BACKGROUND: Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D structure analysis has been utilised in order to discover indications of antigenic pressure. The latter has been mostly based on the 3D models of the clonotypic B cell receptor immunoglobulin (BcR IG) amino acid sequences. Therefore, their accuracy is directly dependent on the quality of the model construction algorithms and the specific methods used to compare the ensuing models. Thus far, reliable and robust methods that can group the IG 3D models based on their structural characteristics are missing. RESULTS: Here we propose a novel method for clustering a set of proteins based on their 3D structure focusing on 3D structures of BcR IG from a large series of patients with CLL. The method combines techniques from the areas of bioinformatics, 3D object recognition and machine learning. The clustering procedure is based on the extraction of 3D descriptors, encoding various properties of the local and global geometrical structure of the proteins. The descriptors are extracted from aligned pairs of proteins. A combination of individual 3D descriptors is also used as an additional method. The comparison of the automatically generated clusters to manual annotation by experts shows an increased accuracy when using the 3D descriptors compared to plain bioinformatics-based comparison. The accuracy is increased even more when using the combination of 3D descriptors. CONCLUSIONS: The experimental results verify that the use of 3D descriptors commonly used for 3D object recognition can be effectively applied to distinguishing structural differences of proteins. The proposed approach can be applied to provide hints for the existence of structural groups in a large set of unannotated BcR IG protein files in both CLL and, by logical extension, other contexts where it is relevant to characterize BcR IG structural similarity. The method does not present any limitations in application and can be extended to other types of proteins.

• Klíčová slova
• 3D protein descriptors, CLL protein clustering, descriptor fusion,
• MeSH
• anotace sekvence MeSH
• automatizace MeSH
• chronická lymfatická leukemie metabolismus   MeSH
• databáze proteinů MeSH
• imunoglobuliny chemie   MeSH
• lidé MeSH
• sekvence aminokyselin MeSH
• zobrazování trojrozměrné * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• imunoglobuliny MeSH

The purpose of this quick guide is to help new modelers who have little or no background in comparative modeling yet are keen to produce high-resolution protein 3D structures for their study by following systematic good modeling practices, using affordable personal computers or online computational resources. Through the available experimental 3D-structure repositories, the modeler should be able to access and use the atomic coordinates for building homology models. We also aim to provide the modeler with a rationale behind making a simple list of atomic coordinates suitable for computational analysis abiding to principles of physics (e.g., molecular mechanics). Keeping that objective in mind, these quick tips cover the process of homology modeling and some postmodeling computations such as molecular docking and molecular dynamics (MD). A brief section was left for modeling nonprotein molecules, and a short case study of homology modeling is discussed.

• MeSH
• algoritmy MeSH
• aminokyseliny chemie   MeSH
• biologické modely MeSH
• databáze proteinů MeSH
• internet MeSH
• ionty MeSH
• koncentrace vodíkových iontů MeSH
• ligandy MeSH
• počítačová simulace MeSH
• posttranslační úpravy proteinů MeSH
• proteiny chemie   MeSH
• rozpouštědla MeSH
• sbalování proteinů MeSH
• simulace molekulární dynamiky MeSH
• simulace molekulového dockingu MeSH
• software MeSH
• strojové učení MeSH
• strukturní homologie proteinů MeSH
• voda MeSH
• výpočetní biologie metody   MeSH
• zobrazování trojrozměrné metody   MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• aminokyseliny MeSH
• ionty MeSH
• ligandy MeSH
• proteiny MeSH
• rozpouštědla MeSH
• voda MeSH

PDBsum is a web server providing structural information on the entries in the Protein Data Bank (PDB). The analyses are primarily image-based and include protein secondary structure, protein-ligand and protein-DNA interactions, PROCHECK analyses of structural quality, and many others. The 3D structures can be viewed interactively in RasMol, PyMOL, and a JavaScript viewer called 3Dmol.js. Users can upload their own PDB files and obtain a set of password-protected PDBsum analyses for each. The server is freely accessible to all at: http://www.ebi.ac.uk/pdbsum.

• Klíčová slova
• 3D protein structure, PDB, PDBsum, enzymes, molecular interactions, protein database, protein structure analysis, schematic diagrams,
• MeSH
• databáze proteinů * MeSH
• internet * MeSH
• molekulární modely * MeSH
• sekundární struktura proteinů * MeSH
• software * MeSH
• zobrazování trojrozměrné * MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Proteins are naturally formed by domains edging their functional and structural properties. A domain out of the context of an entire protein can retain its structure and to some extent also function on its own. These properties rationalize construction of artificial fusion multidomain proteins with unique combination of various functions. Information on the specific functional and structural characteristics of individual domains in the context of new artificial fusion proteins is inevitably encoded in sequential order of composing domains defining their mutual spatial positions. So the challenges in designing new proteins with new domain combinations lie dominantly in structure/function prediction and its context dependency. Despite the enormous body of publications on artificial fusion proteins, the task of their structure/function prediction is complex and nontrivial. The degree of spatial freedom facilitated by a linker between domains and their mutual orientation driven by noncovalent interactions is beyond a simple and straightforward methodology to predict their structure with reasonable accuracy. In the presented manuscript, we tested methodology using available modeling tools and computational methods. We show that the process and methodology of such prediction are not straightforward and must be done with care even when recently introduced AlphaFold II is used. We also addressed a question of benchmarking standards for prediction of multidomain protein structures-x-ray or Nuclear Magnetic Resonance experiments. On the study of six two-domain protein chimeras as well as their composing domains and their x-ray structures selected from PDB, we conclude that the major obstacle for justified prediction is inappropriate sampling of the conformational space by the explored methods. On the other hands, we can still address particular steps of the methodology and improve the process of chimera proteins prediction.

