Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as a promising tool for helping experimental scientists predict ACPs. However, the performance of existing methods still needs to be improved. In this study, we present a novel approach for the accurate prediction of ACPs, which involves the following two steps: (i) We applied a two-step feature selection protocol on seven feature encodings that cover various aspects of sequence information (composition-based, physicochemical properties and profiles) and obtained their corresponding optimal feature-based models. The resultant predicted probabilities of ACPs were further utilized as feature vectors. (ii) The predicted probability feature vectors were in turn used as an input to support vector machine to develop the final prediction model called mACPpred. Cross-validation analysis showed that the proposed predictor performs significantly better than individual feature encodings. Furthermore, mACPpred significantly outperformed the existing methods compared in this study when objectively evaluated on an independent dataset.

2’-O-methylation (2OM) is the most common post-transcriptional modification of RNA. It plays a crucial role in RNA splicing, RNA stability and innate immunity. Despite advances in high-throughput detection, the chemical stability of 2OM makes it difficult to detect and map in messenger RNA. Therefore, bioinformatics tools have been developed using machine learning (ML) algorithms to identify 2OM sites. These tools have made significant progress, but their performances remain unsatisfactory and need further improvement. In this study, we introduced H2Opred, a novel hybrid deep learning (HDL) model for accurately identifying 2OM sites in human RNA. Notably, this is the first application of HDL in developing four nucleotide-specific models [adenine (A2OM), cytosine (C2OM), guanine (G2OM) and uracil (U2OM)] as well as a generic model (N2OM). H2Opred incorporated both stacked 1D convolutional neural network (1D-CNN) blocks and stacked attention-based bidirectional gated recurrent unit (Bi-GRU-Att) blocks. 1D-CNN blocks learned effective feature representations from 14 conventional descriptors, while Bi-GRU-Att blocks learned feature representations from five natural language processing-based embeddings extracted from RNA sequences. H2Opred integrated these feature representations to make the final prediction. Rigorous cross-validation analysis demonstrated that H2Opred consistently outperforms conventional ML-based single-feature models on five different datasets. Moreover, the generic model of H2Opred demonstrated a remarkable performance on both training and testing datasets, significantly outperforming the existing predictor and other four nucleotide-specific H2Opred models. To enhance accessibility and usability, we have deployed a user-friendly web server for H2Opred, accessible at https://balalab-skku.org/H2Opred/. This platform will serve as an invaluable tool for accurately predicting 2OM sites within human RNA, thereby facilitating broader applications in relevant research endeavors.

Background: Acidophilic members of the genus Streptomyces can be a good source for novel secondary metabolites and degradative enzymes of biopolymers. In this study, a genome-based approach on Streptomyces yeochonensis CN732, a representative neutrotolerant acidophilic streptomycete, was employed to examine the biosynthetic as well as enzymatic potential, and also presence of any genetic tools for adaptation in acidic environment. Results: A high quality draft genome (7.8 Mb) of S. yeochonensis CN732 was obtained with a G + C content of 73.53% and 6549 protein coding genes. The in silico analysis predicted presence of multiple biosynthetic gene clusters (BGCs), which showed similarity with those for antimicrobial, anticancer or antiparasitic compounds. However, the low levels of similarity with known BGCs for most cases suggested novelty of the metabolites from those predicted gene clusters. The production of various novel metabolites was also confirmed from the combined high performance liquid chromatography-mass spectrometry analysis. Through comparative genome analysis with related Streptomyces species, genes specific to strain CN732 and also those specific to neutrotolerant acidophilic species could be identified, which showed that genes for metabolism in diverse environment were enriched among acidophilic species. In addition, the presence of strain specific genes for carbohydrate active enzymes (CAZyme) along with many other singletons indicated uniqueness of the genetic makeup of strain CN732. The presence of cysteine transpeptidases (sortases) among the BGCs was also observed from this study, which implies their putative roles in the biosynthesis of secondary metabolites. Conclusions: This study highlights the bioactive potential of strain CN732, an acidophilic streptomycete with regard to secondary metabolite production and biodegradation potential using genomics based approach. The comparative genome analysis revealed genes specific to CN732 and also those among acidophilic species, which could give some insights into the adaptation of microbial life in acidic environment.

