Computational Systems Biology

Computational Systems Biology 

We have been working on the development of computational models for unraveling the molecular mechanisms underlying enzyme inhibition and protein-ligand interactions. These computational models can be used to predict binding affinity of a potential inhibitor for an enzyme; such knowledge has the potential to speed up drug discovery and decrease the cost of development of new drugs (de Ávila et al., 2017, Pintro & Azevedo, 2017). Furthermore, the availability of computational models to predict binding affinity based on the atomic coordinates of protein-ligand complexes adds flexibility to the process of drug discovery, since it allows us to computationally test different scenarios where a potential new drug may interact with a protein target (Xavier et al., 2016, Heck et al., 2017). We developed the program SAnDReS  (Xavier et al., 2016) to create machine-learning models targeted to the biological system of interested. We have successfully employed SAnDReS to study coagulation factor Xa (Xavier et al., 2016), cyclin-dependent kinases (de Ávila et al., 2017; Levin et al., 2018), HIV-1 protease (Pintro & de Azevedo, 2017), estrogen receptor (Amaral et al., 2018), cannabinoid receptor 1 (Russo & de Azevedo, 2018), and 3-dehydroquinate dehydratase (de Ávila & de Azevedo, 2018). Also, we used SAnDReS to develop a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes (Bitencourt-Ferreira & de Azevedo Jr., 2018).

References  

Amaral MEA, Nery LR, Leite CE, de Azevedo Junior WF, Campos MM. Pre-clinical effects of metformin and aspirin on the cell lines of different breast cancer subtypes. Invest New Drugs. 2018. doi: 10.1007/s10637-018-0568-y.   PubMed    PDF   

Bitencourt-Ferreira G, de Azevedo Jr. WF. Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes. Biophys Chem. 2018; 240: 63–69.   PubMed   PDF  

de Ávila MB, Xavier MM, Pintro VO, de Azevedo WF. Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.  Biochem Biophys Res Commun. 2017; 494: 305-10.  PubMed   PDF   

de Ávila MB, de Azevedo WF Jr. Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase. Chem Biol Drug Des. 2018. doi: 10.1111/cbdd.13312.   PubMed   PDF       

Heck GS, Pintro VO, Pereira RR, de Ávila MB, Levin NMB, de Azevedo WF. Supervised Machine Learning Methods Applied to Predict Ligand-Binding Affinity. Curr Med Chem. 2017; 24(23): 2459-70.   PubMed   PDF    

Levin NMB, Pintro VO, Bitencourt-Ferreira G, Mattos BB, Silvério AC, de Azevedo Jr. WF. Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys Chem. 2018; 235: 1–8.  PubMed   PDF        

Pintro VO, Azevedo WF. Optimized Virtual Screening Workflow. Towards Target-Based Polynomial Scoring Functions for HIV-1 Protease. Comb Chem High Throughput Screen. 2017. doi: 10.2174/1386207320666171121110019.   PubMed         

Russo S, De Azevedo WF. Advances in the Understanding of the Cannabinoid Receptor 1 - Focusing on the Inverse Agonists Interactions. Curr Med Chem. 2018. doi: 10.2174/0929867325666180417165247   PubMed   PDF      

Xavier MM, Heck GS, de Avila MB, Levin NM, Pintro VO, Carvalho NL, Azevedo WF Jr. SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions. Comb Chem High Throughput Screen. 2016; 19(10): 801-12.   Link   PubMed   Go To SAnDReS   PDF