• Klíčová slova
• 3D structure prediction, fusion proteins, molecular simulations, x-ray crystallography,
• MeSH
• proteinové domény MeSH
• proteiny * chemie   MeSH
• rekombinantní fúzní proteiny * chemie   MeSH
• rentgenové záření MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• proteiny * MeSH
• rekombinantní fúzní proteiny * MeSH

Three-dimensional (3D) printing technology has attracted great attention for prototyping different electrochemical sensor devices. However, chiral recognition remains a crucial challenge for electrochemical sensors with similar physicochemical properties such as enantiomers. In this work, a magnetic covalent organic framework (COF) and bovine serum albumin (BSA) (as the chiral surface) functionalized 3D-printed electrochemical chiral sensor is reported for the first time. The characterization of the chiral biomolecule-COF 3D-printed constructure was performed by scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and energy-dispersive X-ray spectroscopy (EDX). A tryptophan (Trp) enantiomer was chosen as the model chiral molecule to estimate the chiral recognition ability of the magnetic COF and BSA-based 3DE (Fe3O4@COF@BSA/3DE). We have demonstrated that the Fe3O4@COF@BSA/3DE exhibited excellent chiral recognition to l-Trp as compared to d-Trp. The chiral protein-COF sensing interface was used to determine the concentration of l-Trp in a racemic mixture of d-Trp and l-Trp. This strategy of on-demand fabrication of 3D-printed protein-COF-modified electrodes opens up new approaches for enantiomer recognition.

• MeSH
• 3D tisk MeSH
• biosenzitivní techniky * MeSH
• elektrochemické techniky MeSH
• elektrody MeSH
• porézní koordinační polymery * MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• porézní koordinační polymery * MeSH

SUMMARY: Secondary structures provide a deep insight into the protein architecture. They can serve for comparison between individual protein family members. The most straightforward way how to deal with protein secondary structure is its visualization using 2D diagrams. Several software tools for the generation of 2D diagrams were developed. Unfortunately, they create 2D diagrams based on only a single protein. Therefore, 2D diagrams of two proteins from one family markedly differ. For this reason, we developed the 2DProts database, which contains secondary structure 2D diagrams for all domains from the CATH and all proteins from PDB databases. These 2D diagrams are generated based on a whole protein family, and they also consider information about the 3D arrangement of secondary structure elements. Moreover, 2DProts database contains multiple 2D diagrams, which provide an overview of a whole protein family's secondary structures. 2DProts is updated weekly and is integrated into CATH. AVAILABILITY AND IMPLEMENTATION: Freely accessible at https://2dprots.ncbr.muni.cz. The web interface was implemented in JavaScript. The database was implemented in Python. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

• MeSH
• databáze faktografické MeSH
• proteiny * chemie   MeSH
• sekundární struktura proteinů MeSH
• software * MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• proteiny * MeSH

In this paper, we present a novel algorithm for measuring protein similarity based on their 3-D structure (protein tertiary structure). The algorithm used a suffix tree for discovering common parts of main chains of all proteins appearing in the current research collaboratory for structural bioinformatics protein data bank (PDB). By identifying these common parts, we build a vector model and use some classical information retrieval (IR) algorithms based on the vector model to measure the similarity between proteins--all to all protein similarity. For the calculation of protein similarity, we use term frequency × inverse document frequency ( tf × idf ) term weighing schema and cosine similarity measure. The goal of this paper is to introduce new protein similarity metric based on suffix trees and IR methods. Whole current PDB database was used to demonstrate very good time complexity of the algorithm as well as high precision. We have chosen the structural classification of proteins (SCOP) database for verification of the precision of our algorithm because it is maintained primarily by humans. The next success of this paper would be the ability to determine SCOP categories of proteins not included in the latest version of the SCOP database (v. 1.75) with nearly 100% precision.

