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.
Tribology isn’t exactly an area with which us chemists are most familiar, yet chemistry has a great impact on this branch of physics of high industrial importance. Tribology is basically the science which studies the causes and consequences of friction between surfaces.
The plastic bag industry requires the use of chemical additives to reduce the electrostatic adherence between sheets of plastic. My good old friend Dr. Armando Gama has studied through Dissipative Particle Dynamics (DPD) coarse-grained simulations the friction coefficients of having two slightly different molecules: erukamide and behenamide, which only differ in the presence of a double bond between carbon atoms 12 and 13 (Fig1).In order to study the electronic aspects that give rise to different tribological effects in these very similar molecules, four chains of each kind were bounded to a frozen graphene surface (four bonds apart to prevent steric crowding) and were optimized at the B97D/6-31G(d,p) level of theory.
Double bonds in erukamide pile together through pi-pi stacking interactions (Fig2) which are absent in behenamide which is why these last ones are able to slide better between each other (Fig3). Interaction energies calculated for the inner chains at the same level of theory are 44.21 and 34.46 kcal/mol for erukamide and behenamide, respectively. As per the suggestion of a referee we extended the calculations to a 2D system by placing seven molecules on graphene, which once again was kept at the optimized geometry of its isolated state, at four bonds of separation in order to prevent steric crowding (Fig 4).
The paper is now available at JPC-A. Thanks to Dr. Gama for this great opportunity to work with his team, I know it wont be the last.
It is with great pleasure that I announce the graduation of another member of our research group: Luis Enrique “Kike” Aguilar defended his BSc thesis yesterday and is now counting the days left for the Autumn when he’ll move to the Netherlands for a masters in computational chemistry.
Luis Enrique, Kike, calculated the interaction energies of 144 different inclusion complexes where calix and thia-calix[n]arenes were once again the chosen hosts (36 of them) and two drugs for the treatment of chronic myeloid leukemia (CML), namely Sorafenib and Bosutinib, were the guests.
The publication of the corresponding article in which we once again were fortunate enough to count with the collaboration of Dr. Rodrigo Galindo from Utah University in the molecular dynamics section, is still pending but we’re confident enough that it wont take much longer until it’s out there.
Kike is a very diligent student with great learning skills, I’m sure he’ll succeed in any enterprise he sets himself off. Congratulations, Kike! Thanks for being a part of our research but more importantly for being a part of our community.
Happy new year to all my readers!
Having a new paper published is always a matter of happiness for this computational chemist but this time I’m excedingly excited about anouncing the publishing of a paper in the Journal of Chemical Theory and Computation, which is my highest ranked publication so far! It also establishes the consolidation of our research group at CCIQS as a solid and competitive group within the field of theoretical and computational chemistry. The title of our paper is “In Silico design of monomolecular drug carriers for the tyrosine kinase inhibitor drug Imatinib based on calix- and thiacalix[n]arene host molecules. A DFT and Molecular Dynamics study“.
In this article we aimed towards finding a suitable (thia-) calix[n]arene based drug delivery agent for the drug Imatinib (Gleevec by Novartis), which is a broadly used powerful Tyrosine Kinase III inhibitor used in the treatment of Chronic Myeloid Leukaemia and, to a lesser extent, Gastrointestinal Stromal Tumors; although Imatinib (IMB) exhibits a bioavailability close to 90% most of it is excreted, becomes bound to serum proteins or gets accumulated in other tissues such as the heart causing several undesired side effects which ultimately limit its use. By using a molecular capsule we can increase the molecular weight of the drug thus increasing its retention, and at the same time we can prevent Imatinib to bind, in its active form, to undesired proteins.
We suggested 36 different calix and thia-calix[n]arenes (CX) as possible candidates; IMB-CX complexes were manually docked and then optimized at the B97D/6-31G(d,p) level of theory; Stephan Grimme’s B97D functional was selected for its inclusion of dispersion terms, so important in describing π-π interactions. Intermolecular interaction energies were calculated under the Natural Bond Order approximation; a stable complex was needed but a too stable complex would never deliver its drug payload! This brings us to the next part of the study. A monomolecular drug delivery agent must be able to form a stable complex with the drug but it must also be able to release it. Molecular Dynamics simulations (+100 ns) and umbrella sampling methods were used to analyse the release of the drug into the aqueous media.
Potential Mean Force profiles for the four most stable complexes for position N1 and N2 from the QM simulations are shown below (Red, complexes in the N1 position, blue, N2 position). These plots, derived from the MD simulations give us an idea of the final destination of the drug respect of the calixarene carrier. In the next image, the three preferred structures (rotaxane-like; inside; released) for the final outcome of the delivery process are shown. The stability of the complexes was also assessed by calculating the values of ΔG binding through the use of the Poisson equations.
