Multi-substrate selectivity based on key loops and non-homologous domains: new insight into ALKBH family

The role of RNA methylation in tumor immunity and its potential in immunotherapy

RNA methylation, a prevalent post-transcriptional modification, has garnered considerable attention in research circles. It exerts regulatory control over diverse biological functions by modulating RNA splicing, translation, transport, and stability. Notably, studies have illuminated the substantial impact of RNA methylation on tumor immunity. The primary types of RNA methylation encompass N6-methyladenosine (m6A), 5-methylcytosine (m5C), N1-methyladenosine (m1A), and N7-methylguanosine (m7G), and 3-methylcytidine (m3C). Compelling evidence underscores the involvement of RNA methylation in regulating the tumor microenvironment (TME). By affecting RNA translation and stability through the "writers", "erasers" and "readers", RNA methylation exerts influence over the dysregulation of immune cells and immune factors. Consequently, RNA methylation plays a pivotal role in modulating tumor immunity and mediating various biological behaviors, encompassing proliferation, invasion, metastasis, etc. In this review, we discussed the mechanisms and functions of several RNA methylations, providing a comprehensive overview of their biological roles and underlying mechanisms within the tumor microenvironment and among immunocytes. By exploring how these RNA modifications mediate tumor immune evasion, we also examine their potential applications in immunotherapy. This review aims to provide novel insights and strategies for identifying novel targets in RNA methylation and advancing cancer immunotherapy efficacy.

Research progress of N1-methyladenosine RNA modification in cancer

N1-methyladenosine (m1A) is a post-transcriptionally modified RNA molecule that plays a pivotal role in the regulation of various biological functions and activities. Especially in cancer cell invasion, proliferation and cell cycle regulation. Over recent years, there has been a burgeoning interest in investigating the m1A modification of RNA. Most studies have focused on the regulation of m1A in cancer enrichment areas and different regions. This review provides a comprehensive overview of the methodologies employed for the detection of m1A modification. Furthermore, this review delves into the key players in m1A modification, known as the "writers," "erasers," and "readers." m1A modification is modified by the m1A methyltransferases, or writers, such as TRMT6, TRMT61A, TRMT61B, TRMT10C, NML, and, removed by the demethylases, or erasers, including FTO and ALKBH1, ALKBH3. It is recognized by m1A-binding proteins YTHDF1, TYHDF2, TYHDF3, and TYHDC1, also known as "readers". Additionally, we explore the intricate relationship between m1A modification and its regulators and their implications for the development and progression of specific types of cancer, we discuss how m1A modification can potentially facilitate the discovery of novel approaches for cancer diagnosis, treatment, and prognosis. Our summary of m1A methylated adenosine modification detection methods and regulatory mechanisms in various cancers provides useful insights for cancer diagnosis, treatment, and prognosis. Video Abstract.

The Role of Key Amino Acids of the Human Fe(II)/2OG-Dependent Dioxygenase ALKBH3 in Structural Dynamics and Repair Activity toward Methylated DNA

Non-heme dioxygenases of the AlkB family hold a unique position among enzymes that repair alkyl lesions in nucleic acids. These enzymes activate the Fe(II) ion and molecular oxygen through the coupled decarboxylation of the 2-oxoglutarate co-substrate to subsequently oxidize the substrate. ALKBH3 is a human homolog of E. coli AlkB, which displays a specific activity toward N1-methyladenine and N3-methylcytosine bases in single-stranded DNA. Due to the lack of a DNA-bound structure of ALKBH3, the basis of its substrate specificity and structure-function relationships requires further exploration. Here we have combined biochemical and biophysical approaches with site-directed mutational analysis to elucidate the role of key amino acids in maintaining the secondary structure and catalytic activity of ALKBH3. Using stopped-flow fluorescence spectroscopy we have shown that conformational dynamics play a crucial role in the catalytic repair process catalyzed by ALKBH3. A transient kinetic mechanism, which comprises the steps of the specific substrate binding, eversion, and anchoring within the DNA-binding cleft, has been described quantitatively by rate and equilibrium constants. Through CD spectroscopy, we demonstrated that replacing side chains of Tyr143, Leu177, and His191 with alanine results in significant alterations in the secondary structure content of ALKBH3 and decreases the stability of mutant proteins. The bulky side chain of Tyr143 is critical for binding the methylated base and stabilizing its flipped-out conformation, while its hydroxyl group is likely involved in facilitating the product release. The removal of the Leu177 and His191 side chains substantially affects the secondary structure content and conformational flexibility, leading to the complete inactivation of the protein. The mutants lacking enzymatic activity exhibit a marked decrease in antiparallel β-strands, offset by an increase in the helical component.

Structural Basis of Nucleic Acid Recognition and 6mA Demethylation by Caenorhabditis elegans NMAD-1A

N6-methyladenine (6mA) of DNA is an emerging epigenetic mark in the genomes of Chlamydomonas, Caenorhabditis elegans, and mammals recently. Levels of 6mA undergo drastic fluctuation and thus affect fertility during meiosis and early embryogenesis. Here, we showed three complex structures of 6mA demethylase C. elegans NMAD-1A, a canonical isoform of NMAD-1 (F09F7.7). Biochemical results revealed that NMAD-1A prefers 6mA Bubble or Bulge DNAs. Structural studies of NMAD-1A revealed an unexpected "stretch-out" conformation of its Flip2 region, a conserved element that is usually bent over the catalytic center to facilitate substrate base flipping in other DNA demethylases. Moreover, the wide channel between the Flip1 and Flip2 of the NMAD-1A explained the observed preference of NMAD-1A for unpairing substrates, of which the flipped 6mA was primed for catalysis. Structural analysis and mutagenesis studies confirmed that key elements such as carboxy-terminal domain (CTD) and hypothetical zinc finger domain (ZFD) critically contributed to structural integrity, catalytic activity, and nucleosome binding. Collectively, our biochemical and structural studies suggest that NMAD-1A prefers to regulate 6mA in the unpairing regions and is thus possibly associated with dynamic chromosome regulation and meiosis regulation.

