Evolutionary analysis reveals the role of a non-catalytic domain of peptidyl arginine deiminase 2 in transcriptional regulation

Peptidyl arginine deiminases (PADIs) catalyze protein citrullination, a post-translational conversion of arginine to citrulline. The most widely expressed member of this family, PADI2, regulates cellular processes that impact several diseases. We hypothesized that we could gain new insights into PADI2 function through a systematic evolutionary and structural analysis. Here, we identify 20 positively selected PADI2 residues, 16 of which are structurally exposed and maintain PADI2 interactions with cognate proteins. Many of these selected residues reside in non-catalytic regions of PADI2. We validate the importance of a prominent loop in the middle domain that encompasses PADI2 L162, a residue under positive selection. This site is essential for interaction with the transcription elongation factor (P-TEFb) and mediates the active transcription of the oncogenes c-MYC, and CCNB1, as well as impacting cellular proliferation. These insights could be key to understanding and addressing the role of the PADI2 c-MYC axis in cancer progression.

CM2D3: Furnishing the human interactome with structural models of protein complexes derived by comparative modeling and docking

The human interactome is composed of around half a million interactions according to recent estimations and it is only for a small fraction of those that three-dimensional structural information is available. Indeed, the structural coverage of the human interactome is very low and given the complexity and time-consuming requirements of solving protein structures this problem will remain for the foreseeable future. Structural models, or predictions, of protein complexes can provide valuable information when the experimentally determined 3D structures are not available. Here we present CM2D3, a relational database containing structural models of the whole human interactome derived both from comparative modeling and data-driven docking. Starting from a consensus interactome derived from integrating several interactomics databases, a strategy was devised to derive structural models by computational means. Currently, CM2D3 includes 33338 structural models of which 5121 derived from comparative modeling and the remaining from docking. Of the latter, the structures of 14554 complexes were derived from monomers modeled by M4T while the rest were modeled with structures as predicted by AlphaFold2. Lastly, CM2D3 complements existing resources by focusing on models derived from both free-docking, as opposed to template-based docking, and hence expanding the available structural information on protein complexes to the scientific community. Database URL:http://www.bioinsilico.org/CM2D3.

Theoretical 3D Modeling of NLRP3 Inflammasome Complex

The NOD-like receptor pyrin domain containing 3 (NLRP3) is a multidomain protein that plays a key role in innate immune response. Structures of NLRP3 in different conformational states and bound to cognate partners are available. In this chapter we present an approach to model the oligomeric structure of NLRP3 by homology modeling using multiple templates, symmetry, and refinement. The overall process presented here represents advanced exercise in structural modeling that provides unique insights into the biological role and activation of NLRP3 oligomer. Finally, the same approach can be easily adapted to the rest of the members of the NLRP family.

Prediction of Adverse Drug Reaction Linked to Protein Targets Using Network-Based Information and Machine Learning

Drug discovery attrition rates, particularly at advanced clinical trial stages, are high because of unexpected adverse drug reactions (ADR) elicited by novel drug candidates. Predicting undesirable ADRs produced by the modulation of certain protein targets would contribute to developing safer drugs, thereby reducing economic losses associated with high attrition rates. As opposed to the more traditional drug-centric approach, we propose a target-centric approach to predict associations between protein targets and ADRs. The implementation of the predictor is based on a machine learning classifier that integrates a set of eight independent network-based features. These include a network diffusion-based score, identification of protein modules based on network clustering algorithms, functional similarity among proteins, network distance to proteins that are part of safety panels used in preclinical drug development, set of network descriptors in the form of degree and betweenness centrality measurements, and conservation. This diverse set of descriptors were used to generate predictors based on different machine learning classifiers ranging from specific models for individual ADR to higher levels of abstraction as per MEDDRA hierarchy such as system organ class. The results obtained from the different machine-learning classifiers, namely, support vector machine, random forest, and neural network were further analyzed as a meta-predictor exploiting three different voting systems, namely, jury vote, consensus vote, and red flag, obtaining different models for each of the ADRs in analysis. The level of accuracy of the predictors justifies the identification of problematic protein targets both at the level of individual ADR as well as a set of related ADRs grouped in common system organ classes. As an example, the prediction of ventricular tachycardia achieved an accuracy and precision of 0.83 and 0.90, respectively, and a Matthew correlation coefficient of 0.70. We believe that this approach is a good complement to the existing methodologies devised to foresee potential liabilities in preclinical drug discovery. The method is available through the DocTOR utility at GitHub (https://github.com/cristian931/DocTOR).

In silico identification of two peptides with antibacterial activity against multidrug-resistant Staphylococcus aureus

Here we report two antimicrobial peptides (AMPs), HG2 and HG4 identified from a rumen microbiome metagenomic dataset, with activity against multidrug-resistant (MDR) bacteria, especially methicillin-resistant Staphylococcus aureus (MRSA) strains, a major hospital and community-acquired pathogen. We employed the classifier model design to analyse, visualise, and interpret AMP activities. This approach allowed in silico discrimination of promising lead AMP candidates for experimental evaluation. The lead AMPs, HG2 and HG4, are fast-acting and show anti-biofilm and anti-inflammatory activities in vitro and demonstrated little toxicity to human primary cell lines. The peptides were effective in vivo within a Galleria mellonella model of MRSA USA300 infection. In terms of mechanism of action, HG2 and HG4 appear to interact with the cytoplasmic membrane of target cells and may inhibit other cellular processes, whilst preferentially binding to bacterial lipids over human cell lipids. Therefore, these AMPs may offer additional therapeutic templates for MDR bacterial infections.

Novel SIX6 mutations cause recessively inherited congenital cataract, microcornea, and corneal opacification with or without coloboma and microphthalmia

Purpose

To investigate the molecular basis of recessively inherited congenital cataract, microcornea, and corneal opacification with or without coloboma and microphthalmia in two consanguineous families.

Methods

Conventional autozygosity mapping was performed using single nucleotide polymorphism (SNP) microarrays. Whole-exome sequencing was completed on genomic DNA from one affected member of each family. Exome sequence data were also used for homozygosity mapping and copy number variation analysis. PCR and Sanger sequencing were used to confirm the identification of mutations and to screen further patients. Evolutionary conservation of protein sequences was assessed using CLUSTALW, and protein structures were modeled using PyMol.

Results

In family MEP68, a novel homozygous nucleotide substitution in SIX6 was found, c.547G>C, that converts the evolutionarily conserved aspartic acid residue at the 183^rd amino acid in the protein to a histidine, p.(Asp183His). This residue mapped to the third helix of the DNA-binding homeobox domain in SIX6, which interacts with the major groove of double-stranded DNA. This interaction is likely to be disrupted by the mutation. In family F1332, a novel homozygous 1034 bp deletion that encompasses the first exon of SIX6 was identified, chr14:g.60975890_60976923del. Both mutations segregated with the disease phenotype as expected for a recessive condition and were absent from publicly available variant databases.

Conclusions

Our findings expand the mutation spectrum in this form of inherited eye disease and confirm that homozygous human SIX6 mutations cause a developmental spectrum of ocular phenotypes that includes not only the previously described features of microphthalmia, coloboma, and congenital cataract but also corneal abnormalities.

Inhibition of Viral Membrane Fusion by Peptides and Approaches to Peptide Design

Fusion of lipid-enveloped viruses with the cellular plasma membrane or the endosome membrane is mediated by viral envelope proteins that undergo large conformational changes following binding to receptors. The HIV-1 fusion protein gp41 undergoes a transition into a "six-helix bundle" after binding of the surface protein gp120 to the CD4 receptor and a co-receptor. Synthetic peptides that mimic part of this structure interfere with the formation of the helix structure and inhibit membrane fusion. This approach also works with the S spike protein of SARS-CoV-2. Here we review the peptide inhibitors of membrane fusion involved in infection by influenza virus, HIV-1, MERS and SARS coronaviruses, hepatitis viruses, paramyxoviruses, flaviviruses, herpesviruses and filoviruses. We also describe recent computational methods used for the identification of peptide sequences that can interact strongly with protein interfaces, with special emphasis on SARS-CoV-2, using the PePI-Covid19 database.

