Efficient Variants Research Articles

Superluminescent variants of Gaussia luciferase in living animal deep-tissue imaging

Gaussia luciferase (Gluc) is widely recognized as a powerful bioluminescent tool for biosensing and molecular imaging. However, its utility in mammalian deep tissue imaging and high-throughput applications is hampered by two primary limitations: the emission of blue light and the transient nature of its bioluminescence reaction. In this study, we have engineered two novel luciferases, BeM and SuM, by incorporating known amino acid mutations that have demonstrated the ability to stabilize, enhance, or shift the wavelength of Gluc-catalyzed luminescence towards the red spectrum. The BeM and SuM-coelenterazine systems exhibited a more than 100-fold increase in bioluminescence intensity in vitro, and over a 10-fold enhancement in vivo, compared to the conventional firefly luciferase-D-luciferin system. This significant improvement enables the noninvasive deep tissue imaging of a small number of cells within living mice. BeM and SuM emerge as the most efficient Gaussia luciferase variants to date, showcasing their immense potential as bioengineered light sources. Their applications could span various fields, ranging from fundamental scientific research to clinical diagnostics and therapeutic interventions, marking a significant leap forward in bioluminescence technology.

Pan-genome analysis of 13 Spinacia accessions reveals structural variations associated with sex chromosome evolution and domestication traits in spinach.

Structural variations (SVs) are major genetic variants that can be involved in the origin, adaptation and domestication of species. However, the identification and characterization of SVs in Spinacia species are rare due to the lack of a pan-genome. Here, we report eight chromosome-scale assemblies of cultivated spinach and its two wild species. After integration with five existing assemblies, we constructed a comprehensive Spinacia pan-genome and identified 193 661 pan-SVs, which were genotyped in 452 Spinacia accessions. Our pan-SVs enabled genome-wide association study identified signals associated with sex and clarified the evolutionary direction of spinach. Most sex-linked SVs (86%) were biased to occur on the Y chromosome during the evolution of the sex-linked region, resulting in reduced Y-linked gene expression. The frequency of pan-SVs among Spinacia accessions further illustrated the contribution of these SVs to domestication, such as bolting time and seed dormancy. Furthermore, compared with SNPs, pan-SVs act as efficient variants in genomic selection (GS) because of their ability to capture missing heritability information and higher prediction accuracy. Overall, this study provides a valuable resource for spinach genomics and highlights the potential utility of pan-SV in crop improvement and breeding programmes.

Bioenergetic assessment of the use of fertilizers for potatoes in the northern region of Kazakhstan

In field experiments, the high energy efficiency of the use of mineral fertilizers for potatoes on chestnut soils of northern Kazakhstan was revealed. The energy efficiency of the use of nitrogen, phosphorus and potash fertilizers has been established. The energy consumption of nitrogen fertilizers per 1 kg of increase in the yield of the main product, depending on the doses of fertilizers, ranged from 6223,3 MJ (efficiency = 2,2 units) to 15129,6 MJ (efficiency = 1,0 units). The energy content of 1 kg of phosphorous fertilizers used by increasing the yield of potato tubers (c) was estimated from 11712 MJ (efficiency = 1,9 units) to 18300 MJ (efficiency = 2,5 units), which indicates high energy efficiency. Energy costs varied in the range of 5130,5 MJ against the background of N45K45 and 7208,3 MJ against the best variant of N45P90K45. The energy efficiency from the use of potash fertilizers is estimated at 2,2 units for variants K45 and K90 against the background of N45P45. Energy costs are reduced by obtaining 1 c of potato tubers in more efficient variants in terms of the energy efficiency of the fertilizers used. Knowing the energy costs (Ao, MJ) for growing crops and the energy content (Vf0, MJ/ha) in the yield of products, it is possible to conduct a bioenergetic assessment of the effectiveness of crop cultivation in the practice of applying fertilizers. The purpose of the research is to identify the bioenergetic efficiency of the use of mineral fertilizers for potatoes on chestnut soil in the conditions of the Northern region of Kazakhstan.

