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!
2017 was a complicated year for various reasons here in Mexico (and some personal health issues) but nonetheless I’m very proud of the performance of everyone at the lab whose hard work and great skills keep pushing our research forward.
Four new members joined the team and have presented their work at the national meeting for CompChem for the first time. Also, for the first time, one of my students, Gustavo Mondragón, gave a talk at this meeting with great success about his research on the Fenna Matthews Olson complex of photosynthetic bacteria.
The opportunity to attend WATOC at Munich presented me the great chance to meet wonderful people from around the world and was even kindly and undeservingly invited to write the prologue for an introductory DFT book by Prof. Pedro Cerón from Spain. I hope to Jeep up with the collaborations abroad such as the one with the Mirkin group at Nortgwestern and the one with my dear friend Kunsagi-Mate Sándor at Pecsi Tudomanyegyetem (Hungary), among many others; I’m thankful for their trust in our capabilities.
Two members got their BSc degrees, Marco an Durbis, the latter also single handedly paved the way for us to develop a new research line on the in silico drug developing front; his relentless work has also been praised by the QSAR team at the Institute of Chemistry with which he has collaborated by performing toxicity calculations for the agrochemical industry as well as by designing educational courses aimed to the dissemination of our work and QSAR in general among regulatory offices and potential clients. We’re sad to see him go next fall but at the same time we’re glad to know his scientific skills will further develop.
I cannot thank the team enough: Alejandra Barrera, Gustavo Mondragón, Durbis Castillo, Fernando Uribe, Juan Guzman, Alberto Olmedo, Eduardo Cruz, Ricardo Loaiza and Marco Garcia; may 2018 be a great year for all of you.
And to all the readers thank you for your kind words, I’m glad this little space which is about to become nine years old is regarded as useful; to all of you I wish a great 2018!
One of the most popular posts in this blog has to do with calculating Fukui indexes, however, when dealing with a large number of molecules, our described methodology can become cumbersome since it requires to manually extract the population analysis from two or three different output files and then performing the arithmetic on them separately with a spreadsheet or something.
Our new team member Ricardo Loaiza has written a python script that takes the three aforementioned files and yields a .csv file with the calculated Fukui indexes, and it even points out which of the atoms exhibit the largest values so if you have a large molecule you don’t have to manually check for them. We have also a batch version which takes all the files in any given directory and performs the Fukui calculations for each, provided it can find file triads with the naming requirements described below.
Output files must be named filename.log (the N electrons reference state), filename_plus.log (the state with N+1 electrons) and filename_minus.log (the N-1 electrons state). Another restriction is that so far these scripts only work with NBO population analysis as provided by the NBO3.1 program available in the various versions of Gaussian. I imagine the listing is similar in NBO5.x and NBO6.x and so it should work if you do the population analysis with them.
The syntax for the single molecule version is:
python fukui.py filename.log filename_minus.log filename_plus.log
For the batch version is:
(Por Lote means In Batch in Spanish.)
These scripts are available via GitHub. We hope you find them useful, and you do please let us know whether here at the comments section or at our GitHub site.
A yearly tradition of this Comp.Chem. lab and many others throughout our nation is to attend the Mexican Meeting on Theoretical Physical Chemistry to share news, progress and also a few drinks and laughs. This year the RMFQT was held in Puebla and although unfortunately I was not able to attend this lab was proudly represented by its current members. Gustavo Mondragón gave a talk about his progress on his photosynthesis research linking to the previous work of María Eugenia Sandoval already presented in previous editions; kudos to Gustavo for performing remarkably and thanks to all those who gave us their valuable feedback and criticism. Also, five posters were presented successfully, I can only thank the entire team for representing our laboratory in such an admirable way, and a special mention to the junior members, I hope this was the first of many scientific events they attend and may you deeply enjoy each one of them.
Among the invited speakers, the RMFQT had the honor to welcome Prof. John Perdew (yes, the P in PBE); the team took the opportunity of getting a lovely picture with him.
Here is the official presentation of the newest members of our group:
Alejandra Barrera (hyperpolarizabilty calculations on hypothetical poly-calyx[n]arenes for the search of NLO materials)
Fernando Uribe (Interaction energy calculations for non-canonical nucleotides)
Juan Guzmán (Reaction mechanisms calculations for catalyzed organic reactions)
We thank the organizing committee for giving us the opportunity to actively participate in this edition of the RMFQT, we eagerly await for next year as every year.
Recently, the journal ACS Central Science asked me to write a viewpoint for their First Reactions section about a research article by Prof. Alán Aspuru-Guzik from Harvard University on the evolution of the Fenna-Matthews-Olson (FMO) complex. It was a very rewarding experience to write this piece since we are very close to having our own work on FMO published as well (stay tuned!). The FMO complex remains a great research opportunity for understanding photosynthesis and thus the origin of life itself.
In said article, Aspuru-Guzik’s team climbed their way up a computationally generated phylogenetic tree for the FMO from different green sulfur bacteria by creating small successive mutations on the protein at a time while also calculating their photochemical properties. The idea is pretty simple and brilliant: perform a series of “educated guesses” on the structure of FMO’s ancestors (there are no fossil records of FMO so this ‘educated guesses’ are the next best thing) and find at what point the photochemistry goes awry. In the end the question is which led the way? did the photochemistry led the way of the evolution of FMO or did the evolution of FMO led to improved photochemistry?
Since both the article and viewpoint are both published as open access by the ACS, I wont take too much space here re-writing the whole thing and will instead exhort you to read them both.
Thanks for doing so!
