NETWORK INFERENCE DRIVEN DRUG DISCOVERY

The application of rational drug design principles in the era of network-pharmacology requires the investigation of drug-target and target-target interactions in order to design new drugs. The presented research was aimed at developing novel computational methods that enable the efficient analysis of complex biomedical data and to promote the hypothesis generation in the context of translational research. The three chapters of the Dissertation relate to various segments of drug discovery and development process. The first chapter introduces the integrated predictive drug discovery platform „SmartGraph”. The novel collaborative-filtering based algorithm „Target Based Recommender (TBR)” was developed in the framework of this project and was validated on a set of 28,270 experimentally determined bioactivity data points involving 1,882 compounds and 869 targets. The TBR is integrated into the SmartGraph platform. The graphical interface of SmartGraph enables data analysis and hypothesis generation even for investigators without substantial bioinformatics knowledge. The platform can be utilized in the context of target identification, drug-target prediction and drug repurposing. The second chapter of the Dissertation introduces an information theory inspired dynamic network model and the novel “Luminosity Diffusion (LD)” algorithm. The model can be utilized to prioritize protein targets for drug discovery purposes on the basis of available information and the importance of the targets. The importance of targets is accounted for in the information flow simulation process and is derived merely from network topology. The LD algorithm was validated on 8,010 relations of 794 proteins extracted from the Target Central Resource Database developed in the framework of the “Illuminating the Druggable Genome” project. The last chapter discusses a fundamental problem pertaining to the generation of similarity network of molecules and their clustering. The network generation process relies on the selection of a similarity threshold. The presented work introduces a network topology based systematic solution for selecting this threshold so that the likelihood of a reasonable clustering can be increased. Furthermore, the work proposes a solution for generating so-called “pseudo-reference clustering” for large molecular data sets for performance evaluation purposes. The results of this chapter are applicable in the lead identification and development processes

Relating the shape of protein binding sites to binding affinity profiles: is there an association?

<p>Abstract</p> <p>Background</p> <p>Various pattern-based methods exist that use <it>in vitro </it>or <it>in silico </it>affinity profiles for classification and functional examination of proteins. Nevertheless, the connection between the protein affinity profiles and the structural characteristics of the binding sites is still unclear. Our aim was to investigate the association between virtual drug screening results (calculated binding free energy values) and the geometry of protein binding sites. Molecular Affinity Fingerprints (MAFs) were determined for 154 proteins based on their molecular docking energy results for 1,255 FDA-approved drugs. Protein binding site geometries were characterized by 420 PocketPicker descriptors. The basic underlying component structure of MAFs and binding site geometries, respectively, were examined by principal component analysis; association between principal components extracted from these two sets of variables was then investigated by canonical correlation and redundancy analyses.</p> <p>Results</p> <p>PCA analysis of the MAF variables provided 30 factors which explained 71.4% of the total variance of the energy values while 13 factors were obtained from the PocketPicker descriptors which cumulatively explained 94.1% of the total variance. Canonical correlation analysis resulted in 3 statistically significant canonical factor pairs with correlation values of 0.87, 0.84 and 0.77, respectively. Redundancy analysis indicated that PocketPicker descriptor factors explain 6.9% of the variance of the MAF factor set while MAF factors explain 15.9% of the total variance of PocketPicker descriptor factors. Based on the salient structures of the factor pairs, we identified a clear-cut association between the shape and bulkiness of the drug molecules and the protein binding site descriptors.</p> <p>Conclusions</p> <p>This is the first study to investigate complex multivariate associations between affinity profiles and the geometric properties of protein binding sites. We found that, except for few specific cases, the shapes of the binding pockets have relatively low weights in the determination of the affinity profiles of proteins. Since the MAF profile is closely related to the target specificity of ligand binding sites we can conclude that the shape of the binding site is not a pivotal factor in selecting drug targets. Nonetheless, based on strong specific associations between certain MAF profiles and specific geometric descriptors we identified, the shapes of the binding sites do have a crucial role in virtual drug design for certain drug categories, including morphine derivatives, benzodiazepines, barbiturates and antihistamines.</p

