history about MX and presents the developments in methods as well as technologies in MX. With many exciting current techniques like X-ray Free Electron Lasers (XFELs) and more developments awaiting in the upcoming years, MX has the potential to contribute significantly to the growth of modern biology
Four plasmepsins (PMs) in the food vacuole of Plasmodium falciparum are attractive drug targets. Based on our structural and biochemical studies, we propose a unique mechanism of auto-activation of vacuolar plasmepsin zymogens that under acidic conditions can form a catalytically competent active site. One monomer cleaves the prosegment of the other one through a trans-activation process, resulting in formation of mature enzyme.
X-ray crystallography is currently the most favored method for structural determination of proteins and other macromolecules. The requisite for a successful X-ray crystallographic application is to obtain single crystals of the target protein. Data is then collected by diffracting X-ray from the single crystal that has an ordered pattern of atomic orientation. The assembly of atoms and molecules in the crystal can be deduced from the measurement of X-ray scattering.
At present, more than 120,000 protein structures resolved by X-ray crystallography experiments have been deposited in protein databank, accounting for nearly 90% of the resolved proteins, suggesting the predominant popularity of X-ray crystallography in structural determination.
Creative Biostructure provides X-ray crystallography services in our state-of-the-art facilities and has developed an X-ray crystallography pipeline that covers all technical stages from gene synthesis to structure determination. Our experienced scientists work closely with the clients to ensure rapid turnaround and reliable results.
Advantages of X-ray Crystallography
- To obtain a three-dimensional structure of protein/ protein-protein complex/ protein-small molecule complex with high atomic resolution and to provide structural information is useful for understanding protein function and accelerates the process of rational drug design.
- It is not limited by the molecular weight of the sample.
- It helps reduce the technical barrier of obtaining single crystals of specific proteins, such as membrane protein, that are difficult to process by conventional methods.
- The algorithm of protein structure analysis is well developed, and the established model has high confidence.
What We Do — Integrated Gene to Structure Services
Our services come with well-defined methodologies and instruments yet offer a great deal of flexibility upon customer's requirements. We can prepare crystallization-grade proteins from scratch or obtain them from customers. Besides, Creative Biostructure has multiple ready-made high-purity proteins and crystal structures developed internally that can be used for co-crystallization and compound soaking.
Creative Biostructure can provide various crystallization strategies, particularly for membrane protein crystallization. The detailed services are summarized as follows.
|Featured Crystallization Services||Feature|
|Co-crystallization||Co-crystallization retains the unique crystalline structures with their multiple components (e.g., proteins, DNA/RNA, chemical compounds, metal ions).|
|Bicelle-Protein Crystallization||Providing a more bilayer-like environment for membrane proteins than in detergent micelles, enabling the use of standard crystallization screening methodology for membrane proteins.|
|Lipidic Cubic Phase (LCP) Crystallization||LCP has a membrane-mimetic matrix suitable for stabilization and crystallization of membrane proteins in lipidic environment.|
|Controlled In Meso Phase (CIMP) Crystallization||CIMP stabilizes membrane proteins in meso phase and allows for direct monitoring of phase transformation and crystallization events.|
|Trace Fluorescent Labeling Crystallization||Great for the detection and identification of crystals by covalently labeling a fluorescent probe on the protein.|
|Crystallization Chaperone Strategies||Co-crystallization of challenging membrane protein targets with soluble protein chaperones.|
|Crystallization with Mutant Library Approaches||Improvement of protein solubility and crystallization assisted by mutant library construction and screening.|
Your Specific Sample Types for CrystallizationMembrane Protein Antibody-Antigen Complex Discuss Details with Our Experts
Preparation of PSII complex
Cyanobacterial PSII membrane protein complex was purified from a thermophilic cyanobacterium Thermosynechococcus vulcanus, as described previously. 28 , 29 Further, 1 mM 6-aminocaproic acid and benzamidine were added to the sample preparation buffers to avoid unexpected protein degradation by endogenous enzymes. The purified membrane protein complex was suspended in a buffer containing 30 mM Mes (pH 6.0), 20 mM NaCl, and 3 mM CaCl2 and exposed under saturating the white light illumination for 0, 15, 60, 180, and 300 min at 4 °C, respectively.
