FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties

The evolutionary novelty of insect defensins: from bacterial killing to toxin neutralization

Insect host defense comprises two complementary dimensions, microbial killing-mediated resistance and microbial toxin neutralization-mediated resilience, both jointly providing protection against pathogen infections. Insect defensins are a class of effectors of innate immunity primarily responsible for resistance to Gram-positive bacteria. Here, we report a newly originated gene from an ancestral defensin via genetic deletion following gene duplication in Drosophila virilis, which confers an enhanced resilience to Gram-positive bacterial infection. This gene encodes an 18-mer arginine-rich peptide (termed DvirARP) with differences from its parent gene in its pattern of expression, structure and function. DvirARP specifically expresses in D. virilis female adults with a constitutive manner. It adopts a novel fold with a 310 helix and a two CXC motif-containing loop stabilized by two disulfide bridges. DvirARP exhibits no activity on the majority of microorganisms tested and only a weak activity against two Gram-positive bacteria. DvirARP knockout flies are viable and have no obvious defect in reproductivity but they are more susceptible to the DvirARP-resistant Staphylococcus aureus infection than the wild type files, which can be attributable to its ability in neutralization of the S. aureus secreted toxins. Phylogenetic distribution analysis reveals that DvirARP is restrictedly present in the Drosophila subgenus, but independent deletion variations also occur in defensins from the Sophophora subgenus, in support of the evolvability of this class of immune effectors. Our work illustrates for the first time how a duplicate resistance-mediated gene evolves an ability to increase the resilience of a subset of Drosophila species against bacterial infection.

A single mutation in the mosquito (Aedes aegypti) olfactory receptor 8 causes loss of function to 1-octen-3-ol

The host-seeking behavior of mosquitoes have long been established to be primarily odor-mediated. In this process, olfactory receptors (Ors) play a critical role. 1-Octen-3-ol is a common volatile compound that is attractive to hematophagous arthropods such as mosquitos. The olfactory receptor 8 (AaOr8) on the tip of the stylet and maxillary palp of Aedes aegypti is tuned to 1-octen-3-ol, which is required for mosquitoes to quickly find blood vessels from a vertebrate host. However, little is known about the interaction of AaOr8 with 1-octen-3-ol which was studied in vivo and in silico in this study. The molecular binding poses and energies between ligands and the receptor were investigated. Three mutants of AaOr8 were cloned and compared with in vivo calcium imaging utilizing heterologous expression systems. As a result, our findings imply that a genetic disruption including targeted modification of Ors genes may be used to reduce mosquito bites.

Design, synthesis and preliminary biological evaluation of rivastigmine-INDY hybrids as multitarget ligands against Alzheimer's disease by targeting butyrylcholinesterase and DYRK1A/CLK1 kinases

Based on a multitarget approach implementing rivastigmine-INDY hybrids 1, we identified a set of pseudo-irreversible carbamate-type inhibitors of eqBuChE that, after carbamate transfer at the active site serine residue, released the corresponding INDY analogues 2 endowed with hDYRK1A/hCLK1 kinases inhibitory properties. A SAR study and molecular docking investigation of both series of compounds 1 and 2 revealed that appropriate structural modifications at the carbamate moiety and at the N-appendage of the benzothiazole core led to potent and selective eqBuChE inhibitors with IC50 up to 27 nM and potent hDYRK1A and hCLK1 inhibitors with IC50 up to 106 nM and 17 nM respectively. Pleasingly, identification of the matched pair of compounds 1b/2b with a good balance between inhibition of eqBuChE and hDYRK1A/hCLK1 kinases (IC50 = 68 nM and IC50 = 529/54 nM, respectively) further validated our multitarget approach based on a sequential mechanism of action. In addition, target compound 1b exhibited a suitable ADMET profile, including good brain permeability and high stability in PBS, encouraging further biological investigation as a drug candidate.

Draft genome sequence of novel Candidatus Ornithobacterium hominis carrying antimicrobial resistance genes in Egypt

BACKGROUND

Candidatus Ornithobacterium hominis (O. hominis), which was identified in nasopharyngeal swabs from Egypt, has been associated with respiratory disorders in humans. O. hominis, a recently identified member of the Flavobacteriaceae family, belongs to the largest family within the Bacteroidetes phylum. This family includes hundreds of species and 90 genera, including major human pathogens such as Capnocytophaga canimorsus and Elizabethkingia meningoseptica. Herein, we presented two draft genome assemblies of O. hominis that were extracted from metagenomic data using the Illumina sequencing method. The alignment of reads against the O. hominis genome was accomplished using BLASTN, and the reads with significant hits were extracted using Seqtk and assembled using SPAdes. The primary goal of this study was to obtain a more profound understanding of the genomic landscape of O. hominis, with an emphasis on identifying the associated virulence, antimicrobial genes, and distinct defense mechanisms to shed light on the potential role of O. hominis in human respiratory infections.

RESULTS

The genome size was estimated to be 1.84 Mb, including 1,931,660 base pairs (bp), with 1,837 predicted coding regions and a G+C content of 35.62%. Genes encoding gliding motility, antibiotic resistance (20 genes), and the toxA gene were all included in the genome assembly. Gliding motility lipoproteins (GldD, GldJ, GldN, and GldH) and the gliding motility-associated ABC transporter substrate-binding protein, which acts as a crucial virulence mechanism in Flavobacterium species, were identified. The genome contained unique genes encoding proteins, such as the ParE1 toxin that defend against the actions of quinolone and other antibiotics. The cobalt-zinc-cadmium resistance gene encoding the protein CzcB, which is necessary for metal resistance, urease regulation, and colonization, was also detected. Several multidrug resistance genes encoding proteins were identified, such as MexB, MdtK, YheI, and VanC.

CONCLUSION

Our study focused on identifying virulence factors, and antimicrobial resistance genes present in the core genome of O. hominis. These findings provide valuable insights into the potential pathogenicity and antibiotic susceptibility of O. hominis.

Identification of selective inhibitors for phosphodiesterase 5A using e-pharmacophore modelling and large-scale virtual screening-based structure guided drug discovery approaches

The inhibition of Phosphodiesterase 5A (PDEA5) has the potential to modulate pulmonary arterial hypertension and cardiovascular diseases. Exploring the cross-reactivity of clinically available PDE5A therapeutics with PDE6A is intriguing in order to develop highly selective PDE5A compounds in cardiovascular arena. In the current study, we leveraged e-pharmacophore based screening and molecular dynamics (MD) simulation to discover more selective PDE5A inhibitors as compared to the PDE6A catalytic domain. e-Pharmacophore based mapping of the CoCoCo database (7 million compounds: ∼ 150,000,000 conformers), followed by Glide docking, MM-GBSA, and protein-inhibitor interaction analysis, revealed 1536427, 4832637 and 6788240 as stable, tight binders of PDE5A instead of PDE6A. These compounds adhere to Lipinski Rule of Five (RO5) and ADME/Tox criteria. MD simulations analysis showed that 1536427 stays stable and tightly binds to catalytic (Q-region) core of PDE5A catalytic domain as compared to sildenafil. Pronounced inward motions of the hydrophobic (H-region) and Lid region indicate the closure of PDE5A-1536427 complex, whereas this region in PDE6A-1536427 is more open. Significant differences in the interactions, stability, and dynamics of 1536427 were observed in the catalytic domain of PDE6A, demonstrating less specificity for PDE6A in comparison to PDE5A. After lead optimization and therapeutic interventions, this proposed lead may emerge as a promising PDE5A selective inhibitor.Communicated by Ramaswamy H. Sarma.

nPoRe: n-polymer realigner for improved pileup-based variant calling

Despite recent improvements in nanopore basecalling accuracy, germline variant calling of small insertions and deletions (INDELs) remains poor. Although precision and recall for single nucleotide polymorphisms (SNPs) now exceeds 99.5%, INDEL recall remains below 80% for standard R9.4.1 flow cells. We show that read phasing and realignment can recover a significant portion of false negative INDELs. In particular, we extend Needleman-Wunsch affine gap alignment by introducing new gap penalties for more accurately aligning repeated n-polymer sequences such as homopolymers ([Formula: see text]) and tandem repeats ([Formula: see text]). At the same precision, haplotype phasing improves INDEL recall from 63.76 to [Formula: see text] and nPoRe realignment improves it further to [Formula: see text].

