Category Archives: Research
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.
Having a symposium right after the winter holidays is a great way to get back in touch with colleagues and students; we get to hear how their work is progressing and more importantly I get forced to become focused once again after a few weeks of just not paying much attention to anything related to work.
This year our group has happily gained some additions and sadly seen some others leave in search of a better future. María Eugenia “Maru” Sandoval gave a talk on the work she did on Singlet Fission (SF) in the Fenna-Matthews-Olson (FMO) complex during a three month stay at the Basque Country University in Spain under the supervision of Dr. David Casanova. Aside her calculations regarding Förster theory and a modification to Marcus’ equation, Singlet Fission was explored by her as a possible mechanism in which the Photosynthetic complex FMO might transfer solar energy from the antennae to the reaction center; one that might explain the high efficiency of it.
SF is a fascinating phenomenon: So you get an excited state S1 for a molecule1 that has been struck with a suitable photon; this excited state can either radiate back to the ground state (S0) but if there were two degenerate and coupled triplets whose energies are similar to half the S1 energy then the excited state might decay into [TT]1, hence singlet fission. In some cases (e.g. polyacene crystals) one of these triplets might be located in an adjacent molecule, this creates a hole in a second molecule due to the same single photon! This means creating twice the current albeit at half the voltage in photovoltaic materials. Maru has explored the possibility of SF occurring in natural systems and we think we might be on to something; she will defend her masters thesis any day now and we should see a publication later on this year. After that, she is pondering a few interesting options for her PhD.
On the poster session, our lab was represented by Marycarmen Reséndiz, Gustavo Mondragón and Guillermo Caballero. Durbis Pazos just now joined our group so he didn’t have to present a poster but nevertheless showed up gladly to support his colleagues. Gustavo will work on other aspects regarding the photochemistry of the FMO complex while Marycarmen is working on calculating the electronic interactions of chemically modified nucleotides when incorporated into DNA strands. Guillermo had a poster on his calculations for another reaction mechanism that caught his eye while still working with the experimentalists. I’m pleased to say that Guillermo is close to being published and also close to leaving us in order to get a PhD in a prestigious university that shall remain unnamed.
Thank you guys for keeping up the good work and maintaining the quality of the research we do, here is to a year full of success both in and out of the lab! Any success this lab has is due to you.
Well, I only contributed with the theoretical section by doing electronic structure calculations, so it isn’t really a paper we can ascribe to this particular lab, however it is really nice to see my name in JACS along such a prominent researcher as Prof. Chad Mirkin from Northwestern University, in a work closely related to my area of research interest as macrocyclic recognition agents.
In this manuscript, a calixarene is allosterically opened and closed reversibly by coordinating different kinds of ligands to a platinum center linked to the macrocycle. (This approach has been referred to as the weak link approach.) I recently visited Northwestern and had a great time with José Mendez-Arroyo, the first author, who showed me around and opened the possibility for further work between our research groups.
Closed, semi-open and fully open conformations; selectivity is modulated through cavity size. (Ligands: Green = Chloride; Blue = Cyanide)
Here at UNAM we calculated the interaction energies for the two guests that were successfully inserted into the cavity: N-methyl-pyridinium (Eint = 57.4 kcal/mol) and Pyridine-N-oxide (Eint = +200.0 kcal/mol). Below you can see the electrostatic potential mapped onto the electron density isosurface for one of the adducts. Relative orientation of the hosts within the cavity follows the expected (anti-) alignment of mutual dipole moments. At this level of theory, we could easily be inclined to assert that the most stable interaction is indeed the one from the semi-open compound and that this in turn is due to the fact that host and guest are packed closer together but there is also an orbital issue: Pyridine Oxide is a better electron acceptor than N-Me-pyridinium and when we take a closer look to the (Natural Bonding) orbitals interacting it becomes evident that a closer location does not necessarily yields a stronger interaction when the electron accepting power of the ligand is weaker (which is, in my opinion, both logic and at the same time a bit counterintuitive, yet fascinating, nonetheless).
All calculations were performed at the B97D/LANL2DZ level of theory with the use of Gaussian09 and NBO3.1 as provided within the former. Computing time at UNAM’s supercomputer known as ‘Miztli‘ is fully acknowledged.
