Category Archives: Research
Photosynthetic organisms are so widespread around the globe they have adapted to various solar lighting conditions to thrive. The bacteria Blastochloris viridis absorbs light in the near infrared region of the electromagnetic spectrum, in fact, it holds the record for the longest wavelength (~1015 nm) absorbing organism whose Light Harvesting complex 1 (LHC1) has been elucidated. Despite their adaptation to a wide number of light conditions, photosynthetic organism can only make use of so many pigments or chromophores; the LHC1 (Figure 1) in B. viridis in fact is made up of Bacteriochlorophyll-b (BChl-b) molecules, one of the most abundant photosynthetic pigments on Earth, whose main absorption in solution (MeOH) is observed at 795 nm.
So, how can B. viridis use BChl-b molecules to absorb near IR radiation and how does it achieve this remarkable red-shifting effect? The LHC1 structure was solved in 2018 by Qian et al. through Cryo-EM at a 2.9 Å resolution; it is comprised of 17 protein subunits surrounding the so called photosynthetic pigments special pair. Each of these subunits is made up of three α-helix structures surrounding two BChl-b and one dihydroneurosporene (DHN) molecule for a total of 34 of these photosynthetic pigments inside the LHC and 17 DHN molecules interacting between the protein structures and the
main BChl-b pigments.
It was Dr. Jacinto Sandoval and Gustavo “Gus” Mondragón who brought this facts to our attention during their survey of potential candidates for calculating exotic exciton transfer mechanisms in photosynthetic organisms, part of Gustavo’s PhD thesis. To them, it was clear from the start that some sort of cooperative effect between pigments was operating and possibly leading to the red-shifted absorption, therefore a careful dissection of all possible pigments combinations was carried out and their UV-Vis spectra were calculated at the CAMB3LYP/cc-pVDZ on PBE0/6-31G(d) optimized geometries, leading to the systems shown below in figure 2.
System B7 reproduced the red-shifted absorption at 1026 nm, but since the original structure was fitted from the Cryo-EM with a 2.9 Å resolution, “Gus” suggested reaching out to the group of Prof. Andrés Gerardo Cisneros and Dr. Jorge Nochebuena at UT Dallas for carrying out QM/MM calculations; this optimization included the proteins surrounding the pigments in the MM layer and the interacting residues (Hys coordinated to Mg2+ ions in BChl-b) along the chromophores were incorporated into the QM layer, however the thus obtained minima for the B7 system lost the main absorption in the near-IR region, therefore, Dr. Nochebuena suggested running an MD simulation (45 ns) and took a random sampling of ten structures (Figure 3).
All structures in the sampling reproduced the red-shifted absorption (~1000 nm) successfully proving that cooperative and dynamic effects allow B. viridis to perform photosynthesis with low energy radiation (Figure 4). Therefore, close intermolecular interactions along with thermal/dynamical fluctuations allow for a regular pigment such as BChl-b to form near-IR absorbing photosystems for organisms to thrive in low conditions of solar light.
If you want to read further details, this work is now published in the Journal of Chemical Theory and Computation of the American Chemical Society. I’ll talk about this and other ventures in photosynthesis next week at the WATOC conference in Vancouver, swing by to talk CompChem!
Prof. Mario Molina was awarded the Nobel Prize in Chemistry in 1995, the same year I started my chemistry education at the chemistry school from the National Autonomous University of Mexico, UNAM, the same school from where he got his undergraduate diploma. To be a chemistry student in the late nineties in Mexico had Prof. Molina as a sort of mythical reference, something to aspire to, a role model, the sort of representation the Latinx and other underrepresented communities still require and seldom get.
I saw him several times at UNAM, where he’d pack any auditorium almost once a year to talk about various research topics, but I remember distinctly the first time I sort of interacted with him. It was 1997 and I attended my first congress, the 5th North America Chemistry Congress. Minutes before the official inauguration which he was supposed to preside, I caught a glimpse of him in the hallways near the main conference room. Being only 19 years old, I thought it’d be a good idea to chase him, ask for his autograph and a picture. He was kind enough not to brush me off and took just a minute to shake my hand, sign my book of abstracts, and get his picture taken with me. But cameras back then relied on the user to place a roll of film correctly. I did not; so the picture, although it happened, it doesn’t exist. Because of this and other anecdotes, that congress cemented my love for chemistry. I never asked for a second picture in the few subsequent occasions I had the pleasure to hear him talk.
