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Although in silico folding based on coevolving residue constraints in the deep‐learning era has transformed protein structure prediction, the contributions of coevolving residues to protein folding, stability, and other functions in physical contexts remain to be clarified and experimentally validated. Herein, the PHD finger module, a well‐known histone reader with distinct subtypes containing subtype‐specific coevolving residues, was used as a model to experimentally assess the contributions of coevolving residues and to clarify their specific roles. The results of the assessment, including proteolysis and thermal unfolding of wildtype and mutant proteins, suggested that coevolving residues have varying contributions, despite their large in silico constraints. Residue positions with large constraints were found to contribute to stability in one subtype but not others. Computational sequence design and generative model‐based energy estimates of individual structures were also implemented to complement the experimental assessment. Sequence design and energy estimates distinguish coevolving residues that contribute to folding from those that do not. The results of proteolytic analysis of mutations at positions contributing to folding were consistent with those suggested by sequence design and energy estimation. Thus, we report a comprehensive assessment of the contributions of coevolving residues, as well as a strategy based on a combination of approaches that should enable detailed understanding of the residue contributions in other large protein families. [ABSTRACT FROM AUTHOR]

Efficient translation mediated by the 5′ untranslated region (5′ UTR) is essential for the robust efficacy of mRNA vaccines. However, the N1-methyl-pseudouridine (m1Ψ) modification of mRNA can impact the translation efficiency of the 5′ UTR. We discovered that the optimal 5′ UTR for m1Ψ-modified mRNA (m1Ψ–5′ UTR) differs significantly from its unmodified counterpart, highlighting the need for a specialized tool for designing m1Ψ–5′ UTRs rather than directly utilizing high-expression endogenous gene 5′ UTRs. In response, we developed a novel machine learning-based tool, Smart5UTR, which employs a deep generative model to identify superior m1Ψ–5′ UTRs in silico. The tailored loss function and network architecture enable Smart5UTR to overcome limitations inherent in existing models. As a result, Smart5UTR can successfully design superior 5′ UTRs, greatly benefiting mRNA vaccine development. Notably, Smart5UTR-designed superior 5′ UTRs significantly enhanced antibody titers induced by COVID-19 mRNA vaccines against the Delta and Omicron variants of SARS-CoV-2, surpassing the performance of vaccines using high-expression endogenous gene 5′ UTRs.

Efficient translation mediated by the 5′ untranslated region (5′ UTR) is essential for the robust efficacy of mRNA vaccines. However, the N 1-methyl-pseudouridine (m1 Ψ) modification of mRNA can impact the translation efficiency of the 5′ UTR. We discovered that the optimal 5′ UTR for m1 Ψ -modified mRNA (m1 Ψ – 5′ UTR) differs significantly from its unmodified counterpart, highlighting the need for a specialized tool for designing m1 Ψ – 5′ UTRs rather than directly utilizing high-expression endogenous gene 5′ UTRs. In response, we developed a novel machine learning-based tool, Smart5UTR, which employs a deep generative model to identify superior m1 Ψ – 5′ UTRs in silico. The tailored loss function and network architecture enable Smart5UTR to overcome limitations inherent in existing models. As a result, Smart5UTR can successfully design superior 5′ UTRs, greatly benefiting mRNA vaccine development. Notably, Smart5UTR-designed superior 5′ UTRs significantly enhanced antibody titers induced by COVID-19 mRNA vaccines against the Delta and Omicron variants of SARS-CoV-2, surpassing the performance of vaccines using high-expression endogenous gene 5′ UTRs. A novel deep generative model, Smart5UTR, designs superior 5′ untranslated region (5′ UTR) for mRNA vaccine with N 1-methyl-pseudouridine modification. Smart5UTR-designed 5′ UTRs enhance the efficacy of mRNA vaccines. [Display omitted] [ABSTRACT FROM AUTHOR]

Introduction: Protein engineering, which aims to improve the properties and functions of proteins, holds great research significance and application value. However, current models that predict the effects of amino acid substitutions often perform poorly when evaluated for precision. Recent research has shown that ProteinMPNN, a large-scale pre-training sequence design model based on protein structure, performs exceptionally well. It is capable of designing mutants with structures similar to the original protein. When applied to the field of protein engineering, the diverse designs for mutation positions generated by this model can be viewed as a more precise mutation range. Methods: We collected three biological experimental datasets and compared the design results of ProteinMPNN for wild-type proteins with the experimental datasets to verify the ability of ProteinMPNN in improving protein fitness. Results: The validation on biological experimental datasets shows that ProteinMPNN has the ability to design mutation types with higher fitness in single and multi-point mutations. We have verified the high accuracy of ProteinMPNN in protein engineering tasks from both positive and negative perspectives. Discussion: Our research indicates that using large-scale pre trained models to design protein mutants provides a new approach for protein engineering, providing strong support for guiding biological experiments and applications in biotechnology. [ABSTRACT FROM AUTHOR]