Designing of potent anti-diabetic molecules by targeting SIK2 using computational approaches

Diabetes mellitus (DM) is one of the major health problems worldwide. WHO have estimated that 439 million people may have DM by the year 2030. Several classes of drugs such as sulfonylureas, meglitinides, thiazolidinediones etc. are available to manage this disease, however, there is no cure for this disease. Salt inducible kinase 2 (SIK2) is expressed several folds in adipose tissue than in normal tissues and thus SIK2 is one of the attractive targets for DM treatment. SIK2 inhibition improves glucose homeostasis. Several analogues have been reported and experimentally proven against SIK for DM treatment. But, identifying potential SIK2 inhibitors with improved efficacy and good pharmacokinetic profiles will be helpful for the effective treatment of DM. The objective of the present study is to identify selective SIK2 inhibitors with good pharmacokinetic profiles. Due to the unavailability of SIK2 structure, the modeled structure of SIK2 will be an important to understand the atomic level of SIK2 inhibitors in the binding site pocket. In this study, different molecular modeling studies such as Homology Modeling, Molecular Docking, Pharmacophore-based virtual screening, MD simulations, Density Functional Theory calculations and WaterMap analysis were performed to identify potential SIK2 inhibitors. Five molecules from different databases such as Binding_4067, TosLab_837067, NCI_349155, Life chemicals_ F2565-0113, Enamine_7623111186 molecules were identified as possible SIK2 inhibitors.

Antiproliferative efficacy of the antioxidant bioactive compounds of defatted seeds of Azadirachta indica and Momordica charantia against the regulatory function of tumor suppressor gene inducing oral carcinoma

The present study focuses on the antiproliferative activity of polyphenolic flavonoids found in defatted seeds of Azadirachta indica and Momordica charantia with the regulatory function of tumor suppressor genes inducing Oral Squamous Cell Carcinoma. Polyphenolic flavonoid in extracts was characterized using chromatographic analysis and has confirmed the presence of quercetin, rutin and tannic acid in the extracts of A. indica and M. charantia. According to DPPH assay and reducing power assays, free radical scavenging was found to be high in ethanolic extract of defatted seeds. Antiproliferative efficacies of defatted seed extracts against KB cell line (mouth) were studied by MTT assay and revealed that aqueous extract of defatted seeds of M. charantia has exhibited maximum antiproliferative activity against KB cells. Antioxidant activity of defatted seed extracts were observed on treated KB cells by determining enzymatic activity (SOD, Cat, and GST) and nonenzyme content (GSH and MDA Content). Using the AutoDock tool, quercetin, rutin and tannin acid revealed that mutant p53, TWIST related protein, TGF-β and Snail I have the best binging energy results. MD simulation was observed on best docking results between the molecule and identified flavonoid by Desmond V 5.9 package . This leads to the conclusion that bioactive extracts with antiproliferative activity, antioxidant capacity and polyphenols with binding efficacy against tumor suppressor gene regulatory function could be used as a herbal remedy.Communicated by Ramaswamy H. Sarma.

SelfAT-Fold: Protein Fold Recognition Based on Residue-Based and Motif-Based Self-Attention Networks

The protein fold recognition is a fundamental and crucial step of tertiary structure determination. In this regard, several computational predictors have been proposed. Recently, the predictive performance has been obviously improved by the fold-specific features generated by deep learning techniques. However, these methods failed to measure the global associations among residues or motifs along the protein sequences. Furthermore, these deep learning techniques are often treated as black boxes without interpretability. Inspired by the similarities between protein sequences and natural language sentences, we applied the self-attention mechanism derived from natural language processing (NLP) field to protein fold recognition. The motif-based self-attention network (MSAN) and the residue-based self-attention network (RSAN) were constructed based on a training set to capture the global associations among the structure motifs and residues along the protein sequences, respectively. The fold-specific attention features trained and generated from the training set were then combined with Support Vector Machines (SVMs) to predict the samples in the widely used LE benchmark dataset, which is fully independent from the training set. Experimental results showed that the proposed two SelfAT-Fold predictors outperformed 34 existing state-of-the-art computational predictors. The two SelfAT-Fold predictors were further tested on an independent dataset SCOP_TEST, and they can achieve stable performance. Furthermore, the fold-specific attention features can be used to analyse the characteristics of protein folds. The trained models and data of SelfAT-Fold can be downloaded from http://bliulab.net/selfAT_fold/.

Structure-guided mutagenesis of a mucin-selective metalloprotease from Akkermansia muciniphila alters substrate preferences

Akkermansia muciniphila, a mucin-degrading microbe found in the human gut, is often associated with positive health outcomes. The abundance of A. muciniphila is modulated by the presence and accessibility of nutrients, which can be derived from diet or host glycoproteins. In particular, the ability to degrade host mucins, a class of proteins carrying densely O-glycosylated domains, provides a competitive advantage in the sustained colonization of niche mucosal environments. Although A. muciniphila is known to rely on mucins as a carbon and nitrogen source, the enzymatic machinery used by this microbe to process mucins in the gut is not yet fully characterized. Here, we focus on the mucin-selective metalloprotease, Amuc_0627 (AM0627), which is known to cleave between adjacent residues carrying truncated core 1 O-glycans. We showed that this enzyme is capable of degrading purified mucin 2 (MUC2), the major protein component of mucus in the gut. An X-ray crystal structure of AM0627 (1.9 Å resolution) revealed O-glycan–binding residues that are conserved between structurally characterized enzymes from the same family. We further rationalized the substrate cleavage motif using molecular modeling to identify nonconserved glycan-interacting residues. We conclude that mutagenesis of these residues resulted in altered substrate preferences down to the glycan level, providing insight into the structural determinants of O-glycan recognition.

Worldwide Protein Data Bank (wwPDB): A virtual treasure for research in biotechnology

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RSCB PDB) provides a wide range of digital data regarding biology and biomedicine. This huge internet resource involves a wide range of important biological data, obtained from experiments around the globe by different scientists. The Worldwide Protein Data Bank (wwPDB) represents a brilliant collection of 3D structure data associated with important and vital biomolecules including nucleic acids (RNAs and DNAs) and proteins. Moreover, this database accumulates knowledge regarding function and evolution of biomacromolecules which supports different disciplines such as biotechnology. 3D structure, functional characteristics and phylogenetic properties of biomacromolecules give a deep understanding of the biomolecules' characteristics. An important advantage of the wwPDB database is the data updating time, which is done every week. This updating process helps users to have the newest data and information for their projects. The data and information in wwPDB can be a great support to have an accurate imagination and illustrations of the biomacromolecules in biotechnology. As demonstrated by the SARS-CoV-2 pandemic, rapidly reliable and accessible biological data for microbiology, immunology, vaccinology, and drug development are critical to address many healthcare-related challenges that are facing humanity. The aim of this paper is to introduce the readers to wwPDB, and to highlight the importance of this database in biotechnology, with the expectation that the number of scientists interested in the utilization of Protein Data Bank's resources will increase substantially in the coming years.

