BACKGROUND: Protein function is determined by many factors, namely by its constitution, spatial arrangement, and dynamic behavior. Studying these factors helps the biochemists and biologists to better understand the protein behavior and to design proteins with modified properties. One of the most common approaches to these studies is to compare the protein structure with other molecules and to reveal similarities and differences in their polypeptide chains. RESULTS: We support the comparison process by proposing a new visualization technique that bridges the gap between traditionally used 1D and 3D representations. By introducing the information about mutual positions of protein chains into the 1D sequential representation the users are able to observe the spatial differences between the proteins without any occlusion commonly present in 3D view. Our representation is designed to serve namely for comparison of multiple proteins or a set of time steps of molecular dynamics simulation. CONCLUSIONS: The novel representation is demonstrated on two usage scenarios. The first scenario aims to compare a set of proteins from the family of cytochromes P450 where the position of the secondary structures has a significant impact on the substrate channeling. The second scenario focuses on the protein flexibility when by comparing a set of time steps our representation helps to reveal the most dynamically changing parts of the protein chain.
LiteMol suite is an innovative solution that enables near-instant delivery of model and experimental biomacromolecular structural data, providing users with an interactive and responsive experience in all modern web browsers and mobile devices. LiteMol suite is a combination of data delivery services (CoordinateServer and DensityServer), compression format (BinaryCIF), and a molecular viewer (LiteMol Viewer). The LiteMol suite is integrated into Protein Data Bank in Europe (PDBe) and other life science web applications (e.g., UniProt, Ensemble, SIB, and CNRS services), it is freely available at https://litemol.org , and its source code is available via GitHub. LiteMol suite provides advanced functionality (annotations and their visualization, powerful selection features), and this chapter will describe their use for visual inspection of protein structures.
- MeSH
- Databases, Protein MeSH
- Internet MeSH
- Web Browser MeSH
- Protein Conformation * MeSH
- Proteins chemistry MeSH
- Software MeSH
- User-Computer Interface MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Europe MeSH
Protein structures contain highly complex systems of voids, making up specific features such as surface clefts or grooves, pockets, protrusions, cavities, pores or channels, and tunnels. Many of them are essential for the migration of solvents, ions and small molecules through proteins, and their binding to the functional sites. Analysis of these structural features is very important for understanding of structure-function relationships, for the design of potential inhibitors or proteins with improved functional properties. Here we critically review existing software tools specialized in rapid identification, visualization, analysis and design of protein tunnels and channels. The strengths and weaknesses of individual tools are reported together with examples of their applications for the analysis and engineering of various biological systems. This review can assist users with selecting a proper software tool for study of their biological problem as well as highlighting possible avenues for further development of existing tools. Development of novel descriptors representing not only geometry, but also electrostatics, hydrophobicity or dynamics, is needed for reliable identification of biologically relevant tunnels and channels.
Motivation: Studying the transport paths of ligands, solvents, or ions in transmembrane proteins and proteins with buried binding sites is fundamental to the understanding of their biological function. A detailed analysis of the structural features influencing the transport paths is also important for engineering proteins for biomedical and biotechnological applications. Results: CAVER Analyst 2.0 is a software tool for quantitative analysis and real-time visualization of tunnels and channels in static and dynamic structures. This version provides the users with many new functions, including advanced techniques for intuitive visual inspection of the spatiotemporal behavior of tunnels and channels. Novel integrated algorithms allow an efficient analysis and data reduction in large protein structures and molecular dynamic simulations. Availability and implementation: CAVER Analyst 2.0 is a multi-platform standalone Java-based application. Binaries and documentation are freely available at www.caver.cz. Supplementary information: Supplementary data are available at Bioinformatics online.
