Network target for screening synergistic drug combinations with application to traditional Chinese medicine

<p>Abstract</p> <p>Background</p> <p>Multicomponent therapeutics offer bright prospects for the control of complex diseases in a synergistic manner. However, finding ways to screen the synergistic combinations from numerous pharmacological agents is still an ongoing challenge.</p> <p>Results</p> <p>In this work, we proposed for the first time a “network target”-based paradigm instead of the traditional "single target"-based paradigm for virtual screening and established an algorithm termed NIMS (Network target-based Identification of Multicomponent Synergy) to prioritize synergistic agent combinations in a high throughput way. NIMS treats a disease-specific biological network as a therapeutic target and assumes that the relationship among agents can be transferred to network interactions among the molecular level entities (targets or responsive gene products) of agents. Then, two parameters in NIMS, Topology Score and Agent Score, are created to evaluate the synergistic relationship between each given agent combinations. Taking the empirical multicomponent system traditional Chinese medicine (TCM) as an illustrative case, we applied NIMS to prioritize synergistic agent pairs from 63 agents on a pathological process instanced by angiogenesis. The NIMS outputs can not only recover five known synergistic agent pairs, but also obtain experimental verification for synergistic candidates combined with, for example, a herbal ingredient Sinomenine, which outperforms the meet/min method. The robustness of NIMS was also showed regarding the background networks, agent genes and topological parameters, respectively. Finally, we characterized the potential mechanisms of multicomponent synergy from a network target perspective.</p> <p>Conclusions</p> <p>NIMS is a first-step computational approach towards identification of synergistic drug combinations at the molecular level. The network target-based approaches may adjust current virtual screen mode and provide a systematic paradigm for facilitating the development of multicomponent therapeutics as well as the modernization of TCM.</p

Identification of drug candidates and repurposing opportunities through compound-target interaction networks

Introduction: System-wide identification of both on- and off-targets of chemical probes provides improved understanding of their therapeutic potential and possible adverse effects, thereby accelerating and de-risking drug discovery process. Given the high costs of experimental profiling of the complete target space of drug-like compounds, computational models offer systematic means for guiding these mapping efforts. These models suggest the most potent interactions for further experimental or pre-clinical evaluation both in cell line models and in patient-derived material.Areas covered: The authors focus here on network-based machine learning models and their use in the prediction of novel compound-target interactions both in target-based and phenotype-based drug discovery applications. While currently being used mainly in complementing the experimentally mapped compound-target networks for drug repurposing applications, such as extending the target space of already approved drugs, these network pharmacology approaches may also suggest completely unexpected and novel investigational probes for drug development.Expert opinion: Although the studies reviewed here have already demonstrated that network-centric modeling approaches have the potential to identify candidate compounds and selective targets in disease networks, many challenges still remain. In particular, these challenges include how to incorporate the cellular context and genetic background into the disease networks to enable more stratified and selective target predictions, as well as how to make the prediction models more realistic for the practical drug discovery and therapeutic applications.Peer reviewe

Hub genes in a pan-cancer co-expression network show potential for predicting drug responses [version 2; referees: 2 approved]

Background: The topological analysis of networks extracted from different types of “omics” data is a useful strategy for characterizing biologically meaningful properties of the complex systems underlying these networks. In particular, the biological significance of highly connected genes in diverse molecular networks has been previously determined using data from several model organisms and phenotypes. Despite such insights, the predictive potential of candidate hubs in gene co-expression networks in the specific context of cancer-related drug experiments remains to be deeply investigated. The examination of such associations may offer opportunities for the accurate prediction of anticancer drug responses.  Methods: Here, we address this problem by: a) analyzing a co-expression network obtained from thousands of cancer cell lines, b) detecting significant network hubs, and c) assessing their capacity to predict drug sensitivity using data from thousands of drug experiments. We investigated the prediction capability of those genes using a multiple linear regression model, independent datasets, comparisons with other models and our own in vitro experiments. Results: These analyses led to the identification of 47 hub genes, which are implicated in a diverse range of cancer-relevant processes and pathways. Overall, encouraging agreements between predicted and observed drug sensitivities were observed in public datasets, as well as in our in vitro validations for four glioblastoma cell lines and four drugs. To facilitate further research, we share our hub-based drug sensitivity prediction model as an online tool. Conclusions: Our research shows that co-expression network hubs are biologically interesting and exhibit potential for predicting drug responses in vitro. These findings motivate further investigations about the relevance and application of our unbiased discovery approach in pre-clinical, translationally-oriented research

BIOINFORMATICS TOOL AND MODEL DEVELOPMENT FOR STUDYING BIOLOGICAL NETWORKS AND PROTEIN-PROTEIN INTERACTIONS

Ph.DDOCTOR OF PHILOSOPH

Targeting allosteric sites of Escherichia coli heat shock protein 70 for antibiotic development