Improving protein fold recognition using triplet network and ensemble deep learning

Protein fold recognition is a critical step toward protein structure and function prediction, aiming at providing the most likely fold type of the query protein. In recent years, the development of deep learning (DL) technique has led to massive advances in this important field, and accordingly, the sensitivity of protein fold recognition has been dramatically improved. Most DL-based methods take an intermediate bottleneck layer as the feature representation of proteins with new fold types. However, this strategy is indirect, inefficient and conditional on the hypothesis that the bottleneck layer's representation is assumed as a good representation of proteins with new fold types. To address the above problem, in this work, we develop a new computational framework by combining triplet network and ensemble DL. We first train a DL-based model, termed FoldNet, which employs triplet loss to train the deep convolutional network. FoldNet directly optimizes the protein fold embedding itself, making the proteins with the same fold types be closer to each other than those with different fold types in the new protein embedding space. Subsequently, using the trained FoldNet, we implement a new residue-residue contact-assisted predictor, termed FoldTR, which improves protein fold recognition. Furthermore, we propose a new ensemble DL method, termed FSD_XGBoost, which combines protein fold embedding with the other two discriminative fold-specific features extracted by two DL-based methods SSAfold and DeepFR. The Top 1 sensitivity of FSD_XGBoost increases to 74.8% at the fold level, which is ~9% higher than that of the state-of-the-art method. Together, the results suggest that fold-specific features extracted by different DL methods complement with each other, and their combination can further improve fold recognition at the fold level. The implemented web server of FoldTR and benchmark datasets are publicly available at http://csbio.njust.edu.cn/bioinf/foldtr/.

COSMIC Cancer Gene Census 3D database: understanding the impacts of mutations on cancer targets

Mutations in hallmark genes are believed to be the main drivers of cancer progression. These mutations are reported in the Catalogue of Somatic Mutations in Cancer (COSMIC). Structural appreciation of where these mutations appear, in protein-protein interfaces, active sites or deoxyribonucleic acid (DNA) interfaces, and predicting the impacts of these mutations using a variety of computational tools are crucial for successful drug discovery and development. Currently, there are 723 genes presented in the COSMIC Cancer Gene Census. Due to the complexity of the gene products, structures of only 87 genes have been solved experimentally with structural coverage between 90% and 100%. Here, we present a comprehensive, user-friendly, web interface (https://cancer-3d.com/) of 714 modelled cancer-related genes, including homo-oligomers, hetero-oligomers, transmembrane proteins and complexes with DNA, ribonucleic acid, ligands and co-factors. Using SDM and mCSM software, we have predicted the impacts of reported mutations on protein stability, protein-protein interfaces affinity and protein-nucleic acid complexes affinity. Furthermore, we also predicted intrinsically disordered regions using DISOPRED3.

An Analysis of Structure-function Co-relation between GLI Oncoprotein and HLA Immune-gene Transcriptional Regulation through Molecular Docking

Background:

GLI proteins play a significant role in the transduction of the Hedgehog (Hh) signaling pathway. A variety of human cancers, including the brain, gastrointestinal, lung, breast, and prostate cancers, demonstrate inappropriate activation of this pathway. GLI helps in proliferation and has an inhibitory role in the differentiation of hematopoietic stem cells. Malignancies may have a defect in differentiation. Different types of malignancies and undifferentiated cells have a low level of HLA expression on their cell surface.

Objective:

Human Leukocytic Antigen (HLA) downregulation is frequently observed in cancer cells. This work is aimed to hypothesize whether this downregulation of HLA molecules is GLI oncoprotein mediated or not. To understand the roles of different types of GLI oncoproteins on different classes of HLA transcriptional machinery was carried out through structure-based modeling and molecular docking studies.

Methods:

To investigate the role of GLI in HLA expression /downregulation is Hh-GLI mediated or not, molecular docking based computational interaction studies were performed between different GLI proteins (GLI1, GLI2, and GLI3) with TATA box binding protein (TBP) and compare the binding efficiencies of different HLA gene (both HLA class I and –II) regulating transcription factors (RelA, RFX5, RFXAP, RFXANK, CIITA, CREB1, and their combinations) with TBP. Due to unavailability of 3D protein structures of GLI2 and cyclin D2 (a natural ligand of GLI1) were modelled followed by structural validation by Ramachandran plot analysis.

Results:

GLI proteins especially, GLI1 and GLI2, have almost similar binding energy of RFX5-RFXANK- RFXAP and CIITA multi-protein complex to TBP but has lower binding energy between RelA to TBP.

Conclusion:

This study suggests that HLA class I may not be downregulated by GLI; however, over-expression of GLI1 is may be responsible for HLA class II downregulation. Thus this protein may be responsible for the maintenance of the undifferentiated state of malignant cells. This study also suggests the implicative role of GLI1 in the early definitive stage of hematopoiesis.

Inter-cellular CRISPR screens reveal regulators of cancer cell phagocytosis

Monoclonal antibody therapies targeting tumour antigens drive cancer cell elimination in large part by triggering macrophage phagocytosis of cancer cells^1-7. However, cancer cells evade phagocytosis using mechanisms that are incompletely understood. Here we develop a platform for unbiased identification of factors that impede antibody-dependent cellular phagocytosis (ADCP) using complementary genome-wide CRISPR knockout and overexpression screens in both cancer cells and macrophages. In cancer cells, beyond known factors such as CD47, we identify many regulators of susceptibility to ADCP, including the poorly characterized enzyme adipocyte plasma membrane-associated protein (APMAP). We find that loss of APMAP synergizes with tumour antigen-targeting monoclonal antibodies and/or CD47-blocking monoclonal antibodies to drive markedly increased phagocytosis across a wide range of cancer cell types, including those that are otherwise resistant to ADCP. Additionally, we show that APMAP loss synergizes with several different tumour-targeting monoclonal antibodies to inhibit tumour growth in mice. Using genome-wide counterscreens in macrophages, we find that the G-protein-coupled receptor GPR84 mediates enhanced phagocytosis of APMAP-deficient cancer cells. This work reveals a cancer-intrinsic regulator of susceptibility to antibody-driven phagocytosis and, more broadly, expands our knowledge of the mechanisms governing cancer resistance to macrophage phagocytosis.

FoldRec-C2C: protein fold recognition by combining cluster-to-cluster model and protein similarity network

As a key for studying the protein structures, protein fold recognition is playing an important role in predicting the protein structures associated with COVID-19 and other important structures. However, the existing computational predictors only focus on the protein pairwise similarity or the similarity between two groups of proteins from 2-folds. However, the homology relationship among proteins is in a hierarchical structure. The global protein similarity network will contribute to the performance improvement. In this study, we proposed a predictor called FoldRec-C2C to globally incorporate the interactions among proteins into the prediction. For the FoldRec-C2C predictor, protein fold recognition problem is treated as an information retrieval task in nature language processing. The initial ranking results were generated by a surprised ranking algorithm Learning to Rank, and then three re-ranking algorithms were performed on the ranking lists to adjust the results globally based on the protein similarity network, including seq-to-seq model, seq-to-cluster model and cluster-to-cluster model (C2C). When tested on a widely used and rigorous benchmark dataset LINDAHL dataset, FoldRec-C2C outperforms other 34 state-of-the-art methods in this field. The source code and data of FoldRec-C2C can be downloaded from http://bliulab.net/FoldRec-C2C/download.

