In silico modeling of the specific inhibitory potential of thiophene-2,3-dihydro-1,5-benzothiazepine against BChE in the formation of β-amyloid plaques associated with Alzheimer's disease

<p>Abstract</p> <p>Background</p> <p>Alzheimer's disease, known to be associated with the gradual loss of memory, is characterized by low concentration of acetylcholine in the hippocampus and cortex part of the brain. Inhibition of acetylcholinesterase has successfully been used as a drug target to treat Alzheimer's disease but drug resistance shown by butyrylcholinesterase remains a matter of concern in treating Alzheimer's disease. Apart from the many other reasons for Alzheimer's disease, its association with the genesis of fibrils by β-amyloid plaques is closely related to the increased activity of butyrylcholinesterase. Although few data are available on the inhibition of butyrylcholinesterase, studies have shown that that butyrylcholinesterase is a genetically validated drug target and its selective inhibition reduces the formation of β-amyloid plaques.</p> <p>Rationale</p> <p>We previously reported the inhibition of cholinesterases by 2,3-dihydro-1, 5-benzothiazepines, and considered this class of compounds as promising inhibitors for the cure of Alzheimer's disease. One compound from the same series, when substituted with a hydroxy group at C-3 in ring A and 2-thienyl moiety as ring B, showed greater activity against butyrylcholinesterase than to acetylcholinesterase. To provide insight into the binding mode of this compound (Compound A), molecular docking in combination with molecular dynamics simulation of 5000 ps in an explicit solvent system was carried out for both cholinesterases.</p> <p>Conclusion</p> <p>Molecular docking studies revealed that the potential of Compound A to inhibit cholinesterases was attributable to the cumulative effects of strong hydrogen bonds, cationic-π, π-π interactions and hydrophobic interactions. A comparison of the docking results of Compound A against both cholinesterases showed that amino acid residues in different sub-sites were engaged to stabilize the docked complex. The relatively high affinity of Compound A for butyrylcholinesterase was due to the additional hydrophobic interaction between the 2-thiophene moiety of Compound A and Ile69. The involvement of one catalytic triad residue (His438) of butyrylcholinesterase with the 3'-hydroxy group on ring A increases the selectivity of Compound A. C-C bond rotation around ring A also stabilizes and enhances the interaction of Compound A with butyrylcholinesterase. Furthermore, the classical network of hydrogen bonding interactions as formed by the catalytic triad of butyrylcholinesterase is disturbed by Compound A. This study may open a new avenue for structure-based drug design for Alzheimer's disease by considering the 3D-pharmacophoric features of the complex responsible for discriminating these two closely-related cholinesterases.</p

Applying the genetic algorithm for determination electrospinning parameters of poly vinylidene fluoride (PVDF) nano fibers: theoretical & experimental analysis

Poly Vinylidene Fluoride (PVDF) because of its piezoelectric properties has been applied in different applications such as smart textiles, medical application and membranes for energy harvesting. It was declared that nanofibres diameters and electrospinning parameters could be enhanced the piezoelectric properties of these materials. The main objective of this paper is applying the Genetic Algorithm (GA) to determine the optimum condition of solution parameters and processing conditions based on the desired diameter size of PVDF fibers to produce the fibers without any structural faults. In this method, The Fitness function was determined by a simple analytical model presented by Fridrikh. Toward approving the GA results the experimental tests were done. the effect of four parameters such as flow rate of the polymer solution, electrospinning voltage, electrospinning distance and polymer concentration on the fiber formation and fiber diameter size of electrospun PVDF fibers have been explored by Scanning Electron Microscopy (SEM) to attest the accuracy of the model. Assessment of experimental and theoretical results show that electrospinning parameters determined by GA method leads to achieve desire fiber diameters. Because of time and energy consuming of electrospinning process, the GA method may be useful to achieve desired fiber diameter by determining electrospinning parameters for polymers prior to fiber production

Electrostatic-field and surface-shape similarity for virtual screening and pose prediction.