Steady-state O2-evolving activity of the purified membrane protein complex was measured using a Clark-type oxygen electrode at 25 °C under saturating red light illumination. 28 , 29 The PSII complex corresponding to 5 μg chlorophyll/mL was dispersed in a buffer containing 0.60 mM recrystallized 2,6-dichlorobenzoquinone, and 10 mM CaCl2, and the O2-evolving rate was measured via changes in O2 concentration.
Cross-linking of PSII complex
The PSII complex samples were suspended in 50 mM N-2-hydroxyethylpiperazine-N-2′-ethanesulfonic acid (HEPES pH 7.4) buffer and diluted to 1 μg/μL. For chemical cross-linking, the samples were incubated with a cross-linker bis(sulfosuccinimidyl)suberate (BS3 1:1, w/w) at room temperature for 1 h before 20 mM NH4HCO3 was added to terminate the reaction. All protein samples were precipitated with five volumes of ice-cold precipitation solution (ethanol/acetone/acetic acid = 50:50:0.1) at −20 °C overnight. The precipitated protein samples were resuspended in denaturing buffer (pH 8.0) containing 6 M guanidine and 50 mM Tris. After reduction with 5 mM tris(2-chloroethyl)phosphate (TCEP a thiol-free reducing agent) for 20 min and alkylation with 10 mM iodoacetamide (IAA) for 15 min in the dark, the protein samples were diluted to 2 M guanidine with 50 mM Tris (pH 8.0) and digested using chymotrypsin for 16 h at 37 °C with an enzyme to substrate ratio of 1/25 (w/w). The digested peptide mixtures were purified using a C18 solid-phase extraction (SPE) column before liquid chromatography (LC)-MS/MS analysis.
Online SCX-RPLC-MS/MS analysis
For the online strong cation exchange (SCX)-reversed-phase LC (RPLC)-MS/MS analysis, a 10 cm SCX column (200 μm i.d.) was prepared as previously described 30 and used as a trapping column as well as the first-dimension LC separation column. A series of stepwise elution with salt concentrations of 150, 250, 350, 450, and 1000 mM NH4AC was used to elute peptides from the SCX column into the second-dimensional capillary column (75 μm, i.d., 15 cm length) packed with C18 particles (3 μm, 120 Å). Each elution lasted 10 min and was followed by equilibrium with 0.1% formic acid (FA) for an additional 15 min. Each elution step was followed by a subsequent RPLC-MS/MS analysis with a 100 min gradient from 5% to 35% acetonitrile (ACN). The flow rate was 300 nL/min. Solutions of 0.1% FA in water and ACN were used as mobile phases A and B, respectively.
An LTQ-Orbitrap Velos mass spectrometer (Thermo, San Jose, CA) was used to perform the CX-MS analyses of proteolytic protein samples. The MS parameters were set as follows: ion transfer capillary 250 °C, spray voltage 2.0 kV, full MS scan from m/z 350 to 1800 with a resolution of 30,000 in centroid mode. Data-dependent MS/MS scans at R = 7500 were performed by selecting the 10 most intense ions in the full MS scan for higher-energy collisional dissociation (HCD) with 35% normalized collision energy. The ions carrying +1, +2, or unknown-charges were excluded from the MS2 scans. The dynamic exclusion was set as follows: repeat count 1, repeat duration 30 s, and exclusion duration 120 s.
Top-down LC-MS/MS analysis
For top-down analysis, the PSII membrane protein complex samples with different photodamage levels were initially precipitated with ice-cold precipitation solution at −20 °C overnight. The precipitated protein samples were redissolved in 70% FA, which was precooled at −20 °C, 31 followed by incubation at −20 °C for 2 min and diluted with six sample volumes of ice-cold water, and injected immediately into LC-MS/MS instrument for analysis. Briefly, 3 μg intact protein samples were loaded on a C5 capillary trap column (200 μm, i.d., 5 cm length) and separated using a C5 capillary column (75 μm, i.d., 20 cm length) at a flow rate of 300 nL/min. A solution of 0.1% FA in water and 80% ACN/0.1% FA were utilized as mobile phases A and B. The column was eluted by a gradient of 20–100% phase B in 150 min, followed by isocratic elution at 100% phase B for 10 min.
