Skip to main content
SpringerLink
Log in

In Silico Updates on Lead Identification for Obesity and Cancer

  • Chapter
  • First Online:
Obesity and Cancer

Abstract

The prevalence of obesity has risen considerably in previous decades in the modern world, and an additional rise is anticipated in the future decades. Several studies have established that this rise in obesity is accompanied by the elevated risk of cancer occurrence. A number of signaling proteins have been recognized to be related to both obesity and cancer. Computer-aided drug discovery methods have proved their worth in recent times by aiding in the discovery of various lifesaving drugs. Application of in silico methods of drug discovery in the context of obesity and cancer holds significant promise in discovering drugs for the prevention of this problem. In the present chapter, we have provided an overview of common targets in obesity and cancer along with the in silico drug discovery methods that might play a significant role in the discovery of drugs for obesity and cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abramyan TM, Snyder JA, Thyparambil AA et al (2016) Cluster analysis of molecular simulation trajectories for systems where both conformation and orientation of the sampled states are important. J Comput Chem 37:1973–1982

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Astuti AD, Mutiara AB (2009) Performance analysis on molecular dynamics simulation of protein using GROMACS. Computer Sci Biol 0893:1–8

    Google Scholar 

  • Balistreri CR, Caruso C, Candore G (2010) The role of adipose tissue and adipokines in obesity-related inflammatory diseases. Mediators Inflamm 2010:1–20

    Article  CAS  Google Scholar 

  • Basen-Engquist K, Chang M (2011) Obesity and cancer risk: recent review and evidence. Curr Oncol Rep 13:71–76

    Article  PubMed  PubMed Central  Google Scholar 

  • Boucher J, Kleinridders A, Kahn CR (2014) Insulin receptor signaling in normal and insulin-resistant states. CSH Perspect Biol 6:1–24

    CAS  Google Scholar 

  • Chen M, Huang J (2019) The expanded role of fatty acid metabolism in cancer: new aspects and targets. Precis Clin Med 2:183–191

    Article  PubMed  PubMed Central  Google Scholar 

  • Colditz GA, Peterson LL (2018) Obesity and cancer: evidence, impact, and future directions. Clin Chem 64:154–162

    Article  CAS  PubMed  Google Scholar 

  • Dar AM, Mir S (2017) Molecular docking: approaches, types, applications and basic challenges. J Anal Bioanal Tech 8:1–3

    Article  CAS  Google Scholar 

  • De Paris R, Quevedo CV, Ruiz DD (2015) Clustering molecular dynamics trajectories for optimizing docking experiments. Comput Intell Neurosci 2015:1–10

    Article  Google Scholar 

  • De Pergola G, Silvestris F (2013) Obesity as a major risk factor for cancer. J Obes 2013:1–12

    Article  CAS  Google Scholar 

  • Dhasmana A, Raza S, Jahan R et al (2019) High-throughput virtual screening (HTVS) of natural compounds and exploration of their biomolecular mechanisms: an in silico approach. In: Khan MS, Ahmad I, Chattopadhyay D (eds) New look to phytomedicine, 1st edn. Academic Press, London, pp 523–548

    Google Scholar 

  • Ezebuo FC, Kushwaha PP, Singh AK et al (2019) In-silico methods of drug design: molecular simulations and free energy calculations. In: Kumar S, Egbuna C (eds) Phytochemistry: an in-silico and in-vitro update, 1st edn. Springer, Singapore, pp 521–533

    Chapter  Google Scholar 

  • Freedland SJ, Aronson WJ (2004) Examining the relationship between obesity and prostate cancer. Rev Urol 6:73–81

    PubMed  PubMed Central  Google Scholar 

  • Gupta N, Sitwala N, Patel K (2014) Pharmacophore modelling, validation, 3D virtual screening, docking, design and in silico ADMET simulation study of histone deacetylase class-1 inhibitors. Med Chem Res 23:4853–4864

    Article  CAS  Google Scholar 

  • Gupta S, Singh AK, Kushwaha PP (2020a) Identification of potential natural inhibitors of SARS-CoV2 main protease by molecular docking and simulation studies. J Biomol Struct Dyn 2020:1–9

    Google Scholar 

  • Gupta S, Singh AK, Prajapati KS et al (2020b) Emerging role of ZBTB7A as an oncogenic driver and transcriptional repressor. Cancer Lett 483:22–34

