MBPpred: Proteome-wide detection of membrane lipid-binding proteins using profile Hidden Markov Models

Rapid proteome-wide prediction of lipid-interacting proteins through ligand-guided structural genomics

Lipids are primary metabolites that play essential roles in multiple cellular pathways. Alterations in lipid metabolism and transport are associated with infectious diseases and cancers. As such, proteins involved in lipid synthesis, trafficking, and modification, are targets for therapeutic intervention. The ability to rapidly detect these proteins can accelerate their biochemical and structural characterization. However, it remains challenging to identify lipid binding motifs in proteins due to a lack of conservation at the amino acids level. Therefore, new bioinformatic tools that can detect conserved features in lipid binding sites are necessary. Here, we present Structure-based Lipid-interacting Pocket Predictor (SLiPP), a structural bioinformatics algorithm that uses machine learning to detect protein cavities capable of binding to lipids in experimental and AlphaFold-predicted protein structures. SLiPP, which can be used at proteome-wide scales, predicts lipid binding pockets with an accuracy of 96.8% and a F1 score of 86.9%. Our analyses revealed that the algorithm relies on hydrophobicity-related features to distinguish lipid binding pockets from those that bind to other ligands. Use of the algorithm to detect lipid binding proteins in the proteomes of various bacteria, yeast, and human have produced hits annotated or verified as lipid binding proteins, and many other uncharacterized proteins whose functions are not discernable from sequence alone. Because of its ability to identify novel lipid binding proteins, SLiPP can spur the discovery of new lipid metabolic and trafficking pathways that can be targeted for therapeutic development.

Identification and expression analyses of the olfactory-related genes in different tissues' transcriptome of a predacious soldier beetle, Podabrus annulatus (Coleoptera, Cantharidae)

We sequenced and analyzed the transcriptomes from different tissues of the soldier beetle, Podabrus annulatus (Coleoptera: Cantharidae), and obtained 75.74 Gb clean reads which were assembled into 95,274 unigenes. Among these transcripts, 25,484 unigenes of highly quality were annotated. Based on annotation and tBLASTn results, we identified a total of 101 candidate olfactory-related genes for the first time, including 11 putative odorant-binding proteins (OBPs), 6 chemosensory proteins (CSP), 50 olfactory receptors (ORs), 25 gustatory receptors (GRs), 6 ionotropic receptors (IRs), and 3 sensory neuron membrane proteins (SNMPs). BLASTX best-hit results indicated that these chemosensory genes were most identical to their respective orthologs from Photinus pyralis. Phylogenetic analyses also revealed that the ORs, GRs, and IRs of Podabrus annulatus are closely related to those of Photinus pyralis. The fragment per kilobase per million mapped fragments (FPKM) values showed that the PannOBP2, PannOBP3, and PannOBP10 were predominantly expressed in the antennae, PannOBP1 in the abdomen-thorax, while others were not identified to be tissue-specific. These olfactory-related differentially expressed genes (DEGs) demonstrated different roles in the olfactory system of Podabrus annulatus. This study establishes the groundwork for future research into the molecular mechanism of olfactory recognition in Podabrus annulatus.

Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications

Membrane proteins mediate a wide spectrum of biological processes, such as signal transduction and cell communication. Due to the arduous and costly nature inherent to the experimental process, membrane proteins have long been devoid of well-resolved atomic-level tertiary structures and, consequently, the understanding of their functional roles underlying a multitude of life activities has been hampered. Currently, computational tools dedicated to furthering the structure-function understanding are primarily focused on utilizing intelligent algorithms to address a variety of site-wise prediction problems (e.g., topology and interaction sites), but are scattered across different computing sources. Moreover, the recent advent of deep learning techniques has immensely expedited the development of computational tools for membrane protein-related prediction problems. Given the growing number of applications optimized particularly by manifold deep neural networks, we herein provide a review on the current status of computational strategies mainly in membrane protein type classification, topology identification, interaction site detection, and pathogenic effect prediction. Meanwhile, we provide an overview of how the entire prediction process proceeds, including database collection, data pre-processing, feature extraction, and method selection. This review is expected to be useful for developing more extendable computational tools specific to membrane proteins.

