Phosphate solubilizing Aspergillus Niger PH1 ameliorates growth and alleviates lead stress in maize through improved photosynthetic and antioxidant response.

Among the several threats to humanity by anthropogenic activities, contamination of the environment by heavy metals is of great concern. Upon entry into the food chain, these metals cause serious hazards to plants and other organisms including humans. Use of microbes for bioremediation of the soil and stress mitigation in plants are among the preferred strategies to provide an efficient, cost-effective, eco-friendly solution of the problem. The current investigation is an attempt in this direction where fungal strain PH1 was isolated from the rhizosphere of Parthenium hysterophorus which was identified as Aspergillus niger by sequence homology of the ITS 1 and ITS 4 regions of the rRNA. The strain was tested for its effect on growth and biochemical parameters as reflection of its potential to mitigate Pb stress in Zea mays exposed to 100, 200 and 500 µg of Pb/g of soil. In the initial screening, it was revealed that the strain has the ability to tolerate lead stress, solubilize insoluble phosphate and produce plant growth promoting hormones (IAA and SA) and other metabolites like phenolics, flavonoids, sugar, protein and lipids. Under 500 µg of Pb/g of soil, Z. mays exhibited significant growth retardation with a reduction of 31% in root length, 30.5% in shoot length, 57.5% in fresh weight and 45.2% in dry weight as compared to control plants. Inoculation of A. niger to Pb treated plants not only restored root and shoot length, rather promoted it to a level significantly higher than the control plants. Association of the strain modulated the physio-hormonal attributes of maize plants that resulted in their better growth which indicated a state of low stress. Additionally, the strain boosted the antioxidant defence system of the maize there by causing a significant reduction in the ascorbic acid peroxidase (1.5%), catalase (19%) and 1,1-diphenyl-2 picrylhydrazyl (DPPH) radical scavenging activity (33.3%), indicating a lower stress condition as compared to their non-inoculated stressed plants. Based on current evidence, this strain can potentially be used as a biofertilizer for Pb-contaminated sites where it will improve overall plant health with the hope of achieving better biological and agricultural yields.

Impact of an alpha helix and a cysteine-cysteine disulfide bond on the resistance of bacterial adhesion pili to stress.

Escherichia coli express adhesion pili that mediate attachment to host cell surfaces and are exposed to body fluids in the urinary and gastrointestinal tracts. Pilin subunits are organized into helical polymers, with a tip adhesin for specific host binding. Pili can elastically unwind when exposed to fluid flow forces, reducing the adhesin load, thereby facilitating sustained attachment. Here we investigate biophysical and structural differences of pili commonly expressed on bacteria that inhabit the urinary and intestinal tracts. Optical tweezers measurements reveal that class 1a pili of uropathogenic E. coli (UPEC), as well as class 1b of enterotoxigenic E. coli (ETEC), undergo an additional conformational change beyond pilus unwinding, providing significantly more elasticity to their structure than ETEC class 5 pili. Examining structural and steered molecular dynamics simulation data, we find that this difference in class 1 pili subunit behavior originates from an α-helical motif that can unfold when exposed to force. A disulfide bond cross-linking ß-strands in class 1 pili stabilizes subunits, allowing them to tolerate higher forces than class 5 pili that lack this covalent bond. We suggest that these extra contributions to pilus resiliency are relevant for the UPEC niche, since resident bacteria are exposed to stronger, more transient drag forces compared to those experienced by ETEC bacteria in the mucosa of the intestinal tract. Interestingly, class 1b ETEC pili include the same structural features seen in UPEC pili, while requiring lower unwinding forces that are more similar to those of class 5 ETEC pili.

DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases.

