Mining for missed sORF-encoded peptides

Quantitative estimates of the regulatory influence of long non-coding RNAs on global gene expression variation using TCGA breast cancer transcriptomic data

Long non-coding RNAs (lncRNAs) have received attention in recent years for their regulatory roles in diverse biological contexts including cancer, yet large gaps remain in our understanding of their mechanisms and global maps of their targets. In this work, we investigated a basic unanswered question of lncRNA systems biology: to what extent can gene expression variation across individuals be attributed to lncRNA-driven regulation? To answer this, we analyzed RNA-seq data from a cohort of breast cancer patients, explaining each gene's expression variation using a small set of automatically selected lncRNA regulators. A key aspect of this analysis is that it accounts for confounding effects of transcription factors (TFs) as common regulators of a lncRNA-mRNA pair, to enrich the explained gene expression for lncRNA-mediated regulation. We found that for 16% of analyzed genes, lncRNAs can explain more than 20% of expression variation. We observed 25-50% of the putative regulator lncRNAs to be in 'cis' to, i.e., overlapping or located proximally to the target gene. This led us to quantify the global regulatory impact of such cis-located lncRNAs, which was found to be substantially greater than that of trans-located lncRNAs. Additionally, by including statistical interaction terms involving lncRNA-protein pairs as predictors in our regression models, we identified cases where a lncRNA's regulatory effect depends on the presence of a TF or RNA-binding protein. Finally, we created a high-confidence lncRNA-gene regulatory network whose edges are supported by co-expression as well as a plausible mechanism such as cis-action, protein scaffolding or competing endogenous RNAs. Our work is a first attempt to quantify the extent of gene expression control exerted globally by lncRNAs, especially those located proximally to their regulatory targets, in a specific biological (breast cancer) context. It also marks a first step towards systematic reconstruction of lncRNA regulatory networks, going beyond the current paradigm of co-expression networks, going beyond the current paradigm of co-expression networks, and motivates future analyses assessing the generalizability of our findings to additional biological contexts.

Microenzymes: Is There Anybody Out There?

Biological macromolecules are found in different shapes and sizes. Among these, enzymes catalyze biochemical reactions and are essential in all organisms, but is there a limit size for them to function properly? Large enzymes such as catalases have hundreds of kDa and are formed by multiple subunits, whereas most enzymes are smaller, with molecular weights of 20-60 kDa. Enzymes smaller than 10 kDa could be called microenzymes and the present literature review brings together evidence of their occurrence in nature. Additionally, bioactive peptides could be a natural source for novel microenzymes hidden in larger peptides and molecular downsizing could be useful to engineer artificial enzymes with low molecular weight improving their stability and heterologous expression. An integrative approach is crucial to discover and determine the amino acid sequences of novel microenzymes, together with their genomic identification and their biochemical biological and evolutionary functions.

Exploring the Potential of Bioactive Peptides: From Natural Sources to Therapeutics

Bioactive peptides, specific protein fragments with positive health effects, are gaining traction in drug development for advantages like enhanced penetration, low toxicity, and rapid clearance. This comprehensive review navigates the intricate landscape of peptide science, covering discovery to functional characterization. Beginning with a peptidomic exploration of natural sources, the review emphasizes the search for novel peptides. Extraction approaches, including enzymatic hydrolysis, microbial fermentation, and specialized methods for disulfide-linked peptides, are extensively covered. Mass spectrometric analysis techniques for data acquisition and identification, such as liquid chromatography, capillary electrophoresis, untargeted peptide analysis, and bioinformatics, are thoroughly outlined. The exploration of peptide bioactivity incorporates various methodologies, from in vitro assays to in silico techniques, including advanced approaches like phage display and cell-based assays. The review also discusses the structure-activity relationship in the context of antimicrobial peptides (AMPs), ACE-inhibitory peptides (ACEs), and antioxidative peptides (AOPs). Concluding with key findings and future research directions, this interdisciplinary review serves as a comprehensive reference, offering a holistic understanding of peptides and their potential therapeutic applications.