Thanks to my co-authors Maria Eugenia Sandoval-Salinas and Dr. Rodrigo Galindo-Murillo for their enormous contributions to this work; without their hard work and commitment to the project this paper wouldn’t have been possible.
Today is truly a landmark in our lab because on this day, María Eugenia “Maru” Sandoval-Salinas has defended her thesis and has thus obtained her B. Sc. in Chemistry. She is the first student under my supervision to achieve this goal, and I hope it won’t be long until we get some more, although now the bar has been set quite high. For the time being, Maru is pursuing a career in the pharmaceutical industry but has every intention of coming back to the lab for her Masters degree; she has a reserved spot here with us at CCIQS.
Maru’s thesis deals mainly, but not exclusively, with calculating the interaction energies of calix- and thia-calix[n]arenes with the tyrosine kinase inhibitor Imatinib, which is widely used in the treatment of Chronic Myeloid Leukemia (CML), in order to rationally design a drug delivery agent for this drug. Her work is (a huge) part of an article currently under revision that I only wish had been published before her defense. Still, we await for that paper to be published in the next few weeks.
Throughout her stay at our lab, Maru was a dedicated student willing to learn new skills every time. As she replied today to one of the questions: “it’s not so much how many calculations I got right, but how many I got wrong!“. I find deep meaning in this sentence, perhaps deep enough as to consider it an aphorism, because indeed the more we try the more we fail, and the more we fail the more we learn and the closer we get to success.
Congratulations, Maru! I personally thank you for all the hard work invested in your thesis, all the long hours in front of the computer and your disposition to learn and work during the last 1.5 years. I’m certain you’ll find success in any venture you undertake; and I’m certain of it because you never stop trying.
It’s been a long time since I last posted something and so many things have happened in our research group! I should catch up with them in short but times have just been quite hectic.
Here is a contribution from Igor Marques at the University of Aveiro in Portugal (Group Website); the original text can be found as a comment in the original NBO Visualization post but it is pretty much the same thing you can find in this post. Here is a link to Chemcraft’s website. Thanks for sharing this, Igor!
=> Examples provided by Igor Marques used Chemcraft Version 1.7, build 365 <=
In the Gaussian input, with the NBORead option included under the population keyword, we should include the PLOT option as illustrated below. The gfoldprint keyword will print the basis set to the output file in the old G03 format. Some visualization programs require a certain format of the basis set to be printed to the output file in order to plot orbitals and other surfaces like the electron density; therefore, if you want to play safe, use gfoldprint, gfprint and gfinput in the same line. gfprint will print the basis set as a list but in the new G09 format, whereas gfinput will print the basis set using Gaussian’s own input format. (The used level of theory and number of shared processors are shown as illustrations only; also the Opt keyword is not fundamental to the visualization of the NBO’s)
%chk=filename.chk %nprocshared=8 #P b3lyp/6-311++g** Opt pop=(full,nboread) gfoldprint filename 0 1 molecular coordinates $NBO BNDIDX PLOT $END
this will generate files from *.31 to *.41
For the visualization of NBOs, you’ll need FILE.31 and FILE.37. Open FILE.31 from chemcraft. It will automatically detect FILE.37 (if in the same directory).
Tools > Orbitals > Render molecular orbitals
select the NBOs of interest (whcih are in the same order of the output),
Adjust settings > OK
On the left side of the window, select the NBO of interest and then click on ‘show isosurface’. Adjust the remaining settings. To represent another orbital, click on ‘keep this surface’ and then select another orbital from the rendered set and follow the previous steps.
> It’s possible to open a formated checkpoint file, containing the NBOs, in chemcraft.
%Chk=filename.chk %nprocshared=4 #P b3lyp/6-311++g** Opt pop=(full,nboread,savenbo) gfoldprint filename 0 1 molecular coordinates $NBO BNDIDX $END
the procedure is identical, but it is only necessary to read the *fchk file and then render the desired orbitals.
However, two problems might arise:
a) Orbitals in the checkpoint are reordered, thus requiring some careful inspection of the output.
b) Sometimes, for a larger molecule, the checkpoint might not be properly saved and the Gaussian job (as previously reported – http://goo.gl/DrSgA ) will end with:
Failed in SchOr1 in NBStor.
Error termination via Lnk1e in /data/programs/g09/l607.exe at Wed Mar 6 15:27:33 2013.
As usual, thanks to all for reading/commenting/rating this and other posts in this blog!
I don’t know why I haven’t written about the Local Bond Order (LBO) before! And a few days ago when I thought about it my immediate reaction was to shy away from it since it would constitute a blatant self-promotion attempt; but hell! this is my blog! A place I’ve created for my blatant self-promotion! So without further ado, I hereby present to you one of my own original contributions to Theoretical Chemistry.
