Statistical deconvolution of enthalpic energetic contributions to MHC-peptide binding affinity

Background: MHC Class I molecules present antigenic peptides to cytotoxic T cells, which forms an integral part of the adaptive immune response. Peptides are bound within a groove formed by the MHC heavy chain. Previous approaches to MHC Class I-peptide binding prediction have largely concentrated on the peptide anchor residues located at the P2 and C-terminus positions. Results: A large dataset comprising MHC-peptide structural complexes was created by re-modelling pre-determined x-ray crystallographic structures. Static energetic analysis, following energy minimisation, was performed on the dataset in order to characterise interactions between bound peptides and the MHC Class I molecule, partitioning the interactions within the groove into van der Waals, electrostatic and total non-bonded energy contributions. Conclusion: The QSAR techniques of Genetic Function Approximation (GFA) and Genetic Partial Least Squares (G/PLS) algorithms were used to identify key interactions between the two molecules by comparing the calculated energy values with experimentally-determined BL50 data. Although the peptide termini binding interactions help ensure the stability of the MHC Class I-peptide complex, the central region of the peptide is also important in defining the specificity of the interaction. As thermodynamic studies indicate that peptide association and dissociation may be driven entropically, it may be necessary to incorporate entropic contributions into future calculations

N-methyl-N-((1-methyl-5-(3-(1-(2-methylbenzyl)piperidin-4-yl)propoxy)-1H-indol-2-yl)methyl)prop-2-yn-1-amine, a new cholinesterase and monoamine oxidase dual inhibitor

On the basis of N-((5-(3-(1-benzylpiperidin-4-yl)propoxy)-1-methyl-1H-indol-2-yl)methyl)-N-methylprop-2-yn-1-amine (II, ASS234) and QSAR predictions, in this work we have designed, synthesized, and evaluated a number of new indole derivatives from which we have identified N-methyl-N-((1-methyl-5-(3-(1-(2-methylbenzyl)piperidin-4-yl)propoxy)-1H-indol-2-yl)methyl)prop-2-yn-1-amine (2, MBA236) as a new cholinesterase and monoamine oxidase dual inhibitor.PostprintPostprintPeer reviewe

Context-aware visual exploration of molecular databases

Facilitating the visual exploration of scientific data has received increasing attention in the past decade or so. Especially in life science related application areas the amount of available data has grown at a breath taking pace. In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds. In contrast to classical visualizations of such spaces we incorporate a specific focus of analysis, for example the outcome of a biological experiment such as high throughout screening results. The presented method uses this experimental data to select molecular fragments of the underlying molecules that have interesting properties and uses the resulting space to generate a two dimensional map based on a singular value decomposition algorithm and a self organizing map. Experiments on real datasets show that the resulting visual landscape groups molecules of similar chemical properties in densely connected regions

Daganatos sejtek rezisztenciáját gátló vegyületek fejlesztése = Development of compounds targeting multidrug resistant cancer

A korszerű daganatellenes terápia jelentős sikerei ellenére a kemoterápiával szemben fellépő rezisztencia (multidrog rezisztencia, MDR) továbbra is megoldásra váró klinikai kihívás. Számos rosszindulatú megbetegedés, valamint az áttétet adó daganatok hatékony kezelése a terápia során rendszerint kialakuló MDR hatás miatt a mai napig nem megoldott. A rezisztens fenotípus gyakran társul az ABC-transzporterek családjába tartozó fehérjék emelkedett expressziójával. E család legismertebb képviselője a Pgp (ABCB1) membránfehérje, mely az ATP energiáját felhasználva megakadályozza a citosztatikus vegyületek sejten belüli felhalmozódását. A farmakogenomikai megközelítés révén lehetővé válik a személyre szabott gyógyítás, a daganatos megbetegedések molekuláris profiljához igazított kemoterápiás kezelés. A kutatás fő célja az volt, hogy a korábban kidolgozott farmakogenomikai módszer segítségével olyan ?MDR-inverz? vegyületeket fedezzünk fel, melyek szelektíven elpusztítják az egyébként multidrog rezisztens sejteket. Fontosabb eredményeink a következő pontokban összegezhetők: (i) módszerünk számos további MDR-inverz vegyületet azonosított; (ii) a szerkezetek analízise lehetővé tette QSAR modellek felállítását; (iii) javaslatot tettünk a vegyületek hatásmechanizmusára. Távlati tervünk, hogy a megismert MDR-inverz vegyületekből kiindulva originális gyógyszerkutatást folytassunk a rákos sejteket szelektíven pusztító molekulák preklinikai fejlesztése céljából. | Despite considerable advances in drug discovery, resistance to chemotherapy confounds the effective treatment of cancer patients. Cancer cells can become resistant to a single drug or they may acquire broad cross-resistance to mechanistically and structurally unrelated drugs (multidrug resistance (MDR)). ATP-Binding Cassette (ABC) proteins comprise the largest protein family, many members of which are of immediate medical importance and relevant to human health. The application of pharmacogenetics has the potential to improve the management of patients, particularly by providing the molecular basis for choosing among the increasing number of chemotherapeutic agents available for the treatment. The major aim of this project was to apply a pharmacogenomic approach to discover ?MDR-inverse? compounds that selectively kill multidrug resistant cancer cells. The results can be summarized as follows: (i) we identified a series of MDR-inverse compounds; (ii) we delineated structural features associated with their cytotoxic activity; (iii) we proposed a mechanism of action for the toxicity of newly identified MDR1-inverse compounds. Our future aim is to establish the framework for the preclinical development of the most promising MDR-inverse molecules, setting the stage for a fresh therapeutic approach that may eventually translate into improved patient care

Automatic generation of alignments for 3D QSAR analyses

Many 3D QSAR methods require the alignment of the molecules in a dataset, which can require a fair amount of manual effort in deciding upon a rational basis for the superposition. This paper describes the use of FBSS, a pro-ram for field-based similarity searching in chemical databases, for generating such alignments automatically. The CoMFA and CoMSIA experiments with several literature datasets show that the QSAR models resulting from the FBSS alignments are broadly comparable in predictive performance with the models resulting from manual alignments

Predicting Skin Permeability by means of Computational Approaches : Reliability and Caveats in Pharmaceutical Studies

© 2019 American Chemical Society.The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.Peer reviewedFinal Accepted Versio