In silico characterisation of the complete Ly6 protein family in Fasciola gigantica supported through transcriptomics of the newly-excysted juveniles

Fasciola gigantica is one of the aetiological trematodes associated with fascioliasis, which heavily impacts food-production systems and human and animal welfare on a global scale. In the absence of a vaccine, fascioliasis control and treatment is restricted to pasture management, such as clean grazing, and a limited array of chemotherapies, to which signs of resistance are beginning to appear. Research into novel control strategies is therefore urgently required and the advent of ‘omics technologies presents considerable opportunity for novel drug and vaccine target discovery. Here, interrogation of the first available F. gigantica newly excysted juvenile (NEJ) transcriptome revealed several protein families of current interest to parasitic flatworm vaccine research, including orthologues of mammalian complement regulator CD59 of the Ly6 family. Ly6 proteins have previously been identified on the tegument of Schistosoma mansoni and induced protective immunity in vaccination trials. Incorporating the recently available F. gigantica genome, the current work revealed 20 novel Ly6 family members in F. gigantica and, in parallel, significantly extended the F. hepatica complement from 3 to 18 members. Phylogenetic analysis revealed several distinct clades within the family, some of which are unique to Fasciola spp. trematodes. Analysis of available proteomic databases also revealed three of the newly discovered FhLy6s were present in extracellular vesicles, which have previously been prioritised in studying the host-parasite interface. The presentation of this new transcriptomic resource, in addition to the Ly6 family proteins here identified, represents a wealth of opportunity for future vaccine research.

Incorporating the recently available F. gigantica genome, the current work revealed 20 novel Ly6 family members in F. gigantica and, in parallel, significantly extended the F. hepatica complement from 3 to 18 members.

Mining drug-target and drug-adverse drug reaction databases to identify target-adverse drug reaction relationships

The level of attrition on drug discovery, particularly at advanced stages, is very high due to unexpected adverse drug reactions (ADRs) caused by drug candidates, and thus, being able to predict undesirable responses when modulating certain protein targets would contribute to the development of safer drugs and have important economic implications. On the one hand, there are a number of databases that compile information of drug-target interactions. On the other hand, there are a number of public resources that compile information on drugs and ADR. It is therefore possible to link target and ADRs using drug entities as connecting elements. Here, we present T-ARDIS (Target-Adverse Reaction Database Integrated Search) database, a resource that provides comprehensive information on proteins and associated ADRs. By combining the information from drug-protein and drug-ADR databases, we statistically identify significant associations between proteins and ADRs. Besides describing the relationship between proteins and ADRs, T-ARDIS provides detailed description about proteins along with the drug and adverse reaction information. Currently T-ARDIS contains over 3000 ADR and 248 targets for a total of more 17 000 pairwise interactions. Each entry can be retrieved through multiple search terms including target Uniprot ID, gene name, adverse effect and drug name. Ultimately, the T-ARDIS database has been created in response to the increasing interest in identifying early in the drug development pipeline potentially problematic protein targets whose modulation could result in ADRs. Database URL: http://www.bioinsilico.org/T-ARDIS.

Efficient elimination of primary B-ALL cells in vitro and in vivo using a novel 4-1BB-based CAR targeting a membrane-distal CD22 epitope

Background

There are few therapeutic options available for patients with B-cell acute lymphoblastic leukemia (B-ALL) relapsing as CD19^– either after chemotherapy or CD19-targeted immunotherapies. CD22-chimeric antigen receptor (CAR) T cells represent an attractive addition to CD19-CAR T cell therapy because they will target both CD22⁺CD19^– B-ALL relapses and CD19^– preleukemic cells. However, the immune escape mechanisms from CD22-CAR T cells, and the potential contribution of the epitope binding of the anti-CD22 single-chain variable fragment (scFv) remain understudied.

Methods

Here, we have developed and comprehensively characterized a novel CD22-CAR (clone hCD22.7) targeting a membrane-distal CD22 epitope and tested its cytotoxic effects against B-ALL cells both in in vitro and in vivo assays.

Results

Conformational epitope mapping, cross-blocking, and molecular docking assays revealed that the hCD22.7 scFv is a high-affinity binding antibody which specifically binds to the ESTKDGKVP sequence, located in the Ig-like V-type domain, the most distal domain of CD22. We observed efficient killing of B-ALL cells in vitro, although the kinetics were dependent on the level of CD22 expression. Importantly, we show an efficient in vivo control of patients with B-ALL derived xenografts with diverse aggressiveness, coupled to long-term hCD22.7-CAR T cell persistence. Remaining leukemic cells at sacrifice maintained full expression of CD22, ruling out CAR pressure-mediated antigen loss. Finally, the immunogenicity capacity of this hCD22.7-scFv was very similar to that of other CD22 scFv previously used in adoptive T cell therapy.

Conclusions

We report a novel, high-affinity hCD22.7 scFv which targets a membrane-distal epitope of CD22. 4-1BB-based hCD22.7-CAR T cells efficiently eliminate clinically relevant B- CD22^high and CD22^low ALL primary samples in vitro and in vivo. Our study supports the clinical translation of this hCD22.7-CAR as either single or tandem CD22–CD19-CAR for both naive and anti-CD19-resistant patients with B-ALL.

On the use of direct-coupling analysis with a reduced alphabet of amino acids combined with super-secondary structure motifs for protein fold prediction

Direct-coupling analysis (DCA) for studying the coevolution of residues in proteins has been widely used to predict the three-dimensional structure of a protein from its sequence. We present RADI/raDIMod, a variation of the original DCA algorithm that groups chemically equivalent residues combined with super-secondary structure motifs to model protein structures. Interestingly, the simplification produced by grouping amino acids into only two groups (polar and non-polar) is still representative of the physicochemical nature that characterizes the protein structure and it is in line with the role of hydrophobic forces in protein-folding funneling. As a result of a compressed alphabet, the number of sequences required for the multiple sequence alignment is reduced. The number of long-range contacts predicted is limited; therefore, our approach requires the use of neighboring sequence-positions. We use the prediction of secondary structure and motifs of super-secondary structures to predict local contacts. We use RADI and raDIMod, a fragment-based protein structure modelling, achieving near native conformations when the number of super-secondary motifs covers >30-50% of the sequence. Interestingly, although different contacts are predicted with different alphabets, they produce similar structures.

The rumen eukaryotome is a source of novel antimicrobial peptides with therapeutic potential

BACKGROUND

The rise of microbial antibiotic resistance is a leading threat to the health of the human population. As such, finding new approaches to tackle these microbes, including development of novel antibiotics is vital.

RESULTS

In this study, we mined a rumen eukaryotic metatranscriptomic library for novel Antimicrobial peptides (AMPs) using computational approaches and thereafter characterised the therapeutic potential of the AMPs. We identified a total of 208 potentially novel AMPs from the ruminal eukaryotome, and characterised one of those, namely Lubelisin. Lubelisin (GIVAWFWRLAR) is an α-helical peptide, 11 amino acid long with theoretical molecular weight of 1373.76 D. In the presence of Lubelisin, strains of methicillin-resistant Staphylococcus aureus (MRSA) USA300 and EMRSA-15 were killed within 30 min of exposure with ≥103 and 104 CFU/mL reduction in viable cells respectively. Cytotoxicity of Lubelisin against both human and sheep erythrocytes was low resulting in a therapeutic index of 0.43. Membrane permeabilisation assays using propidium iodide alongside transmission electron microscopy revealed that cytoplasmic membrane damage may contribute to the antimicrobial activities of Lubelisin.

