Tuning tRNAs for improved translation.

Transfer RNAs have been extensively explored as the molecules that translate the genetic code into proteins. At this interface of genetics and biochemistry, tRNAs direct the efficiency of every major step of translation by interacting with a multitude of binding partners. However, due to the variability of tRNA sequences and the abundance of diverse post-transcriptional modifications, a guidebook linking tRNA sequences to specific translational outcomes has yet to be elucidated. Here, we review substantial efforts that have collectively uncovered tRNA engineering principles that can be used as a guide for the tuning of translation fidelity. These principles have allowed for the development of basic research, expansion of the genetic code with non-canonical amino acids, and tRNA therapeutics.

Rational Molecular Design of a Fluorescent Probe for Selectively Sensing Human Cytochrome P450 2D6.

Cytochrome P450 2D6 (CYP2D6) is a key enzyme that mediates the metabolism of various drugs and endogenous substances in humans. However, its biological role in drug-drug interactions especially mechanism-based inactivation (MBI), and various diseases remains poorly understood, owing to the lack of molecular tools suitable for selectively monitoring CYP2D6 in complex biological systems. Herein, using a tailored molecular strategy, we developed a fluorescent probe BDPM for CYP2D6. BDPM exhibits excellent specificity and imaging capability for CYP2D6, making it suitable for the real-time monitoring of endogenous CYP2D6 activity in living bio-samples. Therefore, our tailored strategy proved useful for constructing the highly selective and enzyme-activated fluorescent probes. BDPM as a molecular tool to explore the critical roles of CYP2D6 in the pathogenesis of diseases, high-throughput screening of inhibitors and intensive investigation of CYP2D6-induced MBI in natural systems.

The impact of digital finance on young People's health insurance participation decisions in China.

Background: The digital finance era has reshaped young people's lifestyles, risk perceptions and insurance participation decisions. Modern youth have to constantly seek for rational social security support and construct individual protection barriers to adapt to new lifestyles and social structures. China's multi-tiered, universal health insurance system is urgently needed to satisfy young people's flexible needs and rational decision-making. Methods: Based on the micro data from 2011 ~ 2021 Chinese General Social Survey (CGSS), this paper uses macro data from Digital Inclusive Finance Index (DIFI) matching to construct probit and m-logit model to assess the impact of the development of digital finance on Chinese youth whether or not to participate in health insurance, and how they choose the concrete type of health insurance. Results: (1) Baseline regression results shows that digital finance has a significant positive effect on Chinese youth's health insurance participation decisions, and has different effects on choices within health insurance types. Strong support for the conclusions is provided by endogeneity and robustness tests. (2) The results of the heterogeneity analysis reveal that the marginal effect of digital finance on young people's health insurance decisions shows urban-rural differences, divergence in levels of self-rated health. (3) The mechanism analysis results suggest that there are two mechanism paths of digital finance on youth health insurance decisions: the household income effect and the subjective well-being effect, and two moderating effects: employment type and family structure. Conclusion: Highlighting the positive value that digital finance brings to the perception of youth insurance participation and the construction of social security systems, it needs to pay close attention to the dynamic changes in employment security and family structure through data, and explore the socio-psychological fluctuation and demand for social security among modern youth. To provide a way forward to achieve the integration of the health insurance system in China and solve the current problem of health insurance equity.

The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction.

In recent years, there has been a notable increase in the scientific community's interest in rational protein design. The prospect of designing an amino acid sequence that can reliably fold into a desired three-dimensional structure and exhibit the intended function is captivating. However, a major challenge in this endeavor lies in accurately predicting the resulting protein structure. The exponential growth of protein databases has fueled the advancement of the field, while newly developed algorithms have pushed the boundaries of what was previously achievable in structure prediction. In particular, using deep learning methods instead of brute force approaches has emerged as a faster and more accurate strategy. These deep-learning techniques leverage the vast amount of data available in protein databases to extract meaningful patterns and predict protein structures with improved precision. In this article, we explore the recent developments in the field of protein structure prediction. We delve into the newly developed methods that leverage deep learning approaches, highlighting their significance and potential for advancing our understanding of protein design.

Factors influencing college students' online rumor refuting behavior during major public health crises: the moderating effect of group norms.

