Mapping Enzymatic Catalysis using the Effective Fragment Molecular Orbital Method: Towards all ab initio Biochemistry

We extend the Effective Fragment Molecular Orbital (EFMO) method to the frozen domain approach where only the geometry of an active part is optimized, while the many-body polarization effects are considered for the whole system. The new approach efficiently mapped out the entire reaction path of chorismate mutase in less than four days using 80 cores on 20 nodes, where the whole system containing 2398 atoms is treated in the ab initio fashion without using any force fields. The reaction path is constructed automatically with the only assumption of defining the reaction coordinate a priori. We determine the reaction barrier of chorismate mutase to be $18.3\pm 3.5$ kcal mol$^{-1}$ for MP2/cc-pVDZ and $19.3\pm 3.6$ for MP2/cc-pVTZ in an ONIOM approach using EFMO-RHF/6-31G(d) for the high and low layers, respectively.Comment: SI not attache

Extending fragment-based free energy calculations with library Monte Carlo simulation: Annealing in interaction space

Pre-calculated libraries of molecular fragment configurations have previously been used as a basis for both equilibrium sampling (via "library-based Monte Carlo") and for obtaining absolute free energies using a polymer-growth formalism. Here, we combine the two approaches to extend the size of systems for which free energies can be calculated. We study a series of all-atom poly-alanine systems in a simple dielectric "solvent" and find that precise free energies can be obtained rapidly. For instance, for 12 residues, less than an hour of single-processor is required. The combined approach is formally equivalent to the "annealed importance sampling" algorithm; instead of annealing by decreasing temperature, however, interactions among fragments are gradually added as the molecule is "grown." We discuss implications for future binding affinity calculations in which a ligand is grown into a binding site

Novel algorithms and high-performance cloud computing enable efficient fully quantum mechanical protein-ligand scoring

Ranking the binding of small molecules to protein receptors through physics-based computation remains challenging. Though inroads have been made using free energy methods, these fail when the underlying classical mechanical force fields are insufficient. In principle, a more accurate approach is provided by quantum mechanical density functional theory (DFT) scoring, but even with approximations, this has yet to become practical on drug discovery-relevant timescales and resources. Here, we describe how to overcome this barrier using algorithms for DFT calculations that scale on widely available cloud architectures, enabling full density functional theory, without approximations, to be applied to protein-ligand complexes with approximately 2500 atoms in tens of minutes. Applying this to a realistic example of 22 ligands binding to MCL1 reveals that density functional scoring outperforms classical free energy perturbation theory for this system. This raises the possibility of broadly applying fully quantum mechanical scoring to real-world drug discovery pipelines.Comment: 15 pages, 5 figures, 1 tabl

Novel algorithms and high-performance cloud computing enable efficient fully quantum mechanical protein-ligand scoring

Ranking the binding of small molecules to protein receptors through physics-based computation remains challenging. Though inroads have been made using free energy methods, these fail when the underlying classical mechanical force fields are insufficient. In principle, a more accurate approach is provided by quantum mechanical density functional theory (DFT) scoring, but even with approximations, this has yet to become practical on drug discovery-relevant timescales and resources. Here, we describe how to overcome this barrier using algorithms for DFT calculations that scale on widely available cloud architectures, enabling full density functional theory, without approximations, to be applied to protein-ligand complexes with approximately 2500 atoms in tens of minutes. Applying this to a realistic example of 22 ligands binding to MCL1 reveals that density functional scoring outperforms classical free energy perturbation theory for this system. This raises the possibility of broadly applying fully quantum mechanical scoring to real-world drug discovery pipelines

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

Computational structure‐based drug design: Predicting target flexibility

The role of molecular modeling in drug design has experienced a significant revamp in the last decade. The increase in computational resources and molecular models, along with software developments, is finally introducing a competitive advantage in early phases of drug discovery. Medium and small companies with strong focus on computational chemistry are being created, some of them having introduced important leads in drug design pipelines. An important source for this success is the extraordinary development of faster and more efficient techniques for describing flexibility in three‐dimensional structural molecular modeling. At different levels, from docking techniques to atomistic molecular dynamics, conformational sampling between receptor and drug results in improved predictions, such as screening enrichment, discovery of transient cavities, etc. In this review article we perform an extensive analysis of these modeling techniques, dividing them into high and low throughput, and emphasizing in their application to drug design studies. We finalize the review with a section describing our Monte Carlo method, PELE, recently highlighted as an outstanding advance in an international blind competition and industrial benchmarks.We acknowledge the BSC-CRG-IRB Joint Research Program in Computational Biology. This work was supported by a grant from the Spanish Government CTQ2016-79138-R.J.I. acknowledges support from SVP-2014-068797, awarded by the Spanish Government.Peer ReviewedPostprint (author's final draft

Ligand affinities estimated by quantum chemical calculations

We present quantum chemical estimates of ligand-binding affinities performed, for the first time, at a level of theory for which there is a hope that dispersion and polarization effects are properly accounted for (MP2/cc-pVTZ) and at the same time effects of solvation, entropy, and sampling are included. We have studied the binding of seven biotin analogues to the avidin tetramer. The calculations have been performed by the recently developed PMISP approach (polarizable multipole interactions with supermolecular pairs), which treats electrostatic interactions by multipoles up to quadrupoles, induction by anisotropic polarizabilities, and nonclassical interactions (dispersion, exchange repulsion, etc.) by explicit quantum chemical calculations, using a fragmentation approach, except for long-range interactions that are treated by standard molecular-mechanics Lennard-Jones terms. In order to include effects of sampling, 10 snapshots from a molecular dynamics simulation are studied for each biotin analogue. Solvation energies are estimated by the polarized continuum model (PCM), coupled to the multipole-polarizability model. Entropy effects are estimated from vibrational frequencies, calculated at the molecular mechanics level. We encounter several problems, not previously discussed, illustrating that we are first to apply such a method. For example, the PCM model is, in the present implementation, questionable for large molecules, owing to the use of a surface definition that gives numerous small cavities in a protein