Beta-lactamases (BLs) are enzymes localized in the periplasmic space of bacterial pathogens, where they confer resistance to beta-lactam antibiotics. Experimental identification of BLs is costly yet crucial to understand beta-lactam resistance mechanisms. To address this issue, we present DeepBL, a deep learning-based approach by incorporating sequence-derived features to enable high-throughput prediction of BLs. Specifically, DeepBL is implemented based on the Small VGGNet architecture and the TensorFlow deep learning library. Furthermore, the performance of DeepBL models is investigated in relation to the sequence redundancy level and negative sample selection in the benchmark dataset. The models are trained on datasets of varying sequence redundancy thresholds, and the model performance is evaluated by extensive benchmarking tests. Using the optimized DeepBL model, we perform proteome-wide screening for all reviewed bacterium protein sequences available from the UniProt database. These results are freely accessible at the DeepBL webserver at http://deepbl.erc.monash.edu.au/.

Prioritization of infectious epitopes for translational investigation in type 1 diabetes etiology.

Molecular mimicry is one mechanism by which infectious agents are thought to trigger islet autoimmunity in type 1 diabetes. With a growing number of reported infectious agents and islet antigens, strategies to prioritize the study of infectious agents are critically needed to expedite translational research into the etiology of type 1 diabetes. In this work, we developed an in-silico pipeline for assessing molecular mimicry in type 1 diabetes etiology based on sequence homology, empirical binding affinity to specific MHC molecules, and empirical potential for T-cell immunogenicity. We then assess whether potential molecular mimics were conserved across other pathogens known to infect humans. Overall, we identified 61 potentially high-impact molecular mimics showing sequence homology, strong empirical binding affinity, and empirical immunogenicity linked with specific MHC molecules. We further found that peptide sequences from 32 of these potential molecular mimics were conserved across several human pathogens. These findings facilitate translational evaluation of molecular mimicry in type 1 diabetes etiology by providing a curated and prioritized list of peptides from infectious agents for etiopathologic investigation. These results may also provide evidence for generation of infectious and HLA-specific preclinical models and inform future screening and preventative efforts in genetically susceptible populations.

PanDelos-frags: A methodology for discovering pangenomic content of incomplete microbial assemblies.

Pangenomics was originally defined as the problem of comparing the composition of genes into gene families within a set of bacterial isolates belonging to the same species. The problem requires the calculation of sequence homology among such genes. When combined with metagenomics, namely for human microbiome composition analysis, gene-oriented pangenome detection becomes a promising method to decipher ecosystem functions and population-level evolution. Established computational tools are able to investigate the genetic content of isolates for which a complete genomic sequence is available. However, there is a plethora of incomplete genomes that are available on public resources, which only a few tools may analyze. Incomplete means that the process for reconstructing their genomic sequence is not complete, and only fragments of their sequence are currently available. However, the information contained in these fragments may play an essential role in the analyses. Here, we present PanDelos-frags, a computational tool which exploits and extends previous results in analyzing complete genomes. It provides a new methodology for inferring missing genetic information and thus for managing incomplete genomes. PanDelos-frags outperforms state-of-the-art approaches in reconstructing gene families in synthetic benchmarks and in a real use case of metagenomics. PanDelos-frags is publicly available at https://github.com/InfOmics/PanDelos-frags.

Genetic studies of heat stress regulation in goat during hot climatic condition.

Various direct and indirect environmental constraints have an impact on livestock performance. The physiological parameters, such as rectal temperature, heart rate, and respiratory rate, are the primary indicators of thermal stress. Under a stressed environment temperature humidity index (THI) had established as a vital measurement to identify the thermal stress in livestock. THI in association with climatic variations can define the environmental effect as stressful or comfortable for livestock. Goats are small ruminants that adapt to a wide range of ecological variations due to their anatomical and physiological characteristics. However, the productivity of animals declines at the individual level during thermal stress. Stress tolerance can be determined through genetic studies associated with at the cellular level using physiological as well as molecular approaches. Information on genetic association with thermal stress in goats is scanty, this severely affects their survival and hence productivity of livestock. The ever-increasing demand for food across the globe needs deciphering novel molecular markers as well as stress indicators that play a vital role in livestock improvement. This review represents an analysis of current knowledge of phenotypic differences during thermal stress and signifies the importance of physiological responses and their association at the cellular level in goats. The regulation of vital genes associated with thermal stress such as Aquaporins (AQP 0, 1, 2, 4, 5, 6, 8), aquaglyceroporins (AQP3, 7, 9, and 10) and super-aquaporins (AQP 11, 12); BAX inhibitors such as PERK (PKR like ER kinase), IRE 1(inositol-requiring-1); Redox regulating genes such as NOX; Transport of Na+ and K+ such as ATPase (ATP1A1) and several heat shock proteins have been implicated in heat-stress related adaptations have been elucidated. As these changes have a significant impact on production performance as well as on livestock productivity. Such efforts may help in the development of molecular markers and will assist the breeders to develop heat-tolerant goats with improved productivity.

