protein folding

Coupling intensified push–pull technology with insect frass biofertilization improves land productivity, protein yield and carbon stocks

We evaluated agricultural innovations such as push-pull (PP), vegetable-integrated push-pull (VIPP), and black soldier fly frass fertilizer (BSFFF) for their transformative potential in rural smallholder agriculture. Using 12 cropping systems comprising two crops (maize and cowpea), BSFFF and diversified systems (PP and VIPP), we conducted a 2-year multilocation trial to evaluate impacts on crop productivity, economic benefits, and carbon stocks. Diversified cropping increased above-ground biomass, carbon stocks, and total crop yield by 4.7–11.2-fold with significant increments upon addition of BSFFF. Diverse cropping and BSFFF increased maize grain yield by more than 2-fold. Cowpea yield responded positively to BSFFF. High total yield in diverse cropping was contributed by Desmodium (12.2–12.5 t/ha) and Brachiaria (2.3–2.4 t/ha). Diverse cropping recorded high land equivalent ratios (2.0–3.3) and wheat equivalent yields (3.2–3.7) with a significant contribution of BSFFF. The highest protein yield was in diverse cropping systems, with significant contributions from cowpea and Desmodium. Diverse cropping resulted in high annual economic benefits ($6864–$7681), with more returns when BSFFF was included ($7528–$8340). Comprehensive multidimensional analysis showed VIPP coupled with BSFFF to be superior for all assessed parameters. Crop diversification accompanied by frass-based organic fertilizer improves system productivity, financial gains, carbon storage, and potentially resilience and environmental sustainability.

YES1, a plasma-based biomarker for cavernous malformations

OBJECTIVE Cerebral cavernous malformations (CCMs) are low-flow vascular malformations commonly found in the central nervous system with the potential to cause neurological deficits, seizures, and headache through bleeding and growth. CCMs are typically assessed with imaging such as MRI or CT. However, there is a clinical need to reduce the cost, risk (with sedation/anesthesia in children), and travel associated with these imaging studies. Prior work from the authors’ group and others have highlighted the potential utility of noninvasive biomarkers as tools to potentially complement the use of imaging. Here, the authors present YES1, a member of the Src family kinases, as a potential plasma-based biomarker associated with the presence of CCM. METHODS Plasma samples from symptomatic patients with pathology-confirmed CCM (n = 16) (aged 1–22 years) and healthy matched control subjects (n = 24) (aged 1–19 years) were obtained and compared after institutional review board approval. Protein levels of CCM and control plasma were analyzed using the Olink Proximity Extension Assay (PEA), and ELISA was used to confirm findings. Patient-derived primary cells and CCM tissues were stained for YES1. CCM tissues were sequenced to evaluate the mutational profile of YES1. RESULTS Compared to healthy controls, patients with CCM showed significantly higher levels of plasma YES1 (approximately 4.5-fold higher, p = 0.002). Analysis confirmed the presence of YES1 in the source CCM tissue, linking pathology with putative biomarker expression. Whole exome sequencing (WES) revealed functional mutations in the gene encoding for YES1, providing an avenue of investigation to explore and to explain the overexpression of YES1. CONCLUSIONS Here, for the first time, the authors present a putative diagnostic biomarker for CCM, YES1, that was demonstrated to be significantly elevated in the plasma of affected patients, with high correlative expression in the source tissue. Together, these data further support the development of CCM-related noninvasive biomarkers and may add insight into their pathogenesis.

NitroXAI: An Interpretable Deep Learning Framework for Human S-Nitrosylation Site Prediction

NitroXAI v0.1.1 is an interpretable framework for residue-level S-nitrosylation (SNO) site prediction. The release provides two frozen ensemble predictors for identifying candidate S-nitrosylated cysteineresidues from complete protein sequences: • SNO-CLIM: a lightweight CNN-BiLSTM baseline using handcrafted biochemical and positional features.• NitroXAI: a hybrid model integrating handcrafted features with full-protein contextual ESM-2 residue embeddings through attention-based fusion. Both packages support residue-level prediction, all-cysteine scanning, FASTA and optional UniProt workflows, batch prediction, and Integrated Gradients-based interpretation. The bundled models, fold-specific scalers, ensemble rules, and decision thresholds are frozen from the final training notebooks. Inference does not retrain models, refit scalers, optimize thresholds, or fine-tune ESM-2. NitroXAI is intended for computational prioritization and hypothesis generation. Predictions represent candidate S-nitrosylation sites and require experimental validation. This release contains frozen inference assets and interpretability workflows. The complete training pipeline, processed datasets, and associated materials will be released separately upon publication. The MIT License applies to software and original released assets in this record. Third-party resources, including UniProt retrieval and ESM-2 weights, remain subject to their respective terms.

