Biology subjects

Hejtmanek, L.

Publications and source records attributed to Hejtmanek, L..

3 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↗

bioRxiv2022

Incidental Temporal Binding in Rats: A Novel Behavioral Task Relevant to Episodic Memory

We designed a behavioral task called One-Trial Trace Escape Reaction (OTTER), in which rats incidentally associate two temporally discontinuous stimuli: a neutral acoustic cue (CS) with an aversive stimulus (US) which occurs two seconds later (CS-2s-US sequence). Rats are first habituated to two similar environmental contexts (A and B), each consisting of an interconnected dark and light chamber. Next, rats experience the CS-2s-US sequence in the dark chamber of one of the contexts (either A or B); the US is terminated immediately after a rat escapes into the light chamber. The CS-2s-US sequence is presented only once to ensure the incidental acquisition of the association. The recall is tested 24 h later when rats are presented with only the CS in the alternate context (B or A), and their behavioral response is observed. Our results show that 59 % of the rats responded to the CS by escaping to the light chamber, although they experienced only one CS-2s-US pairing. The OTTER task offers a flexible high throughput tool to study memory acquired incidentally after a single experience. Incidental acquisition of association between temporally discontinuous events is highly relevant to episodic memory formation.

animal behavior and cognition↗

bioRxiv2019

Path integration in large-scale space and with novel geometries: Comparing Vector Addition and Encoding-Error Models

Path integration is thought to rely on vestibular and proprioceptive cues yet most studies in humans involve primarily visual input, providing limited insight into their contributions. We developed a paradigm involving walking in an omnidirectional treadmill in which participants were guided on two legs of a triangle and then found their back way to origin. In Experiment 1, we tested a range of different triangle types while keeping distance relatively constant to determine the influence of spatial geometry. Participants overshot the angle they needed to turn and undershot the distance they needed to walk, with no consistent effect of triangle type. In Experiment 2, we manipulated distance while keeping angle relatively constant to determine how path integration operated over both shorter and longer distances. Participants underestimated the distance they needed to walk to the origin, with error increasing as a function of the walked distance. To attempt to account for our findings, we developed computational models involving vector addition, the second of which included terms for the influence of past trials on the current one. We compared against a previously developed model of human path integration, the Encoding Error model. We found that the vector addition models captured the tendency of participants to under-encode guided legs of the triangles and an influence of past trials on current trials. Together, our findings expand our understanding of body-based contributions to human path integration, further suggesting the value of vector addition models in understanding these important components of human navigation.\n\nAuthor SummaryHow do we remember where we have been? One important mechanism for doing so is called path integration, which refers to the ability to track ones position in space with only self-motion cues. By tracking the direction and distance we have walked, we can create a mental arrow from the current location to the origin, termed the homing vector. Previous studies have shown that the homing vector is subject to systematic distortions depending on previously experienced paths, yet what influences these patterns of errors, particularly in humans, remains uncertain. In this study, we compare two models of path integration based on participants walking two legs of a triangle without vision and then completing the third leg based on their estimate of the homing vector. We found no effect of triangle shape on systematic errors, while path length scaled the systematic errors logarithmically, similar to Weber-Fechner law. While we show that both models captured participants behavior, a model based on vector addition best captured the patterns of error in the homing vector. Our study therefore has important implications for how humans track their location, suggesting that vector-based models provide a reasonable and simple explanation for how we do so.

animal behavior and cognition↗