DREAMM: a web-based server for drugging protein-membrane interfaces as a novel workflow for targeted drug design

SUMMARY

The allosteric modulation of peripheral membrane proteins (PMPs) by targeting protein-membrane interactions with drug-like molecules represents a new promising therapeutic strategy for proteins currently considered undruggable. However, the accessibility of protein-membrane interfaces by small molecules has been so far unexplored, possibly due to the complexity of the interface, the limited protein-membrane structural information and the lack of computational workflows to study it. Herein, we present a pipeline for drugging protein-membrane interfaces using the DREAMM (Drugging pRotein mEmbrAne Machine learning Method) web server. DREAMM works in the back end with a fast and robust ensemble machine learning algorithm for identifying protein-membrane interfaces of PMPs. Additionally, DREAMM also identifies binding pockets in the vicinity of the predicted membrane-penetrating amino acids in protein conformational ensembles provided by the user or generated within DREAMM.

AVAILABILITY AND IMPLEMENTATION

DREAMM web server is accessible via https://dreamm.ni4os.eu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification and function analysis of yellow-leaf mutant (YX-yl) of broomcorn millet

BACKGROUND

Broomcorn millet is highly tolerant to drought and barren soil. Changes in chlorophyll content directly affect leaf color, which subsequently leadsleading to poor photosynthetic performance and reduced crop yield. Herein, we isolated a yellow leaf mutant (YX-yl) using a forward genetics approach and evaluated its agronomic traits, photosynthetic pigment content, chloroplast ultrastructure, and chlorophyll precursors. Furthermore, the molecular mechanism of yellowing was explored using transcriptome sequencing.

RESULTS

The YX-yl mutant showed significantly decreased plant height and low yield. The leaves exhibited a yellow-green phenotype and poor photosynthetic capacity during the entire growth period. The content of chlorophyll a, chlorophyll b, and carotenoids in YX-yl leaves was lower than that in wild-type leaves. Chlorophyll precursor analysis results showed that chlorophyll biosynthesis in YX-yl was hindered by the conversion of porphobilinogen to protoporphyrin IX. Examination of chloroplast ultrastructure in the leaves revealed that the chloroplasts of YX-yl accumulated on one side of the cell. Moreover, the chloroplast structure of YX-yl was degraded. The inner and outer membranes of the chloroplasts could not be distinguished well. The numbers of grana and grana thylakoids in the chloroplasts were low. The transcriptome of the yellowing mutant YX-yl was sequenced and compared with that of the wild type. Nine chlorophyll-related genes with significantly different expression profiles were identified: PmUROD, PmCPO, PmGSAM, PmPBDG, PmLHCP, PmCAO, PmVDE, PmGluTR, and PmPNPT. The proteins encoded by these genes were located in the chloroplast, chloroplast membrane, chloroplast thylakoid membrane, and chloroplast matrix and were mainly involved in chlorophyll biosynthesis and redox-related enzyme regulation.

CONCLUSIONS

YX-yl is an ideal material for studying pigment metabolism mechanisms. Changes in the expression patterns of some genes between YX-yl and the wild type led to differences in chloroplast structures and enzyme activities in the chlorophyll biosynthesis pathway, ultimately resulting in a yellowing phenotype in the YX-yl mutant. Our findings provide an insight to the molecular mechanisms of leaf color formation and chloroplast development in broomcorn millet.