EnsembleFam: towards more accurate protein family prediction in the twilight zone.

BACKGROUND: Current protein family modeling methods like profile Hidden Markov Model (pHMM), k-mer based methods, and deep learning-based methods do not provide very accurate protein function prediction for proteins in the twilight zone, due to low sequence similarity to reference proteins with known functions. RESULTS: We present a novel method EnsembleFam, aiming at better function prediction for proteins in the twilight zone. EnsembleFam extracts the core characteristics of a protein family using similarity and dissimilarity features calculated from sequence homology relations. EnsembleFam trains three separate Support Vector Machine (SVM) classifiers for each family using these features, and an ensemble prediction is made to classify novel proteins into these families. Extensive experiments are conducted using the Clusters of Orthologous Groups (COG) dataset and G Protein-Coupled Receptor (GPCR) dataset. EnsembleFam not only outperforms state-of-the-art methods on the overall dataset but also provides a much more accurate prediction for twilight zone proteins. CONCLUSIONS: EnsembleFam, a machine learning method to model protein families, can be used to better identify members with very low sequence homology. Using EnsembleFam protein functions can be predicted  using just sequence information with better accuracy than state-of-the-art methods.

First report of maize yellow mosaic virus causing maize reddening in Henan,China.

A novel polerovirus maize yellow mosaic virus (MaYMV) has been discovered in Asia (Chen et al. 2016; Lim et al. 2018; Sun et al. 2019; Wang et al. 2016), East Africa (Guadie et al. 2018; Massawe et al. 2018) and South America (Gonçalves et al. 2017). MaMYV was first reported to infect maize (Zea mays L.) showing yellow mosaic symptoms on the leaves in Yunnan, Guizhou, and yellowing and dwarfing symptoms on the leaves in Anhui provinces of China in 2016 (Chen et al. 2016; Wang et al. 2016). An East African isolate of MaYMV has recently been shown to induce leaf reddening in several maize genotypes (Stewart et al. 2020). To our knowledge the leaf reddening symptoms in maize was not reported in China and MaYMV was not reported in Henan province, China. A survey of viral diseases on maize was carried out during the autumn of 2021 in Zhengzhou (Henan province), China. During the survey, the leaves showing reddening symptoms were observed on maize plants in all four fields investigated. Symptomatic leaves of 12 plants from four fields of Xingyang county, Zhengzhou (n=12) were collected and mixed for metatranscriptomics sequencing, and total RNA was extracted and subjected to an rRNA removal procedure using a Ribo-zero Magnetic kit according to the manufacturer's instructions (Epicentre, an Illumina® company). cDNA libraries were constructed using a TruSeq™ RNA sample prep kit (Illumina). Barcoded libraries were paired-end sequenced on an Illumina HiSeq X ten platform at Shanghai Biotechnology Co., Ltd. (Shanghai, China) according to the manufacturer's instructions (www.illumina.com). In total 67607392 clean reads were de novo assembled using CLC Genomics Workbench (version:6.0.4). 105796 contigs were obtained. The assembled contigs were queried by homology search tools (BLASTn and BLASTx) against public database(GenBank). One 5,457 nucleotide (nt) long contig with the most reads of 558826 was obtained and blast analysis showed it shared 99.3% nt sequence identity (99% coverage) with MaYMV Yunnan4 isolate (KU291100).. According to the sequencing data no other plant viruses except MaYMV were present in the sequencing data. To confirm the presence of this virus, twelve leaf samples showing reddening symptoms were detected by RT-PCR using specific primer pairs for CP full length open reading frame (F: ATGAATACGGGAGGTAGAAA, R: CTATTTCGGGTTTTGAACAT). Amplicons with expected size of 594 bp were gained in seven samples and three of them were cloned into pMD18T vector and sequenced. The three isolates (OM417795, OM417796, and OM417797) shared 99.16% to 99.83% nt sequence identity with MaYMV-Yunnan3 isolate (KU291100). Further P0 sequence analysis of the three samples (OM417798, OM417799, and OM417800) with primer pairs F: ATGGGGGGAGTGCCTAAAGC/R: TCATAACTGATGGAATTCCC showed they shared 99.5% to 99.62% nt sequence identity with MaYMV-Yunnan3 isolate.To our knowledge, this is the first report of the occurrence of MaYMV infecting maize in Henan, China. Besides, our finding firstly discovered reddening symptoms caused by MaYMV on maize in China which is different from the previous symptoms observed in the other three provinces of China possibly due to the different maize varieties grown in different areas. According to our investigation, maize showing reddening symptoms was common in the fields. Henan province is the main corn production area in China. Corn leaf aphid (Rhopalosiphum maidis), the insect vector of MaYMV, is an important pest of corn in Henan province, thereby the occurrence of MaYMV might cause potential threat to maize production in China.

