Efficient Variants Research Articles

Engineered acetaldehyde dehydrogenase for the efficient degradation of acetaldehyde

Acetaldehyde is highly cytotoxic and widely presents in food and the environment. Aldehyde dehydrogenase (ALDH) can degrade acetaldehyde to non-toxic acetic acid, showing potential for acetaldehyde elimination. However, a lack of high-throughput methods for screening efficient variants is a significant obstacle to ALDH design. Here, we established a visualized high-throughput method to screen recombinantly expressed ALDH variants in Bacillus subtilis by fluorescent probes of dual-acceptor cyanine-based in response to NADH, the acetaldehyde degradation product. Molecular docking revealed key amino acids in the binding region of acetaldehyde to ALDH. Combined with saturation mutagenesis and visualization high-throughput methods, a variant ALDHS273N with an activity of 119.82 U·mL−1 was screened. The optimal reaction temperature and pH of ALDHS273N were 60 °C and 9.0, respectively. ALDHS273N showed stability at 30–50 °C and pH 5.0–9.0. The activity of ALDHS273N was increased to 263.52 U∙mL−1 by fermentation optimization, which was 5.58 times that of ALDHWT. The degradation rate of ALDHS273N to 100 mmol L−1 acetaldehyde was 87.34% within 2 h, which was 4.2 times that of the wild enzyme (20.81%). As far as we know, this is the ALDH with the highest activity reported so far, and it is also the first time that ALDH has been used for the efficient degradation of acetaldehyde. Overall, the reported high-throughput screening method and developed mutants represent a significant advance in green bio-elimination technologies of acetaldehyde.

Trends in in-silico guided engineering of efficient polyethylene terephthalate (PET) hydrolyzing enzymes to enable bio-recycling and upcycling of PET

Polyethylene terephthalate (PET) is the largest produced polyester globally, and less than 30% of all the PET produced globally (∼6 billion pounds annually) is currently recycled into lower-quality products. The major drawbacks in current recycling methods (mechanical and chemical), have inspired the exploration of potentially efficient and sustainable PET depolymerization using biological approaches. Researchers have discovered efficient PET hydrolyzing enzymes in the plastisphere and have demonstrated the selective degradation of PET to original monomers thus enabling biological recycling or upcycling. However, several significant hurdles such as the less efficiency of the hydrolytic reaction, low thermostability of the enzymes, and the inability of the enzyme to depolymerize crystalline PET must be addressed in order to establish techno-economically feasible commercial-scale biological PET recycling or upcycling processes. Researchers leverage a synthetic biology-based design; build, test, and learn (DBTL) methodology to develop commercially applicable efficient PET hydrolyzing enzymes through 1) high-throughput metagenomic and proteomic approaches to discover new PET hydrolyzing enzymes with superior properties: and, 2) enzyme engineering approaches to modify and optimize PET hydrolyzing properties. Recently, in-silico platforms including molecular mechanics and machine learning concepts are emerging as innovative tools for the development of more efficient and effective PET recycling through the exploration of novel mutations in PET hydrolyzing enzymes. In-silico-guided PET hydrolyzing enzyme engineering with DBTL cycles enables the rapid development of efficient variants of enzymes over tedious conventional enzyme engineering methods such as random or directed evolution. This review highlights the potential of in-silico-guided PET degrading enzyme engineering to create more efficient variants, including Ideonella sakaiensis PETase (IsPETase) and leaf-branch compost cutinases (LCC). Furthermore, future research prospects are discussed to enable a sustainable circular economy through the bioconversion of PET to original or high-value platform chemicals.

Fast training of a transformer for global multi-horizon time series forecasting on tensor processing units

Time Series Forecasting (TSF) is essential to key domains, and the Transformer neural network has advanced the state-of-the-art on global, multi-horizon TSF benchmarks. The quadratic time and memory complexity of the Vanilla Transformer (VT) hinders its application to Big Data environments; therefore, multiple efficient variants of the VT that lower complexity via sparse self-attention have been proposed. However, less complex algorithms do not directly produce faster executions, and machine learning models for Big Data are typically trained on accelerators designed for dense-matrix computation that render slower performance with sparse matrices. To better compare the accuracy-speed trade-off of the VT and its variants, it is essential to test them on such accelerators. We implemented a cloud-based VT on Tensor Processing Units to address this task. Experiments on large-scale datasets show that our Transformer achieves good predictive performance when compared to state-of-the-art models while reducing training times from hours to under 2 min.