• MeSH
• algoritmy * MeSH
• data mining metody   MeSH
• databáze proteinů MeSH
• lidé MeSH
• proteiny chemie   MeSH
• reprodukovatelnost výsledků MeSH
• strukturní homologie proteinů * MeSH
• terciární struktura proteinů * MeSH
• umělá inteligence MeSH
• výpočetní biologie metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• proteiny MeSH

Recent advances in protein 3D structure prediction using deep learning have focused on the importance of amino acid residue-residue connections (i.e., pairwise atomic contacts) for accuracy at the expense of mechanistic interpretability. Therefore, we decided to perform a series of analyses based on an alternative framework of residue-residue connections making primary use of the TOP2018 dataset. This framework of residue-residue connections is derived from amino acid residue pairing models both historic and new, all based on genetic principles complemented by relevant biophysical principles. Of these pairing models, three new models (named the GU, Transmuted and Shift pairing models) exhibit the highest observed-over-expected ratios and highest correlations in statistical analyses with various intra- and inter-chain datasets, in comparison to the remaining models. In addition, these new pairing models are universally frequent across different connection ranges, secondary structure connections, and protein sizes. Accordingly, following further statistical and other analyses described herein, we have come to a major conclusion that all three pairing models together could represent the basis of a universal proteomic code (second genetic code) sufficient, in and of itself, to "encode" for both protein folding mechanisms and protein-protein interactions.

• Klíčová slova
• Contact map, Protein 3D structure, Protein folding, Protein-protein interactions, Proteomic code, Sense-antisense,
• MeSH
• aminokyseliny * chemie   genetika   MeSH
• databáze proteinů MeSH
• lidé MeSH
• molekulární modely * MeSH
• proteiny * chemie   genetika   metabolismus   MeSH
• proteomika * MeSH
• sbalování proteinů * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• aminokyseliny * MeSH
• proteiny * MeSH

Invadopodia and podosomes have been intensively studied because of their involvement in the degradation of extracellular matrix. As both structures have been studied mostly on thin matrices, their commonly reported shapes and characteristics may differ from those in vivo. To assess the morphology of invadopodia in a complex 3D environment, we observed invadopodial formation in cells grown on a dense matrix based on cell-free dermis. We have found that invadopodia differ in morphology when cells grown on the dermis-based matrix and thin substrates are compared. The cells grown on the dermis-based matrix display invadopodia which are formed by a thick protruding base rich in F-actin, phospho-paxillin, phospho-cortactin and phosphotyrosine signal, from which numerous thin filaments protrude into the matrix. The protruding filaments are composed of an F-actin core and are free of phospho-paxillin and phospho-cortactin but capped by phosphotyrosine signal. Furthermore, we found that a matrix-degrading activity is localized to the base of invadopodia and not along the matrix-penetrating protrusions. Our description of invadopodial structures on a dermis-based matrix should greatly aid the development of new criteria for the identification of invadopodia in vivo, and opens up the possibility of studying the invadopodia-related signaling in a more physiological environment.

• MeSH
• aktiny metabolismus   MeSH
• buněčné kultury MeSH
• buněčné výběžky metabolismus   ultrastruktura   MeSH
• cytoskelet metabolismus   MeSH
• elektronová mikroskopie MeSH
• experimentální sarkom metabolismus   ultrastruktura   MeSH
• extracelulární matrix metabolismus   fyziologie   ultrastruktura   MeSH
• fluorescenční protilátková technika MeSH
• kortaktin metabolismus   MeSH
• krysa rodu Rattus MeSH
• lidé MeSH
• nádorové buněčné linie MeSH
• prasata MeSH
• signální transdukce MeSH
• zobrazování trojrozměrné MeSH
• zvířata MeSH
• Check Tag
• krysa rodu Rattus MeSH
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• aktiny MeSH
• kortaktin MeSH

Binding of fatty acids to cryptogein, the proteinaceous elicitor from Phytophthora, was studied by using molecular docking and quantitative structure-activity relationships analysis. Fatty acids bind to the groove located inside the cavity of cryptogein. The structure-activity model was constructed for the set of 27 different saturated and unsaturated fatty acids explaining 87% (81% cross-validated) of the quantitative variance in their binding affinity. The difference in binding between saturated and unsaturated fatty acids was described in the model by three electronic descriptors: the energy of the lowest unoccupied molecular orbital, the energy of the highest occupied molecular orbital, and the heat of formation. The presence of double bonds in the ligand generally resulted in stronger binding. The difference in binding within the group of saturated fatty acids was explained by two steric descriptors, i.e., ellipsoidal volume and inertia moment of length, and one hydrophobicity descriptor, i.e., lipophility. The developed model predicted strong binding for two biologically important molecules, geranylgeranyol and farnesol playing an important role in plant signaling as lipid anchors of some membrane proteins. Elicitin mutants selectively binding only one type of ligand were designed for future experimental studies.

• MeSH
• bílkoviny řas chemie   MeSH
• fungální proteiny MeSH
• konformace proteinů MeSH
• kvantitativní vztahy mezi strukturou a aktivitou * MeSH
• ligandy MeSH
• mastné kyseliny chemie   MeSH
• mutace MeSH
• substituce aminokyselin MeSH
• vazba proteinů MeSH
• vazebná místa MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• bílkoviny řas MeSH
• cryptogein protein, Phytophthora cryptogea MeSH Prohlížeč
• fungální proteiny MeSH
• ligandy MeSH
• mastné kyseliny MeSH