Sequence Of Residues Research Articles

Integrated Method of Future Capacity and RUL Prediction for Lithium‐Ion Batteries Based on CEEMD‐Transformer‐LSTM Model

ABSTRACTAccurately predict the remaining useful life (RUL) of lithium‐ion batteries for energy storage is of critical significance to ensure the safety and reliability of electric vehicles, which can offer efficient early warning signals in a timely manner. Considering nonlinear changes in the aging trajectory of lithium‐ion batteries, a method for predicting the RUL of lithium‐ion batteries was proposed in this study based on a complementary ensemble empirical mode decomposition (CEEMD) as well as transformer and long short‐term memory (LSTM) neural network dual‐drive machine learning model. First, the CEEMD algorithm was adopted to decompose the raw aging data of lithium‐ion batteries into intrinsic mode function (IMF) sequences and residual sequence, where the number of modal layers was produced by the proposed posterior feedback entropy and relevance (PFER) method. Second, prediction models of LSTM and transformer neural networks were established to predict IMF and residual sequences. Simultaneously, the sparrow search algorithm (SSA) was used to obtain the optimal value of the hyperparameter learning rate for the RUL prediction model. Finally, the predicted IMF and residual sequences were combined to comprehensively calculate the future lifespan aging trajectory of lithium‐ion batteries. The aging data of two groups of lithium‐ion batteries were obtained from the CALCE at the University of Maryland as well as the laboratory at AQNU University to verify the proposed method. Experimental results demonstrated that the proposed method can effectively predict the RUL of lithium‐ion batteries; moreover, it exhibited better robustness and generalization ability.

Three Stages of Nascent Protein Translocation Through the Ribosome Exit Tunnel.

All proteins in living organisms are produced in ribosomes that facilitate the translation of genetic information into a sequence of amino acid residues. During translation, the ribosome undergoes initiation, elongation, termination, and recycling. In fact, peptide bonds are formed only during the elongation phase, which comprises periodic association of transfer RNAs and multiple auxiliary proteins with the ribosome and the addition of an amino acid to the nascent polypeptide one at a time. The protein spends a considerable amount of time attached to the ribosome. Here, we conceptually divide this portion of the protein lifetime into three stages. We define each stage on the basis of the position of the N-terminus of the nascent polypeptide within the ribosome exit tunnel and the context of the catalytic center. We argue that nascent polypeptides experience a variety of forces that determine how they translocate through the tunnel and interact with the tunnel walls. We review current knowledge about nascent polypeptide translocation and identify several white spots in our understanding of the birth of proteins.

Kinetics of Polyampholyte Dimerization: Influence of Charge Sequences

Polyampholytes (PAs) exhibit complex behaviors in various environments influenced by their charge distribution. This study focuses on the kinetics of dimerization of PAs, aiming to elucidate the underlying mechanisms and clarify relevant characteristics of the charge sequence. We focus on PAs with non-zero net charges, employing molecular dynamics simulations and theoretical analyses to examine how charge sequences influence the rates of dimer formation and dissociation. Our findings reveal that the charge sequence of tails and the blockiness of the minority charge group markedly influence the kinetics of dimerization: large blockiness and tails with a high number of majority-type charges slow down the dissociation of dimers. Additionally, the presence of an extended (central) block of the majority charge promotes structural diversity. Within dimer states, blocks alternate between intra- and inter-chain contacts. The duration times in the dimer states are significantly longer than the typical dwell times of block inter-contacts, with a notable extension when multiple blocks are engaged. Intrinsically disordered proteins (IDPs) play crucial roles in cellular functions, primarily due to their ability to undergo rapid conformational changes and form transient complexes. These properties largely depend on the sequence of charged residues. We provide insights into the fundamental principles governing the structural and dynamical properties of polyampholytic IDP, emphasizing the importance of sequence-specific effects on both aggregation and dissociation.

