The Molecular Docking of MAX Fungal Effectors with Plant HMA Domain-Binding Proteins.

Fungal effector proteins are important in mediating disease infections in agriculturally important crops. These secreted small proteins are known to interact with their respective host receptor binding partners in the host, either inside the cells or in the apoplastic space, depending on the localisation of the effector proteins. Consequently, it is important to understand the interactions between fungal effector proteins and their target host receptor binding partners, particularly since this can be used for the selection of potential plant resistance or susceptibility-related proteins that can be applied to the breeding of new cultivars with disease resistance. In this study, molecular docking simulations were used to characterise protein-protein interactions between effector and plant receptors. Benchmarking was undertaken using available experimental structures of effector-host receptor complexes to optimise simulation parameters, which were then used to predict the structures and mediating interactions of effector proteins with host receptor binding partners that have not yet been characterised experimentally. Rigid docking was applied for both the so-called bound and unbound docking of MAX effectors with plant HMA domain protein partners. All bound complexes used for benchmarking were correctly predicted, with 84% being ranked as the top docking pose using the ZDOCK scoring function. In the case of unbound complexes, a minimum of 95% of known residues were predicted to be part of the interacting interface on the host receptor binding partner, and at least 87% of known residues were predicted to be part of the interacting interface on the effector protein. Hydrophobic interactions were found to dominate the formation of effector-plant protein complexes. An optimised set of docking parameters based on the use of ZDOCK and ZRANK scoring functions were established to enable the prediction of near-native docking poses involving different binding interfaces on plant HMA domain proteins. Whilst this study was limited by the availability of the experimentally determined complexed structures of effectors and host receptor binding partners, we demonstrated the potential of molecular docking simulations to predict the likely interactions between effectors and their respective host receptor binding partners. This computational approach may accelerate the process of the discovery of putative interacting plant partners of effector proteins and contribute to effector-assisted marker discovery, thereby supporting the breeding of disease-resistant crops.

Ab Initio Modelling of the Structure of ToxA-like and MAX Fungal Effector Proteins.

Pathogenic fungal diseases in crops are mediated by the release of effector proteins that facilitate infection. Characterising the structure of these fungal effectors is vital to understanding their virulence mechanisms and interactions with their hosts, which is crucial in the breeding of plant cultivars for disease resistance. Several effectors have been identified and validated experimentally; however, their lack of sequence conservation often impedes the identification and prediction of their structure using sequence similarity approaches. Structural similarity has, nonetheless, been observed within fungal effector protein families, creating interest in validating the use of computational methods to predict their tertiary structure from their sequence. We used Rosetta ab initio modelling to predict the structures of members of the ToxA-like and MAX effector families for which experimental structures are known to validate this method. An optimised approach was then used to predict the structures of phenotypically validated effectors lacking known structures. Rosetta was found to successfully predict the structure of fungal effectors in the ToxA-like and MAX families, as well as phenotypically validated but structurally unconfirmed effector sequences. Interestingly, potential new effector structural families were identified on the basis of comparisons with structural homologues and the identification of associated protein domains.

Template-Based Modelling of the Structure of Fungal Effector Proteins.

The discovery of new fungal effector proteins is necessary to enable the screening of cultivars for disease resistance. Sequence-based bioinformatics methods have been used for this purpose, but only a limited number of functional effector proteins have been successfully predicted and subsequently validated experimentally. A significant obstacle is that many fungal effector proteins discovered so far lack sequence similarity or conserved sequence motifs. The availability of experimentally determined three-dimensional (3D) structures of a number of effector proteins has recently highlighted structural similarities amongst groups of sequence-dissimilar fungal effectors, enabling the search for similar structural folds amongst effector sequence candidates. We have applied template-based modelling to predict the 3D structures of candidate effector sequences obtained from bioinformatics predictions and the PHI-BASE database. Structural matches were found not only with ToxA- and MAX-like effector candidates but also with non-fungal effector-like proteins-including plant defensins and animal venoms-suggesting the broad conservation of ancestral structural folds amongst cytotoxic peptides from a diverse range of distant species. Accurate modelling of fungal effectors were achieved using RaptorX. The utility of predicted structures of effector proteins lies in the prediction of their interactions with plant receptors through molecular docking, which will improve the understanding of effector-plant interactions.

An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens.

Fungal plant-pathogens promote infection of their hosts through the release of 'effectors'-a broad class of cytotoxic or virulence-promoting molecules. Effectors may be recognised by resistance or sensitivity receptors in the host, which can determine disease outcomes. Accurate prediction of effectors remains a major challenge in plant pathology, but if achieved will facilitate rapid improvements to host disease resistance. This study presents a novel tool and pipeline for the ranking of predicted effector candidates-Predector-which interfaces with multiple software tools and methods, aggregates disparate features that are relevant to fungal effector proteins, and applies a pairwise learning to rank approach. Predector outperformed a typical combination of secretion and effector prediction methods in terms of ranking performance when applied to a curated set of confirmed effectors derived from multiple species. We present Predector ( https://github.com/ccdmb/predector ) as a useful tool for the ranking of predicted effector candidates, which also aggregates and reports additional supporting information relevant to effector and secretome prediction in a simple, efficient, and reproducible manner.

Does the SARS-CoV-2 Spike Protein Receptor Binding Domain Interact Effectively with the DPP4 (CD26) Receptor? A Molecular Docking Study.

