Tag: nevin manimala

bioRxiv [Preprint]. 2026 Feb 2:2026.01.30.702623. doi: 10.64898/2026.01.30.702623.

ABSTRACT

Synapses are fundamental building blocks of cortical circuits, yet their geometry is typically regarded as a local property, independent of mesoscale architecture. The prevailing assumption is that synaptic clefts are isotropically oriented in space. Here, this assumption was tested by analyzing approximately 117 million synaptic clefts from two independent 1 mm ³ electron microscopy datasets: the human H01 middle temporal gyrus and the mouse MICrONS primary visual cortex. Across both volumes, synaptic cleft orientations are not randomly distributed, but instead show statistically significant and spatially coherent directional biases across cortical layers. This mesoscale anisotropy is conserved across species, yet is stronger and more consistent in human association cortex than in mouse sensory cortex. These findings reveal an unrecognized dimension of cortical microarchitecture and suggest that synaptic geometry contributes to circuit organization, mesoscale connectivity, and interactions with endogenous or externally applied electric fields.

PMID:41676682 | PMC:PMC12889479 | DOI:10.64898/2026.01.30.702623

bioRxiv [Preprint]. 2026 Feb 6:2026.02.04.703700. doi: 10.64898/2026.02.04.703700.

ABSTRACT

Protein-protein interactions governed by conformationally heterogeneous domains remain difficult to drug because ligand-competent states are often absent from single static structures. Here, we present AtlasNMR, a statistical framework that transforms multi-model NMR ensembles into screening-ready conformational hypotheses for small molecule discovery. Using the neuronal nitric oxide synthase (nNOS) PDZ domain that engages the adaptor protein CAPON (NOS1AP) as a model system, AtlasNMR identified two representative conformational states capturing the dominant and minor populations of the NMR ensemble. Ensemble-based virtual screening followed by consensus ranking yielded MC-3 , a small molecule modulator that disrupts the NOS1-NOS1AP interaction in live cells and directly engages the nNOS PDZ domain. MC-3 produced convergent neuroprotective effects in disease-relevant neuronal models by reducing amyloid-β-induced cytotoxicity, suppressing NMDA-driven nitrosative stress, and attenuating pathological tau phosphorylation, while exhibiting a balanced early lead-like ADME and safety profile. Together, this work establishes a generalizable strategy for exploiting NMR ensemble heterogeneity to enable small molecule discovery against dynamic protein-protein interfaces.

PMID:41676628 | PMC:PMC12889626 | DOI:10.64898/2026.02.04.703700

bioRxiv [Preprint]. 2026 Feb 8:2026.02.05.704068. doi: 10.64898/2026.02.05.704068.

