MULocDeep: A deep-learning framework for protein subcellular and suborganellar localization prediction with residue-level interpretation

ProLoc-IHS: Multi-label protein subcellular localization based on immunohistochemical images and sequence information

Immunohistochemistry (IHC) imaging is a powerful technique to study the subcelluar localization (SCL) of human proteins in both normal and pathological tissues. As the manual annotation of localization for IHC images is time-consuming and the number of annotated is limited, a computational tool is necessary to analyze IHC images. However, existing prediction models rarely incorporate protein sequences. In this paper, a novel protein SCL prediction model for IHC images, ProLoc-IHS, is proposed by combining with sequence features. First, a bimodal dataset is curated including IHC images and protein sequences, which are derived from the Human Protein Atlas (HPA) and UniProt respectively. Then, ProLoc-IHS extracts embeddings from IHC images and protein sequences using a visual language model, Vision Transformer (Vit), and a protein language model, ProtT5, respectively. Subsequently, these embeddings are fused using a cross-attention module, and the fused features are input into the feature learning module of ProLoc-IHS, which contains a multi-head attention mechanism, a feedforward neural network and a residual connection. Finally, binary cross entropy (BCE) and focal loss function are incorporated into the feature learning module to solve multi-label classification tasks. Experimental results show that ProLoc-IHS outperforms other prediction models. The newly curated dataset and ProLoc-IHS code are available at https://github.com/xinshuaiiii/ProLoc-IHS.

Multiple Pathways to Red Carotenoid Coloration: House Finches (Haemorhous mexicanus) Do Not Use CYP2J19 to Produce Red Plumage

The carotenoid-based colours of birds are a celebrated example of biological diversity and an important system for the study of evolution. Recently, a two-step mechanism, with the enzymes cytochrome P450 2J19 (CYP2J19) and 3-hydroxybutyrate dehydrogenase 1-like (BDH1L), was described for the biosynthesis of red ketocarotenoids from yellow dietary carotenoids in the retina and plumage of birds. A common assumption has been that all birds with ketocarotenoid-based plumage coloration used this CYP2J19/BDH1L mechanism to produce red feathers. We tested this assumption in house finches (Haemorhous mexicanus) by examining the catalytic function of the house finch homologues of these enzymes and tracking their expression in birds growing new feathers. We found that CYP2J19 and BDH1L did not catalyse the production of 3-hydroxy-echinenone (3-OH-echinenone), the primary red plumage pigment of house finches, when provided with common dietary carotenoid substrates. Moreover, gene expression analyses revealed little to no expression of CYP2J19 in liver tissue or growing feather follicles, the putative sites of pigment metabolism in moulting house finches. Finally, although the hepatic mitochondria of house finches have high concentrations of 3-OH-echinenone, observations using fluorescent markers suggest that both CYP2J19 and BDH1L localise to the endomembrane system rather than the mitochondria. We propose that house finches and other birds that deposit 3-OH-echinenone as their primary red plumage pigment use an alternative enzymatic pathway to produce their characteristic red ketocarotenoid-based coloration.

Identification of pennaceous barbule cell factor (PBCF), a novel gene with spatiotemporal expression in barbule cells during feather development

Bird contour feathers exhibit a complex hierarchical structure composed of a rachis, barbs, and barbules, with barbules playing a crucial role in maintaining feather structure and function. Understanding the molecular mechanisms underlying barbule formation is essential for advancing our knowledge of avian biology and evolution. In this study, we identified a novel gene, pennaceous barbule cell factor (PBCF), using microarray analysis, RT-PCR, and in situ hybridization. PBCF is expressed in barbule cells adjacent to the ramus during pennaceous barbule formation, where these cells fuse with the ramus to establish the feather's branching structure. PBCF expression occurs transiently after melanin pigmentation of the barbule plates but before the expression of barbule-specific keratin 1 (BlSK1). Orthologues of PBCF, predicted to be secreted proteins, are conserved across avian species, with potential homologues detected in reptiles, suggesting an evolutionary lineage-specific adaptation. Additionally, PBCF is expressed in non-vacuolated notochord cells and the extra-embryonic ectoderm of the yolk sac, hinting at its broader developmental significance. The PBCF gene produces two mRNA isoforms via alternative splicing, encoding a secreted protein and a glycophosphatidylinositol (GPI)-anchored membrane-bound protein, indicating functional versatility. These findings suggest that PBCF may be involved as an avian-specific extracellular matrix component in cell adhesion and/or communication, potentially contributing to both feather development and embryogenesis. Further investigation of PBCF's role in feather evolution and its potential functions in other vertebrates could provide new insights into the interplay between development and evolution.

Deep learning prioritizes cancer mutations that alter protein nucleocytoplasmic shuttling to drive tumorigenesis

Genetic variants can affect protein function by driving aberrant subcellular localization. However, comprehensive analysis of how mutations promote tumor progression by influencing nuclear localization is currently lacking. Here, we systematically characterize potential shuttling-attacking mutations (SAMs) across cancers through developing the deep learning model pSAM for the ab initio decoding of the sequence determinants of nucleocytoplasmic shuttling. Leveraging cancer mutations across 11 cancer types, we find that SAMs enrich functional genetic variations and critical genes in cancer. We experimentally validate a dozen SAMs, among which R14M in PTEN, P255L in CHFR, etc. are identified to disrupt the nuclear localization signals through interfering their interactions with importins. Further studies confirm that the nucleocytoplasmic shuttling altered by SAMs in PTEN and CHFR rewire the downstream signaling and eliminate their function of tumor suppression. Thus, this study will help to understand the molecular traits of nucleocytoplasmic shuttling and their dysfunctions mediated by genetic variants.

Protein codes promote selective subcellular compartmentalization

Cells have evolved mechanisms to distribute ~10 billion protein molecules to subcellular compartments where diverse proteins involved in shared functions must assemble. In this study, we demonstrate that proteins with shared functions share amino acid sequence codes that guide them to compartment destinations. We developed a protein language model, ProtGPS, that predicts with high performance the compartment localization of human proteins excluded from the training set. ProtGPS successfully guided generation of novel protein sequences that selectively assemble in the nucleolus. ProtGPS identified pathological mutations that change this code and lead to altered subcellular localization of proteins. Our results indicate that protein sequences contain not only a folding code but also a previously unrecognized code governing their distribution to diverse subcellular compartments.

Navigating the challenges of engineering composite specialized metabolite pathways in plants

Plants are a valuable source of diverse specialized metabolites with numerous applications. However, these compounds are often produced in limited quantities, particularly under unfavorable ecological conditions. To achieve sufficient levels of target metabolites, alternative strategies such as pathway engineering in heterologous systems like microbes (e.g., bacteria and fungi) or cell-free systems can be employed. Another approach is plant engineering, which aims to either enhance the native production in the original plant or reconstruct the target pathway in a model plant system. Although increasing metabolite production in the native plant is a promising strategy, these source plants are often exotic and pose significant challenges for genetic manipulation. Effective pathway engineering requires comprehensive prior knowledge of the genes and enzymes involved, as well as the precursor, intermediate, branching, and final metabolites. Thus, a thorough elucidation of the biosynthetic pathway is closely linked to successful metabolic engineering in host or model systems. In this review, we highlight recent advances in strategies for biosynthetic pathway elucidation and metabolic engineering. We focus on efforts to engineer complex, multi-step pathways that require the expression of at least eight genes for transient and three genes for stable transformation. Reports on the engineering of complex pathways in stably transformed plants remain relatively scarce. We discuss the major hurdles in pathway elucidation and strategies for overcoming them, followed by an overview of achievements, challenges, and solutions in pathway reconstitution through metabolic engineering. Recent advances including computer-based predictions offer valuable platforms for the sustainable production of specialized metabolites in plants.

Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions

Alignments in bioinformatics refer to the arrangement of sequences to identify regions of similarity that can indicate functional, structural, or evolutionary relationships. They are crucial for bioinformaticians as they enable accurate predictions and analyses in various applications, including protein subcellular localization. The predictive model used in this article is based on a deep - convolutional architecture. We tested configurations of Deep N-to-1 convolutional neural networks of various depths and widths during experimentation for the evaluation of better-performing values across a diverse set of eight classes. For without alignment assessment, sequences are encoded using one-hot encoding, converting each character into a numerical representation, which is straightforward for non-numerical data and useful for machine learning models. For with alignments assessment, multiple sequence alignments (MSAs) are created using PSI-BLAST, capturing evolutionary information by calculating frequencies of residues and gaps. The average difference in peak performance between models with alignments and without alignments is approximately 15.82%. The average difference in the highest accuracy achieved with alignments compared with without alignments is approximately 15.16%. Thus, extensive experimentation indicates that higher alignment accuracy implies a more reliable model and improved prediction accuracy, which can be trusted to deliver consistent performance across different layers and classes of subcellular localization predictions. This research provides valuable insights into prediction accuracies with and without alignments, offering bioinformaticians an effective tool for better understanding while potentially reducing the need for extensive experimental validations. The source code and datasets are available at http://distilldeep.ucd.ie/SCL8/.

AtSubP-2.0: An integrated web server for the annotation of Arabidopsis proteome subcellular localization using deep learning

The organization of subcellular components in a cell is critical for its function and studying cellular processes, protein-protein interactions, identifying potential drug targets, network analysis, and other systems biology mechanisms. Determining protein localization experimentally is time-consuming and expensive. Due to the need for meticulous experimentation, validation, and data analysis, computational methods provide a quick and accurate alternative. Arabidopsis thaliana, a beneficial model organism in plant biology, facilitates experimentation and applies to other plants. Predicting its proteins' subcellular localization can improve our understanding of cellular processes and have applications in crop improvement and biotechnology. We propose AtSubP-2.0, an extension of our previously developed and widely used AtSubP v1.0 tool for annotating the Arabidopsis proteome. For precise protein subcellular localization prediction, AtSubP-2.0 employs a four-phase strategy. The first phase differentiates between single and dual localization with accuracy (97.66% in fivefold training/testing, 98.10% on independent data) and high Matthews correlation coefficient (0.88 training, 0.90 independent). Single localized proteins are classified into 12 locations at the second phase, with accuracy (98.37% in fivefold training/testing, 97.43% on independent data) and Matthews correlation coefficient (0.94 training, 0.91 independent). The third phase categorizes dual location proteins into nine classes with accuracy (99.65% in fivefold training/testing, 98.16% on independent data) and Matthews correlation coefficient (0.92 training, 0.87 independent). We also employed a fourth phase that classifies the membrane type proteins predicted in phase I into single-pass and multi-pass membrane with accuracy (98% in fivefold training/testing, 98.55% on independent data) and a high Matthews correlation coefficient (0.95 training, 0.97 independent). A web-based prediction server has been implemented for community use and is freely available at https://kaabil.net/AtSubP2/, including a standalone version. AtSubP2 will help researchers to better understand organelle-specific functions, cellular processes, and regulatory mechanisms important for plant growth, development, and response to environmental stimuli.

Computational epitope-based vaccine design with bioinformatics approach; a review

The significance of vaccine development has gained heightened importance in light of the COVID-19 pandemic. In such critical circumstances, global citizens anticipate researchers in this field to swiftly identify a vaccine candidate to combat the pandemic's root cause. It is widely recognized that the vaccine design process is traditionally both time-consuming and costly. However, a specialized subfield within bioinformatics, known as "multi-epitope vaccine design" or "reverse vaccinology," has significantly decreased the time and costs of the vaccine design process. The methodology reverses itself in this subfield and finds a potential vaccine candidate by analyzing the pathogen's genome. Leveraging the tools available in this domain, we strive to pinpoint the most suitable antigen for crafting a vaccine against our target. Once the optimal antigen is identified, the next step involves uncovering epitopes within this antigen. The immune system recognizes particular areas of an antigen as epitopes. By characterizing these crucial segments, we gain the opportunity to design a vaccine centered around these epitopes. Subsequently, after identifying and assembling the vital epitopes with the assistance of linkers and adjuvants, our vaccine candidate can be formulated. Finally, employing computational techniques, we can thoroughly evaluate the designed vaccine. This review article comprehensively covers the entire multi-epitope vaccine development process, starting from obtaining the pathogen's genome to identifying the relevant vaccine candidate and concluding with an evaluation. Furthermore, we will delve into the essential tools needed at each stage, comparing and introducing them.

CAX-INTERACTING PROTEIN4 depletion causes early lethality and pre-mRNA missplicing in Arabidopsis

Zinc knuckle (ZCCHC) motif-containing proteins are present in unicellular and multicellular eukaryotes, and most ZCCHC proteins with known functions participate in the metabolism of various classes of RNA, such as mRNAs, ribosomal RNAs, and microRNAs. The Arabidopsis (Arabidopsis thaliana) genome encodes 69 ZCCHC-containing proteins; however, the functions of most remain unclear. One of these proteins, CAX-INTERACTING PROTEIN 4 (CXIP4, encoded by AT2G28910), has been classified as a PTHR31437 family member. This family includes human Splicing regulatory glutamine/lysine-rich protein 1 (SREK1)-interacting protein 1 (SREK1IP1), which is thought to function in pre-mRNA splicing and RNA methylation. Metazoan SREK1IP1-like and plant CXIP4-like proteins only share a ZCCHC motif, and their functions remain almost entirely unknown. Here, we studied two loss-of-function alleles of Arabidopsis CXIP4: cxip4-1 is likely null and shows early lethality, and cxip4-2 is hypomorphic and viable, with pleiotropic morphological defects. The cxip4-2 mutant exhibited deregulation of defense genes and upregulation of transcription factor genes, some of which might explain its developmental defects. The cxip4-2 mutant also exhibited increased intron retention events, being more evident in cxip4-1. The specific functions of misspliced genes, such as those involved in "gene silencing by DNA methylation" and "mRNA polyadenylation factor" suggest that CXIP4 has additional functions. In cxip4-2 plants, polyadenylated RNAs accumulate in the nucleus; these could be misspliced mRNAs. The CXIP4 protein localizes to the nucleus in a pattern resembling nuclear speckles rich in splicing factors. Therefore, CXIP4 is required for plant development and survival and mRNA maturation.

In-silico identification of putatively functional intergenic small open reading frames in the cucumber genome and their predicted response to biotic and abiotic stresses

The availability of high-throughput sequencing technologies increased our understanding of different genomes. However, the genomes of all living organisms still have many unidentified coding sequences. The increased number of missing small open reading frames (sORFs) is due to the length threshold used in most gene identification tools, which is true in the genic and, more importantly and surprisingly, in the intergenic regions. Scanning the cucumber genome intergenic regions revealed 420 723 sORF. We excluded 3850 sORF with similarities to annotated cucumber proteins. To propose the functionality of the remaining 416 873 sORF, we calculated their codon adaptation index (CAI). We found 398 937 novel sORF (nsORF) with CAI ≥ 0.7 that were further used for downstream analysis. Searching against the Rfam database revealed 109 nsORFs similar to multiple RNA families. Using SignalP-5.0 and NLS, identified 11 592 signal peptides. Five predicted proteins interacting with Meloidogyne incognita and Powdery mildew proteins were selected using published transcriptome data of host-pathogen interactions. Gene ontology enrichment interpreted the function of those proteins, illustrating that nsORFs' expression could contribute to the cucumber's response to biotic and abiotic stresses. This research highlights the importance of previously overlooked nsORFs in the cucumber genome and provides novel insights into their potential functions.