Application of Recycled Filling to Improve the Purification Performance of Confectionery Wastewater in a Vertical Anaerobic Labyrinth Flow Bioreactor

Anaerobic wastewater treatment is, in many cases, a justified alternative to typical activated sludge processes, from a technological, economic, and ecological point of view. The optimisation of fermentation reactors is primarily concerned with increasing the biodegradation of organic compounds and biogas production, as well as improving efficiency in the removal of nitrogen and phosphorus compounds. The aim of the research was to determine the impact of using low-cost recycled filling on the efficiency of treating real confectionery wastewater in a vertical anaerobic labyrinth flow bioreactor. The experiments focused on selecting the organic loading rate that would allow for the effective biodegradation and removal of pollutants, as well as the efficient production of biomethane. It was found that the tested reactor can operate efficiently at a maximum organic loading rate (OLR) of 7.0–8.0 g of chemical oxygen demand (COD)/L·d. In this OLR range, high efficiency was guaranteed for both wastewater treatment and biogas production. However, increasing the OLR value to 8.0 g COD/L·d had a significant negative effect on the methane (CH4) content in the biogas. The most efficient variants achieved a biodegradation efficiency of around 90% of the organic compounds, a CH4 content of over 70% in the biogas, and a biogas yield of over 400 L/kg of COD removed. A significant influence of the applied OLR on the ratio of free organic acids (FOS) to total alkaline capacity (TAC) and pH was observed, as well as a strong correlation of these indicators with the specific biogas yield and CH4 content. The application of a solution based on the use of a hybrid system of anaerobic granulated sludge and an anaerobic filter resulted in an efficient treatment process and an almost complete elimination of suspensions from the wastewater.

Directed Evolution and Immobilization of Lactobacillus brevis Alcohol Dehydrogenase for Chemo-Enzymatic Synthesis of Rivastigmine.

Rivastigmine is one of the several pharmaceuticals widely prescribed for the treatment of Alzheimer's disease. However, its practical synthesis still faces many issues, such as the involvement of toxic metals and harsh reaction conditions. Herein, we report a chemo-enzymatic synthesis of Rivastigmine. The key chiral intermediate was synthesized by an engineered alcohol dehydrogenase from Lactobacillus brevis (LbADH). A semi-rational approach was employed to improve its catalytic activity and thermal stability. Several LbADH variants were obtained with a remarkable increase in activity and melting temperature. Exploration of the substrate scope of these variants demonstrated improved activities toward various ketones, especially acetophenone analogs. To further recycle and reuse the biocatalyst, one LbADH variant and glucose dehydrogenase were co-immobilized on nanoparticles. By integrating enzymatic and chemical steps, Rivastigmine was successfully synthesized with an overall yield of 66 %. This study offers an efficient chemo-enzymatic route for Rivastigmine and provides several efficient LbADH variants with a broad range of potential applications.

Semirational Design Strategy To Enhance the Thermostability and Catalytic Activity of Cytochrome P450 105D7 for the Degradation of the Pharmaceutically Active Compounds: Diclofenac.

Pharmaceutically active compounds are an important category of emerging pollutants, and their biological transformation processes in the environment are crucial for understanding and evaluating the migration, transformation, and environmental fate of emerging pollutants. The cytochrome P450 105 enzyme family has been proven to play an important role in the degradation of exogenous environmental pollutants. However, its thermostability and catalytic activity still need to be improved to better adapt to complex environmental conditions. This work elucidates the key mechanisms and important residues of the degradation reaction through multiple computational strategies, establishes a mutation library, and obtains 21 single-point mutation designs. Experimental verification showed that 16 single mutants had enhanced thermostability, with the R89F and L197Y mutants showing the highest increases in thermostability at 135 and 119% relative to the wild-type enzyme, respectively. Additionally, as a result of the higher specific activity of D390Q, it was selected for combination mutagenesis, ultimately resulting in three combination mutants (R89F/L197Y, R89F/D390Q, and R89F/L197Y/D390Q) with enhanced thermostability and catalytic activity. This study provides a modification approach for constructing efficient enzyme variants through semirational design and can contribute to the development of control technologies for emerging pollutants.

Small proteins, great promises: Geographic bioprospecting of Bowman–Birk protease inhibitors and domestication side‐effects in African cowpea (Vigna unguiculata L.)