Maxwell or concomitant fields imprint additional phases on the transverse magnetization. This concomitant phase may cause severe image artifacts like signal voids or distort the quantitative parameters due to the induced intravoxel dephasing. In particular, double diffusion encoding (DDE) schemes with two pairs of bipolar diffusion-weighting gradients separated by a refocusing radiofrequency (RF) pulse are prone to concomitant field-induced artifacts. In this work, a method for reducing concomitant field effects in these DDE sequences based on additional oscillating gradients is presented. These oscillating gradient pulses obtained by constrained optimization were added to the original gradient waveforms. The modified sequences reduced the accumulated concomitant phase without significant changes in the original sequence characteristics. The proposed method was applied to a DDE acquisition scheme consisting of 60 pairs of diffusion wave vectors. For phantom as well as for in vivo experiments, a considerable increase in the signal-to-noise ratio (SNR) was obtained. For phantom measurements with a diffusion weighting of b = 2000 s/mm2 for each of the gradient pairs, an SNR increase of up to 40% was observed for a transversal slice that had a distance of 5 cm from the isocenter. For equivalent slice parameters, in vivo measurements in the brain of a healthy volunteer exhibited an increase in SNR of up to 35% for b = 750 s/mm2 for each weighting. These findings are supported by corresponding simulations, which also predict a positive effect on the SNR. In summary, the presented method leads to an SNR gain without additional RF refocusing pulses. • Proposes a novel method for concomitant field effect reduction for double diffusion encoding. • Demonstrates a positive effect on the SNR in phantom and in vivo. • Reveals importance for clinical high-amplitude gradient systems. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic poses a disruptive impact on public health and the global economy. Fortunately, the development of COVID-19 vaccines based on in vitro-transcribed messenger RNA (IVT mRNA) has been a breakthrough in medical history, benefiting billions of people with its high effectiveness, safety profile, and ease of large-scale production. This success is the result of decades of continuous RNA research, which has led to significant improvements in the stability and expression level of IVT mRNA through various approaches such as sequence optimization and improved preparation processes. IVT mRNA sequence optimization has been shown to have a positive effect on enhancing the mRNA expression level. The innovation of IVT mRNA purification technology is also indispensable, as the purity of IVT mRNA directly affects the success of downstream vaccine preparation processes and the potential for inducing unwanted side effects in therapeutic applications. Despite the progress made, challenges related to IVT mRNA sequence design and purification still require further attention to enhance the quality of IVT mRNA in the future. In this review, we discuss the latest innovative progress in IVT mRNA design and purification to further improve its clinical efficacy. [ABSTRACT FROM AUTHOR]

PurposeVarious excitation strategies have been proposed for dynamic imaging of hyperpolarized agents such as [1-13 C]-pyruvate, but the impact of these strategies on quantitative evaluation of signal evolution remains unclear. To better understand their relative performance, we compared the accuracy and repeatability of measurements made using variable excitation angle strategies and conventional constant excitation angle strategies.MethodsSignal evolution for constant and variable excitation angle schedules was simulated using a pharmacokinetic model of hyperpolarized pyruvate with 2 chemical pools and 2 physical compartments. Noisy synthetic data were then fit using the same pharmacokinetic model with the apparent chemical exchange term as an unknown, and fit results were compared with simulation parameters to determine accuracy and reproducibility.ResultsConstant excitations and a variable excitation strategy that maximizes the HP lactate signal yielded data that supported quantitative analyses with similar accuracy and repeatability. Variable excitation angle strategies that were designed to produce a constant signal level resulted in lower signal and worse quantitative accuracy and repeatability, particularly for longer acquisition times.ConclusionsThese results suggest that either constant excitation angle or variable excitation angles that attempt to maximize total signal, as opposed to maintaining a constant signal level, are preferred for metabolic quantification using hyperpolarized pyruvate.

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Large language models (LLMs) are having transformative impacts across a wide range of scientific fields, particularly in the biomedical sciences. Just as the goal of natural language processing is to understand sequences of words, a major objective in biology is to understand biological sequences. Genomic language models (gLMs), which are LLMs trained on DNA sequences, have the potential to significantly advance our understanding of genomes and how DNA elements at various scales interact to give rise to complex functions. To showcase this potential, we highlight key applications of gLMs, including functional constraint prediction, sequence design, and transfer learning. Despite notable recent progress, however, developing effective and efficient gLMs presents numerous challenges, especially for species with large, complex genomes. Here, we discuss major considerations for developing and evaluating gLMs., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Unimodular sequences with good correlation and spectral properties are desirable in numerous applications such as active remote sensing and communication systems. Therefore, designing sequences with stopband and correlation sidelobe constraints has gained a lot of attention in the last few decades. In this paper, we propose a fast and efficient iterative algorithm to design unimodular and sparse frequency waveforms with low aperiodic/periodic autocorrelation sidelobes and desired stopband properties. In our approach, the bi-objective optimization problem which minimizes both the integrated sidelobe level (ISL) of the autocorrelation function and the power density in the spectral stopbands is first turned into an unconstrained single objective optimization problem and then is treated as a nonlinear large-scale problem. For the solution of the problem, we develop an algorithm based on Limited Memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) Quasi-Newton optimization method. Unlike most gradient based algorithms which employ line searches to deduce the step length, owing to L-BFGS method, unit step length is taken as a general rule to avoid the cost of computation at every iteration with very few exceptions. The calculation of gradient is based on Fast Fourier Transform and Hadamard product operations and thus the algorithm is fast and computationally efficient. Moreover, the algorithm is space efficient and its low-memory feature makes it possible to generate long sequences. Several numerical examples are presented to validate the efficacy of the proposed method and to show its superiority over other state-of-art algorithms.