A computational framework for modeling functional protein-protein interactions.

Protein interactions and their assemblies assist in understanding the cellular mechanisms through the knowledge of interactome. Despite recent advances, a vast number of interacting protein complexes is not annotated by three-dimensional structures. Therefore, a computational framework is a suitable alternative to fill the large gap between identified interactions and the interactions with known structures. In this work, we develop an automated computational framework for modeling functionally related protein-complex structures utilizing GO-based semantic similarity technique and co-evolutionary information of the interaction sites. The framework can consider protein sequence and structure information as input and employ both rigid-body docking and template-based modeling exploiting the existing structural templates and sequence homology information from the PDB. Our framework combines geometric as well as physicochemical features for re-ranking the docking decoys. The proposed framework has an 83% success rate when tested on a benchmark dataset while considering Top1 models for template-based modeling and Top10 models for the docking pipeline. We believe that our computational framework can be used for any pair of proteins with higher confidence to identify the functional protein-protein interactions.

Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction.

The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins' 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug-target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery.

How to choose templates for modeling of protein complexes: Insights from benchmarking template-based docking.

Comparative docking is based on experimentally determined structures of protein-protein complexes (templates), following the paradigm that proteins with similar sequences and/or structures form similar complexes. Modeling utilizing structure similarity of target monomers to template complexes significantly expands structural coverage of the interactome. Template-based docking by structure alignment can be performed for the entire structures or by aligning targets to the bound interfaces of the experimentally determined complexes. Systematic benchmarking of docking protocols based on full and interface structure alignment showed that both protocols perform similarly, with top 1 docking success rate 26%. However, in terms of the models' quality, the interface-based docking performed marginally better. The interface-based docking is preferable when one would suspect a significant conformational change in the full protein structure upon binding, for example, a rearrangement of the domains in multidomain proteins. Importantly, if the same structure is selected as the top template by both full and interface alignment, the docking success rate increases 2-fold for both top 1 and top 10 predictions. Matching structural annotations of the target and template proteins for template detection, as a computationally less expensive alternative to structural alignment, did not improve the docking performance. Sophisticated remote sequence homology detection added templates to the pool of those identified by structure-based alignment, suggesting that for practical docking, the combination of the structure alignment protocols and the remote sequence homology detection may be useful in order to avoid potential flaws in generation of the structural templates library.

[Analysis of homology and drug sensitivity of vaginal isolates of 10 patients with recurrent vulvovaginal candidiasis in recurrent episodes].

