Sequence Level Research Articles

Developing the Lung Graph-Based Machine Learning Model for Identification of Fibrotic Interstitial Lung Diseases

Accurate detection of fibrotic interstitial lung disease (f-ILD) is conducive to early intervention. Our aim was to develop a lung graph-based machine learning model to identify f-ILD. A total of 417 HRCTs from 279 patients with confirmed ILD (156 f-ILD and 123 non-f-ILD) were included in this study. A lung graph-based machine learning model based on HRCT was developed for aiding clinician to diagnose f-ILD. In this approach, local radiomics features were extracted from an automatically generated geometric atlas of the lung and used to build a series of specific lung graph models. Encoding these lung graphs, a lung descriptor was gained and became as a characterization of global radiomics feature distribution to diagnose f-ILD. The Weighted Ensemble model showed the best predictive performance in cross-validation. The classification accuracy of the model was significantly higher than that of the three radiologists at both the CT sequence level and the patient level. At the patient level, the diagnostic accuracy of the model versus radiologists A, B, and C was 0.986 (95% CI 0.959 to 1.000), 0.918 (95% CI 0.849 to 0.973), 0.822 (95% CI 0.726 to 0.904), and 0.904 (95% CI 0.836 to 0.973), respectively. There was a statistically significant difference in AUC values between the model and 3 physicians (p < 0.05). The lung graph-based machine learning model could identify f-ILD, and the diagnostic performance exceeded radiologists which could aid clinicians to assess ILD objectively.Graphical Given a sequence of HRCT slices from a patient, the lung field is first automatically extracted. Next, this lung region is divided into 36 sub-regions using geometric rules, obtaining a lung atlas. And then, the lung graph is built based on 3D radiomics features of each sub-region of the lung atlas. Finally, the model’s predictions were compared to the physicians’ assessment results.

C2CDB: an advanced platform integrating comprehensive information and analysis tools of cancer-related circRNAs.

Circular RNAs (circRNAs) play important roles in gene expression and their involvement in tumorigenesis is emerging. circRNA-related database is a powerful tool for researchers to investigate circRNAs. However, existing databases lack advanced platform integrating comprehensive information and analysis tools of cancer-related circRNAs. We developed a comprehensive platform called CircRNA to Cancer Database (C2CDB), encompassing 318 158 cancer-related circRNAs expressed in tumors and adjacent tissues across 30 types of cancers. C2CDB provides basic details such as sequence and expression levels of circRNAs, as well as crucial insights into biological mechanisms, including miRNA binding, RNA-binding protein interaction, coding potential, base modification, mutation, and secondary structure. Moreover, C2CDB collects an extensive compilation of published literature on cancer circRNAs, extracting and presenting pivotal content encompassing biological functions, underlying mechanisms, and molecular tools in these studies. Additionally, C2CDB offers integrated tools to analyse three potential mechanisms: circRNA-miRNA ceRNA interaction, circRNA encoding, and circRNA biogenesis, facilitating investigators with convenient access to highly reliable information. To enhance clarity and organization, C2CDB has meticulously curated and integrated the previously chaotic nomenclature of circRNAs, addressing the prevailing confusion and ambiguity surrounding their designations. C2CDB is freely available at http://pengyonglab.com/c2cdb.

Determining the esterase activity of peptides and peptide assemblies.

Catalytic peptides are gaining attention as alternatives to enzymes, especially in industrial applications. Recent advances in peptide design have improved their catalytic efficiency with approaches such as self-assembly and metal ion complexation. However, the fundamental principles governing peptide catalysis at the sequence level are still being explored. Ester hydrolysis, a well-studied reaction, serves as a widely employed method to evaluate the catalytic potential of peptides. The standard colorimetric reaction involving para-nitrophenyl acetate hydrolysis acts as a benchmark assay, providing a straightforward and efficient screening method for rapidly identifying potential catalysts. However, maintaining standardized conditions is crucial for reproducible results, given that factors such as pH, temperature, and substrate concentration can introduce unwanted variability. This necessity becomes particularly pronounced when working with peptides, which often exhibit slower reaction rates compared to enzymes, making even minor variations significantly influential on the final outcome. In this context, we present a refined protocol for assessing the catalytic activity of peptides and peptide assemblies, addressing critical considerations for reproducibility and accuracy.