Peptidomics and Machine Learning-based Evaluation of Noncoding RNA-Derived Micropeptides in Breast Cancer: Expression Patterns and Functional/Therapeutic Insights

Breast cancer is a highly heterogeneous disease characterized by different subtypes arising from molecular alterations that give the disease different phenotypes, clinical behaviors, and prognostic. The noncoding RNA (ncRNA)-derived micropeptides (MPs) represent a novel layer of complexity in cancer study once they can be biologically active and can present potential as biomarkers and also in therapeutics. However, few large-scale studies address the expression of these peptides at the peptidomics level or evaluate their functions and potential in peptide-based therapeutics for breast cancer. In this study, we propose deepening the landscape of ncRNA-derived MPs in breast cancer subtypes and advance the comprehension of the relevance of these molecules to the disease. First, we constructed a 16,349 unique putative MP sequence data set by integrating 2 previously published lists of predicted ncRNA-derived MPs. We evaluated its expression on high-throughput mass spectrometry data of breast tumor samples from different subtypes. Next, we applied several machine and deep learning tools, such as AntiCP 2.0, MULocDeep, PEPstrMOD, Peptipedia, and PreAIP, to predict its functions, cellular localization, tertiary structure, physicochemical features, and other properties related to therapeutics. We identified 58 peptides expressed on breast tissue, including 27 differentially expressed MPs in tumor compared with nontumor samples and MPs exhibiting tumor or subtype specificity. These peptides presented physicochemical features compatible with the canonical proteome and were predicted to influence the tumor immune environment and participate in cell communication, metabolism, and signaling processes. In addition, some MPs presented potential as anticancer, antiinflammatory, and antiangiogenic molecules. Our data demonstrate that MPs derived from ncRNAs have expression patterns associated with specific breast cancer subtypes and tumor specificity, thus highlighting their potential as biomarkers for molecular classification. We also reinforce the relevance of MPs as biologically active molecules that play a role in breast tumorigenesis, besides their potential in peptide-based therapeutics.

Protein subcellular localization prediction tools

Protein subcellular localization prediction is of great significance in bioinformatics and biological research. Most of the proteins do not have experimentally determined localization information, computational prediction methods and tools have been acting as an active research area for more than two decades now. Knowledge of the subcellular location of a protein provides valuable information about its functionalities, the functioning of the cell, and other possible interactions with proteins. Fast, reliable, and accurate predictors provides platforms to harness the abundance of sequence data to predict subcellular locations accordingly. During the last decade, there has been a considerable amount of research effort aimed at developing subcellular localization predictors. This paper reviews recent subcellular localization prediction tools in the Eukaryotic, Prokaryotic, and Virus-based categories followed by a detailed analysis. Each predictor is discussed based on its main features, strengths, weaknesses, algorithms used, prediction techniques, and analysis. This review is supported by prediction tools taxonomies that highlight their rele- vant area and examples for uncomplicated categorization and ease of understandability. These taxonomies help users find suitable tools according to their needs. Furthermore, recent research gaps and challenges are discussed to cover areas that need the utmost attention. This survey provides an in-depth analysis of the most recent prediction tools to facilitate readers and can be considered a quick guide for researchers to identify and explore the recent literature advancements.

Transcriptome-wide investigation and functional characterization reveal a terpene synthase involved in γ-terpinene biosynthesis in Monarda citriodora

Monarda citriodora Cerv. ex Lag. is a rich source of industrially important compounds like γ-terpinene, carvacrol, thymol and thymoquinone. Understanding the regulation of γ-terpinene biosynthesis, a precursor for other monoterpenes, could facilitate upscaling of these metabolites in M. citriodora. Therefore, the present study aimed to unravel and characterize the terpene synthase (TPS) involved in γ-terpinene biosynthesis. Homology searches revealed 33 TPS members in the transcriptome assembly of M. citriodora. Based on the correlation of expression patterns and phytochemical profile, McTPS22 emerged as the putative TPS for γ-terpinene biosynthesis. Molecular docking suggested geranyl diphosphate (GPP) as a potential substrate for McTPS22. Heterologous expression in Escherichia coli and Nicotiana benthamiana confirmed the role of McTPS22 in γ-terpinene biosynthesis. Both in-silico prediction and confocal microscopy indicated plastidial localization of the McTPS22. Gene co-expression network analysis revealed 507 genes interacting with McTPS22, including 80 transcription factors (TFs). Of these, 46 TFs had binding sites in the McTPS22 promoter, and 36 showed significant correlations with γ-terpinene accumulation, suggesting they may be potential regulators. Promoter analysis indicated regulation by phytohormones and abiotic factors, confirmed by phytohormone elicitation and QRT-PCR. The histochemical GUS staining suggested that McTPS22 is primarily active in the glandular trichomes of M. citriodora. The present work provides insights into the molecular regulation of biosynthesis of γ-terpinene in M. citriodora.

AI-driven precision subcellular navigation with fluorescent probes

Precise navigation within intricate biological systems is pivotal for comprehending cellular functions and diagnosing diseases. Fluorescent molecular probes, designed to target specific biological molecules, are indispensable tools for this endeavor. This paper delves into the revolutionary potential of artificial intelligence (AI) in crafting highly precise and effective fluorescent probes. We will discuss how AI can be employed to: design new subcellular dyes by optimizing physicochemical properties; design prospective subcellular targeting probes based on specific receptors; quantitatively explore the potential chemical laws of fluorescent molecules to optimize the optical properties of fluorescent probes; optimize the comprehensive properties of the probe and guide the construction of multifunctional targeting probes. Additionally, we showcase recent AI-driven advancements in probe development and their successful biomedical applications, while addressing challenges and outlining future directions towards transforming subcellular research, diagnostics, and drug discovery.

Gene Flow Between Populations With Highly Divergent Mitogenomes in the Australian Stingless Bee, Tetragonula hockingsi

Coadaptation of mitochondrial and nuclear genes is essential for proper cellular function. When populations become isolated, theory predicts that they should maintain mito-nuclear coadaptation in each population, even as they diverge in genotype. Mito-nuclear incompatibilities may therefore arise when individuals from populations with divergent co-evolved mito-nuclear gene sets are re-united and hybridise, contributing to selection against inter-population hybrids and, potentially, to speciation. Here, we explored genetic divergence and gene flow between populations of a stingless bee (Tetragonula hockingsi) that have highly divergent mitogenomes. We identified three distinct populations across the species' 2500 km range on the east coast of Queensland (Australia): 'Cape York', 'Northern', and 'Southern'. The mitogenomes of each population showed > 12% pairwise nucleotide divergence from each other, and > 7% pairwise amino acid divergence. Based on nuclear SNPs from reduced representation sequencing, we identified at least two zones of gene flow between populations: a narrow natural zone between Northern and Southern populations (coinciding with a biogeographic barrier, the Burdekin Gap), and an artificial zone at the southern edge of the species' distribution, where Cape York, Northern, and Southern mito-lineages have been brought together in recent decades due to beekeeping. In the artificial hybrid zone, we also confirmed that males of all three mito-lineages were attracted to the mating aggregations of Southern queens, consistent with inter-population hybridisation. Populations of T. hockingsi thus appear to be in the 'grey zone' of the speciation continuum, having strong genetic differentiation but incomplete reproductive isolation. Among the nuclear SNPs most differentiated between Northern and Southern populations, several were associated with genes involved in mitochondrial function, consistent with populations having co-diverged mito-nuclear gene sets. Our observations suggest that coadapted sets of mitochondrial and nuclear genes unique to each population of T. hockingsi may play a role in maintaining population boundaries, though more study is needed to confirm the fitness costs of mito-nuclear incompatibilities in hybrid individuals.