Q-Exactive hybrid quadrupole-Orbitrap mass spectrometer (Thermo) was utilized for the top-down MS analyses. The MS parameters were set as follows: ion transfer capillary 320 °C, spray voltage 2.0 kV, full MS scan from m/z 500 to 2500 with a resolution of 120,000. Data-dependent MS/MS scans were performed by selecting the five most intense ions in the full MS scan for HCD with a normalized collision energy of 25%. The MS/MS scans were also acquired by the Orbitrap mass analyzer with a resolution of 60,000, and the automatic gain control (AGC) target was set to 1 × 10 6 with a max injection time of 500 ms. The ions carrying charge <+2 were excluded, and the dynamic exclusion time was set as 180 s.
CX-MS data analysis
The sequences of protein subunits within the cyanobacterial PSII complex were obtained from UniProt ( http://www.uniprot.org/). Cross-linked peptides were identified using the pLink 2.0 software, 32 with its search parameters set as follows: instrument, HCD precursor mass tolerance, 20 ppm fragment mass tolerance, 20 ppm cross-linker BS3 (cross-linking sites K and protein N-terminus, cross-link mass-shift 138.068 Da, monolink mass-shift 156.079 Da) fixed modification carbamidomethyl cysteine (+57.02146 Da) variable modification oxidation of methionine (+15.99492 Da) peptide length, minimum six amino acids and maximum 60 amino acids per chain peptide mass, minimum 600 and maximum 6000 Da per chain enzyme, chymotrypsin, with up to five missed cleavage sites per cross-link. The results were filtered, requiring a false discovery rate (FDR) < 5% and an scoring of matches (SVM) score of ≥1. The MS2 spectra were annotated using pLabel, requiring a mass deviation of ≤20 ppm.
Mapping of the cross-links on structural models was performed using Pymol, and schematic visualizations of cross-links were generated with xiNET. 33
Top-down data analysis
RAW LC-MS/MS data files were searched against ProSightPC (version 126.96.36.199 Thermo) using a Thrash algorithm. Intact precursor and fragment ion masses were searched using a logic tree that submitted all data to an absolute mass search, followed by a biomarker search for all data that did not yield an ID with an expected hit (E) value less than 1E-4 in the absolute mass search. The precursors with a mass >750 and more than five fragments were searched. The precursor and fragment mass tolerances were set at 10 and 15 ppm, respectively. FDR < 1% was controlled with an E value below 1E-4 in the biomarker search.
To identify oxidized amino acid residues, the files from the top-down analysis were searched against MSPathFinder (version 1.0.6510). 34 Mass error tolerance was set to 10 ppm for the precursors and fragments. A maximum of seven types of dynamic modifications was allowed on each sequence. The dynamic modifications included were methionine/tryptophan/phenylalanine/leucine/aspartate/glutamate oxidation, cysteine dehydrogen, and protein N-terminal formylation. Other search parameters were set as follows: minimum–maximum (min–max) sequence lengths 21–300, min–max precursor ion charges 2–30, min–max fragment ion charges 1–15, min–max sequence masses 3000–50,000. The results were filtered, requiring a score of ≤1E-4 and a Q-value of ≤0.01.
The MS proteomics data have been deposited on the ProteomeXchange Consortium via the PRIDE 35 partner repository with the dataset identifier PXD021369.