The utility of engineering enzyme activity is expanding with the development of biotechnology. Conventional methods have limited applicability as they require high-throughput screening or three-dimensional structures to direct target residues of activity control. An alternative method uses sequence evolution of natural selection. A repertoire of mutations was selected for fine-tuning enzyme activities to adapt to varying environments during the evolution. Here, we devised a strategy called s equence c o-evolutionary a nalysis to co n trol the e fficiency of e nzyme r eactions (SCANEER), which scans the evolution of protein sequences and direct mutation strategy to improve enzyme activity. We hypothesized that amino acid pairs for various enzyme activity were encoded in the evolutionary history of protein sequences, whereas loss-of-function mutations were avoided since those are depleted during the evolution. SCANEER successfully predicted the enzyme activities of beta-lactamase and aminoglycoside 3′-phosphotransferase. SCANEER was further experimentally validated to control the activities of three different enzymes of great interest in chemical production: cis-aconitate decarboxylase, α-ketoglutaric semialdehyde dehydrogenase, and inositol oxygenase. Activity-enhancing mutations that improve substrate-binding affinity or turnover rate were found at sites distal from known active sites or ligand-binding pockets. We provide SCANEER to control desired enzyme activity through a user-friendly webserver. • We developed a novel enzyme activity prediction tool based on sequence evolutionary information. • It uses sequence information inferred from evolutionary history of target enzymes as input. • It finds activity-enhancing mutations in distal site from the active site, challenging in structure-guided predictions. • Our method successfully predicts enzyme activity-enhancing mutations in a broad range of enzymes. [ABSTRACT FROM AUTHOR]

We formulate and investigate the problem of distributed channel rendezvous in collision-prone wireless networks. Existing researches on this topic are mainly devoted to designing channel hopping sequences, each pair of which can overlap on a common channel within bounded delay. However, this overlap-based canonical rendezvous design does not take into account channel collision, which may render existing rendezvous algorithms fail to achieve bounded delay in collision-prone environment. Motivated by this observation, we formulate and investigate the collision-aware channel rendezvous problem in a generic scenario, where a collision occurs if more than $C$ packets overlap in time on a same channel. Our generic formulation allows to model both the baseline single packet reception model with $C=1$ and the more sophisticated multiple packet reception model with $C > 1$. We further abstract the collision-aware rendezvous problem as the problem of constructing a robust rendezvous system. We establish the theoretical limit of the problem, guided by which we design a collision-resilient distributed rendezvous algorithm with truly bounded rendezvous delay. We then demonstrate the performance of our rendezvous algorithm both analytically and numerically. [ABSTRACT FROM AUTHOR]

Abstract Background Optimization of DNA and protein sequences based on Machine Learning models is becoming a powerful tool for molecular design. Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are first approximated by a continuous representation, which is then iteratively optimized with respect to the predictor oracle by gradient ascent. While elegant, the current version of the method suffers from vanishing gradients and may cause predictor pathologies leading to poor convergence. Results Here, we introduce Fast SeqProp, an improved activation maximization method that combines straight-through approximation with normalization across the parameters of the input sequence distribution. Fast SeqProp overcomes bottlenecks in earlier methods arising from input parameters becoming skewed during optimization. Compared to prior methods, Fast SeqProp results in up to 100-fold faster convergence while also finding improved fitness optima for many applications. We demonstrate Fast SeqProp’s capabilities by designing DNA and protein sequences for six deep learning predictors, including a protein structure predictor. Conclusions Fast SeqProp offers a reliable and efficient method for general-purpose sequence optimization through a differentiable fitness predictor. As demonstrated on a variety of deep learning models, the method is widely applicable, and can incorporate various regularization techniques to maintain confidence in the sequence designs. As a design tool, Fast SeqProp may aid in the development of novel molecules, drug therapies and vaccines.

Improving resolution and sensitivity will widen possible medical applications of magnetic particle imaging. Pulsed excitation promises such benefits, at the cost of more complex hardware solutions and restrictions on drive field amplitude and frequency. State-of-the-art systems utilize a sinusoidal excitation to drive superparamagnetic nanoparticles into the non-linear part of their magnetization curve, which creates a spectrum with a clear separation of direct feed-through and higher harmonics caused by the particles response. One challenge for rectangular excitation is the discrimination of particle and excitation signals, both broad-band. Another is the drive-field sequence itself, as particles that are not placed at the same spatial position, may react simultaneously and are not separable by their signal phase or shape. To overcome this potential loss of information in spatial encoding for high amplitudes, a superposition of shifting fields and drive-field rotations is proposed in this work. Upon close view, a system matrix approach is capable to maintain resolution, independent of the sequence, if the response to pulsed sequences still encodes information within the phase. Data from an Arbitrary Waveform Magnetic Particle Spectrometer with offsets in two spatial dimensions is measured and calibrated to guarantee device independence. Multiple sequence types and waveforms are compared, based on frequency space image reconstruction from emulated signals, that are derived from measured particle responses. A resolution of 1.0 mT (0.8 mm for a gradient of (−1.25,−1.25,2.5) Tm−1) in x- and y-direction was achieved and a superior sensitivity for pulsed sequences was detected on the basis of reference phantoms. [ABSTRACT FROM AUTHOR]

