Identification of potential antimicrobial targets using methods of network systems biology Eivind Almaas Dept. of Biotehnology & Food Science Norwegian University of Science &Technology

Drug Action Resistance: Increased expression of enzyme substitute Mutation in the enzyme active site Mutations to increase expression of efflux proteins to transport drug out of cell Antibiotics are chemical agents that kill or slow down the growth of micro-organisms

How can we make it difficult for an organism to circumvent our antibiotics?

Two possible approaches from Network Systems Biology: Focus on proteins’ active sites Focus on vulnerabilities of metabolic network structure

Protein Active Site Specific sites in proteins responsible for their function Each active site has a specific shape, so it will only perform a specific job Focus on active site  substrate-centric view Joining things together Ripping things apart 5

Protein Active Site Network (ASN) Hypotheses: Similar active sites structures  similar function and role ASN neighbors of a drug target: Could be cause of potential drug side effects Could be alternative drug targets Suggest existing evolutionary alternatives to target drug Approach not based on pre-conceived notion of protein similarity

Active-Site Network: approach For simplicity, focus only on metalloproteins Define active site as small region centered on metal. Include all atoms within region, regardless of residue origin. Use all Protein Data Bank (PDB) metalloproteins containing (~10k): Co, Cu, Fe, Mn, Mo, Ni, V, W, Zn 1. Identify active site (r~5Å) of all metalloproteins with PDB structure 2. Determine optimal alignment for every structure pair 3. Extract statistically significant pairs and generate network. Selected target Likely targets Possible targets Approx. 1/3 of all proteins are metalloproteins Metal ions are critical to the protein’s function, structure & stability Numerous metalloproteins important therapeutic targets, not only for antimicrobial drugs: Example -- Methionine aminopeptidase (MetAP) enzymes

Example: ASN identifies proteins with similar function Structure of 2axt 2axt: Function found at the Oxygen Evolving Complex (OEC) in photosystem II, utilizing 4 Mn atoms at active site. Loll et. al, Nature 438;1040. Comparing 2axt to a limited set of all known bi-manganese proteins in PDB: No obvious sequence homology and a lack of broader structural homology Several bi-manganese enzymes share similar active-site geometry, of which all are known for secondary catalase activity involving water/peroxide/oxygen redox chemistry. J. Raymond et al, Coord. Chem. Rev. 252, 377 (2008).

ASN: the metalloprotein network CO CU FE MN MO NI V W ZN

Metal distribution in network neighborhoods 1-step 2-step Tendency for one metal to be present in neighborhood. Clear suppression in case of Fe, weak suppression for Cu

Nearest neighbors in ASN share function Estimate functional similarity along a link through EC values

Two possible approaches from Network Systems Biology: Focus on proteins’ active sites Focus on vulnerabilities of metabolic network structure

Mathematical approach: Flux Balance Analysis Most used (and only currently realistic) method for modeling genome-scale metabolism Based on: List of all possible reactions Mass conservation Steady-state Optimization of a cellular objective

Success of growth analysis in wild-type E. coli Example application: Success of growth analysis in wild-type E. coli E. coli growth on acetate very well described by FBA and PhPP analysis: Overall average error in measured vs. predicted uptake / secretion fluxes of 5.8% Edwards et al, Nature biotech 19 (2001)

Metabolic plasticity: adaptation to different nutrient environments Sample 30,000 different optimal conditions randomly and uniformly Metabolic network adapts to environmental changes using: (a) Flux plasticity (changes in flux rates) (b) Structural plasticity (reaction [de-] activation) Flux plasticity Structural plasticity

Metabolic plasticity: adaptation to different nutrient environments Sample 30,000 different optimal conditions randomly and uniformly Metabolic network adapts to environmental changes using: (a) Flux plasticity (changes in flux rates) (b) Structural plasticity (reaction [de-] activation) There exists a group of reactions NOT subject to structural plasticity: the metabolic core These reactions must play a key role in maintaining the metabolism’s overall functional integrity

The Metabolic Core A connected set of reactions that are ALWAYS active  not random effect The larger the network, the smaller the core  a collective network effect

The metabolic core is essential The core is highly essential: 75% lethal (only 20% in non-core) for E. coli. 84% lethal (16% non-core) for S. cerevisiae. The core is highly evolutionary conserved in E.coli: 72% of core enzymes (48% of non-core). The core fluxes and corresponding gene-expression patterns significantly correlated  Metabolic core is highly essential, conserved and synchronized Metabolic core of E. coli: Potential targets for antibiotic treatment Several current antibiotic targets present: Sulfonamides (Folate biosynthesis) Trimethoprim ( -- # -- ) Cycloserine (Peptidoglycan biosynth) Fosfomycin ( -- # -- ) Large parts of central carbon metabolism not included. E. Almaas, et al, PLoS Comput. Biol. 1(7):e68 (2005)

Are some essential reactions more difficult to supplant by HGT? Organism cannot live (produce biomass) without essential genes Use constraint-based modeling to identify essential genes: Trivial! Are the essential genes “robust”… ie. can the organism get alternative genes through HGT easily? Recent analysis of metabolic network structure suggests some reactions are more difficult to supplant than others [Barve & Wagner (PNAS, 2012)]

A) B) Using FBA to assess gene essentiality Case A): Gene knockout is not essential – biomass can still be produced Case B): Gene knockout is essential – biomass is impossible to produce

Identification of robust targets for antimicrobials Generate “universe” of possible metabolic reactions Randomly sample possible metabolic networks with fixed characteristics Assess essentiality of each reaction for each random network sample.

Consequence of adding reaction “universe” to a genome-scale metabolic model

Analysis of randomly generated networks

Exact enumeration of alternative pathways E. coli M. tuberculosis E. coli M. tuberculosis W Want few alternative paths, all with large length

Synthetic lethality analysis Reactions that are non-essential during single-reaction inhibition may be essential when paired Analyze all double-knockout possibilities for randomly generated networks  Systematically identify reaction pairs that have high degree of vulnerability

Network of synthetically lethal reaction pairs Escherichia coli Methanosarcina barkeri

Summary Studying protein active-site structure similarity network (ASN) reveals unbiased similarity map of protein function ASN may be applied to assess viability of potential drug targets identify new potential targets of existing drugs Constraint-based modeling of metabolic networks can be used to identify vulnerable reaction steps, singly or doubly (synthetic lethals) From FBA analysis: Some reactions are difficult to supplant when inhibited because few viable alternatives exist among microbes Some reactions participate in many synthetic lethal pairs and could serve as antimicrobial combination targets.

THANKS!! http://www.ntnu.edu/almaaslab