Parameter-efficient fine-tuning on large protein language models improves signal peptide prediction

Signal peptides (SPs) play a crucial role in protein translocation in cells. The development of large protein language models (PLMs) and prompt-based learning provide a new opportunity for SP prediction, especially for the categories with limited annotated data. We present a parameter-efficient fine-tuning (PEFT) framework for SP prediction, PEFT-SP, to effectively utilize pretrained PLMs. We integrated low-rank adaptation (LoRA) into ESM-2 models to better leverage the protein sequence evolutionary knowledge of PLMs. Experiments show that PEFT-SP using LoRA enhances state-of-the-art results, leading to a maximum Matthews correlation coefficient (MCC) gain of 87.3% for SPs with small training samples and an overall MCC gain of 6.1%. Furthermore, we also employed two other PEFT methods, prompt tuning and adapter tuning, in ESM-2 for SP prediction. More elaborate experiments show that PEFT-SP using adapter tuning can also improve the state-of-the-art results by up to 28.1% MCC gain for SPs with small training samples and an overall MCC gain of 3.8%. LoRA requires fewer computing resources and less memory than the adapter tuning during the training stage, making it possible to adapt larger and more powerful protein models for SP prediction.

Role of Squalene Epoxidase Gene (SQE1) in the Response of the Lichen Lobaria pulmonaria to Temperature Stress

Currently, due to the increasing impact of anthropogenic factors and changes in solar activity, the temperature on Earth is rising, posing a threat to biodiversity. Lichens are among the most sensitive organisms to climate change. Elevated ambient temperatures can have a significant impact on lichens, resulting in more frequent and intense drying events that can impede metabolic activity. It has been suggested that the possession of a diverse sterol composition may contribute to the tolerance of lichens to adverse temperatures and other biotic and abiotic stresses. The major sterol found in lichens is ergosterol (ERG); however, the regulation of the ERG biosynthetic pathway, specifically the step of epoxidation of squalene to 2,3-oxidosqualene catalyzed by squalene epoxidase during stress, has not been extensively studied. In this study, we used lichen Lobaria pulmonaria as a model species that is well known to be sensitive to air pollution and habitat loss. Using in silico analysis, we identified cDNAs encoding squalene epoxidase from L. pulmonaria, designating them as LpSQE1 for the mycobiont and SrSQE1 for the photobiont Symbiochloris reticulata. Our results showed that compared with a control kept at room temperature (+20 °C), mild temperatures (+4 °C and +30 °C) did not affect the physiology of L. pulmonaria, assessed by changes in membrane integrity, respiration rates, and PSII activity. An extreme negative temperature (-20 °C) noticeably inhibited respiration but did not affect membrane stability. In contrast, treating lichen with a high positive temperature (+40 °C) significantly reduced all physiological parameters. Quantitative PCR analysis revealed that exposing thalli to -20 °C, +4 °C, +30 °C, and +40 °C stimulated the expression levels of LpSQE1 and SrSQE1 and led to a significant upregulation of Hsps. These data provide new information regarding the roles of sterols and Hsps in the response of lichens to climate change.

Artificial targeting of the NEIL1 DNA glycosylase to the mitochondria

The human NEIL1 DNA glycosylase is one of 11 mammalian glycosylases that initiate base excision repair. While substrate preference, catalytic mechanism, and structural information of NEIL1's ordered residues are available, limited information on its subcellular localization, compounded by relatively low endogenous expression levels, have impeded our understanding of NEIL1. Here, we employed a previously developed computational framework to optimize the mitochondrial localization signal of NEIL1, enabling the visualization of its specific targeting to the mitochondrion via confocal microscopy. While we observed clear mitochondrial localization and increased glycosylase/lyase activity in mitochondrial extracts from low-moderate NEIL1 expression, high NEIL1 mitochondrial expression levels proved harmful, potentially leading to cell death.

Multiple types of nuclear localization signals in Entamoeba histolytica

Entamoeba histolytica is a protozoan parasite that belongs to the Amoebozoa supergroup whose study related to the nucleocytoplasmic transport of proteins through the nucleus is poorly studied. In this work, we have performed in silico predictions of the potential nuclear localization signals (NLS) corresponding to the proteome of 8201 proteins from Entamoeba histolytica annotated in the AmoebaDB database. We have found the presence of monopartite nuclear localization signals (MNLSs), bipartite nuclear localization signals (BNLSs), and non-canonical monopartite NLSs with lengths exceeding 20 amino acid residues. Additionally, we detected a new type of NLS consisting of multiple juxtaposed bipartite NLSs (JNLSs) that have not been described in any eukaryotic organism. Also, we have generated consensus sequences for the nuclear import of proteins with the NLSs obtained. Docking experiments between EhImportin α and an MNLS, BNLS, and JNLS outlined the interacting residues between the Importin and cargo proteins, emphasizing their putative roles in nuclear import. By transfecting HA-tagged protein constructs, we assessed the nuclear localization of MNLS (U1A and U2AF1), JMNLS (U2AF2), and non-canonical NLS (N-terminus of Pol ll) in vivo. Our data provide the basis for understanding the nuclear transport process in E. histolytica.

Genome-Wide Identification of a Maize Chitinase Gene Family and the Induction of Its Expression by Fusarium verticillioides (Sacc.) Nirenberg (1976) Infection

Maize chitinases are involved in chitin hydrolysis. Chitinases are distributed across various organisms including animals, plants, and fungi and are grouped into different glycosyl hydrolase families and classes, depending on protein structure. However, many chitinase functions and their interactions with other plant proteins remain unknown. The economic importance of maize (Zea mays L.) makes it relevant for studying the function of plant chitinases and their biological roles. This work aims to identify chitinase genes in the maize genome to study their gene structure, family/class classification, cis-related elements, and gene expression under biotic stress, such as Fusarium verticillioides infection. Thirty-nine chitinase genes were identified and found to be distributed in three glycosyl hydrolase (GH) families (18, 19 and 20). Likewise, the conserved domains and motifs were identified in each GH family member. The identified cis-regulatory elements are involved in plant development, hormone response, defense, and abiotic stress response. Chitinase protein-interaction network analysis predicted that they interact mainly with cell wall proteins. qRT-PCR analysis confirmed in silico data showing that ten different maize chitinase genes are induced in the presence of F. verticillioides, and that they could have several roles in pathogen infection depending on chitinase structure and cell wall localization.

Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology

The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.