Purpose: Electrocardiography (ECG) and respiratory-gated preclinical cardiac CEST-MRI acquisitions are difficult because of variable saturation recovery with T 1 , RF interference in the ECG signal, and offset-to-offset variation in Z-magnetization and cardiac phase introduced by changes in cardiac frequency and trigger delays., Methods: The proposed method consists of segmented saturation modules with radial FLASH readouts and golden angle progression. The segmented saturation blocks drive the system to steady-state, and because center k-space is sampled repeatedly, steady-state saturation dominates contrast during gridding and reconstruction. Ten complete Z-spectra were acquired in healthy mice using both ECG and respiratory-gated and ungated methods. Z-spectra were also acquired at multiple saturation B 1 values to optimize for amide and Cr contrasts., Results: There was no significant difference between CEST contrasts (amide, Cr, magnetization transfer) calculated from images acquired using ECG and respiratory-gated and ungated methods (p = 0.27, 0.11, 0.47). A saturation power of 1.8μT provides optimal contrast amplitudes for both amide and total Cr contrast without significantly complicating CEST contrast quantification because of water direct saturation, magnetization transfer, and RF spillover between amide and Cr pools. Further, variability in CEST contrast measurements was significantly reduced using the ungated radial FLASH acquisition (p = 0.002, 0.006 for amide and Cr, respectively)., Conclusion: This method enables CEST mapping in the murine myocardium without the need for cardiac or respiratory gating. Quantitative CEST contrasts are consistent with those obtained using gated sequences, and per-contrast variance is significantly reduced. This approach makes preclinical cardiac CEST-MRI easily accessible, even for investigators without prior experience in cardiac imaging., (© 2024 International Society for Magnetic Resonance in Medicine.)

Intrinsically disordered proteins and protein regions (IDRs) make up around 30% of the human proteome where they play essential roles in dictating and regulating many core biological processes. While IDRs are often studied as isolated domains, in naturally occurring proteins most IDRs are found adjacent to folded domains, where they exist as either N- or C-terminal tails or as linkers connecting two folded domains. Prior work has shown that charge properties of IDRs can influence their conformational behavior, both in isolation and in the context of folded domains. In contrast, the converse scenario is less well-explored: how do the charge properties of folded domains influence IDR conformational behavior? To answer this question, we combined a large-scale structural bioinformatics analysis with all-atom implicit solvent simulations of both rationally designed and naturally occurring proteins. Our results reveal three key takeaways. Firstly, the relative position and accessibility of charged residues across the surface of a folded domain can dictate IDR conformational behavior, overriding expectations based on net surface charge properties. Secondly, naturally occurring proteins possess multiple charge patches that are physically accessible to local IDRs. Finally, even modest changes in the local electrostatic environment of a folded domain can substantially modulate IDR-folded domain interactions. Taken together, our results suggest that folded domain surfaces can act as local determinants of IDR conformational behavior.

Massive machine type communications (mMTC) is one of the three major scenarios in 5G wireless networks. mMTC is an uplink-dominant scenario that supports massive connectivity of small-size packets and sporadic traffic. To meet the requirement of mMTC, non-orthogonal multiple access (NOMA) with grant-free transmission is considered a promising technology because of its higher spectrum efficiency and lower signaling overhead. Request-and-grant-based scheduling process is eliminated in grant-free transmission and thus the base stations need to identify active users first before data detection. Previously, several user activity detection (UAD) algorithms were proposed and they can obtain good performance in flat-fading channels. However, the performance of these works degrades significantly in doubly selective fading channels. In this work, we propose an effective solution for uplink grant-free NOMA with a new user sequence scheme and a novel UAD algorithm suitable for practical channel scenarios. Simulation results validated that the proposed sequences can mitigate the inter-user interference at the base stations, and the proposed UAD algorithm can achieve better detection accuracy. Moreover, detailed complexity analysis showed that the proposed UAD algorithm has relatively low complexity and good scalability.

Purpose: To develop a 3D MR technique to simultaneously acquire proton multiparametric maps (T1, T2, and proton density) and sodium density weighted images over the whole brain. Methods: We implemented a 3D stack‐of‐stars MR pulse sequence which consists of interleaved proton (1H) and sodium (23Na) excitations, tailored slice encoding gradients that can encode the same slice for both nuclei, and simultaneous readout with different radial trajectories (1H, full‐radial; 23Na, center‐out radial). The receive chain of our 7T scanner was modified to enable simultaneous acquisition of 1H and 23Na signal. A heuristically optimized flip angle train was implemented for proton MR fingerprinting (MRF). The SNR and the accuracy of proton T1 and T2 were evaluated in phantoms. Finally, in vivo application of the method was demonstrated in five healthy subjects. Results: The SNR for the simultaneous measurement was almost identical to that for the single‐nucleus measurements (<2% change). The proton T1 and T2 maps remained similar to the results from a reference 2D MRF technique (normalized RMS error in T1 ≈ 4.2% and T2 ≈ 11.3%). Measurements in healthy subjects corroborated these results and demonstrated the feasibility of our method for in vivo application. The in vivo T1 values measured using our method were lower than the results measured by other conventional techniques. Conclusions: With the 3D simultaneous implementation, we were able to acquire sodium and proton density weighted images in addition to proton T1, T2, and B1+ from 1H MRF that covers the whole brain volume within 21 min. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review covers five essential aspects of CMR to provide a comprehensive overview ranging from CVDs to CMR pulse sequence design, acquisition protocols, motion handling, image reconstruction and quantitative analysis of the obtained data. (1) The basic MR physics of CMR is introduced. Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. Commonly perceived artifacts and potential countermeasures are discussed for these methods. (2) CMR methods for identifying CVDs are illustrated. Basic anatomy and functional processes are described to understand the cardiac pathologies and how they can be captured by CMR imaging. (3) The planning and conduct of a complete CMR exam which is targeted for the respective pathology is shown. Building blocks are illustrated to create an efficient and patient-centered workflow. Further strategies to cope with challenging patients are discussed. (4) Imaging acceleration and reconstruction techniques are presented that enable acquisition of spatial, temporal, and parametric dynamics of the cardiac cycle. The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this purpose are summarized. Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance.

