Design and optimization of K-Ras protein inhibitors as anticancer agents using deltarasin as a case study

K-Ras serves as an important component of signalling pathways involved in cell cycle control. Proper functioning K-Ras is regulated by phosphodiesterase δ (PDEδ). Deltarasin binds to this prenyl-binding protein thus inhibiting its interaction with K-Ras and hence disrupting Ras signalling. The objective of this study is to use Deltarasin as a template for further iteration of the design of novel drugs with potential clinical use in the management of malignancies. Deltarasin was constructed using SYBYL-X ® V1.2, followed by analysis of the critical interactions with the amino acids lining the Ligand Binding Pocket (LBP). Seeds were modelled based on the Deltarasin scaffold and Virtual Screening (VS) was used to identify ‘hits’, using the same molecule as a template. SYBYL-X ®, X-SCORE® , LigBuilder® , Visual Molecular Dynamics (VMD), Accelrys® Draw, Accelrys® Discovery Studio v3.5, Protein Data Bank and ZINCPharmer® were all used to generate results. The main outcome measures of this research project are to discover and optimise in silico high binding affinity of PDEδ inhibitory drug molecules, as well as molecule display, Ligand Binding Affinity (LBA) and Ligand Binding Energy (LBE) calculations, seed generation and ultimately de novo design. Based on reviewed SAR studies, nine seeds were generated using SYBYL-X ® V1.2. The POCKET and GROW algorithm of LigBuilder® V1.2 were used to generate in silico molecules for each seed. Surflexdocking in SYBYL-X ® V1.2 resulted in five molecules with a total docking score of six or greater. De novo molecules created and optimized, present viable leads for high-throughput screening, leading to identification of novel PDEδ inhibitors for use as anti-cancer agents.peer-reviewe

Design of novel compounds with the potential of dual PPARγ/α modulation for the management of metabolic syndrome

This study sought to identify a single molecule capable of managing all three manifestations of metabolic syndrome–hyperglycaemia, dyslipidaemia and hypertension. Two Protein Data Bank (PDB) depositions were selected and used to establish the baseline affinity that any designed molecule in this study should ideally exceed in order to be considered for further optimisation. These were PDB depositions 3VN2 and 2P54 describing the bound co-ordinates of the Peroxisome Proliferator Activated Receptor (PPAR) partial agonist and Angiotensin II Receptor (Ang(II)R) blocker telmisartan and of the experimental PPAR fibrate agonist GW590735 bound to their respective cognate receptors. These small molecules were extracted from their cognate receptors, docked into their non-cognate counterparts, conformational analysis performed, and the optimal conformers were selected as template scaffolds in two parallel processes. The first was a fragment based de novo approach. Here, molecular moieties from the optimal telmisartan and GW590735 scaffolds modelled in their non-cognate targets and considered critical to binding were identified and modelled, in order to produce seed structures capable of sustaining molecular growth at user-directed sites designated as H.spc atoms subsequent to their being docked within the non-cognate Ligand Binding Pockets (LBPs). The second approach was a Virtual Screening (VS) exercise. Here, the optimal telmisartan and GW590735 conformers were submitted as query molecules to VS databases both individually and in the form of a consensus pharmacophore. This VS exercise identified structurally diverse molecules which were electronically and spatially similar to the queries and which were capable of modulating the target receptors. The molecular cohorts identified through both VS and the de novo approaches were filtered for Lipinski Rule compliance. The molecules that survived filtering were then re-docked into the non-cognate PPAR and/or _LBPs, conformational analysis re-performed and the affinity of the optimal conformer measured for its cognate receptor quantified. Comparison was made to the baseline and non-cognate receptor affinities previously established, and the molecules exhibiting dual affinities exceeding baseline values were selected for further optimisation. The use of the “tried and tested” Ang(II)R blocker and fibrate scaffolds as templates predisposes to the identification of novel structures devoid of unacceptable toxicity.peer-reviewe

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

Vector synthesis: a media archaeological investigation into sound-modulated light

Vector Synthesis is a computational art project inspired by theories of media archaeology, by the history of computer and video art, and by the use of discarded and obsolete technologies such as the Cathode Ray Tube monitor. This text explores the military and techno-scientific legacies at the birth of modern computing, and charts attempts by artists of the subsequent two decades to decouple these tools from their destructive origins. Using this history as a basis, the author then describes a media archaeological, real time performance system using audio synthesis and vector graphics display techniques to investigate direct, synesthetic relationships between sound and image. Key to this system, realized in the Pure Data programming environment, is a didactic, open source approach which encourages reuse and modification by other artists within the experimental audiovisual arts community.Holzer, Dere

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub

Washington University Record, February 8, 2002

https://digitalcommons.wustl.edu/record/1922/thumbnail.jp