Deciphering the decisive factors driving fate bifurcations in somatic cell reprogramming

Single-cell studies have demonstrated that somatic cell reprogramming is a continuous process of cell fates transition. Only partial reprogramming intermediates can overcome the molecular bottlenecks to acquire pluripotency. To decipher the underlying decisive factors driving cell fate, we identified induced pluripotent stem cells or stromal-like cells (iPSCs/SLCs) and iPSCs or trophoblast-like cells (iPSCs/TLCs) fate bifurcations by reconstructing cellular trajectory. The mesenchymal-epithelial transition and the activation of pluripotency networks are the main molecular series in successful reprogramming. Correspondingly, intermediates diverge into SLCs accompanied by the inhibition of cell cycle genes and the activation of extracellular matrix genes, whereas the TLCs fate is characterized by the up-regulation of placenta development genes. Combining putative gene regulatory networks, seven (Taf7, Ezh2, Klf2, etc.) and three key factors (Cdc5l, Klf4, and Nanog) were individually identified as drivers of the successful reprogramming by triggering downstream pluripotent networks during iPSCs/SLCs and iPSCs/TLCs fate bifurcation. Conversely, 11 factors (Cebpb, Sox4, Junb, etc.) and four factors (Gata2, Jund, Ctnnb1, etc.) drive SLCs fate and TLCs fate, respectively. Our study sheds new light on the understanding of decisive factors driving cell fate, which is helpful for improving reprogramming efficiency through manipulating cell fates to avoid alternative fates.

Functional characterisation of Fe (II) and 2OG-dependent dioxygenase TcALKBH4 in the red flour beetle, Tribolium castaneum

Alpha-ketoglutarate-dependent dioxygenase ALKB homologue 4 (ALKBH4) is a member of the Fe (II) and 2-oxoglutarate-dependent ALKB homologue family that plays important roles in epigenetic regulation by alkyl lesions removal in mammals. However, the roles of ALKBH4 in insects are not clear. Here, TcALKBH4 was cloned and functionally characterised in Tribolium castaneum. Temporal expression revealed that TcALKBH4 was highly expressed in early embryos and early pupae. Spatial expression showed that TcALKBH4 was highly expressed in the adult testis, and followed by the ovary. RNA interference targeting TcALKBH4 at different developmental stages in T. castaneum led to apparent phenotypes including the failure of development in larvae, the reduction of food intake and the deficiency of fertility in adult. However, further dot blot analyses showed that TcALKBH4 RNAi does not seem to influence 6 mA levels in vivo. qRT-PCR was used to further explore the underlying molecular mechanisms; the result showed that TcALKBH4 mediates the development of larvae possibly through 20E signalling pathway, and the fertility of female and male adult might be regulated by the expression of vitellogenesis and JH signalling pathway, respectively. Altogether, these findings will provide new insights into the potential function of ALKBH4 in insects.

RNA modification: mechanisms and therapeutic targets

RNA modifications are dynamic and reversible chemical modifications on substrate RNA that are regulated by specific modifying enzymes. They play important roles in the regulation of many biological processes in various diseases, such as the development of cancer and other diseases. With the help of advanced sequencing technologies, the role of RNA modifications has caught increasing attention in human diseases in scientific research. In this review, we briefly summarized the basic mechanisms of several common RNA modifications, including m6A, m5C, m1A, m7G, Ψ, A-to-I editing and ac4C. Importantly, we discussed their potential functions in human diseases, including cancer, neurological disorders, cardiovascular diseases, metabolic diseases, genetic and developmental diseases, as well as immune disorders. Through the "writing-erasing-reading" mechanisms, RNA modifications regulate the stability, translation, and localization of pivotal disease-related mRNAs to manipulate disease development. Moreover, we also highlighted in this review all currently available RNA-modifier-targeting small molecular inhibitors or activators, most of which are designed against m6A-related enzymes, such as METTL3, FTO and ALKBH5. This review provides clues for potential clinical therapy as well as future study directions in the RNA modification field. More in-depth studies on RNA modifications, their roles in human diseases and further development of their inhibitors or activators are needed for a thorough understanding of epitranscriptomics as well as diagnosis, treatment, and prognosis of human diseases.

m6A-regulated tumor glycolysis: new advances in epigenetics and metabolism

Glycolytic reprogramming is one of the most important features of cancer and plays an integral role in the progression of cancer. In cancer cells, changes in glucose metabolism meet the needs of self-proliferation, angiogenesis and lymphangiogenesis, metastasis, and also affect the immune escape, prognosis evaluation and therapeutic effect of cancer. The n6-methyladenosine (m6A) modification of RNA is widespread in eukaryotic cells. Dynamic and reversible m6A modifications are widely involved in the regulation of cancer stem cell renewal and differentiation, tumor therapy resistance, tumor microenvironment, tumor immune escape, and tumor metabolism. Lately, more and more evidences show that m6A modification can affect the glycolysis process of tumors in a variety of ways to regulate the biological behavior of tumors. In this review, we discussed the role of glycolysis in tumor genesis and development, and elaborated in detail the profound impact of m6A modification on different tumor by regulating glycolysis. We believe that m6A modified glycolysis has great significance and potential for tumor treatment.

Circular RNAs in ferroptosis: regulation mechanism and potential clinical application in disease

Ferroptosis, an iron-dependent non-apoptotic form of cell death, is reportedly involved in the pathogenesis of various diseases, particularly tumors, organ injury, and degenerative pathologies. Several signaling molecules and pathways have been found to be involved in the regulation of ferroptosis, including polyunsaturated fatty acid peroxidation, glutathione/glutathione peroxidase 4, the cysteine/glutamate antiporter system Xc-, ferroptosis suppressor protein 1/ubiquinone, and iron metabolism. An increasing amount of evidence suggests that circular RNAs (circRNAs), which have a stable circular structure, play important regulatory roles in the ferroptosis pathways that contribute to disease progression. Hence, ferroptosis-inhibiting and ferroptosis-stimulating circRNAs have potential as novel diagnostic markers or therapeutic targets for cancers, infarctions, organ injuries, and diabetes complications linked to ferroptosis. In this review, we summarize the roles that circRNAs play in the molecular mechanisms and regulatory networks of ferroptosis and their potential clinical applications in ferroptosis-related diseases. This review furthers our understanding of the roles of ferroptosis-related circRNAs and provides new perspectives on ferroptosis regulation and new directions for the diagnosis, treatment, and prognosis of ferroptosis-related diseases.

Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance

Transcription factors are protein molecules that act as regulators of gene expression. Aberrant protein activity of transcription factors can have a significant impact on tumor progression and metastasis in tumor patients. In this study, 868 immune-related transcription factors were identified from the transcription factor activity profile of 1823 ovarian cancer patients. The prognosis-related transcription factors were identified through univariate Cox analysis and random survival tree analysis, and two distinct clustering subtypes were subsequently derived based on these transcription factors. We assessed the clinical significance and genomics landscape of the two clustering subtypes and found statistically significant differences in prognosis, response to immunotherapy, and chemotherapy among ovarian cancer patients with different subtypes. Multi-scale Embedded Gene Co-expression Network Analysis was used to identify differential gene modules between the two clustering subtypes, which allowed us to conduct further analysis of biological pathways that exhibited significant differences between them. Finally, a ceRNA network was constructed to analyze lncRNA-miRNA-mRNA regulatory pairs with differential expression levels between two clustering subtypes. We expected that our study may provide some useful references for stratifying and treating patients with ovarian cancer.