CONCLUSIONS

We demonstrate that the rumen eukaryotome is a viable source for the discovery of antimicrobial molecules for the treatment of bacterial infections and further development of these may provide part of the potential solution to the ongoing problem of antimicrobial resistance. The role of these AMPs in the ecological warfare within the rumen is also currently unknown.

A Comparison of Differential Gene Expression in Response to the Onset of Water Stress Between Three Hybrid Brachiaria Genotypes

Brachiaria (Trin.) Griseb. (syn. Urochloa P. Beauv.) is a C4 grass genus belonging to the Panicoideae. Native to Africa, these grasses are now widely grown as forages in tropical areas worldwide and are the subject of intensive breeding, particularly in South America. Tolerance to abiotic stresses such as aluminum and drought are major breeding objectives. In this study, we present the transcriptomic profiling of leaves and roots of three Brachiaria interspecific hybrid genotypes with the onset of water stress, Br12/3659-17 (gt-17), Br12/2360-9 (gt-9), and Br12/3868-18 (gt-18), previously characterized as having good, intermediate and poor tolerance to drought, respectively, in germplasm evaluation programs. RNA was extracted from leaf and root tissue of plants at estimated growing medium water contents (EWC) of 35, 15, and 5%. Differentially expressed genes (DEGs) were compared between different EWCs, 35/15, 15/5, and 35/5 using DESeq2. Overall, the proportions of DEGs enriched in all three genotypes varied in a genotype-dependent manner in relation to EWC comparison, with intermediate and sensitive gt-9 and gt-18 being more similar to each other than to drought tolerant gt-17. More specifically, GO terms relating to carbohydrate and cell wall metabolism in the leaves were enriched by up-regulated DEGs in gt-9 and gt-18, but by down-regulated DEGs in gt-17. Across all genotypes, analysis of DEG enzyme activities indicated an excess of down-regulated putative apoplastic peroxidases in the roots as water stress increased. This suggests that changes in root cell-wall architecture may be an important component of the response to water stress in Brachiaria.

On the prediction of DNA-binding preferences of C2H2-ZF domains using structural models: application on human CTCF

Cis2-His2 zinc finger (C2H2-ZF) proteins are the largest family of transcription factors in human and higher metazoans. To date, the DNA-binding preferences of many members of this family remain unknown. We have developed a computational method to predict their DNA-binding preferences. We have computed theoretical position weight matrices (PWMs) of proteins composed by C2H2-ZF domains, with the only requirement of an input structure. We have predicted more than two-third of a single zinc-finger domain binding site for about 70% variants of Zif268, a classical member of this family. We have successfully matched between 60 and 90% of the binding-site motif of examples of proteins composed by three C2H2-ZF domains in JASPAR, a standard database of PWMs. The tests are used as a proof of the capacity to scan a DNA fragment and find the potential binding sites of transcription-factors formed by C2H2-ZF domains. As an example, we have tested the approach to predict the DNA-binding preferences of the human chromatin binding factor CTCF. We offer a server to model the structure of a zinc-finger protein and predict its PWM.

A new genome allows the identification of genes associated with natural variation in aluminium tolerance in Brachiaria grasses

Toxic concentrations of aluminium cations and low phosphorus availability are the main yield-limiting factors in acidic soils, which represent half of the potentially available arable land. Brachiaria grasses, which are commonly sown as forage in the tropics because of their resilience and low demand for nutrients, show greater tolerance to high concentrations of aluminium cations (Al3+) than most other grass crops. In this work, we explored the natural variation in tolerance to Al3+ between high and low tolerant Brachiaria species and characterized their transcriptional differences during stress. We identified three QTLs (quantitative trait loci) associated with root vigour during Al3+ stress in their hybrid progeny. By integrating these results with a new Brachiaria reference genome, we identified 30 genes putatively responsible for Al3+ tolerance in Brachiaria. We observed differential expression during stress of genes involved in RNA translation, response signalling, cell wall composition, and vesicle location homologous to aluminium-induced proteins involved in limiting uptake or localizing the toxin. However, there was limited regulation of malate transporters in Brachiaria, which suggests that exudation of organic acids and other external tolerance mechanisms, common in other grasses, might not be relevant in Brachiaria. The contrasting regulation of RNA translation and response signalling suggests that response timing is critical in high Al3+-tolerant Brachiaria.

Evaluation of Computationally Designed Peptides against TWEAK, a Cytokine of the Tumour Necrosis Factor Ligand Family

The tumour necrosis factor-like weak inducer of apoptosis (TWEAK) is a member of the tumour necrosis factor ligand family and has been shown to be overexpressed in tumoral cells together with the fibroblast growth factor–inducible 14 (Fn14) receptor. TWEAK-Fn14 interaction triggers a set of intracellular pathways responsible for tumour cell invasion and migration, as well as proliferation and angiogenesis. Hence, modulation of the TWEAK-Fn14 interaction is an important therapeutic goal. The targeting of protein-protein interactions by external agents, e.g., drugs, remains a substantial challenge. Given their intrinsic features, as well as recent advances that improve their pharmacological profiles, peptides have arisen as promising agents in this regard. Here, we report, by in silico structural design validated by cell-based and in vitro assays, the discovery of four peptides able to target TWEAK. Our results show that, when added to TWEAK-dependent cellular cultures, peptides cause a down-regulation of genes that are part of TWEAK-Fn14 signalling pathway. The direct, physical interaction between the peptides and TWEAK was further elucidated in an in vitro assay which confirmed that the bioactivity shown in cell-based assays was due to the targeting of TWEAK. The results presented here are framed within early pre-clinical drug development and therefore these peptide hits represent a starting point for the development of novel therapeutic agents. Our approach exemplifies the powerful combination of in silico and experimental efforts to quickly identify peptides with desirable traits.

An ensemble learning approach for modeling the systems biology of drug-induced injury

BACKGROUND

Drug-induced liver injury (DILI) is an adverse reaction caused by the intake of drugs of common use that produces liver damage. The impact of DILI is estimated to affect around 20 in 100,000 inhabitants worldwide each year. Despite being one of the main causes of liver failure, the pathophysiology and mechanisms of DILI are poorly understood. In the present study, we developed an ensemble learning approach based on different features (CMap gene expression, chemical structures, drug targets) to predict drugs that might cause DILI and gain a better understanding of the mechanisms linked to the adverse reaction.

RESULTS

We searched for gene signatures in CMap gene expression data by using two approaches: phenotype-gene associations data from DisGeNET, and a non-parametric test comparing gene expression of DILI-Concern and No-DILI-Concern drugs (as per DILIrank definitions). The average accuracy of the classifiers in both approaches was 69%. We used chemical structures as features, obtaining an accuracy of 65%. The combination of both types of features produced an accuracy around 63%, but improved the independent hold-out test up to 67%. The use of drug-target associations as feature obtained the best accuracy (70%) in the independent hold-out test.

CONCLUSIONS

When using CMap gene expression data, searching for a specific gene signature among the landmark genes improves the quality of the classifiers, but it is still limited by the intrinsic noise of the dataset. When using chemical structures as a feature, the structural diversity of the known DILI-causing drugs hampers the prediction, which is a similar problem as for the use of gene expression information. The combination of both features did not improve the quality of the classifiers but increased the robustness as shown on independent hold-out tests. The use of drug-target associations as feature improved the prediction, specially the specificity, and the results were comparable to previous research studies.

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/ .