Societal Impact StatementThe legume crop cowpea is grown worldwide, but 90% of the world's total share is produced in Africa. It is a promising species due to its resilience properties, balance of macro and micronutrients and presence of health‐promoting bioactive compounds. In African countries, cowpea has a crucial role in guaranteeing food security as a subsistence crop for families and commercial income for small farmers. The discovery of compounds with high nutraceutical value and bioactive properties supports socio‐economic policies to improve health and nutrition, especially in low‐ and middle‐income countries. In turn, this encourages biodiversity protection and crop enhancement programmes.Summary Bowman–Birk protease inhibitors (BBIs) are a restricted group of small proteins in plants mainly involved in defence mechanisms against pests. BBIs are demonstrated to be active components capable of reducing the viabilities of different cancer cell lines. BBI bioactivity is directly linked to the inhibition capacity, but the variability and the efficiency against the physiological targets of different BBI isoforms remain still unexplored. We analysed the natural genetic diversity of two main genes encoding BBI trypsin‐trypsin (BBI‐TT) and trypsin‐chymotrypsin (BBI‐TC) in wild and domesticated cowpea samples mainly spread in Sub‐Saharan Africa. We analysed DNA sequences and respective amino acidic isoforms/isoproteins to explore signs of natural selection and haplotype relationships. Moreover, we calculated the binding energy between BBIs and their biological targets to identify which are the most efficient inhibitors and their geographical locations. We found a high level of haplotype diversity for both genes, almost exclusively in wild accessions and detected positive and negative selection signals in the amino acid sequences. Furthermore, in the wild diversity pool, some BBI‐TT and BBI‐TC mature proteins were potentially better interactors with the physiological targets. The long interaction between plant‐pathogen has selected new and useful isoforms in wild lineages that have allowed the chances of survival of the species to improve. On the other hand, the domestication process has produced an intense bottleneck leaving only poorly efficient BBI variants. In addition to providing information on the natural diversity and evolution of BBIs, our work discusses the potential applications in agriculture and human health.

Transition Path Sampling Study of Engineered Enzymes That Catalyze the Morita-Baylis-Hillman Reaction: Why Is Enzyme Design so Difficult?

It is hoped that artificial enzymes designed in laboratories can be efficient alternatives to chemical catalysts that have been used to synthesize organic molecules. However, the design of artificial enzymes is challenging and requires a detailed molecular-level analysis to understand the mechanism they promote in order to design efficient variants. In this study, we computationally investigate the mechanism of proficient Morita-Baylis-Hillman enzymes developed using a combination of computational design and directed evolution. The powerful transition path sampling method coupled with in-depth post-processing analysis has been successfully used to elucidate the different chemical pathways, transition states, protein dynamics, and free energy barriers of reactions catalyzed by such laboratory-optimized enzymes. This research provides an explanation for how different chemical modifications in an enzyme affect its catalytic activity in ways that are not predictable by static design algorithms.

Wasm-Mutate: Fast and effective binary diversification for WebAssembly

WebAssembly is the fourth officially endorsed Web language. It is recognized because of its efficiency and design, focused on security. Yet, its swiftly expanding ecosystem lacks robust software diversification systems. We introduce Wasm-Mutate, a diversification engine specifically designed for WebAssembly. Our engine meets several essential criteria: 1) To quickly generate functionally identical, yet behaviorally diverse, WebAssembly variants, 2) To be universally applicable to any WebAssembly program, irrespective of the source programming language, and 3) Generated variants should counter side-channels. By leveraging an e-graph data structure, Wasm-Mutate is implemented to meet both speed and efficacy. We evaluate Wasm-Mutate by conducting experiments on 404 programs, which include real-world applications. Our results highlight that Wasm-Mutate can produce tens of thousands of unique and efficient WebAssembly variants within minutes. Significantly, Wasm-Mutate can safeguard WebAssembly binaries against timing side-channel attacks, especially those of the Spectre type.