The genus Streptacidiphilus represents a group of acidophilic actinobacteria within the family Streptomycetaceae, and currently encompasses 15 validly named species, which include five recent additions within the last two years. Considering the potential of the related genera within the family, namely Streptomyces and Kitasatospora, these relatively new members of the family can also be a promising source for novel secondary metabolites. At present, 15 genome data for 11 species from this genus are available, which can provide valuable information on their biology including the potential for metabolite production as well as enzymatic activities in comparison to the neighboring taxa. In this study, the genome sequences of 11 Streptacidiphilus species were subjected to the comparative analysis together with selected Streptomyces and Kitasatospora genomes. This study represents the first comprehensive comparative genomic analysis of the genus Streptacidiphilus. The results indicate that the genomes of Streptacidiphilus contained various secondary metabolite (SM) producing biosynthetic gene clusters (BGCs), some of them exclusively identified in Streptacidiphilus only. Several of these clusters may potentially code for SMs that may have a broad range of bioactivities, such as antibacterial, antifungal, antimalarial and antitumor activities. The biodegradation capabilities of Streptacidiphilus were also explored by investigating the hydrolytic enzymes for complex carbohydrates. Although all genomes were enriched with carbohydrate-active enzymes (CAZymes), their numbers in the genomes of some strains such as Streptacidiphilus carbonis NBRC 100919^T were higher as compared to well-known carbohydrate degrading organisms. These distinctive features of each Streptacidiphilus species make them interesting candidates for future studies with respect to their potential for SM production and enzymatic activities.

Chinese mitten crab (CMC) or Eriocheir sinensis is a strong osmoregulator that can keep rigorous cellular homeostasis. CMC can flourish in freshwater, as well as seawater, habitats and represents the most important species for freshwater aquaculture. Salt stress can have direct effects on several stages (e.g., reproduction, molting, growth, etc.) of the CMC life cycle. To get a better overview of the genes involved in the gills of CMC under different salinity conditions, we conducted an RNA-Seq meta-analysis on the transcriptomes of four publicly available datasets. The meta-analysis identified 405 differentially expressed transcripts (DETs), of which 40% were classified into various transporter classes, including accessory factors and primary active transporters as the major transport classes. A network analysis of the DETs revealed that adaptation to salinity is a highly regulated mechanism in which different functional modules play essential roles. To the best of our knowledge, this study is the first to conduct a transcriptome meta-analysis of gills from crab RNA-Seq datasets under salinity. Additionally, this study is also the first to focus on the differential expression of diverse transporters and channels (transportome) in CMC. Our meta-analysis opens new avenues for a better understanding of the osmoregulation mechanism and the selection of potential transporters associated with salinity change.

Bacteria belonging to the genus Aneurinibacillus within the family Paenibacillaceae are Gram-positive, endospore-forming, and rod-shaped bacteria inhabiting diverse environments. Currently, there are eight validly described species of Aneurinibacillus; however, several unclassified species have also been reported. Aneurinibacillus spp. have shown the potential for producing secondary metabolites (SMs) and demonstrated diverse types of enzyme activities. These features make them promising candidates with industrial implications. At present, genomes of 9 unique species from the genus Aneurinibacillus are available, which can be utilized to decipher invaluable information on their biosynthetic potential as well as enzyme activities. In this work, we performed the comparative genome analyses of nine Aneurinibacillus species representing the first such comprehensive study of this genus at the genome level. We focused on discovering the biosynthetic, biodegradation, and heavy metal resistance potential of this under-investigated genus. The results indicate that the genomes of Aneurinibacillus contain SM-producing regions with diverse bioactivities, including antimicrobial and antiviral activities. Several carbohydrate-active enzymes (CAZymes) and genes involved in heavy metal resistance were also identified. Additionally, a broad range of enzyme classes were also identified in the Aneurinibacillus pan-genomes, making this group of bacteria potential candidates for future investigations with industrial applications.