Generating Efficient Methanomethylophilus alvus Pyrrolysyl-tRNA Synthetases for Structurally Diverse Non-Canonical Amino Acids.

Genetic code expansion (GCE) technologies commonly use the pyrrolysyl-tRNA synthetase (PylRS)/tRNAPyl pairs from Methanosarcina mazei (Mm) and Methanosarcina barkeri (Mb) for site-specific incorporation of non-canonical amino acids (ncAAs) into proteins. Recently a homologous PylRS/tRNAPyl pair from Candidatus Methanomethylophilus alvus Mx1201 (Ma) was developed that, lacking the N-terminal tRNA-recognition domain of most PylRSs, overcomes insolubility, instability, and proteolysis issues seen with Mb/Mm PylRSs. An open question is how to alter Ma PylRS specificity to encode specific ncAAs with high efficiency. Prior work focused on "transplanting" ncAA substrate specificity by reconstructing the same active site mutations found in functional Mm/Mb PylRSs in Ma PylRS. Here, we found that this strategy produced low-efficiency Ma PylRSs for encoding three structurally diverse ncAAs: acridonyl-alanine (Acd), 3-nitro-tyrosine, and m-methyl-tetrazinyl-phenylalanine (Tet3.0-Me). On the other hand, efficient Ma PylRS variants were generated by a conventional life/death selection process from a large library of active site mutants: for Acd encoding, one variant was highly functional in HEK293T cells at just 10 μM Acd; for nitroY encoding, two variants also encoded 3-chloro, 3-bromo-, and 3-iodo-tyrosine at high efficiency; and for Tet-3.0-Me, all variants were more functional at lower ncAA concentrations. All Ma PylRS variants identified through selection had at least two different active site residues when compared with their Mb PylRS counterparts. We conclude that Ma and Mm/Mb PylRSs are sufficiently different that "active site transplantation" yields suboptimal Ma GCE systems. This work establishes a paradigm for expanding the utility of the promising Ma PylRS/tRNAPyl GCE platform.

Photoenzymatic decarboxylation: A promising way to produce sustainable aviation fuels and fine chemicals

As one of the fastest-growing carbon emission sources, the aviation sector is severely restricted by carbon emission reduction targets. Sustainable aviation fuel (SAF) has emerged as the most potential alternative to traditional aviation fuel, but harsh production technologies limit its commercialization. Fatty acids photodecarboxylase from Chlorella variabilis NC64A (CvFAP), the latest discovered photoenzyme, provides promising approaches to produce various carbon–neutral biofuels and fine chemicals. This review highlights the state-of-the-art strategies to enhance the application of CvFAP in carbon–neutral biofuel and fine chemicals production, including supplementing alkane as decoy molecular, screening efficient CvFAP variants with directed evolution, constructing genetic strains, employing biphasic catalytic system, and immobilizing CvFAP in an efficient photobioreactor. Furthermore, future opportunities are suggested to enhance photoenzymatic decarboxylation and explore the catalytic mechanism of CvFAP. This review provides a broad context to improve CvFAP catalysis and advance its potential applications.

Efficient Adaptive Online Learning via Frequent Directions.

By employing time-varying proximal functions, adaptive subgradient methods (ADAGRAD) have improved the regret bound and been widely used in online learning and optimization. However, ADAGRAD with full matrix proximal functions (ADA-FULL) cannot handle large-scale problems due to the impractical O(d³) time and O(d²) space complexities, though it has better performance when gradients are correlated. In this paper, we propose two efficient variants of ADA-FULL via a matrix sketching technique called frequent directions (FD). The first variant named as ADA-FD directly utilizes FD to maintain and manipulate low-rank matrices, which reduces the space and time complexities to O(τd) and O(τ²d) respectively, where d is the dimensionality and τ << d is the sketching size. The second variant named as ADA-FFD further adopts a doubling trick to accelerate FD used in ADA-FD, which reduces the average time complexity to O(τd) while only doubles the space complexity of ADA-FD. Theoretical analysis reveals that the regret of ADA-FD and ADA-FFD is close to that of ADA-FULL as long as the outer product matrix of gradients is approximately low-rank. Experimental results demonstrate the efficiency and effectiveness of our algorithms.