Predicting protein–membrane interfaces of peripheral membrane proteins using ensemble machine learning

Abnormal protein–membrane attachment is involved in deregulated cellular pathways and in disease. Therefore, the possibility to modulate protein–membrane interactions represents a new promising therapeutic strategy for peripheral membrane proteins that have been considered so far undruggable. A major obstacle in this drug design strategy is that the membrane-binding domains of peripheral membrane proteins are usually unknown. The development of fast and efficient algorithms predicting the protein–membrane interface would shed light into the accessibility of membrane–protein interfaces by drug-like molecules. Herein, we describe an ensemble machine learning methodology and algorithm for predicting membrane-penetrating amino acids. We utilize available experimental data from the literature for training 21 machine learning classifiers and meta-classifiers. Evaluation of the best ensemble classifier model accuracy yields a macro-averaged F1 score = 0.92 and a Matthews correlation coefficient = 0.84 for predicting correctly membrane-penetrating amino acids on unknown proteins of a validation set. The python code for predicting protein–membrane interfaces of peripheral membrane proteins is available at https://github.com/zoecournia/DREAMM.

Comparative Transcriptomic Analysis Reveals the Effects of Drought on the Biosynthesis of Methyleugenol in Asarum sieboldii Miq

Asarum sieboldii Miq., a perennial herb in the family Aristolochiaceae, is widely used to treat colds, fever, headache and toothache in China. However, little is known about the drought-tolerance characteristics of A. sieboldii. In this study, to elucidate the molecular–genetic mechanisms of drought-stress tolerance of A. sieboldii, RNA-seq was conducted. In total, 53,344 unigenes were assembled, and 28,715 unigenes were annotated. A total of 6444 differential-expression unigenes (DEGs) were found, which were mainly enriched in phenylpropanoid, starch and sucrose metabolic pathways. Drought stress revealed significant up-regulation of the unigenes encoding PAL, C4H, HCT, C3H, CCR and IGS in the methyleugenol-biosynthesis pathway. Under the condition of maintaining drought for 15 days and 30 days, drought stress reduced the biosynthesis of volatile oil by 24% and 38%, respectively, while the production of key medicinal ingredients (such as methyl eugenol) was increased. These results provide valuable information about the diverse mechanisms of drought resistance in the A. sieboldii, and the changes in the expression of the genes involved in methyleugenol biosynthesis in response to drought stress.

Resource allocation strategies among vegetative growth, sexual reproduction, asexual reproduction and defense during growing season of Aconitum kusnezoffii Reichb

Natural plants must actively allocate their limited resources for survival and reproduction. Although vegetative growth, sexual reproduction, asexual reproduction and defense are all basic processes in the life cycle of plants, the strategies used to allocate resources between these processes are poorly understood. These processes are conspicuous in naturally grown Aconitum kusnezoffii Reichb., which makes it a suitable study subject. Here, the morphology, dry matter, total organic carbon, total nitrogen and aconitum alkaloid levels of shoot, principal root (PR) and lateral roots were measured throughout the growing season. Then, transcriptome and metabolite content analyses were performed. We found that vegetative growth began first. After vegetative growth ceased, sexual development began. Flower organ development was accompanied by increased photosynthesis and the PR consumed temporarily stored resources after flower formation. Asexual propagule development initiated earlier than sexual reproduction and kept accumulating resources after that. Development was slow before flower formation, mainly manifesting as increasing length; then, after flower formation it accelerated via enhanced material transport and accumulation. Defense compounds were maintained at low levels before flowering. In particular, the turnover of defense compounds was enhanced before and after flower bud emergence, providing resources for other processes. After flower formation, defense compounds were accumulated. The pattern found herein provides a vivid example for further studies on resource allocation strategies. The exciting finding that the PR, as a more direct storage site for photosynthate, is a buffer unit for resources, and that defense compounds can be reused for other processes, suggests a need to explore potential mechanisms.