CTFNet: Long-Sequence Time-Series Forecasting Based on Convolution and Time-Frequency Analysis.

Although current time-series forecasting methods have significantly improved the state-of-the-art (SOTA) results for long-sequence time-series forecasting (LSTF), they still have difficulty in capturing and extracting the features and dependencies of long-term sequences and suffer from information utilization bottlenecks and high-computational complexity. To address these issues, a lightweight single-hidden layer feedforward neural network (SLFN) combining convolution mapping and time-frequency decomposition called CTFNet is proposed with three distinctive characteristics. First, time-domain (TD) feature mining-in this article, a method for extracting the long-term correlation of horizontal TD features based on matrix factorization is proposed, which can effectively capture the interdependence among different sample points of a long time series. Second, multitask frequency-domain (FD) feature mining-this can effectively extract different frequency feature information of time-series data from the FD and minimize the loss of data features. Integrating multiscale dilated convolutions, simultaneously focusing on both global and local context feature dependencies at the sequence level, and mining the long-term dependencies of the multiscale frequency information and the spatial dependencies among the different scale frequency information, break the bottleneck of data utilization, and ensure the integrity of feature extraction. Third, highly efficient-the CTFNet model has a short training time and fast inference speed. Our empirical studies with nine benchmark datasets show that compared with state-of-the-art methods, CTFNet can reduce prediction error by 64.7% and 53.7% for multivariate and univariate time series, respectively.

Sweet complexity: O-linked protein glycosylation in pathogenic Neisseria.

The genus Neisseria, which colonizes mucosal surfaces, includes both commensal and pathogenic species that are exclusive to humans. The two pathogenic Neisseria species are closely related but cause quite different diseases, meningococcal sepsis and meningitis (Neisseria meningitidis) and sexually transmitted gonorrhea (Neisseria gonorrhoeae). Although obvious differences in bacterial niches and mechanisms for transmission exists, pathogenic Neisseria have high levels of conservation at the levels of nucleotide sequences, gene content and synteny. Species of Neisseria express broad-spectrum O-linked protein glycosylation where the glycoproteins are largely transmembrane proteins or lipoproteins localized on the cell surface or in the periplasm. There are diverse functions among the identified glycoproteins, for example type IV biogenesis proteins, proteins involved in antimicrobial resistance, as well as surface proteins that have been suggested as vaccine candidates. The most abundant glycoprotein, PilE, is the major subunit of pili which are an important colonization factor. The glycans attached can vary extensively due to phase variation of protein glycosylation (pgl) genes and polymorphic pgl gene content. The exact roles of glycosylation in Neisseria remains to be determined, but increasing evidence suggests that glycan variability can be a strategy to evade the human immune system. In addition, pathogenic and commensal Neisseria appear to have significant glycosylation differences. Here, the current knowledge and implications of protein glycosylation genes, glycan diversity, glycoproteins and immunogenicity in pathogenic Neisseria are summarized and discussed.

Unlocking epitope similarity: A comparative analysis of the American manatee (Trichechus manatus) IgA and human IgA through an immuno-informatics approach.

The American manatee (Trichechus manatus), experiencing population declines due to various threats, is the focus of conservation efforts that include the capture, rehabilitation, and release of orphaned calves when their mothers are unable to care for them. These efforts are compromised by the use of commercially available milk substitutes that lack essential components found in natural manatee breast milk, particularly immunoglobulin A (IgA). IgA plays a crucial role in nurturing the immune mucosal system and fostering a healthy microbiota. However, research on IgA in non-maternally fed manatees is limited due to the lack of species-specific reagents. To address this gap, our study employs immuno-informatics analysis to compare IgA sequences from manatees with those from other species, aiming to explore epitope similarity and sharing. We compared the protein sequence of manatee IgA with available IgA sequences, assessing similarity at the sequence, 3D structures, and epitope levels. Our findings reveal that human IgA exhibits the highest similarity in terms of sequence and 3D structure. Additionally, epitope analysis shows high conservation, identity, and similarity of predicted epitopes compared to human IgA. Future studies should focus on functional analysis using human IgA polyclonal reagents to detect manatee IgA in breast milk. Our findings highlight the potential of comparative analysis in advancing the understanding of immunology in non-human animals and overcoming challenges associated with the scarcity of species-specific reagents.

