Biology subjects

Svobodova, R.

Publications and source records attributed to Svobodova, R..

6 recordsLinked to original sources

bioRxiv2026

AlphaFind v2: Similarity Search in AlphaFold DB and TED Domains across Structural Contexts

The availability of large-scale protein structure collections enables structure-based analysis of their function and evolution beyond what is possible from sequence alone. However, applying three-dimensional structure comparison at scale remains computationally demanding and limits practical exploration of large experimental and predicted collections. This creates a need for fast, structure-based search methods that retain biological relevance while enabling large-scale exploration. In this paper, we present AlphaFind v2, an application for finding structurally similar proteins in the AlphaFold Database (https://alphafold.ebi.ac.uk/) of predicted structures. AlphaFind v2 uses fast pre-filtering via state-of-the-art protein embeddings that preserve structural information, followed by refinement with US-align. The application presents multiple complementary search modes, including (i) search over full protein chains, (ii) search aware of the AlphaFold pLDDT metric, restricting similarity computation to the most stable and structurally relevant regions, (iii) search over protein domains from the TED database (https://ted.cathdb.info/), and (iv) a multidomain search mode, combining multiple chain-level domain matches within a single score and alignment. The application accepts protein identifiers and returns similar proteins with metrics, rich metadata, and interactive superpositions. AlphaFind v2 additionally allows searching within an organism or CATH label and matches the proteins with experimental structures. AlphaFind v2 is accessible at https://alphafind.ics.muni.cz/. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/710735v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@5e7ce7org.highwire.dtl.DTLVardef@15a3458org.highwire.dtl.DTLVardef@12299ccorg.highwire.dtl.DTLVardef@9f21af_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Per-residue optimisation of protein structures:Rapid alternative to optimisation with constrainedalpha carbons

In recent years, the number of known protein structures has increased significantly. Predictive algorithms and experimental methods provide the positions of protein residues relative to each other with high accuracy. However, the local quality of the protein structure, including bond lengths, angles, and positions of individual atoms, often lacks the same level of precision. For this reason, protein structures are usually optimised by a force field prior to their application in further research sensitive to structural quality. Protein structure optimisation, however, is computationally challenging. In this paper, we introduce a general method Per-residue optimisation of protein structures: Rapid alternative to optimisation with constrained alpha carbons (PROPTIMUS RAPHAN). Rather than optimising the entire protein structure at once, PROPTIMUS RAPHAN divides the structure into overlapping residual substructures and optimises each substructure individually. This approach results in computational time that scales linearly with the size of the structure. Additionally, we present PROPTIMUS RAPHANGFN-FF, a reference implementation of our method employing a generic, almost QM-accurate force field, GFN-FF. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/690085v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1e9fc5borg.highwire.dtl.DTLVardef@b6d70aorg.highwire.dtl.DTLVardef@1e00427org.highwire.dtl.DTLVardef@30c66f_HPS_FORMAT_FIGEXP M_FIG C_FIG We tested PROPTIMUS RAPHANGFN-FF on 461 AlphaFold DB structures and demonstrated that our approach achieves results comparable to the optimisation of the structure with constrained alpha carbons in significantly less time. Scientific ContributionThe main contribution of this work is the PROPTI-MUS RAPHAN method and its reference parallelisable implementation PROP-TIMUS RAPHANGFN-FF. Because the time requirement increases linearly with the size of the structure, PROPTIMUS RAPHANGFN-FF optimises on average 5 000 atoms per hour and a common CPU. Therefore, prior to any research sensitive to protein structure quality, our method can be employed to obtain protein structures closer to QM-accuracy.

bioinformatics↗

bioRxiv2024

GOLEM: A tool for visualizing the distribution of Gene regulatOry eLEMents within the plant promoters with a focus on male gametophyte

