SUMMARY: PDBImages is an innovative, open-source Node.js package that harnesses the power of the popular macromolecule structure visualization software Mol*. Designed for use by the scientific community, PDBImages provides a means to generate high-quality images for PDB and AlphaFold DB models. Its unique ability to render and save images directly to files in a browserless mode sets it apart, offering users a streamlined, automated process for macromolecular structure visualization. Here, we detail the implementation of PDBImages, enumerating its diverse image types, and elaborating on its user-friendly setup. This powerful tool opens a new gateway for researchers to visualize, analyse, and share their work, fostering a deeper understanding of bioinformatics. AVAILABILITY AND IMPLEMENTATION: PDBImages is available as an npm package from https://www.npmjs.com/package/pdb-images. The source code is available from https://github.com/PDBeurope/pdb-images.

• MeSH
• molekulární struktura MeSH
• software * MeSH
• výpočetní biologie * metody   MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.

• Publikační typ
• časopisecké články MeSH
• přehledy 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

Protein structural families are groups of homologous proteins defined by the organization of secondary structure elements (SSEs). Nowadays, many families contain vast numbers of structures, and the SSEs can help to orient within them. Communities around specific protein families have even developed specialized SSE annotations, always assigning the same name to the equivalent SSEs in homologous proteins. A detailed analysis of the groups of equivalent SSEs provides an overview of the studied family and enriches the analysis of any particular protein at hand. We developed a workflow for the analysis of the secondary structure anatomy of a protein family. We applied this analysis to the model family of cytochromes P450 (CYPs)-a family of important biotransformation enzymes with a community-wide used SSE annotation. We report the occurrence, typical length and amino acid sequence for the equivalent SSE groups, the conservation/variability of these properties and relationship to the substrate recognition sites. We also suggest a generic residue numbering scheme for the CYP family. Comparing the bacterial and eukaryotic part of the family highlights the significant differences and reveals a well-known anomalous group of bacterial CYPs with some typically eukaryotic features. Our workflow for SSE annotation for CYP and other families can be freely used at address https://sestra.ncbr.muni.cz .

• MeSH
• lidé MeSH
• sekvenční analýza proteinů metody   MeSH
• simulace molekulární dynamiky MeSH
• software * MeSH
• systém (enzymů) cytochromů P-450 chemie   MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• systém (enzymů) cytochromů P-450 MeSH