SmartGraph API: Programmatic Knowledge Mining in Network- Pharmacology Setting

The recent SmartGraph platform facilitates the execution of complex drug-discovery workflows with ease in the network-pharmacology paradigm. However, at the time of its publication, we identified the need for the development of an Application Programming Interface (API) that could promote biomedical data integration and hypothesis generation in an automated manner. This need was magnified at the time of the COVID-19 pandemic. This study addresses this hiatus. Most functionalities of the original platform were implemented in the SmartGraph API. We demonstrate that by using the API it is possible to transform the original semi-automated workflow behind the Neo4COVID19 database to a fully automated one. The availability of the SmartGraph API lends a significant improvement to the programmatic integration of networkpharmacology- oriented knowledge graphs and analytics, as well as predictive functionalities and workflows

Drug Effect Prediction by Polypharmacology-Based Interaction Profiling.

Most drugs exert their effects via multitarget interactions, as hypothesized by polypharmacology. While these multitarget interactions are responsible for the clinical effect profiles of drugs, current methods have failed to uncover the complex relationships between them. Here, we introduce an approach which is able to relate complex drug-protein interaction profiles with effect profiles. Structural data and registered effect profiles of all small-molecule drugs were collected, and interactions to a series of nontarget protein binding sites of each drug were calculated. Statistical analyses confirmed a close relationship between the studied 177 major effect categories and interaction profiles of ca. 1200 FDA-approved small-molecule drugs. On the basis of this relationship, the effect profiles of drugs were revealed in their entirety, and hitherto uncovered effects could be predicted in a systematic manner. Our results show that the prediction power is independent of the composition of the protein set used for interaction profile generation

Canvass: A Crowd-Sourced, Natural-Product Screening Library for Exploring Biological Space.

Natural products and their derivatives continue to be wellsprings of nascent therapeutic potential. However, many laboratories have limited resources for biological evaluation, leaving their previously isolated or synthesized compounds largely or completely untested. To address this issue, the Canvass library of natural products was assembled, in collaboration with academic and industry researchers, for quantitative high-throughput screening (qHTS) across a diverse set of cell-based and biochemical assays. Characterization of the library in terms of physicochemical properties, structural diversity, and similarity to compounds in publicly available libraries indicates that the Canvass library contains many structural elements in common with approved drugs. The assay data generated were analyzed using a variety of quality control metrics, and the resultant assay profiles were explored using statistical methods, such as clustering and compound promiscuity analyses. Individual compounds were then sorted by structural class and activity profiles. Differential behavior based on these classifications, as well as noteworthy activities, are outlined herein. One such highlight is the activity of (-)-2(S)-cathafoline, which was found to stabilize calcium levels in the endoplasmic reticulum. The workflow described here illustrates a pilot effort to broadly survey the biological potential of natural products by utilizing the power of automation and high-throughput screening

Canvass: A Crowd-Sourced, Natural-Product Screening Library for Exploring Biological Space.

Natural products and their derivatives continue to be wellsprings of nascent therapeutic potential. However, many laboratories have limited resources for biological evaluation, leaving their previously isolated or synthesized compounds largely or completely untested. To address this issue, the Canvass library of natural products was assembled, in collaboration with academic and industry researchers, for quantitative high-throughput screening (qHTS) across a diverse set of cell-based and biochemical assays. Characterization of the library in terms of physicochemical properties, structural diversity, and similarity to compounds in publicly available libraries indicates that the Canvass library contains many structural elements in common with approved drugs. The assay data generated were analyzed using a variety of quality control metrics, and the resultant assay profiles were explored using statistical methods, such as clustering and compound promiscuity analyses. Individual compounds were then sorted by structural class and activity profiles. Differential behavior based on these classifications, as well as noteworthy activities, are outlined herein. One such highlight is the activity of (-)-2(S)-cathafoline, which was found to stabilize calcium levels in the endoplasmic reticulum. The workflow described here illustrates a pilot effort to broadly survey the biological potential of natural products by utilizing the power of automation and high-throughput screening