What are the benefits of elucidating the crystal structure of a protein? - Biology
Experimental Data Snapshot
- Method: X-RAY DIFFRACTION
- Resolution: 3.00 Å
- R-Value Free: 0.284
- R-Value Work: 0.210
- R-Value Observed: 0.210
wwPDB Validation   3D Report Full Report
Mutual Conformational Adaptations in Antigen and Antibody Upon Complex Formation between an Fab and HIV-1 Capsid Protein P24
(2000) Structure 8: 1069
- PubMed: 11080628  Search on PubMed
- DOI: 10.1016/s0969-2126(00)00507-4
- Primary Citation of Related Structures:
- PubMed Abstract:
Elucidating the structural basis of antigen-antibody recognition ideally requires a structural comparison of free and complexed components. To this end we have studied a mouse monoclonal antibody, denoted 13B5, raised against p24, the capsid protein of HIV-1 .
Elucidating the structural basis of antigen-antibody recognition ideally requires a structural comparison of free and complexed components. To this end we have studied a mouse monoclonal antibody, denoted 13B5, raised against p24, the capsid protein of HIV-1. We have previously described the first crystal structure of intact p24 as visualized in the Fab13B5-p24 complex. Here we report the structure of the uncomplexed Fab13B5 at 1.8 A resolution and analyze the Fab-p24 interface and the conformational changes occurring upon complex formation.
SPring-8, the large synchrotron radiation facility
A research team consisting of assistant professor Ken Kitano, research scientist Sun-Yong Kim, and professor Toshio Hakoshima of Nara Institute of Science and Technology has succeeded for the first time in elucidating the function of the Werner helicase, the protein responsible for premature aging syndrome, three dimensionally.
In cell division, the double helix structure of DNA, namely, two DNA strands twisted around each other, should be cleanly separated and each strand should be copied. In this process, a group of protein enzymes called helicases *2 play an important role in separating (unwinding) the DNA helix. One of them, the Werner helicase, is known to cause Werner's syndrome, *1 a type of premature aging syndrome that has a relatively high incidence among Japanese people, when it mutates.
The research team investigated the function of the Werner helicase in a healthy person using the facilities of SPring-8. As a result, they elucidated that a knifelike structure protruding from the surface of the Werner helicase separates the two strands of DNA by prizing them apart. Also, they found that this knifelike structure has the most suitable shape for unwinding tangled DNA, which causes the aging process. These findings are expected to provide new information for researchers seeking a treatment for premature aging syndromes as well as other aging-related diseases, particularly types of cancer.
These research achievements were published in the American scientific journal Structure on 10 February 2010 and were introduced in the Preview as a highlight of the issue.
1. Background of research
Premature aging syndromes are rare diseases in which overall aging accelerates when the patients are still young. Recently, Hutchinson-Gilford syndrome (also called progeria, in which children begin to age rapidly immediately after birth) has become known to the public because overseas patients suffering from this syndrome were the subject of documentary programs. However, another genetic disease called Werner's syndrome is more prevalent in Japanese people. While it is estimated that there are only several thousand patients with Werner's syndrome worldwide, 70% of those patients are Japanese. In Werner's syndrome, patients age at double the normal rate because of an abnormality in a protein called the Werner helicase. The Werner helicase is known to unwind DNA having unique structures (four-stranded intermediates called Holliday junctions and telomeres located at the ends of chromosomes, *3 which regulate the cell lifetime Fig. 2A) that cannot be unwound by ordinary helicases. However, the mechanism of unwinding by the Werner helicase had not been elucidated.
2. Experimental methods and results
To elucidate the relationship between proteins and premature aging syndromes, the researchers examined the effect of the Werner helicase on the DNA in a healthy person by X-ray crystal structure analysis. *4 The shapes of molecules were observed by crystallizing the central part (RecQ domain) of the Werner helicase bound to double-stranded DNA and exposing the obtained crystals to the high-brilliance X-ray of the Structural Biology I Beamline (BL41XU) and the Macromolecular Assemblies Beamline (BL44XU) in SPring-8.
As a result, the state of the Werner helicase as it begins to unwind the DNA was elucidated three dimensionally (Fig. 1). The Werner helicase has a protruding structure named a "DNA-unwinding knife" and unwinds the double helix of the DNA while rotating around it, similarly to the peeling of an apple with a knife. Moreover, this unwinding knife has an elongated shape protruding from the molecular surface, which is the most suitable shape for unwinding DNA with a complicated structure (Fig. 2B). It is considered that the Werner helicase prevents the occurrence of premature aging syndromes by unwinding DNA structures such as Holliday junctions and telomeres with the DNA-unwinding knife.