Structure-Guided Computational Approaches to Unravel Druggable Proteomic Landscape of Mycobacterium leprae

Leprosy, caused by Mycobacterium leprae (M. leprae), is treated with a multidrug regimen comprising Dapsone, Rifampicin, and Clofazimine. These drugs exhibit bacteriostatic, bactericidal and anti-inflammatory properties, respectively, and control the dissemination of infection in the host. However, the current treatment is not cost-effective, does not favor patient compliance due to its long duration (12 months) and does not protect against the incumbent nerve damage, which is a severe leprosy complication. The chronic infectious peripheral neuropathy associated with the disease is primarily due to the bacterial components infiltrating the Schwann cells that protect neuronal axons, thereby inducing a demyelinating phenotype. There is a need to discover novel/repurposed drugs that can act as short duration and effective alternatives to the existing treatment regimens, preventing nerve damage and consequent disability associated with the disease. Mycobacterium leprae is an obligate pathogen resulting in experimental intractability to cultivate the bacillus in vitro and limiting drug discovery efforts to repositioning screens in mouse footpad models. The dearth of knowledge related to structural proteomics of M. leprae, coupled with emerging antimicrobial resistance to all the three drugs in the multidrug therapy, poses a need for concerted novel drug discovery efforts. A comprehensive understanding of the proteomic landscape of M. leprae is indispensable to unravel druggable targets that are essential for bacterial survival and predilection of human neuronal Schwann cells. Of the 1,614 protein-coding genes in the genome of M. leprae, only 17 protein structures are available in the Protein Data Bank. In this review, we discussed efforts made to model the proteome of M. leprae using a suite of software for protein modeling that has been developed in the Blundell laboratory. Precise template selection by employing sequence-structure homology recognition software, multi-template modeling of the monomeric models and accurate quality assessment are the hallmarks of the modeling process. Tools that map interfaces and enable building of homo-oligomers are discussed in the context of interface stability. Other software is described to determine the druggable proteome by using information related to the chokepoint analysis of the metabolic pathways, gene essentiality, homology to human proteins, functional sites, druggable pockets and fragment hotspot maps.

Development of Novel Potential Pleiotropic Compounds of Interest in Alzheimer's Disease Treatment through Rigidification Strategy

The development of Multi-Target Directed Ligand is of clear interest for the treatment of multifactorial pathology such as Alzheimer’s disease (AD). In this context, acetylcholinesterase (AChE) inhibitors have been modulated in order to generate novel pleiotropic compounds targeting a second protein of therapeutic interest in AD. Among them, donecopride was the first example of a dual acetylcholinesterase inhibitor and 5-HT₄ receptor agonist. In order to explore the structural diversity around this preclinical candidate we have explored the preparation of novel constrained analogs through late-stage rigidification strategy. A series of phenylpyrazoles was prepared in a late-stage functionalization process and all compounds were evaluated in vitro towards AChE and 5-HTRs. A docking study was performed in order to better explain the observed SAR towards AChE, 5-HT₄R and 5-HT₆R and this study led to the description of novel ligand targeting both AChE and 5-HT₆R.

Diverse evolutionary origins of microbial [4 + 2]-cyclases in natural product biosynthesis

Natural [4 + 2]-cyclases catalyze concerted cycloaddition during biosynthesis of over 400 natural products reported. Microbial [4 + 2]-cyclases are structurally diverse with a broad range of substrates. Thus far, about 52 putative microbial [4 + 2]-cyclases of 13 different types have been characterized, with over 20 crystal structures. However, how these cyclases have evolved during natural product biosynthesis remains elusive. Structural and phylogenetic analyses suggest that these different types of [4 + 2]-cyclases might have diverse evolutionary origins, such as reductases, dehydratases, methyltransferases, oxidases, etc. Divergent evolution of enzyme function might have occurred in these different families. Understanding the independent evolutionary history of these cyclases would provide new insights into their catalysis mechanisms and the biocatalyst design.

SARS-CoV-2 3D database: understanding the coronavirus proteome and evaluating possible drug targets

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a rapidly growing infectious disease, widely spread with high mortality rates. Since the release of the SARS-CoV-2 genome sequence in March 2020, there has been an international focus on developing target-based drug discovery, which also requires knowledge of the 3D structure of the proteome. Where there are no experimentally solved structures, our group has created 3D models with coverage of 97.5% and characterized them using state-of-the-art computational approaches. Models of protomers and oligomers, together with predictions of substrate and allosteric binding sites, protein-ligand docking, SARS-CoV-2 protein interactions with human proteins, impacts of mutations, and mapped solved experimental structures are freely available for download. These are implemented in SARS CoV-2 3D, a comprehensive and user-friendly database, available at https://sars3d.com/. This provides essential information for drug discovery, both to evaluate targets and design new potential therapeutics.

Synthesis, Biological Evaluation, and 3D-QSAR Studies of N-(Substituted pyridine-4-yl)-1-(substituted phenyl)-5-trifluoromethyl-1H-pyrazole-4-carboxamide Derivatives as Potential Succinate Dehydrogenase Inhibitors

A series of new fungicides that can inhibit the succinate dehydrogenase (SDH) was classified and named as SDH inhibitors by the Fungicide Resistance Action Committee in 2009. To develop more potential SDH inhibitors, we designed and synthesized a novel series of N-(substituted pyridine-4-yl)-1-(substituted phenyl)-5-trifluoromethyl-1H-pyrazole-4-carboxamide derivatives, 4a-4i, namely, 5a-5h, 6a-6h, and 7a-7j. The bioassay results demonstrated that some title compounds exhibited excellent antifungal activity against four tested phytopathogenic fungi (Gibberella zea, Fusarium oxysporum, Cytospora mandshurica, and Phytophthora infestans). The EC₅₀ values were 1.8 μg/mL for 7a against G. zeae, 1.5 and 3.6 μg/mL for 7c against F. oxysporum and C. mandshurica, respectively, and 6.8 μg/mL for 7f against P. infestans. The SDH enzymatic activity testing revealed that the IC₅₀ values of 4c, 5f, 7f, and penthiopyrad were 12.5, 135.3, 6.9, and 223.9 μg/mL, respectively. The molecular docking results of this series of title compounds with SDH model demonstrated that the compounds could completely locate inside of the pocket, the body fragment formed H bonds, and the phenyl ring showed a π-π interaction with Arg59, suggesting that these novel 5-trifluoromethyl-pyrazole-4-carboxamide derivatives might target SDH. These results could provide a benchmark for understanding the antifungal activity against the phytopathogenic fungus P. infestans and prompt us to discover more potent SDH inhibitors.

Protein domain identification methods and online resources

Protein domains are the basic units of proteins that can fold, function, and evolve independently. Knowledge of protein domains is critical for protein classification, understanding their biological functions, annotating their evolutionary mechanisms and protein design. Thus, over the past two decades, a number of protein domain identification approaches have been developed, and a variety of protein domain databases have also been constructed. This review divides protein domain prediction methods into two categories, namely sequence-based and structure-based. These methods are introduced in detail, and their advantages and limitations are compared. Furthermore, this review also provides a comprehensive overview of popular online protein domain sequence and structure databases. Finally, we discuss potential improvements of these prediction methods.

SPServer: split-statistical potentials for the analysis of protein structures and protein-protein interactions

BACKGROUND

Statistical potentials, also named knowledge-based potentials, are scoring functions derived from empirical data that can be used to evaluate the quality of protein folds and protein-protein interaction (PPI) structures. In previous works we decomposed the statistical potentials in different terms, named Split-Statistical Potentials, accounting for the type of amino acid pairs, their hydrophobicity, solvent accessibility and type of secondary structure. These potentials have been successfully used to identify near-native structures in protein structure prediction, rank protein docking poses, and predict PPI binding affinities.