CXIP4 depletion causes early lethality and pre-mRNA missplicing in Arabidopsis

Zinc knuckle (ZCCHC) motif-containing proteins are present in unicellular and multicellular eukaryotes and most ZCCHC proteins with known functions participate in the metabolism of various classes of RNA, such as mRNAs, ribosomal RNAs, and microRNAs. The Arabidopsis (Arabidopsis thaliana) genome encodes 69 ZCCHC-containing proteins, but the functions of most remain unclear. One of these proteins is CAX-INTERACTING PROTEIN 4 (CXIP4), which has been classified as a PTHR31437 family member, along with human SREK1-interacting protein 1 (SREK1IP1), which is thought to function in pre-mRNA splicing and RNA methylation. Metazoan SREK1IP1-like and plant CXIP4-like proteins only share a ZCCHC motif, and their functions remain almost entirely unknown. We studied two loss-of-function alleles of Arabidopsis CXIP4, the first mutations in PTHR31437 family genes described to date: cxip4-1 is likely null and shows early lethality, and cxip4-2 is hypomorphic and viable, with pleiotropic morphological defects. The cxip4-2 mutant exhibited deregulation of defense genes and upregulation of transcription factor encoding genes, some of which might explain its developmental defects. This mutant also exhibited increased intron retention events, and the specific functions of misspliced genes, such as those involved in "gene silencing by DNA methylation" and "mRNA polyadenylation factor" suggest that CXIP4 has additional functions. The CXIP4 protein localizes to the nucleus in a pattern resembling nuclear speckles, which are rich in splicing factors. Therefore, CXIP4 is required for plant survival and proper development, and mRNA maturation.

Protein codes promote selective subcellular compartmentalization

Cells have evolved mechanisms to distribute ~10 billion protein molecules to subcellular compartments where diverse proteins involved in shared functions must efficiently assemble. Here, we demonstrate that proteins with shared functions share amino acid sequence codes that guide them to compartment destinations. A protein language model, ProtGPS, was developed that predicts with high performance the compartment localization of human proteins excluded from the training set. ProtGPS successfully guided generation of novel protein sequences that selectively assemble in targeted subcellular compartments. ProtGPS also identified pathological mutations that change this code and lead to altered subcellular localization of proteins. Our results indicate that protein sequences contain not only a folding code, but also a previously unrecognized code governing their distribution in specific cellular compartments.

Deep hybrid modeling of a HEK293 process: Combining long short-term memory networks with first principles equations

The combination of physical equations with deep learning is becoming a promising methodology for bioprocess digitalization. In this paper, we investigate for the first time the combination of long short-term memory (LSTM) networks with first principles equations in a hybrid workflow to describe human embryonic kidney 293 (HEK293) culture dynamics. Experimental data of 27 extracellular state variables in 20 fed-batch HEK293 cultures were collected in a parallel high throughput 250 mL cultivation system in an industrial process development setting. The adaptive moment estimation method with stochastic regularization and cross-validation were employed for deep learning. A total of 784 hybrid models with varying deep neural network architectures, depths, layers sizes and node activation functions were compared. In most scenarios, hybrid LSTM models outperformed classical hybrid Feedforward Neural Network (FFNN) models in terms of training and testing error. Hybrid LSTM models revealed to be less sensitive to data resampling than FFNN hybrid models. As disadvantages, Hybrid LSTM models are in general more complex (higher number of parameters) and have a higher computation cost than FFNN hybrid models. The hybrid model with the highest prediction accuracy consisted in a LSTM network with seven internal states connected in series with dynamic material balance equations. This hybrid model correctly predicted the dynamics of the 27 state variables (R2 = 0.93 in the test data set), including biomass, key substrates, amino acids and metabolic by-products for around 10 cultivation days.

A Review for Artificial Intelligence Based Protein Subcellular Localization

Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer's disease. Knowing where a target protein resides within a cell will give insights into tailored drug design for a disease. As the gold validation standard, the conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, and fluorescent biomarker tags for protein subcellular location identification. However, the booming era of proteomics and high-throughput sequencing generates tons of newly discovered proteins, making protein subcellular localization by wet-lab experiments a mission impossible. To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. In this article, we review the latest advances in AI-based method development in three typical types of approaches, including sequence-based, knowledge-based, and image-based methods. We also elaborately discuss existing challenges and future directions in AI-based method development in this research field.

ML-FGAT: Identification of multi-label protein subcellular localization by interpretable graph attention networks and feature-generative adversarial networks

The prediction of multi-label protein subcellular localization (SCL) is a pivotal area in bioinformatics research. Recent advancements in protein structure research have facilitated the application of graph neural networks. This paper introduces a novel approach termed ML-FGAT. The approach begins by extracting node information of proteins from sequence data, physical-chemical properties, evolutionary insights, and structural details. Subsequently, various evolutionary techniques are integrated to consolidate multi-view information. A linear discriminant analysis framework, grounded on entropy weight, is then employed to reduce the dimensionality of the merged features. To enhance the robustness of the model, the training dataset is augmented using feature-generative adversarial networks. For the primary prediction step, graph attention networks are employed to determine multi-label protein SCL, leveraging both node and neighboring information. The interpretability is enhanced by analyzing the attention weight parameters. The training is based on the Gram-positive bacteria dataset, while validation employs newly constructed datasets: human, virus, Gram-negative bacteria, plant, and SARS-CoV-2. Following a leave-one-out cross-validation procedure, ML-FGAT demonstrates noteworthy superiority in this domain.

PLANT UNCOUPLING MITOCHONDRIAL PROTEIN 2 localizes to the Golgi

In contrast to its close homolog PLANT UNCOUPLING MITOCHONDRIAL PROTEIN 1 (UCP1), which is an abundant carrier protein in the mitochondria, UCP2 localizes to the Golgi.

Improving Signal and Transit Peptide Predictions Using AlphaFold2-predicted Protein Structures

Many proteins contain cleavable signal or transit peptides that direct them to their final subcellular locations. Such peptides are usually predicted from sequence alone using methods such as TargetP 2.0 and SignalP 6.0. While these methods are usually very accurate, we show here that an analysis of a protein's AlphaFold2-predicted structure can often be used to identify false positive predictions. We start by showing that when given a protein's full-length sequence, AlphaFold2 builds experimentally annotated signal and transit peptides in orientations that point away from the main body of the protein. This indicates that AlphaFold2 correctly identifies that a signal is not destined to be part of the mature protein's structure and suggests, as a corollary, that predicted signals that AlphaFold2 folds with high confidence into the main body of the protein are likely to be false positives. To explore this idea, we analyzed predicted signal peptides in 48 proteomes made available in DeepMind's AlphaFold2 database (https://alphafold.ebi.ac.uk). Applying TargetP 2.0 and SignalP 6.0 to the 561,562 proteins in the database results in 95,236 being predicted to contain a cleavable signal or transit peptide. In 95.1% of these cases, the AlphaFold2 structure of the full-length protein is fully consistent with the prediction of TargetP 2.0 or SignalP 6.0. In the remaining 4.9% of cases where the AlphaFold2 structure does not appear consistent with the prediction, the signal is often only predicted with low confidence. The potential false positives identified here may be useful for training even more accurate signal prediction methods.

Gene Stacking and Stoichiometric Expression of ER-Targeted Constructs Using "2A" Self-Cleaving Peptides

Simultaneous stoichiometric expression of multiple genes plays a major part in modern research and biotechnology. Traditional methods for incorporating multiple transgenes (or "gene stacking") have drawbacks such as long time frames, uneven gene expression, gene silencing, and segregation derived from the use of multiple promoters. 2A self-cleaving peptides have emerged over the last two decades as a functional gene stacking method and have been used in plants for the co-expression of multiple genes under a single promoter. Here we describe design features of multicistronic polyproteins using 2A peptides for co-expression in plant cells and targeting to the endoplasmic reticulum (ER). We designed up to quad-cistronic vectors that could target proteins in tandem to the ER. We also exemplify the incorporation of self-excising intein domains within 2A polypeptides, to remove residue additions. These features could aid in the design of stoichiometric protein co-expression strategies in plants in combination with targeting to different subcellular compartments.

