Generic placeholder image

Current Topics in Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

Research Article

Predicting Metabolic Reaction Networks with Perturbation-Theory Machine Learning (PTML) Models

Author(s): Karel Diéguez-Santana, Gerardo M. Casañola-Martin, James R. Green, Bakhtiyor Rasulev and Humberto González-Díaz*

Volume 21, Issue 9, 2021

Published on: 31 March, 2021

Page: [819 - 827] Pages: 9

DOI: 10.2174/1568026621666210331161144

Price: $65

Abstract

Background: Checking the connectivity (structure) of complex Metabolic Reaction Networks (MRNs) models proposed for new microorganisms with promising properties is an important goal for chemical biology.

Objective: In principle, we can perform a hand-on checking (Manual Curation). However, this is a challenging task due to the high number of combinations of pairs of nodes (possible metabolic reactions).

Results: The CPTML linear model obtained using the LDA algorithm is able to discriminate nodes (metabolites) with the correct assignation of reactions from incorrect nodes with values of accuracy, specificity, and sensitivity in the range of 85-100% in both training and external validation data series.

Methods: In this work, we used Combinatorial Perturbation Theory and Machine Learning techniques to seek a CPTML model for MRNs >40 organisms compiled by Barabasis’ group. First, we quantified the local structure of a very large set of nodes in each MRN using a new class of node index called Markov linear indices fk. Next, we calculated CPT operators for 150000 combinations of query and reference nodes of MRNs. Last, we used these CPT operators as inputs of different ML algorithms.

Conclusion: Meanwhile, PTML models based on Bayesian network, J48-Decision Tree and Random Forest algorithms were identified as the three best non-linear models with accuracy greater than 97.5%. The present work opens the door to the study of MRNs of multiple organisms using PTML models.

Keywords: Metabolic pathways, Complex networks, Combinatorial perturbation theory models, Machine learning, linear invariants, Markov chains.