RESULTS

Here, we present the SPServer, a web server that applies the Split-Statistical Potentials to analyze protein folds and protein interfaces. SPServer provides global scores as well as residue/residue-pair profiles presented as score plots and maps. This level of detail allows users to: (1) identify potentially problematic regions on protein structures; (2) identify disrupting amino acid pairs in protein interfaces; and (3) compare and analyze the quality of tertiary and quaternary structural models.

CONCLUSIONS

While there are many web servers that provide scoring functions to assess the quality of either protein folds or PPI structures, SPServer integrates both aspects in a unique easy-to-use web server. Moreover, the server permits to locally assess the quality of the structures and interfaces at a residue level and provides tools to compare the local assessment between structures. SERVER ADDRESS: https://sbi.upf.edu/spserver/ .

Protein Structure Modeling with MODELLER

Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized at atomic resolution using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. In the following chapter, we present an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of a similar protocol has resulted in models of useful accuracy for domains in more than half of all known protein sequences.

Substitution scoring matrices for proteins - An overview

Sequence analysis is the primary and simplest approach to discover structural, functional and evolutionary details of related proteins. All the alignment based approaches of sequence analysis make use of amino acid substitution matrices, and the accuracy of the results largely depends on the type of scoring matrices used to perform alignment tasks. An amino acid substitution matrix is a 20 × 20 matrix in which the individual elements encapsulate the rates at which each of the 20 amino acid residues in proteins are substituted by other amino acid residues over time. In contrast to most globular/ordered proteins whose amino acids composition is considered as standard, there are several classes of proteins (e.g., transmembrane proteins) in which certain types of amino acid (e.g., hydrophobic residues) are enriched. These compositional differences among various classes of proteins are manifested in their underlying residue substitution frequencies. Therefore, each of the compositionally distinct class of proteins or protein segments should be studied using specific scoring matrices that reflect their distinct residue substitution pattern. In this review, we describe the development and application of various substitution scoring matrices peculiar to proteins with standard and biased compositions. Along with most commonly used standard matrices (PAM, BLOSUM, MD and VTML) that act as default parameters in various homologs search and alignment tools, different substitution scoring matrices specific to compositionally distinct class of proteins are discussed in detail.

Efficient rational modification of non-ribosomal peptides by adenylation domain substitution

Non-ribosomal peptide synthetase (NRPS) enzymes form modular assembly-lines, wherein each module governs the incorporation of a specific monomer into a short peptide product. Modules are comprised of one or more key domains, including adenylation (A) domains, which recognise and activate the monomer substrate; condensation (C) domains, which catalyse amide bond formation; and thiolation (T) domains, which shuttle reaction intermediates between catalytic domains. This arrangement offers prospects for rational peptide modification via substitution of substrate-specifying domains. For over 20 years, it has been considered that C domains play key roles in proof-reading the substrate; a presumption that has greatly complicated rational NRPS redesign. Here we present evidence from both directed and natural evolution studies that any substrate-specifying role for C domains is likely to be the exception rather than the rule, and that novel non-ribosomal peptides can be generated by substitution of A domains alone. We identify permissive A domain recombination boundaries and show that these allow us to efficiently generate modified pyoverdine peptides at high yields. We further demonstrate the transferability of our approach in the PheATE-ProCAT model system originally used to infer C domain substrate specificity, generating modified dipeptide products at yields that are inconsistent with the prevailing dogma.

ProtFold-DFG: protein fold recognition by combining Directed Fusion Graph and PageRank algorithm

As one of the most important tasks in protein structure prediction, protein fold recognition has attracted more and more attention. In this regard, some computational predictors have been proposed with the development of machine learning and artificial intelligence techniques. However, these existing computational methods are still suffering from some disadvantages. In this regard, we propose a new network-based predictor called ProtFold-DFG for protein fold recognition. We propose the Directed Fusion Graph (DFG) to fuse the ranking lists generated by different methods, which employs the transitive closure to incorporate more relationships among proteins and uses the KL divergence to calculate the relationship between two proteins so as to improve its generalization ability. Finally, the PageRank algorithm is performed on the DFG to accurately recognize the protein folds by considering the global interactions among proteins in the DFG. Tested on a widely used and rigorous benchmark data set, LINDAHL dataset, experimental results show that the ProtFold-DFG outperforms the other 35 competing methods, indicating that ProtFold-DFG will be a useful method for protein fold recognition. The source code and data of ProtFold-DFG can be downloaded from http://bliulab.net/ProtFold-DFG/download.

Genomics, Computational Biology and Drug Discovery for Mycobacterial Infections: Fighting the Emergence of Resistance

Tuberculosis (TB) and leprosy are mycobacterial infections caused by Mycobacterium tuberculosis and Mycobacterium leprae respectively. These diseases continue to be endemic in developing countries where the cost of new medicines presents major challenges. The situation is further exacerbated by the emergence of resistance to many front-line antibiotics. A priority now is to design new antimycobacterials that are not only effective in combatting the diseases but are also less likely to give rise to resistance. In both these respects understanding the structure of drug targets in M. tuberculosis and M. leprae is crucial. In this review we describe structure-guided approaches to understanding the impacts of mutations that give rise to antimycobacterial resistance and the use of this information in the design of new medicines.

Novel adamantyl retinoid-related molecules with POLA1 inhibitory activity

Atypical retinoids (AR) or retinoid-related molecules (RRMs) represent a promising class of antitumor compounds. Among AR, E-3-(3'-adamantan-1-yl-4'-hydroxybiphenyl-4-yl)acrylic acid (adarotene), has been extensively investigated. In the present work we report the results of our efforts to develop new adarotene-related atypical retinoids endowed also with POLA1 inhibitory activity. The effects of the synthesized compounds on cell growth were determined on a panel of human and hematological cancer cell lines. The most promising compounds showed antitumor activity against several tumor histotypes and increased cytotoxic activity against an adarotene-resistant cell line, compared to the parent molecule. The antitumor activity of a selected compound was evaluated on HT-29 human colon carcinoma and human mesothelioma (MM487) xenografts. Particularly significant was the in vivo activity of the compound as a single agent compared to adarotene and cisplatin, against pleural mesothelioma MM487. No reduction of mice body weight was observed, thus suggesting a higher tolerability with respect to the parent compound adarotene.