“Practical elimination” principle in the Akkuyu Nuclear Power Plant design

The paper considers the implementation of the “practical elimination” principle in the design of the Akkuyu NPP with VVER1200 reactors being under construction in Turkey. The “practical elimination” principle is defined as follows: for accident sequences or phenomena that contribute or lead to unacceptable radiological consequences for the public or the environment, it shall be shown with a high level of confidence that their occurrence is highly unlikely. “Practical elimination” is proved predominantly by results of a Level 2 probabilistic safety assessment. The implementation of the “practical elimination” principle was considered at the level of accident sequences leading to a large radioactive release. It has been shown that each accident sequence leading to an unacceptable release has the probability of occurrence below 4.45Е–8 per reactor per year, while their total probability not exceeding the value of 6.17Е–7 per reactor per year. For the phenomena inside the containment area during severe accidents, including hydrogen detonation, a large thermal explosion, direct containment heating, overpressure in the containment volume, and the containment damage at later stages due to the basement melting through, their “practical elimination” has been demonstrated. The paper also considers specific hard-to-assess scenarios of beyond design basis accidents, for which the applicability of the “practical elimination” principle is assessed as well: a major positive reactivity insertion, a rupture of the reactor pressure vessel and other largescale components, damage of fuel elements in the spent fuel pool, severe accidents with the containment bypass or containment failure, and severe accidents accompanied the means for mitigating with their consequences being unavailable. Criteria have been developed and used for the “practical elimination” assessment. A sensitivity analysis was undertaken as part of the Level 2 probabilistic safety assessment which has shown that estimated values slightly depend on the analytical assumptions, as well as on the random change in the parameters that the affect the progression of severe accidents.

Single-cell transcriptome sequencing of plant leaf expressing anti-HER2 VHH–FcK cancer therapeutic protein

The transgenic plant is a promising strategy for the production of highly valuable biotherapeutic proteins such as recombinant vaccines and antibodies. To achieve an efficient level of protein production, codon sequences and expression cassette elements need to be optimized. However, the systematical expression of recombinant proteins in plant biomass can generally be controlled for the production of therapeutic proteins after the generation of transgenic plants. Without understanding the transgene expression patterns in plant tissue, it is difficult to enhance further production levels. In this study, single-cell RNA-sequencing (scRNA-seq) analysis of transgenic tobacco (Nicotiana tabacum) leaf, expressing an immunotherapeutic llama antibody against breast cancer, anti-HER2 VHH–Fc, was conducted to obtain data on the expression pattern of tissue-specific cells. These high-quality scRNA-seq data enabled the identification of gene expression patterns by cell types, which can be applied to select the best cell types or tissues for the high production of these recombinant antibodies. These data provide a foundation to elucidate the mechanisms that regulate the biosynthesis of recombinant proteins in N. tabacum.

Strategies to Name Metallo-β-Lactamases and Number Their Amino Acid Residues.

Metallo-β-lactamases (MBLs), also known as class B β-lactamases (BBLs), are Zn(II)-containing enzymes able to inactivate a broad range of β-lactams, the most commonly used antibiotics, including life-saving carbapenems. They have been known for about six decades, yet they have only gained much attention as a clinical problem for about three decades. The naming conventions of these enzymes have changed over time and followed various strategies, sometimes leading to confusion. We are summarizing the naming strategies of the currently known MBLs. These enzymes are quite diverse on the amino acid sequence level but structurally similar. Problems trying to describe conserved residues, such as Zn(II) ligands and other catalytically important residues, which have different numbers in different sequences, have led to the establishment of a standard numbering scheme for BBLs. While well intended, the standard numbering scheme is not trivial and has not been applied consistently. We revisit this standard numbering scheme and suggest some strategies for how its implementation could be made more accessible to researchers. Standard numbering facilitates the comparison of different enzymes as well as their interaction with novel antibiotics and BBL inhibitors.