ACE2 has been established as the main receptor for SARS-CoV-2. Since other human coronaviruses are known to use co-receptors for viral cell entry, it has been suggested that DPP4 (CD26) could be a potential additional binding target or co-receptor, supported by early molecular docking simulation studies. However, recent biophysical studies have shown this interaction to be very weak. We have conducted detailed molecular docking simulations to predict the potential binding interactions between the receptor binding domain (RBD) of the spike protein of SARS-CoV-2 and DPP4 and compare them with the interactions observed in the experimentally determined structure of the complex of MERS-CoV with DPP4. Whilst the overall binding mode of the RBD of SARS-CoV-2 to DPP4 is predicted to be similar to that observed in the MERS-CoV-DPP4 complex, including a number of equivalent interactions, important differences in the amino acid sequences of SARS-CoV-2 and MERS-CoV result in substantially weakened interactions with DPP4. This is shown to arise from differences in the predicted proximity, nature and secondary structure at the binding interface on the RBD of SARS-CoV-2. These findings do not support DPP4 being a significant receptor for SARS-CoV-2.

Characterisation and Mutagenesis Study of An Alternative Sigma Factor Gene (hrpL) from Erwinia mallotivora Reveal Its Central Role in Papaya Dieback Disease.

The alternative sigma (σ) factor E, RpoE or HrpL, has been reported to be involved in stress- and pathogenicity-related transcription initiation in Escherichia coli and many other Gram-negative bacteria, including Erwinia spp. and Pseudomonas spp. A previous study identified the hrpL/rpoE transcript as one of the significant differentially expressed genes (DEGs) during early E. mallotivora infection in papaya and those data serve as the basis of the current project. Here, the full coding DNA sequence (CDS) of hrpL from E. mallotivora (EmhrpL) was determined to be 549 bp long, and it encoded a 21.3 kDa HrpL protein that possessed two highly conserved sigma-70 (σ70) motifs-σR2 and σR4. Nucleotide sequence alignment revealed the hrpL from E. mallotivora shared high sequence similarity to rpoE/hrpL from E. tracheiphila (83%), E. pyrifoliae (81%), and E. tasmaniensis (80%). Phylogenetics analysis indicated hrpL from E. mallotivora to be monophyletic with rpoEs/hrpLs from Pantoea vagans, E. herbicola, and E. tracheiphila. Structural analysis postulated that the E. mallotivora's alternative σ factor was non-transmembranic and was an extracytoplasmic function (ECF) protein-characteristics shared by other σ factors in different bacterial species. Notably, the protein-protein interaction (PPI) study through molecular docking suggested the σ factor could be possibly inhibited by an anti-σ. Finally, a knockout of hrpL in E. mallotivora (ΔEmhrpL) resulted in avirulence in four-month-old papaya plants. These findings have revealed that the hrpL is a necessary element in E. mallotivora pathogenicity and also predicted that the gene can be inhibited by an anti-σ.

Computational Analysis of Gynura bicolor Bioactive Compounds as Dipeptidyl Peptidase-IV Inhibitor.

The inhibition of dipeptidyl peptidase-IV (DPPIV) is a popular route for the treatment of type-2 diabetes. Commercially available gliptin-based drugs such as sitagliptin, anagliptin, linagliptin, saxagliptin, and alogliptin were specifically developed as DPPIV inhibitors for diabetic patients. The use of Gynura bicolor in treating diabetes had been reported in various in vitro experiments. However, an understanding of the inhibitory actions of G. bicolor bioactive compounds on DPPIV is still lacking and this may provide crucial information for the development of more potent and natural sources of DPPIV inhibitors. Evaluation of G. bicolor bioactive compounds for potent DPPIV inhibitors was computationally conducted using Lead IT and iGEMDOCK software, and the best free-binding energy scores for G. bicolor bioactive compounds were evaluated in comparison with the commercial DPPIV inhibitors, sitagliptin, anagliptin, linagliptin, saxagliptin, and alogliptin. Drug-likeness and absorption, distribution, metabolism, and excretion (ADME) analysis were also performed. Based on molecular docking analysis, four of the identified bioactive compounds in G. bicolor, 3-caffeoylquinic acid, 5-O-caffeoylquinic acid, 3,4-dicaffeoylquinic acid, and trans-5-p-coumaroylquinic acid, resulted in lower free-binding energy scores when compared with two of the commercially available gliptin inhibitors. The results revealed that bioactive compounds in G. bicolor are potential natural inhibitors of DPPIV.

Analysis of MAP kinase MPK4/MEKK1/MKK genes of Carica papaya L. comparative to other plant homologues.

Mitogen-activated protein kinase 4 (MPK4) interacts with the (Mitogen-activated protein kinase kinase kinase 1) MEKK1/ Mitogenactivated protein kinase kinase 1 (MKK1)/ Mitogen-activated protein kinase kinase 2 (MKK2) complex to affect its function in plant development or against pathogen attacks. The KEGG (Kyoto Encyclopedia of Genes and Genomes) network analysis of Arabidopsis thaliana revealed close interactions between those four genes in the same plant-pathogen interaction pathway, which warrants further study of these genes due to their evolutionary conservation in different plant species. Through targeting the signature sequence in MPK4 of papaya using orthologs from Arabidopsis, the predicted sequence of MPK4 was studied using a comparative in silico approach between different plant species and the MAP cascade complex of MEKK1/MKK1/MKK2. This paper reported that MPK4 was highly conserved in papaya with 93% identical across more than 500 bases compared in each species predicted. Slight variations found in the MEKK1/MKK1/MKK2 complex nevertheless still illustrated sequence similarities between most of the species. Localization of each gene in the cascade network was also predicted, potentiating future functional verification of these genes interactions using knock out or/and gene silencing tactics.