Artificial mutagenesis and protein engineering have laid the foundation for antigenic characterization and universal vaccine design for influenza viruses. However, many methods used in this process require manual sequence editing and protein expression, limiting their efficiency and utility in high-throughput applications. More streamlined in silico tools allowing researchers to properly analyze and visualize influenza viral protein sequences with accurate nomenclature are necessary to improve antigen design and productivity. To address this need, we developed Librator, a system for analyzing and designing custom protein sequences of influenza virus hemagglutinin (HA) and neuraminidase (NA) glycoproteins. Within Librator's graphical interface, users can easily interrogate viral sequences and phylogenies, visualize antigen structures and conservation, mutate target residues and design custom antigens. Librator also provides optimized fragment design for Gibson Assembly of HA and NA expression constructs based on peptide conservation of all historical HA and NA sequences, ensuring fragments are reusable and compatible across related subtypes, thereby promoting reagent savings. Finally, the program facilitates single-cell immune profiling, epitope mapping of monoclonal antibodies and mosaic protein design. Using Librator - based antigen construction, we demonstrate that antigenicity can be readily transferred between HA molecules of H3, but not H1, lineage viruses. Altogether, Librator is a valuable tool for analyzing influenza virus HA and NA proteins and provides an efficient resource for optimizing recombinant influenza antigen synthesis. [ABSTRACT FROM AUTHOR]

A versatile sequence set design with both low cubic metric (CM) and low maximum-absolute-crosscorrelation is proposed for pilot-less sequence-based transmission as a solution to current pilot/symbol-based 5G design in coverage-limited scenarios. Based on generalized second-order Reed-Muller sequences that provide already sequence length flexibility and good correlation properties, a modified construction is introduced to achieve a very low CM for any resource allocation. New sequences inherently incorporate a symbol spreading pattern matching the signal bandwidth, leading to a windowing effect that limits the time-domain signal variations. Simulation results show that the proposed design effectively improves CMs and outperforms error rates of current 5G NR uplink control signals. [ABSTRACT FROM AUTHOR]

Sequences with low correlation have important applications in communications, radar, cryptography, and also in compressed sensing. The ultimate objective of this paper is to design a new family of polyphase sequences with low correlation. Our main contribution is the construction of such a family using additive and multiplicative characters over Galois rings. The proposed sequences have lengths N = pm − 1, family size M = pkm − 1, and a maximum magnitude θ max = p k − 1 p m , where k is an integer with 1 ≤ k < m. [ABSTRACT FROM AUTHOR]

Quantitative susceptibility mapping (QSM) is a promising non-invasive method for obtaining information relating to oxygen metabolism. However, the optimal acquisition sequence and QSM reconstruction method for reliable venous susceptibility measurements are unknown. Full flow compensation is generally recommended to correct for the influence of venous blood flow, although the effect of flow compensation on the accuracy of venous susceptibility values has not been systematically evaluated. In this study, we investigated the effect of different acquisition sequences, including different flow compensation schemes, and different QSM reconstruction methods on venous susceptibilities.Ten healthy subjects were scanned with five or six distinct QSM sequence designs using monopolar readout gradients and different flow compensation schemes. All data sets were processed using six different QSM pipelines and venous blood susceptibility was evaluated in whole-brain segmentations of the venous vasculature and single veins. The quality of vein segmentations and the accuracy of venous susceptibility values were analyzed and compared between all combinations of sequences and reconstruction methods.The influence of the QSM reconstruction method on average venous susceptibility values was found to be 2.7–11.6 times greater than the influence of the acquisition sequence, including flow compensation. The majority of the investigated QSM reconstruction methods tended to underestimate venous susceptibility values in the vein segmentations that were obtained.In summary, we found that multi-echo gradient-echo acquisition sequences without full flow compensation yielded venous susceptibility values comparable to sequences with full flow compensation. However, the QSM reconstruction method had a great influence on susceptibility values and thus needs to be selected carefully for accurate venous QSM.

Accurate variant effect prediction has broad impacts on protein engineering. Recent machine learning approaches toward this end are based on representation learning, by which feature vectors are learned and generated from unlabeled sequences. However, it is unclear how to effectively learn evolutionary properties of an engineering target protein from homologous sequences, taking into account the protein's sequence-level structure called domain architecture (DA). Additionally, no optimal protocols are established for incorporating such properties into Transformer, the neural network well-known to perform the best in natural language processing research. This article proposes DA-aware evolutionary fine-tuning, or 'evotuning', protocols for Transformer-based variant effect prediction, considering various combinations of homology search, fine-tuning and sequence vectorization strategies. We exhaustively evaluated our protocols on diverse proteins with different functions and DAs. The results indicated that our protocols achieved significantly better performances than previous DA-unaware ones. The visualizations of attention maps suggested that the structural information was incorporated by evotuning without direct supervision, possibly leading to better prediction accuracy. [ABSTRACT FROM AUTHOR]