The Metal-binding Protein Atlas (MbPA): an integrated database for curating metalloproteins in all aspects

Metal-binding proteins are essential for the vital activities and engage in their roles by acting in concert with metal cations. MbPA (The Metal-binding Protein Atlas) is the most comprehensive resource up to now dedicated to curating metal-binding proteins. Currently, it contains 106373 entries and 440187 sites related to 54 metals and 8169 species. Users can view all metal-binding proteins and species-specific proteins in MbPA. There are also metal-proteomics data that quantitatively describes protein expression in different tissues and organs. By analyzing the data of the amino acid residues at the metal-binding site, it is found that about 80% of the metal ions tend to bind to cysteine, aspartic acid, glutamic acid, and histidine. Moreover, we use Diversity Measure to confirm that the diversity of metal-binding is specific in different area of periodic table, and further elucidate the binding modes of 19 transition metals on 20 amino acids. In addition, MbPA also embraces 6855 potential pathogenic mutations related to metalloprotein. The resource is freely available at http://bioinfor.imu.edu.cn/mbpa.

The role of demethylase AlkB homologs in cancer

The AlkB family (ALKBH1-8 and FTO), a member of the Fe (II)- and α-ketoglutarate-dependent dioxygenase superfamily, has shown the ability to catalyze the demethylation of a variety of substrates, including DNA, RNA, and histones. Methylation is one of the natural organisms’ most prevalent forms of epigenetic modifications. Methylation and demethylation processes on genetic material regulate gene transcription and expression. A wide variety of enzymes are involved in these processes. The methylation levels of DNA, RNA, and histones are highly conserved. Stable methylation levels at different stages can coordinate the regulation of gene expression, DNA repair, and DNA replication. Dynamic methylation changes are essential for the abilities of cell growth, differentiation, and division. In some malignancies, the methylation of DNA, RNA, and histones is frequently altered. To date, nine AlkB homologs as demethylases have been identified in numerous cancers’ biological processes. In this review, we summarize the latest advances in the research of the structures, enzymatic activities, and substrates of the AlkB homologs and the role of these nine homologs as demethylases in cancer genesis, progression, metastasis, and invasion. We provide some new directions for the AlkB homologs in cancer research. In addition, the AlkB family is expected to be a new target for tumor diagnosis and treatment.

ALKBH4 Stabilization Is Required for Arsenic-Induced 6mA DNA Methylation Inhibition, Keratinocyte Malignant Transformation, and Tumorigenicity

Inorganic arsenic is one of the well-known human skin carcinogens. However, the molecular mechanism by which arsenic promotes carcinogenesis remains unclear. Previous studies have established that epigenetic changes, including changes in DNA methylation, are among the critical mechanisms that drive carcinogenesis. N6-methyladenine (6mA) methylation on DNA is a widespread epigenetic modification that was initially found on bacterial and phage DNA. Only recently has 6mA been identified in mammalian genomes. However, the function of 6mA in gene expression and cancer development is not well understood. Here, we show that chronic low doses of arsenic induce malignant transformation and tumorigenesis in keratinocytes and lead to the upregulation of ALKBH4 and downregulation of 6mA on DNA. We found that reduced 6mA levels in response to low levels of arsenic were mediated by the upregulation of the 6mA DNA demethylase ALKBH4. Moreover, we found that arsenic increased ALKBH4 protein levels and that ALKBH4 deletion impaired arsenic-induced tumorigenicity in vitro and in mice. Mechanistically, we found that arsenic promoted ALKBH4 protein stability through reduced autophagy. Together, our findings reveal that the DNA 6mA demethylaseALKBH4 promotes arsenic tumorigenicity and establishes ALKBH4 as a promising target for arsenic-induced tumorigenesis.

Competitive binding of TET1 and DNMT3A/B cooperates the DNA methylation pattern in human embryonic stem cells

DNMT3A/B and TET1 play indispensable roles in regulating DNA methylation that undergoes extensive reprogramming during mammalian embryogenesis. Yet the competitive and cooperative relationships between TET1 and DNMT3A/B remain largely unknown in the human embryonic stem cells. Here, we revealed that the main DNA-binding domain of TET1 contains more positive charges by using charge reduction of amino acid alphabet, followed by DNMT3A and DNMT3B. The genome-wide binding profiles showed that TET1 prefers binding to the proximal promoters and CpG islands compared with DNMT3A/B. Moreover, the binding regions of these three transcription factors can be divided into specific and co-binding regions. And a stronger inhibitory effect of DNMT3A on TET1 demethylation was observed in co-binding regions. Furthermore, we integrated TET1 knockout data to further discuss the competitive binding patterns of TET1 and DNMT3A/B. The lack of TET1 increased the occupation of DNMT3A/B at the specific binding regions of TET1 causing focal hypermethylation. The knockout of TET1 was also accompanied by a reduction of DNMT3A/B binding in the co-binding regions, further confirming the cooperative binding function between TET1 and DNMT3A/B. In conclusion, our studies found that the competitive binding of TET1 and DNMT3A/B cooperatively shapes the global DNA methylation pattern in human embryonic stem cells.

Genome-wide identification of the AlkB homologs gene family, PagALKBH9B and PagALKBH10B regulated salt stress response in Populus

The AlkB homologs (ALKBH) gene family regulates N⁶-methyladenosine (m⁶A) RNA methylation and is involved in plant growth and the abiotic stress response. Poplar is an important model plant for studying perennial woody plants. Poplars typically have a long juvenile period of 7-10 years, requiring long periods of time for studies of flowering or mature wood properties. Consequently, functional studies of the ALKBH genes in Populus species have been limited. Based on AtALKBHs sequence similarity with Arabidopsis thaliana, 23 PagALKBHs were identified in the genome of the poplar 84K hybrid genotype (P. alba  ×  P. tremula var. glandulosa), and gene structures and conserved domains were confirmed between homologs. The PagALKBH proteins were classified into six groups based on conserved sequence compared with human, Arabidopsis, maize, rice, wheat, tomato, barley, and grape. All homologs of PagALKBHs were tissue-specific; most were highly expressed in leaves. ALKBH9B and ALKBH10B are m⁶A demethylases and overexpression of their homologs PagALKBH9B and PagALKBH10B reduced m⁶A RNA methylation in transgenic lines. The number of adventitious roots and the biomass accumulation of transgenic lines decreased compared with WT. Therefore, PagALKBH9B and PagALKBH10B mediate m⁶A RNA demethylation and play a regulatory role in poplar growth and development. Overexpression of PagALKBH9B and PagALKBH10B can reduce the accumulation of H₂O₂ and oxidative damage by increasing the activities of SOD, POD, and CAT, and enhancing protection for Chl a/b, thereby increasing the salt tolerance of transgenic lines. However, overexpression lines were more sensitive to drought stress due to reduced proline content. This research revealed comprehensive information about the PagALKBH gene family and their roles in growth and development and responsing to salt stress of poplar.