Physical aspects of quantile residual lifetime sequence

Abstract The modeling of count data is found in many fields, such as statistical physics, public health, medicine, epidemiology, applied science, sociology, and agriculture. In many physical situations, it has been observed that many times in the real world, the original variables may be continuous in nature, but discrete by observation. In this study, the α {\rm{\alpha }} -quantile residual life function for discrete lifetime models is defined and some attributes are investigated. The relation between this measure and the hazard rate function is studied. We discuss how this measure could be useful for finding the burn-in time of a lifetime dataset. Then, a new stochastic order based on the α {\rm{\alpha }} -quantile residual life is proposed and studied.

SARS-CoV-2 S protein harbors furin cleavage site located in a short loop between antiparallel β-strand

Furin cleavage site (FCS) of the SARS-CoV-2 S protein, which connects the S1/S2 junction, is essential for facilitating fusion with the host cells. Wild-type (Wt) SARS-CoV-2 S protein, PDB ID: 6yvb, lacks a sequence of amino acid residues, including the FCS that links the S1/S2 junction. For the first time, we demonstrated that a stretch of 14 amino acid residues (677QTNSPRRARSVASQ689) forms an antiparallel β-sheet comprising of PRRAR sequence in the FCS within a short loop. Upon comparing the loop content of the S1/S2 junction with that of Wt SARS-CoV-2 containing PRRAR in the FCS, we observed a decrease in antiparallel β-sheet content and an increase in loop content in the B.1.1.7 variant with HRRAR in the FCS. This short loop within antiparallel β-sheet can serve as a docking site for various proteases, including TMPRSS2 and α1AT. We performed a 300-ns simulation of the SARS-CoV-2 receptor binding domain (RBD) using several antibacterial and antiviral ligands commonly used to treat various infections. Our findings indicate that the receptor binding domain (RBD) comprising the receptor binding motif (RBM) utilizes β6 and a significant portion of the loop to bind with ligands, suggesting its potential for treating SARS-CoV-2 infections.

Understanding Antimicrobial Peptide Synergy: Differential Binding Interactions and Their Impact on Membrane Integrity.

Research on antimicrobial peptides (AMPs) has been conducted as a solution to overcome antibiotic resistance. In particular, the synergistic effect that appears when two or more AMPs are used in combination has been observed. To find an effective synergistic combination, it is necessary to understand the underlying mechanism. However, a consistent explanation for this phenomenon has not yet been provided due to limitations in experimentally determining or predicting the structure of the heteroaggregates formed by the interactions between different AMPs and the interaction of the aggregate surface with the lipid membrane surface. In this study, we conducted molecular dynamics simulations for two heterogeneous aggregates of melittin-indolicidin and pexiganan-indolicidin to observe their structures in the solution phase and their interactions with the lipid membrane. We aimed to determine how the surfaces of these aggregates interact with the lipid membrane. Due to the different amino acid residue sequence characteristics of melittin and pexiganan, we found that when the two AMPs bind to indolicidin, they form aggregates with completely different structural characteristics. Accordingly, the sequence characteristics of pexiganan, which exhibits a relatively unstable structure compared to melittin in aqueous solution or on lipid membranes, allow for a more stable interaction with the lipid membrane when forming aggregates with indolicidin, effectively inhibiting the integrity of the lipid membranes. We also found that the amino acid residues forming the surface of the AMP aggregate show differential binding strengths to different lipid species forming the lipid membrane, thereby disrupting the membrane in a way that weakens its integrity. Through this, we provided insight into the basic principle of how the synergistic effect of AMPs occurs.