A comparative survey of the influence of small self-cleaving ribozymes on gene expression in human cell culture

ABSTRACT Self-cleaving ribozymes are versatile tools for synthetic biologists when it comes to controlling gene expression. Up to date, 12 different classes are known, and over the past decades more and more details about their structure, cleavage mechanisms and natural environments have been uncovered. However, when these motifs are applied to mammalian gene expression constructs, the outcome can often be unexpected. A variety of factors, such as surrounding sequences and positioning of the ribozyme influences the activity and hence performance of catalytic RNAs. While some information about the efficiency of individual ribozymes (each tested in specific contexts) is known, general trends obtained from standardized, comparable experiments are lacking, complicating decisions such as which ribozyme to choose and where to insert it into the target mRNA. In many cases, application-specific optimization is required, which can be very laborious. Here, we systematically compared different classes of ribozymes within the 3’-UTR of a given reporter gene. We then examined position-dependent effects of the best-performing ribozymes. Moreover, we tested additional variants of already widely used hammerhead ribozymes originating from various organisms. We were able to identify functional structures suited for aptazyme design and generated highly efficient hammerhead ribozyme variants originating from the human genome. The present dataset will aide decisions about how to apply ribozymes for affecting gene expression as well as for developing ribozyme-based switches for controlling gene expression in human cells.

The screening vector system of morphogenic regulators in Fabaceae

Legumes are important agricultural and food crops, however, some legume species have a low regenerative capacityin vitro, which complicates obtaining the genetically modified plants with improved properties and analysing gene function.
 To search for genes that stimulate somatic embryogenesis and to increase the regeneration frequency in legumesin vitro, we designed a screening vector system that will allow faster cloning of genes encoding potential regulators of morphogenesis by preserving restriction sites in the final vector. The construction of vectors is based on the Golden Gate [1] modular cloning method. Using type II restriction endonucleases, DNA fragments form sticky ends and are combined in a given order to form multigene constructs intended forAgrobacterium-mediated transformation. In order to identify efficient variants for gene expression, we used a number of promoters: CaMV 35S(long), CaMV 35S(double), nopaline synthase (nos), actin 2 (act2), and a number of terminators: 35S CaMV, nos, act2 forMtWOX9-1overexpression.Medicago truncatulaWOX9-1 (MtWOX9-1) is a WUSCHEL- related homeobox transcription factor for which a positive effect on the formation of somatic embryos in callus culture was previously shown [2].
 Based on the analysis of embryogenic tissue, the optimal combination of promoters and terminators will be selected to assemble the vector for screening of morphogenetic regulators.
 The work is supported by the Ministry of Science and Higher Education of the Russian Federation (Agreement 075-10-2021-093, Project PBB-RND-2243).

Targeted C-to-T and A-to-G dual mutagenesis system for RhtA transporter in vivo evolution.

T7 RNA polymerase (T7RNAP) has been fused with cytosine or adenine deaminase individually, enabling in vivo C-to-T or A-to-G transitions on DNA sequence downstream of T7 promoter, and greatly accelerated directed protein evolution. However, its base conversion type is limited. In this study, we created a dual-functional system for simultaneous C-to-T and A-to-G in vivo mutagenesis, called T7-DualMuta, by fusing T7RNAP with both cytidine deaminase (PmCDA1) and a highly active adenine deaminase (TadA-8e). The C-to-T and A-to-G mutagenesis frequencies of T7-DualMuta were 4.02 × 10-3 and 1.20 × 10-2, respectively, with 24 h culturing and distributed mutations evenly across the target gene. The T7-DualMuta system was used to in vivo directed evolution of L-homoserine transporter RhtA, resulting in efficient variants that carried the four types of base conversions by T7-DualMuta. The evolved variants greatly increased the host growth rates at L-homoserine concentrations of 8 g/L, which was not previously achieved, and demonstrated the great in vivo evolution capacity. The novel molecular device T7-DualMuta efficiently provides both C/G-to-T/A and A/T-to-G/C mutagenesis on target regions, making it useful for various applications and research in Enzymology and Synthetic Biology studies. It also represents an important expansion of the base editing toolbox.ImportanceA T7-DualMuta system for simultaneous C-to-T and A-to-G in vivo mutagenesis was created. The mutagenesis frequency was 4.02 × 107 fold higher than the spontaneous mutation, which was reported to be approximately 10-10 bases per nucleotide per generation. This mutant system can be utilized for various applications and research in Enzymology and Synthetic Biology studies.