Identification of subgenomic mRNAs derived from the coronavirus 1a/1b protein gene: Implications for coronavirus transcription

Synthesis of coronavirus subgenomic mRNA (sgmRNA) is guided by the transcription regulatory sequence (TRS). sgmRNA derived from the body TRS (TRS-B) located at the 1a/1b protein gene is designated 1ab/sgmRNA. In the current study, we comprehensively identified the 1ab/sgmRNAs synthesized from TRS-Bs located at the 1a/1b protein genes of different coronavirus genera both in vitro and in vivo by RT‒PCR and sequencing. The results suggested that the degree of sequence homology between the leader TRS (TRS-L) and TRS-B may not be a decisive factor for 1ab/sgmRNA synthesis. This observation led us to revisit the coronavirus transcription mechanism and to propose that the disassociation of coronavirus polymerase from the viral genome may be a prerequisite for sgmRNA synthesis. Once the polymerase can disassociate at TRS-B, the sequence homology between TRS-L and TRS-B is important for sgmRNA synthesis. The study therefore extends our understanding of transcription mechanisms.

LncHLEF promotes hepatic lipid synthesis through miR-2188-3p/GATA6 axis and encoding peptides and enhances intramuscular fat deposition via exosome

Long noncoding RNAs (lncRNAs) have emergingly been implicated in mammalian lipid metabolism. However, their biological functions and regulatory mechanisms underlying adipogenesis remain largely elusive in chicken. Here, we systematically characterized the genome-wide full-length lncRNAs in the livers of pre- and peak-laying hens, and identified a novel intergenic lncRNA, lncHLEF, an RNA macromolecule with a calculated molecular weight of 433 kDa. lncHLEF was primarily distributed in cytoplasm of chicken hepatocyte and significantly up-regulated in livers of peak-laying hens. Functionally, lncHLEF could promote hepatocyte lipid droplet formation, triglycerides and total cholesterol contents. Mechanistically, lncHLEF could not only serve as a competitive endogenous RNA to modulate miR-2188-3p/GATA6 axis, but also encode three small functional polypeptides that directly interact with ACLY protein to enable its stabilization. Importantly, adeno-associated virus-mediated liver-specific lncHLEF overexpression resulted in increased hepatic lipid synthesis and intramuscular fat (IMF) deposition, but did not alter abdominal fat (AbF) deposition. Furthermore, hepatocyte lncHLEF could be delivered into intramuscular and abdominal preadipocytes via hepatocyte-secreted exosome to enhance intramuscular preadipocytes differentiation without altering abdominal preadipocytes differentiation. In conclusion, this study revealed that the lncHLEF could promote hepatic lipid synthesis through two independent regulatory mechanisms, and could enhance IMF deposition via hepatocyte-adipocyte communications mediated by exosome.

RNAcode_Web - Convenient identification of evolutionary conserved protein coding regions

The differentiation of regions with coding potential from non-coding regions remains a key task in computational biology. Methods such as RNAcode that exploit patterns of sequence conservation for this task have a substantial advantage in classification accuracy in particular for short coding sequences, compared to methods that rely on a single input sequence. However, they require sequence alignments as input. Frequently, suitable multiple sequence alignments are not readily available and are tedious, and sometimes difficult to construct. We therefore introduce here a new web service that provides access to the well-known coding sequence detector RNAcode with minimal user overhead. It requires as input only a single target nucleotide sequence. The service automates the collection, selection, and preparation of homologous sequences from the NCBI database, as well as the construction of the multiple sequence alignment that are needed as input for RNAcode. The service automatizes the entire pre- and postprocessing and thus makes the investigation of specific genomic regions for previously unannotated coding regions, such as small peptides or additional introns, a simple task that is easily accessible to non-expert users. RNAcode_Web is accessible online at rnacode.bioinf.uni-leipzig.de.