The Globin Gene Family in Arthropods: Evolution and Functional Diversity

Globins are small heme-proteins that reversibly bind oxygen. Their most prominent roles in vertebrates are the transport and storage of O₂ for oxidative energy metabolism, but recent research has suggested alternative, non-respiratory globin functions. In the species-rich and ecologically highly diverse taxon of arthropods, the copper-containing hemocyanin is considered the main respiratory protein. However, recent studies have suggested the presence of globin genes and their proteins in arthropod taxa, including model species like Drosophila. To systematically assess the taxonomic distribution, evolution and diversity of globins in arthropods, we systematically searched transcriptome and genome sequence data and found a conserved, widespread occurrence of three globin classes in arthropods: hemoglobin-like (HbL), globin X (GbX), and globin X-like (GbXL) protein lineages. These globin types were previously identified in protostome and deuterostome animals including vertebrates, suggesting their early ancestry in Metazoa. The HbL genes show multiple, lineage-specific gene duplications in all major arthropod clades. Some HbL genes (e.g., Glob2 and 3 of Drosophila) display particularly fast substitution rates, possibly indicating the evolution of novel functions, e.g., in spermatogenesis. In contrast, arthropod GbX and GbXL globin genes show high evolutionary stability: GbXL is represented by a single-copy gene in all arthropod groups except Brachycera, and representatives of the GbX clade are present in all examined taxa except holometabolan insects. GbX and GbXL both show a brain-specific expression. Most arthropod GbX and GbXL proteins, but also some HbL variants, include sequence motifs indicative of potential N-terminal acylation (i.e., N-myristoylation, 3C-palmitoylation). All arthropods except for the brachyceran Diptera harbor at least one such potentially acylated globin copy, confirming the hypothesis of an essential, conserved globin function associated with the cell membrane. In contrast to other animals, the fourth ancient globin lineage, represented by neuroglobin, appears to be absent in arthropods, and the putative arthropod orthologs of the fifth metazoan globin lineage, androglobin, lack a recognizable globin domain. Thus, the remarkable evolutionary stability of some globin variants is contrasted by occasional dynamic gene multiplication or even loss of otherwise strongly conserved globin lineages in arthropod phylogeny.

A Personal History of Using Crystals and Crystallography to Understand Biology and Advanced Drug Discovery

Over the past 60 years, the use of crystals to define structures of complexes using X-ray analysis has contributed to the discovery of new medicines in a very significant way. This has been in understanding not only small-molecule inhibitors of proteins, such as enzymes, but also protein or peptide hormones or growth factors that bind to cell surface receptors. Experimental structures from crystallography have also been exploited in software to allow prediction of structures of important targets based on knowledge of homologues. Crystals and crystallography continue to contribute to drug design and provide a successful example of academia–industry collaboration.

Proposed model of the Dictyostelium cAMP receptors bound to cAMP

3',5'-cyclic adenosine monophosphate (cAMP) is well known as a ubiquitous intracellular messenger regulating a diverse array of cellular processes. However, for a group of social amoebae or Dictyostelia undergoing starvation, intracellular cAMP is secreted in a pulsatile manner to their exterior. This then uniquely acts as a first messenger, triggering aggregation of the starving amoebae followed by their developmental progression towards multicellular fruiting bodies formation. Such developmental signalling for extracellularly-acting cAMP is well studied in the popular dictyostelid, Dictyostelium discoideum, and is mediated by a distinct family ('class E') of G protein-coupled receptors (GPCRs) collectively designated as the cAMP receptors (cARs). Whilst the biochemical aspects of these receptors are well characterised, little is known about their overall 3D architecture and structural basis for cAMP recognition and subtype-dependent changes in binding affinity. Using a ligand docking-guided homology modelling approach, we hereby present for the first time, plausible models of active forms of the cARs from D. discoideum. Our models highlight some structural features that may underlie the differential affinities of cAR isoforms for cAMP binding and also suggest few residues that may play important roles for the activation mechanism of this GPCR family.

Structure of the RECK CC domain, an evolutionary anomaly

Five small protein domains, the CC-domains, at the N terminus of the RECK protein, play essential roles in signaling by WNT7A and WNT7B in the context of central nervous system angiogenesis and blood-brain barrier formation and maintenance. We have determined the structure of CC domain 4 (CC4) at 1.65-Å resolution and find that it folds into a compact four-helix bundle with three disulfide bonds. The CC4 structure, together with homology modeling of CC1, reveals the surface locations of critical residues that were shown in previous mutagenesis studies to mediate GPR124 binding and WNT7A/WNT7B recognition and signaling. Surprisingly, sequence and structural homology searches reveal no other cell-surface or secreted domains in vertebrates that resemble the CC domain, a pattern that is in striking contrast to other ancient and similarly sized domains, such as Epidermal Growth Factor, Fibronectin Type 3, Immunoglobulin, and Thrombospondin type 1 domains, which are collectively present in hundreds of proteins.

Genome3D: integrating a collaborative data pipeline to expand the depth and breadth of consensus protein structure annotation

Genome3D (https://www.genome3d.eu) is a freely available resource that provides consensus structural annotations for representative protein sequences taken from a selection of model organisms. Since the last NAR update in 2015, the method of data submission has been overhauled, with annotations now being 'pushed' to the database via an API. As a result, contributing groups are now able to manage their own structural annotations, making the resource more flexible and maintainable. The new submission protocol brings a number of additional benefits including: providing instant validation of data and avoiding the requirement to synchronise releases between resources. It also makes it possible to implement the submission of these structural annotations as an automated part of existing internal workflows. In turn, these improvements facilitate Genome3D being opened up to new prediction algorithms and groups. For the latest release of Genome3D (v2.1), the underlying dataset of sequences used as prediction targets has been updated using the latest reference proteomes available in UniProtKB. A number of new reference proteomes have also been added of particular interest to the wider scientific community: cow, pig, wheat and mycobacterium tuberculosis. These additions, along with improvements to the underlying predictions from contributing resources, has ensured that the number of annotations in Genome3D has nearly doubled since the last NAR update article. The new API has also been used to facilitate the dissemination of Genome3D data into InterPro, thereby widening the visibility of both the annotation data and annotation algorithms.

A community proposal to integrate structural bioinformatics activities in ELIXIR (3D-Bioinfo Community)

Structural bioinformatics provides the scientific methods and tools to analyse, archive, validate, and present the biomolecular structure data generated by the structural biology community. It also provides an important link with the genomics community, as structural bioinformaticians also use the extensive sequence data to predict protein structures and their functional sites. A very broad and active community of structural bioinformaticians exists across Europe, and 3D-Bioinfo will establish formal platforms to address their needs and better integrate their activities and initiatives. Our mission will be to strengthen the ties with the structural biology research communities in Europe covering life sciences, as well as chemistry and physics and to bridge the gap between these researchers in order to fully realize the potential of structural bioinformatics. Our Community will also undertake dedicated educational, training and outreach efforts to facilitate this, bringing new insights and thus facilitating the development of much needed innovative applications e.g. for human health, drug and protein design. Our combined efforts will be of critical importance to keep the European research efforts competitive in this respect.

Here we highlight the major European contributions to the field of structural bioinformatics, the most pressing challenges remaining and how Europe-wide interactions, enabled by ELIXIR and its platforms, will help in addressing these challenges and in coordinating structural bioinformatics resources across Europe. In particular, we present recent activities and future plans to consolidate an ELIXIR 3D-Bioinfo Community in structural bioinformatics and propose means to develop better links across the community. These include building new consortia, organising workshops to establish data standards and seeking community agreement on benchmark data sets and strategies. We also highlight existing and planned collaborations with other ELIXIR Communities and other European infrastructures, such as the structural biology community supported by Instruct-ERIC, with whom we have synergies and overlapping common interests.

Homology Modeling and Virtual Screening Studies of Antigen MLAA-42 Protein: Identification of Novel Drug Candidates against Leukemia-An In Silico Approach

Monocytic leukemia-associated antigen-42 (MLAA-42) is associated with excessive cell division and progression of leukemia. Thus, human MLAA-42 is considered as a promising target for designing of new lead molecules for leukemia treatment. Herein, the 3D model of the target was generated by homology modeling technique. The model was then evaluated using various cheminformatics servers. Moreover, the virtual screening studies were performed to explore the possible binding patterns of ligand molecules to MLAA's active site pocket. Thirteen ligand molecules from the ChemBank^™ database were identified as they showed good binding affinities, scaffold diversity, and preferential ADME properties which may act as potent drug candidates against leukemia. The study provides the way to identify novel therapeutics with optimal efficacy, targeting MLAA-42.

