Application of machine learning on understanding biomolecule interactions in cellular machinery

Deciphering Ferroptosis: From Molecular Pathways to Machine Learning-Guided Therapeutic Innovation

Ferroptosis is a unique form of cell death reliant on iron and lipid peroxidation. It disrupts redox balance, causing cell death by damaging the plasma membrane, with inducers acting through enzymatic pathways or transport systems. In cancer treatment, suppressing ferroptosis or circumventing it holds significant promise. Beyond cancer, ferroptosis affects aging, organs, metabolism, and nervous system. Understanding ferroptosis mechanisms holds promise for uncovering novel therapeutic strategies across a spectrum of diseases. However, detection and regulation of this regulated cell death are still mired with challenges. The dearth of cell, tissue, or organ-specific biomarkers muted the pharmacological use of ferroptosis. This review covers recent studies on ferroptosis, detailing its properties, key genes, metabolic pathways, and regulatory networks, emphasizing the interaction between cellular signaling and ferroptotic cell death. It also summarizes recent findings on ferroptosis inducers, inhibitors, and regulators, highlighting their potential therapeutic applications across diseases. The review addresses challenges in utilizing ferroptosis therapeutically and explores the use of machine learning to uncover complex patterns in ferroptosis-related data, aiding in the discovery of biomarkers, predictive models, and therapeutic targets. Finally, it discusses emerging research areas and the importance of continued investigation to harness the full therapeutic potential of targeting ferroptosis.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.

Integrated machine learning methods with oversampling technique for regional suitability prediction of waste-to-energy incineration projects

China's tiered strategy to enhance county-level waste incineration for energy aligns with the sustainable development goals (SDGs), emphasizing the need for comprehensive assessments of waste-to-energy (WtE) plant suitability. Traditional assessment methodologies face challenges, particularly in suggesting innovative site alternatives, adapting to new data sets, and their dependence on strict assumptions. This study introduced enhancements in three pivotal dimensions. Methodologically, it leverages data-driven machine learning (ML) approaches to capture the complex relationships essential for site selection, reducing dependency on strict assumptions. In terms of predictive performance, the integration of oversampling with stacked ensemble models enhances the diversity and generalizability of ML models. The area under curve (AUC) scores from four ML models, enhanced by the oversampled dataset, demonstrated significant improvements compared to the original dataset. The stacking model excelled, achieving a score of 92%. It also led in overall Precision and Recall, reaching 85.2% and 85.08% respectively. Nevertheless, a noticeable discrepancy existed in Precision and Recall for positive classes. The stacking model topped Precision scores at 83.1%, followed by eXtreme Gradient Boosting (XGBoost) (82.61%). In terms of Recall, XGBoost recorded the lowest at 85.07%, while the other three classifiers all marked 88.06%. From an industry applicability standpoint, the stacking model provides innovative location alternatives and demonstrates adaptability in Hunan province, offering a reusable tool for WtE location. In conclusion, this study not only enhances the methodological aspects of WtE site selection but also provides practical and adaptable solutions, contributing positively to sustainable waste management practices.

Tunnel engineering of gas-converting enzymes for inhibitor retardation and substrate acceleration

Carbon monoxide dehydrogenase (CODH), formate dehydrogenase (FDH), hydrogenase (H2ase), and nitrogenase (N2ase) are crucial enzymatic catalysts that facilitate the conversion of industrially significant gases such as CO, CO2, H2, and N2. The tunnels in the gas-converting enzymes serve as conduits for these low molecular weight gases to access deeply buried catalytic sites. The identification of the substrate tunnels is imperative for comprehending the substrate selectivity mechanism underlying these gas-converting enzymes. This knowledge also holds substantial value for industrial applications, particularly in addressing the challenges associated with separation and utilization of byproduct gases. In this comprehensive review, we delve into the emerging field of tunnel engineering, presenting a range of approaches and analyses. Additionally, we propose methodologies for the systematic design of enzymes, with the ultimate goal of advancing protein engineering strategies.

