Attracting cavities for docking. Replacing the rough energy landscape of the protein by a smooth attracting landscape.

Molecular docking is a computational approach for predicting the most probable position of ligands in the binding sites of macromolecules and constitutes the cornerstone of structure-based computer-aided drug design. Here, we present a new algorithm called Attracting Cavities that allows molecular docking to be performed by simple energy minimizations only. The approach consists in transiently replacing the rough potential energy hypersurface of the protein by a smooth attracting potential driving the ligands into protein cavities. The actual protein energy landscape is reintroduced in a second step to refine the ligand position. The scoring function of Attracting Cavities is based on the CHARMM force field and the FACTS solvation model. The approach was tested on the 85 experimental ligand-protein structures included in the Astex diverse set and achieved a success rate of 80% in reproducing the experimental binding mode starting from a completely randomized ligand conformer. The algorithm thus compares favorably with current state-of-the-art docking programs

Synthesis and characterization of Polyindole and its catalytic performance study as a heterogeneous catalyst

The catalytic performance study of polyindole as a heterogeneous catalyst is reported for the synthesis of 3,3'-arylmethylene-bis-1H-Indole derivatives using various substituted aldehydes and indole under reflux reaction condition with good to excellent yield. Polyindole was synthesized by chemical oxidative polymerization using citric acid as a dopant. The synthesized polymer was well characterized by various spectroscopic techniques like FT-IR, XRD, FESEM, etc. The XRD pattern confirms the partially crystalline nature of polyindole. The FESEM images of polyindole revealed the formation of irregularly shaped particulate nature with size in the range of 0.2 to 6 micron. In FT-IR spectrum, the major peak at similar to 3400 cm(-1) indicates N-H stretching and at 1564-1624 cm(-1) indicates C-C stretching of benzenoid ring of indole. The presence of peak at similar to 3400 cm(-1) indicates that the polymerization does not occur at nitrogen. The present protocol has certain advantages like recyclability, low loading of the catalyst, low-cost and efficient use of polyindole as a heterogeneous catalyst

Computational Treatment of Metalloproteins

Metalloproteins present a considerable challenge for modeling, especially when the starting point is far from thermodynamic equilibrium. Examples include formidable problems such as metalloprotein folding and structure prediction upon metal addition, removal, or even just replacement; metalloenzyme design, where stabilization of a transition state of the catalyzed reaction in the specific binding pocket around the metal needs to be achieved; docking to metal-containing sites and design of metalloenzyme inhibitors. Even more conservative computations, such as elucidations of the mechanisms and energetics of the reaction catalyzed by natural metalloenzymes, are often nontrivial. The reason is the vast span of time and length scales over which these proteins operate, and thus the resultant difficulties in estimating their energies and free energies. It is required to perform extensive sampling, properly treat the electronic structure of the bound metal or metals, and seamlessly merge the required techniques to assess energies and entropies, or their changes, for the entire system. Additionally, the machinery needs to be computationally affordable. Although a great advancement has been made over the years, including some of the seminal works resulting in the 2013 Nobel Prize in chemistry, many aforementioned exciting applications remain far from reach. We review the methodology on the forefront of the field, including several promising methods developed in our lab that bring us closer to the desired modern goals. We further highlight their performance by a few examples of applications