CELL-E 2: Translating Proteins to Pictures and Back with a Bidirectional Text-to-Image Transformer

We present CELL-E 2, a novel bidirectional transformer that can generate images depicting protein subcellular localization from the amino acid sequences (and vice versa). Protein localization is a challenging problem that requires integrating sequence and image information, which most existing methods ignore. CELL-E 2 extends the work of CELL-E, not only capturing the spatial complexity of protein localization and produce probability estimates of localization atop a nucleus image, but also being able to generate sequences from images, enabling de novo protein design. We train and finetune CELL-E 2 on two large-scale datasets of human proteins. We also demonstrate how to use CELL-E 2 to create hundreds of novel nuclear localization signals (NLS). Results and interactive demos are featured at https://bohuanglab.github.io/CELL-E_2/.

Alternative splicing variants of stimulator of interferon genes (STING) from Asian seabass (Lates calcarifer) and their immune response against red spotted grouper nervous necrosis virus (RGNNV)

The Stimulator of Interferon Genes (STING, also known as MITA/ERYS/MPYS) is an adaptor molecule that plays a crucial role in the RLR pathway and responds to DNA and RNA viruses. In the present study, we have identified two novel isoforms of STING (the canonical form named as LcSTINGa and its alternative splicing isoform named as LcSTINGb) from teleost Lates calcarifer. LcSTINGa has an ORF of 1230 bp, encoding a 409 amino acid protein, while its alternative splicing variant, LcSTINGb, features an ORF of 987 bp, encoding 328 amino acids. LcSTINGa is predicted to contain four transmembrane helices, whereas LcSTINGb has only two. The Lates STING protein showed about 86.85% identity with Perca flavescens, 86.45% with Seriola and 39.51% with Homo sapiens. The tissue distribution studies revealed that the STING variants were constitutively expressed in all the tissues examined, with the highest expression in blood. In-vivo upregulation of LcSTINGa and LcSTINGb mRNA following immune challenge with poly (I:C), Red-spotted grouper nervous necrosis virus (RGNNV) and zymosan A suggests its significance in the immune response.

Prediction of submitochondrial proteins localization based on Gene Ontology

Mitochondria, which are double-membrane bound organelles commonly found in eukaryotic cells, play a fundamental role as sites for cellular energy production. Within the mitochondria, there exist substructures called submitochondria, and specific proteins associated with submitochondria have been implicated in various human diseases. Therefore, comprehending the precise localization of these submitochondrial proteins is of utmost importance. Such knowledge not only aids in unraveling their role in the pathogenesis of diseases but also facilitates the development of therapeutic drugs and diagnostic methods. In this study, we proposed a novel method based on Gene Ontology (GO) to predict the localization of the submitochondrial proteins, called GO-Submito. More specifically, the GO-Submito fine-tuned pre-training Bidirectional Encoder Representations from Transformers models to encode GO annotations into vectors. Subsequently, the Multi-head Attention Mechanism was employed to fuse these encoded vectors of GO annotations, enabling precise localization prediction. Through comprehensive evaluation, our results demonstrated that GO-Submito outperforms existing methods, offering a reliable and efficient tool for precisely localizing submitochondrial proteins.

Characterization of thraustochytrid-specific sterol O-acyltransferase: modification of DGAT2-like enzyme to increase the sterol production in Aurantiochytrium limacinum mh0186

IMPORTANCE

Since the global market for sterols and vitamin D are grown with a high compound annual growth rate, a sustainable source of these compounds is required to keep up with the increasing demand. Thraustochytrid is a marine oleaginous microorganism that can synthesize several sterols, which are stored as SE in lipid droplets. DGAT2C is an unconventional SE synthase specific to thraustochytrids. Although the primary structure of DGAT2C shows high similarities with that of DGAT, DGAT2C utilizes sterol as an acceptor substrate instead of diacylglycerol. In this study, we examined more detailed enzymatic properties, intracellular localization, and structure-activity relationship of DGAT2C. Furthermore, we successfully developed a method to increase sterol and provitamin D3 productivity of thraustochytrid by more than threefold in the process of elucidating the function of the DGAT2C-specific N-terminal region. Our findings could lead to sustainable sterol and vitamin D production using thraustochytrid.

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.

Disclosing the locale of transmembrane proteins within cellular alcove by machine learning approach: systematic review and meta analysis

Protein subcellular localization is a promising research question in Proteomics and associated fields, including Biological Sciences, Biomedical Engineering, Computational Biology, Bioinformatics, Proteomics, Artificial Intelligence, and Biophysics. However, computational techniques are preferred to explore this attribute for a massive number of proteins. The byproduct of this conjunction yields diversified location identifiers of proteins. These protein subcellular localization identifiers are unique regarding the database used, organisms, Machine Learning Technique, and accuracy. Despite the availability of these identifiers, the majority of the work has been done on the subcellular localization of proteins and, less work has been done specifically on locations of transmembrane proteins. This systematic review accounts for computational techniques implemented on transmembrane protein localization. Moreover, a literature search on PubMed, Science Direct, and IEEE Databases disclosed no systematic review or meta-analysis on the cell's transmembrane protein locale. A Systematic review was formed under the guidelines of PRISMA by using Science Direct, PubMed, and IEEE Databases. Journal publications from 2000 to 2023 were taken into consideration and screened. This review has focused only on computational studies rather than experimental techniques. 1004 studies were reviewed and were categorized as relevant and non-relevant according to inclusion and exclusion criteria. All the screening was done through Endnote after importing citations. This systematic review characterizes the gap in targeting the locale of the transmembrane protein and will aid researchers in exploring its new horizons.Communicated by Ramaswamy H. Sarma.

Characterization of two tripartite motif-containing genes from Asian Seabass Lates calcarifer and their expression in response to virus infection and microbial molecular motifs

OBJECTIVE

We identified two tripartite motif (TRIM) genes, LcTRIM21 and LcTRIM39, from the Asian Seabass Lates calcarifer, and examined their responses to experimental betanodavirus infection and stimulation with microbial pathogen-associated molecular patterns.

METHODS

Genes encoding LcTRIM21 and LcTRIM39 were identified, cloned, and sequenced from the Asian Seabass. We analyzed the sequence using a variety of bioinformatics tools to determine protein structure, localization, and establish a phylogenetic tree. By using quantitative real-time PCR, we analyzed expression profiles of the LcTRIM21 and LcTRIM39 genes in response to betanodavirus challenge as well as molecular pathogen-associated molecular patterns like poly(I:C) and Zymosan A. The tissue distribution pattern of these genes was also examined in healthy animals.

RESULT

Asian Seabass homologues of the TRIM gene, LcTRIM21 and LcTRIM39, were cloned, both encoding proteins with 547 amino acids. LcTRIM21 is predicted to have an isoelectric point of 6.32 and a molecular mass of 62.11 kilodaltons, while LcTRIM39 has an isoelectric point of 5.57 and a molecular mass of 62.11 kilodaltons. LcTRIM21 and LcTRIM39 homologues were predicted to be localized in cytoplasm by in silico protein localization. Structurally, both proteins contain an N-terminal really interesting new gene (RING) zinc-finger domain, B-box domain, coiled-coil domain and C-terminal PRY/SPRY domain. Most tissues and organs examined showed constitutive expression of LcTRIM21 and LcTRIM39. Upon poly(I:C) challenge or red-spotted grouper nervous necrosis virus infection, LcTRIM21 and LcTRIM39 mRNA expression was significantly upregulated, suggesting that they may play a critical antiviral role against fish viruses. LcTRIM21 and LcTRIM39 expression were also upregulated by administration of the glucan Zymosan A.