ABSTRACT

Kinase family proteins constitute the second largest protein class targeted in drug development efforts, most prominently to treat cancer, but also several other diseases associated with kinase dysfunction. In this work we focus on type II kinase inhibitors which bind to the “classical” inactive conformation of the protein kinase catalytic domain where the DFG motif has a ″DFG-out″ orientation and the activation loop is folded. Many Tyrosine kinases (TKs) exhibit strong binding affinity with a wide spectrum of type II inhibitors while serine/threonine kinases (STKs) often bind more weakly. Recent work suggests this difference is largely due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs. STKs. The binding affinity of a type II inhibitor to its kinase target can be decomposed into a sum of two contributions: (1) the free energy cost to reorganize the protein from the active to inactive state, and (2) the binding affinity of the type II inhibitor to the inactive kinase conformation. In previous work we used a Potts statistical energy potential based on sequence co variation to thread sequences over ensembles of active and inactive kinase structures. The threading function was used to estimate the free energy cost to reorganize kinases from the active to classical inactive conformation, and we showed that this estimator is consistent with the results of molecular dynamics free energy simulations for a small set of STKs and TKs. In the current study, we analyze the results of a large-scale study of the binding affinities of 50 type II inhibitors to 348 kinases, of which the results for 16 of the 50 type II inhibitors were reported in an earlier study (the “Davis dataset”). The binding data for the remaining 34 type II inhibitors to the panel of 348 kinases were recently obtained (the “Schrödinger dataset”). We use the Potts statistical energy model to investigate the contribution of protein reorganization to the selectivity of the large kinase panel against the set of 50 type II inhibitors, and find that protein reorganization makes a significant contribution to the selectivity. The AUC of the receiver operator characteristic curve is ≈0.8. We report the results of an internal “blind test”, that shows how Potts threading energies can provide more accurate estimates of kinase selectivity than corresponding predictions using experimental results of small sample size. We discuss why two STK phylogenetic kinase families, STE and CMGC, appear to contain many outliers, and how to improve the ability to predict kinase selectivity with a more complete analysis of the kinase conformational landscape. We compare the performance of Potts threading for predicting binding properties of the large set of (50) Type II inhibitors to 348 kinases, with those of a sequence-based purely machine learning model, DeepDTAGen, a publicly available machine learning model that was trained on the complete Davis dataset, including both Type I and Type II kinase inhibitors. We observe that DeepDTAGen performs well on binding predictions for the 16 type II inhibitors in the Davis dataset, but performs poorly on binding predictions for the 34 type II inhibitors against 348 kinases in the Schrödinger dataset.

PMID:41676586 | PMC:PMC12889613 | DOI:10.64898/2026.02.05.704068

bioRxiv [Preprint]. 2026 Feb 7:2026.02.04.703900. doi: 10.64898/2026.02.04.703900.

ABSTRACT

Detecting a sex difference in response to a treatment or intervention, often reported as a ‘sex-specific effect,’ requires statistical comparison of the response across sex. Here, we investigated analytical approaches used to test for such effects in the behavioral and brain sciences. Of 200 recent articles containing terms such as ‘sex-specific’ or ‘gender-dependent’ in their titles, only 24% presented appropriate evidence supporting the claim: the effect was compared statistically across sex and results consistent with the claim were reported. In most articles (58%), no test was conducted that could have supported the title claim. Only 15% of studies on non-human animals supported the claim with appropriate evidence, which was significantly less frequently than studies on human participants (34%; p = 0.002). The use of appropriate analytical approaches was unrelated to journal rank or the citation impact of the article. We conclude that claims of sex/gender-dependent effects in the behavioral and brain sciences are only infrequently supported by appropriate evidence.

PMID:41676574 | PMC:PMC12889599 | DOI:10.64898/2026.02.04.703900

bioRxiv [Preprint]. 2026 Feb 6:2026.02.03.703561. doi: 10.64898/2026.02.03.703561.

ABSTRACT

Directed evolution is a powerful and widely used technique for protein engineering, and reducing the cost of iterated experimental observations has become a major priority for practitioners. A number of recent efforts to use machine-learning-based predictors to improve sequence selection have led to remarkable improvements in efficiency, but the sparse data at each experimental iteration restricts these approaches to extremely simple models. Adapting large-scale pre-trained protein language models using experimental data offers an alternative that we show productively leverages the strong inductive biases of the natural distribution of protein sequences to navigate high-dimensional, combinatorially large fitness landscapes. Our approach uses a general-purpose “post-training” algorithm grounded in statistical physics that employs quantitative experimental rankings to directly produce a sampler for diverse, high fitness sequences with fewer data points than competing methods. The resulting adapted protein language model can itself be studied and interpreted, shedding further light on the biophysical characteristics of highly fit sequences and their properties.

PMID:41676563 | PMC:PMC12889597 | DOI:10.64898/2026.02.03.703561

bioRxiv [Preprint]. 2026 Feb 6:2026.02.04.703796. doi: 10.64898/2026.02.04.703796.