Nonsense-Mediated mRNA Decay: Mechanistic Insights and Physiological Significance

Nonsense-mediated mRNA decay (NMD) is an evolutionarily conserved surveillance mechanism across eukaryotes and also regulates the expression of physiological transcripts, thus involved in gene regulation. It essentially ensures recognition and removal of aberrant transcripts. Therefore, the NMD protects the cellular system by restricting the synthesis of truncated proteins, potentially by eliminating the faulty mRNAs. NMD is an evolutionarily conserved surveillance mechanism across eukaryotes and also regulates the expression of physiological transcripts, thus involved in gene regulation as well. Primarily, the NMD machinery scans and differentiates the aberrant and non-aberrant transcripts. A myriad of cellular dysfunctions arise due to production of truncated proteins, so the NMD core proteins, the up-frameshift factors (UPFs) recognizes the faulty mRNAs and further recruits factors resulting in the mRNA degradation. NMD exhibits astounding variability in its ability in regulating cellular mechanisms including both pathological and physiological events. But, the detailed underlying molecular mechanisms in NMD remains blurred and require extensive investigation to gain insights on cellular homeostasis. The complexity in understanding of NMD pathway arises due to the involvement of numerous proteins, molecular interactions and their functioning in different steps of this process. Moreover methods such as alternative splicing generates numerous isoforms of mRNA, so it makes difficulties in understanding the impact of alternative splicing on the efficiency of NMD functioning. Role of NMD in cancer development is very complex. Studies have shown that in some cases cancer cells use NMD pathway as a tool to exploit the NMD mechanism to maintain tumor microenvironment. A greater level of understanding about the intricate mechanism of how tumor used NMD pathway for their benefits, a strategy can be developed for targeting and inhibiting NMD factors involved in pro-tumor activity. There are very little amount of information available about the NMD pathway, how it discriminate mRNAs that are targeted by NMD from those that are not. This review highlights our current understanding of NMD, specifically the regulatory mechanisms and attempts to outline less explored questions that warrant further investigations. Taken as a whole, a detailed molecular understanding of the NMD mechanism could lead to wide-ranging applications for improving cellular homeostasis and paving out strategies in combating pathological disorders leaping forward toward achieving United Nations sustainable development goals (SDG 3: Good health and well-being).

Combinatorial protein engineering and transporter engineering for efficient synthesis of L-Carnosine in Escherichia coli

L-Carnosine has various physiological functions and is widely used in cosmetics, medicine, food additives, and other fields. However, the yield of L-Carnosine obtained by biological methods is far from the level of industrial production. Herein, a cell factory for efficient synthesis of L-Carnosine was constructed based on transporter engineering and protein engineering. Firstly, a dipeptidase (SmpepD) was screened from Serratia marcescens through genome mining to construct a cell factory for synthesizing L-Carnosine. Subsequently, through rationally designed SmPepD, a double mutant T168S/G148D increased the L-Carnosine yield by 41.6% was obtained. Then, yeaS, a gene encoding the exporter of L-histidine, was deleted to further increase the production of L-Carnosine. Finally, L-Carnosine was produced by one-pot biotransformation in a 5 L bioreactor under optimized conditions with a yield of 133.2 mM. This study represented the highest yield of L-Carnosine synthesized in microorganisms and provided a biosynthetic pathway for the industrial production of L-Carnosine.

Evaluation of Long Non-coding RNA (LncRNA) in the Pathogenesis of Chemotherapy Resistance in Cervical Cancer: Diagnostic and Prognostic Approach

Cervical cancer (CC), caused by human papillomavirus (HPV), is a leading cause of female malignancies worldwide. Therefore, understanding the underlying mechanisms of CC development and identifying novel therapeutic targets are significantly important. Cisplatin resistance is a significant challenge in the management of CC. Recent studies highlighted the critical role of long non-coding RNAs (lncRNAs) in modulation of cisplatin resistance. This comprehensive review aims to collect the current understanding roles of lncRNAs and their involvement in cisplatin resistance in CC by highlighting key processes of cancer progression, including apoptosis, proliferation, angiogenesis and epithelial-to-mesenchymal transition (EMT). We discussed the role of lncRNA in CC resistance to cisplatin through molecular pathways and examined gene expression changes. We also discussed treatment strategies and factors that reduce CC resistance to cisplatin by targeting them.

Artificial intelligence and machine learning for smart bioprocesses

In recent years, the digital transformation of bioprocesses, which focuses on interconnectivity, online monitoring, process automation, artificial intelligence (AI) and machine learning (ML), and real-time data acquisition, has gained considerable attention. AI can systematically analyze and forecast high-dimensional data obtained from the operating dynamics of bioprocess, allowing for precise control and synchronization of the process to improve performance and efficiency. Data-driven bioprocessing is a promising technology for tackling emerging challenges in bioprocesses, such as resource availability, parameter dimensionality, nonlinearity, risk mitigation, and complex metabolisms. This special issue entitled "Machine Learning for Smart Bioprocesses (MLSB-2022)" was conceptualized to incorporate some of the recent advances in applications of emerging tools such as ML and AI in bioprocesses. This VSI: MLSB-2022 contains 23 manuscripts, and summarizes the major findings that can serve as a valuable resource for researchers to learn major advances in applications of ML and AI in bioprocesses.