    Article  CAS  PubMed  Google Scholar 

  • Kashyap N, Kushwaha PP, Singh AK et al (2020) Phytochemicals, cancer and miRNAs: an in-silico approach. In: Kumar S, Egbuna C (eds) Phytochemistry: an in-silico and in-vitro update, 1st edn. Springer, Singapore, pp 421–459

    Google Scholar 

  • Kerzmann A, Fuhrmann J, Kohlbacher O et al (2008) BALLDock/SLICK: a new method for protein-carbohydrate docking. J Chem Inf Model 48:1616–1625

    Article  CAS  PubMed  Google Scholar 

  • Kirschner MA, Schneider G, Ertel NH et al (1982) Obesity, androgens, estrogens, and cancer risk. Cancer Res 42:3281–3285

    Google Scholar 

  • Krishna S, Shukla S, Lakra AD et al (2017) Identification of potent inhibitors of DNA methyltransferase 1 (DNMT1) through a pharmacophore-based virtual screening approach. J Mol Graph Model 75:174–188

    Article  CAS  PubMed  Google Scholar 

  • Kumar A, Kushwaha PP, Varddhan S et al (2020) Fats and oils as sources of bioactive molecules. In: Egbuna C, Dable-Tupas G (eds) Functional foods and nutraceuticals. Springer, Cham, pp 99–108

    Chapter  Google Scholar 

  • Kushwaha PP, Vardhan PS, Kapewangolo P et al (2019) Bulbine frutescens phytochemical inhibits notch signaling pathway and induces apoptosis in triple negative and luminal breast cancer cells. Life Sci 234:1–15

    Article  CAS  Google Scholar 

  • Kushwaha PP, Das S, Singh AK (2020a) Drug development: drug delivery carriers and clinical trial. In: Kumar S (ed) Clinical biochemistry and drug development: from fundamentals to output. CRC Press, London, pp 163–178

    Chapter  Google Scholar 

  • Kushwaha PP, Malik R, Rawat SG et al (2020b) Drug development: in silico, in vivo, and system biology approach. In: Kumar S (ed) Clinical biochemistry and drug development: from fundamentals to output. CRC Press, London, pp 145–162

    Chapter  Google Scholar 

  • Kushwaha PP, Malik R, Vardhan PS et al (2020c) Human disease drug therapy and drug discovery. In: Kumar S (ed) Clinical biochemistry and drug development: from fundamentals to output. CRC Press, London, pp 133–144

    Chapter  Google Scholar 

  • Kushwaha PP, Kumar A, Maurya S et al (2020d) Bulbine frutescens phytochemicals as a promising anti-cancer drug discovery source: a computational study. In: Kumar S, Egbuna C (eds) Phytochemistry: an in-silico and in-vitro update, 1st edn. Springer, Singapore, pp 491–510

    Google Scholar 

  • Kushwaha PP, Maurya SK, Singh A et al (2020e) Bulbine frutescens phytochemicals as novel ABC-transporter inhibitor: a molecular docking and molecular dynamics simulation study. J Cancer Metastasis Treat 2021:1–13

    Google Scholar 

  • Kushwaha PP, Dash S, Singh AK et al (2020f) Cell membrane receptor. In: Kumar S (ed) Clinical biochemistry and drug development: from fundamentals to output. CRC Press, London, pp 93–108

    Chapter  Google Scholar 

  • Kushwaha PP, Gupta S, Singh AK et al (2020g) MicroRNA targeting nicotinamide adenine dinucleotide phosphate oxidases in cancer. Antioxid Redox Signal 32:267–284

    Article  CAS  PubMed  Google Scholar 

  • Meng XY, Zhang HX, Mezei M et al (2011) Molecular docking: a powerful approach for structure-based drug discovery. Curr Comput-Aid Drug 7:146–157

    Article  CAS  Google Scholar 

  • Muhammed MT, Aki-Yalcin E (2019) Homology modeling in drug discovery: overview, current applications, and future perspectives. Chem Biol Drug Des 93:12–20

    Article  CAS  PubMed  Google Scholar 

  • Mullen M, Gonzalez-Perez RR (2016) Leptin-induced JAK/STAT signaling and cancer growth. Vaccine 4:1–17

    Google Scholar 

  • Neves BJ, Braga RC, Melo-Filho CC (2018) QSAR-based virtual screening: advances and applications in drug discovery. Front Pharmacol 9:1–7

    Article  CAS  Google Scholar 

  • Pirogova E, Istivan T, Gan E et al (2011) Advances in methods for therapeutic peptide discovery, design and development. Curr Pharm Biotechnol 12:1117–1127