We introduce a new method for rapid computation of 3D molecular similarity that combines electrostatic field comparison with comparison of molecular surface-shape and directional hydrogen-bonding preferences (called "eSim"). Rather than employing heuristic "colors" or user-defined molecular feature types to represent conformation-dependent molecular electrostatics, eSim calculates the similarity of the electrostatic fields of two molecules (in addition to shape and hydrogen-bonding). We present detailed virtual screening performance data on the standard 102 target DUD-E set. In its moderately fast screening mode, eSim running on a single computing core is capable of processing over 60 molecules per second. In this mode, eSim performed significantly better than all alternate methods for which full DUD-E data were available (mean ROC area of 0.74, p [Formula: see text], by paired t-test, compared with the best performing alternate method). In addition, for 92 targets of the DUD-E set where multiple ligand-bound crystal structures were available, screening performance was assessed using alternate ligands or sets thereof (in their bound poses) as similarity targets. Using the joint alignment of five ligands for each protein target, mean ROC area exceeded 0.82 for the 92 targets. Design-focused application of ligand similarity methods depends on accurate predictions of geometric molecular relationships. We comprehensively assessed pose prediction accuracy by curating nearly 400,000 bound ligand pose pairs across the DUD-E targets. Overall, beginning from agnostic initial poses, we observed an 80% success rate for RMSD [Formula: see text] Å  among the top 20 predicted eSim poses. These examples were split roughly 50/50 into cases with high direct atomic overlap (where a shared scaffold exists between a pair) and low direct atomic overlap (where where a ligand pair has dissimilar scaffolds but largely occupies the same space). Within the high direct atomic overlap subset, the pose prediction success rate was 93%. For the more challenging subset (where dissimilar scaffolds are to be aligned), the success rate was 70%. The eSim approach enables both large-scale screening and rational design of ligands and is rooted in physically meaningful, non-heuristic, molecular comparisons

A review of estimation of distribution algorithms in bioinformatics

Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain

The application of multiobjective optimisation to protein-ligand docking

Despite the intense efforts that have been devoted to the development of scoring functions for protein-ligand docking, they are still limited in their ability to identify the correct binding pose of a ligand within a protein binding site. A deeper understanding of the intricacies of scoring functions is therefore essential in order to develop these effectively. The aim of the work described in this thesis is to analyse the individual interaction energy types which form the individual components of a force field-based scoring function. To do this, & protein-ligand docking algorithm that is based on multiobjective optimisation has been developed. Multiobjective optimisation allows for the optimisation of several objectives simultaneously and this has been applied to the individual interaction energy types of the GRID scoring function. Traditionally these interaction energy types are summed together and the total energy is used to guide the search. By using individual energy types during optimisation, their roles can be better understood. The interaction energy types that have been used here are the electrostatic and hydrogen bond interactions combined, and van der Waals interactions. The algorithm is first tested on two datasets containing twenty complexes. The results show that the different interaction energy types have varying influences when it comes to successfully docking certain complexes, and that it is important to fmd the right balance of interaction energy types so as to find correct solutions. Ofthe twenty complexes, the algorithm found correct solutions for fifteen. To improve the performance of the algorithm, a few enhancements were introduced. This includes a simplex minimisation process with a Lamarckian element. The algorithm was retested on the twenty complexes, and the newer version was found to outperform the original version, finding correct solutions for seventeen of the twenty complexes. To extensively study the capabilities of the algorithm, it was tested on varied datasets, including the FlexX dataset. The algoritlun's performance was also compared to a single-objective docking tool, Q-fit. The comparison betw~en the multiobjective and single-objective methodologies revealed that single-objective methods can sometimes fail at finding correct docked solutions because they are unable to correctly balance the interaction energy types comprising a scoring function. The study also showed that a multiobjective optimisation method can reveal the reasons why a given docking algorithm may fail at fmding a correct solution. Finally, the algorithm was extended to incorporate desolvation energy as a third objective. Though these results are preliminary, they revealed some interesting relationships between the different objectives.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Development of an evolutionary algorithm for crystal structure prediction

Die vorliegende Dissertation befasst sich mit der theoretischen Vorhersage neuer Materialien. Ein evolutionärer Algorithmus, der zur Lösung dieses globalen Optimierungsproblems Konzepte der natürlichen Evolution imitiert, wurde entwickelt und ist als Programmpaket EVO frei verfügbar. EVO findet zuverlässig sowohl bekannte als auch neuartige Kristallstrukturen. Beispielsweise wurden die Strukturen von Germaniumnitrofluorid, einer neue Borschicht und mit dem gekreuzten Graphen einer bisher unbekannte Kohlenstoffstruktur gefunden. Ferner wurde in der Arbeit gezeigt, dass das reine Auffinden solcher Strukturen der erste Teil einer erfolgreichen Vorhersage ist. Weitere aufwendige Berechnungen sind nötig, die Aufschluss über die Stabilität der hypothetischen Struktur geben und Aussagen über zu erwartende Materialeigenschaften liefern