The cytosolic lncRNA dutA affects STATa signaling and developmental commitment in Dictyostelium

STATa is a pivotal transcription factor for Dictyostelium development. dutA is the most abundant RNA transcribed by RNA polymerase II in Dictyostelium, and its functional interplay with STATa has been suggested. This study demonstrates that dutA RNA molecules are distributed as spot-like structures in the cytoplasm, and that its cell type-specific expression changes dramatically during development. dutA RNA was exclusively detectable in the prespore region of slugs and then predominantly localized in prestalk cells, including the organizer region, at the Mexican hat stage before most dutA transcripts, excluding those in prestalk O cells, disappeared as culmination proceeded. dutA RNA was not translated into small peptides from any potential open reading frame, which confirmed that it is a cytoplasmic lncRNA. Ectopic expression of dutA RNA in the organizer region of slugs caused a prolonged slug migration period. In addition, buffered suspension-cultured cells of the strain displayed reduced STATa nuclear translocation and phosphorylation on Tyr702. Analysis of gene expression in various dutA mutants revealed changes in the levels of several STATa-regulated genes, such as the transcription factors mybC and gtaG, which might affect the phenotype. dutA RNA may regulate several mRNA species, thereby playing an indirect role in STATa activation.

LINC00998-encoded micropeptide SMIM30 promotes the G1/S transition of cell cycle by regulating cytosolic calcium level

The biological functions of short open reading frame (sORF)-encoded micropeptides remain largely unknown. Here, we report that LINC00998, a previously annotated lncRNA, was upregulated in multiple cancer types and the sORF on LINC00998 encoded a micropeptide named SMIM30. SMIM30 was localized in the membranes of the endoplasmic reticulum (ER) and mitochondria. Silencing SMIM30 inhibited the proliferation of hepatoma cells in vitro and suppressed the growth of tumor xenografts and N-nitrosodiethylamine-induced hepatoma. Overexpression of the 5'UTR-sORF sequence of LINC00998, encoding wild-type SMIM30, enhanced tumor cell growth, but this was abolished when a premature stop codon was introduced into the sORF via single-base deletion. Gain- and loss-of-function studies revealed that SMIM30 peptide but not LINC00998 reduced cytosolic calcium level, increased CDK4, cyclin E2, phosphorylated-Rb and E2F1, and promoted the G1/S phase transition and cell proliferation. The effect of SMIM30 silencing was attenuated by a calcium chelator or the agonist of sarco/endoplasmic reticulum calcium ATPase (SERCA) pump. These findings suggest a novel function of micropeptide SMIM30 in promoting G1/S transition and cell proliferation by enhancing SERCA activity and reducing cytosolic calcium level.

Mitochondrion-located peptides and their pleiotropic physiological functions

With the development of advanced technologies, many small open reading frames (sORFs) have been found to be translated into micropeptides. Interestingly, a considerable proportion of micropeptides are located in mitochondria, which are designated here as mitochondrion-located peptides (MLPs). These MLPs often contain a transmembrane domain and show a high degree of conservation across species. They usually act as co-factors of large proteins and play regulatory roles in mitochondria such as electron transport in the respiratory chain, reactive oxygen species (ROS) production, metabolic homeostasis, and so on. Deficiency of MLPs disturbs diverse physiological processes including immunity, differentiation, and metabolism both in vivo and in vitro. These findings reveal crucial functions for MLPs and provide fresh insights into diverse mitochondrion-associated biological processes and diseases.

Identification of Differentially Expressed Intronic Transcripts in Osteosarcoma

Over the past decade; the discovery and characterization of long noncoding RNAs (lncRNAs) have revealed that they play a major role in the development of various diseases; including cancer. Intronic transcripts are one of the most fascinating lncRNAs that are located within intron regions of protein-coding genes, which have the advantage of encoding micropeptides. There have been several studies looking at intronic transcript expression profiles in cancer; but almost none in osteosarcoma. To overcome this problem; we have investigated differentially expressed intronic transcripts between osteosarcoma and normal bone tissues. The results highlighted that NRG1-IT1; FGF14-IT1; and HAO2-IT1 were downregulated; whereas ER3-IT1; SND1-IT1; ANKRD44-IT1; AGAP1-IT1; DIP2A-IT1; LMO7DN-IT1; SLIT2-IT1; RNF216-IT1; and TCF7L1-IT1 were upregulated in osteosarcoma tissues compared to normal bone tissues. Furthermore, we identified if the transcripts encode micropeptides and the transcripts' locations in a cell.