A receptor for the complement regulator factor H increases transmission of trypanosomes to tsetse flies

Persistent pathogens have evolved to avoid elimination by the mammalian immune system including mechanisms to evade complement. Infections with African trypanosomes can persist for years and cause human and animal disease throughout sub-Saharan Africa. It is not known how trypanosomes limit the action of the alternative complement pathway. Here we identify an African trypanosome receptor for mammalian factor H, a negative regulator of the alternative pathway. Structural studies show how the receptor binds ligand, leaving inhibitory domains of factor H free to inactivate complement C3b deposited on the trypanosome surface. Receptor expression is highest in developmental stages transmitted to the tsetse fly vector and those exposed to blood meals in the tsetse gut. Receptor gene deletion reduced tsetse infection, identifying this receptor as a virulence factor for transmission. This demonstrates how a pathogen evolved a molecular mechanism to increase transmission to an insect vector by exploitation of a mammalian complement regulator.

Dominant Noonan syndrome-causing LZTR1 mutations specifically affect the Kelch domain substrate-recognition surface and enhance RAS-MAPK signaling

Noonan syndrome (NS), the most common RASopathy, is caused by mutations affecting signaling through RAS and the MAPK cascade. Recently, genome scanning has discovered novel genes implicated in NS, whose function in RAS-MAPK signaling remains obscure, suggesting the existence of unrecognized circuits contributing to signal modulation in this pathway. Among these genes, leucine zipper-like transcriptional regulator 1 (LZTR1) encodes a functionally poorly characterized member of the BTB/POZ protein superfamily. Two classes of germline LZTR1 mutations underlie dominant and recessive forms of NS, while constitutional monoallelic, mostly inactivating, mutations in the same gene cause schwannomatosis, a cancer-prone disorder clinically distinct from NS. Here we show that dominant NS-causing LZTR1 mutations do not affect significantly protein stability and subcellular localization. We provide the first evidence that these mutations, but not the missense changes occurring as biallelic mutations in recessive NS, enhance stimulus-dependent RAS-MAPK signaling, which is triggered, at least in part, by an increased RAS protein pool. Moreover, we document that dominant NS-causing mutations do not perturb binding of LZTR1 to CUL3, a scaffold coordinating the assembly of a multimeric complex catalyzing protein ubiquitination but are predicted to affect the surface of the Kelch domain mediating substrate binding to the complex. Collectively, our data suggest a model in which LZTR1 contributes to the ubiquitinationof protein(s) functioning as positive modulator(s) of the RAS-MAPK signaling pathway. In this model, LZTR1 mutations are predicted to variably impair binding of these substrates to the multi-component ligase complex and their efficient ubiquitination and degradation, resulting in MAPK signaling upregulation.

Update of the GRIP web service

G protein-coupled receptors (GPCRs) can form homodimers, heterodimers, or higher-order molecular complexes (oligomers). The reports on the change of functions through the oligomerization have been accumulated. Inhibition of GPCR oligomerization without affecting the protomer's overall structure would clarify the oligomer-specific functions although inhibition experiments are costly and require accurate information about the interface location. Unfortunately, the number of experimentally determined interfaces is limited. The precise prediction of the oligomerization interfaces is, therefore, useful for inhibition experiments to examine the oligomer-specific functions, which would accelerate investigations of the GPCR signaling. However, interface prediction for GPCR oligomerization is difficult because different GPCR subtypes belonging to the same subfamily often use different structural regions as their interfaces. We previously developed a high-performance method to predict the interfaces for GPCR oligomerization, by identifying the conserved surfaces with the sequence and structure information. Then, the structural characteristic of a GPCR structure is regarded to be a thick-tube like conformation that is approximately perpendicular to the membrane plane. Our method had successfully predicted all of the interfaces available on that day. We had launched a web server for our interface prediction of GPCRs (GRIP). We have improved the previous version of GRIP server and enhanced its usability. First, we discarded the approximation of the GPCR structure as the thick-tube-like conformation. This improvement increased the number of structures for the prediction. Second, the FUGUE-based template recommendation service was introduced to facilitate the choice of an appropriate structure for the prediction. The new prediction server is available at http://grip.b.dendai.ac.jp/∼grip/.

Identification of AIDS-Associated Kaposi Sarcoma: A Functional Genomics Approach

BACKGROUND

Kaposi sarcoma-associated herpes virus (KSHV) is one of the most common causal agents of Kaposi Sarcoma (KS) in individuals with HIV-infections. The virus has gained attention over the past few decades due to its remarkable pathogenic mechanisms. A group of genes, ORF71, ORF72, and ORF73, are expressed as polycistronic mRNAs and the functions of ORF71 and ORF72 in KSHV are already reported in the literature. However, the function of ORF73 has remained a mystery. The aim of this study is to conduct comprehensive exploratory experiments to clarify the role of ORF73 in KSHV pathology and discover markers of AIDS-associated KSHV-induced KS by bioinformatic approaches.

METHODS AND RESULTS

We searched for homologues of ORF-73 and attempted to predict protein-protein interactions (PPI) based on GeneCards and UniProtKB, utilizing Position-Specific Iterated BLAST (PSI-BLAST). We applied Gene Ontology (GO) and KEGG pathway analyses to identify highly conserved regions between ORF-73 and p53to help us identify potential markers with predominant hits and interactions in the KEGG pathway associated with host apoptosis and cell arrest. The protein p53 is selected because it is an important tumor suppressor antigen. To identify the potential roles of the candidate markers at the molecular level, we used PSIPRED keeping the conserved domains as the major parameters to predict secondary structures. We based the FUGE interpretation consolidations of the sequence-structure comparisons on distance homology, where the score for the amino acids matching the insertion/deletion (indels) detected were based on structures compared to the FUGE database of structural profiles. We also calculated the compatibility scores of sequence alignments accordingly. Based on the PSI-BLAST homologues, we checked the disordered structures predicted using PSI-Pred and DISO-Pred for developing a hidden Markov model (HMM). We further applied these HMMs models based on the alignment of constructed 3D models between the known structure and the HMM of our sequence. Moreover, stable homology and structurally conserved domains confirmed that ORF-73 maybe an important prognostic marker for AIDS-associated KS.

CONCLUSION

Collectively, similar variants of ORF-73 markers involved in the immune response may interact with targeted host proteins as predicted by our computational analysis. This work also suggests the existence of potential conformational changes that need to be further explored to help elucidate the role of immune signaling during KS towards the development of therapeutic applications.

The Genome3D Consortium for Structural Annotations of Selected Model Organisms

Genome3D consortium is a collaborative project involving protein structure prediction and annotation resources developed by six world-leading structural bioinformatics groups, based in the United Kingdom (namely Blundell, Murzin, Gough, Sternberg, Orengo, and Jones). The main objective of Genome3D serves as a common portal to provide both predicted models and annotations of proteins in model organisms, using several resources developed by these labs such as CATH-Gene3D, DOMSERF, pDomTHREADER, PHYRE, SUPERFAMILY, FUGUE/TOCATTA, and VIVACE. These resources primarily use SCOP- and/or CATH-based protein domain assignments. Another objective of Genome3D is to compare structural classifications of protein domains in CATH and SCOP databases and to provide a consensus mapping of CATH and SCOP protein superfamilies. CATH/SCOP mapping analyses led to the identification of total of 1429 consensus superfamilies.Currently, Genome3D provides structural annotations for ten model organisms, including Homo sapiens, Arabidopsis thaliana, Mus musculus, Escherichia coli, Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Plasmodium falciparum, Staphylococcus aureus, and Schizosaccharomyces pombe. Thus, Genome3D serves as a common gateway to each structure prediction/annotation resource and allows users to perform comparative assessment of the predictions. It, thus, assists researchers to broaden their perspective on structure/function predictions of their query protein of interest in selected model organisms.

