Software

FRAGMENT GDB: Fragment Graph Database

Availability

Click here to access FGDB

Citing FGDB

If you publish results obtained by using FGDB please cite:
Toti, D., Macari, G., Barbierato, E., Polticelli, F. (2022) FGDB: a comprehensive graph database of ligand fragments from the Protein Data Bank. Database, Volume 2022, 2022, baac044. DOI: 10.1093/database/baac044

FGDB is a graph database of ligand fragments extracted and generated from the protein entries available in the Protein Data Bank. FGDB is meant to support and elicit campaigns of fragment-based drug design, by enabling users to query it in order to construct ad-hoc, target-specific libraries. In this regard, the database features more than 17,000 fragments, typically small, highly-soluble and chemically-stable molecules expressed via their canonical SMILES representation. For these fragments, the database provides information related to their contact frequencies with the amino acids, the ligands they are contained in, and the proteins the latter bind to. The graph database can be queried via standard web forms and textual searches by a number of identifiers (SMILES, ligand and protein PDB ids, etc.), as well as via graphical queries that can be performed against the graph itself, providing users with an intuitive and effective view upon the underlying biological entities. Further search mechanisms via advanced conjunctive/disjunctive/negated textual queries are also possible, in order to allow scientists to look for specific relationships and export their results for further studies.

LIBRAWA: Ligand Binding site Recognition Web Application

Availability

Click here to access LIBRA Web Application

Citing LIBRA WA

If you publish results obtained by using LIBRAWA please cite:
Toti, D., Viet Hung, L., Tortosa, V., Brandi, V., Polticelli, F. (2017) LIBRA-WA: a web application for ligand binding site detection and protein function recognition. Bioinformatics 34 (5), 878-880. DOI: 10.1093/bioinformatics/btx715

LIBRA Web Application is an online portal where users can exploit LIBRA+'s capabilities in recognizing the presence and identity of active sites and/or ligand binding sites given a protein's structural model. With a free registration, users are given a personal space where they can launch and schedule multiple recognitions, check out the resulting three-dimensional alignments and browse ligand clusters. Results produced in LIBRAWA are backward-compatible with LIBRA+ and can thus be exported in LIBRA+'s format to be accessed offline from the desktop application.

LIBRA+: Ligand Binding site Recognition Application Plus

Download

LIBRA+.zip (v. 2017-09-15, requires Java 1.7+)
Installation instructions: README.txt

Citing LIBRA+

If you publish results obtained by using LIBRA+ please cite:
Viet Hung, L., Caprari, S., Bizai, M., Toti, D., Polticelli, F. (2015) LIBRA: LIgand Binding site Recognition Application. Bioinformatics 31 (24), 4020-4022.
DOI: 10.1093/bioinformatics/btv489

LIBRA+ is an upgraded version of LIBRA, a tool that, given a protein's structural model, predicts the presence and identity of active sites and/or ligand binding sites. The algorithm implemented by LIBRA+ is based on a graph theory approach to find the largest subset of similar residues between an input protein and a collection of known functional sites. For this purpose, the algorithm makes use of two predefined databases for active sites and ligand binding sites, respectively derived from the Catalytic Site Atlas and the Protein Data Bank.

With respect to its earlier version, LIBRA+ now features a refined and enhanced scoring system, an updated ligand database and the grouping of ligands into clusters based on their structural similarity. Instructions for installing LIBRA+, running the program and analyzing the output can be found in the README file available under the download link.

DockingApp: Application for Docking and Virtual Screening

Download

Linux x64: DockingApp_Linux_x64.tar.gz
Mac OSX x64: DockingApp_Mac_x64.zip
Windows x64: DockingApp_Windows_x64.zip
Installation instructions (all versions): README.txt

Citing DockingApp

If you publish results obtained by using DockingApp please cite:
Di Muzio, E., Toti, D., Polticelli, F. (2017) DockingApp: a user friendly interface for facilitated docking simulations with AutoDock Vina. Journal of Computer-Aided Molecular Design 31 (2), 213-218.
DOI: 10.1007/s10822-016-0006-1

DockingApp is a user-friendly graphical application for carrying out molecular docking and virtual screening tasks, meant to enable non-experienced users to easily perform such activities and browse the docking results via a three-dimensional visualization.