Hormone biosynthesis and metabolism members of 2OGD superfamily are involved in berry development and respond to MeJA and ABA treatment of Vitis vinifera L

BACKGROUND

Hormones play an indispensable role during fruit ripening, nine clades in 2-oxoglutarate-dependent dioxygenase (2OGD) superfamily are responsible for the hormone biosynthesis and metabolism, but less information is known about them.

RESULTS

A total of 163 Vv2OGD superfamily members were identified from grape genome, which were mainly expanded by local (tandem and proximal) duplication. Phylogenetic analysis of 2OGD members in grape and Arabidopsis indicates 37 members in Vv2OGD superfamily are related to hormone biosynthesis and metabolism process (Vv2OGD-H), which could be divided into 9 clades, gibberellin (GA) 3-oxidase (GA3ox), GA 20-oxidase (GA20ox), carbon-19 GA 2-oxidase (C₁₉-GA2ox), carbon-20 GA 2-oxidase (C₂₀-GA2ox), 1-aminocyclopropane-1-carboxylic acid oxidase (ACO), dioxygenase for auxin oxidation (DAO), lateral branching oxidoreductas (LBO), downy mildew resistant 6 and DMR6-like oxygenase (DMR6/DLO) and jasmonate-induced oxygenase (JOX). Sixteen of these 37 Vv2OGD-Hs are expressed in grape berry, in which the expression patterns of VvGA2oxs, VvDAOs and VvJOXs shows a correlation with the change patterns of GAs, indole-3-acetic acid (IAA) and jasmonates (JAs), indicating the involvement of these genes in grape berry development by regulating corresponding hormones. Twelve Vv2OGD-Hs respond to methyl JA (MeJA) treatment, of which eight may lead to the inhibition of the ripening process by the crosstalk of JAs-salicylic acids (SAs), JAs-GAs and JAs-JAs, while seven Vv2OGD-Hs respond to ABA treatment may be responsible for the promotion of ripening process by the interplay of abscisic acid (ABA)-strigolactones (SLs), ABA-SAs, ABA-GAs, ABA-JAs. Especially, VvLBO1 reach an expression peak near véraison and up-regulate about four times after ABA treatment, which implies SLs and ABA-SLs crosstalk may be related to the onset of berry ripening in grape.

CONCLUSIONS

This study provides valuable clues and new insights for the mechanism research of Vv2OGD-Hs in hormones regulation during the grape berry development.

A Statistical Analysis of the Sequence and Structure of Thermophilic and Non-Thermophilic Proteins

Thermophilic proteins have various practical applications in theoretical research and in industry. In recent years, the demand for thermophilic proteins on an industrial scale has been increasing; therefore, the engineering of thermophilic proteins has become a hot direction in the field of protein engineering. However, the exact mechanism of thermostability of proteins is not yet known, for engineering thermophilic proteins knowing the basis of thermostability is necessary. In order to understand the basis of the thermostability in proteins, we have made a statistical analysis of the sequences, secondary structures, hydrogen bonds, salt bridges, DHA (Donor-Hydrogen-Accepter) angles, and bond lengths of ten pairs of thermophilic proteins and their non-thermophilic orthologous. Our findings suggest that polar amino acids contribute to thermostability in proteins by forming hydrogen bonds and salt bridges which provide resistance against protein denaturation. Short bond length and a wider DHA angle provide greater bond stability in thermophilic proteins. Moreover, the increased frequency of aromatic amino acids in thermophilic proteins contributes to thermal stability by forming more aromatic interactions. Additionally, the coil, helix, and loop in the secondary structure also contribute to thermostability.

ALKBH family members as novel biomarkers and prognostic factors in human breast cancer

Breast cancer is the most common lethal carcinoma worldwide and better targeted therapies are still worthy of exploration, having had some great successes already. Abnormal expression of ALKBH members were found in various cancers, and the roles played by it were the focus of attention. The ALKBH gene family encodes nine homologous enzymes (ALKBH1-8 and FTO) to repair DNA or RNA depending on Fe2+ and α-ketoglutarate (α-KG), which is related to carcinogenesis. In this study, we applied several databases to explore the roles of ALKBHs in breast cancer. We found that ALKBH members were abnormal expression in breast cancer and associated with tumor stage and subclasses. Higher alteration rates of ALKBH family were found in breast cancer. Function enrichment revealed that several cancer-associated signal pathways were related to ALKBH family such as PI3K-Akt signaling pathway and axon guidance. Infiltration of immune cells (Eosinophiles, NK CD56bright cells, mast cells, T helper cells and so on) were strongly related to ALKBHs. Moreover, we further found that there was strong correlation between ALKBH7 and higher age, later T stage, ER/PR positive and post-menopause of breast cancer patients, and patients with higher ALKBH7 expression had shorter overall survival (OS) and post progression survival (PPS). In conclusion, our findings may provide novel insights into ALKBH-targeted therapy for breast cancer patients, and ALKBH7 may be a potential prognostic biomarker.

Conformational Dynamics of Human ALKBH2 Dioxygenase in the Course of DNA Repair as Revealed by Stopped-Flow Fluorescence Spectroscopy

Elucidation of physicochemical mechanisms of enzymatic processes is one of the main tasks of modern biology. High efficiency and selectivity of enzymatic catalysis are mostly ensured by conformational dynamics of enzymes and substrates. Here, we applied a stopped-flow kinetic analysis based on fluorescent spectroscopy to investigate mechanisms of conformational transformations during the removal of alkylated bases from DNA by ALKBH2, a human homolog of Escherichia coli AlkB dioxygenase. This enzyme protects genomic DNA against various alkyl lesions through a sophisticated catalytic mechanism supported by a cofactor (Fe(II)), a cosubstrate (2-oxoglutarate), and O₂. We present here a comparative study of conformational dynamics in complexes of the ALKBH2 protein with double-stranded DNA substrates containing N1-methyladenine, N3-methylcytosine, or 1,N6-ethenoadenine. By means of fluorescent labels of different types, simultaneous detection of conformational transitions in the protein globule and DNA substrate molecule was performed. Fitting of the kinetic curves by a nonlinear-regression method yielded a molecular mechanism and rate constants of its individual steps. The results shed light on overall conformational dynamics of ALKBH2 and damaged DNA during the catalytic cycle.