Objective: To detect karyotype homology of vaginal isolates from patients with recurrent vulvovaginal candidiasis (RVVC) in recurrent episodes, and to discuss changes of susceptibility of Candida strains to antifungal drugs with clinical progress. Method: s Ten patients were recruited from Beijing Obstetrics and Gynecology Hospital, Capital Medical University from September 2018 to June 2019, who were firstly diagnosed with RVVC. Vaginal discharges were collected before first treatment and after first relapse. Vaginal strains were isolated, purificated and identificated. Then karyotype of 20 strains isolated from 10 patients were detected by restriction endonuclease analysis of genomic DNA (REAG) using enzyme BssHⅡand pulsed field gel electrophoresis (PFGE) methods, and sensitivity of clinical isolates to 5 antifungal drugs (clostridium, fluconazole, miconazole, itraconazole and nystatin) was also detected using disk diffusion method. Result: s (1) All 20 strains of 10 patients with RVVC were Candida albicans, and their chromosomes were extremely similar after BssHⅡ enzyme digestion. The gene bands of isolated strains from the same patient were completely identical. (2) After clinical medication, the sensitivity of vaginal isolates to azoles was generally decreased, but remained highly sensitive to nystatin, nystatin (first and second clinical isolates: 100% sensitivity and 100% sensitivity)>clotrimazole (100% sensitivity and 90% sensitivity)>fluconazole (80% sensitivity and 70% sensitivity)>itraconazole (60% sensitivity and 50% sensitivity)>miconazole (30% sensitivity and 20% sensitivity). Conclusions: (1) The latency of the same colonized strain in the vagina may be the cause of repeated RVVC episodes. (2) Antifungal agents could selectively induce drug resistance to Candidas, and Candidas show cross-resistance to antifungal agents. Repeated fungal culture and drug sensitivity test in patients with RVVC are very necessary for correct selection of antifungals.

Comparative sequence and structure analysis of eIF1A and eIF1AD.

BACKGROUND: Eukaryotic translation initiation factor 1A (eIF1A) is universally conserved in all organisms. It has multiple functions in translation initiation, including assembly of the ribosomal pre-initiation complexes, mRNA binding, scanning, and ribosomal subunit joining. eIF1A binds directly to the small ribosomal subunit, as well as to several other translation initiation factors. The structure of an eIF1A homolog, the eIF1A domain-containing protein (eIF1AD) was recently determined but its biological functions are unknown. Since eIF1AD has a known structure, as well as a homolog, whose structure and functions have been extensively studied, it is a very attractive target for sequence and structure analysis. RESULTS: Structure/sequence analysis of eIF1AD found significant conservation in the surfaces corresponding to the ribosome-binding surfaces of its paralog eIF1A, including a nearly invariant surface-exposed tryptophan residue, which plays an important role in the interaction of eIF1A with the ribosome. These results indicate that eIF1AD may bind to the ribosome, similar to its paralog eIF1A, and could have roles in ribosome biogenenesis or regulation of translation. We identified conserved surfaces and sequence motifs in the folded domain as well as the C-terminal tail of eIF1AD, which are likely protein-protein interaction sites. The roles of these regions for eIF1AD function remain to be determined. We have also identified a set of trypanosomatid-specific surface determinants in eIF1A that could be a promising target for development of treatments against these parasites. CONCLUSIONS: The results described here identify regions in eIF1A and eIF1AD that are likely to play major functional roles and are promising therapeutic targets. Our findings and hypotheses will promote new research and help elucidate the functions of eIF1AD.

Tarin, a Potential Immunomodulator and COX-Inhibitor Lectin Found in Taro (Colocasia esculenta).

