Biology subjects

Ataei, S.

Publications and source records attributed to Ataei, S..

4 recordsLinked to original sources

bioRxiv2026

How Awakenings Shape Dream Recall: A Multilevel Study

Dream recall varies substantially both between individuals and from night to night within the same individual. Although nocturnal awakenings are thought to facilitate the encoding and later retrieval of dream experiences, it remains unclear whether dream recall is shaped primarily by awakening frequency or by more specific awakening characteristics, including duration, sleep stage, and timing within the night. Here, we analyzed two cohorts: cohort 1 consisted of 708 adults spanning the full range of dream recall frequency, assessed across three waves with home sleep recordings and questionnaire-based dream recall frequency measures; cohort 2 consisted of 124 adults with high dream recall frequency, assessed across multiple nights with home sleep recordings and daily dream reports. Using multilevel models with within-between decomposition, we examined trait-like and state-like associations between awakening measures and dream recall outcomes. At the trait level, both questionnaire-based dream recall frequency in cohort 1 and daily dream recall (i.e., a sense of having dreamed) in cohort 2 were associated with a specific nocturnal awakening profile: more habitual long REM awakenings and short NREM awakenings, with REM awakening effects remaining robust after adjustment for sleep duration. At the state level, in cohort 2, nights with more short and medium REM awakenings than usual increased the likelihood of morning dream recall, whereas nights with more long REM awakenings than usual increased the likelihood of morning dream content recall (i.e., remembering dream content). These findings support the arousal-retrieval and functional state-shift models, while highlighting important nuances in the associations between nocturnal awakenings and different dream recall outcomes.

neuroscience↗

bioRxiv2025

TooTranslator: Zero-Shot Classification of Specific Substrates for Transport Proteins by Language Embedding Alignment of Proteins and Chemicals

Transmembrane transport proteins mediate selective movement of ions and metabolites across membranes. Experimental characterization of their substrate specificity is limited. For novel class discovery with limited data, zero-shot learning aims to assign labels to test samples whose label has not been seen previously during training of the model. We introduce TooTranslator, a regression-based model that aligns embeddings from pre-trained protein (ProtBERT), chemical (ChemBERTa), and text (SciB-ERT) language models into a shared latent space, enabling substrate prediction by minimizing distances between protein and substrate embeddings. TooTranslator tackles zero-shot learning for the task of predicting the specific substrate of transmembrane transport proteins. Models using four loss functions are evaluated on protein test sets with seen and unseen substrates. We find no statistically significant difference in performance of the four loss functions. For tests with seen substrates, the models compare with the state-of-the art in performance. For tests with unseen (but known) substrate a top-k approach is needed as only 4.3% of test cases predict the correct label as the nearest label. For top-10 that rises to 17%; for top-50 rises to 54%, and top-100 reaches 80%. TooTranslator demonstrates the potential of multimodal embedding alignment for open-world protein function inference.

bioinformatics↗

bioRxiv2022

Predicting the specific substrate for transmembrane transport proteins using BERT language model

Transmembrane transport proteins play a vital role in cells metabolism by the selective passage of substrates through the cell membrane. Metabolic network reconstruction requires transport reactions that describe the specific substrate transported as well as the metabolic reactions of enzyme catalysis. In this paper, we apply BERT (Bidirectional Encoder Representations from Transformers) language model for protein sequences to predict one of 12 specific substrates. Our UniProt-ICAT-100 dataset is automatically constructed from UniProt using the ChEBI and GO ontologies to identify 4,112 proteins transporting 12 inorganic anion or cation substrates. We classified this dataset using three different models including Logistic Regression with an MCC of 0.81 and accuracy of 97.5%; Feed-forward Neural Networks classifier with an MCC of 0.88 and accuracy of 98.5%. Our third model utilizes a Fine-tuned BERT language model to predict the specific substrate with an MCC of 0.95 and accuracy of 99.3% on an independent test set.

bioinformatics↗

bioRxiv2019

High-yield Production of Recombinant Platelet Factor 4 Protein by Harnessing and Honing the Gram-negative Bacterial Secretory Apparatus

BackgroundPlatelet factor 4 is a cytokine released into the bloodstream by activated platelets and plays a pivotal role in heparin-induced thrombocytopenia etiology and diagnosis. Therefore, a sustainable source of recombinant PF4 with structural and functional similarity to its native form is urgently needed to be used in diagnostic procedures. To this end, a three-in-one primary construct was designed and custom synthesized based on the pET26b backbone from which three secondary constructs could be derived each capable of employing either type I, type II secretory or cytoplasmic pathways. Protein expression and secretion were performed in Escherichia coli BL-21 (DE3) and were confirmed by SDS-PAGE and Western blotting. To further enhance protein secretion, the effect of several controllable factors including IPTG, Triton X-100, Sucrose, and Glycine were individually investigated at first. In the next step, according to fractional factorial approach, the synergistic effect of IPTG, Triton X-100, and Glycine on secretion was further investigated. To ascertain the structure and function of the secreted recombinant proteins, Dynamic light scattering was utilized and confirmed rPF4 tetramerization and heparin-mediated ultra-large complex formation. Moreover, Raman spectroscopy was exploited to determine the rPF4 secondary structure. ResultsType II secretory pathway was proven to be superior over type I in case of rPF4 secretion into the extracellular milieu. Protein secretion mediated by Type II was enhanced to approximately more than 700 g/ml. Large quantities of native rPF4 up to 20 mg was purified upon a minor scale up to 40 ml of culture medium. Dynamic light scattering unveiled native rPF4 quaternary structure revealing the formation of tetramers having an average size of 10 nm and formation of larger complexes of approximately 100-1200 nm in size following heparin supplementation, implying proper protein folding, tetramerization, and antigenicity. Analysis of the Zeta potential on approximately 600 g/ml of rPF4 revealed a 98 mV positive charge which further confirms protein folding. Moreover, rPF4 secondary structure was determined to be 43.5% Random coil, 32.5% {beta}-sheet, 18.6 % -helix and 4.9 % Turn, which is in perfect agreement with the native structure. Conclusionour results indicate that the gram-negative type II bacterial secretory system holds a great promise to be employed as a reliable protein production strategy with favorable industrial application. However, further efforts are required to realize the full potential of secretory pathways regarding their application to proteins with distinct characteristics. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/830851v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@fd47fdorg.highwire.dtl.DTLVardef@8163c3org.highwire.dtl.DTLVardef@bcbb5eorg.highwire.dtl.DTLVardef@b145d5_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract. rPF4 secretion mediated by type 2 secretory system. The pelB signal sequence directs protein export into the extracellular milieu through the SecYEG translocon complex in a process assisted by SecB chaperone. A) Indicates protein secretion before supplementation with additives and B) indicates secretion in the presence of additives. C_FIG

bioengineering↗