DEVELOPMENT AND APPLICATION OF A QM/MM DOCKING ALGORITHM

Molecular docking has become a routine application in drug design projects, when structural information about the target is available. Its task is to predict the binding mode of a ligand within the binding site of a macromolecule, usually an enzyme or a receptor. Docking algorithms are composed of two main components: I ) pose generation and 2) scoring. The first component ailns to generate different ligand poses on the surface of the protein, while the goal of the second component is to score and to rank the generated ligand poses in order to identify the native binding mode as its global minimum. Many docking programs have been developed, but their accuracy is limited due to difficulties in correctly treating protein flexibility, polarization, charge transfer and covalent interactions, for example with transition metal ions. To address the challenges of treating polarization and covalent interactions in docking, we developed a hybrid quantum mechanical/molecular mechanical (QM/MM) scoring fonction based on the semiempirical self-consistent charge density functional tight-binding (SCC-DFTB) method and the CHARMM force field. To benchmark this scoring function, we created a publicly available data set of high­ quality X-ray structures of zinc metalloproteins. For the zinc-binding data set, QMIMM scoring yielded a significantly improved success rate compared to the classical scoring fonction (77.0% vs 61.5%), while for zinc non-binding data set success rate remained constant (49 .1%). Compared to widely use docking programs AutoDock and AutoDock Vina, our QMIMM scoring scheme performed significantly better and also improved the prediction of correct zinc binding geometries. In the following, we implemented this scoring function into our in-house docking code Attracting Cavities to perform on-the-fly QM/MM docking. We tested this implementation on our previously developed zinc dataset, the Astex diverse dataset and a newly developed challenging set of 87 heme proteins. We found an improved success rate for the zinc-binding dataset using the QM/MM on-the-fly docking algorithm (65.5% vs 55.7%). On the Astex dataset, the QM/MM success rate remained close to the classical success rate (71.8% vs 75.3 %). For the heme-binding dataset, the improvement due to the QM/MM treatment is even more striking (success rate of 62.2% vs 18.9%) than for the zinc metalloproteins. These results demonstrate that our QM/MM approach yields improved results for metalloproteins, which pose a severe challenge to classical docking codes due to strong polarization or the formation of bonds of covalent nature . On the other hand, for the common protein/ligand complexes present in the Astex dataset, the QM/MM docking provides results on par with the classical force-field approach. With the current work we bring docking to a next level by introducing the accuracy of QM-derived interactions in a fully automatized docking process. This enhanced accuracy will be used for real-life drug design applications. -- Le docking de molécules est devenu une tâche usuelle pour les projets de chimie medicinale, lorsque des informations sur la structure de la cible therapeutique sont disponibles. Son objectif principal est la prédiction du mode de liaison d'un ligand à l'interieur d'une macro-structure (protéine). Les algorithmes de docking sont composés de deux parties principales: 1) la génération de poses et 2) le scoring de ces dernières. La première partie a pour but de générer différentes conformations structurelles de ligands à la surface de la protéine alors que la deuxième joint une valeur physico-chimique permettant de classer et de minimiser les poses générées dans la première partie. De nom breux programmes de docking ont été développés, mais leur précision reste limitée dû aux difficultés de traiter correctement différentes propriétés, telles que la fléxibilité de la protéine, la polarisation, le transfert de charges et les interactions covalentes (ex : les métaux de transition). Afin de répondre au défi que pose la polarisation et les interactions covalentes lors d'un docking, une fonction de score a été développée. Celle-ci est un hybride entre méchanique quantique et méchanique moléculaire (QMIMM) basée sur la méthode fonctionelle semi-empirique SCC-DFTB et le champ de force de méchanique moleculaire classique (CHARMM). Afin d'évaluer notre function de score, un ensemble de structures moléculaires contenant du zinc et provenant d'images déduite par rayons X de hautes resolution fut créé, testé et mis à disposition du public. Pour cet ensemble, le score donné par la méthode QM/MM améliore de manière significative le taux de réussite du docking en comparaison avec la function classique de score (77.0% contre 61.5%), alors que pour l'ensemble de données avec un zinc allosterique, le taux de succès est resté constant (49.1%). En comparaison avec les programmes de docking AutoDock et AutoDock Vina, notre fonction de score QMIMM fournit de meilleurs résultats et trouve des positions acceptable de liaisons entre un ligand et le ion zinc. Par la suite, cette fonction de score a été incorporée au programme (Attracting Cavities) précédemment développé dans le groupe de Molecular Modeling du SIB, afin d'effectuer des docking avec évalutation QMIMM à la volée. Nous avons testé cette implémentation sur différents ensembles de donnée : notre ensemble contenant du zinc, l'ensemble diversifié d'Astex ainsi qu'un nouvel ensemble de 87 protéines contenant une hème structure. Le taux de réussite du docking pour l'ensemble contenant du zinc en utilisant notre fonction QM!MM est meilleur qu'avec la fonction classique (65.5% contre 55.7%). Sur l'ensemble d'Astex, le taux de réussite est proche du taux avec la fonction classique (71.8% contre 75.3 %). Pour l'ensemble contenant des hèmes, le gain en utilisant notre fonction QMIMM est encore plus évident (62.2% contre 18.9%). Ces résultats mettent en évidence que notre approche donne de meilleurs résultats pour les protéines contenant des atomes métalliques, qui sont un véritable défi pour les méthodes de docking classiques à cause de leur forte polarisation et de la potentielle formation de liaisons covalentes. Néanmoins, pour les complexes ligand / protéine standards présent dans Astex, l'approche QMIMM donne des résultats équivalents aux méthodes classiques utilisant un champ de force moléculaire. Avec ce travail, nous améliorons distinctement le docking en introduisant la précision des interactions dérivées de la méchanique quantique dans un processus de docking automatisé. Cette précision améliorée peut alors être utilisée pour de la recherche médicale

