The Effective Fragment Molecular Orbital Method for Fragments Connected by Covalent Bonds

We extend the effective fragment molecular orbital method (EFMO) into treating fragments connected by covalent bonds. The accuracy of EFMO is compared to FMO and conventional ab initio electronic structure methods for polypeptides including proteins. Errors in energy for RHF and MP2 are within 2 kcal/mol for neutral polypeptides and 6 kcal/mol for charged polypeptides similar to FMO but obtained two to five times faster. For proteins, the errors are also within a few kcal/mol of the FMO results. We developed both the RHF and MP2 gradient for EFMO. Compared to ab initio, the EFMO optimized structures had an RMSD of 0.40 and 0.44 {\AA} for RHF and MP2, respectively.Comment: Revised manuscrip

Characterizing Rhodopsin-Arrestin Interactions with the Fragment Molecular Orbital (FMO) Method

Arrestin binding to G protein-coupled receptors (GPCRs) plays a vital role in receptor signaling. Recently, the crystal structure of rhodopsin bound to activated visual arrestin was resolved using XFEL (X-ray free electron laser). However, even with the crystal structure in hand, our ability to understand GPCR-arrestin binding is limited by the availability of accurate tools to explore receptor-arrestin interactions. We applied fragment molecular orbital (FMO) method to explore the interactions formed between the residues of rhodopsin and arrestin. FMO enables ab initio approaches to be applied to systems that conventional quantum mechanical (QM) methods would be too compute-expensive. The FMO calculations detected 35 significant interactions involved in rhodopsin-arrestin binding formed by 25 residues of rhodopsin and 28 residues of arrestin. Two major regions of interaction were identified: at the C-terminal tail of rhodopsin (D330-S343) and where the "finger loop" (G69-T79) of arrestin directly inserts into rhodopsin active core. Out of these 35 interactions, 23 were mainly electrostatic and 12 hydrophobic in nature

Speed and accuracy: Having your cake and eating it too

Since the first ab initio methods were developed, the ultimate goal of quantum chemistry has been to provide insights, not readily accessible through experiment, into chemical phenomena. Over the years, two different paths to this end have been taken. The first path provides as accurate a description of relatively small systems as modern computer hardware will allow. The second path follows the desire to perform simulations on systems of physically relevant sizes while sacrificing a certain level of accuracy. The merging of these two paths has allowed for the accurate modeling of large molecular systems through the use of novel theoretical methods. The largest barrier to achieving the goal of accurate calculations on large systems has been the computational requirements of many modern theoretical methods. While these methods are capable of providing the desired level of accuracy, the prohibitive computational requirements can limit system sizes to tens of atoms. By decomposing large chemical systems into more computationally tractable pieces, fragmentation methods have the capability to reduce this barrier and allow for highly accurate descriptions of large molecular systems such as proteins, bulk phase solutions and polymers and nano-scale systems

Quantum chemical studies of light harvesting

The design of optimal light-harvesting (supra)molecular systems and materials is one of the most challenging frontiers of science. Theoretical methods and computational models play a fundamental role in this difficult task, as they allow the establishment of structural blueprints inspired by natural photosynthetic organisms that can be applied to the design of novel artificial light-harvesting devices. Among theoretical strategies, the application of quantum chemical tools represents an important reality that has already reached an evident degree of maturity, although it still has to show its real potentials. This Review presents an overview of the state of the art of this strategy, showing the actual fields of applicability but also indicating its current limitations, which need to be solved in future developments

Fragmentation Methods: A Route to Accurate Calculations on Large Systems

Theoretical chemists have always strived to perform quantum mechanics (QM) calculations on larger and larger molecules and molecular systems, as well as condensed phase species, that are frequently much larger than the current state-of-the-art would suggest is possible. The desire to study species (with acceptable accuracy) that are larger than appears to be feasible has naturally led to the development of novel methods, including semiempirical approaches, reduced scaling methods, and fragmentation methods. The focus of the present review is on fragmentation methods, in which a large molecule or molecular system is made more computationally tractable by explicitly considering only one part (fragment) of the whole in any particular calculation. If one can divide a species of interest into fragments, employ some level of ab initio QM to calculate the wave function, energy, and properties of each fragment, and then combine the results from the fragment calculations to predict the same properties for the whole, the possibility exists that the accuracy of the outcome can approach that which would be obtained from a full (nonfragmented) calculation. It is this goal that drives the development of fragmentation methods

Characterizing Protein-Protein Interactions with the Fragment Molecular Orbital Method

Proteins are vital components of living systems, serving as building blocks, molecular machines, enzymes, receptors, ion channels, sensors, and transporters. Protein-protein interactions (PPIs) are a key part of their function. There are more than 645,000 reported disease-relevant PPIs in the human interactome, but drugs have been developed for only 2% of these targets. The advances in PPI-focused drug discovery are highly dependent on the availability of structural data and accurate computational tools for analysis of this data. Quantum mechanical approaches are often too expensive computationally, but the fragment molecular orbital (FMO) method offers an excellent solution that combines accuracy, speed and the ability to reveal key interactions that would otherwise be hard to detect. FMO provides essential information for PPI drug discovery, namely, identification of key interactions formed between residues of two proteins, including their strength (in kcal/mol) and their chemical nature (electrostatic or hydrophobic). In this chapter, we have demonstrated how three different FMO-based approaches (pair interaction energy analysis (PIE analysis), subsystem analysis (SA) and analysis of protein residue networks (PRNs)) have been applied to study PPI in three protein-protein complexes

A needed response: Fragment molecular orbital analytic gradients

Ab initio quantum chemistry seeks to describe and elucidate chemical species and processes using quantum mechanics. For dynamical chemical processes, molecular dynamics (MD), where the atoms of a chemical system move according to Newton\u27s laws of motion, is frequently used. MD calculations have historically used classical mechanics rather than quantum mechanics to describe the evolution of a chemical system. The use of classical mechanics with MD has proven to be a great success, but classical MD has deficiencies, since quantum mechanics must be used to describe important chemical phenomena such as bond breaking or excited states accurately. With the increase of computer power over the past half-century, ab initio MD (AIMD) methods that describe a chemical system using quantum mechanics have been developed to eliminate the deficiencies of classical MD. Unfortunately, the application of AIMD is limited to small systems and short time scales since standard quantum chemical methods exhibit non-linear scaling with system size. More recently, new approaches have circumvented the non-linear scaling of quantum chemical methods by exploiting the fact that most chemical interactions are local and therefore distant interactions can be approximated or even ignored. Other methods obtain quantum mechanical accuracy at a cost associated with classical mechanics by deriving a classical force field directly from ab initio calculations. Individually and in combination, methods that eliminate the non-linear scaling of standard ab initio methods have the potential to extend the reach of AIMD to larger systems such as surfaces, molecular clusters, bulk liquids, and proteins