This study integrates SOR (Stimuli-Organism-Response) theoretical framework and rational behavior theory within a theoretical framework, incorporating group norms as a moderating factor to investigate the psychological mechanisms influencing Chinese college students' online rumor-refutation behavior amidst public health crises. Using the structural equation modeling research method, data was collected via questionnaires from 1,254 participants in the context of the COVID-19 pandemic. The findings indicate that both online and offline information seeking are positively correlated with college students' attitudes and subjective norms. Moreover, the attitudes and subjective norms of college students are positively correlated with the online rumor refuting behavior. Furthermore, group norms serve to strengthen the connection between college students' attitudes and their engagement in online refuting rumors. These results illuminate the psychological underpinnings driving college students' online rumor-refuting actions, offering practical and policy implications for effectively managing rumor behaviors.

Harnessing the potential of microbial keratinases for bioconversion of keratin waste.

The increasing global consumption of poultry meat has led to the generation of a vast quantity of feather keratin waste daily, posing significant environmental challenges due to improper disposal methods. A growing focus is on utilizing keratinous polymeric waste, amounting to millions of tons annually. Keratins are biochemically rigid, fibrous, recalcitrant, physiologically insoluble, and resistant to most common proteolytic enzymes. Microbial biodegradation of feather keratin provides a viable solution for augmenting feather waste's nutritional value while mitigating environmental contamination. This approach offers an alternative to traditional physical and chemical treatments. This review focuses on the recent findings and work trends in the field of keratin degradation by microorganisms (bacteria, actinomycetes, and fungi) via keratinolytic and proteolytic enzymes, as well as the limitations and challenges encountered due to the low thermal stability of keratinase, and degradation in the complex environmental conditions. Therefore, recent biotechnological interventions such as designing novel keratinase with high keratinolytic activity, thermostability, and binding affinity have been elaborated here. Enhancing protein structural rigidity through critical engineering approaches, such as rational design, has shown promise in improving the thermal stability of proteins. Concurrently, metagenomic annotation offers insights into the genetic foundations of keratin breakdown, primarily predicting metabolic potential and identifying probable keratinases. This may extend the understanding of microbial keratinolytic mechanisms in a complex community, recognizing the significance of synergistic interactions, which could be further utilized in optimizing industrial keratin degradation processes.

Clinical Efficacy of Taxol Plus Platinum (TP) Chemotherapy Combined with Delayed Administration of PD-1 Inhibitors in Patients with Locally Advanced, Recurrent or Metastatic Esophageal Squamous Cell Carcinoma: A Retrospective Study.

Purpose: Immune checkpoint inhibitors (ICIs) combined with chemotherapy have become the first-line standard treatment for locally advanced or metastatic esophageal squamous cell carcinoma (ESCC). The evidence also demonstrates improved synergistic effects of chemotherapy when combined with delayed administration of ICIs. In this study, we conducted a retrospective investigation into the treatment efficacy of taxol plus platinum (TP) chemotherapy combined with delayed administration of PD-1 inhibitors for ESCC patients. Patients and Methods: Clinical data of ESCC patients who received PD-1 inhibitors 3-5 days after TP chemotherapy as first-line treatment was retrospectively reviewed between January 2019 and April 2023. Clinical outcomes and treatment safety were analyzed. The potential roles of neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and pan-immune-inflammation value (PIV) were investigated. Results: A total of 34 locally advanced, recurrent or metastatic ESCC patients received PD-1 inhibitors 3-5 days following TP chemotherapy were included. The objective response rate (ORR) and disease control rate (DCR) were 85.3% and 97.1% respectively. The median progression-free survival (PFS) and overall survival (OS) were 13.2 and 19.1 month respectively. Seven patients received radical surgery, 1 patient achieved pathologic complete response (pCR) and 3 patients achieved major pathologic response (MPR). Among the 27 patients without surgery, the median PFS and OS were 9.7 and 19.1 month respectively. A more favorable prognosis was correlated with NLR less than 3 at the 3rd and 4th cycle of immunochemotherapy. No significant correlations between other parameters (PLR, MLR and PIV) and prognosis were observed. A total of 22 patients developed grade 3-4 toxicity events. Conclusion: The optimized sequence of PD-1 inhibitors administered 3-5 days after TP chemotherapy as the first-line treatment of ESCC demonstrated favorable treatment efficacy. Pretreatment NLR of less than 3 at the 3rd and 4th cycle of immunochemotherapy is associated with a better prognosis.

The influence of various polymer coatings on the in vitro and in vivo properties of PLGA nanoparticles: Comprehensive study.