Background: Optimization of DNA and protein sequences based on Machine Learning models is becoming a powerful tool for molecular design. Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are first approximated by a continuous representation, which is then iteratively optimized with respect to the predictor oracle by gradient ascent. While elegant, the current version of the method suffers from vanishing gradients and may cause predictor pathologies leading to poor convergence. Results: Here, we introduce Fast SeqProp, an improved activation maximization method that combines straight-through approximation with normalization across the parameters of the input sequence distribution. Fast SeqProp overcomes bottlenecks in earlier methods arising from input parameters becoming skewed during optimization. Compared to prior methods, Fast SeqProp results in up to 100-fold faster convergence while also finding improved fitness optima for many applications. We demonstrate Fast SeqProp's capabilities by designing DNA and protein sequences for six deep learning predictors, including a protein structure predictor. Conclusions: Fast SeqProp offers a reliable and efficient method for general-purpose sequence optimization through a differentiable fitness predictor. As demonstrated on a variety of deep learning models, the method is widely applicable, and can incorporate various regularization techniques to maintain confidence in the sequence designs. As a design tool, Fast SeqProp may aid in the development of novel molecules, drug therapies and vaccines. [ABSTRACT FROM AUTHOR]

One objective of this paper is to propose a novel class of sequence pairs, called “quasi-orthogonal Z-complementary pairs (QOZCPs)”, each depicting Z-complementary property for their aperiodic auto-correlation sums and also having a low correlation zone when their aperiodic cross-correlation is considered. Construction of QOZCPs based on Successively Distributed Algorithms under Majorization Minimization (SDAMM) is presented. Another objective of this paper is to apply the proposed QOZCPs in fully polarimetric radar systems and analyse the corresponding ambiguity functions. It turns out that QOZCP waveforms are much more Doppler resilient than the known Golay complementary waveforms. [ABSTRACT FROM AUTHOR]

UDP-glucose is a key metabolite in carbohydrate metabolism and plays a vital role in glycosyl transfer reactions. Its significance spans across the food and agricultural industries. This study focuses on UDP-glucose synthesis via multienzyme catalysis using dextrin, incorporating UTP production and ATP regeneration modules to reduce costs. To address thermal stability limitations of the key UDP-glucose pyrophosphorylase (UGP), a deep learning-based protein sequence design approach and ancestral sequence reconstruction are employed to engineer a thermally stable UGP variant. The engineered UGP variant is significantly 500-fold more thermally stable at 60 °C and has a half-life of 49.8 h compared to the wild-type enzyme. MD simulations and umbrella sampling calculations provide insights into the mechanism behind the enhanced thermal stability. Experimental validation demonstrates that the engineered UGP variant can produce 52.6 mM UDP-glucose within 6 h in an in vitro cascade reaction. This study offers practical insights for efficient UDP-glucose synthesis methods.

Although in silico folding based on coevolving residue constraints in the deep-learning era has transformed protein structure prediction, the contributions of coevolving residues to protein folding, stability, and other functions in physical contexts remain to be clarified and experimentally validated. Herein, the PHD finger module, a well-known histone reader with distinct subtypes containing subtype-specific coevolving residues, was used as a model to experimentally assess the contributions of coevolving residues and to clarify their specific roles. The results of the assessment, including proteolysis and thermal unfolding of wildtype and mutant proteins, suggested that coevolving residues have varying contributions, despite their large in silico constraints. Residue positions with large constraints were found to contribute to stability in one subtype but not others. Computational sequence design and generative model-based energy estimates of individual structures were also implemented to complement the experimental assessment. Sequence design and energy estimates distinguish coevolving residues that contribute to folding from those that do not. The results of proteolytic analysis of mutations at positions contributing to folding were consistent with those suggested by sequence design and energy estimation. Thus, we report a comprehensive assessment of the contributions of coevolving residues, as well as a strategy based on a combination of approaches that should enable detailed understanding of the residue contributions in other large protein families., (© 2024 The Protein Society.)

Amore efficient new channel coding scheme was required for NRby the service features and capability indices of 5G.Due to its excellent performance,Polar code was specified as the control channel coding scheme for 5G enhancedmobile broadband(eMBB)scenario.An overview of Polar code was presented and themain existing research issues of the standardization for new radio(NR)technology oriented Polar code,including code construction,sequence design,rate-matching and channel interleaving,etc,were elaborated.Then some specific analysis and comparisons of the Polar code schemes which were proposed by 3GPPmember institutes in RAN1 conferences recently were presented.Finally,a summary of the standardization works of Polar code was given on the basis of the above discussions.