Deep learning-based transcription factor activity for stratification of breast cancer patients

Transcription factors directly bind to DNA and regulate the expression of the gene, causing epigenetic modification of the DNA. They often mediate epigenetic parameters of transcriptional and posttranscriptional mechanisms, and their expression activities can be used to characterize genomic aberrations in cancer cell. In this study, the activity profile of transcription factors inferred by VIPER algorithm. The autoencoder model was applied for compressing the transcription factor activity profile for obtaining more useful transformed features for stratifying patients into two different breast cancer subtypes. The deep learning-based subtypes exhibited superior prognostic value and yielded better risk-stratification than the transcription factor activity-based method. Importantly, according to transformed features, a deep neural network was constructed to predict the subtypes, and achieved the accuracy of 94.98% and area under the ROC curve of 0.9663, respectively. The proposed subtypes were found to be significantly associated with immune infiltration, tumor immunogenicity and so on. Furthermore, the ceRNA network was constructed for the breast cancer subtypes. Besides, 11 master regulators were found to be associated with patients in cluster 1. Given the robustness performance of our deep learning model over multiple breast cancer cohorts, we expected this model may be useful in the area of prognosis prediction and lead some possibility for personalized medicine in breast cancer patients.

RaacFold: a webserver for 3D visualization and analysis of protein structure by using reduced amino acid alphabets

Protein structure exhibits greater complexity and diversity than DNA structure, and usually affects the interpretation of the function, interactions and biological annotations. Reduced amino acid alphabets (Raaa) exhibit a powerful ability to decrease protein complexity and identify functional conserved regions, which motivated us to create RaacFold. The RaacFold provides 687 reduced amino acid clusters (Raac) based on 58 reduction methods and offers three analysis tools: Protein Analysis, Align Analysis, and Multi Analysis. The Protein Analysis and Align Analysis provide reduced representations of sequence-structure according to physicochemical similarities and computational biology strategies. With the simplified representations, the protein structure can be viewed more concise and clearer to capture biological insight than the unreduced structure. Thus, the design of artificial protein will be more convenient, and redundant interference is avoided. In addition, Multi Analysis allows users to explore biophysical variation and conservation in the evolution of protein structure and function. This supplies important information for the identification and exploration of the nonhomologous functions of paralogs. Simultaneously, RaacFold provides powerful 2D and 3D rendering performance with advanced parameters for sequences, structures, and related annotations. RaacFold is freely available at http://bioinfor.imu.edu.cn/raacfold.

m1A RNA Modification in Gene Expression Regulation

N1-methyladenosine (m1A) is a prevalent and reversible post-transcriptional RNA modification that decorates tRNA, rRNA and mRNA. Recent studies based on technical advances in analytical chemistry and high-throughput sequencing methods have revealed the crucial roles of m1A RNA modification in gene regulation and biological processes. In this review, we focus on progress in the study of m1A methyltransferases, m1A demethylases and m1A-dependent RNA-binding proteins and highlight the biological mechanisms and functions of m1A RNA modification, as well as its association with human disease. We also summarize the current understanding of detection approaches for m1A RNA modification.

Structural insights into the interactions and epigenetic functions of human nucleic acid repair protein ALKBH6

Human AlkB homolog 6, ALKBH6, plays key roles in nucleic acid damage repair and tumor therapy. However, no precise structural and functional information are available for this protein. In this study, we determined atomic resolution crystal structures of human holo-ALKBH6 and its complex with ligands. AlkB members bind nucleic acids by NRLs (nucleotide recognition lids, also called Flips), which can recognize DNA/RNA and flip methylated lesions. We found that ALKBH6 has unusual Flip1 and Flip2 domains, distinct from other AlkB family members both in sequence and conformation. Moreover, we show that its unique Flip3 domain has multiple unreported functions, such as discriminating against double-stranded nucleic acids, blocking the active center, binding other proteins, and in suppressing tumor growth. Structural analyses and substrate screening reveal how ALKBH6 discriminates between different types of nucleic acids and may also function as a nucleic acid demethylase. Structure-based interacting partner screening not only uncovered an unidentified interaction of transcription repressor ZMYND11 and ALKBH6 in tumor suppression but also revealed cross talk between histone modification and nucleic acid modification in epigenetic regulation. Taken together, these results shed light on the molecular mechanism underlying ALKBH6-associated nucleic acid damage repair and tumor therapy.

Evolutionary History of RNA Modifications at N6-Adenosine Originating from the R-M System in Eukaryotes and Prokaryotes

Simple Summary

The m⁶A is the most abundant and well-studied modification of mRNA, and plays an important role in transcription and translation. It is known to be evolutionarily conserved machinery present in the last eukaryotic common ancestor (LECA). The writers and erasers responsible for adding or removing m⁶A belong to specific protein families, respectively, suggesting that these members are evolutionarily related. However, only some of these mRNA m⁶A modification-associated proteins have been studied from an evolutionary perspective, while there has been no comprehensive and systematic analysis of the distributions and evolutionary history of N6mA-associated proteins in the three kingdoms of life. In this study, we identified orthologues of all the reported N6mA-associated proteins in 88 organisms from three kingdoms of life and comprehensively reconstructed the evolutionary history of the RNA N6mA modification machinery. The results demonstrate that RNA N6mA-MTases are derived from at least two different types of prokaryotic DNA MTases (class α and β MTases). As the m⁶A reader, YTH proteins may be acquired by LECA from an individual prokaryotic YTH-domain protein that evolved from the N-terminals of an R-M system endonuclease. In addition, the origin of eukaryotic ALKBH family proteins is inferred to be driven by at least two occasions of independent HTG from the bacterial ALKB family.

Abstract

Methylation at the N6-position of adenosine (N6mA) on mRNA (m⁶A) is one of the most widespread, highly selective and dynamically regulated RNA modifications and plays an important role in transcription and translation. In the present study, a comprehensive analysis of phylogenetic relationships, conserved domain sequence characteristics and protein structure comparisons were employed to explore the distribution of RNA N6mA modification (m⁶A, m^6,6A, m⁶Am, m^{6, 6}Am and m⁶t⁶A)-associated proteins (writers, readers and erasers) in three kingdoms of life and reveal the evolutionary history of these modifications. These findings further confirmed that the restriction-modification (R-M) system is the origin of DNA and RNA N6mA modifications. Among them, the existing mRNA m⁶A modification system derived from the last eukaryotic common ancestor (LECA) is the evolutionary product of elements from the last universal common ancestor (LUCA) or driven by horizontal gene transfer (HGT) from bacterial elements. The subsequent massive gene gains and losses contribute to the development of unique and diverse functions in distinct species. Particularly, RNA methyltransferases (MTases) as the writer responsible for adding N6mA marks on mRNA and ncRNAs may have evolved from class α and β prokaryotic “orphan” MTases originating from the R-M system. The reader, YTH proteins that specifically recognize the m⁶A deposit, may be acquired by LECA from an individual prokaryotic YTH-domain protein that evolved from N-terminals of an R-M system endonuclease. The eraser, which emerged from the ALKB family (ALKBH5 and FTO) in eukaryotes, may be driven by independent HTG from bacterial ALKB proteins. The evolutionary history of RNA N6mA modifications was inferred in the present study, which will deepen our understanding of these modifications in different species.