Probing the sORF-Encoded Peptides of Deinococcus radiodurans in Response to Extreme Stress

Organisms have developed different mechanisms to respond to stresses. However, the roles of small ORF-encoded peptides (SEPs) in these regulatory systems remain elusive, which is partially because of the lack of comprehensive knowledge regarding these biomolecules. We chose the extremophile Deinococcus radiodurans R1 as a model species and conducted large-scale profiling of the SEPs related to the stress response. The integrated workflow consisting of multiple omics approaches for SEP identification was streamlined, and an SEPome of D. radiodurans containing 109 novel and high-confidence SEPs was drafted. Forty-four percent of these SEPs were predicted to function as antimicrobial peptides. Quantitative peptidomics analysis indicated that the expression of SEP068184 was upregulated upon oxidative treatment and gamma irradiation of the bacteria. SEP068184 was conserved in Deinococcus and exhibited negative regulation of oxidative stress resistance in a comparative phenotypic assay of its mutants. Further quantitative and interactive proteomics analyses suggested that SEP068184 might function through metabolic pathways and interact with cytoplasmic proteins. Collectively, our findings demonstrate that SEPs are involved in the regulation of oxidative resistance, and the SEPome dataset provides a rich resource for research on the molecular mechanisms of the response to extreme stress in organisms.

Identifying LncRNA-Encoded Short Peptides Using Optimized Hybrid Features and Ensemble Learning

Long non-coding RNA (lncRNA) contains short open reading frames (sORFs), and sORFs-encoded short peptides (SEPs) have become the focus of scientific studies due to their crucial role in life activities. The identification of SEPs is vital to further understanding their regulatory function. Bioinformatics methods can quickly identify SEPs to provide credible candidate sequences for verifying SEPs by biological experimenrts. However, there is a lack of methods for identifying SEPs directly. In this study, a machine learning method to identify SEPs of plant lncRNA (ISPL) is proposed. Hybrid features including sequence features and physicochemical features are extracted manually or adaptively to construct different modal features. In order to keep the stability of feature selection, the non-linear correction applied in Max-Relevance-Max-Distance (nocRD) feature selection method is proposed, which integrates multiple feature ranking results and uses the iterative random forest for different modal features dimensionality reduction. Classification models with different modal features are constructed, and their outputs are combined for ensemble classification. The experimental results show that the accuracy of ISPL is 89.86% percent on the independent test set, which will have important implications for further studies of functional genomic.

Message in hand: the application of CRISPRi, RNAi, and LncRNA in adenocarcinoma

Gene editing interference technology has been flourishing for more than 30 years. It has always been a common means to interfere with the expression of particular genes. Today it has shown a broad application prospect in clinical treatment, especially in adenocarcinoma treatment. In just a few years, the CRISPRi technology has attracted much z attention with its precise targeting and convenient operability significantly promoted the transformation from bench to bedside, and won the Nobel Prize in Chemistry 2020. In recent years, the importance of non-coding RNA has led LncRNA research to the center. At the same time, it also recalls the surprises obtained in laboratory and clinic research by RNAi technologies such as microRNA, siRNA, and shRNA at the beginning of the century. Therefore, this article focuses on CRISPRi, RNAi, and LncRNA to review their gene interference mechanisms currently expected to be translational research. Their applications and differences in adenocarcinoma research will also be described powerfully. It will provide a helpful reference for scientists to understand better and apply several RNA interference technologies.

False discovery rate: the Achilles' heel of proteogenomics

Proteogenomics refers to the integrated analysis of the genome and proteome that leverages mass-spectrometry (MS)-based proteomics data to improve genome annotations, understand gene expression control through proteoforms and find sequence variants to develop novel insights for disease classification and therapeutic strategies. However, proteogenomic studies often suffer from reduced sensitivity and specificity due to inflated database size. To control the error rates, proteogenomics depends on the target-decoy search strategy, the de-facto method for false discovery rate (FDR) estimation in proteomics. The proteogenomic databases constructed from three- or six-frame nucleotide database translation not only increase the search space and compute-time but also violate the equivalence of target and decoy databases. These searches result in poorer separation between target and decoy scores, leading to stringent FDR thresholds. Understanding these factors and applying modified strategies such as two-pass database search or peptide-class-specific FDR can result in a better interpretation of MS data without introducing additional statistical biases. Based on these considerations, a user can interpret the proteogenomics results appropriately and control false positives and negatives in a more informed manner. In this review, first, we briefly discuss the proteogenomic workflows and limitations in database construction, followed by various considerations that can influence potential novel discoveries in a proteogenomic study. We conclude with suggestions to counter these challenges for better proteogenomic data interpretation.