Deciphering the QR Code of the CRISPR-Cas9 System: Synergy between Gln768 (Q) and Arg976 (R).

Markov state models (MSMs) and machine learning (ML) algorithms can extrapolate the long-time-scale behavior of large biomolecules from molecular dynamics (MD) trajectories. In this study, an MD-MSM-ML scheme has been applied to probe the large endonuclease (Cas9) in the bacterial adaptive immunity CRISPR-Cas9 system. CRISPR has become a programmable and state-of-the-art powerful genome editing tool that has already revolutionized life sciences. CRISPR-Cas9 is programmed to process specific DNA sequences in the genome. However, human/biomedical applications are compromised by off-target DNA damage. Characterization of Cas9 at the structural and biophysical levels is a prerequisite for the development of efficient and high-fidelity Cas9 variants. The Cas9 wild type and two variants (R63A-R66A-R70A, R69A-R71A-R74A-R78A) are studied herein. The configurational space of Cas9 is provided with a focus on the conformations of the side chains of two residues (Gln768 and Arg976). A model for the synergy between those two residues is proposed. The results are discussed within the context of experimental literature. The results and methodology can be exploited for the study of large biomolecules in general and for the engineering of more efficient and safer Cas9 variants for applications.

A Single-Pass and One-Round Message Authentication Encryption for Limited IoT Devices

In this work, we propose three efficient variants of a message authentication encryption (MAE) algorithm, which is based on the dynamic key-dependent concept and dynamic operation mode to reach a high level of security. These variants consist of a single pass and a single round, in addition to the use of common operations for the encryption and authentication processes to reduce the required execution time and resources. Accordingly, the proposed scheme outperforms the existing solutions that are based on the static approach with multiple rounds. Furthermore, to reduce the overhead associated with the regeneration of the dynamic key and the corresponding cryptographic primitives, we propose a simple, yet effective update process. In such a scheme, even when the same plaintext is processed, it will be encrypted and authenticated using different cryptographic primitives (substitution and permutation tables in addition to round keys), which guards against the existing cryptanalysis techniques. The experimental results show that the proposed MAE variants are more efficient than the counter with cipher block chaining message authentication code (CCM), Galois message authentication code (GMAC), offset codebook mode (OCB), and the Chacha20-poly1305. The best performance is achieved with the third MAE variant that presents a high throughput with an enhancement of at least 373% compared to CCM, 90% compared to GCM, 23% compared to OCB, and 22% compared to Chacha20-poly1305.

Towards a general approach for tailoring the hydrophobic binding site of phenylalanine ammonia-lyases

Unnatural substituted amino acids play an important role as chiral building blocks, especially for pharmaceutical industry, where the synthesis of chiral biologically active molecules still represents an open challenge. Recently, modification of the hydrophobic binding pocket of phenylalanine ammonia-lyase from Petroselinum crispum (PcPAL) resulted in specifically tailored PcPAL variants, contributing to a rational design template for PAL-activity enhancements towards the differently substituted substrate analogues. Within this study we tested the general applicability of this rational design model in case of PALs, of different sources, such as from Arabidopsis thaliana (AtPAL) and Rhodosporidium toruloides (RtPAL). With some exceptions, the results support that the positions of substrate specificity modulating residues are conserved among PALs, thus the mutation with beneficial effect for PAL-activity enhancement can be predicted using the established rational design model. Accordingly, the study supports that tailoring PALs of different origins and different substrate scope, can be performed through a general method. Moreover, the fact that AtPAL variants I461V, L133A and L257V, all outperformed in terms of catalytic efficiency the corresponding, previously reported, highly efficient PcPAL variants, of identical catalytic site, suggests that not only catalytic site differences influence the PAL-activity, thus for the selection of the optimal PAL-biocatalysts for a targeted process, screening of PALs from different origins, should be included.