In heterogeneous multi-cell networks (HetNets), inter-and intra-cell interference are critical issues impairing the performance. In this paper, the frequency-hopping (FH) technique based on an attractive FH sequence (FHS) set with two-level Hamming correlations is exploited in such networks. Motivating from the idea of no-and low-hit-zone FHS sets, a construction algorithm of the new FHS set is proposed by utilizing the interleaving technique. Then, the performance metric of such FHS set in terms of Hamming correlation properties are derived, which are verified by an example. Compared with classic FHS sets (patterns), the proposed FHS set offers a zero Hamming cross-correlation for intra-cell users, and a low Hamming cross-correlation for inter-cell users (i.e., multi-level quality-of-services). The intra-and inter-interference in multi-cell HetNets can be significantly reduced by the proposed sequence-based FH technique. [ABSTRACT FROM AUTHOR]

Membrane proteins are involved in many aspects of cellular biology; for example, they regulate how cells interact with their environment, so such proteins are important drug targets. The rapid advancement in the field of immune effector cell therapy has been expanding the horizons of synthetic membrane receptors in the areas of cell-based immunotherapy and cellular medicine. However, the investigation of membrane proteins, which are key constituents of cells, is hampered by the difficulty and complexity of their in vitro synthesis, which is of unpredictable yield. Cell-free synthesis is herein employed to unravel the impact of the expression construct on gene transcription and translation, without the complex regulatory mechanisms of cellular systems. Through the systematic design of plasmids in the immediacy of the start of the target gene, it was possible to identify translation initiation and the conformation of mRNA as the main factors governing the cell-free expression efficiency of the human voltage-dependent anion channel (VDAC), which is a relevant membrane protein in drug-based therapy. A simple translation initiation model was developed to quantitatively assess the expression potential for the designed constructs. A scoring function that quantifies the feasibility of the formation of the translation initiation complex through the ribosome–mRNA hybridization energy and the accessibility of the mRNA segment binding to the ribosome is proposed. The scoring function enables one to optimize plasmid sequences and semi-quantitatively predict protein expression efficiencies. This scoring function is publicly available as webservice XenoExpressO at University of Vienna, Austria.

In this paper, we propose and evaluate a novel double-sequence low-resolution frequency synchronization method in millimeter-wave (mmWave) systems. In our system model, the base station uses analog beams to send the synchronization signal with infinite-resolution digital-to-analog converters. The user equipment employs a fully digital front end to detect the synchronization signal with low-resolution analog-to-digital converters (ADCs). The key ingredient of the proposed method is the custom designed synchronization sequence pairs, from which there exists an invertible function (a ratio metric) of the carrier frequency offset (CFO) to be estimated. We use numerical examples to show that the ratio metric is robust to the quantization distortion. To implement our proposed method in practice, we propose to optimize the double-sequence design parameters such that: (i) for each individual user, the impact of the quantization distortion on the CFO estimation accuracy is minimized, and (ii) the resulting frequency range of estimation can capture as many users’ CFOs as possible. Numerical results reveal that our proposed algorithm can provide a flexible means to estimate CFO in a variety of low-resolution settings. [ABSTRACT FROM AUTHOR]

Sequences with low autocorrelation sidelobes are needed in a diverse set of active sensing applications, including radar, sonar, communications, and biomedicine. The recently proposed and widely adopted methods of the Cyclic Algorithm-New (CAN) and Periodic CAN (PeCAN) are known to be computationally efficient, particularly as they employ fast Fourier transform (FFT) operations to design unimodular (i.e., unit-modulus) sequences with good autocorrelation properties. However, these cyclic algorithms cannot be directly used to design binary sequences with good autocorrelation properties due to the extremely multimodal nature of the associated optimization objectives. In this paper, we combine the notion of converging functions with the CAN and PeCAN frameworks, to considerably enhance their performances for binary sequence synthesis. Moreover, the convergence of these algorithms are established and their convergence properties are thoroughly analyzed. Numerical examples are provided to show that the proposed algorithms can outperform existing approaches for aperiodic and periodic binary sequence synthesis, especially for long sequence designs. [ABSTRACT FROM AUTHOR]

The topic of sequence design has received considerable attention due to its wide applications in active sensing. One important desired property for the design sequence is the spectral shape. In this paper, the sequence design problem is formulated by minimizing the regularized spectral level ratio subject to a peak-to-average power ratio constraint. Then, two algorithms are proposed by combining both the Dinkelbach's algorithm and the majorization–minimzation (MM) method organically. Specifically, by using the Dinkelbach's algorithm, the challenging fractional programming problem can be handled by solving a series of subproblems, which are further solved via the MM method. The numerical experiments verify the effectiveness of the optimization metric and demonstrate the performance of the proposed algorithms compared with the benchmark. [ABSTRACT FROM AUTHOR]

This paper is focused on the optimal design and performance analysis of a novel class of no-hit-zone frequency-hopping sequences (NHZ-FHSs) in quasi-synchronous FH multiple-access (QS-FHMA) systems. Although traditional NHZ-FHSs can offer interference-free FHMA performance when the signal arrival delay ($\tau $) does not exceed the width of NHZ ($Z_{\mathrm{ nh}}$), i.e., $|\tau |\leq Z_{\mathrm{ nh}}$ , the same performance cannot be guaranteed for $|\tau |>Z_{\mathrm{ nh}}$ in which traditional NHZ-FHZs may have large Hamming correlation (which denotes the total number of frequency hits of sequences) and consequently poor error probability performance. Since the strict quasi-synchronization (i.e., $|\tau |\leq Z_{\mathrm{ nh}}$) may be hard to maintain at all times in practical FHMA networks (e.g., infrastructureless ad hoc networks), it is also important to minimize the Hamming correlation for time-shifts outside of the NHZ. Such FHSs are called strong NHZ-FHSs (SNHZ-FHSs) in this paper. We derive a lower bound on the maximum Hamming correlation outside of the NHZ and then present a design algorithm for the optimal SNHZ-FHS set meeting our proposed lower bound with equality. We analyze the bit-error-rate (BER) performance of the FHMA system employing the proposed SNHZ-FHS sets using the average Hamming correlation function. The theoretical analysis and simulation results show that the proposed optimal SNHZ-FHS sets are feasible for practical FHMA networks with relaxed timing requirement and enhanced BER performance. [ABSTRACT FROM AUTHOR]