Small open reading frames in plant research: from prediction to functional characterization

Gene prediction is a laborious and time-consuming task. The advancement of sequencing technologies and bioinformatics tools, coupled with accelerated rate of ribosome profiling and mass spectrometry development, have made identification of small open reading frames (sORFs) (< 100 codons) in various plant genomes possible. The past 50 years have seen sORFs being isolated from many organisms. However, to date, a comprehensive sORF annotation pipeline is as yet unavailable, hence, addressed in our review. Here, we also provide current information on classification and functions of plant sORFs and their potential applications in crop improvement programs.

Exploring the Peptide Potential of Genomes

Recent studies attribute a central role to the noncoding genome in the emergence of novel genes. The widespread transcription of noncoding regions and the pervasive translation of the resulting RNAs offer to the organisms a vast reservoir of novel peptides. Although the majority of these peptides are anticipated as deleterious or neutral, and thereby expected to be degraded right away or short-lived in evolutionary history, some of them can confer an advantage to the organism. The latter can be further subjected to natural selection and be established as novel genes. In any case, characterizing the structural properties of these pervasively translated peptides is crucial to understand (1) their impact on the cell and (2) how some of these peptides, derived from presumed noncoding regions, can give rise to structured and functional de novo proteins. Therefore, we present a protocol that aims to explore the potential of a genome to produce novel peptides. It consists in annotating all the open reading frames (ORFs) of a genome (i.e., coding and noncoding ones) and characterizing the fold potential and other structural properties of their corresponding potential peptides. Here, we apply our protocol to a small genome and show how to apply it to very large genomes. Finally, we present a case study which aims to probe the fold potential of a set of 721 translated ORFs in mouse lncRNAs, identified with ribosome profiling experiments. Interestingly, we show that the distribution of their fold potential is different from that of the nontranslated lncRNAs and more generally from the other noncoding ORFs of the mouse.

Advances in the identification of long non-coding RNA binding proteins

Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nt without evident protein coding function. They play important regulatory roles in many biological processes, e.g., gene regulation, chromatin remodeling, and cell fate determination during development. Dysregulation of lncRNAs has been observed in various diseases including cancer. Interacting with proteins is a crucial way for lncRNAs to play their biological roles. Therefore, the characterization of lncRNA binding proteins is important to understand their functions and to delineate the underlying molecular mechanism. Large-scale studies based on mass spectrometry have characterized over a thousand new RNA binding proteins without known RNA-binding domains, thus revealing the complexity and diversity of RNA-protein interactions. In addition, several methods have been developed to identify the binding proteins for particular RNAs of interest. Here we review the progress of the RNA-centric methods for the identification of RNA-protein interactions, focusing on the studies involving lncRNAs, and discuss their strengths and limitations.

LncRNA-Encoded Short Peptides Identification Using Feature Subset Recombination and Ensemble Learning

Long non-coding RNA (lncRNA), which is a type of non-coding RNA, was reported to contain short open reading frames (sORFs). SORFs-encoded short peptides (SEPs) have been demonstrated to play a crucial role in regulating the biological processes such as growth, development, and resistance response. The identification of SEPs is vital to further understanding their function. However, there is still a lack of methods for identifying SEPs effectively and rapidly. In this study, a novel method for lncRNA-encoded short peptides identification based on feature subset recombination and ensemble learning, lncPepid, is developed. lncPepid transforms the data of Zea mays and Arabidopsis thaliana into hybrid features from two aspects including sequence composition and physicochemical properties separately. It optimizes hybrid features by proposing a novel weighted iteration-based feature selection method to recombine a stable subset that characterizes SEPs effectively. Different classification models with different optimized features are constructed and tested separately. The outputs of the optimal models are integrated for ensemble classification to improve efficiency. Experimental results manifest that the geometric mean of sensitivity and specificity of lncPepid is about 70% on the identification of functional SEPs derived from multiple species. It is an effective and rapid method for the identification of lncRNA-encoded short peptides. This study can be extended to the research on SEPs from other species and have crucial implications for further findings and studies of functional genomics.