During the course of my graduate years I grew interested in weakly bonded inorganic systems, namely those with secondary interactions in bidentate ligands such as xanthates, dithiocarboxylates, dithiocarbamates and so on. Description of the resulting geometries around the central metallic atom involved the invocation of secondary interactions defined purely by geometrical parameters (Alcock, 1972) in which these were defined as present if the interatomic distance was longer than the sum of their covalent radii and yet smaller than the sum of their van der Waals radii. This definition is subject to a lot of constrictions such as the accuracy of the measurement, which in turn is related to the quality of the monocrystal used in the X-ray difraction experiment; the used definition of covalent radii (Pauling, Bondi, etc.); and most importantly, it doesn’t shed light on the roles of crystal packing, intermolecular contacts, and the energetics of the interaction.
This is why in 2004 we developed a simple yet useful definition of bond order which could account for a single molecule in vacuo the strength and relevance of the secondary interaction, relative to the well defined covalent bonds.
Barroso-Flores, J. et al. Journal of Organometallic Chemistry 689 (2004) 2096–2102 http://dx.doi.org/10.1016/j.jorganchem.2004.03.035,
Let a Molecular Orbital be defined as a wavefunction ψi which in turn may be constructed by a linear combination of Atomic Orbitals (or atom centered basis set functions) φj
We define ζLBO in the following way, where we explicitly take into account a doubly occupied orbital (hence the multiplication by 2) and therefore we are assuming a closed shell configuration in the Restricted formalism.
The summation is carried over all the orbitals which belong to atom A1 and those of atom A2.
Simplifying we yield,
where Sjk is the overlap integral for the φj and φk functions.
By summing over all i MOs we have accomplished with this definition to project all the MO’s onto the space of those functions centered on atoms A1 and A2. This definition is purely quantum mechanical in nature and is independent from any geometric requirement of such interacting atoms (i.e. interatomic distance) thus can be used as a complement to the internuclear distance argument to assess the interaction between them. This definition also results very simple and easy to calculate for all you need are the coefficients to the LCAO expansion and the respective overlap integrals.
Unfortunately, the Local Bond Order hasn’t found much echo, partly due to the fact that it is hidden in a missapropriate journal. I hope someone finds it interesting and useful; if so, don’t forget to cite it appropriately 😉
A new paper has been published and that is always good news. The paper entitled “Synthesis of new γ-lactones from preactivated monosubstituted pyrazines and TMS–ketene acetals” coauthored by Azucena Garduño-Alva, M. Carmen Ortega-Alfaro, José G. López-Cortés, Isabel Chávez, Joaquin Barroso-Flores, Rubén A. Toscano, Henri Rudler and Cecilio Álvarez-Toledano was a fruitful collaboration between several researchers from within the UNAM Institute of Chemistry and from other labs.
Therein, the lactone formation from pyrazines is analyzed, with some resulting orientations not quite in accordance with the common orientation patterns yield by electrondonor and electronwithrdawing groups. In order to assess the electronic structure of the intermediates and its influence on the resulting orientations, I performed a Fukui analysis based on the Natural Population formalism.
Azucena Garduño-Alva, M. Carmen Ortega-Alfaro, José G. López-Cortés, Isabel Chávez, Joaquin Barroso-Flores, Rubén A. Toscano, Henri Rudler, Cecilio Álvarez-Toledano
Canadian Journal of Chemistry, 2012, 90(5): 469-482, 10.1139/v2012-016
The Natural Bond Orbitals Deletion analysis provides an excellent approach to the assessment of bonding energy within a single molecular fragment or between many. It deletes specific elements of the Fock matrix (this means it sets their values to 0.000) and then re-diagonalizes it in order to find the difference in energy respect to the original matrix. About nine different kinds of deletions are available, which will be briefly summarized in the following section.
One of the main strengths of the NBO derived methods is their almost complete basis set independence, which allows us to obtain comparable numbers under different levels of theory.
Both G03 and G09 use the NBO3.1 program. The 5.0 version is sold separately by their creators, namely prof. Frank Weinhold, who can be contacted through their website. It’s not available for geometry optimizations (gradients),
some people insist on trying to get a different geometry by eliminating a certain interaction and that is just not possible directly with the NBODel method It is indeed possible to perform NBODeletions along with optimizations in G09 (Thanks to prof. Weinhold for his clarifying message) but there are some restrictions: molecular coordinates should be in Z-Matrix format and the number of variables to be optimized should not exceed 50; prof. Weinhold also recommends to use print=0 in the $NBO keylist in order to prevent the output files to become too big. Be sure to start with a proper geometry (close to the desired minimum) since given the nature of this analysis some apretiable geometry effects are to be expected.