3. Future expectations
Even in a healthy person, genes are damaged in daily life and such damaged genes cause aging and may cause cancer. In particular, telomeres are important parts of chromosomes that regulate the cell lifetime. Because telomeres have a complicated loop structure, it is considered to be difficult to maintain their length by the function of ordinary proteins alone. In patients with Werner's syndrome, the telomeres tend to become shorter because of mutation of the Werner helicase, resulting in the accelerated aging of the body. There is still no technique for curing patients with shortened telomeres. However, the continued examination of the shapes of proteins may lead to the discovery of a means of maintaining the original length of telomeres, namely, a means of keeping cells young. Also, it has been reported that inhibition of the Werner helicase activity suppresses the growth of cancer. These research achievements are expected to be applied to the development of anticancer drugs for the general public.
Fig. 1 DNA-unwinding knife of Werner helicase observed in this study
It has the shape of a knife being held by a hand.
(A) The DNA-unwinding knife of the Werner helicase is so fine that it can be inserted into narrow gaps in the DNA structure, which ordinary helicases cannot enter. Owing to this feature, the DNA-unwinding knife appears to be used for unwinding complicated DNA structures.
(B) The Werner helicase unwinding a Holliday junction (simulation model on the basis of this study). The RecQ domains (blue) consisting of two molecules insert the DNA-unwinding knife into the narrow branch points of the Holliday junction, and each domain starts to unwind the DNA structures on the left and right.
*1 Werner's syndrome
This is a premature aging syndrome named after its discoverer Otto Werner. While the symptoms of progeria appear immediately after birth, in the case of Werner's syndrome, overall aging starts to accelerate rapidly when the patients are in their mid-teens. The mean life expectancy of a patient with Werner's syndrome is 46 years, and no treatment for this disease has yet been discovered. It is estimated that one out of several hundred Japanese people is a latent carrier of Werner's syndrome, namely, a carrier of the mutation of the Werner gene, even if he/she does not develop the disease.
*2 DNA helicases
DNA helicases are proteins that separate the double helix of DNA into single strands. They can be classified into various types. The amino-acid sequence of the Werner helicase is similar to that of the Bloom helicase, which causes Bloom syndrome (a rare disease in which the patients frequently develop cancer in their childhood) and that of the RECQL4 helicase, which causes Rothmund-Thomson syndrome. These helicases are generally called RecQ family helicases. The results of this study indicated that the Bloom helicase also has a similar DNA-unwinding knife.
Telomeres are the regions of DNA at the ends of chromosomes that shorten every time a cell divides. They are sometimes described as an "aging clock." Telomeres were the subject of the research awarded the 2009 Nobel Prize in Physiology or Medicine.
*4 X-ray crystal structure analysis
This is a method of determining the three-dimensional structure of a protein or DNA molecule by crystallizing the molecules and exposing the crystals to an X-ray beam.
For more information, please contact:
Dr. Ken KITANO (Nara Institute of Science and Technology)
Kinesins were discovered in 1985, based on their motility in cytoplasm extruded from the giant axon of the squid. 
They turned out as MT-based anterograde intracellular transport motors.  The founding member of this superfamily, kinesin-1, was isolated as a heterotetrameric fast axonal organelle transport motor consisting of 2 identical motor subunits (KHC) and 2 "light chains" (KLC) via microtubule affinity purification from neuronal cell extracts.  Subsequently, a different, heterotrimeric plus-end-directed MT-based motor named kinesin-2, consisting of 2 distinct KHC-related motor subunits and an accessory "KAP" subunit, was purified from echinoderm egg/embryo extracts  and is best known for its role in transporting protein complexes (IFT particles) along axonemes during cilium biogenesis.  Molecular genetic and genomic approaches have led to the recognition that the kinesins form a diverse superfamily of motors that are responsible for multiple intracellular motility events in eukaryotic cells.     For example, the genomes of mammals encode more than 40 kinesin proteins,  organized into at least 14 families named kinesin-1 through kinesin-14. 