Nanoparticles based on poly(lactic-co-glycolic acid) (PLGA) with various surface chemistry are widely used in biomedicine for theranostic applications. The nature of the external coating of nanoparticles has a significant influence on their efficiency as drug carriers or visualization agents. However, information about the mechanisms of nanoparticle accumulation in tumors and the influence of their surface properties on biodistribution is scarce due to the lack of systematic evaluation. Here we investigate the effect of different polymer coatings of the surface on in vitro and in vivo properties of PLGA nanoparticles. Namely, cell binding efficiency, cytotoxicity, efficiency of fluorescent bioimaging, and tumor accumulation were tested. The highest binding efficiency in vitro and cytotoxicity were observed for positively charged polymers. Interestingly, in vivo fluorescent visualization of tumor-bearing mice and quantitative measurements of biodistribution of magnetite-loaded nanoparticles indicated different dependences of accumulation in tumors on the coating of PLGA nanoparticles. This means that nanoparticle surface properties can simultaneously enhance imaging efficiency and decrease quantitative accumulation in tumors. The obtained data demonstrate the complexity of the dependence of nanoparticles' effectiveness for theranostic applications on surface features. We believe that this study will contribute to the rational design of nanoparticles for effective cancer diagnostics and therapy.

Rational adaptation in word production: Strong conceptual ability reduces the effect of lexical impairments on verb retrieval in aphasia.

Language users rely on both linguistic and conceptual processing abilities to efficiently comprehend or produce language. According to the principle of rational adaptation, the degree to which a cognitive system relies on one process vs. another can change under different conditions or disease states with the goal of optimizing behavior. In this study, we investigated rational adaptation in reliance on linguistic versus conceptual processing in aphasia, an acquired disorder of language. In individuals living with aphasia, verb-retrieval impairments are a pervasive deficit that negatively impacts communicative function. As such, we examined evidence of adaptation in verb production, using parallel measures to index impairment in two of verb naming's critical subcomponents: conceptual and linguistic processing. These component processes were evaluated using a standardized assessment battery designed to contrast non-linguistic (picture input) and linguistic (word input) tasks of conceptual action knowledge. The results indicate that non-linguistic conceptual action processing can be impaired in people with aphasia and contributes to verb-retrieval impairments. Furthermore, relatively unimpaired conceptual action processing can ameliorate the influence of linguistic processing deficits on verb-retrieval impairments. These findings are consistent with rational adaptation accounts, indicating that conceptual processing plays a key role in language function and can be leveraged in rehabilitation to improve verb retrieval in adults with chronic aphasia.

Rational Design of Dual-Functionalized Gd@C82 Nanoparticles to Relieve Neuronal Cytotoxicity in Alzheimer's Disease via Inhibition of Aß Aggregation.

The accumulation of amyloid-ß (Aß) peptides is a major hallmark of Alzheimer's disease (AD) and plays a crucial role in its pathogenesis. Particularly, the structured oligomeric species rich in ß-sheet formations were implicated in neuronal organelle damage. Addressing this formidable challenge requires identifying candidates capable of inhibiting peptide aggregation or disaggregating preformed oligomers for effective antiaggregation-based AD therapy. Here, we present a dual-functional nanoinhibitor meticulously designed to target the aggregation driving force and amyloid fibril spatial structure. Leveraging the exceptional structural stability and facile tailoring capability of endohedral metallofullerene Gd@C82, we introduce desired hydrogen-binding sites and charged groups, which are abundant on its surface for specific designs. Impressively, these designs endow the resultant functionalized-Gd@C82 nanoparticles (f-Gd@C82 NPs) with high capability of redirecting peptide self-assembly toward disordered, off-pathway species, obstructing the early growth of protofibrils, and disaggregating the preformed well-ordered protofibrils or even mature Aß fibrils. This results in considerable alleviation of Aß peptide-induced neuronal cytotoxicity, rescuing neuronal death and synaptic loss in primary neuron models. Notably, these modifications significantly improved the dispersibility of f-Gd@C82 NPs, thus substantially enhancing its bioavailability. Moreover, f-Gd@C82 NPs demonstrate excellent cytocompatibility with various cell lines and possess the ability to penetrate the blood-brain barrier in mice. Large-scale molecular dynamics simulations illuminate the inhibition and disaggregation mechanisms. Our design successfully overcomes the limitations of other nanocandidates, which often overly rely on hydrophobic interactions or photothermal conversion properties, and offers a viable direction for developing anti-AD agents through the inhibition and even reversal of Aß aggregation.

Efficient production of a highly active lysozyme from European flat oyster Ostrea edulis.