The general syntax for its usage includes the string pop=NBODel in the route section of the GaussianX input file. Then, at the end of the file, the following is required:
--End of Input File-- --blank line-- $NBO $END $DEL Interactions to be deleted $END
ENTIRE BLOCKS OF ATOMS
In this kind of deletion one is able to delete all the elements between specific groups of atoms, as if their orbitals (and hence their common Fock elements) did not overlap.
--End of Input File-- --blank line-- $NBO $END $DEL ZERO 2 ATOM BLOCKS 2 BY 3 1 2 3 4 5 3 BY 2 3 4 5 1 2 $END --blank line--
The first line after $DEL indicates how many groups of atoms will be set to zero and the following lines indicate how many atoms belong to each group (i.e. the size of each block which in this case are 2 and 3, respectively). After this line the groups of atoms are listed, in this example all elements from atoms 1 and 2 with those of atoms 3, 4 and 5 will become zero. The next three lines are used for symmetry, so all the interactions from (1,2)->(3,4,5) are deleted along with (3,4,5)->(1,2)
DELETIONS BETWEEN ENTIRE MOLECULAR FRAGMENTS (Intermolecular deletions)
If we want to assess the interaction energy between two molecules, the previous method would consume a lot of time in declaring the size of each block with every atom of each molecule in it, plus there seems to be a limit to the size of the block. In this kind of deletion one is able to delete all the elements between two or more molecular fragments.
--End of Input File-- --blank line-- $NBO $END $DEL ZERO 2 DELOC FROM 1 TO 2 FROM 2 TO 1 $END --blank line--
The delocalizations can also be calculated only in one direction (FROM 1 to 2), in the case above both interactions 1->2 and 2->1 have been deleted. The input for a trimer in which all three fragments interacted with each other would look like this:
ZERO 6 DELOC FROM 1 TO 2 FROM 2 TO 1 FROM 2 TO 3 FROM 3 TO 2 FROM 1 TO 3 FROM 3 TO 1
In short, the number of bilateral delocalizations to be deleted is equal to twice the number of edges in a graph depicting the intermolecular interactions (A post on topology in chemistry is now due).
Reading the output file
Almost at the very end of the output file the following section can be found:
>>>>>>>>>> Convergence criterion not met.
SCF Done: E(RHF) = -4728.57245403 A.U. after 2 cycles
Convg = 0.2354D-03 -V/T = 2.0012
Energy of deletion : -4728.572454034
Total SCF energy : -4728.604640956
Energy change : 0.032187 a.u., 20.198 kcal/mol
The warning about the convergence can be disregarded without any concern about the accuracy of the outcome and it will show in every $DEL calculation. The SCF energy displayed in the second line is the energy corresponding to the modified Fock Matrix, which is the same as the one labeled as Energy of deletion. The Total SCF energy corresponds to the original Fock Matrix; the difference between them is labeled as Energy change and the value is reported in both atomic units as well as kcal/mol.
Some common errors and possible solutions
–> Sometimes you get the following error message at the beginning of the calculation making it crash:
** ERROR IN INITNF. NUMBER OF VARIABLES ( 57) **
** INCORRECT (SHOULD BE BETWEEN 1 AND 50) **
I have found that changing the molecule specification section from Z-matrix to Cartesian coordinates, or vice versa, overcomes this difficulty. Also, if the Opt keyword appears in the route section the previous message will be shown. Opt is
not available under the NBODel method (read the first paragraph for the proper correction).
–> Possible conflicts between NBODel and the usage of DFT methods:
In some revisions of Gaussian 03 there is a conflict when using NBODel and DFT methods. The IOp(5/48=10000) should be included to repair such conflict. This issue was solved in some revision of Gaussian 03 but I don’t know which, so try this if you have problems. Gaussian09 has taken care of the issue although still the usage of DFT to obtain NBODel calculations is not advised.
–> The following error is not self-explanatory:
Error termination via Lnk1e in ‘/../../path’
This particular error arises from the absence of the ‘$NBO $END’ line before the $DEL instruction. The previous line may or may not include additional keywords. If you are interested in computing some kind of deletion energy just leave the line as presented above in all previous examples. My guess is that the $DEL instruction does not calculate the corresponding NBO’s from which to make the deletion but it rather takes all the results from the $NBO instruction and works from there. Bottom line: don’t forget this line!
As with other posts tagged as ‘white papers’, this one will be updated and expanded every time new information is found. In the mean time, thanks to everyone for reading, commenting and rating, this keeps me going with the blog. Have you encountered problems with NBODel methods? share your experiences and solutions with the rest in the comments section.
Have a nice day!