EVaDe: Efficient and Lightweight Mirai Variants Detection via Approximate Largest Submatrix Search

The Mirai botnet, notorious for launching significant Distributed Denial of Service (DDoS) attacks and crippling portions of internet services in late 2016, has emerged as a significant threat. Its threat is magnified by the open-source nature of the original Mirai code, which enables a propagation and evolution rate that surpasses traditional malware and frequently defies common sense. As the primary targets of Mirai attacks, Internet of Things (IoT) devices must promptly adapt to the evolving variations of the Mirai threat scenario. In practice, however, IoT devices are frequently constrained by insufficient security detection resources. Therefore, there is an urgent need for a lightweight framework capable of handling Mirai variants and dynamically updating its rule set in order to effectively counter the threat. In response to these challenges, we present Efficient and lightweight Mirai Variants Detection (EVaDe), a novel, lightweight framework for detecting Mirai. EVaDe unleashes the power of sample function mining to efficiently automate the generation of detection rules, requiring limited hardware resources while maintaining effectiveness against Mirai and its numerous variants. In addition, to improve the efficacy of rule generation, we propose a sophisticated algorithm designed to optimize the maximum submatrix problem, thereby facilitating the efficient and rapid extraction of malicious rules from the sample group. We validated the experiments on actual IoT devices with significantly compressed performance overheads. An average sample detection time of 5 ms to make sure the system can be deployed in real production. According to the result, the approach has an average detection rate of 95% for Mirai and its variants, which beats every other well-known piece of commercial antivirus software on the market by 3% to 56%.

Mini-PE, a prime editor with compact Cas9 and truncated reverse transcriptase

Prime editor (PE) is a versatile genome editing tool that does not need extra DNA donors or inducing double-strand breaks. However, invivo implementation of PE remains a challenge because of its oversized composition. In this study, we screened out the smallest truncated Moloney murine leukemia virus (MMLV) reverse transcriptase (RT) with the F155Y mutation to keep gene editing efficiency. We discovered the most efficient gene editing variants of MMLV RT with the smallest size. After optimization of the pegRNAs and incorporation with nick sgRNAs, the mini-PE delivered up to 10% precise editing at target sites in human and mouse cells. It also edited the mouse Hsf1 gene in the mouse retina precisely after delivery with adeno-associated viruses (AAVs), although the editing efficiency was lower than 1%. We will focus on improving the editing efficiency of mini-PE and exploiting its therapeutic potential against human genetic diseases.

Computational investigation on the binding modes of PET polymer to PETase

Poly(ethylene terephthalate) (PET) has been widely utilized in daily life, but its non-degradability has induced severe environmental and health problems. Recently, PETase, which has been isolated from bacterium Ideonella sakaiensisis, was reported to have the highest PET degradation activity and specificity under room temperature, but no crystal structure for PET in complex with PETase has been reported. To provide deep insight into the binding mode of PET polymer on PETase and the binding interactions, we employed molecular docking and molecular dynamics simulations to study the substrate binding at the atomic level. Different PET oligomers have been studied with chain lengths varying from 2 to 8. In addition, the binding energies and hot-spot residues were analyzed to gain better insights into the binding mechanism by MM/GBSA approach. The PET oligomers adopt stable and reactive conformations in a shallow cleft on a flat surface of PETase. The binding cleft can only accommodate four moieties, and others beyond the region will be stabilized by the π–stacking interactions with Trp156 at the terephthalic acid terminal. Our studies provide a clear picture of how the binding mode of PET polymer and its interactions with PETase change with the chain length. Those studies would provide useful information for the rational design of catalytically more efficient PETase variants toward plastic degradation. Communicated by Ramaswamy H. Sarma

Exploiting the implicit independence assumption for learning directed graphical models

Bayesian network classifiers (BNCs) provide a sound formalism for representing probabilistic knowledge and reasoning with uncertainty. Explicit independence assumptions can effectively and efficiently reduce the size of the search space for solving the NP-complete problem of structure learning. Strong conditional dependencies, when added to the network topology of BNC, can relax the independence assumptions, whereas the weak ones may result in biased estimates of conditional probability and degradation in generalization performance. In this paper, we propose an extension to the k-dependence Bayesian classifier (KDB) that achieves the bias/variance trade-off by verifying the rationality of implicit independence assumptions implicated. The informational and probabilistic dependency relationships represented in the learned robust topologies will be more appropriate for fitting labeled and unlabeled data, respectively. The comprehensive experimental results on 40 UCI datasets show that our proposed algorithm achieves competitive classification performance when compared to state-of-the-art BNC learners and their efficient variants in terms of zero-one loss, root mean square error (RMSE), bias and variance.