In-silico drug repurposing study predicts the combination of pirfenidone and melatonin as a promising candidate therapy to reduce SARS-CoV-2 infection progression and respiratory distress caused by cytokine storm

From January 2020, COVID-19 is spreading around the world producing serious respiratory symptoms in infected patients that in some cases can be complicated by the severe acute respiratory syndrome, sepsis and septic shock, multiorgan failure, including acute kidney injury and cardiac injury. Cost and time efficient approaches to reduce the burthen of the disease are needed. To find potential COVID-19 treatments among the whole arsenal of existing drugs, we combined system biology and artificial intelligence-based approaches. The drug combination of pirfenidone and melatonin has been identified as a candidate treatment that may contribute to reduce the virus infection. Starting from different drug targets the effect of the drugs converges on human proteins with a known role in SARS-CoV-2 infection cycle. Simultaneously, GUILDify v2.0 web server has been used as an alternative method to corroborate the effect of pirfenidone and melatonin against the infection of SARS-CoV-2. We have also predicted a potential therapeutic effect of the drug combination over the respiratory associated pathology, thus tackling at the same time two important issues in COVID-19. These evidences, together with the fact that from a medical point of view both drugs are considered safe and can be combined with the current standard of care treatments for COVID-19 makes this combination very attractive for treating patients at stage II, non-severe symptomatic patients with the presence of virus and those patients who are at risk of developing severe pulmonary complications.

A Functional Kinase Is Necessary for Cyclin-Dependent Kinase G1 (CDKG1) to Maintain Fertility at High Ambient Temperature in Arabidopsis

Maintaining fertility in a fluctuating environment is key to the reproductive success of flowering plants. Meiosis and pollen formation are particularly sensitive to changes in growing conditions, especially temperature. We have previously identified cyclin-dependent kinase G1 (CDKG1) as a master regulator of temperature-dependent meiosis and this may involve the regulation of alternative splicing (AS), including of its own transcript. CDKG1 mRNA can undergo several AS events, potentially producing two protein variants: CDKG1L and CDKG1S, differing in their N-terminal domain which may be involved in co-factor interaction. In leaves, both isoforms have distinct temperature-dependent functions on target mRNA processing, but their role in pollen development is unknown. In the present study, we characterize the role of CDKG1L and CDKG1S in maintaining Arabidopsis fertility. We show that the long (L) form is necessary and sufficient to rescue the fertility defects of the cdkg1-1 mutant, while the short (S) form is unable to rescue fertility. On the other hand, an extra copy of CDKG1L reduces fertility. In addition, mutation of the ATP binding pocket of the kinase indicates that kinase activity is necessary for the function of CDKG1. Kinase mutants of CDKG1L and CDKG1S correctly localize to the cell nucleus and nucleus and cytoplasm, respectively, but are unable to rescue either the fertility or the splicing defects of the cdkg1-1 mutant. Furthermore, we show that there is partial functional overlap between CDKG1 and its paralog CDKG2 that could in part be explained by overlapping gene expression.

Differential gene expression and gene ontologies associated with increasing water-stress in leaf and root transcriptomes of perennial ryegrass (Lolium perenne)

Perennial ryegrass (Lolium perenne) is a forage and amenity grass species widely cultivated in temperate regions worldwide. As such, perennial ryegrass populations are exposed to a range of environmental conditions and stresses on a seasonal basis and from year to year. One source of potential stress is limitation on water availability. The ability of these perennial grasses to be able to withstand and recover after periods of water limitation or drought can be a key component of grassland performance. Thus, we were interested in looking at changes in patterns of gene expression associated with increasing water stress. Clones of a single genotype of perennial ryegrass were grown under non-flowering growth room conditions in vermiculite supplemented with nutrient solution. Leaf and root tissue was sampled at 4 times in quadruplicate relating to estimated water contents of 35%, 15%, 5% and 1%. RNA was extracted and RNAseq used to generate transcriptome profiles at each sampling point. Transcriptomes were assembled using the published reference genome sequence and differential gene expression analysed using 3 different programmes, DESeq2, edgeR and limma (with the voom transformation), individually and in combination, deriving Early, Middle and Late stage comparisons. Identified differentially expressed genes were then associated with enriched GO terms using BLAST2GO. For the leaf, up-regulated differentially expressed genes were strongly associated with GO terms only during the Early stage and the majority of GO terms were associated with only down-regulated genes at the Middle or Late stages. For the roots, few differentially expressed genes were identified at either Early or Middle stages. Only one replicate at 1% estimated water content produced high quality data for the root, however, this indicated a high level of differential expression. Again the majority of enriched GO terms were associated with down-regulated genes. The performance of the different analysis programmes and the annotations associated with identified differentially expressed genes is discussed.

Integrating a newly developed BAC-based physical mapping resource for Lolium perenne with a genome-wide association study across a L. perenne European ecotype collection identifies genomic contexts associated with agriculturally important traits

BACKGROUND AND AIMS

Lolium perenne (perennial ryegrass) is the most widely cultivated forage and amenity grass species in temperate areas worldwide and there is a need to understand the genetic architectures of key agricultural traits and crop characteristics that deliver wider environmental services. Our aim was to identify genomic regions associated with agriculturally important traits by integrating a bacterial artificial chromosome (BAC)-based physical map with a genome-wide association study (GWAS).

METHODS

BAC-based physical maps for L. perenne were constructed from ~212 000 high-information-content fingerprints using Fingerprint Contig and Linear Topology Contig software. BAC clones were associated with both BAC-end sequences and a partial minimum tiling path sequence. A panel of 716 L. perenne diploid genotypes from 90 European accessions was assessed in the field over 2 years, and genotyped using a Lolium Infinium SNP array. The GWAS was carried out using a linear mixed model implemented in TASSEL, and extended genomic regions associated with significant markers were identified through integration with the physical map.

KEY RESULTS

Between ~3600 and 7500 physical map contigs were derived, depending on the software and probability thresholds used, and integrated with ~35 k sequenced BAC clones to develop a resource predicted to span the majority of the L. perenne genome. From the GWAS, eight different loci were significantly associated with heading date, plant width, plant biomass and water-soluble carbohydrate accumulation, seven of which could be associated with physical map contigs. This allowed the identification of a number of candidate genes.

CONCLUSIONS

Combining the physical mapping resource with the GWAS has allowed us to extend the search for candidate genes across larger regions of the L. perenne genome and identified a number of interesting gene model annotations. These physical maps will aid in validating future sequence-based assemblies of the L. perenne genome.

On the mechanisms of protein interactions: predicting their affinity from unbound tertiary structures

Motivation

The characterization of the protein–protein association mechanisms is crucial to understanding how biological processes occur. It has been previously shown that the early formation of non-specific encounters enhances the realization of the stereospecific (i.e. native) complex by reducing the dimensionality of the search process. The association rate for the formation of such complex plays a crucial role in the cell biology and depends on how the partners diffuse to be close to each other. Predicting the binding free energy of proteins provides new opportunities to modulate and control protein–protein interactions. However, existing methods require the 3D structure of the complex to predict its affinity, severely limiting their application to interactions with known structures.

Results

We present a new approach that relies on the unbound protein structures and protein docking to predict protein–protein binding affinities. Through the study of the docking space (i.e. decoys), the method predicts the binding affinity of the query proteins when the actual structure of the complex itself is unknown. We tested our approach on a set of globular and soluble proteins of the newest affinity benchmark, obtaining accuracy values comparable to other state-of-art methods: a 0.4 correlation coefficient between the experimental and predicted values of ΔG and an error < 3 Kcal/mol.

Availability and implementation

The binding affinity predictor is implemented and available at http://sbi.upf.edu/BADock and https://github.com/badocksbi/BADock.