CONCLUSION

The TRIM-containing gene is an E3 ubiquitin ligase that exhibits antiviral activity by targeting viral proteins via proteasome-mediated ubiquitination. TRIM proteins can be explored for the discovery of antivirals and strategies to combat diseases like viral nervous necrosis, that threaten seabass aquaculture.

The boundary of life and death: changes in mitochondrial and cytosolic proteomes associated with programmed cell death of Arabidopsis thaliana suspension culture cells

Introduction

Despite the critical role of programmed cell death (PCD) in plant development and defense responses, its regulation is not fully understood. It has been proposed that mitochondria may be important in the control of the early stages of plant PCD, but the details of this regulation are currently unknown.

Methods

We used Arabidopsis thaliana cell suspension culture, a model system that enables induction and precise monitoring of PCD rates, as well as chemical manipulation of this process to generate a quantitative profile of the alterations in mitochondrial and cytosolic proteomes associated with early stages of plant PCD induced by heat stress. The cells were subjected to PCD-inducing heat levels (10 min, 54°C), with/without the calcium channel inhibitor and PCD blocker LaCl3. The stress treatment was followed by separation of cytosolic and mitochondrial fractions and mass spectrometry-based proteome analysis.

Results

Heat stress induced rapid and extensive changes in protein abundance in both fractions, with release of mitochondrial proteins into the cytosol upon PCD induction. In our system, LaCl3 appeared to act downstream of cell death initiation signal, as it did not affect the release of mitochondrial proteins, but instead partially inhibited changes occurring in the cytosolic fraction, including upregulation of proteins with hydrolytic activity.

Discussion

We characterized changes in protein abundance and localization associated with the early stages of heat stress-induced PCD. Collectively, the generated data provide new insights into the regulation of cell death and survival decisions in plant cells.

Genome analysis of Cephalotrichum gorgonifer and identification of the biosynthetic pathway for rasfonin, an inhibitor of KRAS dependent cancer

BACKGROUND

Fungi are important sources for bioactive compounds that find their applications in many important sectors like in the pharma-, food- or agricultural industries. In an environmental monitoring project for fungi involved in soil nitrogen cycling we also isolated Cephalotrichum gorgonifer (strain NG_p51). In the course of strain characterisation work we found that this strain is able to naturally produce high amounts of rasfonin, a polyketide inducing autophagy, apoptosis, necroptosis in human cell lines and showing anti-tumor activity in KRAS-dependent cancer cells.

RESULTS

In order to elucidate the biosynthetic pathway of rasfonin, the strain was genome sequenced, annotated, submitted to transcriptome analysis and genetic transformation was established. Biosynthetic gene cluster (BGC) prediction revealed the existence of 22 BGCs of which the majority was not expressed under our experimental conditions. In silico prediction revealed two BGCs with a suite of enzymes possibly involved in rasfonin biosynthesis. Experimental verification by gene-knock out of the key enzyme genes showed that one of the predicted BGCs is indeed responsible for rasfonin biosynthesis.

CONCLUSIONS

This study identified a biosynthetic gene cluster containing a key-gene responsible for rasfonin production. Additionally, molecular tools were established for the non-model fungus Cephalotrichum gorgonifer which allows strain engineering and heterologous expression of the BGC for high rasfonin producing strains and the biosynthesis of rasfonin derivates for diverse applications.

DeepSP: A Deep Learning Framework for Spatial Proteomics

The study of protein subcellular localization (PSL) is a fundamental step toward understanding the mechanism of protein function. The recent development of mass spectrometry (MS)-based spatial proteomics to quantify the distribution of proteins across subcellular fractions provides us a high-throughput approach to predict unknown PSLs based on known PSLs. However, the accuracy of PSL annotations in spatial proteomics is limited by the performance of existing PSL predictors based on traditional machine learning algorithms. In this study, we present a novel deep learning framework named DeepSP for PSL prediction of an MS-based spatial proteomics data set. DeepSP constructs the new feature map of a difference matrix by capturing detailed changes between different subcellular fractions of protein occupancy profiles and uses the convolutional block attention module to improve the prediction performance of PSL. DeepSP achieved significant improvement in accuracy and robustness for PSL prediction in independent test sets and unknown PSL prediction compared to current state-of-the-art machine learning predictors. As an efficient and robust framework for PSL prediction, DeepSP is expected to facilitate spatial proteomics studies and contributes to the elucidation of protein functions and the regulation of biological processes.

MULocDeep web service for protein localization prediction and visualization at subcellular and suborganellar levels

Predicting protein localization and understanding its mechanisms are critical in biology and pathology. In this context, we propose a new web application of MULocDeep with improved performance, result interpretation, and visualization. By transferring the original model into species-specific models, MULocDeep achieved competitive prediction performance at the subcellular level against other state-of-the-art methods. It uniquely provides a comprehensive localization prediction at the suborganellar level. Besides prediction, our web service quantifies the contribution of single amino acids to localization for individual proteins; for a group of proteins, common motifs or potential targeting-related regions can be derived. Furthermore, the visualizations of targeting mechanism analyses can be downloaded for publication-ready figures. The MULocDeep web service is available at https://www.mu-loc.org/.

Immunology: Meta-learning for T cell-receptor binding specificity and beyond

Predicting whether T-cell receptors bind to specific peptides is a challenging problem as the majority of binding examples in the training data involves only a few peptides. A new approach employs meta-learning to improve predictions for binding to peptides for which no or little binding data exists.

Phylogenetic trees, conserved motifs and predicted subcellular localization for transcription factor families in pearl millet

OBJECTIVES

Pearl millet (Pennisetum glaucum) is a cereal crop that is tolerant to a high temperature, a drought and a nutrient-poor condition. Characterizing pearl millet proteins can help to improve productivity of pearl millet and other crops. Transcription factors in general are proteins that regulate transcription of their target genes and thereby regulate diverse processes. Some transcription factor families in pearl millet were characterized in previous studies, but most of them are not. The objective of the data presented was to characterize amino acid sequences for most transcription factors in pearl millet.

DATA DESCRIPTION

Sequences of 2395 pearl millet proteins that have transcription factor-associated domains were extracted. Subcellular and suborganellar localization of these proteins was predicted by MULocDeep. Conserved domains in these sequences were confirmed by CD-Search. These proteins were classified into 85 families on the basis of those conserved domains. A phylogenetic tree including pearl millet proteins and their counterparts in Arabidopsis thaliana and rice was constructed for each of these families. Sequence motifs were identified by MEME for each of these families.

The Intracellular and Secreted Sides of Osteopontin and Their Putative Physiopathological Roles

Classically, osteopontin (OPN) has been described as a secreted glycophosprotein. Indeed, most data concerning its physiological and pathological roles are mainly related to the secreted OPN (sOPN). However, there are several instances in which intracellular OPN (iOPN) has been described, presenting some specific roles in distinct experimental models, such as in the immune system, cancer cells, and neurological disorders. We herein aimed to highlight and discuss some of these secreted and intracellular roles of OPN and their putative clinical and biological impacts. Moreover, by consolidating data from the OPN protein database, we also analyzed the occurrence of signal peptide (SP) sequences and putative subcellular localization, especially concerning currently known OPN splicing variants (OPN-SV). Comprehending the roles of OPN in its distinct cellular and tissue environments may provide data regarding the additional applications of this protein as biomarkers and targets for therapeutic purposes, besides further describing its pleiotropic roles.

