The application of quantum mechanics in structure-based drug design

Electronic structure, dielectric response, and surface charge distribution of RGD (1FUV) peptide

Long and short range molecular interactions govern molecular recognition and self-assembly of biological macromolecules. Microscopic parameters in the theories of these molecular interactions are either phenomenological or need to be calculated within a microscopic theory. We report a unified methodology for the ab initio quantum mechanical (QM) calculation that yields all the microscopic parameters, namely the partial charges as well as the frequency-dependent dielectric response function, that can then be taken as input for macroscopic theories of electrostatic, polar, and van der Waals-London dispersion intermolecular forces. We apply this methodology to obtain the electronic structure of the cyclic tripeptide RGD-4C (1FUV). This ab initio unified methodology yields the relevant parameters entering the long range interactions of biological macromolecules, providing accurate data for the partial charge distribution and the frequency-dependent dielectric response function of this peptide. These microscopic parameters determine the range and strength of the intricate intermolecular interactions between potential docking sites of the RGD-4C ligand and its integrin receptor.

Potency prediction of β-secretase (BACE-1) inhibitors using density functional methods

Scoring potency is a main challenge for structure based drug design. Inductive effects of subtle variations in the ligand are not possible to accurately predict by classical computational chemistry methods. In this study, the problem of predicting potency of ligands with electronic variations participating in key interactions with the protein was addressed. The potency was predicted for a large set of cyclic amidine and guanidine cores extracted from β-secretase (BACE-1) inhibitors. All cores were of similar size and had equal interaction motifs but were diverse with respect to electronic substitutions. A density functional theory approach, in combination with a representation of the active site of a protein using only key residues, was shown to be predictive. This computational approach was used to guide and support drug design, within the time frame of a normal drug discovery design cycle.

Quantum mechanics-based scoring rationalizes the irreversible inactivation of parasitic Schistosoma mansoni cysteine peptidase by vinyl sulfone inhibitors

The quantum mechanics (QM)-based scoring function that we previously developed for the description of noncovalent binding in protein-ligand complexes has been modified and extended to treat covalent binding of inhibitory ligands. The enhancements are (i) the description of the covalent bond breakage and formation using hybrid QM/semiempirical QM (QM/SQM) restrained optimizations and (ii) the addition of the new ΔG(cov)' term to the noncovalent score, describing the "free" energy difference between the covalent and noncovalent complexes. This enhanced QM-based scoring function is applied to a series of 20 vinyl sulfone-based inhibitory compounds inactivating the cysteine peptidase cathepsin B1 of the Schistosoma mansoni parasite (SmCB1). The available X-ray structure of the SmCB1 in complex with a potent vinyl sulfone inhibitor K11017 is used as a template to build the other covalently bound complexes and to model the derived noncovalent complexes. We present the correlation of the covalent score and its constituents with the experimental binding data. Four outliers are identified. They contain bulky R1' substituents structurally divergent from the template, which might induce larger protein rearrangements than could be accurately modeled. In summary, we propose a new computational approach and an optimal protocol for the rapid evaluation and prospective design of covalent inhibitors with a conserved binding mode.

Antibody Drug Conjugate bioinformatics: drug delivery through the letterbox

Antibodies appear to be the first line of defence in the adaptive immune response of vertebrates and thereby are involved in a multitude of biochemical mechanisms, such as regulation of infection, autoimmunity, and cancer. It goes without saying that a full understanding of antibody function is required for the development of novel antibody-interacting drugs. These drugs are the Antibody Drug Conjugates (ADCs), which are a new type of targeted therapy, used for example for cancer. They consist of an antibody (or antibody fragment such as a single-chain variable fragment [scFv]) linked to a payload drug (often cytotoxic). Because of the targeting, the side effects should be lower and give a wider therapeutic window. Overall, the underlying principle of ADCs is to discern the delivery of a drug that is cytotoxic to a target that is cancerous, hoping to increase the antitumoural potency of the original drug by reducing adverse effects and side effects, such as toxicity of the cancer target. This is a pioneering field that employs state-of-the-art computational and molecular biology methods in the fight against cancer using ADCs.

Molecular modeling of vasopressin receptor and in silico screening of V1b receptor antagonists

INTRODUCTION

G protein-coupled receptors (GPCRs) are integral membrane proteins which contain seven-transmembrane-spanning alpha-helices. GPCR-mediated signaling has been associated with various human diseases, positioning GPCRs as attractive targets in the drug discovery field. Recently, through advances in protein engineering and crystallography, the number of resolved GPCR structures has increased dramatically. This growing availability of GPCR structures has greatly accelerated structure-based drug design (SBDD) and in silico screening for GPCR-targeted drug discovery.

AREAS COVERED

The authors introduce the current status of X-ray crystallography of GPCRs and what has been revealed from the resolved crystal structures. They also review the recent advances in SBDD and in silico screening for GPCR-targeted drug discovery and discuss a docking study, using homology modeling, with the discovery of potent antagonists of the vasopressin 1b receptor.

EXPERT OPINION

Several innovative protein engineering techniques and crystallographic methods have greatly accelerated SBDD, not only for already-resolved GPCRs but also for those structures which remain unclear. These technological advances are expected to enable the determination of GPCR-fragment complexes, making it practical to perform fragment-based drug discovery. This paves the way for a new era of GPCR-targeted drug discovery.

Molecular Modeling Studies of Piperidine Derivatives as New Acetylcholinesterase Inhibitors against Neurodegenerative Diseases

Neurodegenerative disorders are related to the progressive loss of structure or function and, eventually, death of neurons. These processes are responsible for diseases like Parkinson’s, Alzheimer’s, and Huntington’s, and the main molecular target for the drug design against these illnesses today is the enzyme acetylcholinesterase (AChE). Following this line, in the present work, we applied docking techniques to study some piperidine derivative inhibitors of AChE and further propose structures of six new AChE inhibitors as potential new drugs against neurodegenerative disorders. The best inhibitor proposed was submitted to additional molecular dynamics simulations steps.