Evaluating the effectiveness of embeddings from five Protein Language Models, including ProtBERT-BFD, ESM-2, ProtALBERT, ProLLaMA, and ProtGPT-2, as input features for various machine learning classifiers suggests that while current embeddings offer strong performance, further advancements in feature extraction and model architectures are needed to significantly boost strict accuracy.
Abstract
Determining the subcellular localization of proteins is critical for understanding their functional roles. With the rapid expansion of protein sequence databases, traditional experimental and homology-based approaches for Protein Subcellular Localization (PSCL) prediction are becoming increasingly impractical. Although several computational approaches have been developed, achieving high performance in terms of strict accuracy for multi-label PSCL prediction remains a significant challenge. This study evaluates the effectiveness of embeddings from five Protein Language Models (PLMs), including ProtBERT-BFD, ESM-2, ProtALBERT, ProLLaMA, and ProtGPT-2, as input features for various machine learning classifiers. Through five-fold cross-validation on the Swiss-Prot dataset, the results show that encoder-based PLMs, particularly ESM-2, combined with a Support Vector Machine (SVM) employing a polynomial kernel, consistently achieve the best performance. This configuration demonstrates a modest yet consistent improvement in strict-accuracy metrics compared to other model combinations. The proposed work offers a comprehensive evaluation of different PLM architectures and classifier complexities, achieving a strict accuracy of 0.58 while providing valuable insights into their performance trade-offs. This represents a 3 percentage point improvement over existing state-of-the-art models like DeepLoc 2.0. These results suggest that while current embeddings offer strong performance, further advancements in feature extraction and model architectures are needed to significantly boost strict accuracy.
This study constructed a pH-responsive P-TN/SF@Fe-Cur composite coating that demonstrated significant anti-infective, anti-inflammatory, antioxidant, pro-angiogenic, and pro-osteogenic effects in rat subcutaneous infection and femoral defect models.
ProteinReasoner is developed, a multimodal generative protein foundation model that sequentially connects amino acid sequence, evolutionary constraints and three-dimensional structure within a shared autoregressive architecture and suggests a general route towards reasoning across interdependent representations in other scientific domains.
Chaozhong Liu, Linlin Chao, Shaomin Ji et al.· bioRxiv· 1 citation
Due to its importance and wide adoption, wheat cultivation is promptly required to shift towards sustainable practices, reducing the dependency on chemical components. Among bio-based solutions aimed at securing the sustainability of wheat cultivation, biostimulants offer a versatile platform of eco-friendly tools assuring sustainability and profitability. Microalgae present a concrete example of a biostimulant source due to their richness in metabolites and high value products. Therefore, this study evaluated the biostimulant potential of eleven eco-extracts prepared from soil-isolated microalgae strains. Eco-extracts applied via soil drench at low dose (0.1 g/L) were investigated for their biostimulant effects on wheat growth, physiology, yield, and quality under controlled conditions. Results demonstrated significant ameliorations in treated plants as compared to the control, with no phytoinhibitory effects. Remarkable enhancements were notable in growth parameters such as shoot and root lengths (+40-70%), physiological traits such as total chlorophyll and stomatal conductance (+7-52%), yield components in the example of grain number per spike and thousand grain weight (+17-103%), and grain quality namely protein and polyphenol content (+2-fold to 4-fold). Similarly, phosphorus accumulation and uptake were significantly improved, while soil physicochemical status was ameliorated, indicating enhanced fertility. Multivariate analysis and composite index ranking marked Chlorella sp. GA18, Chlorella sp. GA65, Scenedesmus sp. GA69, and Chlorococcum sp. GA63 as eco-extracts with consistent performances across all plant traits. These findings highlighted the promising potential of integrating microalgae-based eco-friendly extracts in sustainable wheat cultivation.
Amer Chabili, Z. Hakkoum, F. Minaoui et al.· Plant Science· 1 citation
Effective control of fluid flows is critical across transportation, energy and medicine, where it can increase lift, reduce drag, enhance mixing and attenuate noise1-3. Yet fluids are notoriously difficult to control because they involve high-dimensional, nonlinear and multiscale dynamics that resist conventional approaches4-6. Reinforcement learning has driven remarkable progress in fields such as protein folding and complex games, which have shared benchmarks and standardized environments7-10. Fluid dynamics has lacked such infrastructure, so each controller is typically tuned to a single geometry and operating condition, making progress difficult to accumulate, transfer and compare11-13. Here we introduce HydroGym, a solver-independent reinforcement learning platform providing more than 60 validated, openly available flow control environments spanning from canonical laminar flows to complex turbulent flows, with systematic progression in the Reynolds number up to Re = 4 × 105, and Mach number variations in two and three dimensions. Across these environments, agents repeatedly discover robust control principles, including boundary layer manipulation, disruption of acoustic feedback and reorganization of turbulent wakes. Critically, we demonstrate a proof of concept for zero-shot transfer, in which agents that are trained exclusively in inexpensive surrogate environments are deployed to challenging real-world scenarios such as a three-dimensional wing section. We achieve a 38% reduction in local skin friction while reducing exploration costs by four orders of magnitude compared with direct on-wing optimization. As this transfer exploits shared near-wall physics, the breadth of generalization remains open, suggesting a new pathway for research toward policy generalization across computationally prohibitive simulation environments. By offering a common, extensible foundation for reproducible research, HydroGym moves flow control from isolated case studies toward a cohesive community effort.
Christian Lagemann, Sajeda Mokbel, Miro Gondrum et al.· Nature· 1 citation
Results identify Cryaa as a critical regulator of ER stress and demonstrate that its reduction promotes UPR activation and subsequent photoreceptor apoptosis in the rd9 model, revealing a key role for Cryaa in XLRP pathology and may provide a novel therapeutic perspective for this disease.
Mingzhu Yang, Ruiqi Qiu, S. Yao et al.· Cell Death & Disease· 0 citations
Current evidence linking mitochondrial dysfunction, ER stress, and ER-mitochondrial crosstalk to the pathogenesis of chronic pain is summarized and their potentials as therapeutic targets are discussed.
A. Yadawa, Sufang Liu, F. Tao· Brain Science· 0 citations