Supplementary information

Supplementary data are available at Bioinformatics online.

Arginine Citrullination at the C-Terminal Domain Controls RNA Polymerase II Transcription

The post-translational modification of key residues at the C-terminal domain of RNA polymerase II (RNAP2-CTD) coordinates transcription, splicing, and RNA processing by modulating its capacity to act as a landing platform for a variety of protein complexes. Here, we identify a new modification at the CTD, the deimination of arginine and its conversion to citrulline by peptidyl arginine deiminase 2 (PADI2), an enzyme that has been associated with several diseases, including cancer. We show that, among PADI family members, only PADI2 citrullinates R1810 (Cit1810) at repeat 31 of the CTD. Depletion of PADI2 or loss of R1810 results in accumulation of RNAP2 at transcription start sites, reduced gene expression, and inhibition of cell proliferation. Cit1810 is needed for interaction with the P-TEFb (positive transcription elongation factor b) kinase complex and for its recruitment to chromatin. In this way, CTD-Cit1810 favors RNAP2 pause release and efficient transcription in breast cancer cells.

Methyl-CpG-binding (SmMBD2/3) and chromobox (SmCBX) proteins are required for neoblast proliferation and oviposition in the parasitic blood fluke Schistosoma mansoni

While schistosomiasis remains a significant health problem in low to middle income countries, it also represents a recently recognised threat to more economically-developed regions. Until a vaccine is developed, this neglected infectious disease is primarily controlled by praziquantel, a drug with a currently unknown mechanism of action. By further elucidating how Schistosoma molecular components cooperate to regulate parasite developmental processes, next generation targets will be identified. Here, we continue our studies on schistosome epigenetic participants and characterise the function of a DNA methylation reader, the Schistosoma mansoni methyl-CpG-binding domain protein (SmMBD2/3). Firstly, we demonstrate that SmMBD2/3 contains amino acid features essential for 5-methyl cytosine (5mC) binding and illustrate that adult schistosome nuclear extracts (females > males) contain this activity. We subsequently show that SmMBD2/3 translocates into nuclear compartments of transfected murine NIH-3T3 fibroblasts and recombinant SmMBD2/3 exhibits 5mC binding activity. Secondly, using a yeast-two hybrid (Y2H) screen, we show that SmMBD2/3 interacts with the chromo shadow domain (CSD) of an epigenetic adaptor, S. mansoni chromobox protein (SmCBX). Moreover, fluorescent in situ hybridisation (FISH) mediated co-localisation of Smmbd2/3 and Smcbx to mesenchymal cells as well as somatic- and reproductive- stem cells confirms the Y2H results and demonstrates that these interacting partners are ubiquitously expressed and found within both differentiated as well as proliferating cells. Finally, using RNA interference, we reveal that depletion of Smmbd2/3 or Smcbx in adult females leads to significant reductions (46-58%) in the number of proliferating somatic stem cells (PSCs or neoblasts) as well as in the quantity of in vitro laid eggs. Collectively, these results further expand upon the schistosome components involved in epigenetic processes and suggest that pharmacological inhibition of SmMBD2/3 and/or SmCBX biology could prove useful in the development of future schistosomiasis control strategies.

The rumen microbiome: an underexplored resource for novel antimicrobial discovery

Antimicrobial peptides (AMPs) are promising drug candidates to target multi-drug resistant bacteria. The rumen microbiome presents an underexplored resource for the discovery of novel microbial enzymes and metabolites, including AMPs. Using functional screening and computational approaches, we identified 181 potentially novel AMPs from a rumen bacterial metagenome. Here, we show that three of the selected AMPs (Lynronne-1, Lynronne-2 and Lynronne-3) were effective against numerous bacterial pathogens, including methicillin-resistant Staphylococcus aureus (MRSA). No decrease in MRSA susceptibility was observed after 25 days of sub-lethal exposure to these AMPs. The AMPs bound preferentially to bacterial membrane lipids and induced membrane permeability leading to cytoplasmic leakage. Topical administration of Lynronne-1 (10% w/v) to a mouse model of MRSA wound infection elicited a significant reduction in bacterial counts, which was comparable to treatment with 2% mupirocin ointment. Our findings indicate that the rumen microbiome may provide viable alternative antimicrobials for future therapeutic application.

Anti-microbial molecules made by microbes in the gut of ruminant animals could become new weapons against antibiotic-resistant infections. An international team of researchers led by Sharon Huws at Queen’s University Belfast, UK, identified three anti-microbial peptides in the rumen of animals such as cattle, sheep and goats. The peptides—short proteins—were highly active in laboratory trials against several clinically important drug-resistant infections. These included methicillin resistant Staphylococcus aureus (MRSA), a notorious cause of life-threatening infections, especially in patients with weakened immunity. There is growing interest in using peptides as alternatives to existing antibiotics. The findings, initiated by examining a ‘library’ of molecular data, suggest that the rumen is an under-explored resource that may harbor many medically useful antimicrobials. The possibilities should be investigated further, with promising molecules being tested in clinical conditions.

Buwchitin: A Ruminal Peptide with Antimicrobial Potential against Enterococcus faecalis

Antimicrobial peptides (AMPs) are gaining popularity as alternatives for treatment of bacterial infections and recent advances in omics technologies provide new platforms for AMP discovery. We sought to determine the antibacterial activity of a novel antimicrobial peptide, buwchitin, against Enterococcus faecalis. Buwchitin was identified from a rumen bacterial metagenome library, cloned, expressed and purified. The antimicrobial activity of the recombinant peptide was assessed using a broth microdilution susceptibility assay to determine the peptide's killing kinetics against selected bacterial strains. The killing mechanism of buwchitin was investigated further by monitoring its ability to cause membrane depolarization (diSC₃(5) method) and morphological changes in E. faecalis cells. Transmission electron micrographs of buwchitin treated E. faecalis cells showed intact outer membranes with blebbing, but no major damaging effects and cell morphology changes. Buwchitin had negligible cytotoxicity against defibrinated sheep erythrocytes. Although no significant membrane leakage and depolarization was observed, buwchitin at minimum inhibitory concentration (MIC) was bacteriostatic against E. faecalis cells and inhibited growth in vitro by 70% when compared to untreated cells. These findings suggest that buwchitin, a rumen derived peptide, has potential for antimicrobial activity against E. faecalis.

Insight into the machinery that oils chromatin dynamics

The packaging of genetic information in form of chromatin within the nucleus provides cells with the ability to store and protect massive amounts of information within a compact space. Storing information within chromatin allows selective access to specific DNA sequences by regulating the various levels of chromatin structure from nucleosomes, to chromatin fibers, loops and topological associating domains (TADs) using mechanisms that are being progressively unravelled. However, a relatively unexplored aspect is the energetic cost of changing the chromatin configuration to gain access to DNA information. Among the enzymes responsible for regulating chromatin access are the ATP-dependent chromatin remodellers that act on nucleosomes and use the energy of ATP hydrolysis to make chromatin DNA more accessible. It is assumed that the ATP used by these enzymes is provided by the mitochondria or by cytoplasmic glycolysis. We hypothesize that though this may be the case for cells in steady state, when gene expression has to be globally reprogramed in response to externals signals or stress conditions, the cell directs energy production to the cell nucleus, where rapid chromatin reorganization is needed for cell survival. We discovered that in response to hormones a nuclear ATP synthesis mechanism is activated that utilizing ADP-ribose and pyrophosphate as substrates. ¹ This extra view aims to put this process within its historical context, to describe the enzymatic steps in detail, to propose a possible structure of the ATP synthesising enzyme, and to shed light on how this may link to other reactions within the cell providing a perspective for future lines of investigation.