Identification of Helicobacter pylori Membrane Proteins Using Sequence-Based Features

Helicobacter pylori (H. pylori) is the most common risk factor for gastric cancer worldwide. The membrane proteins of the H. pylori are involved in bacterial adherence and play a vital role in the field of drug discovery. Thus, an accurate and cost-effective computational model is needed to predict the uncharacterized membrane proteins of H. pylori. In this study, a reliable benchmark dataset consisted of 114 membrane and 219 nonmembrane proteins was constructed based on UniProt. A support vector machine- (SVM-) based model was developed for discriminating H. pylori membrane proteins from nonmembrane proteins by using sequence information. Cross-validation showed that our method achieved good performance with an accuracy of 91.29%. It is anticipated that the proposed model will be useful for the annotation of H. pylori membrane proteins and the development of new anti-H. pylori agents.

SNAREs-SAP: SNARE Proteins Identification With PSSM Profiles

Soluble N-ethylmaleimide sensitive factor activating protein receptor (SNARE) proteins are a large family of transmembrane proteins located in organelles and vesicles. The important roles of SNARE proteins include initiating the vesicle fusion process and activating and fusing proteins as they undergo exocytosis activity, and SNARE proteins are also vital for the transport regulation of membrane proteins and non-regulatory vesicles. Therefore, there is great significance in establishing a method to efficiently identify SNARE proteins. However, the identification accuracy of the existing methods such as SNARE CNN is not satisfied. In our study, we developed a method based on a support vector machine (SVM) that can effectively recognize SNARE proteins. We used the position-specific scoring matrix (PSSM) method to extract features of SNARE protein sequences, used the support vector machine recursive elimination correlation bias reduction (SVM-RFE-CBR) algorithm to rank the importance of features, and then screened out the optimal subset of feature data based on the sorted results. We input the feature data into the model when building the model, used 10-fold crossing validation for training, and tested model performance by using an independent dataset. In independent tests, the ability of our method to identify SNARE proteins achieved a sensitivity of 68%, specificity of 94%, accuracy of 92%, area under the curve (AUC) of 84%, and Matthew’s correlation coefficient (MCC) of 0.48. The results of the experiment show that the common evaluation indicators of our method are excellent, indicating that our method performs better than other existing classification methods in identifying SNARE proteins.

WGBS combined with RNA-seq analysis revealed that Dnmt1 affects the methylation modification and gene expression changes during mouse oocyte vitrification

Understanding the molecular level changes of oocyte cryopreservation and the subsequent warming process is essential for improving the oocyte cryopreservation technologies. Here, we collected the mature metaphase II (MII) oocytes from mice and vitrified. After thawing, single-cell whole-genome bisulphite sequencing (scWGBS) and single-cell RNA sequencing (scRNA-seq) were used to investigate the molecular attributes of this process. Compared to the fresh oocytes, the vitrified oocytes had lower global methylation and gene expression levels, and 1426 genes up-regulated and 3321 genes down-regulated. The 1426 up-regulated differentially expressed genes (DEGs) in the vitrified oocytes were mainly associated with the histone ubiquitination, while the 3321 down-regulated genes were mainly enriched in the mitochondrion organisation and ATP metabolism processes. The differentially methylated regions (DMRs) were mainly located in promoter, intron and exon region of genes, and the length of DMRs in the vitrified oocytes were also significantly lower than that of the fresh oocytes. Notably, there were no significant difference in the expression levels of DNA demethylases (Tet1, Tet2 and Tet3) and methyltransferases (Dnmt3a and Dnmt3b) between two treatments of oocytes. However, Dnmt1 and kcnq1ot1, which are responsible for maintaining DNA methylation, were significantly down regulated in the vitrified oocytes. Gene regulatory network (GRN) analysis showed the Dnmt1 and kcnq1ot1 play a core role in regulating methylation and expression levels of downstream genes. Moreover, some genes associated with oocyte quality were significantly down-regulated in the vitrified oocytes. The present data provides a new perspective for understanding the impact of vitrification on oocytes.

Pseudo-188D: Phage Protein Prediction Based on a Model of Pseudo-188D

Phages have seriously affected the biochemical systems of the world, and not only are phages related to our health, but medical treatments for many cancers and skin infections are related to phages; therefore, this paper sought to identify phage proteins. In this paper, a Pseudo-188D model was established. The digital features of the phage were extracted by PseudoKNC, an appropriate vector was selected by the AdaBoost tool, and features were extracted by 188D. Then, the extracted digital features were combined together, and finally, the viral proteins of the phage were predicted by a stochastic gradient descent algorithm. Our model effect reached 93.4853%. To verify the stability of our model, we randomly selected 80% of the downloaded data to train the model and used the remaining 20% of the data to verify the robustness of our model.

DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins

Drug targets are biological macromolecules or biomolecule structures capable of specifically binding a therapeutic effect with a particular drug or regulating physiological functions. Due to the important value and role of drug targets in recent years, the prediction of potential drug targets has become a research hotspot. The key to the research and development of modern new drugs is first to identify potential drug targets. In this paper, a new predictor, DrugHybrid_BS, is developed based on hybrid features and Bagging-SVM to identify potentially druggable proteins. This method combines the three features of monoDiKGap (k = 2), cross-covariance, and grouped amino acid composition. It removes redundant features and analyses key features through MRMD and MRMD2.0. The cross-validation results show that 96.9944% of the potentially druggable proteins can be accurately identified, and the accuracy of the independent test set has reached 96.5665%. This all means that DrugHybrid_BS has the potential to become a useful predictive tool for druggable proteins. In addition, the hybrid key features can identify 80.0343% of the potentially druggable proteins combined with Bagging-SVM, which indicates the significance of this part of the features for research.

KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

DNA-binding protein (DBP) is a protein with a special DNA binding domain that is associated with many important molecular biological mechanisms. Rapid development of computational methods has made it possible to predict DBP on a large scale; however, existing methods do not fully integrate DBP-related features, resulting in rough prediction results. In this article, we develop a DNA-binding protein identification method called KK-DBP. To improve prediction accuracy, we propose a feature extraction method that fuses multiple PSSM features. The experimental results show a prediction accuracy on the independent test dataset PDB186 of 81.22%, which is the highest of all existing methods.

tRNA modifications and their potential roles in pancreatic cancer

Since the breakthrough discovery of N6-methyladenosine (m⁶A), the field of RNA epitranscriptomics has attracted increasing interest in the biological sciences. Transfer RNAs (tRNAs) are extensively modified, and various modifications play a crucial role in the formation and stability of tRNA, which is universally required for accurate and efficient functioning of tRNA. Abnormal tRNA modification can lead to tRNA degradation or specific cleavage of tRNA into fragmented derivatives, thus affecting the translation process and frequently accompanying a variety of human diseases. Increasing evidence suggests that tRNA modification pathways are also misregulated in human cancers. In this review, we summarize tRNA modifications and their biological functions, describe the type and frequency of tRNA modification alterations in cancer, and highlight variations in tRNA-modifying enzymes and the multiple functions that they regulate in different types of cancers. Furthermore, the current implications and the potential role of tRNA modifications in the progression of pancreatic cancer are discussed. Collectively, this review describes recent advances in tRNA modification in cancers and its potential significance in pancreatic cancer. Further study of the mechanism of tRNA modifications in pancreatic cancer may provide possibilities for therapies targeting enzymes responsible for regulating tRNA modifications in pancreatic cancer.

Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design

The global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has led to a dramatic loss of human life worldwide. Despite many efforts, the development of effective drugs and vaccines for this novel virus will take considerable time. Artificial intelligence (AI) and machine learning (ML) offer promising solutions that could accelerate the discovery and optimization of new antivirals. Motivated by this, in this paper, we present an extensive survey on the application of AI and ML for combating COVID-19 based on the rapidly emerging literature. Particularly, we point out the challenges and future directions associated with state-of-the-art solutions to effectively control the COVID-19 pandemic. We hope that this review provides researchers with new insights into the ways AI and ML fight and have fought the COVID-19 outbreak.

Biosynthesis and Degradation of Sulfur Modifications in tRNAs

Various sulfur-containing biomolecules include iron–sulfur clusters that act as cofactors for enzymes, sulfur-containing vitamins such as thiamin, and sulfur-modified nucleosides in RNA, in addition to methionine and cysteine in proteins. Sulfur-containing nucleosides are post-transcriptionally introduced into tRNA molecules, where they ensure precise codon recognition or stabilization of tRNA structure, thereby maintaining cellular proteome integrity. Modulating sulfur modification controls the translation efficiency of specific groups of genes, allowing organisms to adapt to specific environments. The biosynthesis of tRNA sulfur nucleosides involves elaborate ‘sulfur trafficking systems’ within cellular sulfur metabolism and ‘modification enzymes’ that incorporate sulfur atoms into tRNA. This review provides an up-to-date overview of advances in our knowledge of the mechanisms involved. It covers the functions, biosynthesis, and biodegradation of sulfur-containing nucleosides as well as the reaction mechanisms of biosynthetic enzymes catalyzed by the iron–sulfur clusters, and identification of enzymes involved in the de-modification of sulfur atoms of RNA. The mechanistic similarity of these opposite reactions is discussed. Mutations in genes related to these pathways can cause human diseases (e.g., cancer, diabetes, and mitochondrial diseases), emphasizing the importance of these pathways.

DNA Demethylation in the Processes of Repair and Epigenetic Regulation Performed by 2-Ketoglutarate-Dependent DNA Dioxygenases

Site-specific DNA methylation plays an important role in epigenetic regulation of gene expression. Chemical methylation of DNA, including the formation of various methylated nitrogenous bases, leads to the formation of genotoxic modifications that impair DNA functions. Despite the fact that different pathways give rise to methyl groups in DNA, the main pathway for their removal is oxidative demethylation, which is catalyzed by nonheme Fe(II)/α-ketoglutarate–dependent DNA dioxygenases. DNA dioxygenases share a common catalytic mechanism of the oxidation of the alkyl groups on nitrogenous bases in nucleic acids. This review presents generalized data on the catalytic mechanism of action of DNA dioxygenases and on the participation of typical representatives of this superfamily, such as prokaryotic enzyme AlkB and eukaryotic enzymes ALKBH1–8 and TET1–3, in both processes of direct repair of alkylated DNA adducts and in the removal of an epigenetic mark (5-methylcytosine).

The bioinformatics and experimental analysis of AlkB family for prognosis and immune cell infiltration in hepatocellular carcinoma

Background

Serving as N6-methyladenosine demethylases, the AlkB family is involved in the tumorigenesis of hepatocellular carcinoma (HCC). However, the molecular profiles and clinical values of the AlkB family in HCC are not well known.

Methods

Several bioinformatics tools and in vitro experiments were used to identify the immune-related profiles and prognostic values of AlkB family in HCC.

Results

In this study expression levels of ALKBH1/2/3/4/7 were all remarkably increased in HCC tissues when compared with normal tissues. Quantitative PCR (qPCR) and immunohistochemistry were used to validate the expression of AlkB family members in HCC tissues and normal liver tissues. In addition, high expression levels of ALKBH4 were negatively correlated with overall survival (OS) and disease-free survival (DFS) in patients with HCC. Increased ALKBH4 was also associated with pathological stage in HCC patients. The molecular profiles of AlkB family in HCC were mainly associated with peptidyl-serine modification, peptidyl-tyrosine modification, regulation of metal ion transport, etc. Furthermore, tumor-infiltrating immune cell analysis indicated that ALKBH1/2/3/4/5/6/7/8 and FTO were related to the infiltration of different immune cell, such as CD8+ T cells, macrophages, neutrophils, dendritic cells and CD4+ T cells. We also discovered that the methylation levels of ALKBH1/2/4/5/6/8 and FTO were remarkably reduced in HCC tissues.

Conclusions

Collectively, our findings may deepen the understanding of specific molecular profiles of the AlkB family in HCC pathology. In particular, ALKBH4 could serve as a promising prognostic candidate for treating HCC, and these results might potentiate the development of more reliable therapeutic strategies for patients with HCC.

Deep-4mCW2V: A sequence-based predictor to identify N4-methylcytosine sites in Escherichia coli

N4-methylcytosine (4mC) is a type of DNA modification which could regulate several biological progressions such as transcription regulation, replication and gene expressions. Precisely recognizing 4mC sites in genomic sequences can provide specific knowledge about their genetic roles. This study aimed to develop a deep learning-based model to predict 4mC sites in the Escherichia coli. In the model, DNA sequences were encoded by word embedding technique 'word2vec'. The obtained features were inputted into 1-D convolutional neural network (CNN) to discriminate 4mC sites from non-4mC sites in Escherichia coli genome. The examination on independent dataset showed that our model could yield the overall accuracy of 0.861, which was about 4.3% higher than the existing model. To provide convenience to scholars, we provided the data and source code of the model which can be freely download from https://github.com/linDing-groups/Deep-4mCW2V.