Prof. Molina was an advocate of green and sustainable sources of energies. His work predicted the existence of a hole in the ozone layer and his struggle brought change into the banning of CFCs and other substances which interfere with the replenishment of ozone in the sub-stratosphere. Today, his legacy remains but also do his pending battles in the quest for new policies that favor the use of green alternative forms of energy. May he rest in peace and may we continue his example.
Just as I was thinking about the state of Mexican scientific environment in the global scale, Prof. Dr. Gabriel Merino from CINVESTAV comes and gets this prize awarded by the International Center for Theoretical Physics (ICTP) and the Quantum ESPRESSO Foundation, showing us all that great science is possible even under pressing circumstances.
This prize is awarded biennially to a young scientist for outstanding contributions in the field of quantum-mechanical materials and molecular modeling, performed in a developing country or emerging economy,and in the case of Dr. Merino it is awarded not only for his contributions to theory and applications but also by his contributions to the prediction of novel systems that violate standard chemical paradigms, broadening the scope of concepts like aromaticity, coordination and chemical bond. The list of his contributions is very long despite his young age and there are barely any topic in chemistry or materials science that escapes his interest.
Gabriel is also one of the leading organizers of the Mexican Theoretical Physical Chemistry Meeting, an unstoppable mentor with many of his former students now leading research teams of their own. He is pretty much a force of nature.
Congratulations to Dr. Gabriel Merino, his team, CINVESTAV and thanks for being such an inspiration and a good friend at the same time.
The video below is a sad recount of the scientific conditions in Mexico that have driven an enormous amount of brain power to other countries. Doing science is always a hard endeavour but in developing countries is also filled with so many hurdles that it makes you wonder if it is all worth the constant frustration.
That is why I think it is even more important for the Latin American community to make our science visible, and special issues like this one from the International Journal of Quantum Chemistry goes a long way in doing so. This is not the first time IJQC devotes a special issue to the Comp.Chem. done south of the proverbial border, a full issue devoted to the Mexican Physical Chemistry Meetings (RMFQT) was also published six years ago.
I believe these special issues in mainstream journals are great ways of promoting our work in a collected way that stresses our particular lines of research instead of having them spread a number of journals. Also, and I may be ostracized for this, but I think coming up with a new journal for a specific geographical community represents a lot of effort that takes an enormous amount of time to take off and thus gain visibility.
For these reasons I’ve been cooking up some ideas for the next RMFQT website. I don’t pretend to say that my colleagues need any shoutouts from my part -I could only be so lucky to produce such fine pieces of research myself- but it wouldn’t hurt to have a more established online presence as a community.
¡Viva la ciencia Latinoamericana!
We’ve expanded the scope of our research interests from quantum mechanical calculations to docking and MedChem for over a year now; it has been a very interesting ride and a very rich avenue of research to explore. Durbis Castillo has led -out of his own initiative- this project and today he presents us with a guest post on the nuances of his project. Bear in mind that the detail of the calculations and a small -very targeted- tutorial on MAESTRO will be provided later in further posts and that making all this decisions required a long process of trial and error, we can only thank Dr. Antonio Romo for his help in minimizing the time this process took.
HIV is a tricky virus, and even though many of the steps included in its lifecycle are druggable, the chemical machinery making it work has been quite elusive since research groups started studying it. Highly Active Antiretroviral Therapy (HAART) works thanks to the combination of several drugs targeting different proteins such as the HIV protease or reverse transcriptase.
In 1998 the elucidation of the gp120 envelope glycoprotein crystal structure introduced a new step in the drug discovery race: HIV entry. Since drugs targeting gp120 have not been widely explored or developed, we decided to use common methodologies like docking (rigid and fit-induced) and ADME predictions to address the following question: How can we easily discover a molecule that inhibits gp120 binding to the lymphocyte CD4 receptor without having to synthesize it first? The answer was to perform a virtual screening with a bottleneck methodology based on docking calculations.
Docking methodologies are often looked as insufficient, careless or even unscientific, since the algorithms they are founded upon are not as accurate or descriptive as the ones that support DFT or ab initio calculations, for example. But there is a huge advantage to simpler operations: less computational resources are required. Then, following Russia’s example when making tanks during the WWII, why not make thousands or millions of docking calculations to quickly explore an entire chemical space and find which molecules are more likely to bind the protein?