Taro (Colocasia esculenta) corm is a rustic staple food, rich in small starch granules, fibers, and bioactive phytoconstituents such as flavonoids, alkaloids, sterols, tannins, phytates, micronutrients, and proteins, including tarin, a GNA-related lectin. Tarin exhibits recognized biocide activities against viruses and insects, has antitumoral properties and is an immunomodulator molecule candidate. It has been isolated in highly purified form (>90%) from taro corms through low-cost and single-step affinity chromatography. It comprises 2-domain 27 to 28 kDa protomer, posttranslational cleaved into 2 nonidentical monomers, 11.9 and 12.6 kDa, held by noncovalent binding. At least 10 tarin isoforms sharing over 70% similarity have been described. The monomers assume the ß-prism II fold, consisting of 3 antiparallel ß-sheets formed by 4 ß-strands each. Tarin exhibits an expanded-binding site for complex and high-mannose N-glycan chains 49, 212, 213, 358, 465, and 477 found on cell surface antigens of viruses, insects, cancer, and hematopoietic cells, explaining its broad biological activities. Tarin may stimulate innate and adaptive immune responses, enabling hosts to recover from infections or immunosuppressed status inherent to several pathological conditions. In a murine model, tarin stimulates the in vitro and in vivo proliferation of total spleen and bone marrow cells, especially B lymphocytes. Granulocyte repopulation has also been demonstrated in long-term mice bone marrow cell cultures. As a potential immunomodulator, tarin, administered to immunosuppressed mice, attenuated cyclophosphamide-induced leukopenia. We propose a molecular model that unites the potential prophylactic and therapeutic action of tarin on hematopoietic and cancer cells, as a potential immunomodulator.

Homolog detection using global sequence properties suggests an alternate view of structural encoding in protein sequences.

We show that a Fourier-based sequence distance function is able to identify structural homologs of target sequences with high accuracy. It is shown that Fourier distances correlate very strongly with independently determined structural distances between molecules, a property of the method that is not attainable using conventional representations. It is further shown that the ability of the Fourier approach to identify protein folds is statistically far in excess of random expectation. It is then shown that, in actual searches for structural homologs of selected target sequences, the Fourier approach gives excellent results. On the basis of these results, we suggest that the global information detected by the Fourier representation is an essential feature of structure encoding in protein sequences and a key to structural homology detection.

LEON-BIS: multiple alignment evaluation of sequence neighbours using a Bayesian inference system.

BACKGROUND: A standard procedure in many areas of bioinformatics is to use a multiple sequence alignment (MSA) as the basis for various types of homology-based inference. Applications include 3D structure modelling, protein functional annotation, prediction of molecular interactions, etc. These applications, however sophisticated, are generally highly sensitive to the alignment used, and neglecting non-homologous or uncertain regions in the alignment can lead to significant bias in the subsequent inferences. RESULTS: Here, we present a new method, LEON-BIS, which uses a robust Bayesian framework to estimate the homologous relations between sequences in a protein multiple alignment. Sequences are clustered into sub-families and relations are predicted at different levels, including 'core blocks', 'regions' and full-length proteins. The accuracy and reliability of the predictions are demonstrated in large-scale comparisons using well annotated alignment databases, where the homologous sequence segments are detected with very high sensitivity and specificity. CONCLUSIONS: LEON-BIS uses robust Bayesian statistics to distinguish the portions of multiple sequence alignments that are conserved either across the whole family or within subfamilies. LEON-BIS should thus be useful for automatic, high-throughput genome annotations, 2D/3D structure predictions, protein-protein interaction predictions etc.

Lack of PRKD2 and PRKD3 kinase domain somatic mutations in PRKD1 wild-type classic polymorphous low-grade adenocarcinomas of the salivary gland.

AIMS: Polymorphous low-grade adenocarcinoma (PLGA) is the second most common intra-oral salivary gland malignancy. The vast majority of PLGAs harbour a PRKD1 E710D hot-spot somatic mutation or somatic rearrangements of PRKD1, PRKD2 or PRKD3. Given the kinase domain homology among PRKD1, PRKD2 and PRKD3, we sought to define whether PLGAs lacking PRKD1 somatic mutations or PRKD gene family rearrangements would be driven by somatic mutations affecting the kinase domains of PRKD2 or PRKD3. METHODS AND RESULTS: DNA was extracted from eight microdissected PLGAs lacking PRKD1 somatic mutations or PRKD gene family rearrangements. Samples were thoroughly centrally reviewed, microdissected and subjected to Sanger sequencing of the kinase domains of the PRKD2 and PRKD3 genes. None of the PLGAs lacking PRKD1 somatic mutations or PRKD gene family rearrangements harboured somatic mutations in the kinase domains of the PRKD2 or PRKD3 genes. CONCLUSION: PLGAs lacking PRKD1 somatic mutations or PRKD gene family rearrangements are unlikely to harbour somatic mutations in the kinase domains of PRKD2 or PRKD3. Further studies are warranted to define the driver genetic events in this subgroup of PLGAs.