BackgroundThe regulation of gene expression during tissue development is very complex. A key mechanism of gene regulation is the recognition of regulatory motifs, also known as cis-regulatory elements (CREs), by various proteins in gene promoter regions. Localization of these motifs near the transcription start site (TSS) or translation start site (ATG) is crucial for transcription initiation and rate. Transcription levels of individual genes, regulated by these motifs, can vary significantly across tissues and developmental stages, especially in processes like sexual reproduction. However, the precise localization and visualization of regulatory motifs in relation to gene expression in specific tissues can be challenging. ResultsHere, we introduce a program called GOLEM (Gene regulatOry eLEMents) which enables users to precisely locate any motif of interest with respect to TSS or ATG within the relevant plant genomes across the plant Tree of Life (Marchantia, Physcomitrium, Amborella, Oryza, Zea, Solanum and Arabidopsis). The visualization of the motifs is performed with respect to the transcript levels of particular genes in leaves and male reproductive tissues and can be compared with genome-wide distribution regardless of the transcription level. Additionally, genes with specific CREs at defined positions and high expression in selected tissues can be exported for further analysis. GOLEMs functionality is illustrated by its application to conserved motifs (e.g. TATA-box, ABRE, I-box, and TC-element), as well as to male gametophyte-related motifs (e.g. LAT52, MEF2, ARR10_core, and DOF_core). ConclusionGOLEM is a freely available tool (https://golem.ncbr.muni.cz) for tracking the precise localization and distribution of any CREs of interest in plant gene promoters. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=59 SRC="FIGDIR/small/606583v1_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@13c1fc9org.highwire.dtl.DTLVardef@4b034dorg.highwire.dtl.DTLVardef@c5e28aorg.highwire.dtl.DTLVardef@19c0ce4_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2024

Rare ring conformations in PDB: Facts or wishful thinking?

Protein structural data are highly valuable for research, and many significant results have been published on their basis. A key point for their credibility and applicability is their quality. An important facet of protein structure quality is the validation of ligands. Some aspects of ligand quality have already been validated by established quality metrics. However, validation of ring conformation has yet to be comprehensively performed despite rings strongly influencing the formation of the ligands scaffold and shape. Most rings form several conformations that differ in their stability. The most stable ones occur frequently in nature and should, therefore, be found in Protein Data Bank (PDB) structures. In this article, we examined which conformations of rings occur in PDB structures. Our analysis focused on conformations of all cyclopentane, cyclohexane, and benzene rings in the PDB. Specifically, we examined 123 264 rings of 24 763 distinct ligands, which instances occur in 44 022 protein structures. In general, we found that most of the rings (98.32 %) are in energetically favourable conformations. Surprisingly, the existence of most of the energetically unfavourable ring conformations (2 067 samples, 1.68 %) is not supported by experimental data. Only 291 unfavourable ring conformations (0.24 %) are backed by experimental data that are accurate enough to distinguish the conformation, which shows that the existence of energetically unfavourable ring conformations is rarely supported by structural or experimental evidence. Our results suggest that each occurrence of untypical ring conformation in the PDB may indicate a potential error and should be carefully analysed.

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bioRxiv2024

AlphaFind: Discover structure similarity across the entire known proteome

AlphaFind is a web-based search engine that provides fast structure-based retrieval in the entire set of AlphaFold DB structures. Unlike other protein processing tools, AlphaFind is focused entirely on tertiary structure, automatically extracting the main 3D features of each protein chain and using a machine learning model to find the most similar structures. This indexing approach and the 3D feature extraction method used by AlphaFind have both demonstrated remarkable scalability to large datasets as well as to large protein structures. The web application itself has been designed with a focus on clarity and ease of use. The searcher accepts any valid Uniprot ID, PDB ID or gene symbol as input, and returns a set of similar protein chains from AlphaFold DB, including various similarity metrics between the query and each of the retrieved results. In addition to the main search functionality, the application provides 3D visualizations of protein structure superpositions in order to allow researchers to instantly analyze the structural similarity of the retrieved results. The AlphaFind web application is available online for free and without any registration at https://alphafind.fi.muni.cz. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/580465v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1bf5ae8org.highwire.dtl.DTLVardef@1e975d7org.highwire.dtl.DTLVardef@378598org.highwire.dtl.DTLVardef@123e439_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

Uncovering of Cytochrome P450 Anatomy by SecStrAnnotator

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 an anomalous group of bacterial CYPs with some typically eukaryotic features. Application of our workflow to other families could produce equally surprising findings.

bioinformatics↗