And this is exactly what we did. We built a piperazine-based dataset of 16.3 million compounds, all of them including fragments that are reported in the medicinal chemistry literature, thus having two main characteristics, synthetic accessibility and pharmacological activity. These 16.3 million compounds were thoroughly filtered through several docking steps, each one of them being more accurate and comprehensive than the previous one, abruptly eliminating poorly fitted molecules, leaving us with a total of 275 candidates that were redocked in a different crystal structure and a different program (consensus docking).
After analyzing the ADME properties of the candidates, with descriptors such as human oral absorption and possible metabolic reactions, as well as the Induced-Fit Docking score of these molecules, ten ligands were selected as the best ones inside the analyzed chemical space. You can see ligand 255 (figure 1) as an example of the molecules that obtained the best scores throughout the docking steps.
Many of the colleague researchers related to this kind of topics asked “Why didn’t you download a set of molecules from Zinc or Maybridge?” And the answer to this question includes three aspects: first we wanted to test a combinatorial approach to drug design, second, we wanted to test whether including a piperazine as the core of the set of molecules would immediately grant them activity and high potency, and finally, a built database will always confer a higher degree of novelty to the possible hits when compared to commercially available compounds whose synthesis has already been developed. However, this last point needs to be addressed by an organic chemist since none of the molecules from our database have ever been synthesized (any takers?).
Right now, we are trying to explore further through molecular dynamics simulations using Desmond and Amber. Other future goals for this project include screening large databases of commercial and novel compounds with gp120 and other proteins involved in the HIV lifecycle. Also, we remain open to collaborate with anyone interested in taking the challenge to synthesize our molecules, as well as performing the biochemical assays to get an idea of their activity.
More details on MD simulations and the path of our first virtual hits to follow. Anyone interested in reading my thesis work can contact me through my linkedin profile at https://www.linkedin.com/in/durbisjaviercp/. An article is under preparation and will soon be submitted, stay tuned!
Communication of scientific findings is an essential skill for any scientist, yet it’s one of those things some students are reluctant to do partially because of the infamous blank page scare. Once they are confronted to writing their thesis or papers they make some common mistakes like for instance not thinking who their audience is or not adhering to the main points. One of the the highest form of communication, believe it or not, is gossip, because gossip goes straight to the point, is juicy (i.e. interesting) and seldom needs contextualization i.e. you deliver it just to the right audience (that’s why gossiping about friends to your relatives is almost never fun) and you do it at the right time (that’s the difference between gossips and anecdotes). Therefore, I tell my students to write as if they were gossiping; treat your research in a good narrative way, because a poor narrative can make your results be overlooked.
I’ve read too many theses in which conclusions are about how well the methods work, and unless your thesis has to do with developing a new method, that is a terrible mistake. Methods work well, that is why they are established methods.
Take the following example for a piece of gossip: Say you are in a committed monogamous relationship and you have the feeling your significant other is cheating on you. This is your hypothesis. This hypothesis is supported by their strange behavior, that would be the evidence supporting your hypothesis; but be careful because there could also be anecdotal evidence which isn’t significant to your own as in the spouse of a friend had this behavior when cheating ergo mine is cheating too. The use of anecdotal evidence to support a hypothesis should be avoided like the plague. Then, you need an experimental setup to prove, or even better disprove, your hypothesis. To that end you could hack into your better half’s email, have them followed either by yourself or a third party, confronting their friends, snooping their phone, just basically about anything that might give you some information. This is the core of your research: your data. But data is meaningless without a conclusion, some people think data should speak for itself and let each reader come up with their own conclusions so they don’t get biased by your own vision and while there is some truth to that, your data makes sense in a context that you helped develop so providing your own conclusions is needed or we aren’t scientists but stamp collectors.
This is when most students make a terrible mistake because here is where gossip skills come in handy: When asked by friends (peers) what was it that you found out, most students will try to convince them that they knew the best algorithms for hacking a phone or that they were super conspicuous when following their partners or even how important was the new method for installing a third party app on their phones to have a text message sent every time their phone when outside a certain area, and yeah, by the way, I found them in bed together. Ultimately their question is left unanswered and the true conclusion lies buried in a lengthy boring description of the work performed; remember, you performed all that work to reach an ultimate goal not just for the sake of performing it.