Food allergomics based on high-throughput and bioinformatics technologies

Food allergy is a serious food safety problem worldwide, and the investigation of food allergens is the foundation of preventing and treating them, but relevant knowledge is far from sufficient. With the advent of the "big data era", it has been possible to investigate food allergens by high-throughput methods, proposing the concept of allergomics. Allergomics is the discipline studying the repertoire of allergens, which has relatively higher throughput and is faster and more sensitive than conventional methods. This review introduces the basis of allergomics and summarizes its major strategies and applications. Particularly, strategies based on immunoblotting, phage display, allergen microarray, and bioinformatics are reviewed in detail, and the advantages and limitations of each strategy are discussed. Finally, further development of allergomics is predicted. This provides basic theories and recent advances in food allergomics research, which could be insightful for both food allergy research and practical applications.

Fold-LTR-TCP: protein fold recognition based on triadic closure principle

As an important task in protein structure and function studies, protein fold recognition has attracted more and more attention. The existing computational predictors in this field treat this task as a multi-classification problem, ignoring the relationship among proteins in the dataset. However, previous studies showed that their relationship is critical for protein homology analysis. In this study, the protein fold recognition is treated as an information retrieval task. The Learning to Rank model (LTR) was employed to retrieve the query protein against the template proteins to find the template proteins in the same fold with the query protein in a supervised manner. The triadic closure principle (TCP) was performed on the ranking list generated by the LTR to improve its accuracy by considering the relationship among the query protein and the template proteins in the ranking list. Finally, a predictor called Fold-LTR-TCP was proposed. The rigorous test on the LE benchmark dataset showed that the Fold-LTR-TCP predictor achieved an accuracy of 73.2%, outperforming all the other competing methods.

MotifCNN-fold: protein fold recognition based on fold-specific features extracted by motif-based convolutional neural networks

Protein fold recognition is one of the most critical tasks to explore the structures and functions of the proteins based on their primary sequence information. The existing protein fold recognition approaches rely on features reflecting the characteristics of protein folds. However, the feature extraction methods are still the bottleneck of the performance improvement of these methods. In this paper, we proposed two new feature extraction methods called MotifCNN and MotifDCNN to extract more discriminative fold-specific features based on structural motif kernels to construct the motif-based convolutional neural networks (CNNs). The pairwise sequence similarity scores calculated based on fold-specific features are then fed into support vector machines to construct the predictor for fold recognition, and a predictor called MotifCNN-fold has been proposed. Experimental results on the benchmark dataset showed that MotifCNN-fold obviously outperformed all the other competing methods. In particular, the fold-specific features extracted by MotifCNN and MotifDCNN are more discriminative than the fold-specific features extracted by other deep learning techniques, indicating that incorporating the structural motifs into the CNN is able to capture the characteristics of protein folds.

Regulatory and evolutionary roles of pseudo γ-butyrolactone receptors in antibiotic biosynthesis and resistance

Bacteria modulate their physiological behavior by responding to various signal molecules. The signals are received by cognate receptors, which usually mediate transcriptional regulation. Streptomyces employ γ-butyrolactones (GBLs) and cognate GBL receptors (GblRs) to regulate secondary metabolism and morphological development. However, there are additional transcriptional regulators called pseudo GblR regulators, which cannot bind GBLs and are not directly associated with GBL synthase. The pseudo GblR regulators may act as transcriptional repressors and respond to antibiotic signals. They play regulatory roles in coordination of antibiotic biosynthesis by connecting the hormone feed-forward loops and the antibiotic feedback loops. As the TetR family members, they might also have evolutionary roles between the transcriptional regulators of quorum sensing and antibiotic resistance. Understanding the regulatory and evolutionary roles of the pseudo GblR family would be helpful for fine-tuning regulation of antibiotic biosynthesis and resistance.

Mabellini: a genome-wide database for understanding the structural proteome and evaluating prospective antimicrobial targets of the emerging pathogen Mycobacterium abscessus

Mycobacterium abscessus, a rapid growing, multidrug resistant, nontuberculous mycobacteria, can cause a wide range of opportunistic infections, particularly in immunocompromised individuals. M. abscessus has emerged as a growing threat to patients with cystic fibrosis, where it causes accelerated inflammatory lung damage, is difficult and sometimes impossible to treat and can prevent safe transplantation. There is therefore an urgent unmet need to develop new therapeutic strategies. The elucidation of the M. abscessus genome in 2009 opened a wide range of research possibilities in the field of drug discovery that can be more effectively exploited upon the characterization of the structural proteome. Where there are no experimental structures, we have used the available amino acid sequences to create 3D models of the majority of the remaining proteins that constitute the M. abscessus proteome (3394 proteins and over 13 000 models) using a range of up-to-date computational tools, many developed by our own group. The models are freely available for download in an on-line database, together with quality data and functional annotation. Furthermore, we have developed an intuitive and user-friendly web interface (http://www.mabellinidb.science) that enables easy browsing, querying and retrieval of the proteins of interest. We believe that this resource will be of use in evaluating the prospective targets for design of antimicrobial agents and will serve as a cornerstone to support the development of new molecules to treat M. abscessus infections.

DeepSVM-fold: protein fold recognition by combining support vector machines and pairwise sequence similarity scores generated by deep learning networks

Protein fold recognition is critical for studying the structures and functions of proteins. The existing protein fold recognition approaches failed to efficiently calculate the pairwise sequence similarity scores of the proteins in the same fold sharing low sequence similarities. Furthermore, the existing feature vectorization strategies are not able to measure the global relationships among proteins from different protein folds. In this article, we proposed a new computational predictor called DeepSVM-fold for protein fold recognition by introducing a new feature vector based on the pairwise sequence similarity scores calculated from the fold-specific features extracted by deep learning networks. The feature vectors are then fed into a support vector machine to construct the predictor. Experimental results on the benchmark dataset (LE) show that DeepSVM-fold obviously outperforms all the other competing methods.

The Arabidopsis thaliana gene annotated by the locus tag At3g08860 encodes alanine aminotransferase

The aminotransferase gene family in the model plant Arabidopsis thaliana consists of 44 genes, eight of which are suggested to be alanine aminotransferases. One of the putative alanine aminotransferases genes, At3g08860, was attributed the function of alanine:glyoxylate aminotransferase/β-alanine:pyruvate aminotransferase based on the analysis of gene expression networks and homology to other β-alanine aminotransferases in plants. It was earlier demonstrated that At3g08860 is specifically upregulated in response to osmotic stress, but not other stresses (β-alanine is an osmoprotectant in plants). Furthermore, it was shown that the expression of At3g08860 is highly coordinated with the genes of the uracil degradation pathway leading to the non-proteinogenic amino acid β-alanine. These evidence were suggestive of the involvement of At3g08860 in β-alanine metabolism. However, direct experimental evidence for the function of At3g08860 was lacking, and therefore, the goal of this study was to elucidate the function of the uncharacterized aminotransferase annotated by the locus tag At3g08860. The cDNA of At3g08860 was demonstrated to functionally complement two E. coli mutants auxotrophic for the amino acids, L-alanine (proteinogenic) and β-alanine (non-proteinogenic). Enzyme activity using purified recombinant At3g08860 further demonstrated that the enzyme is endowed with L-alanine:glyoxylate aminotransferase activity.