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BINANA specifications


Unique identifier OMICS_14097
Alternative name BINing ANAlyzer
Software type Package/Module
Interface Command line interface
Restrictions to use None
Input data Receptor file,ligand file
Input format PDBQT
Output data Ligand binding description
Operating system Unix/Linux, Mac OS, Windows
Programming languages Python
License GNU General Public License version 3.0
Computer skills Advanced
Version 1.2.0
Stability Stable
Maintained Yes




No version available

Publication for BINing ANAlyzer

BINANA citations


Resistance related metabolic pathways for drug target identification in Mycobacterium tuberculosis

BMC Bioinformatics
PMCID: 4745158
PMID: 26856535
DOI: 10.1186/s12859-016-0898-8

[…] ed with lowest energy values at the top. The top ten lowest energy binding modes for each compound were visually inspected in PyMol and were further analysed using PoseView and BINANA programs [, ]. PoseView determines four types of interactions namely; i) hydrogen bonds, ii) hydrophobic, iii) metal interactions and iv) π interactions, while BINANA was used to calculate all […]


Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening

PMCID: 4832270
PMID: 27110292
DOI: 10.1002/wcms.1225

[…] ase was performed, the estimated EFs in the top 10 hits were 10.3 (using the average of top 24 networks) and 6.9 (using Vina). In a subsequent study, NNScore 2.0 was presented using NN regression and BINANA features and Vina energetic terms as features. On the nine selected target classes, NNScore 2.0 (average AUC 0.59) was shown to outperform popular classical SFs such as AutoDockfast (0.51), Au […]


Combined computational and experimental analysis of a complex of ribonuclease III and the regulatory macrodomain protein, YmdB

PMCID: 4329070
PMID: 25546632
DOI: 10.1002/prot.24751

[…] below 1.5 Å, indicating that steric clashes, rotamer quality, and Ramachandran quality of the complexes are within the average values for X-ray structures of 1.5 Å resolution. Finally, the algorithm Binana (with default parameters), followed by a visual double-check, was used to identify the main protein-protein interactions. […]


A Virtual Screening Approach For Identifying Plants with Anti H5N1 Neuraminidase Activity

J Chem Inf Model
PMCID: 4340357
PMID: 25555059
DOI: 10.1021/ci500405g

[…] e potential binding modes of these analogues. The AutoDock pose of Garcinone C is shown in Figure A and B. Potential interactions between the docked compound and the NA receptor were identified using BINANA, PoseView,, Visual Molecular Dynamics, and visual inspection. Garcinone C may form hydrogen bonds with the Y347, R118, Y406, D151, and Q276 side chains (Figure A), as well as extensive cation-π […]


Crystal structure of the DdrB/ssDNA complex from Deinococcus radiodurans reveals a DNA binding surface involving higher order oligomeric states

Nucleic Acids Res
PMCID: 3834827
PMID: 23975200
DOI: 10.1093/nar/gkt759
call_split See protocol

[…] Analysis of protein–protein interfaces was performed using the PISA server from PDBe (). Assessment of protein–ssDNA interactions was carried out with the aid of NUCPLOT () and BINANA 1.0.0 (). Input files for BINANA in pdbqt format were generated with AutoDockTools () using calculated Gasteiger charges and merged non-polar hydrogens. […]


Comparing Neural Network Scoring Functions and the State of the Art: Applications to Common Library Screening

J Chem Inf Model
PMCID: 3735370
PMID: 23734946
DOI: 10.1021/ci400042y

[…] nergy of those close contacts; the number of ligand atoms of each atom type; and the number of ligand rotatable bonds. For NNScore 2.0, the input additionally included the descriptors provided by the BINANA algorithm (counts of the number of hydrophobic, π–π, hydrogen-bond, and salt-bridge interactions), as well as the components of the Vina scoring function (steric, hydrophobic, hydrogen-bond, an […]


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BINANA institution(s)
Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA, USA; Department of Chemistry and Biochemistry, NSF Center for Theoretical Biological Physics, National Biomedical Computation Resource, University of California San Diego, La Jolla, CA, USA; Department of Pharmacology, University of California San Diego, La Jolla, CA, USA; Howard Hughes Medical Institute, University of California San Diego, La Jolla, CA, USA
BINANA funding source(s)
This work was funding from NIH GM31749, NSF MCB-0506593, and MCA93S013

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