Writers say that every sentence in a book should either move the story forward or show character; in the same way, every section of your scientific written piece should help make the point of your research, keep the why and the what distinct from the how, and don’t be afraid about treating your research as the best piece of gossip you’ve had in years because if you are a science student it is.
As is the case of proteins, the functioning of DNA is highly dependent on its 3D structure and not just only on its sequence but the difference is that protein tertiary structure has an enormous variety whereas DNA is (almost) always a double helix with little variations. The canonical base pairs AT, CG stabilize the famous double helix but the same cannot be guaranteed when non-canonical -unnatural- base pairs (UBPs) are introduced.
When I first took a look at Romesberg’s UBPS, d5SICS and dNaM (throughout the study referred to as X and Y see Fig.1) it was evident that they could not form hydrogen bonds, in the end they’re substituted naphtalenes with no discernible ways of creating a synton like their natural counterparts. That’s when I called Dr. Rodrigo Galindo at Utah University who is one of the developers of the AMBER code and who is very knowledgeable on matters of DNA structure and dynamics; he immediately got on board and soon enough we were launching molecular dynamics simulations and quantum mechanical calculations. That was more than two years ago.
Our latest paper in Phys.Chem.Chem.Phys. deals with the dynamical and structural stability of a DNA strand in which Romesberg’s UBPs are introduced sequentially one pair at a time into Dickerson’s dodecamer (a palindromic sequence) from the Protein Data Bank. Therein d5SICS-dNaM pair were inserted right in the middle forming a trisdecamer; as expected, +10 microseconds molecular dynamics simulations exhibited the same stability as the control dodecamer (Fig.2 left). We didn’t need to go far enough into the substitutions to get the double helix to go awry within a couple of microseconds: Three non-consecutive inclusions of UBPs were enough to get a less regular structure (Fig. 2 right); with five, a globular structure was obtained for which is not possible to get a proper average of the most populated structures.
X and Y don’t form hydrogen bonds so the pairing is pretty much forced by the scaffold of the rest of the DNA’s double helix. There are some controversies as to how X and Y fit together, whether they overlap or just wedge between each other and according to our results, the pairing suggests that a C1-C1′ distance of 11 Å is most stable consistent with the wedging conformation. Still much work is needed to understand the pairing between X and Y and even more so to get a pair of useful UBPs. More papers on this topic in the near future.
In the past I’ve avoided this topic for various reasons. First, because I strongly believe that focusing on labels perpetuates them, and as scientists, we should always rise above them, for is science and not scientists what’s important. I remember my former PhD advisor, Prof. Cogordan, saying that “Liberties are exercised, not demanded“. Take Rosa Parks, for instance, her refusal to move to the back of the bus was an exercise of her liberty, and one that moved to a profound change, alas not without turmoil. But should I really call it a label? since it applies to roughly half the potential brain power available in the planet it then becomes a relevant question. Are equality and political correctness mutually exclusive terms?
It could be argued that I talk from a privileged position being a male scientist but since I’m a Mexican, non-white, non-US-based, male scientist those privileges are only so many.
I first began drafting this post way back before November 2016, when the misogyny displayed by a presidential candidate was in everyone’s mind to such a large extent that even when it even seemed prone to cause his demise it didn’t. The women’s march in D.C. has proven the topic to be still quite relevant though, and next April 22nd, Earth Day, a scientists march will take place to protest against policies that put science -and therefore mankind- in jeopardy. Some particular issues associated with the march will be the communication gag orders against scientific federal agencies; the consequences of the travel-ban to scientists from black-listed countries and, of course, the threat of having a misogynistic environment on the status of women in STEM careers.
Fact: There is a clear selection bias since there is still a large number disparity between men and women in academia throughout the world and since the number of academic position is growing at a much lower rate than the number of scientists competing for such positions, the race has become tighter and usually women take the worst part of the deal. There is a leaking pipeline in which women don’t reach the end of the race. I imagine in some cases it may have to do with maternity as it is still conservatively perceived by most countries but issues like harassment and condescension are not to be ignored.