Bipolar fuzzy based least squares twin bounded support vector machine

Data classification is a key domain of research in real-world applications. One of the big challenges of real-world data classification is to tackle the presence of noise and outliers. In this paper, we handle the computational cost of Bipolar evaluation pairs score value based support vector machine model by formulating two efficient variants, referred to as bipolar fuzzy least squares support vector machines and bipolar fuzzy least squares twin bounded support vector machines, in which the score value is obtained as a bipolar evaluation pair with membership and non-membership functions. The solution of primal problems is attained by solving a system of linear equations that leads to less training time complexity unlike other fuzzy based twin models obtain the solution by handling two quadratic programming problems. Furthermore, our proposed models minimize the impact of noise in the data and facilitate the separation of support vectors from noise. The proposed works have been computationally analyzed on many publicly available real-world benchmark datasets as well as simulated artificial datasets in the non-linear case for different significant levels of noise, namely 0% (noise-free) and 5% (noise-corrupted). In comparison to other similar models, our suggested model is showing phenomenal generalization performance by controlling the negative impact of noise and needs substantially less training time. The results of the proposed models are also validated using quality metrics including AUC, F1-score, G-mean, and Precision Predictive Value.

Sink Mobility-Based Energy Efficient Routing Algorithm Variants in WSN

Sink Mobility-Based Energy Efficient Routing Algorithm Variants in WSN

A quasi-Newton proximal bundle method using gradient sampling technique for minimizing nonsmooth convex functions

This study aims to merge the well-established ideas of bundle and Gradient Sampling (GS) methods to develop an algorithm for locating a minimizer of a nonsmooth convex function. In the proposed method, with the help of the GS technique, we sample a number of differentiable auxiliary points around the current iterate. Then, by applying the standard techniques used in bundle methods, we construct a polyhedral (piecewise linear) model of the objective function. Moreover, by performing quasi-Newton updates on the set of auxiliary points, this polyhedral model is augmented with a regularization term that enjoys second-order information. If required, this initial model is improved by the techniques frequently used in GS and bundle methods. We analyse the global convergence of the proposed method. As opposed to the original GS method and some of its variants, our convergence analysis is independent of the size of the sample. In our numerical experiments, various aspects of the proposed method are examined using a variety of test problems. In particular, in contrast with many variants of bundle methods, we will see that the user can supply gradients approximately. Moreover, we compare the proposed method with some efficient variants of GS and bundle methods.

Directed Evolution Pipeline for the Improvement of Orthogonal Translation Machinery for Genetic Code Expansion at Sense Codons.

The expansion of the genetic code beyond a single type of noncanonical amino acid (ncAA) is hindered by inefficient machinery for reassigning the meaning of sense codons. A major obstacle to using directed evolution to improve the efficiency of sense codon reassignment is that fractional sense codon reassignments lead to heterogeneous mixtures of full-length proteins with either a ncAA or a natural amino acid incorporated in response to the targeted codon. In stop codon suppression systems, missed incorporations lead to truncated proteins; improvements in activity may be inferred from increased protein yields or the production of downstream reporters. In sense codon reassignment, the heterogeneous proteins produced greatly complicate the development of screens for variants of the orthogonal machinery with improved activity. We describe the use of a previously-reported fluorescence-based screen for sense codon reassignment as the first step in a directed evolution workflow to improve the incorporation of a ncAA in response to the Arg AGG sense codon. We first screened a library with diversity introduced into both the orthogonal Methanocaldococcus jannaschii tyrosyl tRNA anticodon loop and the cognate aminoacyl tRNA synthetase (aaRS) anticodon binding domain for variants that improved incorporation of tyrosine in response to the AGG codon. The most efficient variants produced fluorescent proteins at levels indistinguishable from the E. coli translation machinery decoding tyrosine codons. Mutations to the M. jannaschii aaRS that were found to improve tyrosine incorporation were transplanted onto a M. jannaschii aaRS evolved for the incorporation of para-azidophenylalanine. Improved ncAA incorporation was evident using fluorescence- and mass-based reporters. The described workflow is generalizable and should enable the rapid tailoring of orthogonal machinery capable of activating diverse ncAAs to any sense codon target. We evaluated the selection based improvements of the orthogonal pair in a host genomically engineered for reduced target codon competition. Using this particular system for evaluation of arginine AGG codon reassignment, however, E. coli strains with genomes engineered to remove competing tRNAs did not outperform a standard laboratory E. coli strain in sense codon reassignment.

