QuTIE: Quantum optimization for Target Identification by Enzymes

Target Identification by Enzymes (TIE) problem aims to identify the set of enzymes in a given metabolic network, such that their inhibition eliminates a given set of target compounds associated with a disease while incurring minimum damage to the rest of the compounds. This is an NP-complete problem, and thus optimal solutions using classical computers fail to scale to large metabolic networks. In this paper, we consider the TIE problem for identifying drug targets in metabolic networks. We develop the first quantum optimization solution, called QuTIE (Quantum optimization for Target Identification by Enzymes), to this NP-complete problem. We do that by developing an equivalent formulation of the TIE problem in Quadratic Unconstrained Binary Optimization (QUBO) form, then mapping it to a logical graph, which is then embedded on a hardware graph on a quantum computer. Our experimental results on 27 metabolic networks from Escherichia coli, Homo sapiens, and Mus musculus show that QuTIE yields solutions which are optimal or almost optimal. Our experiments also demonstrate that QuTIE can successfully identify enzyme targets already verified in wet-lab experiments for 14 major disease classes

Two-stage flux balance analysis of metabolic networks for drug target identification

<p>Abstract</p> <p>Background</p> <p>Efficient identification of drug targets is one of major challenges for drug discovery and drug development. Traditional approaches to drug target identification include literature search-based target prioritization and <it>in vitro</it> binding assays which are both time-consuming and labor intensive. Computational integration of different knowledge sources is a more effective alternative. Wealth of omics data generated from genomic, proteomic and metabolomic techniques changes the way researchers view drug targets and provides unprecedent opportunities for drug target identification.</p> <p>Results</p> <p>In this paper, we develop a method based on flux balance analysis (FBA) of metabolic networks to identify potential drug targets. This method consists of two linear programming (LP) models, which first finds the steady optimal fluxes of reactions and the mass flows of metabolites in the pathologic state and then determines the fluxes and mass flows in the medication state with the minimal side effect caused by the medication. Drug targets are identified by comparing the fluxes of reactions in both states and examining the change of reaction fluxes. We give an illustrative example to show that the drug target identification problem can be solved effectively by our method, then apply it to a hyperuricemia-related purine metabolic pathway. Known drug targets for hyperuricemia are correctly identified by our two-stage FBA method, and the side effects of these targets are also taken into account. A number of other promising drug targets are found to be both effective and safe.</p> <p>Conclusions</p> <p>Our method is an efficient procedure for drug target identification through flux balance analysis of large-scale metabolic networks. It can generate testable predictions, provide insights into drug action mechanisms and guide experimental design of drug discovery.</p

Drugs targeting the retinoblastoma binding protein 6 (RBBP6): "the collision of computers and biochemistry"

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfillment of the requirements for the Master of Science degree. 2 November 2017.Screening methodologies have identified specific targets that could serve as potential therapeutic markers in cancer drug design, and the Retinoblastoma binding protein 6 (RBBP6) which is predominately expressed in lung and breast cancers is one critical protein identified. This study seeks to understand the 3D structure of RBBP6 domains, with emphasis on cancer. Three of these domains have been studied in this project, i.e. the Domain With No Name (DWNN), RING Finger, and the p53-binding domain. The ubiquitin-like structure of the DWNN has implicated this domain as a ubiquitin-like modifier of other proteins such as p53, whilst the RING Finger domain has intrinsic E3 Ligase activity like MDM2 the prototypical negative regulator of p53. The DWNN and RING Finger domains have resolved solution NMR structures, whilst the p53-binding domain has none. Thus, the first initiative undertaken was to model the RBBP6 p53-binding domain using I-TASSER and eThread-Modeller web-severs. Our results demonstrated that this domain mainly constitutes of alpha-helices and loop structures. Structural quality validations of both I-TASSER and eThread-Modeller models were further assessed using Swiss-Model and ProSA (Protein structure analysis) web-servers. Analyses were focussed on specific statistical parameters (Anolea, DFire, QMEAN, ProCheck and the ProSA Z-score). Results from these analyses show that the first I-TASSER model is the best possible representation of the RBBP6 p53-binding domain depicting minimal deviation from native state. Furthermore, screening and docking studies were performed using Schrödinger-Maestro v10.7: Glide SP and drug-like molecules that would potentially serve as agonist or antagonist of RBBP6 were identified.MT 201

The era of big data: Genome-scale modelling meets machine learning

With omics data being generated at an unprecedented rate, genome-scale modelling has become pivotal in its organisation and analysis. However, machine learning methods have been gaining ground in cases where knowledge is insufficient to represent the mechanisms underlying such data or as a means for data curation prior to attempting mechanistic modelling. We discuss the latest advances in genome-scale modelling and the development of optimisation algorithms for network and error reduction, intracellular constraining and applications to strain design. We further review applications of supervised and unsupervised machine learning methods to omics datasets from microbial and mammalian cell systems and present efforts to harness the potential of both modelling approaches through hybrid modelling