Fact: Scientific curiosity is innate to all human beings -which confirms the above mentioned bias- therefore talking about encouraging young women to pursuit a career in STEM is plain stupid; they don’t need to be encouraged they must stop being discouraged somewhere along the path. The playing field for both genders should be leveled or science risks loosing half the population in these dire times in which all the brain power available is much needed. Also, I fear the continuous talk about these disadvantages could be off-putting for future generations of women who might be interested in undertaking STEM careers. Leveling the field for female and male scientists should be done and not just demanded but details about the mechanisms to accomplish it are still unclear and vary from one institution to another. Here in Mexico, for instance, all public universities have collective contracts, therefore every scientist in a given level earns as much as another in the same level. In other countries salaries are personally negotiated and therefore each scientists earnings vary, which has led to women earning less on average. Now, the ease with which levels are climbed within an institution are also a matter for debate. Does this mean that earnings and positions are the main problems women face in academia? Could they be the best starting points? Is the rate of enrollment the root of the problem? If so, are us teachers and professors to blame?
Another reason why I avoided this topic was because it would seem so patronizing on my part to give a shout-out to women whose work in computational chemistry I so much admire when I myself could only aspire to one day have work of their quality. They definitely don’t need my praises because they have well earned all our admiration. Nonetheless, here is a link to a great directory of women working in computational chemistry in which some great names are found such as Anna Krylov, Gloria Tabacchi, Romelia Salomón, Patricia Hunt, and so many more great scientists from all over the world. Here in Mexico we count with names such as Margarita Bernal, Patrizia Calaminici, Annia Galano, Estela Mayoral and so many other. It is hard to make a comprehensive list, and as I said before I could only aspire to have work with the same quality as theirs. The importance of recognizing and promoting women to take a career in computational chemistry will in short be addressed by the FemEx-NL-2017 conference next June 22nd in the Netherlands; their motto is “Promoting female excellence in theoretical and computational chemistry”, certainly a worthy and noble endeavor for a problem far from solved.
Perhaps another good reason for writing this post lies in the image below. It is a true statement but we should analyze the causality for it and fix whatever it is we’re doing wrong because it is certainly not the plumbing:
— David Mobley (@davidlmobley) May 17, 2016
I have a daughter. I want her to be able to do whatever she wants when she grows up without deterrence from unfairness. I want a world for her without labels so she never has the option of playing ‘The Woman Card’. It wouldn’t be fair for anyone around her.
This wont be the last post on this topic. Please share your views in the comments and criticism section. They are all welcome.
Guillermo Caballero, a graduate student from this lab, has written this two-part post on the nuances to be considered when searching for transition states in the theoretical assessment of reaction mechanisms. He’s been quite successful in getting beautiful energy profiles for organic reaction mechanisms, some of which have even explained why some reactions do not occur! A paper in Tetrahedron has just been accepted but we’ll talk about it in another post. I wanted Guillermo to share his insight into this hard practice of computational chemistry so he wrote the following post. Enjoy!
Yes, finding a transition state (TS) can be one of the most challenging tasks in computational chemistry, it requires both a good choice of keywords in your route section and all of your chemical intuition as well. Herein I give you some good tricks when you have to find a transition state using Gaussian 09 Rev. D1
METHOD 1. The first option you should try is to use the opt=qst2 keyword. With this method you provide the structures of your reagents and your products, then the program uses the quadratic synchronous transit algorithm to find a possible transition state structure and then optimize it to a first order saddle point. Here is an example of the input file.
link 0 --blank line-- #p b3lyp/6-31G(d,p) opt=qst2 geom=connectivity freq=noraman --blank line-- Charge Multiplicity Coordinates of reagents --blank line-- Charge Multiplicity Coordinates of products --blank line---
It is mandatory that the numbering must be the same in the reagents and the products otherwise the calculation will crash. To verify that the label for a given atom is the same in reagents and products you can go to Edit, then Connection. This opens a new window were you can manually modify the numbering scheme. I suggest you to work in a split window in gaussview so you can see at the same time your reagents and products.
The keyword freq=noraman is used to calculate the frequencies for your optimized structure, it is important because for a TS you must only observe one imaginary frequency, if not, then that is not a TS and you have to use another method. It also occurs that despite you find a first order saddle point, the imaginary frequency does not correspond to the bond forming or bond breaking in your TS, thus, you should use another method. I will give you advice later in the text for when this happens. When you use the noraman in this keyword you are not calculating the Raman frequencies, which for the purpose of a TS is unnecessary and saves computing time. Frequency analysis MUST be performed AT THE VERY SAME LEVEL OF THEORY at which the optimization is performed.