Engineered genomic attachment sites for site-specific recombinases enable high-efficiency integration in plants and human cells

Abstract Here we developed a DNA-centric strategy for optimizing site-specific recombination by rationally engineering chimeric attachment sites. The high-activity att variants enhance Bxb1-mediated integration efficiency in human cells and plants. Among these att variants, the engineered attB(V111) site achieved 51.9% integration efficiency in HEK293T cells (1.7-fold versus wild-type attB ) and 35.6% in rice protoplasts (4.4-fold versus wild-type attB ). When paired with an engineered single protein mutant in the Bxb1 catalytic domain, the optimized system achieved targeted integration efficiencies of 31% for a CD19 chimeric antigen receptor cassette and 25% for an ornithine transcarbamylase expression cassette in human cells. In rice, these engineered variants enabled integration of a 5.8 kb herbicide-resistance cassette at a targeted genomic locus, with stable integration detected in 24% of regenerated plants. Oxford Nanopore-based long-read sequencing of edited plants reveals complete and precise insertion with high specificity. Propagation of edited seedlings to T1 plants confirms heritable editing to future generations. This approach provides a safe, broadly applicable approach for recombinase-based genome editing.

NitroXAI: An Interpretable Deep Learning Framework for Human S-Nitrosylation Site Prediction

NitroXAI v0.1.1 is an interpretable framework for residue-level S-nitrosylation (SNO) site prediction. The release provides two frozen ensemble predictors for identifying candidate S-nitrosylated cysteineresidues from complete protein sequences: • SNO-CLIM: a lightweight CNN-BiLSTM baseline using handcrafted biochemical and positional features.• NitroXAI: a hybrid model integrating handcrafted features with full-protein contextual ESM-2 residue embeddings through attention-based fusion. Both packages support residue-level prediction, all-cysteine scanning, FASTA and optional UniProt workflows, batch prediction, and Integrated Gradients-based interpretation. The bundled models, fold-specific scalers, ensemble rules, and decision thresholds are frozen from the final training notebooks. Inference does not retrain models, refit scalers, optimize thresholds, or fine-tune ESM-2. NitroXAI is intended for computational prioritization and hypothesis generation. Predictions represent candidate S-nitrosylation sites and require experimental validation. This release contains frozen inference assets and interpretability workflows. The complete training pipeline, processed datasets, and associated materials will be released separately upon publication. The MIT License applies to software and original released assets in this record. Third-party resources, including UniProt retrieval and ESM-2 weights, remain subject to their respective terms.

Decoding protein mutational fitness through evolutionary sequence intelligence: a large-scale ESM-2 benchmark across 217 DMS assays

Quantifying molecular specificity in excipient-driven antibody solubilization

It is shown that excipient-mediated solubilization of therapeutic antibodies is markedly molecularly specific, and the integration of high-throughput experimentation with molecular feature analysis offers a foundation for improving the understanding and prediction of antibody-specific formulation behavior.

FoldKit: A Python library for efficient storage and retrieval of co-folding predictions

FoldKit is introduced, a Python package for efficient storage and analysis of large-scale AF3 co-folding results that reduces storage requirements and facilitating programmatic access to relevant outputs, which facilitates large-scale computational studies of biomolecular interactions.

Dehydrocorydaline Accelerates Palatal Wound Healing in Mice Through Suppression of the p38 MAPK/CCL2 Axis and Macrophage Chemotaxis: A Preliminary Study

Investigating whether DHC accelerates palatal wound healing and to elucidate the role of the p38 mitogen-activated protein kinase (MAPK)/CCL2 signaling axis in DHC-mediated regulation of macrophage chemotaxis demonstrated that DHC accelerated palatal wound healing 1.6-fold in mice.

5 A multivariate plasma biomarker model enabling safe exclusion of glioblastoma

Glioblastoma (GBM) is the most aggressive primary brain tumor and remains difficult to diagnose due to reliance on MRI and invasive biopsies, which are limited by accessibility, morbidity, and radiologic uncertainty. Blood-based biomarkers may provide a minimally-invasive strategy to support early diagnostic-triage. In this retrospective case-control study, plasma samples from 72 patients with newly-diagnosed GBM and 102 age-matched healthy controls were analyzed. Seven circulating proteins were quantified using ultrasensitive- and clinical-grade immunoassays: glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), tau, neuron-specific enolase (NSE), myeloperoxidase (MPO), and polymorphonuclear-(PMN) elastase. Diagnostic performance was assessed using receiver-operating-characteristic (ROC) analysis. Multivariate logistic regression with 10-fold cross-validation and 1,000-iteration bootstrap resampling evaluated model robustness, with emphasis on sensitivity and negative predictive value (NPV) for exclusion-use. Among individual plasma biomarkers, GFAP demonstrated near-perfect separation between GBM and controls (AUC = 0.995), followed by NfL (AUC = 0.957), whereas tau, NSE, MPO, and PMN-elastase showed more modest individual performance. A multivariate logistic regression model integrating GFAP, NfL, tau, NSE, MPO, and PMN-elastase achieved near-perfect classification of GBM-versus-controls (AUC = 0.999), outperforming all individual biomarkers. At the optimal probability threshold, sensitivity reached 98.6% with 100% specificity, with no GBM cases misclassified as negative. Ten-fold cross-validation yielded a mean AUC of 0.984 with consistently high-sensitivity and specificity across folds, indicating minimal overfitting. Bootstrap resampling (1,000-iterations) confirmed stability of discriminative performance. When extrapolated to population-level prevalence, the model maintained an extremely high NPV (>0.999), while PPV remained low, consistent with its intended role as a high-sensitivity exclusion tool rather than a confirmatory test. A plasma-based multivariate biomarker model enables accurate, safe exclusion of glioblastoma and supports diagnostic triage alongside neuro-imaging.

