Development and Application of Web-Based Open Source Drug Discovery Platforms Yuri Pevzner University of South Florida, Yuri [email protected]
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Structure-Based Function Prediction of Uncharacterized Protein Using Binding Sites Comparison
Structure-Based Function Prediction of Uncharacterized Protein Using Binding Sites Comparison Janez Konc1, Milan Hodosˇcˇek1, Mitja Ogrizek1, Joanna Trykowska Konc1, Dusˇanka Janezˇicˇ1,2* 1 National Institute of Chemistry, Ljubljana, Slovenia, 2 University of Primorska, Faculty of Mathematics, Natural Sciences and Information Technologies, Koper, Slovenia Abstract A challenge in structural genomics is prediction of the function of uncharacterized proteins. When proteins cannot be related to other proteins of known activity, identification of function based on sequence or structural homology is impossible and in such cases it would be useful to assess structurally conserved binding sites in connection with the protein’s function. In this paper, we propose the function of a protein of unknown activity, the Tm1631 protein from Thermotoga maritima, by comparing its predicted binding site to a library containing thousands of candidate structures. The comparison revealed numerous similarities with nucleotide binding sites including specifically, a DNA-binding site of endonuclease IV. We constructed a model of this Tm1631 protein with a DNA-ligand from the newly found similar binding site using ProBiS, and validated this model by molecular dynamics. The interactions predicted by the Tm1631-DNA model corresponded to those known to be important in endonuclease IV-DNA complex model and the corresponding binding free energies, calculated from these models were in close agreement. We thus propose that Tm1631 is a DNA binding enzyme with endonuclease activity that recognizes DNA lesions in which at least two consecutive nucleotides are unpaired. Our approach is general, and can be applied to any protein of unknown function. It might also be useful to guide experimental determination of function of uncharacterized proteins. -
An Improved Branch and Bound Algorithm for the Maximum Clique Problem Janez Konc and Duˇsanka Janeˇziˇc∗
MATCH MATCH Commun. Math. Comput. Chem. 58 (2007) 569-590 Communications in Mathematical and in Computer Chemistry ISSN 0340 - 6253 An improved branch and bound algorithm for the maximum clique problem Janez Konc and Duˇsanka Janeˇziˇc∗ National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia (Received June 21, 2007) Abstract A new algorithm for finding a maximum clique in an undirected graph is described. An approximate coloring algorithm has been improved and used to provide bounds to the size of the maximum clique in a basic algorithm which finds a maximum clique. This basic algorithm was then extended to include dynamically varying bounds. The resulting algorithm is significantly faster than the comparable algorithm. ∗ Author to whom correspondence should be addressed email: [email protected] telephone: 00386-14760200 fax: 00386-14760300 - 570 - 1 Introduction A clique is a subset S of vertices in a graph G such that each pair of vertices in S is connected by an edge. The maximum clique problem is the problem of finding in a given graph the clique with the largest number of vertices. The algorithms for finding a maximum clique are frequently used in chem- ical information, bioinformatics and computational biology applications [1], where their main application is to search for similarity between molecules. These algorithms are used for screening databases of compounds to filter out molecules that are similar to known biologically active molecules and are feasible to be active themselves [2]. Also, these algorithms are used for comparing protein structures, to provide the information about protein function [3] and also the information about possible interactions between proteins [4, 5]. -
Propedia: a Database for Protein–Peptide Identification Based on A
Martins et al. BMC Bioinformatics (2021) 22:1 https://doi.org/10.1186/s12859-020-03881-z DATABASE Open Access Propedia: a database for protein–peptide identifcation based on a hybrid clustering algorithm Pedro M. Martins1 , Lucianna H. Santos1, Diego Mariano1, Felippe C. Queiroz3, Luana L. Bastos1, Isabela de S. Gomes3, Pedro H. C. Fischer5, Rafael E. O. Rocha1, Sabrina A. Silveira3, Leonardo H. F. de Lima5, Mariana T. Q. de Magalhães2, Maria G. A. Oliveira4 and Raquel C. de Melo‑Minardi1* *Correspondence: [email protected] Abstract 1 Laboratory of Bioinformatics Background: Protein–peptide interactions play a fundamental role in a wide variety and Systems (LBS), Department of Computer of biological processes, such as cell signaling, regulatory networks, immune responses, Science, Universidade and enzyme inhibition. Peptides are characterized by low toxicity and small interface Federal de Minas Gerais, Av areas; therefore, they are good targets for therapeutic strategies, rational drug plan‑ Pres. Antônio Carlos, Belo Horizonte, MG 31720‑901, ning and protein inhibition. Approximately 10% of the ethical pharmaceutical market Brazil is protein/peptide‑based. Furthermore, it is estimated that 40% of protein interactions Full list of author information are mediated by peptides. Despite the fast increase in the volume of biological data, is available at the end of the article particularly on sequences and structures, there remains a lack of broad and compre‑ hensive protein–peptide databases and tools that allow the retrieval, characterization and understanding of protein–peptide recognition and consequently support peptide design. Results: We introduce Propedia, a comprehensive and up‑to‑date database with a web interface that permits clustering, searching and visualizing of protein–peptide complexes according to varied criteria. -
Rna 3D Motifs: Identification, Clustering, and Analysis
RNA 3D MOTIFS: IDENTIFICATION, CLUSTERING, AND ANALYSIS ANTON I. PETROV A Dissertation Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY May 2012 Committee: Neocles Leontis, Advisor Craig L. Zirbel Graduate Faculty Representative Paul Morris Scott Rogers Ray Larsen i ABSTRACT Neocles B. Leontis, Advisor Many hairpin and internal RNA 3D motif structures are recurrent, occurring in various types of RNA molecules, not necessarily homologs. Although usually drawn as single-strand “loops” in RNA 2D diagrams, recurrent motifs share a common 3D structure, but can vary in sequence. It is essential to understand the sequence variability of RNA 3D motifs in order to advance the RNA 2D and 3D structure prediction and ncRNA discovery methods, to interpret mutations that affect ncRNAs, and to guide experimental functional studies. The dissertation is organized into two parts as follows. First, the development of a new online resource called RNA 3D Hub is described, which is intended to provide a useful resource for structure modeling and prediction. It houses non-redundant sets of RNA-containing 3D structures, RNA 3D motifs extracted from all RNA 3D structures, and the RNA 3D Motif Atlas, a representative collection of RNA 3D motifs. Unique and stable ids are assigned to all non- redundant equivalence classes of structure files, to all motifs, and to all motif instances. RNA 3D Hub is updated automatically on a regular schedule and is available at http://rna.bgsu.edu/rna3dhub. In the second part of the dissertation, the development of WebFR3D (http://rna.bgsu.edu/webfr3d), a new webserver for finding and aligning RNA 3D motifs, is described and its use in a biologically relevant context is then illustrated using two RNA 3D motifs.