Authentication of variable length messages in quantum key distribution

Authentication plays a critical role in the security of quantum key distribution (QKD) protocols. We propose using Polynomial Hash and its variants for authentication of variable length messages in QKD protocols. Since universal hashing is used not only for authentication in QKD but also in other steps in QKD like error correction and privacy amplification, and also in several other areas of quantum cryptography, Polynomial Hash and its variants as the most efficient universal hash function families can be used in these important steps and areas, as well. We introduce and analyze several efficient variants of Polynomial Hash and, using deep results from number theory, prove that each variant gives an ε-almost-Δ-universal family of hash functions. We also give a general method for transforming any such family to an ε-almost-strongly universal family of hash functions. The latter families can then, among other applications, be used in the Wegman–Carter MAC construction which has been shown to provide a universally composable authentication method in QKD protocols. As Polynomial Hash has found many applications, our constructions and results are potentially of interest in various areas.

Composite Detectors Based on Single-Crystalline Films and Single Crystals of Garnet Compounds.

This manuscript summarizes recent results on the development of composite luminescent materials based on the single-crystalline films and single crystals of simple and mixed garnet compounds obtained by the liquid-phase epitaxy growth method. Such composite materials can be applied as scintillating and thermoluminescent (TL) detectors for radiation monitoring of mixed ionization fluxes, as well as scintillation screens in the microimaging techniques. The film and crystal parts of composite detectors were fabricated from efficient scintillation/TL materials based on Ce3+-, Pr3+-, and Sc3+-doped Lu3Al5O12 garnets, as well as Ce3+-doped Gd3−xAxAl5−yGayO12 mixed garnets, where A = Lu or Tb; x = 0–1; y = 2–3 with significantly different scintillation decay or positions of the main peaks in their TL glow curves. This work also summarizes the results of optical study of films, crystals, and epitaxial structures of these garnet compounds using absorption, cathodoluminescence, and photoluminescence. The scintillation and TL properties of the developed materials under α- and β-particles and γ-quanta excitations were studied as well. The most efficient variants of the composite scintillation and TL detectors for monitoring of composition of mixed beams of ionizing radiation were selected based on the results of this complex study.

On three-term conjugate gradient method for optimization problems with applications on COVID-19 model and robotic motion control

The three-term conjugate gradient (CG) algorithms are among the efficient variants of CG algorithms for solving optimization models. This is due to their simplicity and low memory requirements. On the other hand, the regression model is one of the statistical relationship models whose solution is obtained using one of the least square methods including the CG-like method. In this paper, we present a modification of a three-term conjugate gradient method for unconstrained optimization models and further establish the global convergence under inexact line search. The proposed method was extended to formulate a regression model for the novel coronavirus (COVID-19). The study considers the globally infected cases from January to October 2020 in parameterizing the model. Preliminary results have shown that the proposed method is promising and produces efficient regression model for COVID-19 pandemic. Also, the method was extended to solve a motion control problem involving a two-joint planar robot.

Using selenocysteine-specific reporters to screen for efficient tRNASec variants.

The unique properties of selenocysteine (Sec) have generated an interest in the scientific community to site-specifically incorporate Sec into a protein of choice. Current technologies have rewired the natural Sec-specific translation factor-dependent selenoprotein biosynthesis pathway by harnessing the canonical elongation factor (EF-Tu) to simplify the requirements for Sec incorporation in Escherichia coli. This strategy is versatile and can be applied to Sec incorporation at any position in a protein of interest. However, selenoprotein production is still limited by yield and serine misincorporation. This protocol outlines a method in E. coli to design and optimize tRNA libraries which can be selected and screened for by the use of Sec-specific intein-based reporters. This provides a fast and simple way to engineer tRNAs with enhanced Sec-incorporation ability.