The main advantage for using the qst2 option is that if your calculation is going to crash, it generally crashes at the beginning, in the moment of guessing your transition state structure. Once the program have a guess, it starts the optimization. I suggest you to ask the algorithm to calculate the force constants once, this generally improves on the convergence, it will take slightly more time depending on the size of your structure but it pays off. The keyword in the route section is opt=(qst2,calcfc). Indeed, I hardly encourage you to use the calcfc keyword in any optimization you want to run.
METHOD 2. If method 1 does not work, my next advice is to use the opt=ts keyword. For this method, the coordinates in your input file are those for the TS structure. Here is an example of the input file.
link 0 --blank line-- #p b3lyp/6-31G(d,p) opt=ts geom=connectivity freq=noraman --blank line-- Charge Multiplicity Coordinates of TS --blank line--
The question that arises here is how should I get the coordinates for my TS? Well, honestly this is not a trivial task, here is where you use all the chemistry you know. For example, you can start with the coordinates of your reagents and manually get them closer. If you are forming a bond whose length is to be 1.5Å, then I suggest you to have that length in 1.6Å in your TS. Sometimes this becomes trial and error but the most accurate your TS structure is, based on your chemical knowledge, the easiest to find your TS will be. As another example, if you want to find a TS for a [1,5]-sigmatropic reaction a good TS structure will be putting the hydrogen atom that migrates in the middle point through the way. I have to insist, this method hardly depends on your imagination to elucidate a TS and on your chemistry background.
Most of the time when you use the opt=ts keyword the calculations crashes because of an error in the number of eigenvalues, you can avoid it adding noeigen to the route section; here is an example of the input file, I encourage you to use this method.
link 0 --blank line-- #p b3lyp/6-31G(d,p) opt=(ts,noeigen,calcfc) geom=connectivity freq=noraman --blank line-- Charge Multiplicity Coordinates of TS --blank line--
If you have problems in the optimization steps I suggest you to ask the algorithm to calculate the force constants in every step of the optimization opt=(ts,noeigen,calcall) this is quite a harsh method because will consume long computing time but works well for small molecules and for complicated TSs to find.
Another ‘tricky’ way to get your coordinates for your TS is to run the qst2 calculation, then if it fails, take the second- or the third-step coordinates and used them as a ‘pre-optimized’ set of coordinates for this method.
By the way, here is another useful trick. If you are evaluating a group of TSs, let’s say, if you are varying a functional group among the group, focus on finding the TS for the simplest case, then use this optimized TS as a template where you add the moieties and use this this method. This works pretty well.
For this post we’ll leave it up to here and post the rest of Guillermo’s tricks and advice on finding TS structures next week when we’ll also discuss the use of IRC calculations and some considerations on energy corrections when plotting the full energy profile. In the mean time please take the time to rate, like and share this and other posts.
Thanks for reading!
As part of an ongoing collaboration with the University of Arizona (UA) and the Center for Advanced Research and Studies (CINVESTAV – Saltillo), we are looking into the use of calix[n]arenes for bio-remediation agents capable to extract Arsenic (V) and (III) species from water. Water contamination by arsenic is a pressing issue in northern Mexico and the southern US, therefore any efforts aiming to their elimination has strong social and health repercussions.
As in previous studies, all calixarenes were optimized along with their corresponding guests within the cavity, namely H3AsO4, H2AsO4– and HAsO42- at the DFT level with the so-called Minnesota functionals by Truhlar and Cao, M06-2X/6-31G(d,p) level of theory. Interaction energies were calculated through the NBODel procedure. Calixarenes with R = SO3H and PO3H are the most promising leads. This study is now publishes in the Journal of Inclusion Phenomena and Macrocyclic Chemistry (DOI 10.1007/s10847-016-0617-0) as an online first article.
This article is also the first to be published by our undergraduate (and almost grad student in a month) Gustavo Mondragón who took this project on a side to his own research on photosynthesis.
Now my colleagues in Arizona and Saltillo, Prof. Reyes Sierra and Dr. Eddie López, respectively, will work on the experimental side of the project. Further calculations are being undertaken to extend this study to As(III) and to the use of other potential extracting materials such as metallic nanoparticles to which calixarenes could be covalently linked.