MetaPred2CS: a sequence-based meta-predictor for protein-protein interactions of prokaryotic two-component system proteins

MOTIVATION

Two-component systems (TCS) are the main signalling pathways of prokaryotes, and control a wide range of biological phenomena. Their functioning depends on interactions between TCS proteins, the specificity of which is poorly understood.

RESULTS

The MetaPred2CS web-server interfaces a sequence-based meta-predictor specifically designed to predict pairing of the histidine kinase and response-regulator proteins forming TCSs. MetaPred2CS integrates six sequence-based methods using a support vector machine classifier and has been intensively tested under different benchmarking conditions: (i) species specific gene sets; (ii) neighbouring versus orphan pairs; and (iii) k-fold cross validation on experimentally validated datasets.

AVAILABILITY AND IMPLEMENTATION

Web server at: http://metapred2cs.ibers.aber.ac.uk/, Source code: https://github.com/martinjvickers/MetaPred2CS or implemented as Virtual Machine at: http://metapred2cs.ibers.aber.ac.uk/download CONTACT: naf4@aber.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Prediction of homoprotein and heteroprotein complexes by protein docking and template-based modeling: A CASP-CAPRI experiment

We present the results for CAPRI Round 30, the first joint CASP-CAPRI experiment, which brought together experts from the protein structure prediction and protein-protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact-sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology-built subunit models and the smaller pair-wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323-348. © 2016 Wiley Periodicals, Inc.

The GH51 α-l-arabinofuranosidase from Paenibacillus sp. THS1 is multifunctional, hydrolyzing main-chain and side-chain glycosidic bonds in heteroxylans

BACKGROUND

Conceptually, multi-functional enzymes are attractive because in the case of complex polymer hydrolysis having two or more activities defined by a single enzyme offers the possibility of synergy and reduced enzyme cocktail complexity. Nevertheless, multi-functional enzymes are quite rare and are generally multi-domain assemblies with each activity being defined by a separate protein module. However, a recent report described a GH51 arabinofuranosidase from Alicyclobacillus sp. A4 that displays both α-l-arabinofuranosidase and β-d-xylanase activities, which are defined by a single active site. Following on from this, we describe in detail another multi-functional GH51 arabinofuranosidase and discuss the molecular basis of multifunctionality.

RESULTS

THSAbf is a GH51 α-l-arabinofuranosidase. Characterization revealed that THSAbf is active up to 75 °C, stable at 60 °C and active over a broad pH range (4-7). THSAbf preferentially releases para-nitrophenyl from the l-arabinofuranoside (k cat/K M = 1050 s(-1) mM(-1)) and to some extent from d-galactofuranoside and d-xyloside. THSAbf is active on 4-O-methylglucuronoxylans from birch and beechwood (10.8 and 14.4 U mg(-1), respectively) and on sugar beet branched and linear arabinans (1.1 ± 0.24 and 1.8 ± 0.1 U mg(-1)). Further investigation revealed that like the Alicyclobacillus sp. A4 α-l-arabinofuranosidase, THSAbf also displays endo-xylanase activity, cleaving β-1,4 bonds in heteroxylans. The optimum pH for THASAbf activity is substrate dependent, but ablation of the catalytic nucleophile caused a general loss of activity, indicating the involvement of a single active center. Combining the α-l-arabinofuranosidase with a GH11 endoxylanase did not procure synergy. The molecular modeling of THSAbf revealed a wide active site cleft and clues to explain multi-functionality.

CONCLUSION

The discovery of single active site, multifunctional enzymes such as THSAbf opens up exciting avenues for enzyme engineering and the development of new biomass-degrading cocktails that could considerably reduce enzyme production costs.

Genome-wide prediction of prokaryotic two-component system networks using a sequence-based meta-predictor

BACKGROUND

Two component systems (TCS) are signalling complexes manifested by a histidine kinase (receptor) and a response regulator (effector). They are the most abundant signalling pathways in prokaryotes and control a wide range of biological processes. The pairing of these two components is highly specific, often requiring costly and time-consuming experimental characterisation. Therefore, there is considerable interest in developing accurate prediction tools to lessen the burden of experimental work and cope with the ever-increasing amount of genomic information.

RESULTS

We present a novel meta-predictor, MetaPred2CS, which is based on a support vector machine. MetaPred2CS integrates six sequence-based prediction methods: in-silico two-hybrid, mirror-tree, gene fusion, phylogenetic profiling, gene neighbourhood, and gene operon. To benchmark MetaPred2CS, we also compiled a novel high-quality training dataset of experimentally deduced TCS protein pairs for k-fold cross validation, to act as a gold standard for TCS partnership predictions. Combining individual predictions using MetaPred2CS improved performance when compared to the individual methods and in comparison with a current state-of-the-art meta-predictor.

CONCLUSION

We have developed MetaPred2CS, a support vector machine-based metapredictor for prokaryotic TCS protein pairings. Central to the success of MetaPred2CS is a strategy of integrating individual predictors that improves the overall prediction accuracy, with the in-silico two-hybrid method contributing most to performance. MetaPred2CS outperformed other available systems in our benchmark tests, and is available online at http://metapred2cs.ibers.aber.ac.uk, along with our gold standard dataset of TCS interaction pairs.

VORFFIP-driven dock: V-D2OCK, a fast and accurate protein docking strategy

The experimental determination of the structure of protein complexes cannot keep pace with the generation of interactomic data, hence resulting in an ever-expanding gap. As the structural details of protein complexes are central to a full understanding of the function and dynamics of the cell machinery, alternative strategies are needed to circumvent the bottleneck in structure determination. Computational protein docking is a valid and valuable approach to model the structure of protein complexes. In this work, we describe a novel computational strategy to predict the structure of protein complexes based on data-driven docking: VORFFIP-driven dock (V-D2OCK). This new approach makes use of our newly described method to predict functional sites in protein structures, VORFFIP, to define the region to be sampled during docking and structural clustering to reduce the number of models to be examined by users. V-D2OCK has been benchmarked using a validated and diverse set of protein complexes and compared to a state-of-art docking method. The speed and accuracy compared to contemporary tools justifies the potential use of VD2OCK for high-throughput, genome-wide, protein docking. Finally, we have developed a web interface that allows users to browser and visualize V-D2OCK predictions from the convenience of their web-browsers.

Knowledge-based modeling of peptides at protein interfaces: PiPreD

MOTIVATION

Protein-protein interactions (PPIs) underpin virtually all cellular processes both in health and disease. Modulating the interaction between proteins by means of small (chemical) agents is therefore a promising route for future novel therapeutic interventions. In this context, peptides are gaining momentum as emerging agents for the modulation of PPIs.

RESULTS

We reported a novel computational, structure and knowledge-based approach to model orthosteric peptides to target PPIs: PiPreD. PiPreD relies on a precompiled and bespoken library of structural motifs, iMotifs, extracted from protein complexes and a fast structural modeling algorithm driven by the location of native chemical groups on the interface of the protein target named anchor residues. PiPreD comprehensive and systematically samples the entire interface deriving peptide conformations best suited for the given region on the protein interface. PiPreD complements the existing technologies and provides new solutions for the disruption of selected interactions.

AVAILABILITY AND IMPLEMENTATION

Database and accessory scripts and programs are available upon request to the authors or at http://www.bioinsilico.org/PIPRED.

CONTACT

narcis.fernandez@gmail.com.