[1]
Kamps, D.; Dehmelt, L. Deblurring signal network dynamics. ACS Chem. Biol., 2017, 12(9), 2231-2239.
[http://dx.doi.org/10.1021/acschembio.7b00451] [PMID: 28806053]
[2]
Carbonell, P.; Parutto, P.; Baudier, C.; Junot, C.; Faulon, J-L. Retropath: automated pipeline for embedded metabolic circuits. ACS Synth. Biol., 2014, 3(8), 565-577.
[http://dx.doi.org/10.1021/sb4001273] [PMID: 24131345]
[3]
Stephanopoulos, G. Synthetic biology and metabolic engineering. ACS Synth. Biol., 2012, 1(11), 514-525.
[http://dx.doi.org/10.1021/sb300094q] [PMID: 23656228]
[4]
Libis, V.; Delépine, B.; Faulon, J-L. Expanding biosensing abilities through computer-aided design of metabolic pathways. ACS Synth. Biol., 2016, 5(10), 1076-1085.
[http://dx.doi.org/10.1021/acssynbio.5b00225] [PMID: 27028723]
[5]
Hadadi, N.; Hafner, J.; Shajkofci, A.; Zisaki, A.; Hatzimanikatis, V. ATLAS of biochemistry: A repository of all possible biochemical reactions for synthetic biology and metabolic engineering studies. ACS Synth. Biol., 2016, 5(10), 1155-1166.
[http://dx.doi.org/10.1021/acssynbio.6b00054] [PMID: 27404214]
[6]
Jeong, H.; Tombor, B.; Albert, R.; Oltvai, Z.N.; Barabási, A.L. The large-scale organization of metabolic networks. Nature, 2000, 407(6804), 651-654.
[http://dx.doi.org/10.1038/35036627] [PMID: 11034217]
[7]
Ma, H.; Zeng, A-P. Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics, 2003, 19(2), 270-277.
[http://dx.doi.org/10.1093/bioinformatics/19.2.270] [PMID: 12538249]
[8]
Stanford, N.J.; Lubitz, T.; Smallbone, K.; Klipp, E.; Mendes, P.; Liebermeister, W. Systematic construction of kinetic models from genome-scale metabolic networks. PLoS One, 2013, 8(11), e79195.
[http://dx.doi.org/10.1371/journal.pone.0079195] [PMID: 24324546]
[9]
Boccaletti, S.; Latora, V.; Moreno, Y.; Chavez, M.; Hwang, D.U. Complex networks: Structure and dynamics. Phys. Rep., 2006, 424, 175-308.
[http://dx.doi.org/10.1016/j.physrep.2005.10.009]
[10]
Bonchev, D. On the complexity of directed biological networks. SAR QSAR Environ. Res., 2003, 14(3), 199-214.
[http://dx.doi.org/10.1080/1062936031000101764] [PMID: 12854653]
[11]
Bornholdt, S.; Schuster, H.G. Handbook of Graphs and Complex Networks: From the Genome to the Internet; WILEY-VCH GmbH & CO. KGa.: Wheinheim, 2003.
[12]
Breiger, R. The Analysis of social networks.Handbook of Data Analysis; Hardy, M.; Bryman, A., Eds.; Sage Publications: London, 2004, pp. 505-526.
[http://dx.doi.org/10.4135/9781848608184.n22]
[13]
Jeong, H.; Mason, S.P.; Barabási, A.L.; Oltvai, Z.N. Lethality and centrality in protein networks. Nature, 2001, 411(6833), 41-42.
[http://dx.doi.org/10.1038/35075138] [PMID: 11333967]
[14]
González-Díaz, H.; Arrasate, S.; Gómez-SanJuan, A.; Sotomayor, N.; Lete, E.; Besada-Porto, L.; Ruso, J.M. General theory for multiple input-output perturbations in complex molecular systems. 1. Linear QSPR electronegativity models in physical, organic, and medicinal chemistry. Curr. Top. Med. Chem., 2013, 13(14), 1713-1741.
[http://dx.doi.org/10.2174/1568026611313140011] [PMID: 23889050]
[15]
Box, G.E.P.; Jenkins, G.M. Time series analysis; Holden-Day: Maryland, 1970, p. 553.
[16]
Speck-Planche, A.; Dias Soeiro Cordeiro, M.N. Speeding up early drug discovery in antiviral research: a fragment-based in silico approach for the design of virtual anti-hepatitis c leads. ACS Comb. Sci., 2017, 19(8), 501-512.
[http://dx.doi.org/10.1021/acscombsci.7b00039] [PMID: 28437091]
[17]
Kleandrova, V.V.; Ruso, J.M.; Speck-Planche, A.; Dias Soeiro Cordeiro, M.N. Enabling the discovery and virtual screening of potent and safe antimicrobial peptides. simultaneous prediction of antibacterial activity and cytotoxicity. ACS Comb. Sci., 2016, 18(8), 490-498.
[http://dx.doi.org/10.1021/acscombsci.6b00063] [PMID: 27280735]
[18]
Speck-Planche, A.; Cordeiro, M.N. Computer-aided discovery in antimicrobial research: In silico model for virtual screening of potent and safe anti-pseudomonas agents. Comb. Chem. High Throughput Screen., 2015, 18(3), 305-314.
[http://dx.doi.org/10.2174/1386207318666150305144249] [PMID: 25747443]
[19]
Speck-Planche, A.; Cordeiro, M.N. Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening. ACS Comb. Sci., 2014, 16(2), 78-84.
[http://dx.doi.org/10.1021/co400115s] [PMID: 24383958]
[20]
Vergara-Galicia, J.; Prado-Prado, F.J.; Gonzalez-Diaz, H. Galvez-Markov network transferability indices: review of classic theory and new model for perturbations in metabolic reactions. Curr. Drug Metab., 2014, 15(5), 557-564.
[http://dx.doi.org/10.2174/1389200215666140605125827] [PMID: 24909421]
[21]
Riera-Fernandez, P.; Munteanu, C.R.; Martin-Romalde, R.; Duardo-Sanchez, A.; Gonzalez-Diaz, H. Markov-randic indices for qspr re-evaluation of metabolic, parasite-host, fasciolosis spreading, brain cortex and legal-social complex networks. Curr. Bioinform., 2013, 8, 401-415.