Preselecting Variants from Large-Scale Genome-Wide Association Study Meta-Analyses Increases the Genomic Prediction Accuracy of Growth and Carcass Traits in Large White Pigs.

Preselected variants associated with the trait of interest from genome-wide association studies (GWASs) are available to improve genomic prediction in pigs. The objectives of this study were to use preselected variants from a large GWAS meta-analysis to assess the impact of single-nucleotide polymorphism (SNP) preselection strategies on genome prediction of growth and carcass traits in pigs. We genotyped 1018 Large White pigs using medium (50k) SNP arrays and then imputed SNPs to sequence level by utilizing a reference panel of 1602 whole-genome sequencing samples. We tested the effects of different proportions of selected top SNPs across different SNP preselection strategies on genomic prediction. Finally, we compared the prediction accuracies by employing genomic best linear unbiased prediction (GBLUP), genomic feature BLUP and three weighted GBLUP models. SNP preselection strategies showed an average improvement in accuracy ranging from 0.3 to 2% in comparison to the SNP chip data. The accuracy of genomic prediction exhibited a pattern of initial increase followed by decrease, or continuous decrease across various SNP preselection strategies, as the proportion of selected top SNPs increased. The highest level of prediction accuracy was observed when utilizing 1 or 5% of top SNPs. Compared with the GBLUP model, the utilization of estimated marker effects from a GWAS meta-analysis as SNP weights in the BLUP|GA model improved the accuracy of genomic prediction in different SNP preselection strategies. The new SNP preselection strategies gained from this study bring opportunities for genomic prediction in limited-size populations in pigs.

A General and Convenient Peptide Self-Assembling Mechanism for Developing Supramolecular Versatile Nanomaterials Based on The Biosynthetic Hybrid Amyloid-Resilin Protein.

Self-assembling peptides are valuable building blocks to fabricate supramolecular biomaterials, which have broad applications from biomedicine to biotechnology. However, limited choices to induce different globular proteins into hydrogels hinder these designs. Here, an easy-to-implement and tunable self-assembling strategy, which employs Ure2 amyloidogenic peptide, are described to induce any target proteins to assemble into supramolecular hydrogels alone or in combination with notable compositional control. Furthermore, the collective effect of nanoscale interactions among amyloid nanofibrils and partially disordered elastomeric polypeptides are investigated. This led to many useful macroscopic material properties simultaneously emerging from one pure protein material, i.e. strong adhesion to any substrates under wet conditions, rapidly self--assembling into robust and porous hydrogels, adaptation to remodeling processes, strongly promoting cell adhesion, proliferation and differentiation. Moreover, he demonstrated this supramolecular material's robust performance in vitro and vivo for tissue engineering, cosmetic and hemostasis applications and exhibited superior performance compared to corresponding commercial counterparts. To the best of his knowledge, few pure protein-based materials could meet such seemingly mutually exclusive properties simultaneously. Such versatility renders this novel supramolecular nanomaterial as next-generation functional protein-based materials, and he demonstrated the sequence level modulation of structural order and disorder as an untapped principle to design new proteins.

Energy Content Analysis of the Large 2019 Ridgecrest, California Seismic Sequence

The energy content of ground motion records obtained during the 2019 M6.4, M7.1 Ridgecrest, California earthquakes which occurred closely in space is analyzed in this paper. Wavelet transform as a powerful tool, that is used to evaluate the energy content characteristics including cumulative energy profiles and the total energy for different components of records, was obtained at three stations named China Lake Christmas Canyon, China Lake and Tower 2. Due to the fact that the energy-based parameters could be well correlated with damage distribution, this path has been taken into consideration in this research work with the light of the powerful wavelet tool. For this purpose, the discrete wavelet transform is efficiently used for multi-resolution decomposition of acceleration time history of ground motions and a new framework is presented to estimate the energy level of earthquake sequences. Finally, the pseudo-period corresponding to each corresponding level is introduced and the distribution of energy vs. the pseudo-period is discussed. The results of this paper provide a platform to compare the energy levels of different earthquake sequences in different periods. Analysis showed that wide-band energy distribution could be identified at China Lake Christmas Canyon and Tower 2 stations.