Design of Precise Multiplier Using Inexact Compressor for Digital Signal Processing

In the recent years, error recovery circuits in optimized data path units are adopted with approximate computing methodology. In this paper the novel multipliers have effective utilization in the newly proposed two different 4:2 approximate compressors that generate Error free Sum (ES) and Error free Carry (EC). Proposed ES and Proposed EC in 4:2 compressors are used for performing Partial Product (PP) compression. The structural arrangement utilizes Dadda structure based PP. Due to the regularity of PP arrangement Dadda multiplier is chosen for compressor implementation that favors easy standard cell ASIC design. In this, the proposed compression idealogy are more effective in the smallest n columns, and the accurate compressor in the remaining most significant columns. This limits the error in the multiplier output to be not more than 2n for an n X n multiplication. The choice among the proposed compressors is decided based on the significance of the sum and carry signals on the multiplier result. As an enhancement to the proposed multiplier, we introduce two Area Efficient (AE) variants viz., Proposed-AE (P-AE), and P-AE with Error Recovery (P-AEER). The proposed basic P-AE, and P-AEER designs exhibit 46.7%, 52.9%, and 52.7% PDP reduction respectively when compared to an approximate multiplier of minimal error type and are designed with 90nm ASIC technology. The proposed design and their performance validation are done by using Cadence Encounter. The performance evaluations are carried out using cadence encounter with 90nm ASIC technology. The proposed-basic P-AEA and P-AEER designs demonstrate 46.7%, 52.9% and 52.7% PDP reduction compared to the minimal error approximate multiplier. The proposed multiplier is implemented in digital image processing which revealed 0.9810 Structural SIMilarity Index (SSIM), to the least, and less than 3% deviation in ECG signal processing application.

RFN: A Random-Feature Based Newton Method for Empirical Risk Minimization in Reproducing Kernel Hilbert Spaces

In supervised learning using kernel methods, we often encounter a large-scale finite-sum minimization over a reproducing kernel Hilbert space (RKHS). Large-scale finite-sum problems can be solved using efficient variants of Newton method, where the Hessian is approximated via sub-samples of data. In RKHS, however, the dependence of the penalty function to kernel makes standard sub-sampling approaches inapplicable, since the gram matrix is not readily available in a low-rank form. In this paper, we observe that for this class of problems, one can naturally use kernel approximation to speed up the Newton method. Focusing on randomized features for kernel approximation, we provide a novel second-order algorithm that enjoys local superlinear convergence and global linear convergence (with high probability). We derive the theoretical lower bound for the number of random features required for the approximated Hessian to be close to the true Hessian in the norm sense. Our numerical experiments on real-world data verify the efficiency of our method compared to several benchmarks.

ЕКОНОМЕТРИЧНІ ПІДХОДИ ДО ПРОГНОЗУВАННЯ ФІНАНСОВОГО ЗАБЕЗПЕЧЕННЯ СОЦІАЛЬНО-ЕКОНОМІЧНОГО РОЗВИТКУ РЕГІОНУ

In the given article it is noted that the level of forecasting of processes of social development is determined by the efficiency of planning and management of economy and other spheres. Social and economic forecasting of basic trends of social development allows use of special calculation and logic methods, giving the opportunity to determine parameters of functioning of separate elements of productive forces in their interrelation and interdependence. At the current stage of regional development of the state, the forecasting of the management of social-economic processes in the region is urgent, and the need for their improvement in order to obtain effective tools for determining the main guidelines and directions of regional policy. Predictions that include scientific justification should be central to the planned decisions of state authorities and the implementation of social-economic policies in the region, to determine the main directions of its future development, place and role in the national economy. The process of forming a modern system of forecasting regional development in Ukraine took place under conditions of a large-scale state transformation and reorganization. The change in the political regime and reform of the Ukrainian economy, which began in the 1990s, led to the inversion of the role of the territory in the system of public administration. Regions that previously had very limited rights in the agricultural sector, received the right to make political, economic, social, cultural and other decisions on their own. Economic forecasting is necessary for determining ways of society development and economic resources which provide its achievement, for revealing most likely and economically efficient variants of long-term, medium term and current plans, grounding main directions of economic and technical politics, forecasting the consequences of the made decisions and measures taken at present. Application of econometric models in economics gives the opportunity to distinguish and formally describe the most significant, the most essential relations of economic variables and objects, as well as to get new knowledge about the object in the inductive way. In such model, in the simplified form, by many assumptions, the main dependence between economic indicators is determined.