Improved Identification of Small Open Reading Frames Encoded Peptides by Top-Down Proteomic Approaches and De Novo Sequencing

Small open reading frames (sORFs) have translational potential to produce peptides that play essential roles in various biological processes. Nevertheless, many sORF-encoded peptides (SEPs) are still on the prediction level. Here, we construct a strategy to analyze SEPs by combining top-down and de novo sequencing to improve SEP identification and sequence coverage. With de novo sequencing, we identified 1682 peptides mapping to 2544 human sORFs, which were all first characterized in this work. Two-thirds of these new sORFs have reading frame shifts and use a non-ATG start codon. The top-down approach identified 241 human SEPs, with high sequence coverage. The average length of the peptides from the bottom-up database search was 19 amino acids (AA); from de novo sequencing, it was 9 AA; and from the top-down approach, it was 25 AA. The longer peptide positively boosts the sequence coverage, more efficiently distinguishing SEPs from the known gene coding sequence. Top-down has the advantage of identifying peptides with sequential K/R or high K/R content, which is unfavorable in the bottom-up approach. Our method can explore new coding sORFs and obtain highly accurate sequences of their SEPs, which can also benefit future function research.

An overview on miRNA-encoded peptides in plant biology research

MicroRNAs (miRNAs) are short (21-23 nt) regulatory RNA molecules present in plants and animals which are known for regulating the mRNA target gene expression either by cleavage or translational repression. With the advancements in miRNAs research in plants towards their biogenesis and applications has directed the recent discovery of pri-miRNAs encoding functional peptides or microRNA peptides (miPEPs). These miPEPs are encoded by 5' of pri-miRs containing short ORFs (miORFs). miPEPs are known to enhance the activity of their associated miRNAs by increasing their accumulation and hence downregulating the target genes. Since miPEPs are very specific for each miRNA, they are considered as novel and effective tools for improving traits of interest for plant growth promotion and plant-microbe interaction. Entire peptidome research is the need of the hour. This review thus summarizes recent advancements in miPEP research and its applications as a technology with important agronomical implications with miRNAs augmentation.

ORFLine: a bioinformatic pipeline to prioritise small open reading frames identifies candidate secreted small proteins from lymphocytes

MOTIVATION

The annotation of small open reading frames (smORFs) of less than 100 codons (<300 nucleotides) is challenging due to the large number of such sequences in the genome.

RESULTS

In this study, we developed a computational pipeline, which we have named ORFLine, that stringently identifies smORFs and classifies them according to their position within transcripts. We identified a total of 5744 unique smORFs in datasets from mouse B and T lymphocytes and systematically characterized them using ORFLine. We further searched smORFs for the presence of a signal peptide, which predicted known secreted chemokines as well as novel micropeptides. Four novel micropeptides show evidence of secretion and are therefore candidate mediators of immunoregulatory functions.

AVAILABILITY

Freely available on the web at https://github.com/boboppie/ORFLine.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The role of micropeptides in biology

Micropeptides are small polypeptides coded by small open-reading frames. Progress in computational biology and the analyses of large-scale transcriptomes and proteomes have revealed that mammalian genomes produce a large number of transcripts encoding micropeptides. Many of these have been previously annotated as long noncoding RNAs. The role of micropeptides in cellular homeostasis maintenance has been demonstrated. This review discusses different types of micropeptides as well as methods to identify them, such as computational approaches, ribosome profiling, and mass spectrometry.

