The pharmacophore kernel for virtual screening with support vector machines

We introduce a family of positive definite kernels specifically optimized for the manipulation of 3D structures of molecules with kernel methods. The kernels are based on the comparison of the three-points pharmacophores present in the 3D structures of molecul es, a set of molecular features known to be particularly relevant for virtual screening applications. We present a computationally demanding exact implementation of these kernels, as well as fast approximations related to the classical fingerprint-based approa ches. Experimental results suggest that this new approach outperforms state-of-the-art algorithms based on the 2D structure of mol ecules for the detection of inhibitors of several drug targets

Digital Three-Dimensional Atlas of Quail Development Using High-Resolution MRI

We present an archetypal set of three-dimensional digital atlases of the quail embryo based on microscopic magnetic resonance imaging (µMRI). The atlases are composed of three modules: (1) images of fixed ex ovo quail, ranging in age from embryonic day 5 to 10 (e05 to e10); (2) a coarsely delineated anatomical atlas of the µMRI data; and (3) an organ system–based hierarchical graph linked to the anatomical delineations. The atlas is designed to be accessed using SHIVA, a free Java application. The atlas is extensible and can contain other types of information including anatomical, physiological, and functional descriptors. It can also be linked to online resources and references. This digital atlas provides a framework to place various data types, such as gene expression and cell migration data, within the normal three-dimensional anatomy of the developing quail embryo. This provides a method for the analysis and examination of the spatial relationships among the different types of information within the context of the entire embryo

Molecular Distance Maps: An alignment-free computational tool for analyzing and visualizing DNA sequences\u27 interrelationships

In an attempt to identify and classify species based on genetic evidence, we propose a novel combination of methods to quantify and visualize the interrelationships between thousand of species. This is possible by using Chaos Game Representation (CGR) of DNA sequences to compute genomic signatures which we then compare by computing pairwise distances. In the last step, the original DNA sequences are embedded in a high dimensional space using Multi-Dimensional Scaling (MDS) before everything is projected on a Euclidean 3D space. To start with, we apply this method to a mitochondrial DNA dataset from NCBI containing over 3,000 species. The analysis shows that the oligomer composition of full mtDNA sequences can be a source of taxonomic information, suggesting that this method could be used for unclassified species and taxonomic controversies. Next, we test the hypothesis that CGR-based genomic signature is preserved along a species\u27 genome by comparing inter- and intra-genomic signatures of nuclear DNA sequences from six different organisms, one from each kingdom of life. We also compare six different distances and we assess their performance using statistical measures. Our results support the existence of a genomic signature for a species\u27 genome at the kingdom level. In addition, we test whether CGR-based genomic signatures originating only from nuclear DNA can be used to distinguish between closely-related species and we answer in the negative. To overcome this limitation, we propose the concept of ``composite signatures\u27\u27 which combine information from different types of DNA and we show that they can effectively distinguish all closely-related species under consideration. We also propose the concept of ``assembled signatures\u27\u27 which, among other advantages, do not require a long contiguous DNA sequence but can be built from smaller ones consisting of ~100-300 base pairs. Finally, we design an interactive webtool MoDMaps3D for building three-dimensional Molecular Distance Maps. The user can explore an already existing map or build his/her own using NCBI\u27s accession numbers as input. MoDMaps3D is platform independent, written in Javascript and can run in all major modern browsers

Computational Approaches to Drug Profiling and Drug-Protein Interactions

Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

Modeling regionalized volumetric differences in protein-ligand binding cavities

Identifying elements of protein structures that create differences in protein-ligand binding specificity is an essential method for explaining the molecular mechanisms underlying preferential binding. In some cases, influential mechanisms can be visually identified by experts in structural biology, but subtler mechanisms, whose significance may only be apparent from the analysis of many structures, are harder to find. To assist this process, we present a geometric algorithm and two statistical models for identifying significant structural differences in protein-ligand binding cavities. We demonstrate these methods in an analysis of sequentially nonredundant structural representatives of the canonical serine proteases and the enolase superfamily. Here, we observed that statistically significant structural variations identified experimentally established determinants of specificity. We also observed that an analysis of individual regions inside cavities can reveal areas where small differences in shape can correspond to differences in specificity

Towards Arginase Inhibition: Hybrid SAR Protocol for Property Mapping of Chlorinated N-arylcinnamamides

peer reviewedA series of seventeen 4-chlorocinnamanilides and seventeen 3,4-dichlorocinnamanilides were characterized for their antiplasmodial activity. In vitro screening on a chloroquine-sensitive strain of Plasmodium falciparum 3D7/MRA-102 highlighted that 23 compounds possessed IC50 < 30 µM. Typically, 3,4-dichlorocinnamanilides showed a broader range of activity compared to 4-chlorocinnamanilides. (2E)-N-[3,5-bis(trifluoromethyl)phenyl]-3-(3,4-dichlorophenyl)prop-2-en-amide with IC50 = 1.6 µM was the most effective agent, while the other eight most active derivatives showed IC50 in the range from 1.8 to 4.6 µM. A good correlation between the experimental logk and the estimated clogP was recorded for the whole ensemble of the lipophilicity generators. Moreover, the SAR-mediated similarity assessment of the novel (di)chlorinated N-arylcinnamamides was conducted using the collaborative (hybrid) ligand-based and structure-related protocols. In consequence, an ‘averaged’ selection-driven interaction pattern was produced based in namely ‘pseudo–consensus’ 3D pharmacophore mapping. The molecular docking approach was engaged for the most potent antiplasmodial agents in order to gain an insight into the arginase-inhibitor binding mode. The docking study revealed that (di)chlorinated aromatic (C-phenyl) rings are oriented towards the binuclear manganese cluster in the energetically favorable poses of the chloroquine and the most potent arginase inhibitors. Additionally, the water-mediated hydrogen bonds were formed via carbonyl function present in the new N-arylcinnamamides and the fluorine substituent (alone or in trifluoromethyl group) of N-phenyl ring seems to play a key role in forming the halogen bonds