Machine learning-based classification of deubiquitinase USP26 and its cell proliferation inhibition through stabilizing KLF6 in cervical cancer

ObjectiveWe aim to accurately distinguish ubiquitin-specific proteases (USPs) from other members within the deubiquitinating enzyme families based on protein sequences. Additionally, we seek to elucidate the specific regulatory mechanisms through which USP26 modulates Krüppel-like factor 6 (KLF6) and assess the subsequent effects of this regulation on both the proliferation and migration of cervical cancer cells. MethodsAll the deubiquitinase (DUB) sequences were classified into USPs and non-USPs. Feature vectors, including 188D, n-gram, and 400D dimensions, were extracted from these sequences and subjected to binary classification via the Weka software. Next, thirty human USPs were also analyzed to identify conserved motifs and ascertained evolutionary relationships. Experimentally, more than 90 unique DUB-encoding plasmids were transfected into HeLa cell lines to assess alterations in KLF6 protein levels and to isolate a specific DUB involved in KLF6 regulation. Subsequent experiments utilized both wild-type (WT) USP26 overexpression and shRNA-mediated USP26 knockdown to examine changes in KLF6 protein levels. The half-life experiment was performed to assess the influence of USP26 on KLF6 protein stability. Immunoprecipitation was applied to confirm the USP26-KLF6 interaction, and ubiquitination assays to explore the role of USP26 in KLF6 deubiquitination. Additional cellular assays were conducted to evaluate the effects of USP26 on HeLa cell proliferation and migration. Results1. Among the extracted feature vectors of 188D, 400D, and n-gram, all 12 classifiers demonstrated excellent performance. The RandomForest classifier demonstrated superior performance in this assessment. Phylogenetic analysis of 30 human USPs revealed the presence of nine unique motifs, comprising zinc finger and ubiquitin-specific protease domains. 2. Through a systematic screening of the deubiquitinase library, USP26 was identified as the sole DUB associated with KLF6. 3. USP26 positively regulated the protein level of KLF6, as evidenced by the decrease in KLF6 protein expression upon shUSP26 knockdown in both 293T and Hela cell lines. Additionally, half-life experiments demonstrated that USP26 prolonged the stability of KLF6. 4. Immunoprecipitation experiments revealed a strong interaction between USP26 and KLF6. Notably, the functional interaction domain was mapped to amino acids 285–913 of USP26, as opposed to the 1–295 region. 5. WT USP26 was found to attenuate the ubiquitination levels of KLF6. However, the mutant USP26 abrogated its deubiquitination activity. 6. Functional biological assays demonstrated that overexpression of USP26 inhibited both proliferation and migration of HeLa cells. Conversely, knockdown of USP26 was shown to promote these oncogenic properties. Conclusions1. At the protein sequence level, members of the USP family can be effectively differentiated from non-USP proteins. Furthermore, specific functional motifs have been identified within the sequences of human USPs. 2. The deubiquitinating enzyme USP26 has been shown to target KLF6 for deubiquitination, thereby modulating its stability. Importantly, USP26 plays a pivotal role in the modulation of proliferation and migration in cervical cancer cells.

Analysis of Nucleotide Variations in Human G-Quadruplex Forming Regions Associated with Disease States.

While the role of G quadruplex (G4) structures has been identified in cancers and metabolic disorders, single nucleotide variations (SNVs) and their effect on G4s in disease contexts have not been extensively studied. The COSMIC and CLINVAR databases were used to detect SNVs present in G4s to identify sequence level changes and their effect on the alteration of the G4 secondary structure. A total of 37,515 G4 SNVs in the COSMIC database and 2378 in CLINVAR were identified. Of those, 7236 COSMIC (19.3%) and 457 (19%) of the CLINVAR variants result in G4 loss, while 2728 (COSMIC) and 129 (CLINVAR) SNVs gain a G4 structure. The remaining variants potentially affect the folding energy without affecting the presence of a G4. Analysis of mutational patterns in the G4 structure shows a higher selective pressure (3-fold) in the coding region on the template strand compared to the reverse strand. At the same time, an equal proportion of SNVs were observed among intronic, promoter, and enhancer regions across strands.

