Monthly Archives: December 2020
The war against COVID-19 has been waged in many fronts. The computational chemistry community has done their share during this pandemic to put forward a cure, a vaccine, or a better understanding of the molecular mechanisms behind the human infection by the SARS-CoV-2 virus. As few vaccines show currently their heads and start making their way around the globe to stop the spreading, amidst a climate of disinformation, distrust and political upheaval, all of which pose several challenges yet to be faced aside from the technical and scientific ones.
This is by no means a comprehensive review of the literature, in fact, most of the cited literature herein was observed in Twitter under the #CompChem and #COVID combined hashtags; Summarizing the research by the CompChem community on COVID-19 related topics in a single blog-post would be near to impossible—I trust a book is being written on it as I type these lines.
The structural elucidation of the proteins associated to the SARS-CoV-2 virus is probably the first step required in designing chemical compounds capable of modifying their functions and altering their life-cycle without altering the biochemistry of the hosts. The Coronavirus Structural Taskforce has elucidated the structure of 28 proteins of SARS-CoV-2 aside from the 300+ proteins from the previous SARS-CoV virus using the tools from the FoldIt at home game based on the Rosetta program to heuristically predict the structure of these proteins. Structure based drug design rely on the knowledge of the structure of the active site (hence the name), but in the case of newly discovered proteins for which homology modeling is not entirely feasible, a ligand-based approach named D3Similarity was developed early in the pandemic for identifying the possible active sites by the group of Prof. Zhijian Xu. Mapping of the of the viral genome and proteome was also achieved early on during the first dates of lockdown in the American continent. The information was readily made available and usable for further studies which prompts another challenge: the rapid dissemination, review and evaluation of information to make scientifically sound claims and make data-based decisions. In this regard, the role of preprints cannot be stressed enough. Without a rapid communication, scientific results cannot generate a much needed critical mass to turn all these data into knowledge. As evidenced by the vast majority of the links present in this post, ChemRXiv from the ACS served the much needed function to gather, link and put the data for scientific evaluation out there in order to accelerate the discovery of solutions to the various steps of the virus’ reproductive cycle through various strategies.
The role of supercomputing has been paramount worldwide to the various efforts made in CompChem (read the C&EN piece) in various fronts from structural elucidation, such as the AI driven structural modelling of spike proteins and their infection mechanism led by Prof. Rommie Amaro (UCSD) and Dr. Arvind Ramanathan which was celebrated by the Bell Prize, to development of vaccines. Many Molecular Dynamics simulations have been performed on potential inhibitors of proteins such as the spike protein, in some cases these simulations coupled with cryo-EM microscopy allowed for the elucidation of the hinging mechanism of these spike proteins, their thermodynamic properties, and all atoms-simulations assessed the rigidity of the receptor as the cause of its infectivity. Still, owning these computing resources isn’t always cost effective; that’s why there have been outsourced to companies such as Amazon web services as Pearlman did for the QM/DFT calculations of the binding energy of several drug candidates for the inhibition of the virus’ main protease (MPro). Many other CADD studies are available (here, here, and here). Researchers from all around the world can chip in and join the effort by reaching out to the COVID-19 High Performance Computing Consortium (HPC) which brings together some of the most advanced computing systems to the hands of private and academic researchers with relevant projects aimed to the study of the virus. On the other side of the Atlantic, the Partnership for Advanced Computing in Europe (PRACE) also provides access to advanced computing services for research. As an effort to keep all the developing information curated and concentrated, the COVID-19 Molecular Structure and Therapeutics Hub was created to provide a community-driven data repository and curation service for molecular structures, models, therapeutics, and simulations related to computational research related to therapeutic opportunities.
As described above, molecular dynamics simulations are capital in the assessment of how drugs interact with proteins. But molecular dynamics can only do so much as they’re computing intensive so, the use of Polarizable Force Fields (PFF) algorithms to obtain results in the microseconds regime with high-resolution sampling methods which have been applied also to the modeling of the MPro protein; the phase space is sampled by different MD trajectories which are then tested and selected. Aside from classical simulations, artificial intelligence predictions and docking calculations, also quantum mechanical calculations have been employed in the search for the most intimate interactions governing the mechanisms of inhibition of proteins. In this front, a Fragment Molecular Orbital based analysis was carried out to find which residues in MPro interacted the most with a given inhibitor.