Diagnosis of Abnormal Overcharge Internal Pressure in Lithium Battery Based on Physics‐Informed Neural Network's Residual Distribution

Changes in the internal pressure of lithium batteries often reflect the state of the batteries. Rapid diagnosis of abnormal internal pressure is importance for battery safety. This article proposes a battery overcharge internal pressure abnormality diagnosis method based on the detection of safety vent strain. First, this method establishes a multiphysical model between the strain of the battery's safety vent, surface temperature rise, and state of charge (SOC) under normal internal pressure. Second, a physics‐informed neural network is used to enhance the multiphysical model's accuracy. Third, the residual sequence generated from actual measurements and estimated values, along with an improved quartile range, is used to diagnose internal pressure abnormalities. Battery overcharge experiment results show that this method can diagnose internal pressure abnormalities within 260 s after overcharging and alarm. At this time, the battery has slight bulging, and the SOC is 102.1%. By conducting charging experiments under different conditions, the low false alarm rate of this method is only 0.575%. The proposed method can identify overcharging abnormalities before the lithium battery safety vent is opened, which greatly ensures the safe operation of the battery system.

Efficient Generation of Protein Pockets with PocketGen.

Designing small-molecule-binding proteins, such as enzymes and biosensors, is crucial in protein biology and bioengineering. Generating high-fidelity protein pockets areas where proteins interact with ligand molecules, is challenging due to complex interactions between ligand molecules and proteins, flexibility of ligand molecules and amino acid side chains, and complex sequence-structure dependencies. Here, we introduce PocketGen, a deep generative method for generating the residue sequence and the full-atom structure within the protein pocket region that leverages sequence structure consistency. PocketGen consists of a bilevel graph transformer for structural encoding and a sequence refinement module that uses a protein language model (pLM) for sequence prediction. The bilevel graph transformer captures interactions at multiple granularities (atom-level and residue/ligand-level) and aspects (intra-protein and protein-ligand) with bilevel attention mechanisms. For sequence refinement, a structural adapter using cross-attention is integrated into a pLM to ensure structure-sequence consistency. During training, only the adapter is fine-tuned, while the other layers of the pLM remain unchanged. Experiments show that PocketGen can efficiently generate protein pockets with higher binding affinity and validity than state-of-the-art methods. PocketGen is ten times faster than physics-based methods and achieves a 95% success rate (percentage of generated pockets with higher binding affinity than reference pockets) with over 64% amino acid recovery rate.

Quasi-likelihood analysis for Student-Lévy regression

We consider the quasi-likelihood analysis for a linear regression model driven by a Student-t Lévy process with constant scale and arbitrary degrees of freedom. The model is observed at high frequency over an extending period, under which we can quantify how the sampling frequency affects estimation accuracy. In that setting, joint estimation of trend, scale, and degrees of freedom is a non-trivial problem. The bottleneck is that the Student-t distribution is not closed under convolution, making it difficult to estimate all the parameters fully based on the high-frequency time scale. To efficiently deal with the intricate nature from both theoretical and computational points of view, we propose a two-step quasi-likelihood analysis: first, we make use of the Cauchy quasi-likelihood for estimating the regression-coefficient vector and the scale parameter; then, we construct the sequence of the unit-period cumulative residuals to estimate the remaining degrees of freedom. In particular, using full data in the first step causes a problem stemming from the small-time Cauchy approximation, showing the need for data thinning.

Multi-scale prediction of remaining useful life of lithium-ion batteries based on variational mode decomposition and integrated machine learning

Accurate and reliable prediction of the remaining useful life (RUL) of lithium-ion batteries (LIB) is very important for the safety of power systems. To solve the nonlinear and time-varying problems of LIB aging trajectories, an RUL prediction method based on variational mode decomposition (VMD) and integrated machine learning is proposed. First, the VMD algorithm is used to mine the internal information in LIB data to capture the long-term downward trend and capacity regeneration hidden in the capacity degradation. After that, the Elman neural network optimized by the gray wolf algorithm and Gaussian process regression were used to model and predict the intrinsic mode function (IMF) and residual sequence respectively. Next, the predicted IMF component and residual sequence are fused to obtain the LIB aging trajectory, and the capacity prediction error is corrected through the bidirectional long short-term memory network. Finally, the LIB data set is used as the test data of the algorithm. The results show that the proposed method has better robustness and nonlinear tracking ability, the mean absolute error is controlled within 1.5 %, and the RUL prediction error is within 3 cycles.