In silico evaluation of deleterious nsSNPs in SOD1 and TARDBP genes associated with amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder involving mutations in the SOD1 and TARDBP genes that significantly influence its development. Nonsynonymous SNPs in these genes can disrupt protein stability, folding, and regulatory functions, leading to the loss of motor neurons. This study performed an in silico approach to assess the functional and structural effects of nsSNPs in SOD1 and TARDBP . A total of 5,964 SOD1 and 10,630 TARDBP variants were retrieved from public databases, filtered for the coding region with a MAF ≥ 0.001, and prioritized using CADD. Multiple approaches, including pathogenicity prediction, stability analysis, structural modeling, post-translational modification assessment, and network-based functions, were combined. 12 nsSNPs per gene met the inclusion criteria. Notably, SOD1 variants V15G (rs1202989817) and I19M (rs1182088847) were consistently predicted to be deleterious, showing decreased stability indicated by negative ΔΔG values and localized structural disruptions without global misfolding. Conversely, TARDBP variants G335D (rs80356729) and I222T (rs1570722030) suggested destabilization but yielded mixed predictions regarding the disease association. Network and pathway analyses highlighted SOD1 and TARDBP as key nodes in ALS-related mechanisms, such as oxidative stress, RNA metabolism, proteostasis, and mitochondrial impairment. These findings prioritize structurally destabilizing variants with potential pathogenic relevance in ALS and provide a computational framework for downstream experimental validation of these variants.

Functional Identification of Minimal Promoter Region of Elongation of Very Long Chain Fatty Acids 2 (ELOVL2) Gene in Chicken Hepatocytes

Background: The enzyme elongation of very long chain fatty acids protein 2 (ELOVL2) plays a crucial role in the long-chain fatty acid elongation cycle. The chicken is known to contain abundant quantities of PUFAs that are suitable for human consumption and is regarded as a perfect model species for studying the synthesis of PUFAs. However, the molecular processes controlling the synthesis of PUFAs in chickens need to be further explored, and a better understanding of ELOVL2 regulation may provide information useful for identifying regulatory genetic variants associated with fatty-acid composition and other economically important traits in chickens. Identification of the minimal promoter region will provide a basis for understanding the transcriptional regulation of ELOVL2 and its potential contribution to fatty-acid metabolism so that the function of the gene may be altered to obtain a desired phenotype. Aim: For this purpose, an in vitro cell culture experiment was conducted. This investigation was carried out with the aim of identifying the minimal promoter region of ELOVL2 gene. Place and Duration of Study: The study was carried out at ICAR-Directorate of Poultry Research, Hyderabad, during 2021-2022. Methodology: 0.5 to 1.0 mL of blood was collected from the wing vein of a control-layer chicken and genomic DNA was isolated using the standard phenol-chloroform method. PCR was performed to amplify two overlapping ELOVL2 promoter fragments of 587 bp and 1558 bp using specific primers, cloned into the reporter vector pAcGFP1-1 (promoterless) with GFP as a marker protein, and then transformed into DH5α competent cells. The pAcGFP1-1 recombinant plasmid was transfected into a primary chicken embryo hepatocyte culture to check functionality, which was reflected by the expression level of GFP protein in hepatocyte cells. Results: The expression of GFP in chicken embryo hepatocytes was evaluated through real-time PCR. A 1.19-fold higher GFP expression was observed in cells with the 1558 bp fragment as the promoter than in cells with the 587 bp fragment. Conclusion: Thus, it can be concluded that both the promoter fragments of ELOVL2 are capable of regulating gene expression even in a cell culture system with the larger fragment being more effective. This identified promoter region can be used as a target in further research involving promoter mutations, transcription-factor analysis, gene regulation, and functional characterization of ELOVL2. The observed findings may also support future research in poultry nutrition, lipid metabolism, functional genomics, and molecular breeding.