Overall structure Edit
Members of the kinesin superfamily vary in shape but the prototypical kinesin-1 motor consists of two Kinesin Heavy Chain (KHC) molecules which form a protein dimer (molecule pair) that binds two light chains (KLCs), which are unique for different cargos.
The heavy chain of kinesin-1 comprises a globular head (the motor domain) at the amino terminal end connected via a short, flexible neck linker to the stalk – a long, central alpha-helical coiled coil domain – that ends in a carboxy terminal tail domain which associates with the light-chains. The stalks of two KHCs intertwine to form a coiled coil that directs dimerization of the two KHCs. In most cases transported cargo binds to the kinesin light chains, at the TPR motif sequence of the KLC, but in some cases cargo binds to the C-terminal domains of the heavy chains. 
Kinesin motor domain Edit
The head is the signature of kinesin and its amino acid sequence is well conserved among various kinesins. Each head has two separate binding sites: one for the microtubule and the other for ATP. ATP binding and hydrolysis as well as ADP release change the conformation of the microtubule-binding domains and the orientation of the neck linker with respect to the head this results in the motion of the kinesin. Several structural elements in the Head, including a central beta-sheet domain and the Switch I and II domains, have been implicated as mediating the interactions between the two binding sites and the neck domain. Kinesins are structurally related to G proteins, which hydrolyze GTP instead of ATP. Several structural elements are shared between the two families, notably the Switch I and Switch II domain.
Basic kinesin regulation Edit
Kinesins tend to have low basal enzymatic activity which becomes significant when microtubule-activated.  In addition, many members of the kinesin superfamily can be self-inhibited by the binding of tail domain to the motor domain.  Such self-inhibition can then be relieved via additional regulation such as binding to cargo or cargo adapters.  
In the cell, small molecules, such as gases and glucose, diffuse to where they are needed. Large molecules synthesised in the cell body, intracellular components such as vesicles and organelles such as mitochondria are too large (and the cytosol too crowded) to be able to diffuse to their destinations. Motor proteins fulfill the role of transporting large cargo about the cell to their required destinations. Kinesins are motor proteins that transport such cargo by walking unidirectionally along microtubule tracks hydrolysing one molecule of adenosine triphosphate (ATP) at each step.  It was thought that ATP hydrolysis powered each step, the energy released propelling the head forwards to the next binding site.  However, it has been proposed that the head diffuses forward and the force of binding to the microtubule is what pulls the cargo along.  In addition viruses, HIV for example, exploit kinesins to allow virus particle shuttling after assembly. 
There is significant evidence that cargoes in-vivo are transported by multiple motors.    
Motor proteins travel in a specific direction along a microtubule. Microtubules are polar meaning, the heads only bind to the microtubule in one orientation, while ATP binding gives each step its direction through a process known as neck linker zippering. 
It has been previously known that kinesin move cargo towards the plus (+) end of a microtubule, also known as anterograde transport/orthograde transport.  However, it has been recently discovered that in budding yeast cells kinesin Cin8 (a member of the Kinesin-5 family) can move toward the minus end as well, or retrograde transport. This means, these unique yeast kinesin homotetramers have the novel ability to move bi-directionally.    Kinesin, so far, has only been shown to move toward the minus end when in a group, with motors sliding in the antiparallel direction in an attempt to separate microtubules.  This dual directionality has been observed in identical conditions where free Cin8 molecules move towards the minus end, but cross-linking Cin8 move toward the plus ends of each cross-linked microtubule. One specific study tested the speed at which Cin8 motors moved, their results yielded a range of about 25-55 nm/s, in the direction of the spindle poles.  On an individual basis it has been found that by varying ionic conditions Cin8 motors can become as fast as 380 nm/s.  It is suggested that the bidirectionality of yeast kinesin-5 motors such as Cin8 and Cut7 is a result of coupling with other Cin8 motors and helps to fulfill the role of dynein in budding yeast, as opposed to the human homologue of these motors, the plus directed Eg5.  This discovery in kinesin-14 family proteins (such as Drosophila melanogaster NCD, budding yeast KAR3, and Arabidopsis thaliana ATK5) allows kinesin to walk in the opposite direction, toward microtubule minus end.  This is not typical of kinesin, rather, an exception to the normal direction of movement.