Lysozyme, an antimicrobial agent, is extensively employed in the food and healthcare sectors to facilitate the breakdown of peptidoglycan. However, the methods to improve its catalytic activity and secretory expression still need to be studied. In the present study, twelve lysozymes from different origins were heterologously expressed using the Komagataella phaffii expression system. Among them, the lysozyme from the European flat oyster Ostrea edulis (oeLYZ) showed the highest activity. Via a semi-rational approach to reduce the structural free energy, the double mutant Y15A/S39R (oeLYZdm) with the catalytic activity 1.8-fold greater than that of the wild type was generated. Subsequently, different N-terminal fusion tags were employed to enhance oeLYZdm expression. The fusion with peptide tag 6×Glu resulted in a remarkable increase in the recombinant oeLYZdm expression, from 2.81 × 103 U mL-1 to 2.11 × 104 U mL-1 in shake flask culture, and eventually reaching 2.05 × 105 U mL-1 in a 3-L fermenter. The work produced the greatest amount of heterologous oeLYZ expression in microbial systems that are known to exist. Reducing the structural free energy and employing the N-terminal fusion tags are effective strategies to improve the catalytic activity and secretory expression of lysozyme.

Rational number representation by the approximate number system.

The approximate number system (ANS) enables organisms to represent the approximate number of items in an observed collection, quickly and independently of natural language. Recently, it has been proposed that the ANS goes beyond representing natural numbers by extracting and representing rational numbers (Clarke & Beck, 2021a). Prior work demonstrates that adults and children discriminate ratios in an approximate and ratio-dependent manner, consistent with the hallmarks of the ANS. Here, we use a well-known "connectedness illusion" to provide evidence that these ratio-dependent ratio discriminations are (a) based on the perceived number of items in seen displays (and not just non-numerical confounds), (b) are not dependent on verbal working memory, or explicit counting routines, and (c) involve representations with a part-whole (or subset-superset) format, like a fraction, rather than a part-part format, like a ratio. These results vindicate key predictions of the hypothesis that the ANS represents rational numbers.

Effects of bulk tank milk, waste milk, and pasteurized waste milk on the nutrient utilization, gastrointestinal tract development, and antimicrobial resistance to Escherichia coli in pre-weaned dairy calves.

This study aimed to assess the impact of bulk tank milk (BTM), waste milk (WM), and pasteurized waste milk (PWM) on nutrient digestibility, ruminal and cecal fermentation, gastrointestinal tract (GIT) development, and antimicrobial resistance of fecal Escherichia coli from dairy calves at 2 periods (30 and 60 d of age). Calves were grouped according to body weight, serum protein levels, and breed composition. Three treatments were included: BTM (n = 21), WM from cows under antibiotic treatment (n = 21), and PWM (waste milk submitted to high-temperature, short-time pasteurization; n = 21). A total of 63 calves were used, of which: 18 animals (n = 6 per treatment) evaluated in the period of 4 - 30 d and 45 (n = 15 per treatment) from 4 - 60 d. During the experimental period, a daily intake of 6 L of milk was divided into 2 equal meals, with ad libitum access to water and starter. Milk and feed intakes were recorded daily. Apparent total-tract digestibility and nitrogen balance were conducted from 25 to 29 d of age (n = 6) and from 53 to 57 d of age (n = 15). Animals were euthanized at 30 ± 1 and 60 ± 1 d of age for the assessment of ruminal and cecal fermentation and GIT development. Antimicrobial susceptibility testing was conducted at 1, 30, and 60 d of age (n = 15/treatment). Statistical analysis utilized a linear mixed-effects model for continuous outcomes and generalized linear models for single measurements (R software). Treatments WM and PWM had lower rumen pH, higher ruminal acetate concentration, larger reticulorumen and liver, and a higher prevalence of fecal-resistant E. coli compared with BTM at both 30 and 60 d. Up to 60 d, both BTM and WM treatments exhibited higher digestibility of ether extract and gross energy compared with the PWM, whereas WM and PWM treatments showed increased nitrogen intake and retention compared with the BTM. These findings suggest that pasteurization of waste milk negatively affects nutrient digestibility and calf performance, while also impacting rumen development. Additionally, the use of milk containing antibiotic residue leads to the selection of resistant E. coli in the GIT over time.

Overweight, obesity, and carbohydrate metabolism disorder in workers of an industrial facility in Kazakhstan: early prevention and its management.