Functional precision polymers based on monodisperse oligo(N‐substituted acrylamide)s and oligo(2‐substituted‐α‐hydroxy acid)s have been synthesized. The discrete sequences originate from a direct translation of side‐chain functionality sequences of a peptide with well‐studied properties. The peptide was previously selected to solubilize the photosensitizer meta‐tetra(hydroxyphenyl)chlorin. The resulting peptidomimetic formulation additives preserve the drug solubilization and release characteristics of the parent peptide. In some cases, superior properties are obtained, reaching up to 40 % higher payloads and 27‐times faster initial drug release. [ABSTRACT FROM AUTHOR]

Phase-coded sequences are widely studied as the transmitted signals of active sonars. Recently, several design methods have been developed to generate phased-coded sequences satisfying specific aperiodic or periodic autocorrelation sidelobe level metrics. In this paper, based on the majorization–minimization strategy and the squared iterative acceleration scheme, we propose a method to generate sequences with the periodic weighted integrated sidelobe level metric. Numerical simulations illustrate that the proposed method can effectively suppress the periodic autocorrelation sidelobe levels in specific time lags. Compared with other sequence design methods satisfying the periodic weighted integrated sidelobe level metric, our method improves the computational efficiency significantly. In addition, the proposed sequence demonstrates better matched filter performance in specific range intervals compared with its counterpart. The results suggest that the method could be applied as a valid and real-time design method for transmitted signals of active sonars.

Spider silk is a natural fiber known as "biosteel" with the strongest composite performance, such as high tensile strength and toughness. It is also equipped with excellent biocompatibility and shape memory ability, thus shows great potential in many fields such as biomedicine and tissue engineering. Spider silk is composed of macromolecular spidroin with rich structural diversity. The characteristics of the primary structure of natural spidroin, such as the high repeatability of amino acids in the core repetitive region, the high content of specific amino acids, the large molecular weight, and the high GC content of the spidroin gene, have brought great difficulties in heterologous expression. This review discusses focuses on the relationship between the featured motifs of the microcrystalline region in the repetitive unit of spidroin and its structure, as well as the spinning performance and the heterologous expression. The optimization design for the sequence of spidroin combined with heterologous expression strategy has greatly promoted the development of the biosynthesis of spider silk proteins. This review may facilitate the rational design and efficient synthesis of recombinant spidroin.

In protein modelling and design, an understanding of the relationship between sequence and structure is essential. Using parallel, homotetrameric coiled-coil structures as a model system, we demonstrated that machine learning techniques can be used to predict structural parameters directly from the sequence. Coiled coils are regular protein structures, which are of great interest as building blocks for assembling larger nanostructures. They are composed of two or more alpha-helices wrapped around each other to form a supercoiled bundle. The coiled-coil bundles are defined by four basic structural parameters: topology (parallel or antiparallel), radius, degree of supercoiling, and the rotation of helices around their axes. In parallel coiled coils the latter parameter, describing the hydrophobic core packing geometry, was assumed to show little variation. However, we found that subtle differences between structures of this type were not artifacts of structure determination and could be predicted directly from the sequence. Using this information in modelling narrows the structural parameter space that must be searched and thus significantly reduces the required computational time. Moreover, the sequence-structure rules can be used to explain the effects of point mutations and to shed light on the relationship between hydrophobic core architecture and coiled-coil topology. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel method for constructing new un-correlated asymmetric zero correlation zone (UA-ZCZ) sequence sets by inter-leaving perfect sequences. As a type of ZCZ sequence set, an A-ZCZ sequence set consists of multiple sequence subsets. Different subsets are correlated in conventional A-ZCZ sequence set but uncorrelated in our scheme. In other words, the cross-correlation function (CCF) between two arbitrary sequences which belong to different subsets has quite a large zero cross-correlation zone (ZCCZ). Analytical results demonstrate that the UA-ZCZ sequence set proposed herein is optimal with respect to the upper bound of ZCZ sequence set. Specifically, our scheme enables the exible selection of ZCZ length, which makes it extremely valuable for designing spreading sequences for quasi-synchronous code-division multiple-access (QS-CDMA) systems. [ABSTRACT FROM AUTHOR]

Binary sequence sets with asymptotically optimal auto/cross correlation peak sidelobe level (PSL) growth have been known in the literature for a long time, and their construction has been studied both analytically and numerically. In contrast, it has been a long-standing problem whether we can construct a family of binary sequences whose auto-correlation PSL grows in an optimal manner. In this paper, we devise a construction method of binary sequences with asymptotically optimal PSL growth from the sequence sets with good correlation properties. A key component of the design follows from the observation that if the PSL of the sequence set grows optimally or nearly optimally, then the PSL of the constructed binary sequence will experience a similar growth as a consequence. The proposed construction is simple-to-implement, and is shown to be accomplished in polynomial time. With such a construction, we not only bridge the gap between analytical construction and computational search, but also pave the way to settle the long-standing design problem of binary sequences with an optimal growth of the auto-correlation PSL. [ABSTRACT FROM AUTHOR]