Smotifs as structural local descriptors of supersecondary elements: classification, completeness and applications

Protein structures are made up of periodic and aperiodic structural elements (i.e., α-helices, β-strands and loops). Despite the apparent lack of regular structure, loops have specific conformations and play a central role in the folding, dynamics, and function of proteins. In this article, we reviewed our previous works in the study of protein loops as local supersecondary structural motifs or Smotifs. We reexamined our works about the structural classification of loops (ArchDB) and its application to loop structure prediction (ArchPRED), including the assessment of the limits of knowledge-based loop structure prediction methods. We finalized this article by focusing on the modular nature of proteins and how the concept of Smotifs provides a convenient and practical approach to decompose proteins into strings of concatenated Smotifs and how can this be used in computational protein design and protein structure prediction.

ArchDB 2014: structural classification of loops in proteins

The function of a protein is determined by its three-dimensional structure, which is formed by regular (i.e. β-strands and α-helices) and non-periodic structural units such as loops. Compared to regular structural elements, non-periodic, non-repetitive conformational units enclose a much higher degree of variability—raising difficulties in the identification of regularities, and yet represent an important part of the structure of a protein. Indeed, loops often play a pivotal role in the function of a protein and different aspects of protein folding and dynamics. Therefore, the structural classification of protein loops is an important subject with clear applications in homology modelling, protein structure prediction, protein design (e.g. enzyme design and catalytic loops) and function prediction. ArchDB, the database presented here (freely available at http://sbi.imim.es/archdb), represents such a resource and has been an important asset for the scientific community throughout the years. In this article, we present a completely reworked and updated version of ArchDB. The new version of ArchDB features a novel, fast and user-friendly web-based interface, and a novel graph-based, computationally efficient, clustering algorithm. The current version of ArchDB classifies 149,134 loops in 5739 classes and 9608 subclasses.

A modular perspective of protein structures: application to fragment based loop modeling

Proteins can be decomposed into supersecondary structure modules. We used a generic definition of supersecondary structure elements, so-called Smotifs, which are composed of two flanking regular secondary structures connected by a loop, to explore the evolution and current variety of structure building blocks. Here, we discuss recent observations about the saturation of Smotif geometries in protein structures and how it opens new avenues in protein structure modeling and design. As a first application of these observations we describe our loop conformation modeling algorithm, ArchPred that takes advantage of Smotifs classification. In this application, instead of focusing on specific loop properties the method narrows down possible template conformations in other, often not homologous structures, by identifying the most likely supersecondary structure environment that cradles the loop. Beyond identifying the correct starting supersecondary structure geometry, it takes into account information of fit of anchor residues, sterical clashes, match of predicted and observed dihedral angle preferences, and local sequence signal.

Next generation sequencing identifies mutations in Atonal homolog 7 (ATOH7) in families with global eye developmental defects

The atonal homolog 7 (ATOH7) gene encodes a transcription factor involved in determining the fate of retinal progenitor cells and is particularly required for optic nerve and ganglion cell development. Using a combination of autozygosity mapping and next generation sequencing, we have identified homozygous mutations in this gene, p.E49V and p.P18RfsX69, in two consanguineous families diagnosed with multiple ocular developmental defects, including severe vitreoretinal dysplasia, optic nerve hypoplasia, persistent fetal vasculature, microphthalmia, congenital cataracts, microcornea, corneal opacity and nystagmus. Most of these clinical features overlap with defects in the Norrin/β-catenin signalling pathway that is characterized by dysgenesis of the retinal and hyaloid vasculature. Our findings document Mendelian mutations within ATOH7 and imply a role for this molecule in the development of structures at the front as well as the back of the eye. This work also provides further insights into the function of ATOH7, especially its importance in retinal vascular development and hyaloid regression.

CAPS-DB: a structural classification of helix-capping motifs

The regions of the polypeptide chain immediately preceding or following an α-helix are known as Nt- and Ct cappings, respectively. Cappings play a central role stabilizing α-helices due to lack of intrahelical hydrogen bonds in the first and last turn. Sequence patterns of amino acid type preferences have been derived for cappings but the structural motifs associated to them are still unclassified. CAPS-DB is a database of clusters of structural patterns of different capping types. The clustering algorithm is based in the geometry and the (ϕ–ψ)-space conformation of these regions. CAPS-DB is a relational database that allows the user to search, browse, inspect and retrieve structural data associated to cappings. The contents of CAPS-DB might be of interest to a wide range of scientist covering different areas such as protein design and engineering, structural biology and bioinformatics. The database is accessible at: http://www.bioinsilico.org/CAPSDB.

Homozygous mutations in PXDN cause congenital cataract, corneal opacity, and developmental glaucoma

Anterior segment dysgenesis describes a group of heterogeneous developmental disorders that affect the anterior chamber of the eye and are associated with an increased risk of glaucoma. Here, we report homozygous mutations in peroxidasin (PXDN) in two consanguineous Pakistani families with congenital cataract-microcornea with mild to moderate corneal opacity and in a consanguineous Cambodian family with developmental glaucoma and severe corneal opacification. These results highlight the diverse ocular phenotypes caused by PXDN mutations, which are likely due to differences in genetic background and environmental factors. Peroxidasin is an extracellular matrix-associated protein with peroxidase catalytic activity, and we confirmed localization of the protein to the cornea and lens epithelial layers. Our findings imply that peroxidasin is essential for normal development of the anterior chamber of the eye, where it may have a structural role in supporting cornea and lens architecture as well as an enzymatic role as an antioxidant enzyme in protecting the lens, trabecular meshwork, and cornea against oxidative damage.

Improving the prediction of protein binding sites by combining heterogeneous data and Voronoi diagrams

BACKGROUND

Protein binding site prediction by computational means can yield valuable information that complements and guides experimental approaches to determine the structure of protein complexes. Predictions become even more relevant and timely given the current resolution of protein interaction maps, where there is a very large and still expanding gap between the available information on: (i) which proteins interact and (ii) how proteins interact. Proteins interact through exposed residues that present differential physicochemical properties, and these can be exploited to identify protein interfaces.

RESULTS

Here we present VORFFIP, a novel method for protein binding site prediction. The method makes use of broad set of heterogeneous data and defined of residue environment, by means of Voronoi Diagrams that are integrated by a two-steps Random Forest ensemble classifier. Four sets of residue features (structural, energy terms, sequence conservation, and crystallographic B-factors) used in different combinations together with three definitions of residue environment (Voronoi Diagrams, sequence sliding window, and Euclidian distance) have been analyzed in order to maximize the performance of the method.

CONCLUSIONS

The integration of different forms information such as structural features, energy term, evolutionary conservation and crystallographic B-factors, improves the performance of binding site prediction. Including the information of neighbouring residues also improves the prediction of protein interfaces. Among the different approaches that can be used to define the environment of exposed residues, Voronoi Diagrams provide the most accurate description. Finally, VORFFIP compares favourably to other methods reported in the recent literature.

PCRPi-DB: a database of computationally annotated hot spots in protein interfaces

Protein–protein interactions are central to almost any cellular process. Although typically protein interfaces are large, it is well established that only a relatively small region, the so-called ‘hot spot’, contributes the most to the total binding energy. There is a clear interest in identifying hot spots because of its application in drug discovery and protein design. Presaging Critical Residues in Protein Interfaces Database (PCRPi-DB) is a public repository that archives computationally annotated hot spots in protein complexes for which the 3D structure is known. Hot spots have been annotated using a new and highly accurate computational method developed in the lab. PCRPi-DB is freely available to the scientific community at http://www.bioinsilico.org/PCRPIDB. Besides browsing and querying the contents of the database, extensive documentation and links to relevant on-line resources and contents are available to users. PCRPi-DB is updated on a weekly basis.

Presaging critical residues in protein interfaces-web server (PCRPi-W): a web server to chart hot spots in protein interfaces

Background

It is well established that only a portion of residues that mediate protein-protein interactions (PPIs), the so-called hot spot, contributes the most to the total binding energy, and thus its identification is an important and relevant question that has clear applications in drug discovery and protein design. The experimental identification of hot spots is however a lengthy and costly process, and thus there is an interest in computational tools that can complement and guide experimental efforts.