Introduction: In recent years, the issue of widespread increased body weight in combination with blood glycaemic changes in the able-bodied population of Kazakhstan has become especially acute, which in turn leads to dangerous complications and an increased burden on the country's healthcare structures. Aim: To investigate the effectiveness of early prevention of overweight, obesity, and carbohydrate metabolism disorders in the able-bodied workers at an industrial facility in Kazakhstan.Material and methods: The study was conducted in 2019-2021 using elements of both empirical (observation, comparison, measurement) and theoretical (analysis and synthesis, statistical processing of results with the calculation of the reliability criterion) models of scientific knowledge. Results: The factors that have a correlation with this pathology and the strength of their interaction have been studied. New approaches to primary prevention have been developed, and algorithms have been formulated not only for the early detection of pathologies but also for the most effective and efficient methods of combating the occurrence of such pathologies in the population. Conclusions: To prevent the further spread of the phenomenon of overweight among workers in the industrial sector of Kazakhstan, the following measures are necessary: daily walking (more than 10,000 steps), dosed physical activity in the form of cardio and swimming, and nutrition correction (Mediterranean diet) with alcohol restriction. This prevention significantly reduces body weight and optimizes carbohydrate metabolism.

Engineering principles for rationally design therapeutic strategies against hepatocellular carcinoma.

The search for new therapeutic strategies against cancer has favored the emergence of rationally designed treatments. These treatments have focused on attacking cell plasticity mechanisms to block the transformation of epithelial cells into cancerous cells. The aim of these approaches was to control particularly lethal cancers such as hepatocellular carcinoma. However, they have not been able to control the progression of cancer for unknown reasons. Facing this scenario, emerging areas such as systems biology propose using engineering principles to design and optimize cancer treatments. Beyond the possibilities that this approach might offer, it is necessary to know whether its implementation at a clinical level is viable or not. Therefore, in this paper, we will review the engineering principles that could be applied to rationally design strategies against hepatocellular carcinoma, and discuss whether the necessary elements exist to implement them. In particular, we will emphasize whether these engineering principles could be applied to fight hepatocellular carcinoma.

The two-step strategy for enhancing the specific activity and thermostability of alginate lyase AlyG2 with mechanism for improved thermostability.

To overcome the trade-off challenge encountered in the engineering of alginate lyase AlyG2 from Seonamhaeicola algicola Gy8T and to expand its potential industrial applications, we devised a two-step strategy encompassing activity enhancement followed by thermal stability engineering. To enhance the specific activity of efficient AlyG2, we strategically substituted residues with bulky steric hindrance proximal to the active pocket with glycine or alanine. This led to the generation of three promising positive mutants, with particular emphasis on the T91S mutant, exhibiting a 1.91-fold specific activity compared to the wild type. To mitigate the poor thermal stability of T91S, mutants with negative ΔΔG values in the thermal flexibility region were screened out. Notably, the S72Ya mutant not only displayed 17.96 % further increase in specific activity but also exhibited improved stability compared to T91S, manifesting as a remarkable 30.97 % increase in relative activity following a 1-hour incubation at 42 °C. Furthermore, enhanced kinetic stability was observed. To gain deeper insights into the mechanism underlying the enhanced thermostability of the S72Ya mutant, we conducted molecular dynamics simulations, principal component analysis (PCA), dynamic cross-correlation map (DCCM), and free energy landscape (FEL) analysis. The results unveiled a reduction in the flexibility of the surface loop, a stronger correlation dynamic and a narrower motion subspace in S72Ya system, along with the formation of more stable hydrogen bonds. Collectively, our findings suggest amino acids substitutions resulting in smaller side chains proximate to the active site can positively impact enzyme activity, while reducing the flexibility of surface loops emerges as a pivotal factor in conferring thermal stability. These insights offer valuable guidance and a framework for the engineering of other enzyme types.

Alpha Carbonic Anhydrase from Nitratiruptor tergarcus Engineered for Increased Activity and Thermostability.

The development of carbon capture and storage technologies has resulted in a rising interest in the use of carbonic anhydrases (CAs) for CO2 fixation at elevated temperatures. In this study, we chose to rationally engineer the α-CA (NtCA) from the thermophilic bacterium Nitratiruptor tergarcus, which has been previously suggested to be thermostable by in silico studies. Using a combination of analyses with the DEEPDDG software and available structural knowledge, we selected residues in three regions, namely, the catalytic pocket, the dimeric interface and the surface, in order to increase thermostability and CO2 hydration activity. A total of 13 specific mutations, affecting seven amino acids, were assessed. Single, double and quadruple mutants were produced in Escherichia coli and analyzed. The best-performing mutations that led to improvements in both activity and stability were D168K, a surface mutation, and R210L, a mutation in the dimeric interface. Apart from these, most mutants showed improved thermostability, with mutants R210K and N88K_R210L showing substantial improvements in activity, up to 11-fold. Molecular dynamics simulations, focusing particularly on residue fluctuations, conformational changes and hydrogen bond analysis, elucidated the structural changes imposed by the mutations. Successful engineering of NtCA provided valuable lessons for further engineering of α-CAs.