DockingApp RF: Docking and Replicated Docking with a state-of-the-art scoring function

Download

Linux x64: DockingApp_RF_Linux_x64.tar.gz
Windows x64: DockingApp_RF_Windows_x64.zip
Installation instructions (all versions): README.txt

Citing DockingApp RF

If you publish results obtained by using DockingApp RF please cite:
DockingApp RF: A State-of-the-Art Novel Scoring Function for Molecular Docking in a User-Friendly Interface to AutoDock Vina (2017) International Journal of Molecular Sciences, 21(24), 1-17.
DOI: 10.3390/ijms21249548

DockingApp RF is a user-friendly graphical application for carrying out molecular docking and replicated docking, meant to enable non-experienced users to easily perform such activities and browse the docking results via a three-dimensional visualization. It implements a state-of-the-art scoring function based on a Random Forest algorithm, whose effectiveness has been proven on specific use cases of molecular docking, making DockingApp RF complementary to the classic DockingApp.

PRAISED: Protein-Related Abbreviation Identification, StoragE and Disambiguation

Availability

PRAISED will be online soon.

Citing PRAISED

If you publish results obtained by using PRAISED please cite:
Toti, D., Atzeni, P., Polticelli, F. (2012) Automatic protein abbreviations discovery and resolution from full-text scientific papers: the PRAISED framework. Bio-Algorithms and Medical-Systems. 8, 13-52.
DOI: 10.2478/bams-2012-0002

PRAISED is a lightweight framework for identifying and resolving protein-related abbreviations found in the full text of scientific papers. It is based on a three-phase process where: candidate abbreviations are firstly detected within an unstructured text, based on lexical clues and exclusion rules; abbreviations are then matched with their potential explanation, using syntactical and semantic criteria combined with fitting optimization techniques; and finally, resulting abbreviation-explanation pairs are sorted out according to the domain of interest, by performing an entity recognition phase against repositories of known biological entities.

Acknowledgments

The authors would like to thank Ing. Augusto Accosta for his vital contribution in the development of the PRAISED framework.

LIBRA: Ligand Binding site Recognition Application

Download

LIBRAv1.zip (v. 2015-07-13, requires Java 1.7+)

Citing LIBRA

If you publish results obtained by using LIBRA please cite:
Le Viet Hung, Silvia Caprari, Massimiliano Bizai, Daniele Toti, Fabio Polticelli. LIBRA: LIgand Binding site Recognition Application. Bioinformatics 31 (24), 4020-4022.
DOI: 10.1093/bioinformatics/btv489

LIBRA is a novel software tool that, given a protein's structural model, predicts the presence and identity of active sites and/or ligand binding sites. The algorithm implemented by LIBRA is based on a graph theory approach to find the largest subset of similar residues between an input protein and a collection of known functional sites. For this purpose, the algorithm makes use of two predefined databases for active sites and ligand binding sites, respectively derived from the Catalytic Site Atlas and the Protein Data Bank. Instructions for installing LIBRA, running the program and analyzing the output can be found in the README file provided within the package.
Please note that LIBRA is no longer maintained. Check out its newer version, LIBRA+, described above.

ASSIST: Active Site Similarity Search Tool

Download

ASSISTv1.zip (v. 2013-11-21, requires Java 1.6+)

Citing ASSIST

If you publish results obtained by using ASSIST please cite:
Caprari, S., Toti, D., Viet Hung, L., Di Stefano, M., Polticelli, F. (2014) ASSIST: a fast versatile local structural comparison tool. Bioinformatics 30 (7), 1022-1024.
DOI: 10.1093/bioinformatics/btt664

ASSIST is an application for the detection of catalytic sites in unknown function proteins. ASSIST is based on a local geometric/chemical comparison algorithm implemented to find the largest subset of similar residues (at the sidechain and/or backbone level) between an input protein and query sites/motifs. The user provides a protein structure as input and ASSIST performs a search for known functional sites based on a geometric hashing approach. The known catalytic sites are extracted from Janet Thornton Catalytic Site Atlas. Nonetheless, other functional motifs can be plugged in for the ASSIST execution in the form of coordinate files in PDB format. Instructions for installing ASSIST, running the program and analyzing the output can be found in the README file provided within the package.