Hsp70s are members of the heat shock proteins family with a molecular weight of 70-kDa and are the most abundant group in bacterial and eukaryotic systems, hence the most extensively studied ones. These proteins are molecular chaperones that play a significant role in protein homeostasis by facilitating appropriate folding of proteins, preventing proteins from aggregating and misfolding. They are also involved in translocation of proteins into subcellular compartments and protection of cells against stress. Stress caused by environmental or biological factors affects the functionality of the cell. In response to these stressful conditions, up-regulation of Hsp70s ensures that the cells are protected by balancing out unfolded proteins giving them ample time to repair denatured proteins. Hsp70s is connected to numerous illnesses such as autoimmune and neurodegenerative diseases, bacterial infection, cancer, malaria, and obesity. The multi-functional nature of Hsp70s predisposes them as promising therapeutic targets. Hsp70s play vital roles in various cell developments, and survival pathways, therefore targeting this protein will provide a new avenue towards the discovery of active therapeutic agents for the treatment of a wide range of diseases. Allosteric sites of these proteins in its multi-conformational states have not been explored for inhibitory properties hence the aim of this study. This study aims at identifying allosteric sites that inhibit the ATPase and substrate binding activities using computational approaches. Using E. coli as a model organism, molecular docking for high throughput virtual screening was carried out using 623 compounds from the South African Natural Compounds Database (SANCDB; https://sancdb.rubi.ru.ac.za/) against identified allosteric sites. Ligands with the highest binding affinity (good binders) interacting with critical allosteric residues that are druggable were identified. Molecular dynamics (MD) simulation was also performed on the identified hits to assess for protein-inhibitor complex stability. Finally, principal component analysis (PCA) was performed to understand the structural dynamics of the ligand-free and ligand-bound structures during MD simulation

Structural characterization and selective drug targeting of higher-order DNA G-quadruplex systems.

There is now substantial evidence that guanine-rich regions of DNA form non-B DNA structures known as G-quadruplexes in cells. G-quadruplexes (G4s) are tetraplex DNA structures that form amid four runs of guanines which are stabilized via Hoogsteen hydrogen bonding to form stacked tetrads. DNA G4s have roles in key genomic functions such as regulating gene expression, replication, and telomere homeostasis. Because of their apparent role in disease, G4s are now viewed as important molecular targets for anticancer therapeutics. To date, the structures of many important G4 systems have been solved by NMR or X-ray crystallographic techniques. Small molecules developed to target these structures have shown promising results in treating cancer in vitro and in vivo, however, these compounds commonly lack the selectivity required for clinical success. There is now evidence that long single-stranded G-rich regions can stack or otherwise interact intramolecularly to form G4-multimers, opening a new avenue for rational drug design. For a variety of reasons, G4 multimers are not amenable to NMR or X-ray crystallography. In the current dissertation, I apply a variety of biophysical techniques in an integrative structural biology (ISB) approach to determine the primary conformation of two disputed higher-order G4 systems: (1) the extended human telomere G-quadruplex and (2) the G4-multimer formed within the human telomerase reverse transcriptase (hTERT) gene core promoter. Using the higher-order human telomere structure in virtual drug discovery approaches I demonstrate that novel small molecule scaffolds can be identified which bind to this sequence in vitro. I subsequently summarize the current state of G-quadruplex focused virtual drug discovery in a review that highlights successes and pitfalls of in silico drug screens. I then present the results of a massive virtual drug discovery campaign targeting the hTERT core promoter G4 multimer and show that discovering selective small molecules that target its loops and grooves is feasible. Lastly, I demonstrate that one of these small molecules is effective in down-regulating hTERT transcription in breast cancer cells. Taken together, I present here a rigorous ISB platform that allows for the characterization of higher-order DNA G-quadruplex structures as unique targets for anticancer therapeutic discovery

Computational approaches to complex biological networks

The need of understanding and modeling the biological networks is one of the raisons d'\ueatre and of the driving forces behind the emergence of Systems Biology. Because of its holistic approach and because of the widely different level of complexity of the networks, different mathematical methods have been developed during the years. Some of these computational methods are used in this thesis in order to investigate various properties of different biological systems. The first part deals with the prediction of the perturbation of cellular metabolism induced by drugs. Using Flux Balance Analysis to describe the reconstructed genome-wide metabolic networks, we consider the problem of identifying the most selective drug synergisms for given therapeutic targets. The second part of this thesis considers gene regulatory and large social networks as signed graphs (activation/deactivation or friendship/hostility are rephrased as positive/negative coupling between spins). Using the analogy with an Ising spin glass an analysis of the energy landscape and of the content of \u201cdisorder\u201d 'is carried out. Finally, the last part concerns the study of the spatial heterogeneity of the signaling pathway of rod photoreceptors. The electrophysiological data produced by our collaborators in the Neurobiology laboratory have been analyzed with various dynamical systems giving an insight into the process of ageing of photoreceptors and into the role diffusion in the pathway