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

Analyzing GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method

G-protein-coupled receptors (GPCRs) have enormous physiological and biomedical importance, and therefore it is not surprising that they are the targets of many prescribed drugs. Further progress in GPCR drug discovery is highly dependent on the availability of protein structural information. However, the ability of X-ray crystallography to guide the drug discovery process for GPCR targets is limited by the availability of accurate tools to explore receptor-ligand interactions. Visual inspection and molecular mechanics approaches cannot explain the full complexity of molecular interactions. Quantum mechanics (QM) approaches are often too computationally expensive to be of practical use in time-sensitive situations, 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. Integration of GPCR crystallography or homology modelling with FMO reveals atomistic details of the individual contributions of each residue and water molecule toward ligand binding, including an analysis of their chemical nature. Such information is essential for an efficient structure-based drug design (SBDD) process. In this chapter, we describe how to use FMO in the characterization of GPCR-ligand interactions

Molecular Modeling in Enzyme Design, Toward In Silico Guided Directed Evolution

Directed evolution (DE) creates diversity in subsequent rounds of mutagenesis in the quest of increased protein stability, substrate binding, and catalysis. Although this technique does not require any structural/mechanistic knowledge of the system, the frequency of improved mutations is usually low. For this reason, computational tools are increasingly used to focus the search in sequence space, enhancing the efficiency of laboratory evolution. In particular, molecular modeling methods provide a unique tool to grasp the sequence/structure/function relationship of the protein to evolve, with the only condition that a structural model is provided. With this book chapter, we tried to guide the reader through the state of the art of molecular modeling, discussing their strengths, limitations, and directions. In addition, we suggest a possible future template for in silico directed evolution where we underline two main points: a hierarchical computational protocol combining several different techniques and a synergic effort between simulations and experimental validation.Peer ReviewedPostprint (author's final draft

Fundamentals of drug design from a biophysical viewpoint

Drug design means many things to many people. Commercially the aim is the development of compounds that can be patented and meet a variety of regulatory standards. In drug design, for medical purposes, toxicity and bio-availability are major consideration

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

Discovery of a Non-Peptidic Inhibitor of West Nile Virus NS3 Protease by High-Throughput Docking

An estimated 2.5 billion people are at risk of diseases caused by dengue and West Nile virus. As of today, there are neither vaccines to prevent nor drugs to cure the severe infections caused by these viruses. The NS3 protease is one of the most promising targets for drug development against West Nile virus because it is an essential enzyme for viral replication and because success has been demonstrated with the closely related hepatitis C virus protease. We have discovered a small molecule that inhibits the NS3 protease of West Nile virus by computer-aided high-throughput docking, and validated it using three experimental techniques. The inhibitor has potential to be developed to a drug candidate to combat West Nile virus infections