UNLABELLED: The transport of ligands, ions or solvent molecules into proteins with buried binding sites or through the membrane is enabled by protein tunnels and channels. CAVER Analyst is a software tool for calculation, analysis and real-time visualization of access tunnels and channels in static and dynamic protein structures. It provides an intuitive graphic user interface for setting up the calculation and interactive exploration of identified tunnels/channels and their characteristics. AVAILABILITY AND IMPLEMENTATION: CAVER Analyst is a multi-platform software written in JAVA. Binaries and documentation are freely available for non-commercial use at http://www.caver.cz.
Summary: MolArt fills the gap between sequence and structure visualization by providing a light-weight, interactive environment enabling exploration of sequence annotations in the context of available experimental or predicted protein structures. Provided a UniProt ID, MolArt downloads and displays sequence annotations, sequence-structure mapping and relevant structures. The sequence and structure views are interlinked, enabling sequence annotations being color overlaid over the mapped structures, thus providing an enhanced understanding and interpretation of the available molecular data. Availability and implementation: MolArt is released under the Apache 2 license and is available at https://github.com/davidhoksza/MolArt. The project web page https://davidhoksza.github.io/MolArt/ features examples and applications of the tool.
- MeSH
- Color MeSH
- Protein Conformation * MeSH
- Molecular Structure * MeSH
- Proteins * MeSH
- Software * MeSH
- Computational Biology MeSH
- Publication type
- Journal Article MeSH
Protein structure determines biological function. Accurately conceptualizing 3D protein/ligand structures is thus vital to scientific research and education. Virtual reality (VR) enables protein visualization in stereoscopic 3D, but many VR molecular-visualization programs are expensive and challenging to use; work only on specific VR headsets; rely on complicated model-preparation software; and/or require the user to install separate programs or plugins. Here we introduce ProteinVR, a web-based application that works on various VR setups and operating systems. ProteinVR displays molecular structures within 3D environments that give useful biological context and allow users to situate themselves in 3D space. Our web-based implementation is ideal for hypothesis generation and education in research and large-classroom settings. We release ProteinVR under the open-source BSD-3-Clause license. A copy of the program is available free of charge from http://durrantlab.com/protein-vr/, and a working version can be accessed at http://durrantlab.com/pvr/.
- MeSH
- Internet * MeSH
- Protein Conformation MeSH
- Proteins * chemistry ultrastructure MeSH
- Virtual Reality * MeSH
- Computational Biology methods MeSH
- Imaging, Three-Dimensional methods MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Research Support, N.I.H., Extramural MeSH
Networks of protein-protein interactions (PPI) constitute either stable or transient complexes in every cell. Most of the cellular complexes keep their function, and therefore stay similar, during evolution. The evolutionary constraints preserve most cellular functions via preservation of protein structures and interactions. The evolutionary conservation information is utilized in template-based approaches, like protein structure modeling or docking. Here we use the combination of the template-free docking method with conservation-based selection of the best docking model using our newly developed COZOID tool.We describe a step-by-step protocol for visual selection of docking models, based on their similarity to the original protein complex structure. Using the COZOID tool, we first analyze contact zones of the original complex structure and select contact amino acids for docking restraints. Then we model and dock the homologous proteins. Finally, we utilize different analytical modes of our COZOID tool to select the docking models most similar to the original complex structure.
PrankWeb is an online resource providing an interface to P2Rank, a state-of-the-art method for ligand binding site prediction. P2Rank is a template-free machine learning method based on the prediction of local chemical neighborhood ligandability centered on points placed on a solvent-accessible protein surface. Points with a high ligandability score are then clustered to form the resulting ligand binding sites. In addition, PrankWeb provides a web interface enabling users to easily carry out the prediction and visually inspect the predicted binding sites via an integrated sequence-structure view. Moreover, PrankWeb can determine sequence conservation for the input molecule and use this in both the prediction and result visualization steps. Alongside its online visualization options, PrankWeb also offers the possibility of exporting the results as a PyMOL script for offline visualization. The web frontend communicates with the server side via a REST API. In high-throughput scenarios, therefore, users can utilize the server API directly, bypassing the need for a web-based frontend or installation of the P2Rank application. PrankWeb is available at http://prankweb.cz/, while the web application source code and the P2Rank method can be accessed at https://github.com/jendelel/PrankWebApp and https://github.com/rdk/p2rank, respectively.