The compound shown below in figure 1 is listed by Aldrich as 4,5,6,7-tetrahydroindole, but is it really?
To a hardcore organic chemist it is clear that this is not an indole but a pyrrole because the lack of aromaticity in the fused ring gives this molecule the same reactivity as 2,3-diethyl pyrrole. If you search the ChemSpider database for ‘tetrahydroindole’ the search returns the following compound with the identical chemical formula C8H11N but with a different hydrogenation pattern: 2,3,3a,4-Tetrahydro-1H-indole
The real indole, upon an electrophilic attack, behaves as a free enamine yielding the product shown in figure 3 in which the substitution occurs in position 3. This compound cannot undergo an Aromatic Electrophilic Susbstitution since that would imply the formation of a sigma complex which would disrupt the aromaticity.
On the contrary, the corresponding pyrrole is substituted in position 2
These differences in reactivity towards electrophiles are easily rationalized when we plot their HOMO orbitals (calculated at the M062X/def2TZVP level of theory):
If we calculate the Fukui indexes at the same level of theory we get the highest value for susceptibility towards an electrophilic attack as follows: 0.20 for C(3) in indole and 0.25 for C(2) in pyrrole, consistent with the previous reaction schemes.
So, why is it listed as an indole? why would anyone search for it under that name? Nobody thinks about cyclohexane as 1,3,5-trihydrobenzene. According to my good friend and colleague Dr. Moisés Romero most names for heterocyles are kept even after such dramatic chemical changes due to historical and mnemonic reasons even when the reactivity is entirely different. This is only a nomenclature issue that we have inherited from the times of Hantzsch more than a century ago. We’ve become used to keeping the trivial (or should I say arbitrary) names and further use them as derivations but this could pose an epistemological problem if students cannot recognize which heterocycle presents which reactivity.
So, in a nutshell:
Chemistry makes the chemical and not the structure.
A thing we all know but sometimes is overlooked for the sake of simplicity.
Last week the WATOC congress in Munich was a lot of fun. Our poster on photosynthesis had a great turnout and got a lot of positive feedback as well as many thought provoking questions. One of the highlights of my time there was seeing my former students and knowing they’re all leading successful and happy grad-student lives in Europe, I’m so very proud of them. It was great to connect with old friends and making new ones; a big thank you to all the readers of this little blog who took the time to come and say hi, I’m very glad the blog has been helpful to you.
Below there is an image of our poster (some typos persist).
See you all in 2020!
If you work in the field of photovoltaics or polyacene photochemistry, then you are probably aware of the Singlet Fission (SF) phenomenon. SF can be broadly described as the process where an excited singlet state decays to a couple of degenerate coupled triplet states (via a multiexcitonic state) with roughly half the energy of the original singlet state, which in principle could be centered in two neighboring molecules; this generates two holes with a single photon, i.e. twice the current albeit at half the voltage (Fig 1).
It could also be viewed as the inverse process to triplet-triplet annihilation. An important requirement for SF is that the two triplets to which the singlet decays must be coupled in a 1(TT) state, otherwise the process is spin-forbidden. Unfortunately (from a computational perspective) this also means that the 3(TT) and 5(TT) states are present and should be taken into account, and when it comes to chlorophyll derivatives the task quickly scales.
SF has been observed in polyacenes but so far the only photosynthetic pigments that have proven to exhibit SF are some carotene derivatives; so what about chlorophyll derivatives? For a -very- long time now, we have explored the possibility of finding a naturally-occurring, chlorophyll-based, photosynthetic system in which SF could be possible.
But first things first; The methodology: It was soon enough clear, from María Eugenia Sandoval’s MSc thesis, that TD-DFT wasn’t going to be enough to capture the whole description of the coupled states which give rise to SF. It was then that we started our collaboration with SF expert, Prof. David Casanova from the Basque Country University at Donostia, who suggested the use of Restricted Active Space – Spin Flip in order to account properly for the spin change during decay of the singlet excited state. A set of optimized bacteriochlorophyll-a molecules (BChl-a) were oriented ad-hoc so their Qy transition dipole moments were either parallel or perpendicular; the rate to which SF could be in principle present yielded that both molecules should be in a parallel Qy dipole moments configuration. When translated to a naturally-occurring system we sought in two systems: The Fenna-Matthews-Olson complex (FMO) containing 7 BChl-a molecules and a chlorosome from a mutant photosynthetic bacteria made up of 600 Bchl-d molecules (Fig 2). The FMO complex is a trimeric pigment-protein complex which lies between the antennae complex and the reaction center in green sulfur dependent photosynthetic bacteria such as P. aestuarii or C. tepidium, serving thus as a molecular wire in which is known that the excitonic transfer occurs with quantum coherence, i.e. virtually no energy loss which led us to believe SF could be an operating mechanism. So far it seems it is not present. However, for a crystallographic BChl-d dimer present in the chlorosome it could actually occur even when in competition with fluorescence.
I will keep on blogging more -numerical and computational- details about these results and hopefully about its publication but for now I will wrap this post by giving credit where credit is due: This whole project has been tackled by our former lab member María Eugenia “Maru” Sandoval and Gustavo Mondragón. Finally, after much struggle, we are presenting our results at WATOC 2017 next week on Monday 28th at poster session 01 (PO1-296), so please stop by to say hi and comment on our work so we can improve it and bring it home!
Out of some +1000 twitter accounts I follow about a quarter are related computational chemistry. The following public list isn’t comprehensive and prone to errors and contains researchers, programmers, students, journals, products and companies who gravitate around the use of in silico methods for the understanding and design of chemical and biochemical compounds.