Town gas daily load forecasting based on machine learning combinatorial algorithms: A case study in North China

Town gas daily load forecasting based on machine learning combinatorial algorithms: A case study in North China

Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification.

The emergence of antibiotic-resistant microbes raises a pressing demand for novel alternative treatments. One promising alternative is the antimicrobial peptides (AMPs), a class of innate immunity mediators within the therapeutic peptide realm. AMPs offer salient advantages such as high specificity, cost-effective synthesis, and reduced toxicity. Although some computational methodologies have been proposed to identify potential AMPs with the rapid development of artificial intelligence techniques, there is still ample room to improve their performance. This study proposes a predictive framework which ensembles deep learning and statistical learning methods to screen peptides with antimicrobial activity. We integrate multiple LightGBM classifiers and convolution neural networks which leverages various predicted sequential, structural and physicochemical properties from their residue sequences extracted by diverse machine learning paradigms. Comparative experiments exhibit that our method outperforms other state-of-the-art approaches on an independent test dataset, in terms of representative capability measures. Besides, we analyse the discrimination quality under different varieties of attribute information and it reveals that combination of multiple features could improve prediction. In addition, a case study is carried out to illustrate the exemplary favorable identification effect. We establish a web application at http://amp.denglab.org to provide convenient usage of our proposal and make the predictive framework, source code, and datasets publicly accessible at https://github.com/researchprotein/amp .

Resilient Factor Graph-Based GNSS/IMU/Vision/Odo Integrated Navigation Scheme Enhanced by Noise Approximate Gaussian Estimation in Challenging Environments

The signal blockage and multipath effects of the Global Navigation Satellite System (GNSS) caused by urban canyon scenarios have brought great technical challenges to the positioning and navigation of autonomous vehicles. In this paper, an improved factor graph optimization algorithm enhanced by a resilient noise model is proposed. The measurement noise is resilient and adjusted based on an approximate Gaussian distribution-based estimation. In estimating and adjusting the noise parameters of the measurement model, the error covariance matrix of the multi-sensor fusion positioning system is dynamically optimized to improve the system accuracy. Firstly, according to the approximate Gaussian statistical property of the GNSS/odometer velocity residual sequence, the measured data are divided into an approximate Gaussian fitting region and an approximate Gaussian convergence region. Secondly, the interval is divided according to the measured data, and the corresponding variational Bayesian network and Gaussian mixture model are used to estimate the innovation online. Further, the noise covariance matrix of the adaptive factor graph-based model is dynamically optimized using the estimated noise parameters. Finally, based on low-cost inertial navigation equipment, GNSS, odometer, and vision, the algorithm is implemented and verified using a simulation platform and real-vehicle road test. The experimental results show that in a complex urban road environment, compared with the traditional factor graph fusion localization algorithm, the maximum improvement in accuracy of the proposed algorithm can reach 65.63%, 39.52%, and 42.95% for heading, position, and velocity, respectively.

A hybrid residual correction method with auxiliary particle filter and conditional kernel density estimation for remaining useful life prediction

Predicting the remaining useful life (RUL) is crucial for prognosis and health management and provides an essential foundation for maintenance decisions. This study constructed a hybrid RUL prediction method based on residual correction to enhance prediction accuracy, minimize uncertainty, and mitigate errors caused by local fluctuations in the degeneration state within complex environments. This approach combined an auxiliary particle filter (APF), complementary ensemble empirical mode decomposition (CEEMD), and conditional kernel density estimation (CKDE). First, a Wiener degradation model was established, and the APF was used to acquire the initial state estimate of the equipment and residual difference between actual observations. Second, CEEMD was used to reconstruct the residual sequence and reduce interference. Subsequently, the nonparametric CKDE predicted the residual to correct the filter estimation. The corrected degeneration state value elucidated the local details of the system degradation process, and the point prediction of the RUL at the current time was obtained. The probability density distribution of the remaining life was estimated by substituting the modified degeneration state values into the kernel density estimation (KDE) model. Finally, the method was tested using an aircraft engine dataset. The results demonstrated the efficacy of the proposed method and its superiority over alternative approaches.