Characterization and analysis of full-length transcriptomes from two grasshoppers, Gomphocerus licenti and Mongolotettix japonicus

Acrididae are diverse in size, body shape, behavior, ecology and life history; widely distributed; easy to collect; and important to agriculture. They represent promising model candidates for functional genomics, but their extremely large genomes have hindered this research; establishing a reference transcriptome for a species is the primary means of obtaining genetic information. Here, two Acrididae species, Gomphocerus licenti and Mongolotettix japonicus, were selected for full-length (FL) PacBio transcriptome sequencing. For G. licenti and M. japonicus, respectively, 590,112 and 566,165 circular consensus sequences (CCS) were generated, which identified 458,131 and 428,979 full-length nonchimeric (FLNC) reads. After isoform-level clustering, next-generation sequencing (NGS) short sequences were used for error correction, and remove redundant sequences with CD-HIT, 17,970 and 16,766 unigenes were generated for G. licenti and M. japonicus. In addition, we obtained 17,495 and 16,373 coding sequences, 1,082 and 813 transcription factors, 11,840 and 10,814 simple sequence repeats, and 905 and 706 long noncoding RNAs by analyzing the transcriptomes of G. licenti and M. japonicus, respectively, and 15,803 and 14,846 unigenes were annotated in eight functional databases. This is the first study to sequence FL transcriptomes of G. licenti and M. japonicus, providing valuable genetic resources for further functional genomics research.

Gill transcriptomes reveal expression changes of genes related with immune and ion transport under salinity stress in silvery pomfret (Pampus argenteus)

Salinity is a major ecological factor in the marine environment, and extremely important for the survival, development, and growth of fish. In this study, gill transcriptomes were examined by high-throughput sequencing at three different salinities (12 ppt as low salinity, 22 ppt as control salinity, and 32 ppt as high salinity) in an importantly economical fish silvery pomfret. A total of 187 genes were differentially expressed, including 111 up-regulated and 76 down-regulated transcripts in low-salinity treatment group and 107 genes differentially expressed, including 74 up-regulated and 33 down-regulated transcripts in high-salinity treatment group compared with the control group, respectively. Some pathways including NOD-like receptor signaling pathway, cytokine-cytokine receptor interaction, Toll-like receptor pathway, cardiac muscle contraction, and vascular smooth muscle contraction were significantly enriched. qPCR analysis further confirmed that mRNA expression levels of immune (HSP90A, IL-1β, TNFα, TLR2, IP-10, MIG, CCL19, and IL-11) and ion transport-related genes (WNK2, NPY2R, CFTR, and SLC4A2) significantly changed under salinity stress. Low salinity stress caused more intensive expression changes of immune-related genes than high salinity. These results imply that salinity stress may affect immune function in addition to regulating osmotic pressure in silvery pomfret.

Deep Sequencing and Analysis of Transcriptomes of Pinus koraiensis Sieb. & Zucc

The objective of this research was to study the differences in endogenous hormone levels and the genes related to reproductive development in Chinese pinenut (Pinus koraiensis) trees of different ages. The apical buds of P. koraiensis were collected from 2-, 5-, 10-, 15-, and 30-year-old plants and also from grafted plants. There were three replicates from each group used for transcriptome sequencing. After assembly and annotation, we identified the differentially expressed genes (DEGs) and performed enrichment analysis, pathway analysis, and expression analysis of the DEGs in each sample. The results showed that unigenes related to reproductive development, such as c64070.graph_c0 and c68641.graph_c0, were expressed at relatively low levels at young ages, and that the relative expression gradually increased with increasing plant age. In addition the highest expression levels were reached around 10 and 15 years of age, after which they gradually decreased. Moreover, some unigenes, such as c61855.graph_c0, were annotated as abscisic acid hydroxylase genes, and the expression of c61855.graph_c0 gradually declined with increasing age in P. koraiensis.

