Synthetic multi-species consortia provide valuable insights into the ecological and structural dynamics of complex microbial biofilms. However, the specialized functional contributions of individual components under severe nutrient limitations remain poorly understood. This study investigated the population dynamics, m...
T. Pankratov, A. Hakobjanyan· Ecologies· 0 citations
KinConfBench is introduced, a curated benchmark of 2225 high-quality human kinase chains to evaluate the ability of four state-of-the-art cofolding models—Boltz-2, Chai-1, Protenix, and RoseTTAFold-All-Atom—to recover both canonical and rare conformational states.
Kun-Yang Sun, T. Head-Gordon· npj Drug Discovery· 0 citations
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 an...
E. Omuse, H. Machekano, D. Mutyambai et al.· npj Sustainable Agriculture· 0 citations
A putative diagnostic biomarker for CCM, YES1, was demonstrated to be significantly elevated in the plasma of affected patients, with high correlative expression in the source tissue, which further support the development of CCM-related noninvasive biomarkers and may add insight into their pathogenesis.
Yousef Hattar, J. Sesen, Tyra Martinez et al.· Journal of Neurosurgery: Ped...· 0 citations
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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 biochemica...
Soumyadeep Ray, Ganesh Bagler· Zenodo (CERN European Organi...· 0 citations
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% integrat...
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 biochemica...
Soumyadeep Ray, Ganesh Bagler· Zenodo (CERN European Organi...· 0 citations
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.
Zexiang Han, Nadia A. Erkamp, Rob M. Scrutton et al.· mAbs· 0 citations
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.
Jonathan A. Levine, M. Pathil, Samuel Nitz et al.· 0 citations
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.
Yingyi Chen, Zhao-Nan Liu, Yi-Jia Wang et al.· Biomedicines· 0 citations
A plasma-based multivariate biomarker model enables accurate, safe exclusion of glioblastoma and supports diagnostic triage alongside neuro-imaging and maintains an extremely high NPV (>0.999), consistent with its intended role as a high-sensitivity exclusion tool rather than a confirmatory test.
M. J. Decarpentrie, C. David, J. Favresse et al.· Neuro-Oncology· 0 citations
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.