finDr: A web server for in silico D-peptide ligand identification

Background: Chemotherapy and radiotherapy have negative effects on normal tissues and they are very expensive and lengthy treatments. These disadvantages have recently attracted researchers to the new methods that specifically affect cancerous tissues and have lower damage to normal tissues. One of these methods is the use of intelligent recombinant fusion toxin. The fusion toxin DTGCSF, which consists of linked Diphtheria Toxin (DT) and Granulocyte Colony Stimulate Factor (GCSF), was first studied by Chadwick et al. in 1993 where HATPL linker provided the linking sequence between GCSF and the 486 amino acid sequences of DT. Methods: In this study, the fusion toxin DT389GCSF is evaluated for functional structure in silico. With the idea of the commercial fusion toxin of Ontak, the DT in this fusion protein is designed incomplete for 389 amino acids and is linked to the beginning of the GCSF cytokine via the SG4SM linker (DT389GCSF). The affinity of the DT389GCSF as a ligand with GCSF-R as receptor was compared with DT486GCSF as a ligand with GCSF-R as receptor. Both DT486GCSF and its receptor GCSF-R have been modeled by Easy Modeler2 software. Our fusion protein (DT389GCSF) and GCSF-R are modeled through Modeller software; all of the structures were confirmed by server MDWEB and VMD software. Then, the interaction studies between two proteins are done using protein-protein docking (HADDOCK 2.2 web server) for both the fusion protein in this study and DT486GCSF. Results: The HADDOCK results demonstrate that the interaction of DT389GCSF with GCSF-R is very different and has a more powerful interaction than DT486GCSF with GCSF-R. Conclusion: HADDOCK web server is operative tools for evaluation of protein–protein interactions, therefore, in silico study of DT389GCSF will help with studying the function and the structure of these molecules. Moreover, DT389GCSF may have important new therapeutic applications.

Download Full-text

Evaluation of protein-ligand docking methods on peptide-ligand complexes for docking small ligands to peptides

10.1101/212514 ◽

2017 ◽

Cited By ~ 2

Author(s):

Sandeep Singh ◽

Hemant Kumar Srivastava ◽

Gaurav Kishor ◽

Harinder Singh ◽

Piyush Agrawal ◽

...

Keyword(s):

Success Rate ◽

Scientific Community ◽

Web Server ◽

The Other ◽

Ligand Docking ◽

Peptide Ligand ◽

Autodock Vina ◽

The Past ◽

Link Type ◽

Other Hand

ABSTRACTIn the past, many benchmarking studies have been performed on protein-protein and protein-ligand docking however there is no study on peptide-ligand docking. In this study, we evaluated the performance of seven widely used docking methods (AutoDock, AutoDock Vina, DOCK 6, PLANTS, rDock, GEMDOCK and GOLD) on a dataset of 57 peptide-ligand complexes. Though these methods have been developed for docking ligands to proteins but we evaluate their ability to dock ligands to peptides. First, we compared TOP docking pose of these methods with original complex and achieved average RMSD from 4.74Å for AutoDock to 12.63Å for GEMDOCK. Next we evaluated BEST docking pose of these methods and achieved average RMSD from 3.82Å for AutoDock to 10.83Å for rDock. It has been observed that ranking of docking poses by these methods is not suitable for peptide-ligand docking as performance of their TOP pose is much inferior to their BEST pose. AutoDock clearly shows better performance compared to the other six docking methods based on their TOP docking poses. On the other hand, difference in performance of different docking methods (AutoDock, AutoDock Vina, PLANTS and DOCK 6) was marginal when evaluation was based on their BEST docking pose. Similar trend has been observed when performance is measured in terms of success rate at different cut-off values. In order to facilitate scientific community a web server PLDbench has been developed (http://webs.iiitd.edu.in/raghava/pldbench/).

Download Full-text

Peptide ligand screening of α-synuclein aggregation modulators by in silico panning

BMC Bioinformatics ◽

10.1186/1471-2105-8-451 ◽

2007 ◽

Vol 8 (1) ◽

pp. 451 ◽

Cited By ~ 25

Author(s):

Koichi Abe ◽

Natsuki Kobayashi ◽

Koji Sode ◽

Kazunori Ikebukuro

Keyword(s):

In Silico ◽

Peptide Ligand ◽

Ligand Screening

Download Full-text

3D-PP: A Tool for Discovering Conserved Three-Dimensional Protein Patterns

International Journal of Molecular Sciences ◽

10.3390/ijms20133174 ◽

2019 ◽

Vol 20 (13) ◽

pp. 3174

Author(s):

Alejandro Valdés-Jiménez ◽

Josep-L. Larriba-Pey ◽

Gabriel Núñez-Vivanco ◽

Miguel Reyes-Parada

Keyword(s):