Genetic determinants of host- and virus-derived insertions for hepatitis E virus replication

Hepatitis E virus (HEV) is a long-neglected RNA virus and the major causative agent of acute viral hepatitis in humans. Recent data suggest that HEV has a very heterogeneous hypervariable region (HVR), which can tolerate major genomic rearrangements. In this study, we identify insertions of previously undescribed sequence snippets in serum samples of a ribavirin treatment failure patient. These insertions increase viral replication while not affecting sensitivity towards ribavirin in a subgenomic replicon assay. All insertions contain a predicted nuclear localization sequence and alanine scanning mutagenesis of lysine residues in the HVR influences viral replication. Sequential replacement of lysine residues additionally alters intracellular localization in a fluorescence dye-coupled construct. Furthermore, distinct sequence patterns outside the HVR are identified as viral determinants that recapitulate the enhancing effect. In conclusion, patient-derived insertions can increase HEV replication and synergistically acting viral determinants in and outside the HVR are described. These results will help to understand the underlying principles of viral adaptation by viral- and host-sequence snatching during the clinical course of infection.

Guiding Transient Peptide Assemblies with Structural Elements Embedded in Abiotic Phosphate Fuels

AbstractDespite great progress in the construction of non‐equilibrium systems, most approaches do not consider the structure of the fuel as a critical element to control the processes. Herein, we show that the amino acid side chains (A, F, Nal) in the structure of abiotic phosphates can direct assembly and reactivity during transient structure formation. The fuels bind covalently to substrates and subsequently influence the structures in the assembly process. We focus on the ways in which the phosphate esters guide structure formation and how structures and reactivity cross regulate when constructing assemblies. Through the chemical functionalization of energy‐rich aminoacyl phosphate esters, we are able to control the yield of esters and thioesters upon adding dipeptides containing tyrosine or cysteine residues. The structural elements around the phosphate esters guide the lifetime of the structures formed and their supramolecular assemblies. These properties can be further influenced by the peptide sequence of substrates, incorporating anionic, aliphatic and aromatic residues. Furthermore, we illustrate that oligomerization of esters can be initiated from a single aminoacyl phosphate ester incorporating a tyrosine residue (Y). These findings suggest that activated amino acids with varying reactivity and energy contents can pave the way for designing and fabricating structured fuels.

Guiding Transient Peptide Assemblies with Structural Elements Embedded in Abiotic Phosphate Fuels.

Despite great progress in the construction of non-equilibrium systems, most approaches do not consider the structure of the fuel as a critical element to control the processes. Herein, we show that the amino acid side chains (A, F, Nal) in the structure of abiotic phosphates can direct assembly and reactivity during transient structure formation. The fuels bind covalently to substrates and subsequently influence the structures in the assembly process. We focus on the ways in which the phosphate esters guide structure formation and how structures and reactivity cross regulate when constructing assemblies. Through the chemical functionalization of energy-rich aminoacyl phosphate esters, we are able to control the yield of esters and thioesters upon adding dipeptides containing tyrosine or cysteine residues. The structural elements around the phosphate esters guide the lifetime of the structures formed and their supramolecular assemblies. These properties can be further influenced by the peptide sequence of substrates, incorporating anionic, aliphatic and aromatic residues. Furthermore, we illustrate that oligomerization of esters can be initiated from a single aminoacyl phosphate ester incorporating a tyrosine residue (Y). These findings suggest that activated amino acids with varying reactivity and energy contents can pave the way for designing and fabricating structured fuels.