Full-Length Transcriptome Analysis of Plasmodium falciparum by Single-Molecule Long-Read Sequencing

Malaria, an infectious disease caused by Plasmodium parasites, still accounts for amounts of deaths annually in last decades. Despite the significance of Plasmodium falciparum as a model organism of malaria parasites, our understanding of gene expression of this parasite remains largely elusive since lots of progress on its genome and transcriptome are based on assembly with short sequencing reads. Herein, we report the new version of transcriptome dataset containing all full-length transcripts over the whole asexual blood stages by adopting a full-length sequencing approach with optimized experimental conditions of cDNA library preparation. We have identified a total of 393 alternative splicing (AS) events, 3,623 long non-coding RNAs (lncRNAs), 1,555 alternative polyadenylation (APA) events, 57 transcription factors (TF), 1,721 fusion transcripts in P. falciparum. Furthermore, the shotgun proteome was performed to validate the full-length transcriptome of P. falciparum. More importantly, integration of full-length transcriptomic and proteomic data identified 160 novel small proteins in lncRNA regions. Collectively, this full-length transcriptome dataset with high quality and accuracy and the shotgun proteome analyses shed light on the complex gene expression in malaria parasites and provide a valuable resource for related functional and mechanistic researches on P. falciparum genes.

A Mini-review of Computational Approaches to Predict Functions and Findings of Novel Micro Peptides

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New techniques in bioinformatics and the study of the transcriptome at a wide-scale have uncovered the fact that a large part of the genome is being translated than recently perceived thoughts and research, bringing about the creation of a various quantity of RNA with proteincoding and noncoding potential. A lot of RNA particles have been considered as noncoding due to many reasons, according to developing proofs. Like many sORFs that encode many functional micro peptides have neglected due to their tiny sizes.

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Advanced studies reveal many major biological functions of these sORFs and their encoded micro peptides in a different and wide range of species. All the achievement in the identification of these sORFs and micro peptides is due to the progressive bioinformatics and high-throughput sequencing methods. This field has pulled in more consideration due to the detection of a large number of more sORFs and micro peptides. Nowadays, COVID-19 grabs all the attention of science as it is a sudden outbreak. sORFs of COVID-19 should be revealed for new ways to understand this virus. This review discusses ongoing progress in the systems for the identification and distinguishing proof of sORFs and micro peptides.

LncRNA-Encoded Peptide: Functions and Predicting Methods

Long non-coding RNA (lncRNA) was originally defined as the representative of the non-coding RNAs and unable to encode. However, recent reports suggest that some lncRNAs actually contain open reading frames that encode peptides. These coding products play important roles in the pathogenesis of many diseases. Here, we summarize the regulatory pathways of mammalian lncRNA-encoded peptides in influencing muscle function, mRNA stability, gene expression, and so on. We also address the promoting and inhibiting functions of the peptides in different cancers and other diseases. Then we introduce the computational predicting methods and data resources to predict the coding ability of lncRNA. The intention of this review is to provide references for further coding research and contribute to reveal the potential prospects for targeted tumor therapy.

K27-Linked Diubiquitin Inhibits UCHL3 via an Unusual Kinetic Trap

Functional analysis of lysine 27-linked ubiquitin chains (^K27Ub) is difficult due to the inability to make them through enzymatic methods and due to a lack of model tools and substrates. Here we generate a series of ubiquitin (Ub) tools to study how the deubiquitinase UCHL3 responds to ^K27Ub chains in comparison to lysine 63-linked chains and mono-Ub. From a crystal structure of a complex between UCHL3 and synthetic ^K27Ub₂, we unexpectedly discover that free ^K27Ub₂ and ^K27Ub₂-conjugated substrates are natural inhibitors of UCHL3. Using our Ub tools to profile UCHL3's activity, we generate a quantitative kinetic model of the inhibitory mechanism and we find that ^K27Ub₂ can inhibit UCHL3 covalently, by binding to its catalytic cysteine, and allosterically, by locking its catalytic loop tightly in place. Based on this inhibition mechanism, we propose that UCHL3 and ^K27Ub chains likely sense and regulate each other in cells.