An Interplay between Mitochondrial and ER Targeting of a Bacterial Signal Peptide in Plants

Protein targeting is essential in eukaryotic cells to maintain cell function and organelle identity. Signal peptides are a major type of targeting sequences containing a tripartite structure, which is conserved across all domains in life. They are frequently included in recombinant protein design in plants to increase yields by directing them to the endoplasmic reticulum (ER) or apoplast. The processing of bacterial signal peptides by plant cells is not well understood but could aid in the design of efficient heterologous expression systems. Here we analysed the signal peptide of the enzyme PmoB from methanotrophic bacteria. In plant cells, the PmoB signal peptide targeted proteins to both mitochondria and the ER. This dual localisation was still observed in a mutated version of the signal peptide sequence with enhanced mitochondrial targeting efficiency. Mitochondrial targeting was shown to be dependent on a hydrophobic region involved in transport to the ER. We, therefore, suggest that the dual localisation could be due to an ER-SURF pathway recently characterised in yeast. This work thus sheds light on the processing of bacterial signal peptides by plant cells and proposes a novel pathway for mitochondrial targeting in plants.

Structure based functional annotation of a MYND-less lysine methyl transferase inCandida albicans

Candida albicans is opportunistic pathogenic yeast that is widely distributed throughout the world and is classified as the most critical fungal pathogen group. Candida albicans is a common microbiota of healthy individuals but can cause superficial and invasive infections in immune compromised individuals. Protein Post-translational modifications involving methylation of lysine amino acids stand for a major regulator of eukaryotic transcription, and pathways controlling several cellular processes. SMYD makes up a SET (Su (Var) 3-9, Enhancer-of-zeste and Trithorax) and MYND (Myeloid, Nervy, and DEAF-1) domain containing lysine methyl transferase subfamily that transfers methyl groups from methyl donors onto lysine residues in histones (H3 and H4) and non-histone proteins. The SET domain is the methyltransferase catalytic domain, while MYND participates in both protein and DNA interactions. Well-studied examples of SMYD proteins are five human and two Saccharomyces cerevisiae, constituting examples of histone and non-histone protein lysine methyl transferase members. However, there is limited understanding of SET lysine methyltransferases, including the SMYD subfamily, in the pathogenic fungi Candida albicans. Using bioinformatics tools, we characterized the SMYD domain containing proteins in the important pathogen. We report the presence of an atypical SMYD member (CaO19.3863) as a new lysine methyltransferase that can be a target for antifungal therapy.

DsDBF1, a Type A-5 DREB Gene, Identified and Characterized in the Moss Dicranum scoparium

Plant dehydration-responsive element binding (DREB) transcription factors (TFs) play important roles during stress tolerance by regulating the expression of numerous genes involved in stresses. DREB TFs have been extensively studied in a variety of angiosperms and bryophytes. To date, no information on the identification and characterization of DREB TFs in Dicranum scoparium has been reported. In this study, a new DBF1 gene from D. scoparium was identified by cloning and sequencing. Analysis of the conserved domain and physicochemical properties revealed that DsDBF1 protein has a classic AP2 domain encoding a 238 amino acid polypeptide with a molecular mass of 26 kDa and a pI of 5.98. Subcellular prediction suggested that DsDBF1 is a nuclear and cytoplasmic protein. Phylogenetic analysis showed that DsDBF1 belongs to group A-5 DREBs. Expression analysis by reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) revealed that DsDBF1 was significantly upregulated in response to abiotic stresses such as desiccation/rehydration, exposure to paraquat, CdCl₂, high and freezing temperatures. Taken together, our data suggest that DsDBF1 could be a promising gene candidate to improve stress tolerance in crop plants, and the characterization of TFs of a stress tolerant moss such as D. scoparium provides a better understanding of plant adaptation mechanisms.

Biological Sequence Classification: A Review on Data and General Methods

With the rapid development of biotechnology, the number of biological sequences has grown exponentially. The continuous expansion of biological sequence data promotes the application of machine learning in biological sequences to construct predictive models for mining biological sequence information. There are many branches of biological sequence classification research. In this review, we mainly focus on the function and modification classification of biological sequences based on machine learning. Sequence-based prediction and analysis are the basic tasks to understand the biological functions of DNA, RNA, proteins, and peptides. However, there are hundreds of classification models developed for biological sequences, and the quite varied specific methods seem dizzying at first glance. Here, we aim to establish a long-term support website (http://lab.malab.cn/~acy/BioseqData/home.html), which provides readers with detailed information on the classification method and download links to relevant datasets. We briefly introduce the steps to build an effective model framework for biological sequence data. In addition, a brief introduction to single-cell sequencing data analysis methods and applications in biology is also included. Finally, we discuss the current challenges and future perspectives of biological sequence classification research.

Protein Subcellular Localization Prediction Model Based on Graph Convolutional Network

Protein subcellular localization prediction is an important research area in bioinformatics, which plays an essential role in understanding protein function and mechanism. Many machine learning and deep learning algorithms have been employed for this task, but most of them do not use structural information of proteins. With the advances in protein structure research in recent years, protein contact map prediction has been dramatically enhanced. In this paper, we present GraphLoc, a deep learning model that predicts the localization of proteins at the subcellular level. The cores of the model are a graph convolutional neural network module and a multi-head attention module. The protein topology graph is constructed based on a contact map predicted from protein sequences, which is used as the input of the GCN module to take full advantage of the structural information of proteins. Multi-head attention module learns the weighted contribution of different amino acids to subcellular localization in different feature representation subspaces. Experiments on the benchmark dataset show that the performance of our model is better than others. The code can be accessed at https://github.com/GoodGuy398/GraphLoc . The proposed GraphLoc model consists of three parts. The first part is a graph convolutional network (GCN) module, which utilizes the predicted contact maps to construct protein graph, taking benefit of protein information accordingly. The second part is the multi-head attention module, which learns the weighted contribution of different amino acids in different feature representation subspace, and weighted average the feature map across all amino acid nodes. The last part is a fully connected layer that maps the flatten graph representation vector to another vector with a category number dimension, followed by a softmax layer to predict the protein subcellular localization.

Antiviral radical SAM enzyme viperin homologue from Asian seabass (Lates calcarifer): Molecular characterisation and expression analysis

The host response to virus infection is mediated by the interferon system and its workhorse effector proteins like Interferon-stimulated genes (ISGs). Viperin is an interferon-inducible antiviral protein. In the present study, an antiviral radical SAM enzyme, viperin homologue, was cloned and characterised from teleost, Asian seabass (Lates calcarifer). This cloned viperin cDNA encodes 351 amino acid protein with predicted N-terminal amphipathic alpha-helix, conserved radical S-adenosyl l-methionine (SAM) domain with CxxxCxxC motif and a highly conserved C-terminal domain. Lcviperin gene consists of six exons and five introns. The secondary structure contains nine alpha helices and beta sheets. Viperin from Lates is evolutionarily conserved and shares about 89% identity with Seriola dumerili and 70% identity with human orthologue. Poly(I:C) and RGNNV upregulated Lcviperin during in-vivo challenge studies, providing insight into its antiviral properties. Lates antiviral effector genes like viperin could help in elucidating the host-virus protein interactions and allow the development of improved antiviral strategies against pathogens like betanodavirus that devastate aquaculture of the species.