SOFB is a comprehensive ensemble deep learning approach for elucidating and characterizing protein-nucleic-acid-binding residues

Proteins and nucleic-acids are essential components of living organisms that interact in critical cellular processes. Accurate prediction of nucleic acid-binding residues in proteins can contribute to a better understanding of protein function. However, the discrepancy between protein sequence information and obtained structural and functional data renders most current computational models ineffective. Therefore, it is vital to design computational models based on protein sequence information to identify nucleic acid binding sites in proteins. Here, we implement an ensemble deep learning model-based nucleic-acid-binding residues on proteins identification method, called SOFB, which characterizes protein sequences by learning the semantics of biological dynamics contexts, and then develop an ensemble deep learning-based sequence network to learn feature representation and classification by explicitly modeling dynamic semantic information. Among them, the language learning model, which is constructed from natural language to biological language, captures the underlying relationships of protein sequences, and the ensemble deep learning-based sequence network consisting of different convolutional layers together with Bi-LSTM refines various features for optimal performance. Meanwhile, to address the imbalanced issue, we adopt ensemble learning to train multiple models and then incorporate them. Our experimental results on several DNA/RNA nucleic-acid-binding residue datasets demonstrate that our proposed model outperforms other state-of-the-art methods. In addition, we conduct an interpretability analysis of the identified nucleic acid binding residue sequences based on the attention weights of the language learning model, revealing novel insights into the dynamic semantic information that supports the identified nucleic acid binding residues. SOFB is available at https://github.com/Encryptional/SOFB and https://figshare.com/articles/online_resource/SOFB_figshare_rar/25499452.

Molecular determinants for brain targeting by peptides: a meta-analysis approach with experimental validation.

Blood-brain barrier (BBB) peptide-shuttles (BBBpS) are able to translocate the BBB and reach the brain. Despite the importance of brain targeting in pharmacology, BBBpS are poorly characterized. Currently, their development relies on the empiric assumption that cell-penetrating peptides (CPPs), with proven ability to traverse lipid membranes, will likewise behave as a BBBpS. The relationship between CPPs/BBBpS remains elusive and, to the best of our knowledge, has not hitherto been subject to thorough experimental scrutiny. In this work, we have identified/quantified the main physicochemical properties of BBBpS and then searched for CPPs with these properties, hence potential BBBpS. The specific features found for BBBpS are: (i) small size, (ii) none or few aromatic residues, (iii) hydrophobic, and (iv) slight cationic nature. Then, we selected the 10 scoring best in an ordinary least squares analysis, and tested them in vitro and in vivo. Overall, we identified the molecular determinants for brain targeting by peptides, devised a methodology that can be used to assist in the design of peptides with potential brain penetration from amino acid residue sequences, and found four new BBBpS within the CPP library.

Advancing Accuracy in Sea Level Estimation with GNSS-R: A Fusion of LSTM-DNN-Based Deep Learning and SNR Residual Sequences

The global navigation satellite system reflectometry (GNSS-R) technique has shown promise in retrieving sea levels using signal-to-noise ratio (SNR) data. However, its accuracy and performance are often limited compared to conventional tide gauges, particularly due to constraints in satellite elevation angles. To address these limitations, we propose a methodology integrating Long Short-Term Memory Deep Neural Networks (LSTM-DNN) models, utilising SNR residual sequences as key feature inputs. Our study focuses on the SC02 station, examining elevation angles ranging from 5° to 10°, 5° to 15°, and 5° to 20°. Results reveal notable reductions in root mean square errors (RMSE) of 2.855%, 17.519%, and 15.756%, respectively, showcasing improvements in accuracy across varying elevation angles. Of particular significance is the enhancement in precision observed at higher elevation angles. This underscores the valuable contribution of our approach to nearshore sea level wave height retrieval, promising advancements in the GNSS-R technique.