ABSTRACT

Recent advances in deep learning have led to the development of sequence-to-omics (S2O) models that predict molecular phenotypes directly from DNA sequences. Here, we systematically evaluate the utility of these models, e.g., AlphaGenome, Borzoi, Enformer, and Sei, for improving the reproducibility of genetic fine-mapping across expression quantitative trait loci (eQTL) datasets from Genotype-Tissue Expression (GTEx), Trans-Omics Precision Medicine (TOPMed), and Multi-Ancestry Analysis of Gene Expression (MAGE) projects. We show that purely statistical fine-mapping often yields high replication failure rates (RFRs), but integrating S2O model predictions substantially reduces RFRs and enhances the accuracy of prioritizing SNPs replicated in other consortia. We describe a generalized framework for functionally informed fine-mapping that combines traditional posterior inclusion probabilities (PIPs) from statistical fine-mapping methods with scores from S2O models to generate functionally informed PIPs (fiPIPs) that improve reproducibility. Our findings demonstrate that S2O models, particularly newer ones like AlphaGenome and Borzoi, enable robust identification of replicated variants across consortia, highlighting their promise for scalable, functionally aware genetic mapping.

PMID:41676556 | PMC:PMC12889643 | DOI:10.64898/2026.02.04.703796

BMC Med. 2026 Feb 11. doi: 10.1186/s12916-026-04623-3. Online ahead of print.

ABSTRACT

BACKGROUND: We tested whether inflammation indexed by soluble tumor necrosis factor receptor-1 (sTNFR1) is related to cognitive decline. We examined serum sTNFR1 with cognition in the Health and Retirement Study (HRS) and cerebrospinal fluid (CSF) sTNFR1 with tau pathology and magnetic resonance imaging (MRI)-based atrophy in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Finally, we used Mendelian randomization (MR) to assess associations between genetically proxied sTNFR1 and regional brain volumes.

METHODS: Data were from HRS (2016-2020; N = 6028) and ADNI (N = 287). In HRS, serum sTNFR1 was log-transformed (quartiles); in ADNI, CSF sTNFR1 was analyzed. Global cognition included word recall, serial 7 s, and counting backwards. In ADNI, cognition was measured by the Clinical Dementia Rating-Sum of Boxes (CDR-SB); CSF total tau/phosphorylated tau and longitudinal MRI regional volumes were analyzed. Associations were estimated with linear and linear mixed-effects models adjusted for demographic, clinical, and genetic covariates including apolipoprotein E ε4 (APOE ε4). Incident mild cognitive impairment (MCI)/dementia was modeled with cause-specific Cox and Fine-Gray models. Incremental prediction used optimism-corrected change in area under the curve (AUC; ΔAUC), net reclassification improvement (NRI)/integrated discrimination improvement (IDI), calibration, and decision curve analysis. MR used genome-wide association study (GWAS) statistics to test effects of genetically proxied sTNFR1 on MRI-derived regional volumes.

RESULTS: In HRS (follow-up 4 years), higher serum sTNFR1 was associated with lower baseline cognition and faster decline in global cognition (β = – 0.16/year). Higher sTNFR1 predicted MCI/dementia (Cox HR ≈ 1.17; Fine-Gray sHR ≈ 1.14); among cognitively normal individuals, risk was elevated (OR = 1.30; 95% CI, 1.03-1.63). Adding sTNFR1 to 2- and 4-year prediction models conferred small discrimination gains after internal validation (ΔAUC ≤ 0.003) and minimal or inconsistent net clinical benefit. In ADNI, higher CSF sTNFR1 was associated with greater CSF total tau and phosphorylated tau, and predicted accelerated caudate atrophy. Exploratory MR suggested a nominal association with reduced right inferior temporal volume, limited by instruments.

CONCLUSIONS: sTNFR1 is associated with cognitive decline and tau-related selective neurodegeneration, but provides limited incremental predictive value beyond established risk factors; external validation and replication are warranted.

PMID:41673623 | DOI:10.1186/s12916-026-04623-3

BMC Public Health. 2026 Feb 11. doi: 10.1186/s12889-026-26315-6. Online ahead of print.