Molecular mimicry and autoimmune thyroid disease.

Hypothesized 40 years ago, molecular mimicry has been thereafter demonstrated as an extremely common mechanism by which microbes elude immune response and modulate biosynthetic/metabolic pathways of the host. In genetically predisposed persons and under particular conditions, molecular mimicry between microbial and human antigens can turn a defensive immune response into autoimmunity. Such triggering role and its pathogenetic importance have been investigated and demonstrated for many autoimmune diseases. However, this is not the case for autoimmune thyroid disease, which appears relatively neglected by this field of research. Here we review the available literature on the possible role of molecular mimicry as a trigger of autoimmune thyroid disease. Additionally, we present the results of in silico search for amino acid sequence homologies between some microbial proteins and thyroid autoantigens, and the potential pathogenetic relevance of such homologies. Relevance stems from the overlap with known autoepitopes and the occurrence of specific HLA-DR binding motifs. Bioinformatics data published by our group support and explain the triggering role of Borrelia, Yersinia, Clostridium botulinum, Rickettsia prowazekii and Helicobacter pylori. Our new data suggest the potential pathogenic importance of Toxoplasma gondii, some Bifidobacteria and Lactobacilli, Candida albicans, Treponema pallidum and hepatitis C virus in autoimmune thyroid disease, indicating specific molecular targets for future research. Additionally, the consistency between in silico prediction of cross-reactivity and experimental results shows the reliability and usefulness of bioinformatics tools to precisely identify candidate molecules for in vitro and/or in vivo experiments, or at least narrow down their number.

Identification of WD40 repeats by secondary structure-aided profile-profile alignment.

A WD40 protein typically contains four or more repeats of ~40 residues ended with the Trp-Asp dipeptide, which folds into ß-propellers with four ß strands in each repeat. They often function as scaffolds for protein-protein interactions and are involved in numerous fundamental biological processes. Despite their important functional role, the "velcro" closure of WD40 propellers and the diversity of WD40 repeats make their identification a difficult task. Here we develop a new WD40 Repeat Recognition method (WDRR), which uses predicted secondary structure information to generate candidate repeat segments, and further employs a profile-profile alignment to identify the correct WD40 repeats from candidate segments. In particular, we design a novel alignment scoring function that combines dot product and BLOSUM62, thereby achieving a great balance of sensitivity and accuracy. Taking advantage of these strategies, WDRR could effectively reduce the false positive rate and accurately identify more remote homologous WD40 repeats with precise repeat boundaries. We further use WDRR to re-annotate the Pfam families in the ß-propeller clan (CL0186) and identify a number of WD40 repeat proteins with high confidence across nine model organisms. The WDRR web server and the datasets are available at http://protein.cau.edu.cn/wdrr/.

Clustering and visualizing similarity networks of membrane proteins.

We proposed a fast and unsupervised clustering method, minimum span clustering (MSC), for analyzing the sequence-structure-function relationship of biological networks, and demonstrated its validity in clustering the sequence/structure similarity networks (SSN) of 682 membrane protein (MP) chains. The MSC clustering of MPs based on their sequence information was found to be consistent with their tertiary structures and functions. For the largest seven clusters predicted by MSC, the consistency in chain function within the same cluster is found to be 100%. From analyzing the edge distribution of SSN for MPs, we found a characteristic threshold distance for the boundary between clusters, over which SSN of MPs could be properly clustered by an unsupervised sparsification of the network distance matrix. The clustering results of MPs from both MSC and the unsupervised sparsification methods are consistent with each other, and have high intracluster similarity and low intercluster similarity in sequence, structure, and function. Our study showed a strong sequence-structure-function relationship of MPs. We discussed evidence of convergent evolution of MPs and suggested applications in finding structural similarities and predicting biological functions of MP chains based on their sequence information.