Improving the enzymatic activity and stability of N-carbamoyl hydrolase using deep learning approach.

BACKGROUND: Optically active D-amino acids are widely used as intermediates in the synthesis of antibiotics, insecticides, and peptide hormones. Currently, the two-enzyme cascade reaction is the most efficient way to produce D-amino acids using enzymes DHdt and DCase, but DCase is susceptible to heat inactivation. Here, to enhance the enzymatic activity and thermal stability of DCase, a rational design software "Feitian" was developed based on kcat prediction using the deep learning approach. RESULTS: According to empirical design and prediction of "Feitian" software, six single-point mutants with high kcat value were selected and successfully constructed by site-directed mutagenesis. Out of six, three mutants (Q4C, T212S, and A302C) showed higher enzymatic activity than the wild-type. Furthermore, the combined triple-point mutant DCase-M3 (Q4C/T212S/A302C) exhibited a 4.25-fold increase in activity (29.77 ± 4.52 U) and a 2.25-fold increase in thermal stability as compared to the wild-type, respectively. Through the whole-cell reaction, the high titer of D-HPG (2.57 ± 0.43 mM) was produced by the mutant Q4C/T212S/A302C, which was about 2.04-fold of the wild-type. Molecular dynamics simulation results showed that DCase-M3 significantly enhances the rigidity of the catalytic site and thus increases the activity of DCase-M3. CONCLUSIONS: In this study, an efficient rational design software "Feitian" was successfully developed with a prediction accuracy of about 50% in enzymatic activity. A triple-point mutant DCase-M3 (Q4C/T212S/A302C) with enhanced enzymatic activity and thermostability was successfully obtained, which could be applied to the development of a fully enzymatic process for the industrial production of D-HPG.

Machine learning guided rational design of a non-heme iron-based lysine dioxygenase improves its total turnover number.

Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolution or structure-based rational design campaigns to improve their activities. In recent years, machine learning has been integrated into these workflows to improve the discovery of beneficial enzyme variants. In this work, we combine a structure-based machine-learning algorithm with classical molecular dynamics simulations to down select mutations for rational design of a non-heme iron-dependent lysine dioxygenase, LDO. This approach consistently resulted in functional LDO mutants and circumvents the need for extensive study of mutational activity before-hand. Our rationally designed single mutants purified with up to 2-fold higher yields than WT and displayed higher total turnover numbers (TTN). Combining five such single mutations into a pentamutant variant, LPNYI LDO, leads to a 40% improvement in the TTN (218±3) as compared to WT LDO (TTN = 160±2). Overall, this work offers a low-barrier approach for those seeking to synergize machine learning algorithms with pre-existing protein engineering strategies.

High-content tailoring strategy to improve the multifunctionality of functional nucleic acids.

Functional nucleic acids (FNAs) have attracted increasing attention in recent years due to their diverse physiological functions. The understanding of their conformational recognition mechanisms has advanced through nucleic acid tailoring strategies and sequence optimization. With the development of the FNA tailoring techniques, they have become a methodological guide for nucleic acid repurposing. Therefore, it is necessary to systematize the relationship between FNA tailoring strategies and the development of nucleic acid multifunctionality. This review systematically categorizes eight types of FNA multifunctionality, and introduces the traditional FNA tailoring strategy from five aspects, including deletion, substitution, splitting, fusion and elongation. Based on the current state of FNA modification, a new generation of FNA tailoring strategy, called the high-content tailoring strategy, was unprecedentedly proposed to improve FNA multifunctionality. In addition, the multiple applications of rational tailoring-driven FNA performance enhancement in various fields were comprehensively summarized. The limitations and potential of FNA tailoring and repurposing in the future are also explored in this review. In summary, this review introduces a novel tailoring theory, systematically summarizes eight FNA performance enhancements, and provides a systematic overview of tailoring applications across all categories of FNAs. The high-content tailoring strategy is expected to expand the application scenarios of FNAs in biosensing, biomedicine and materials science, thus promoting the synergistic development of various fields.