Dynamic interplay between target search and recognition for a Type I CRISPR-Cas system

CRISPR-Cas effector complexes enable the defense against foreign nucleic acids and have recently been exploited as molecular tools for precise genome editing at a target locus. To bind and cleave their target, the CRISPR-Cas effectors have to interrogate the entire genome for the presence of a matching sequence. Here we dissect the target search and recognition process of the Type I CRISPR-Cas complex Cascade by simultaneously monitoring DNA binding and R-loop formation by the complex. We directly quantify the effect of DNA supercoiling on the target recognition probability and demonstrate that Cascade uses facilitated diffusion for its target search. We show that target search and target recognition are tightly linked and that DNA supercoiling and limited 1D diffusion need to be considered when understanding target recognition and target search by CRISPR-Cas enzymes and engineering more efficient and precise variants.

Fast Differentiable Matrix Square Root and Inverse Square Root.

Computing the matrix square root and its inverse in a differentiable manner is important in a variety of computer vision tasks. Previous methods either adopt the Singular Value Decomposition (SVD) to explicitly factorize the matrix or use the Newton-Schulz iteration (NS iteration) to derive the approximate solution. However, both methods are not computationally efficient enough in either the forward pass or the backward pass. In this paper, we propose two more efficient variants to compute the differentiable matrix square root and the inverse square root. For the forward propagation, one method is to use Matrix Taylor Polynomial (MTP), and the other method is to use Matrix Padé Approximants (MPA). The backward gradient is computed by iteratively solving the continuous-time Lyapunov equationusing the matrix sign function. A series of numerical tests show that both methods yield considerable speed-up compared with the SVD or the NS iteration. Moreover, we validate the effectiveness of our methods in several real-world applications, including de-correlated batch normalization, second-order vision transformer, global covariance pooling for large-scale and fine-grained recognition, attentive covariance pooling for video recognition, and neural style transfer. The experiments demonstrate that our methods can also achieve competitive and even slightly better performances. Code is available at https://github.com/KingJamesSong/FastDifferentiableMatSqrt.

Efficient Long-Text Understanding with Short-Text Models

AbstractTransformer-based pretrained language models (LMs) are ubiquitous across natural language understanding, but cannot be applied to long sequences such as stories, scientific articles, and long documents due to their quadratic complexity. While a myriad of efficient transformer variants have been proposed, they are typically based on custom implementations that require expensive pretraining from scratch. In this work, we propose SLED: SLiding-Encoder and Decoder, a simple approach for processing long sequences that re-uses and leverages battle-tested short-text pretrained LMs. Specifically, we partition the input into overlapping chunks, encode each with a short-text LM encoder and use the pretrained decoder to fuse information across chunks (fusion-in-decoder). We illustrate through controlled experiments that SLED offers a viable strategy for long text understanding and evaluate our approach on SCROLLS, a benchmark with seven datasets across a wide range of language understanding tasks. We find that SLED is competitive with specialized models that are up to 50x larger and require a dedicated and expensive pretraining step.

Variable Weight Analysis Under Fuzzy Environment and its Application

Fuzzy set theory has attracted many researchers’ interests and many efficient variants have been implemented in support system, decision-making and other fields. Inspired by the idea of variable weight analysis, in this study, we investigate state variable weight vector and its properties, propose two kinds of variable weight aggregation operators such as variable weight averaging (VWA) operator and variable weight geometric (VWG) operator based on three types of fuzzy attribute values. Finally, we apply in ship evaluation and economy collaboration evaluation tasks and make comparison analysis to illustrate the effectiveness of our proposed technique.