Global transcriptome analysis of alfalfa reveals six key biological processes of senescent leaves

Leaf senescence is a complex organized developmental stage limiting the yield of crop plants, and alfalfa is an important forage crop worldwide. However, our understanding of the molecular mechanism of leaf senescence and its influence on biomass in alfalfa is still limited. In this study, RNA sequencing was utilized to identify differentially expressed genes (DEGs) in young, mature, and senescent leaves, and the functions of key genes related to leaf senescence. A total of 163,511 transcripts and 77,901 unigenes were identified from the transcriptome, and 5,133 unigenes were differentially expressed. KEGG enrichment analyses revealed that ribosome and phenylpropanoid biosynthesis pathways, and starch and sucrose metabolism pathways are involved in leaf development and senescence in alfalfa. GO enrichment analyses exhibited that six clusters of DEGs are involved in leaf morphogenesis, leaf development, leaf formation, regulation of leaf development, leaf senescence and negative regulation of the leaf senescence biological process. The WRKY and NAC families of genes mainly consist of transcription factors that are involved in the leaf senescence process. Our results offer a novel interpretation of the molecular mechanisms of leaf senescence in alfalfa.

Sex- and tissue-specific transcriptome analyses and expression profiling of olfactory-related genes in Ceracris nigricornis Walker (Orthoptera: Acrididae)

BACKGROUND

The sophisticated insect olfactory system plays an important role in recognizing external odors and enabling insects to adapt to environment. Foraging, host seeking, mating, ovipositing and other forms of chemical communication are based on olfaction, which requires the participation of multiple olfactory genes. The exclusive evolutionary trend of the olfactory system in Orthoptera insects is an excellent model for studying olfactory evolution, but limited olfaction research is available for these species. The olfactory-related genes of Ceracris nigricornis Walker (Orthoptera: Acrididae), a severe pest of bamboos, have not yet been reported.

RESULTS

We sequenced and analyzed the transcriptomes from different tissues of C. nigricornis and obtained 223.76 Gb clean data that were assembled into 43,603 unigenes with an N50 length of 2235 bp. Among the transcripts, 66.79% of unigenes were annotated. Based on annotation and tBLASTn results, 112 candidate olfactory-related genes were identified for the first time, including 20 odorant-binding proteins (OBPs), 10 chemosensory-binding proteins (CSPs), 71 odorant receptors (ORs), eight ionotropic receptors (IRs) and three sensory neuron membrane proteins (SNMPs). The fragments per kilobase per million mapped fragments (FPKM) values showed that most olfactory-related differentially expressed genes (DEGs) were enriched in the antennae, and these results were confirmed by detecting the expression of olfactory-related genes with quantitative real-time PCR (qRT-PCR). Among these antennae-enriched genes, some were sex-biased, indicating their different roles in the olfactory system of C. nigricornis.

CONCLUSIONS

This study provides the first comprehensive list and expression profiles of olfactory-related genes in C. nigricornis and a foundation for functional studies of these olfactory-related genes at the molecular level.

PerMemDB: A database for eukaryotic peripheral membrane proteins

The majority of all proteins in cells interact with membranes either permanently or temporarily. Peripheral membrane proteins form transient complexes with membrane proteins and/or lipids, via non-covalent interactions and are of outmost importance, due to numerous cellular functions in which they participate. In an effort to collect data regarding this heterogeneous group of proteins we designed and constructed a database, called PerMemDB. PerMemDB is currently the most complete and comprehensive repository of data for eukaryotic peripheral membrane proteins deposited in UniProt or predicted with the use of MBPpred - a computational method that specializes in the detection of proteins that interact non-covalently with membrane lipids, via membrane binding domains. The first version of the database contains 231,770 peripheral membrane proteins from 1009 organisms. All entries have cross-references to other databases, literature references and annotation regarding their interactions with other proteins. Moreover, additional sequence annotation of the characteristic domains that allow these proteins to interact with membranes is available, due to the application of MBPpred. Through the web interface of PerMemDB, users can browse the contents of the database, submit advanced text searches and BLAST queries against the protein sequences deposited in PerMemDB. We expect this repository to serve as a source of information that will allow the scientific community to gain a deeper understanding of the evolution and function of peripheral membrane proteins via the enhancement of proteome-wide analyses. The database is available at: http://bioinformatics.biol.uoa.gr/db=permemdb.