[http://dx.doi.org/10.2174/1574893611308040003]
[22]
Ponce, Y.M.; Garit, J.A.; Torrens, F.; Zaldivar, V.R.; Castro, E.A. Atom, atom-type, and total linear indices of the “molecular pseudograph’s atom adjacency matrix”: application to QSPR/QSAR studies of organic compounds. Molecules, 2004, 9(12), 1100-1123.
[http://dx.doi.org/10.3390/91201100] [PMID: 18007507]
[23]
Bediaga, H.; Arrasate, S.; González-Díaz, H. PTML combinatorial model of chembl compounds assays for multiple types of cancer. ACS Comb. Sci., 2018, 20(11), 621-632.
[http://dx.doi.org/10.1021/acscombsci.8b00090] [PMID: 30240186]
[24]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N. Chemoinformatics in anti-cancer chemotherapy: multi-target QSAR model for the in silico discovery of anti-breast cancer agents. Eur. J. Pharm. Sci., 2012, 47(1), 273-279.
[http://dx.doi.org/10.1016/j.ejps.2012.04.012] [PMID: 22538055]
[25]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N. Unified multi-target approach for the rational in silico design of anti-bladder cancer agents. Anticancer. Agents Med. Chem., 2013, 13(5), 791-800.
[http://dx.doi.org/10.2174/1871520611313050013] [PMID: 23272967]
[26]
Speck-Planche, A.; Kleandrova, V.V.; Cordeiro, M.N. New insights toward the discovery of antibacterial agents: multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugs. Eur. J. Pharm. Sci., 2013, 48(4-5), 812-818.
[http://dx.doi.org/10.1016/j.ejps.2013.01.011] [PMID: 23376211]
[27]
Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N. Multi-target inhibitors for proteins associated with Alzheimer: in silico discovery using fragment-based descriptors. Curr. Alzheimer Res., 2013, 10(2), 117-124.
[http://dx.doi.org/10.2174/1567205011310020001] [PMID: 22515494]
[28]
Marrero-Ponce, Y. Linear indices of the “molecular pseudograph’s atom adjacency matrix”: Definition, significance-interpretation, and application to QSAR analysis of flavone derivatives as HIV-1 integrase inhibitors. J. Chem. Inf. Comput. Sci., 2004, 44(6), 2010-2026.
[http://dx.doi.org/10.1021/ci049950k] [PMID: 15554670]
[29]
Martins Alho, M.A.; Marrero-Ponce, Y.; Barigye, S.J.; Meneses-Marcel, A.; Machado Tugores, Y.; Montero-Torres, A.; Gómez-Barrio, A.; Nogal, J.J.; García-Sánchez, R.N.; Vega, M.C.; Rolón, M.; Martínez-Fernández, A.R.; Escario, J.A.; Pérez-Giménez, F.; Garcia-Domenech, R.; Rivera, N.; Mondragón, R.; Mondragón, M.; Ibarra-Velarde, F.; Lopez-Arencibia, A.; Martín-Navarro, C.; Lorenzo-Morales, J.; Cabrera-Serra, M.G.; Piñero, J.; Tytgat, J.; Chicharro, R.; Arán, V.J. Antiprotozoan lead discovery by aligning dry and wet screening: prediction, synthesis, and biological assay of novel quinoxalinones. Bioorg. Med. Chem., 2014, 22(5), 1568-1585.
[http://dx.doi.org/10.1016/j.bmc.2014.01.036] [PMID: 24513185]
[30]
Rescigno, A.; Casañola-Martin, G.M.; Sanjust, E.; Zucca, P.; Marrero-Ponce, Y. Vanilloid derivatives as tyrosinase inhibitors driven by virtual screening-based QSAR models. Drug Test. Anal., 2011, 3(3), 176-181.
[http://dx.doi.org/10.1002/dta.187] [PMID: 21125547]
[31]
Casañola-Martín, G.M.; Khan, M.T.H.; Marrero-Ponce, Y.; Ather, A.; Sultankhodzhaev, M.N.; Torrens, F. New tyrosinase inhibitors selected by atomic linear indices-based classification models. Bioorg. Med. Chem. Lett., 2006, 16(2), 324-330.
[http://dx.doi.org/10.1016/j.bmcl.2005.09.085] [PMID: 16275084]
[32]
González-Díaz, H.; Duardo-Sanchez, A.; Ubeira, F.M.; Prado-Prado, F.; Pérez-Montoto, L.G.; Concu, R.; Podda, G.; Shen, B. Review of MARCH-INSIDE & complex networks prediction of drugs: ADMET, anti-parasite activity, metabolizing enzymes and cardiotoxicity proteome biomarkers. Curr. Drug Metab., 2010, 11(4), 379-406.
[http://dx.doi.org/10.2174/138920010791514225] [PMID: 20446904]
[33]
Junker, B.H.; Koschützki, D.; Schreiber, F. Exploration of biological network centralities with CentiBiN. BMC Bioinformatics, 2006, 7, 219.
[http://dx.doi.org/10.1186/1471-2105-7-219] [PMID: 16630347]
[34]
Hill, T.; Lewicki, P. STATISTICS Methods and Applications. A Comprehensive Reference for Science, Industry and Data Mining; StatSoft: Tulsa, 2006, Vol. 1, p. 813.
[35]
Witten, H.I.; Frank, E. Data Mining: Practical machine learning tools and techniques, 2nd ed.; Morgan Kaufmann: San Francisco, USA, 2005.
[36]
Breiman, L. Random Forests. Mach. Learn., 2001, 45, 5-32.
[http://dx.doi.org/10.1023/A:1010933404324]
[37]
Quinlan, R. C4.5: Programs for Machine Learning; Morgan Kaufmann Publishers: San Mateo, CA, 1993.
[38]
Breiman, L.; Friedman, J.H.; Olshen, R.A.; Stone, C.J. Classification and Regression Trees; CRC press.: Wadsworth: Monterey, CA, 1984.

Rights & Permissions Print Export Cite as
© 2022 Bentham Science Publishers | Privacy Policy