    Article  CAS  PubMed  Google Scholar 

  • Qing X, Lee XY, De Raeymaecker J et al (2014) Pharmacophore modeling: advances, limitations, and current utility in drug discovery. J Receptor Ligand Channel Res 7:81–92

    Google Scholar 

  • Senapati S, Kumar S, Singh AK et al (2020) Assessment of risk conferred by coding and regulatory variations of TMPRSS2 and CD26 in susceptibility to SARS-CoV-2 infection in human. J Genet 99:1–5

    Article  CAS  Google Scholar 

  • Singh P, Kushwaha PP, Singh AK, Kumar S (2019) Association between nuclear receptor coactivator 1 and stem cell signaling pathway and identification of natural NCOA1 inhibitors: an in-silico study. In: Kumar S, Egbuna C (eds) Phytochemistry: an in-silico and in-vitro update, 1st edn. Springer, Singapore, pp 545–564

    Chapter  Google Scholar 

  • Singh AK, Kushwaha PP, Prajapati KS et al (2020) Identification of FDA approved drugs and nucleoside analogues as potential SARS-CoV-2 A1pp domain inhibitor: an in silico study. Comput Biol Med 130:1–10

    CAS  Google Scholar 

  • Sittel F, Jain A, Stock G (2014) Principal component analysis of molecular dynamics: on the use of Cartesian vs. internal coordinates. J Chem Phys 141:1–9

    Article  CAS  Google Scholar 

  • Stock G, Jain A, Riccardi L et al (2012) Exploring the energy landscape of small peptides and proteins by molecular dynamics simulations. In: Uversky V (ed) Protein and peptide folding, misfolding and non-folding, 1st edn. Wiley, London, pp 65–77

    Google Scholar 

  • Stone TW, McPherson M, Darlington LG (2018) Obesity and cancer: existing and new hypotheses for a causal connection. EBioMedicine 30:14–28

    Article  PubMed  PubMed Central  Google Scholar 

  • Verma S, Prajapati KS, Kushwaha PP et al (2020) Resistance to second generation antiandrogens in prostate cancer: pathways and mechanisms. Cancer Drug Resist 3:1–20

    Google Scholar 

  • Vilar S, Costanzi S (2012) Predicting the biological activities through QSAR analysis and docking-based scoring. Methods Mol Biol 914:271–284

    CAS  PubMed  PubMed Central  Google Scholar 

  • Westley RL, May FE (2013) A twenty-first century cancer epidemic caused by obesity: the involvement of insulin, diabetes, and insulin-like growth factors. Int J Endocrinol 2013:1–38

    Article  CAS  Google Scholar 

  • Xia X (2017) Bioinformatics and drug discovery. Curr Top Med Chem 17(15):1709–1726

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yamashita F, Hashida M (2004) In silico approaches for predicting ADME properties of drugs. Drug Metab Pharmacokinet 19:327–338

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgement

PPK and MS acknowledge financial support from Indian Council of Medical Research (ICMR), India in the form of ICMR-Senior Research fellowship. SK acknowledges University Grants Commission, India and Department of Science and Technology, India for providing financial support in the form of UGC-BSR Research Start-Up-Grant [No. F.30– 372/2017 (BSR)] and DST-SERB Grant [EEQ/2016/000350] respectively. AKS and KSP acknowledge CSIR-India and DBT-India funding agencies for providing financial assistance in the form of Senior Research Fellowship and Junior Research Fellowship respectively.

Author information

Authors and Affiliations

  1. Molecular Signaling & Drug Discovery Laboratory, Department of Biochemistry, Central University of Punjab, Bathinda, Punjab, India

    Atul Kumar Singh, Mohd Shuaib, Prem Prakash Kushwaha, Kumari Sunita Prajapati, Rahul Sharma & Shashank Kumar

Authors
  1. Atul Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohd Shuaib
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Prem Prakash Kushwaha
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kumari Sunita Prajapati
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rahul Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shashank Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Biochemistry, Central University of Punjab, Bathinda, Punjab, India

    Shashank Kumar

  2. Department of Urology, Case Western Reserve University, Cleveland, OH, USA

    Sanjay Gupta

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Singh, A.K., Shuaib, M., Kushwaha, P.P., Prajapati, K.S., Sharma, R., Kumar, S. (2021). In Silico Updates on Lead Identification for Obesity and Cancer. In: Kumar, S., Gupta, S. (eds) Obesity and Cancer. Springer, Singapore. https://doi.org/10.1007/978-981-16-1846-8_13

Download citation

Publish with us

Policies and ethics

Search

Navigation