Nuclear Transfer Arrest Embryos Show Massive Dysregulation of Genes Involved in Transcription Pathways

Somatic cell nuclear transfer (SCNT) technology can reprogram terminally differentiated cell nuclei into a totipotent state. However, the underlying molecular barriers of SCNT embryo development remain incompletely elucidated. Here, we observed that transcription-related pathways were incompletely activated in nuclear transfer arrest (NTA) embryos compared to normal SCNT embryos and in vivo fertilized (WT) embryos, which hinders the development of SCNT embryos. We further revealed the transcription pathway associated gene regulatory networks (GRNs) and found the aberrant transcription pathways can lead to the massive dysregulation of genes in NTA embryos. The predicted target genes of transcription pathways contain a series of crucial factors in WT embryos, which play an important role in catabolic process, pluripotency regulation, epigenetic modification and signal transduction. In NTA embryos, however, these genes were varying degrees of inhibition and show a defect in synergy. Overall, our research found that the incomplete activation of transcription pathways is another potential molecular barrier for SCNT embryos besides the incomplete reprogramming of epigenetic modifications, broadening the understanding of molecular mechanism of SCNT embryonic development.

Identification of cyclin protein using gradient boost decision tree algorithm

Cyclin proteins are capable to regulate the cell cycle by forming a complex with cyclin-dependent kinases to activate cell cycle. Correct recognition of cyclin proteins could provide key clues for studying their functions. However, their sequences share low similarity, which results in poor prediction for sequence similarity-based methods. Thus, it is urgent to construct a machine learning model to identify cyclin proteins. This study aimed to develop a computational model to discriminate cyclin proteins from non-cyclin proteins. In our model, protein sequences were encoded by seven kinds of features that are amino acid composition, composition of k-spaced amino acid pairs, tri peptide composition, pseudo amino acid composition, geary correlation, normalized moreau-broto autocorrelation and composition/transition/distribution. Afterward, these features were optimized by using analysis of variance (ANOVA) and minimum redundancy maximum relevance (mRMR) with incremental feature selection (IFS) technique. A gradient boost decision tree (GBDT) classifier was trained on the optimal features. Five-fold cross-validated results showed that our model would identify cyclins with an accuracy of 93.06% and AUC value of 0.971, which are higher than the two recent studies on the same data.

Identification of Disease-Related 2-Oxoglutarate/Fe (II)-Dependent Oxygenase Based on Reduced Amino Acid Cluster Strategy

The 2-oxoglutarate/Fe (II)-dependent (2OG) oxygenase superfamily is mainly responsible for protein modification, nucleic acid repair and/or modification, and fatty acid metabolism and plays important roles in cancer, cardiovascular disease, and other diseases. They are likely to become new targets for the treatment of cancer and other diseases, so the accurate identification of 2OG oxygenases is of great significance. Many computational methods have been proposed to predict functional proteins to compensate for the time-consuming and expensive experimental identification. However, machine learning has not been applied to the study of 2OG oxygenases. In this study, we developed OGFE_RAAC, a prediction model to identify whether a protein is a 2OG oxygenase. To improve the performance of OGFE_RAAC, 673 amino acid reduction alphabets were used to determine the optimal feature representation scheme by recoding the protein sequence. The 10-fold cross-validation test showed that the accuracy of the model in identifying 2OG oxygenases is 91.04%. Besides, the independent dataset results also proved that the model has excellent generalization and robustness. It is expected to become an effective tool for the identification of 2OG oxygenases. With further research, we have also found that the function of 2OG oxygenases may be related to their polarity and hydrophobicity, which will help the follow-up study on the catalytic mechanism of 2OG oxygenases and the way they interact with the substrate. Based on the model we built, a user-friendly web server was established and can be friendly accessed at http://bioinfor.imu.edu.cn/ogferaac.

RFhy-m2G: Identification of RNA N2-methylguanosine modification sites based on random forest and hybrid features

N2-methylguanosine is a post-transcriptional modification of RNA that is found in eukaryotes and archaea. The biological function of m2G modification discovered so far is to control and stabilize the three-dimensional structure of tRNA and the dynamic barrier of reverse transcription. To discover additional biological functions of m2G, it is necessary to develop time-saving and labor-saving calculation tools to identify m2G. In this paper, based on hybrid features and a random forest, a novel predictor, RFhy-m2G, was developed to identify the m2G modification sites for three species. The hybrid feature used by the predictor is used to fuse the three features of ENAC, PseDNC, and NPPS. These three features include primary sequence derivation properties, physicochemical properties, and position-specific properties. Since there are redundant features in hybrid features, MRMD2.0 is used for optimal feature selection. Through feature analysis, it is found that the optimal hybrid features obtained still contain three kinds of properties, and the hybrid features can more accurately identify m2G modification sites and improve prediction performance. Based on five-fold cross-validation and independent testing to evaluate the prediction model, the accuracies obtained were 0.9982 and 0.9417, respectively. The robustness of the predictor is demonstrated by comparisons with other predictors.

6mA-Pred: identifying DNA N6-methyladenine sites based on deep learning

With the accumulation of data on 6mA modification sites, an increasing number of scholars have begun to focus on the identification of 6mA sites. Despite the recognized importance of 6mA sites, methods for their identification remain lacking, with most existing methods being aimed at their identification in individual species. In the present study, we aimed to develop an identification method suitable for multiple species. Based on previous research, we propose a method for 6mA site recognition. Our experiments prove that the proposed 6mA-Pred method is effective for identifying 6mA sites in genes from taxa such as rice, Mus musculus, and human. A series of experimental results show that 6mA-Pred is an excellent method. We provide the source code used in the study, which can be obtained from http://39.100.246.211:5004/6mA_Pred/.

IHEC_RAAC: a online platform for identifying human enzyme classes via reduced amino acid cluster strategy

Enzymes have been proven to play considerable roles in disease diagnosis and biological functions. The feature extraction that truly reflects the intrinsic properties of protein is the most critical step for the automatic identification of enzymes. Although lots of feature extraction methods have been proposed, some challenges remain. In this study, we developed a predictor called IHEC_RAAC, which has the capability to identify whether a protein is a human enzyme and distinguish the function of the human enzyme. To improve the feature representation ability, protein sequences were encoded by a new feature-vector called 'reduced amino acid cluster'. We calculated 673 amino acid reduction alphabets to determine the optimal feature representative scheme. The tenfold cross-validation test showed that the accuracy of IHEC_RAAC to identify human enzymes was 74.66% and further discriminate the human enzyme classes with an accuracy of 54.78%, which was 2.06% and 8.68% higher than the state-of-the-art predictors, respectively. Additionally, the results from the independent dataset indicated that IHEC_RAAC can effectively predict human enzymes and human enzyme classes to further provide guidance for protein research. A user-friendly web server, IHEC_RAAC, is freely accessible at http://bioinfor.imu.edu.cn/ihecraac .