Bentuk Lagu �Sendah Kerja Rani�

Runggun Semarang Batak Karo Protestant Church (GBKP) celebrates kerja rani and song �Sendah Kerja Rani� is a Karo spiritual song found in the book kitab end-endwhich has cultural values, is still sung at GBKP Runggun Semarang.The purpose of writing this article is to analyze the form of the �Sendah Kerja Rani� song using a qualitative research method, a musicological approach, and an approach that involves reading problems in the scientific field of music, such as form and context. �Sendah Kerja Rani� song is a two-part song consisting of sentence A (a, a') and sentence B (b, y), there are three motifs in the �Sendah Kerja Rani� song namely literal repetition, repetition at sequence level, and interval reduction, then cadence

Engineering minimal tissue inhibitors of metalloproteinase targeting MMPs via gene shuffling and yeast surface display.

Overexpression of specific matrix metalloproteinases (MMPs) has a key role in development of several diseases, such as cancer, neurological disorders, and cardiovascular diseases due to their critical role in degradation and remodeling of the extracellular matrix (ECM). Tissue inhibitors of metalloproteinases (TIMPs), a family of four in humans, are endogenous inhibitors of MMPs. TIMPs have a high level of sequence and structure homology, with a broad range of binding and inhibition to the family of MMPs. It is important to identify the key motifs of TIMPs responsible for inhibition of MMPs to developefficient therapeutics targeting specific MMPs. We used DNA shuffling between the human TIMP family to generate a minimal TIMP hybrid library in yeast to identify the dominant minimal MMP inhibitory regions. The minimal TIMP variants screened toward MMP-3 and MMP-9 using fluorescent-activated cell sorting (FACS). Interestingly, several minimal TIMP variants selected after screening toward MMP-3cd or MMP-9cd, with lengths as short as 20 amino acids, maintained or improved binding to MMP-3 and MMP-9. The TIMP-MMP binding dissociation constant (KD ), in the nM range, and MMP inhibition constants (Ki ), in the pM range, of these minimal TIMP variants were similar to the N-terminal domain of TIMP-1 on the yeast surface and in solution indicating the potency of these minimal variants as MMP inhibitors. We further used molecular modeling simulation, and molecular docking of the minimal TIMP variants in complex with MMP-3cd to understand the binding and inhibition mechanism of these variants.

A methodological approach by capillary electrophoresis coupled to mass spectrometry via electrospray interface for the characterization of short synthetic peptides towards the conception of self-assembled nanotheranostic agents

Nanostructures formed by the self-assembling peptide building blocks are attractive materials for the design of theranostic objects due to their intrinsic biocompatibility, accessible surface chemistry as well as cavitary morphology. Short peptide synthesis and modification are straightforward and give access to a great diversity of sequences, making them very versatile building blocks allowing for the design of thoroughly controlled self-assembled nanostructures. In this work, we developed a new CE-DAD-ESI-MS method to characterize short synthetic amphiphilic peptides in terms of exact sequence and purity level in the low 0.1 mg.mL−1 range, without sample treatment. This study was conducted using a model sequence, described to have pH sensitive self-assembling property. Peptide samples obtained from different synthesis processes (batch or flow, purified or not) were thus separated by capillary zone electrophoresis (CZE). The associated dual UV and MS detection mode allowed to evidence the exact sequence together with the presence of impurities, identified as truncated or non-deprotected sequences, and to quantify their relative proportion in the peptide mixture. Our results demonstrate that the developed CE-DAD-ESI-MS method could be directly applied to the characterization of crude synthetic peptide products, in parallel with the optimization of peptide synthetic pathway to obtain controlled sequences with high synthetic yield and purity, which is crucial for further design of robust peptide based self-assembled nanoarchitectures.

PLM-ARG: antibiotic resistance gene identification using a pretrained protein language model.