Virtual screening is at the heart of the computationally aided drug discovery process, specially high-throughput virtual screening such as the one performed by the group of Andre Fischer at Basel, in which 11 potential drugs were narrowed from a pool of over 600 million compounds that were analyzed as potential protease inhibitors. Repurposing of antiviral drugs, and other entry-inhibiting compounds, is also a major avenue explored in the search for treatments; in the linked study by Shailly Tomar et al. antiviral drugs which are also anti inflammatory are believed to take care of lung inflammation and injury associated to the infection at the same time they tend to disrupt the virus’ infection mechanism. The comeback of Virtual Reality can make virtual screening more cooperative even during lockdown conditions and more ‘tangible’ as the company Nanome has proven with their COVID-19 Town Hall meetings which aim to the modeling of proteins in 3D space. Aside from the de novo and repurposing efforts, the search for peptides against infection by SARS-CoV-2 was an important topic (here and here). More recently, Skariyachan and Gopal turn to natural products from herbal origins for their virtual screening (molecular docking and dynamics). In their perspective the chemical complexity achieved through biosynthesis can overcome the bottleneck of chemical discovery while at the same time turning to the ancient practices of herbal remedies described in Ayurveda. Other researchers like Manish Manish have also turned to libraries of 500,000+ natural compounds to find potential drugs for MPro.
The year is coming to an end but not the pandemic in any way. Now, with the advent of new strains, and the widespread vaccination effort put in place, it is more important than ever to keep the fight strong in our labs but also in our personal habits and responsibilities—the same advices that were given at the beginning of the year are still in effect today and will continue to be for the months to come. I want to wish everyone who reads this a happy holiday season, but above all I want to pay a small tribute to the scientists working relentlessly in one of the largest coordinated scientific efforts in modern history, one that can only be compared to the Moon landing or the Manhattan Project; to those scientists and all the healthcare personnel, may you find rest soon, may your efforts never go unnoticed: Thank you for your service.
Molecular Orbitals (MOs) are linear combinations of Atomic Orbitals (AOs), which in turn are linear combinations of other functions called ‘basis functions’. A basis, or more accurately a basis set, is a collection of functions which obey a set of rules (such as being orthogonal to each other and possibly being normalized) with which all AOs are constructed, and although these are centered on each atomic nucleus, the canonical way in which they are combined yield delocalized MOs; in other words, an MO can occupy a large space spanning several atoms at once. We don’t mind this expansion across a molecule, but what about between two molecules? Calculating the interaction energy between two or more molecular fragments leads to an artificial extra–stabilization term that stems from the fact that electrons in molecule 1 can occupy AO’s (or the basis functions which form them) centered on atoms from molecule 2.
Fundamentally, the interaction energy of any A—B dimer, Eint, is calculated as the energy difference between the dimer and the separately calculated energies for each component (Equation 1).
Eint = EAB – EA – EB (1)
However the calculation of Eint by this method is highly sensitive to the choice of basis set due to the Basis Set Superposition Error (BSSE) described in the first paragraph. The BSSE is particularly troublesome when small basis sets are used, due to the poor description of dispersion interactions but treating this error by just choosing a larger basis set is seldom useful for systems of considerable sizes. The Counterpoise method is a nifty correction to equation 1, in which EA and EB are calculated with the basis set of A and B respectively, i.e., only in EAB a larger basis set (that of A and B simultaneously) is used. The Counterpoise method calculates each component with the AB basis set (Equation 2)
EintCP = EABAB – EAAB– EBAB (2)
where the superscript AB means the whole basis set is used. This is accomplished by using ‘ghost‘ atoms with no nuclei and no electrons but empty basis set functions centered on them.
In Gaussian, BSSE is calculated with the Counterpoise method developed by Boys and Simon. It requires the keyword Counterpoise=N where N is the number of fragments to be considered (for an A—B system, N=2). Each atom in the coordinates list must be specified to which fragment pertains; additionally, the charge and multiplicity for each fragment and the whole supermolecular ensemble must be specified. Follow the example of this hydrogen fluoride dimer.
%chk=HF2.chk #P opt wB97XD/6-31G(d,p) Counterpoise=2 HF dimer 0,1 0,1 0,1 H(Fragment=1) 0.00 0.00 0.00 F(Fragment=1) 0.00 0.00 0.70 H(Fragment=2) 0.00 0.00 1.00 F(Fragment=2) 0.00 0.00 1.70
For closed shell fragments the first line is straightforward but one must pay attention that the first pair of numbers in the charge multiplicity line correspond to the whole ensemble, whereas the folowing pairs correspond to each fragment in consecutive order. Fragments do not need to be specified contiguously, i.e., you don’t need to define all atoms for fragment 1 and after those the atoms for fragment 2, etc. They could be mixed and the program still assigns them correctly. Just as an example I typed wB97XD but any other method, DFT or ab initio, may be used; only semiempirical methods do not admit a BSSE calculation because they don’t make use of a basis set in the first place!
The output provides the corrected energy (in atomic units) for the whole system, as well as the BSSE correction (which added to the previous term yields the un-corrected energy of the system). Gaussian16 also provides these values in kcal/mol as ‘Complexation energies’ first raw (uncorrected) and then the corrected energy.
BSSE is always present and cannot be entirely eliminated because of the use of finite basis sets but it can be correctly dealt with if the Counterpoise method is included.