Ovarian Cancer: Tumor-Specific Urinary Micro-Peptides Profiling as Potential Biomarkers for Early Diagnosis

Ovarian cancer is the second major lethal gynecologic malignancy in developing countries. This study aimed to characterize urinary micro-peptides as potential diagnostic biomarkers for ovarian cancer. In a prospective, longitudinal and case-controlled study and following informed consent, urine and plasma samples were collected from 112 women with histologically-proven ovarian cancer and 200 apparently healthy age-matched volunteers. Urinary micro-peptides were detected and sequenced using SDS-PAGE and Edman degradation technique. Serum CA125 was detected in less than a quarter (23.2%, 26/112) of patients. One or more urinary micro-peptides were detected in about two thirds of the patients (62.5%, 70/112). A total of 40 patients had three bands (57.1%, 40/70), while two bands (15 and 35 kDa) were detected in 28.6% (20/70) of the patients. Isolated 45 kDa band was seen in 14.3% (10/70). No urinary micro-peptide was detected in the volunteers. The 15 and 35 kDa bands disappeared after 6 months of regular chemotherapy, while the 45 kDa band persisted in 2.9% (2/70) of the patients after treatment. The micro-peptides were identified as: Catalase (45 kDa), α-1 Acid Glycoprotein (35 kDa) and Peroxiredoxin-2 (15 kDa). Urinary catalase, α-1 Acid Glycoprotein and Peroxiredoxin-2 can be useful biomarkers for early detection and treatment response of ovarian cancer.

Large-Scale Discovery of Non-conventional Peptides in Maize and Arabidopsis through an Integrated Peptidogenomic Pipeline

Non-conventional peptides (NCPs), which include small open reading frame-encoded peptides, play critical roles in fundamental biological processes. In this study, we developed an integrated peptidogenomic pipeline using high-throughput mass spectra to probe a customized six-frame translation database and applied it to large-scale identification of NCPs in plants.A total of 1993 and 1860 NCPs were unambiguously identified in maize and Arabidopsis, respectively. These NCPs showed distinct characteristics compared with conventional peptides and were derived from introns, 3' UTRs, 5' UTRs, junctions, and intergenic regions. Furthermore, our results showed that translation events in unannotated transcripts occur more broadly than previously thought. In addition, we found that dozens of maize NCPs are enriched within regions associated with phenotypic variations and domestication selection, indicating that they potentially are involved in genetic regulation of complex traits and domestication in maize. Taken together, our study developed an integrated peptidogenomic pipeline for large-scale identification of NCPs in plants, which would facilitate global characterization of NCPs from other plants. The identification of large-scale NCPs in both monocot (maize) and dicot (Arabidopsis) plants indicates that a large portion of plant genome can be translated into biologically functional molecules, which has important implications for functional genomic studies.

Understanding the proteome encoded by "non-coding RNAs": new insights into human genome

A great number of non-coding RNAs (ncRNAs) account for the majority of the genome. The translation of these ncRNAs has been noted but seriously underestimated due to both technological and theoretical limitations. Based on the development of ribosome profiling (Ribo-seq), full length translating RNA analysis (RNC-seq) and mass spectrometry technology, more and more ncRNAs are being found to be translated in different organism, and some of them can produce functional peptides. While recently, not only individual new functional proteins, but also a new proteome have been experimentally discovered to be encoded by endogenous lncRNAs and circRNAs. These new proteins are of biological significance, suggesting the connection of the translation of ncRNAs to human physiology and diseases. Therefore, an in-depth and systematic understanding of the coding capabilities of ncRNAs is necessary for basic biology and medicine. In this review, we summarize the advances in the field of discovering this new proteome, i.e. "ncRNA-coded" proteins.

A novel numerical model for protein sequences analysis based on spherical coordinates and multiple physicochemical properties of amino acids

How to characterize short protein sequences to make an effective connection to their functions is an unsolved problem. Here we propose to map the physicochemical properties of each amino acid onto unit spheres so that each protein sequence can be represented quantitatively. We demonstrate the usefulness of this representation by applying it to the prediction of cell penetrating peptides. We show that its combination with traditional composition features yields the best performance across different datasets, among several methods compared. For the convenience of users, a web server has been established for automatic calculations of the proposed features at http://biophy.dzu.edu.cn/SNumD/.