Antibiotic resistance presents a formidable global challenge to public health and the environment. While considerable endeavors have been dedicated to identify antibiotic resistance genes (ARGs) for assessing the threat of antibiotic resistance, recent extensive investigations using metagenomic and metatranscriptomic approaches have unveiled a noteworthy concern. A significant fraction of proteins defies annotation through conventional sequence similarity-based methods, an issue that extends to ARGs, potentially leading to their under-recognition due to dissimilarities at the sequence level. Herein, we proposed an Artificial Intelligence-powered ARG identification framework using a pretrained large protein language model, enabling ARG identification and resistance category classification simultaneously. The proposed PLM-ARG was developed based on the most comprehensive ARG and related resistance category information (>28K ARGs and associated 29 resistance categories), yielding Matthew's correlation coefficients (MCCs) of 0.983 ± 0.001 by using a 5-fold cross-validation strategy. Furthermore, the PLM-ARG model was verified using an independent validation set and achieved an MCC of 0.838, outperforming other publicly available ARG prediction tools with an improvement range of 51.8%-107.9%. Moreover, the utility of the proposed PLM-ARG model was demonstrated by annotating resistance in the UniProt database and evaluating the impact of ARGs on the Earth's environmental microbiota. PLM-ARG is available for academic purposes at https://github.com/Junwu302/PLM-ARG, and a user-friendly webserver (http://www.unimd.org/PLM-ARG) is also provided.

DisProt in 2024: improving function annotation of intrinsically disordered proteins.

DisProt (URL: https://disprot.org) is the gold standard database for intrinsically disordered proteins and regions, providing valuable information about their functions. The latest version of DisProt brings significant advancements, including a broader representation of functions and an enhanced curation process. These improvements aim to increase both the quality of annotations and their coverage at the sequence level. Higher coverage has been achieved by adopting additional evidence codes. Quality of annotations has been improved by systematically applying Minimum Information About Disorder Experiments (MIADE) principles and reporting all the details of the experimental setup that could potentially influence the structural state of a protein. The DisProt database now includes new thematic datasets and has expanded the adoption of Gene Ontology terms, resulting in an extensive functional repertoire which is automatically propagated to UniProtKB. Finally, we show that DisProt's curated annotations strongly correlate with disorder predictions inferred from AlphaFold2 pLDDT (predicted Local Distance Difference Test) confidence scores. This comparison highlights the utility of DisProt in explaining apparent uncertainty of certain well-defined predicted structures, which often correspond to folding-upon-binding fragments. Overall, DisProt serves as a comprehensive resource, combining experimental evidence of disorder information to enhance our understanding of intrinsically disordered proteins and their functional implications.

Engineering custom morpho- and chemotypes of Populus for sustainable production of biofuels, bioproducts, and biomaterials.

Humans have been modifying plant traits for thousands of years, first through selection (i.e., domestication) then modern breeding, and in the last 30 years, through biotechnology. These modifications have resulted in increased yield, more efficient agronomic practices, and enhanced quality traits. Precision knowledge of gene regulation and function through high-resolution single-cell omics technologies, coupled with the ability to engineer plant genomes at the DNA sequence, chromatin accessibility, and gene expression levels, can enable engineering of complex and complementary traits at the biosystem level. Populus spp., the primary genetic model system for woody perennials, are among the fastest growing trees in temperate zones and are important for both carbon sequestration and global carbon cycling. Ample genomic and transcriptomic resources for poplar are available including emerging single-cell omics datasets. To expand use of poplar outside of valorization of woody biomass, chassis with novel morphotypes in which stem branching and tree height are modified can be fabricated thereby leading to trees with altered leaf to wood ratios. These morphotypes can then be engineered into customized chemotypes that produce high value biofuels, bioproducts, and biomaterials not only in specific organs but also in a cell-type-specific manner. For example, the recent discovery of triterpene production in poplar leaf trichomes can be exploited using cell-type specific regulatory sequences to synthesize high value terpenes such as the jet fuel precursor bisabolene specifically in the trichomes. By spatially and temporally controlling expression, not only can pools of abundant precursors be exploited but engineered molecules can be sequestered in discrete cell structures in the leaf. The structural diversity of the hemicellulose xylan is a barrier to fully utilizing lignocellulose in biomaterial production and by leveraging cell-type-specific omics data, cell wall composition can be modified in a tailored and targeted specific manner to generate poplar wood with novel chemical features that are amenable for processing or advanced manufacturing. Precision engineering poplar as a multi-purpose sustainable feedstock highlights how genome engineering can be used to re-imagine a crop species.