Exploring effectiveness of ab-initio protein-protein docking methods on a novel antibacterial protein complex dataset

The role of RAB12 in inhibiting osteogenic differentiation and driving metabolic dysregulation in osteoporosis

AIMS

The osteogenic differentiation of mesenchymal stem cells (MSCs) is crucial in osteoporosis, and the metabolic level of the bone microenvironment directly affects metabolic dysregulation in postmenopausal women. RAB12 is a member of the small GTPase Rab family proteins, known to play an important role in autophagy. However, the role of RAB12 in the osteogenic differentiation of osteoporotic hMSCs remains unclear.

MATERIALS AND METHOD

Immunohistochemical staining was used to validate the high expression of RAB12 in aged osteoporotic mouse models and ovariectomized (OVX) mouse models. Co-immunoprecipitation (Co-IP) and LC-MS/MS were employed to explore downstream proteins that may interact with RAB12. Adenovirus containing RAB12 siRNA sequences was injected into the tail vein of OVX osteoporotic mice to analyze the impact of the RAB12/PCBP1/GLUT1 axis on MSC osteogenic differentiation.

KEY FINDINGS

We found that RAB12 expression is upregulated in elderly osteoporotic patients and in osteoporotic mouse models. RAB12 negatively regulates the osteogenic differentiation of hMSCs both in vivo and in vitro. RAB12 interacts with the PCBP1 protein, affecting its autophagic degradation when its expression levels change. RAB12 regulates the transcriptional level of GLUT1 by influencing the autophagic degradation of PCBP1, thereby affecting MSC's regulation of glucose uptake, which in turn impacts MSC osteogenic differentiation and metabolic changes.

SIGNIFICANCE

RAB12 negatively regulates osteogenic differentiation through the PCBP1/GLUT1 axis, affecting glucose metabolism levels in the bone microenvironment. RAB12 may serve as a potential target for the treatment of osteoporosis and postmenopausal metabolic dysregulation.

Computational models for prediction of m6A sites using deep learning

RNA modifications play a crucial role in enhancing the structural and functional diversity of RNA molecules and regulating various stages of the RNA life cycle. Among these modifications, N6-Methyladenosine (m6A) is the most common internal modification in eukaryotic mRNAs and has been extensively studied over the past decade. Accurate identification of m6A modification sites is essential for understanding their function and underlying mechanisms. Traditional methods predominantly rely on machine learning techniques to recognize m6A sites, which often fail to capture the contextual features of these sites comprehensively. In this study, we comprehensively summarize previously published methods based on machine learning and deep learning. We also validate multiple deep learning approaches on benchmark dataset, including previously underutilized methods in m6A site prediction, pre-trained models specifically designed for biological sequence and other basic deep learning methods. Additionally, we further analyze the dataset features and interpret the model's predictions to enhance understanding. Our experimental results clearly demonstrate the effectiveness of the deep learning models, elucidating their strong potential in accurately recognizing m6A modification sites.

Bioelectrocatalytic carbon dioxide reduction by an engineered formate dehydrogenase from Thermoanaerobacter kivui

Electrocatalytic carbon dioxide (CO2) reduction by CO2 reductases is a promising approach for biomanufacturing. Among all known biological or chemical catalysts, hydrogen-dependent carbon dioxide reductase from Thermoanaerobacter kivui (TkHDCR) possesses the highest activity toward CO2 reduction. Herein, we engineer TkHDCR to generate an electro-responsive carbon dioxide reductase considering the safety and convenience. To achieve this purpose, a recombinant Escherichia coli TkHDCR overexpression system is established. The formate dehydrogenase is obtained via subunit truncation and rational design, which enables direct electron transfer (DET)-type bioelectrocatalysis with a near-zero overpotential. By applying a constant voltage of -500 mV (vs. SHE) to a mediated electrolytic cell, 22.8 ± 1.6 mM formate is synthesized in 16 h with an average production rate of 7.1 ± 0.5 μmol h-1cm-2, a Faradaic efficiency of 98.9% and a half-cell energy efficiency of 94.4%. This study provides an enzyme candidate for high efficient CO2 reduction and opens up a way to develop paradigm for CO2-based bio-manufacturing.

HDIContact: a novel predictor of residue-residue contacts on hetero-dimer interfaces via sequential information and transfer learning strategy

Proteins maintain the functional order of cell in life by interacting with other proteins. Determination of protein complex structural information gives biological insights for the research of diseases and drugs. Recently, a breakthrough has been made in protein monomer structure prediction. However, due to the limited number of the known protein structure and homologous sequences of complexes, the prediction of residue-residue contacts on hetero-dimer interfaces is still a challenge. In this study, we have developed a deep learning framework for inferring inter-protein residue contacts from sequential information, called HDIContact. We utilized transfer learning strategy to produce Multiple Sequence Alignment (MSA) two-dimensional (2D) embedding based on patterns of concatenated MSA, which could reduce the influence of noise on MSA caused by mismatched sequences or less homology. For MSA 2D embedding, HDIContact took advantage of Bi-directional Long Short-Term Memory (BiLSTM) with two-channel to capture 2D context of residue pairs. Our comprehensive assessment on the Escherichia coli (E. coli) test dataset showed that HDIContact outperformed other state-of-the-art methods, with top precision of 65.96%, the Area Under the Receiver Operating Characteristic curve (AUROC) of 83.08% and the Area Under the Precision Recall curve (AUPR) of 25.02%. In addition, we analyzed the potential of HDIContact for human-virus protein-protein complexes, by achieving top five precision of 80% on O75475-P04584 related to Human Immunodeficiency Virus. All experiments indicated that our method was a valuable technical tool for predicting inter-protein residue contacts, which would be helpful for understanding protein-protein interaction mechanisms.