Books

 

 

 

Projects

 

 

 

Citation

 

Editorships

Current Drug Targets

Frontiers Section Editor (Bioinformatics and Biophysics) for the Current Drug Targets ISSN: 1873-5592

Bentham Link

Current Medicinal Chemistry

Section Editor (Bioinformatics in Drug Design and Discovery) for the Current Medicinal Chemistry ISSN: 1875-533X

Bentham Link

Combinatorial Chemistry and High Throughput Screening

Section Editor (Combinatorial/Medicinal Chemistry) for the Combinatorial Chemistry and High Throughput Screening ISSN: 1875-5402

Bentham Link

Current Bioinformatics

Member of the Editorial Board for the Current Bioinformatics ISSN: 2212-392X (Online) ISSN: 1574-8936 (Print)

Bentham Link

Organic and Medicinal Chemistry International Journal

Member of the Editorial Board for the Organic and Medicinal Chemistry International Journal ISSN: 2474-7610

Bentham Link

Bioengineering International

Section Editor in Chief (Bioinformatics) for Bioengineering International. ISSN 2668-7119

Bioengineering International

 

Taba

Citation

Please cite the following reference (da Silva AD et al., 2020) if the Taba program was useful.        

Da Silva AD, Bitencourt-Ferreira G, de Azevedo WF Jr. Taba: A Tool to Analyze the Binding Affinity. J Comput Chem. 2020; 41(1): 69–73. doi: 10.1002/jcc.26048   PubMed   Publons   




Message posted on September 10, 2021.

Dear Taba Users,

Due to recent changes in the protein data bank, we had to update all programs that we developed for the generation of machine learning models to address protein-ligand binding affinity. The download python scripts of Taba, SAnDReS, and SFSXplorer do not work anymore. I am working on the update of all these programs, and soon I will release new versions. Thank you very much for your patience.

 Stay safe,


                                    Dr. Walter F. de Azevedo, Jr.
                                    I am a freethinker influencing science and technology.

How to Install 

You need to have Python 3 installed on your computer to run Taba. In addition, you also need NumPy (1.14.5*), Matplotlib, scikit-learn (0.19.1*), PyQt4 and SciPy (1.1.0*).
*You can use higher versions as well.

Windows
  • Step 1. Download Taba (available here)
  • Step 2. Unzip the zipped file TABA_dist
  • Step 3. Copy TABA_dist directory to c:\ 
  • Step 4. Open a command prompt window and type: cd c:\TABA_dist 
  • Step 5. Then type: python taba.py 

This launches a GUI window for Taba. That´s it, good Taba session. See help for additional information about how to run Taba. 

Linux
  • Step 1. Download Taba (available here)
  • Step 2. Unzip the zipped file TABA_dist
  • Step 3. Copy TABA_dist directory to the directory of your choice
  • Step 4. Open a terminal and type cd /your personal directory/TABA_dist
  • Step 5. Then type: python taba.py

This launches a GUI window for Taba. That´s it, good Taba session. See help for additional information about how to run Taba.  

Overview 

The basic idea behind the Taba is that the determinant structural features responsible for ligand-binding affinity are already somehow imprinted in the three-dimensional structures of protein-ligand complexes (da Silva AD et al., 2020). When we consider an ensemble of crystallographic structures available at the protein data bank (PDB) (Berman et al., 2000; Veit-Acosta and de Azevedo, 2021), for which ligand-binding information data is available (Wang et al., 2004; Hu et al., 2005; Liu et al., 2007) we have the raw data that can be used by the program Taba to generate a target-based polynomial scoring function. To build this target-based polynomial scoring function (flowchart below), Taba reads all structures available for a biological system of interest and calculates the average distances for each type of pair of atoms. For instance, consider intermolecular Carbon-Carbon distances, where one Carbon belongs to the protein and the second one is in the ligand. Taba calculates the average intermolecular distance for Carbon-Carbon pair. Taba considers this length as the equilibrium distance for a Carbon-Carbon pair, taking an analogy with a mass-spring system. For a given structure, displacement from this equilibrium distance generates an increase in the energy of the system. 



Flowchart showing the main steps used to generate targeted-scoring functions with Taba (da Silva AD et al., 2020). 

For each pair of atoms, Taba calculates the average intermolecular distances. These distances are considered the equilibrium distance for each pair of atoms. We have an equilibrium distance for Carbon-Carbon pair, another for Carbon-Oxygen pair, and so on. The animated figure below shows the oscillation of a mass-spring system, displacement from the equilibrium generates a restoring force that causes the system to move in the contrary direction, in a harmonic motion.

Mass-spring system in an undamped oscillation movement (the program Mathematica generated the above animation, the code is available here).

As we previously highlighted, to apply Taba we need to have an ensemble of crystallographic structures for which ligand-binding affinity is known. This set of structures is used to train our model. In the first round, Taba calculates the average distance for each pair of atoms. On a second round, Taba applies supervised machine learning techniques (Bitencourt-Ferreira et al., 2021; Bitencourt-Ferreira and de Azevedo, 2019)  available in scikit-learn (Pedregosa et al., 2011) to determine the relative weights of each type of pair of atoms. Taba considers intermolecular distances for each pair of atoms as explanatory variables. The response variable is the log of binding affinity, for instance, log(Ki), where Ki is the inhibition constant. Taba considers the following atoms from the protein structure: C, N, O, S, and P. For the ligands, Taba uses the following atoms: C, N, O, S, F, Cl, Br, I, and P.

References    

Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The Protein Data Bank. Nucleic Acids Res. 2000; 28(1): 235–242.   PubMed   

Bitencourt-Ferreira G, de Azevedo WF Jr. Machine Learning to Predict Binding Affinity. Methods Mol Biol. 2019; 2053: 251–273.   PubMed   

Bitencourt-Ferreira G, da Silva AD, de Azevedo WF Jr. Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets. A Study of Cyclin-Dependent Kinase 2. Curr Med Chem. 2021; 28(2): 253–265.   PubMed   

Da Silva AD, Bitencourt-Ferreira G, de Azevedo WF Jr. Taba: A Tool to Analyze the Binding Affinity. J Comput Chem. 2020; 41(1): 69–73.  PubMed   

Hu L, Benson ML, Smith RD, Lerner MG, Carlson HA. Binding MOAD (Mother Of All Databases). Proteins: Struct Funct Genet. 2005; 60(3): 333–340.   PubMed   

Liu T, Lin Y, Wen X, Jorrisen RN, Gilson MK. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res. 2007; 35 (Database issue): D198–201.   PubMed  

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Verplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E. Scikit-learn: Machine Learning in Python. J Mach Learn Res. 2011; 12: 2825–2830.   PDF    

Veit-Acosta M, de Azevedo Junior WF. The Impact of Crystallographic Data for the Development of Machine Learning Models to Predict Protein-Ligand Binding Affinity. Curr Med Chem. doi: 10.2174/0929867328666210210121320.   PubMed   

Wang R, Fang X, Lu Y, Wang S. The PDBbind Database: Collection of Binding Affinities for Protein-Ligand Complexes with Known Three-Dimensional Structures. J. Med. Chem. 2004; 47(12): 2977–2980.   PubMed