- MeSH
- Benchmarking MeSH
- Datasets as Topic MeSH
- Protein Interaction Domains and Motifs MeSH
- Internet MeSH
- Protein Conformation, alpha-Helical MeSH
- Protein Conformation, beta-Strand MeSH
- Humans MeSH
- Ligands MeSH
- Proteins chemistry metabolism MeSH
- Amino Acid Sequence MeSH
- Software * MeSH
- Machine Learning * MeSH
- Thermodynamics MeSH
- Protein Binding MeSH
- Binding Sites MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Studium protein‑proteinových interakcí in vivo se v současné době dostává do popředí zájmu – umožňuje prokázat nebo upřesnit již známé protein‑proteinové interakce a odhalit jejich inhibitory, zachytit konformační změny proteinů, objasnit nebo upřesnit signální kaskády v živé buňce s minimálním ovlivněním jejího buněčného prostředí. Jedním z možných přístupů umožňujících tuto charakteristiku jsou metody využívající rezonančního přenosu energie – fluorescenční (FRET) a jeho pozdější modifikace bioluminiscenční (BRET). Tyto metody jsou založeny na zviditelnění proteinových interakcí pomocí excitace fluorescenčních proteinů, ať už světelně nebo enzymaticky. Tyto přístupy umožňují nejen lokalizovat proteiny v buňce nebo jejich organelách (případně i v malých živočiších), ale i kvantifikovat intenzitu fluorescenčního nebo luminiscenčního signálu a odhalit pevnost vazby mezi interakčními partnery. V tomto příspěvku je objasněn princip metod FRET a BRET, jejich konkrétní aplikace při studiu protein‑proteinových interakcí a jsou popsány dosavadní poznatky získané s využitím těchto metod a upřesňující některé molekulární a buněčné mechanizmy a signalizace související s nádorovou biologií.
Nowadays, in vivo protein‑protein interaction studies have become preferable detecting methods that enable to show or specify (already known) protein interactions and discover their inhibitors. They also facilitate detection of protein conformational changes and discovery or specification of signaling pathways in living cells. One group of in vivo methods enabling these findings is based on fluorescent resonance energy transfer (FRET) and its bioluminescent modification (BRET). They are based on visualization of protein‑protein interactions via light or enzymatic excitation of fluorescent or bioluminescent proteins. These methods allow not only protein localization within the cell or its organelles (or small animals) but they also allow us to quantify fluorescent signals and to discover weak or strong interaction partners. In this review, we explain the principles of FRET and BRET, their applications in the characterization of protein‑protein interactions and we describe several findings using these two methods that clarify molecular and cellular mechanisms and signals related to cancer biology. Key words: FRET – BRET – imaging methods – protein‑protein interaction in vivo This work was supported by the Czech Science Foundation projects P206/12/G151 and 13--00956S, by the European Regional Development Fund and the State Budget of the Czech Republic (RECAMO, CZ.1.05/2.1.00/03.0101) and by MH CZ – DRO (MMCI, 00209805). The authors declare they have no potential conflicts of interest concerning drugs, products, or services used in the study. The Editorial Board declares that the manuscript met the ICMJE “uniform requirements” for biomedical papers. Submitted: 20. 1. 2014 Accepted: 31. 3. 2014
- Keywords
- intramolekulární biosenzory, protein‑proteinové interakce in vivo, intermolekulární biosenzory,
- MeSH
- Biosensing Techniques * MeSH
- Fluorescence MeSH
- Fluorescent Dyes MeSH
- Protein Interaction Mapping * methods MeSH
- Fluorescence Resonance Energy Transfer * methods MeSH
- Protein Folding MeSH
- Bioluminescence Resonance Energy Transfer Techniques * methods MeSH
- Green Fluorescent Proteins MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
