The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein-nucleic acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: Escherichia coli beta-galactosidase with inhibitor, SARS-CoV-2 virus RNA-dependent RNA polymerase with covalently bound nucleotide analog and SARS-CoV-2 virus ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. The quality of submitted ligand models and surrounding atoms were analyzed by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics and contact scores. A composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

• MeSH
• beta-galaktosidasa chemie   metabolismus   MeSH
• COVID-19 virologie   MeSH
• elektronová kryomikroskopie * metody   MeSH
• Escherichia coli MeSH
• konformace proteinů MeSH
• ligandy MeSH
• molekulární modely * MeSH
• reprodukovatelnost výsledků MeSH
• SARS-CoV-2 MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• beta-galaktosidasa MeSH
• ligandy MeSH

Most of the available crystal structures of epidermal growth factor receptor (EGFR) kinase domain, bound to drug inhibitors, originated from ligand-based drug design studies. Here, we used variations in 110 crystal structures to assemble eight distinct families highlighting the C-helix orientation in the N-lobe of the EGFR kinase domain. The families shared similar mutational profiles and similarity in the ligand R-groups (chemical composition, geometry, and charge) facing the C-helix, mutation sites, and DFG domain. For structure-based drug design, we recommend a systematic decision-making process for choice of template, guided by appropriate pairwise fitting and clustering before the molecular docking step. Alternatively, the binding site shape/volume can be used to filter and select the compound libraries.

• MeSH
• erbB receptory antagonisté a inhibitory   chemie   genetika   MeSH
• inhibitory proteinkinas farmakologie   MeSH
• lidé MeSH
• ligandy MeSH
• mutace MeSH
• racionální návrh léčiv metody   MeSH
• rozhodování MeSH
• simulace molekulového dockingu MeSH
• vazebná místa MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• přehledy MeSH
• Názvy látek
• EGFR protein, human MeSH Prohlížeč
• erbB receptory MeSH
• inhibitory proteinkinas MeSH
• ligandy MeSH

Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.

• Klíčová slova
• Artificial intelligence, Collective intelligence, Homology modeling, Machine learning, Protein 3D structure, Structural bioinformatics,
• Publikační typ
• časopisecké články MeSH
• přehledy 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

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

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.

• Klíčová slova
• Molecular sequence analysis, Molecular structure and function, Molecular visualization,
• MeSH
• algoritmy MeSH
• molekulární modely MeSH
• proteiny chemie   MeSH
• sekundární struktura proteinů * MeSH
• sekvence aminokyselin MeSH
• sekvenční seřazení MeSH
• simulace molekulární dynamiky * MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• proteiny MeSH