Principal Findings

Here, we present Presaging Critical Residues in Protein interfaces-Web server (http://www.bioinsilico.org/PCRPi), a web server that implements a recently described and highly accurate computational tool designed to predict critical residues in protein interfaces: PCRPi. PRCPi depends on the integration of structural, energetic, and evolutionary-based measures by using Bayesian Networks (BNs).

Conclusions

PCRPi-W has been designed to provide an easy and convenient access to the broad scientific community. Predictions are readily available for download or presented in a web page that includes among other information links to relevant files, sequence information, and a Jmol applet to visualize and analyze the predictions in the context of the protein structure.

A mutation in the mitochondrial fission gene Dnm1l leads to cardiomyopathy

Mutations in a number of genes have been linked to inherited dilated cardiomyopathy (DCM). However, such mutations account for only a small proportion of the clinical cases emphasising the need for alternative discovery approaches to uncovering novel pathogenic mutations in hitherto unidentified pathways. Accordingly, as part of a large-scale N-ethyl-N-nitrosourea mutagenesis screen, we identified a mouse mutant, Python, which develops DCM. We demonstrate that the Python phenotype is attributable to a dominant fully penetrant mutation in the dynamin-1-like (Dnm1l) gene, which has been shown to be critical for mitochondrial fission. The C452F mutation is in a highly conserved region of the M domain of Dnm1l that alters protein interactions in a yeast two-hybrid system, suggesting that the mutation might alter intramolecular interactions within the Dnm1l monomer. Heterozygous Python fibroblasts exhibit abnormal mitochondria and peroxisomes. Homozygosity for the mutation results in the death of embryos midway though gestation. Heterozygous Python hearts show reduced levels of mitochondria enzyme complexes and suffer from cardiac ATP depletion. The resulting energy deficiency may contribute to cardiomyopathy. This is the first demonstration that a defect in a gene involved in mitochondrial remodelling can result in cardiomyopathy, showing that the function of this gene is needed for the maintenance of normal cellular function in a relatively tissue-specific manner. This disease model attests to the importance of mitochondrial remodelling in the heart; similar defects might underlie human heart muscle disease.

Heart disease is very common. Some cases of heart disease are strongly influenced by lifestyle and diet, whereas others have a strong genetic component. A certain form of heart failure, known as dilated cardiomyopathy (DCM) quite often runs in families suggesting that a defective gene or genes underlie this disease. We describe a new mouse mutant called “Python” which suffers from a heart disease similar to DCM. We were able to pinpoint the defective gene responsible for the disease. This gene is normally involved in the division of mitochondria, the “power plants” of the cell that generate one of the main energy supplies for the cell. This is a unique model that implicates a new gene and mechanism of disease for further investigation.

Homozygous FOXE3 mutations cause non-syndromic, bilateral, total sclerocornea, aphakia, microphthalmia and optic disc coloboma

PURPOSE

To investigate the genetic basis of recessively-inherited congenital, non syndromic, bilateral, total sclerocornea in two consanguineous pedigrees, one from the Punjab province of Pakistan and the other from the Tlaxcala province of Mexico.

METHODS

Ophthalmic examinations were conducted on each family member to confirm their diagnosis and magnetic resonance imaging (MRI) or ultrasonography of the eyes was performed on some family members. Genomic DNA was analyzed by homozygosity mapping using the Affymetrix 6.0 SNP array and linkage was confirmed with polymorphic microsatellite markers. Candidate genes were sequenced.

RESULTS

A diagnosis of autosomal recessive sclerocornea was established for 7 members of the Pakistani and 8 members of the Mexican pedigrees. In the Pakistani family we established linkage to a region on chromosome 1p that contained Forkhead Box E3 (FOXE3), a strong candidate gene since FOXE3 mutations had previously been associated with various anterior segment abnormalities. Sequencing FOXE3 identified the previously reported nonsense mutation, c.720C>A, p.C240X, in the Pakistani pedigree and a novel missense mutation which disrupts an evolutionarily conserved residue in the forkhead domain, c.292T>C, p.Y98H, in the Mexican pedigree. Individuals with heterozygous mutations had no ocular abnormalities. MRI or ultrasonography confirmed that the patients with sclerocornea were also aphakic, had microphthalmia and some had optic disc coloboma.

CONCLUSIONS

This is the fourth report detailing homozygous FOXE3 mutations causing anterior segment abnormalities in human patients. Previous papers have emphasized aphakia and microphthalmia as the primary phenotype, but we find that the initial diagnosis - and perhaps the only one possible in a rural setting - is one of non-syndromic, bilateral, total sclerocornea. Dominantly inherited anterior segment defects have also been noted in association with heterozygous FOXE3 mutations. However the absence of any abnormalities in the FOXE3 heterozygotes described suggests that genetic background and environmental factors plays a role in the penetrance of the mutant allele.

Runx1 binds as a dimeric complex to overlapping Runx1 sites within a palindromic element in the human GM-CSF enhancer

Runx1 is a developmentally regulated transcription factor that is essential for haemopoiesis. Runx1 can bind as a monomer to the core consensus sequence TGTGG, but binds more efficiently as a hetero-dimer together with the non-DNA binding protein CBFβ as a complex termed core binding factor (CBF). Here, we demonstrated that CBF can also assemble as a dimeric complex on two overlapping Runx1 sites within the palindromic sequence TGTGGCTGCCCACA in the human granulocyte macrophage colony-stimulating factor enhancer. Furthermore, we demonstrated that binding of Runx1 to the enhancer is rigidly controlled at the level of chromatin accessibility, and is dependent upon prior induction of NFAT and AP-1, which disrupt a positioned nucleosome in this region. We employed in vivo footprinting to demonstrate that, upon activation of the enhancer, both sites are efficiently occupied. In vitro binding assays confirmed that two CBF complexes can bind this site simultaneously, and transfection assays demonstrated that both sites contribute significantly to enhancer function. Computer modelling based on the Runx1/CBFβ/DNA crystal structure further revealed that two molecules of CBF could potentially bind to this class of palindromic sequence as a dimeric complex in a conformation whereby both Runx1 and CBFβ within the two CBF complexes are closely aligned.

Structural characteristics of novel protein folds

Folds are the basic building blocks of protein structures. Understanding the emergence of novel protein folds is an important step towards understanding the rules governing the evolution of protein structure and function and for developing tools for protein structure modeling and design. We explored the frequency of occurrences of an exhaustively classified library of supersecondary structural elements (Smotifs), in protein structures, in order to identify features that would define a fold as novel compared to previously known structures. We found that a surprisingly small set of Smotifs is sufficient to describe all known folds. Furthermore, novel folds do not require novel Smotifs, but rather are a new combination of existing ones. Novel folds can be typified by the inclusion of a relatively higher number of rarely occurring Smotifs in their structures and, to a lesser extent, by a novel topological combination of commonly occurring Smotifs. When investigating the structural features of Smotifs, we found that the top 10% of most frequent ones have a higher fraction of internal contacts, while some of the most rare motifs are larger, and contain a longer loop region.

Structural genomics efforts aim at exploring the repertoire of three-dimensional structures of protein molecules. While genome scale sequencing projects have already provided us with all the genes of many organisms, it is the three dimensional shape of gene encoded proteins that defines all the interactions among these components. Understanding the versatility and, ultimately, the role of all possible molecular shapes in the cell is a necessary step toward understanding how organisms function. In this work we explored the rules that identify certain shapes as novel compared to all already known structures. The findings of this work provide possible insights into the rules that can be used in future works to identify or design new molecular shapes or to relate folds with each other in a quantitative manner.