Homology Modeling ◽

In Silico ◽

Rational Design ◽

Protein Structures ◽

Three Dimensional ◽

Web Server ◽

Free Access ◽

Protein Patterns ◽

Structural Motifs ◽

The Web

Discovering conserved three-dimensional (3D) patterns among protein structures may provide valuable insights into protein classification, functional annotations or the rational design of multi-target drugs. Thus, several computational tools have been developed to discover and compare protein 3D-patterns. However, most of them only consider previously known 3D-patterns such as orthosteric binding sites or structural motifs. This fact makes necessary the development of new methods for the identification of all possible 3D-patterns that exist in protein structures (allosteric sites, enzyme-cofactor interaction motifs, among others). In this work, we present 3D-PP, a new free access web server for the discovery and recognition all similar 3D amino acid patterns among a set of proteins structures (independent of their sequence similarity). This new tool does not require any previous structural knowledge about ligands, and all data are organized in a high-performance graph database. The input can be a text file with the PDB access codes or a zip file of PDB coordinates regardless of the origin of the structural data: X-ray crystallographic experiments or in silico homology modeling. The results are presented as lists of sequence patterns that can be further analyzed within the web page. We tested the accuracy and suitability of 3D-PP using two sets of proteins coming from the Protein Data Bank: (a) Zinc finger containing and (b) Serotonin target proteins. We also evaluated its usefulness for the discovering of new 3D-patterns, using a set of protein structures coming from in silico homology modeling methodologies, all of which are overexpressed in different types of cancer. Results indicate that 3D-PP is a reliable, flexible and friendly-user tool to identify conserved structural motifs, which could be relevant to improve the knowledge about protein function or classification. The web server can be freely utilized at https://appsbio.utalca.cl/3d-pp/.

Download Full-text

ACID: a free tool for drug repurposing using consensus inverse docking strategy

Journal of Cheminformatics ◽

10.1186/s13321-019-0394-z ◽

2019 ◽

Vol 11 (1) ◽

Cited By ~ 7

Author(s):

Fan Wang ◽

Feng-Xu Wu ◽

Cheng-Zhang Li ◽

Chen-Yang Jia ◽

Sun-Wen Su ◽

...

Keyword(s):

Success Rate ◽

Single Molecule ◽

In Silico ◽

Drug Target ◽

Computational Models ◽

De Novo ◽

Web Server ◽

Drug Repurposing ◽

Promising Alternative ◽

Computational Approaches

AbstractDrug repurposing offers a promising alternative to dramatically shorten the process of traditional de novo development of a drug. These efforts leverage the fact that a single molecule can act on multiple targets and could be beneficial to indications where the additional targets are relevant. Hence, extensive research efforts have been directed toward developing drug based computational approaches. However, many drug based approaches are known to incur low successful rates, due to incomplete modeling of drug-target interactions. There are also many technical limitations to transform theoretical computational models into practical use. Drug based approaches may, thus, still face challenges for drug repurposing task. Upon this challenge, we developed a consensus inverse docking (CID) workflow, which has a ~ 10% enhancement in success rate compared with current best method. Besides, an easily accessible web server named auto in silico consensus inverse docking (ACID) was designed based on this workflow (http://chemyang.ccnu.edu.cn/ccb/server/ACID).

Download Full-text

A Web Server for Designing Molecular Switches Composed of Two Interacting RNAs

International Journal of Molecular Sciences ◽

10.3390/ijms22052720 ◽

2021 ◽

Vol 22 (5) ◽

pp. 2720

Author(s):

Akito Taneda ◽

Kengo Sato

Keyword(s):

In Silico ◽

Web Server ◽

Solution Space ◽

Rna Sequences ◽

Design Algorithm ◽

Multi Objective ◽

Rna Switches ◽

Benchmark Datasets ◽

Switch Design ◽

Rna Interaction

The programmability of RNA–RNA interactions through intermolecular base-pairing has been successfully exploited to design a variety of RNA devices that artificially regulate gene expression. An in silico design for interacting structured RNA sequences that satisfies multiple design criteria becomes a complex multi-objective problem. Although multi-objective optimization is a powerful technique that explores a vast solution space without empirical weights between design objectives, to date, no web service for multi-objective design of RNA switches that utilizes RNA–RNA interaction has been proposed. We developed a web server, which is based on a multi-objective design algorithm called MODENA, to design two interacting RNAs that form a complex in silico. By predicting the secondary structures with RactIP during the design process, we can design RNAs that form a joint secondary structure with an external pseudoknot. The energy barrier upon the complex formation is modeled by an interaction seed that is optimized in the design algorithm. We benchmarked the RNA switch design approaches (MODENA+RactIP and MODENA+RNAcofold) for the target structures based on natural RNA-RNA interactions. As a result, MODENA+RactIP showed high design performance for the benchmark datasets.

Download Full-text