The full citation follows:
A Multi-State, Allosterically-Regulated Molecular Receptor With Switchable Selectivity
Jose Mendez-Arroyo †, Joaquín Barroso-Flores §,Alejo M. Lifschitz †, Amy A. Sarjeant †, Charlotte L. Stern †, and Chad A. Mirkin *†
Thanks to José Mendez-Arroyo for contacting me and giving me the opportunity to collaborate with his research; I’m sure this is the first of many joint projects that will mutually benefit our groups.
Just as last year, the “Dolphin Summer Internship Program” (Programa Delfín) has started and this time it coincided with #RealTimeChem week. Four students from various cities (and accents) around Mexico have come to our lab in Toluca in order to spend about 7 weeks of research in the field of molecular modeling and within our research of molecular recognition in biochemistry. Karen, Cynthia, Jesús and Marco have started their training today as they arrived to CCIQS so we went over the (very) basics of quantum chemistry, the (very) basics of Linux and the basics of Gaussian09. (I should really think about developing some web tutorials or something because this impromptu training is very exhausting!)
Their academic backgrounds are mostly centered around pharmaceutics and biochemistry although their ages range from the second to the fourth year of college education. Computational chemistry is pretty unknown to all of them; I’ll do my best to change that, while at the same time I make them aware of its power as a research tool and as a research field in itself.
Here is to a very productive summer! I hope we manage to get enough data for a paper and, more importantly, that they all get a good experience out of their time here, make new friends and learn something new that enriches their skills in this increasingly competitive world.
Theoretical evaluation of a reaction mechanism is all about finding the right transition states (TS) but there are no guarantees within the available methods to actually find the one we need. Chemical intuition in the proposal of a mechanism is paramount. Let’s remember that a TS is a critical point on a Potential Energy Surface (PES) that is a minimum in every dimension but one. For a PES with more than two degrees of freedom, a hyper-surface, envisioning the location of a TS is a bit tricky, in the case of a three dimensional PES (two degrees of freedom) the saddle point constitutes the location of the TS as depicted in figure 1 by a section of a revolution hyperboloid.
The following procedure considers gas phase calculations. Nevertheless, the use of the SCRF keyword activates the implicit solvent calculation of choice in order to evaluate to some degree the solvent influence on the reaction energetics at different temperatures with the use of the temperature keyword.
The first step consists of a high level optimization of all minimums involved, such as reagents, products and intermediates, with a subsequent frequency analysis that includes no imaginary eigenvalues.
In order to find the structures of the transition states we use in Gaussian the Synchronous Transit-guided Quasi-Newton method  through the keywords QST2 or QST3. In the former case, coordinates for the reagents and products are needed as input; for the latter keyword, coordinates for the TS structure guess is needed also.
#p opt=(qst2,redundant) m062x/6-31++G(d,p) freq=noraman Temperature=373.15 SCRF=(Solvent=Water)
Title card for reagents
Cartesian Coordinates for reagents
Title card for products
Cartesian Coordinates for products
#p opt=(qst3,redundant) m062x/6-31++G(d,p) freq=noraman Temperature=373.15 SCRF=(Solvent=Water)
Title Card for reagents
Cartesian Coordinates for reagents
Title card for products
Cartesian Coordinates for products
Title card for TS
Cartesian Coordinates for TS
NOTE: It is fundamental that the numbering order is kept constant throughout the molecular specifications of all two, or three, input structures. Hence, I recommend to build a set of molecules, save their structure, and then modified the coordinates on the same file to produce the following structure, that way the number for every atom will remain the same for every step.
As I wrote above, there are no guarantees of finding the right TS so many attempts are probably needed. Once we have the optimized structures for all the species involved in our mechanistic proposal we can plot their energies very simply with MS Excel the way we’ve previously described in this blog (reblogged from eutactic.wordpress.com)
Once we’ve succeeded in finding the structure of our TS we may run an Internal Reaction Coordinate (IRC) calculation. This calculation will connect the TS structure to those of the products and the reagents. Initial constant forces are required and these are commonly retrieved from the TS calculation checkpoint file through the RCFC keyword.
#p m062x/6-31++G(d,p) IRC=(Maxpoints=50,RCFC,phase=(2,1))Temperature=373.15 SCRF=(Solvent=Water) geom=allcheck
Finally, the IRC path can be visualized with GaussView from the Results menu. A successful IRC will link both structures along a single reaction coordinate proving that both reagents and products are linked by the obtained TS.
Hat tip to Howard Diaz who has become quite skillful in calculating these mechanisms as proven by his recent poster at the XII RMFQT a couple of weeks back. And as usual thanks to everyone who reads, comments, likes, recommends, rates and shares my silly little posts.