On-the-Fly QM/MM Docking with Attracting Cavities.

We developed a hybrid quantum mechanical/molecular mechanical (QM/MM) on-the-fly docking algorithm to address the challenges of treating polarization and selected metal interactions in docking. The algorithm is based on our classical docking algorithm Attracting Cavities and relies on the semiempirical self-consistent charge density functional tight-binding (SCC-DFTB) method and the CHARMM force field. We benchmarked the performance of this approach on three very diverse data sets: (1) the Astex Diverse set of 85 common noncovalent drug/target complexes formed both by hydrophobic and electrostatic interactions; (2) a zinc metalloprotein data set of 281 complexes, where polarization is strong and ligand/protein interactions are dominated by electrostatic interactions; and (3) a heme protein data set of 72 complexes, where ligand/protein interactions are dominated by covalent ligand/iron binding. Redocking performance of the on-the-fly QM/MM docking algorithm was compared to the performance of classical Attracting Cavities, AutoDock, AutoDock Vina, and GOLD. The results demonstrate that the QM/MM code preserves the high accuracy of most classical scores on the Astex Diverse set, while it yields significant improvements on both sets of metalloproteins at moderate computational cost

Toward on-the-fly quantum mechanical/molecular mechanical (QM/MM) docking: development and benchmark of a scoring function.

We address the challenges of treating polarization and covalent interactions in docking by developing a hybrid quantum mechanical/molecular mechanical (QM/MM) scoring function based on the semiempirical self-consistent charge density functional tight-binding (SCC-DFTB) method and the CHARMM force field. To benchmark this scoring function within the EADock DSS docking algorithm, we created a publicly available dataset of high-quality X-ray structures of zinc metalloproteins ( http://www.molecular-modelling.ch/resources.php ). For zinc-bound ligands (226 complexes), the QM/MM scoring yielded a substantially improved success rate compared to the classical scoring function (77.0% vs 61.5%), while, for allosteric ligands (55 complexes), the success rate remained constant (49.1%). The QM/MM scoring significantly improved the detection of correct zinc-binding geometries and improved the docking success rate by more than 20% for several important drug targets. The performance of both the classical and the QM/MM scoring functions compare favorably to the performance of AutoDock4, AutoDock4Zn, and AutoDock Vina

Corey–Itsuno Reduction of Ketones: A Development of Safe and Inexpensive Process for Synthesis of Some API Intermediates

A safe and inexpensive procedure for asymmetric reduction of ketones using in situ prepared <i>N</i>,<i>N</i>-diethylaniline borane (DEANB) and oxazaborolidine catalyst from sodium borohydride, <i>N</i>,<i>N</i>-diethylaniline hydrochloride and (<i>S</i>)-α,α-diphenylprolinol is described. This protocol is demonstrated successfully to manufacture enantiopure dapoxetine at the plant scale