ABSTRACT

BACKGROUND: Oral health during pregnancy is critical for both maternal and neonatal outcomes, yet awareness and preventive behaviors remain suboptimal. This study evaluated the effect of a web-based educational intervention, grounded in the Health Belief Model (HBM), on caries-preventive oral health behaviors in pregnant women.

METHODS: In a quasi-experimental design, 66 pregnant women in Bushehr, Iran, were randomly assigned to intervention and control groups. The intervention group received a multimedia web-based education program based on HBM constructs, while the control group received routine care. Data on knowledge, HBM constructs, and preventive behaviors were collected before and three months after the intervention using validated questionnaires. Statistical analyses included repeated measures ANOVA to compare changes over time between groups.

RESULTS: Post-intervention, the intervention group showed significant improvements in knowledge and most HBM constructs (perceived susceptibility, severity, benefits, barriers, and self-efficacy) compared to controls (p < 0.05). Although preventive oral health behaviors increased significantly within the intervention group (p = 0.022), between-group differences in behavior change were not statistically significant (p = 0.171).

CONCLUSION: The web-based educational program effectively enhanced pregnant women’s knowledge and health beliefs regarding oral health but did not produce a statistically significant improvement in preventive behaviors compared to routine care. Integrating HBM-based web education offers a flexible, cost-effective approach to promote oral health awareness during pregnancy, though further strategies may be needed to translate knowledge gains into sustained behavioral change.

PMID:41673612 | DOI:10.1186/s12889-026-26315-6

BMC Pulm Med. 2026 Feb 11. doi: 10.1186/s12890-025-04038-5. Online ahead of print.

ABSTRACT

BACKGROUND: Alterations in the central nervous system of patients with asthma have been reported. However, the intricate relationship between asthma and cortical structure remains unclear and challenging to determine. To determine the association between asthma and changes in brain cortical structure, we conducted a two-sample Mendelian randomization study.

METHODS: Genome-wide association studies summary data of asthma in 408,442 participants from the UK Biobank were used to identify genetically predicted asthma. Uncorrelated (r² < 0.001) single nucleotide polymorphisms (SNPs) were selected as instrumental variants. Cortical thickness and surface area were obtained from 51,665 patients from the ENIGMA Consortium as outcomes in this study. Inverse-variance weighted was employed as the primary estimate whereas MR Pleiotropy RESidual Sum and Outlier, MR-Egger, and weighted median were used to detect heterogeneity and pleiotropy.

RESULTS: At the global level, no statistically significant association was observed between asthma and global surface area or cortical thickness. In region-specific analyses, nominal significant associations were identified between asthma and the surface area of the parahippocampal gyrus, both with global weighting (β = 5.07 mm², 95% CI: 0.80 to 9.33, P = 0.0198) and without global weighting (β = 5.92 mm², 95% CI: 0.77 to 11.07, P = 0.0242), as well as with the cortical thickness of the caudal anterior cingulate gyrus, both with global weighting (β = -0.01 mm, 95% CI: -0.02 to -0.001, P = 0.0243) and without global weighting (β = -0.01 mm, 95% CI: -0.02 to -0.001, P = 0.0259). However, none of these associations remained statistically significant after adjustment for multiple comparisons.

CONCLUSIONS: This two-sample Mendelian randomization analysis did not identify a statistically significant association between asthma and global cortical metrics. While nominal associations were observed between asthma and changes in parahippocampal and caudal anterior cingulate, these findings did not remain significant after correction for multiple comparisons and should be interpreted as exploratory. Further research is needed to elucidate potential neuroanatomical alterations associated with asthma.

PMID:41673596 | DOI:10.1186/s12890-025-04038-5

BMC Infect Dis. 2026 Feb 11. doi: 10.1186/s12879-026-12836-3. Online ahead of print.

NO ABSTRACT

PMID:41673590 | DOI:10.1186/s12879-026-12836-3