Another type of motor protein, known as dyneins, move towards the minus end of the microtubule. Thus, they transport cargo from the periphery of the cell towards the center. An example of this would be transport occurring from the terminal boutons of a neuronal axon to the cell body (soma). This is known as retrograde transport.
Kinesin accomplishes transport by "walking" along a microtubule. Two mechanisms have been proposed to account for this movement.
- In the "hand-over-hand" mechanism, the kinesin heads step past one another, alternating the lead position.
- In the "inchworm" mechanism, one kinesin head always leads, moving forward a step before the trailing head catches up.
Despite some remaining controversy, mounting experimental evidence points towards the hand-over-hand mechanism as being more likely.  
ATP binding and hydrolysis cause kinesin to travel via a "seesaw mechanism" about a pivot point.   This seesaw mechanism accounts for observations that the binding of the ATP to the no-nucleotide, microtubule-bound state results in a tilting of the kinesin motor domain relative to the microtubule. Critically, prior to this tilting the neck linker is unable to adopt its motor-head docked, forward-facing conformation. The ATP-induced tilting provides the opportunity for the neck linker to dock in this forward-facing conformation. This model is based on CRYO-EM models of the microtubule-bound kinesin structure which represent the beginning and end states of the process, but cannot resolve the precise details of the transition between the structures.
A number of theoretical models of the molecular motor protein kinesin have been proposed.    Many challenges are encountered in theoretical investigations given the remaining uncertainties about the roles of protein structures, the precise way energy from ATP is transformed into mechanical work, and the roles played by thermal fluctuations. This is a rather active area of research. There is a need especially for approaches which better make a link with the molecular architecture of the protein and data obtained from experimental investigations.
The single-molecule dynamics are already well described  but it seems that these nano scale machines typically work in large teams.
Single-molecule dynamics are based on the distinct chemical states of the motor and observations about its mechanical steps.  For small concentrations of adenosine diphosphate, the motor’s behaviour is governed by the competition of two chemomechanical motor cycles which determine the motor’s stall force. A third cycle becomes important for large ADP concentrations.  Models with a single cycle have been discussed too. Seiferth et al. demonstrated how quantities such as the velocity or the entropy production of a motor change when adjacent states are merged in a multi-cyclic model until eventually the number of cycles is reduced. 
Recent experimental research has shown that kinesins, while moving along microtubules, interact with each other,   the interactions being short range and weak attractive (1.6±0.5 KBT). One model that has been developed takes into account these particle interactions,  where the dynamic rates change accordingly with the energy of interaction. If the energy is positive the rate of creating bonds (q) will be higher while the rate of breaking bonds (r) will be lower. One can understand that the rates of entrance and exit in the microtubule will be changed as well by the energy (See figure 1 in reference 30). If the second site is occupied the rate of entrance will be α*q and if the last but one site is occupied the rate of exit will be β*r. This theoretical approach agrees with the results of Monte Carlo simulations for this model, especially for the limiting case of very large negative energy. The normal totally asymmetric simple exclusion process for (or TASEP) results can be recovered from this model making the energy equal to zero.
In recent years, it has been found that microtubule-based molecular motors (including a number of kinesins) have a role in mitosis (cell division). Kinesins are important for proper spindle length and are involved in sliding microtubules apart within the spindle during prometaphase and metaphase, as well as depolymerizing microtubule minus ends at centrosomes during anaphase.  Specifically, Kinesin-5 family proteins act within the spindle to slide microtubules apart, while the Kinesin 13 family act to depolymerize microtubules.
Human kinesin superfamily members include the following proteins, which in the standardized nomenclature developed by the community of kinesin researchers, are organized into 14 families named kinesin-1 through kinesin-14: