pGenTHREADER and pDomTHREADER: new methods for improved protein fold recognition and superfamily discrimination

Structural modelling and preventive strategy targeting of WSSV hub proteins to combat viral infection in shrimp Penaeus monodon

White spot syndrome virus (WSSV) presents a considerable peril to the aquaculture sector, leading to notable financial consequences on a global scale. Previous studies have identified hub proteins, including WSSV051 and WSSV517, as essential binding elements in the protein interaction network of WSSV. This work further investigates the functional structures and potential applications of WSSV hub complexes in managing WSSV infection. Using computational methodologies, we have successfully generated comprehensive three-dimensional (3D) representations of hub proteins along with their three mutual binding counterparts, elucidating crucial interaction locations. The results of our study indicate that the WSSV051 hub protein demonstrates higher binding energy than WSSV517. Moreover, a unique motif, denoted as "S-S-x(5)-S-x(2)-P," was discovered among the binding proteins. This pattern perhaps contributes to the detection of partners by the hub proteins of WSSV. An antiviral strategy targeting WSSV hub proteins was demonstrated through the oral administration of dual hub double-stranded RNAs to the black tiger shrimp, Penaeus monodon, followed by a challenge assay. The findings demonstrate a decrease in shrimp mortality and a cessation of WSSV multiplication. In conclusion, our research unveils the structural features and dynamic interactions of hub complexes, shedding light on their significance in the WSSV protein network. This highlights the potential of hub protein-based interventions to mitigate the impact of WSSV infection in aquaculture.

DBPMod: a supervised learning model for computational recognition of DNA-binding proteins in model organisms

DNA-binding proteins (DBPs) play critical roles in many biological processes, including gene expression, DNA replication, recombination and repair. Understanding the molecular mechanisms underlying these processes depends on the precise identification of DBPs. In recent times, several computational methods have been developed to identify DBPs. However, because of the generic nature of the models, these models are unable to identify species-specific DBPs with higher accuracy. Therefore, a species-specific computational model is needed to predict species-specific DBPs. In this paper, we introduce the computational DBPMod method, which makes use of a machine learning approach to identify species-specific DBPs. For prediction, both shallow learning algorithms and deep learning models were used, with shallow learning models achieving higher accuracy. Additionally, the evolutionary features outperformed sequence-derived features in terms of accuracy. Five model organisms, including Caenorhabditis elegans, Drosophila melanogaster, Escherichia coli, Homo sapiens and Mus musculus, were used to assess the performance of DBPMod. Five-fold cross-validation and independent test set analyses were used to evaluate the prediction accuracy in terms of area under receiver operating characteristic curve (auROC) and area under precision-recall curve (auPRC), which was found to be ~89-92% and ~89-95%, respectively. The comparative results demonstrate that the DBPMod outperforms 12 current state-of-the-art computational approaches in identifying the DBPs for all five model organisms. We further developed the web server of DBPMod to make it easier for researchers to detect DBPs and is publicly available at https://iasri-sg.icar.gov.in/dbpmod/. DBPMod is expected to be an invaluable tool for discovering DBPs, supplementing the current experimental and computational methods.

Deep learning for the PSIPRED Protein Analysis Workbench

The PSIRED Workbench is a long established and popular bioinformatics web service offering a wide range of machine learning based analyses for characterizing protein structure and function. In this paper we provide an update of the recent additions and developments to the webserver, with a focus on new Deep Learning based methods. We briefly discuss some trends in server usage since the publication of AlphaFold2 and we give an overview of some upcoming developments for the service. The PSIPRED Workbench is available at http://bioinf.cs.ucl.ac.uk/psipred.

The S-component fold: a link between bacterial transporters and receptors

The processes of nutrient uptake and signal sensing are crucial for microbial survival and adaptation. Membrane-embedded proteins involved in these functions (transporters and receptors) are commonly regarded as unrelated in terms of sequence, structure, mechanism of action and evolutionary history. Here, we analyze the protein structural universe using recently developed artificial intelligence-based structure prediction tools, and find an unexpected link between prominent groups of microbial transporters and receptors. The so-called S-components of Energy-Coupling Factor (ECF) transporters, and the membrane domains of sensor histidine kinases of the 5TMR cluster share a structural fold. The discovery of their relatedness manifests a widespread case of prokaryotic "transceptors" (related proteins with transport or receptor function), showcases how artificial intelligence-based structure predictions reveal unchartered evolutionary connections between proteins, and provides new avenues for engineering transport and signaling functions in bacteria.

Molecular and Structural Insights into Buffalo Interleukin-17A

Interleukin-17A is a pro-inflammatory cytokine that plays a key role in the immune response to many pathogens and implicated in autoimmune diseases. This molecule is also involved in providing protection to many bacterial and fungal infections of gastro-intestinal tract and respiratory mucosa. Although molecular aspect of IL-17A has been studied in few species, no data are available for buffalo, which is one of the major sources of milk production in India. Therefore, in the present study, IL-17A gene of Indian Murrah Buffalo origin was cloned, expressed, and analyzed using bioinformatic tools. The coding sequence of buffalo IL-17A gene was cloned in prokaryotic expression vector (pET-28a) followed by its expression, purification, and characterization. A computational analysis was performed to understand the sequence, structure, and evolutionary relationship of buIL-17A. It revealed that the length of buIL-17A sequence without signal peptide is 132 amino acids as in cattle. However, sequence identity is found to be 99% due to one amino substitution difference between buffalo and cattle. After analysis, it can be concluded that buIL-17A recombinant protein can be used as a potential immunobiological reagent for diagnostic and therapeutic purpose.

Bitter taste receptors establish a stable binding affinity with the SARS-CoV-2-spike 1 protein akin to ACE2

COVID-19 is caused by the highly contagious SARS-CoV-2 virus, which originated in Wuhan, China, resulting in the highest worldwide mortality rate. Gustatory dysfunction is common among individuals infected with the Wild-type Wuhan strain. However, there are no reported cases of gustatory dysfunction among patients infected with the mutant delta variant. The reason behind this remains elusive to date. This in-silico-based study aims to unravel this clinical factor by evaluating the overall binding affinity of predominant bitter taste receptors associated with gustatory function (T2R-4, 10, 14, 19, 31, 38, 43, and 46) with the Receptor Binding Domain (RBD) of spike 1 (S1) protein of Wuhan (Wild)/delta-SARS-CoV-2 (mut1-T478K; mut2-E484K) variants. Based on docking and MM/PBSA free binding energy scores, the Wild RBD showed a stronger interaction with T2R-46 compared to the ACE2 protein. However, both delta variant mutants (mut1 and mut2) could not establish a stronger binding affinity with bitter taste receptor proteins, except for T2R-43 against mut1. In conclusion, the delta variants could not establish a better binding affinity with bitter taste receptors, contradicting the Wild variant that determines the severity of gustatory dysfunction among patients exposed to the delta and Wild SARS-CoV-2 variants. The study's inference also proposes T2R-46 as an alternate binding receptor target for RBD-S1 of Wild SARS-CoV-2, augmenting its virulence in all functional organs with compromised α-gustducin interaction and bitter sensitization. This in-silico-based study needs further wet-lab-based validation for a better understanding of the role of T2R-46-based viral entry in the human host.Communicated by Ramaswamy H. Sarma.

Challenges of Protein-Protein Docking of the Membrane Proteins

Despite the recent advances in the determination of high-resolution membrane protein (MP) structures, the structural and functional characterization of MPs remains extremely challenging, mainly due to the hydrophobic nature, low abundance, poor expression, purification, and crystallization difficulties associated with MPs. Whereby the major challenges/hurdles for MP structure determination are associated with the expression, purification, and crystallization procedures. Although there have been significant advances in the experimental determination of MP structures, only a limited number of MP structures (approximately less than 1% of all) are available in the Protein Data Bank (PDB). Therefore, the structures of a large number of MPs still remain unresolved, which leads to the availability of widely unplumbed structural and functional information related to MPs. As a result, recent developments in the drug discovery realm and the significant biological contemplation have led to the development of several novel, low-cost, and time-efficient computational methods that overcome the limitations of experimental approaches, supplement experiments, and provide alternatives for the characterization of MPs. Whereby the fine tuning and optimizations of these computational approaches remains an ongoing endeavor.Computational methods offer a potential way for the elucidation of structural features and the augmentation of currently available MP information. However, the use of computational modeling can be extremely challenging for MPs mainly due to insufficient knowledge of (or gaps in) atomic structures of MPs. Despite the availability of numerous in silico methods for 3D structure determination the applicability of these methods to MPs remains relatively low since all methods are not well-suited or adequate for MPs. However, sophisticated methods for MP structure predictions are constantly being developed and updated to integrate the modifications required for MPs. Currently, different computational methods for (1) MP structure prediction, (2) stability analysis of MPs through molecular dynamics simulations, (3) modeling of MP complexes through docking, (4) prediction of interactions between MPs, and (5) MP interactions with its soluble partner are extensively used. Towards this end, MP docking is widely used. It is notable that the MP docking methods yet few in number might show greater potential in terms of filling the knowledge gap. In this chapter, MP docking methods and associated challenges have been reviewed to improve the applicability, accuracy, and the ability to model macromolecular complexes.

TeM-DTBA: time-efficient drug target binding affinity prediction using multiple modalities with Lasso feature selection

Drug discovery, especially virtual screening and drug repositioning, can be accelerated through deeper understanding and prediction of Drug Target Interactions (DTIs). The advancement of deep learning as well as the time and financial costs associated with conventional wet-lab experiments have made computational methods for DTI prediction more popular. However, the majority of these computational methods handle the DTI problem as a binary classification task, ignoring the quantitative binding affinity that determines the drug efficacy to their target proteins. Moreover, computational space as well as execution time of the model is often ignored over accuracy. To address these challenges, we introduce a novel method, called Time-efficient Multimodal Drug Target Binding Affinity (TeM-DTBA), which predicts the binding affinity between drugs and targets by fusing different modalities based on compound structures and target sequences. We employ the Lasso feature selection method, which lowers the dimensionality of feature vectors and speeds up the proposed model training time by more than 50%. The results from two benchmark datasets demonstrate that our method outperforms state-of-the-art methods in terms of performance. The mean squared errors of 18.8% and 23.19%, achieved on the KIBA and Davis datasets, respectively, suggest that our method is more accurate in predicting drug-target binding affinity.

The impact of ORF19.36.1 in the pathobiology of Candida albicans

BACKGROUND

Our previous proteomics data obtained from Candida albicans recovered after serial passage in a murine model of systemic infection revealed that Orf19.36.1 expression correlates with the virulence of the fungus. Therefore, the impact of ORF19.36.1 upon virulence was tested in this study.

MATERIALS & METHODS

CRISPR-Cas9 technology was used to construct homozygous C. albicans orf19.36.1 null mutants and the phenotypes of these mutants examined in vitro (filamentation, invasion, adhesion, biofilm formation, hydrolase activities) and in vivo assays.

RESULTS

The deletion of ORF19.36.1 did not significantly impact the phenotypes examined or the virulence of C. albicans in two infection models.

CONCLUSION

These results suggest that, although Orf19.36.1 expression correlates with virulence, this protein is not essential for C. albicans pathobiology.

Multi-omic analysis characterizes molecular susceptibility of receptors to SARS-CoV-2 spike protein

In the post COVID-19 era, new SARS-CoV-2 variant strains may continue emerging and long COVID is poised to be another public health challenge. Deciphering the molecular susceptibility of receptors to SARS-CoV-2 spike protein is critical for understanding the immune responses in COVID-19 and the rationale of multi-organ injuries. Currently, such systematic exploration remains limited. Here, we conduct multi-omic analysis of protein binding affinities, transcriptomic expressions, and single-cell atlases to characterize the molecular susceptibility of receptors to SARS-CoV-2 spike protein. Initial affinity analysis explains the domination of delta and omicron variants and demonstrates the strongest affinities between BSG (CD147) receptor and most variants. Further transcriptomic data analysis on 4100 experimental samples and single-cell atlases of 1.4 million cells suggest the potential involvement of BSG in multi-organ injuries and long COVID, and explain the high prevalence of COVID-19 in elders as well as the different risks for patients with underlying diseases. Correlation analysis validated moderate associations between BSG and viral RNA abundance in multiple cell types. Moreover, similar patterns were observed in primates and validated in proteomic expressions. Overall, our findings implicate important therapeutic targets for the development of receptor-specific vaccines and drugs for COVID-19.

Cavity architecture based modulation of ligand binding tunnels in plant START domains

The Steroidogenic acute regulatory protein (StAR)-related lipid transfer (START) domain represents an evolutionarily conserved superfamily of lipid transfer proteins widely distributed across the tree of life. Despite significant expansion in plants, knowledge about this domain remains inadequate in plants. In this work, we explore the role of cavity architectural modulations in START protein evolution and functional diversity. We use deep-learning approaches to generate plant START domain models, followed by surface accessibility studies and a comprehensive structural investigation of the rice START family. We validate 28 rice START domain models, delineate binding cavities, measure pocket volumes, and compare these with mammalian counterparts to understand evolution of binding preferences. Overall, plant START domains retain the ancestral α/β helix-grip signature, but we find subtle variation in cavity architectures, resulting in significantly smaller ligand-binding tunnels in the plant kingdom. We identify cavity lining residues (CLRs) responsible for reduction in ancestral tunnel space, and these appear to be class specific, and unique to plants, providing a mechanism for the observed shift in domain function. For instance, mammalian cavity lining residues A135, G181 and A192 have evolved to larger CLRs across the plant kingdom, contributing to smaller sizes, minimal STARTs being the largest, while members of type-IV HD-Zip family show almost complete obliteration of lipid binding cavities, consistent with their present-day DNA binding functions. In summary, this work quantifies plant START structural & functional divergence, bridging current knowledge gaps.

The Metal-binding Protein Atlas (MbPA): an integrated database for curating metalloproteins in all aspects

Metal-binding proteins are essential for the vital activities and engage in their roles by acting in concert with metal cations. MbPA (The Metal-binding Protein Atlas) is the most comprehensive resource up to now dedicated to curating metal-binding proteins. Currently, it contains 106373 entries and 440187 sites related to 54 metals and 8169 species. Users can view all metal-binding proteins and species-specific proteins in MbPA. There are also metal-proteomics data that quantitatively describes protein expression in different tissues and organs. By analyzing the data of the amino acid residues at the metal-binding site, it is found that about 80% of the metal ions tend to bind to cysteine, aspartic acid, glutamic acid, and histidine. Moreover, we use Diversity Measure to confirm that the diversity of metal-binding is specific in different area of periodic table, and further elucidate the binding modes of 19 transition metals on 20 amino acids. In addition, MbPA also embraces 6855 potential pathogenic mutations related to metalloprotein. The resource is freely available at http://bioinfor.imu.edu.cn/mbpa.

Parallel expansion and divergence of an adhesin family in pathogenic yeasts

Opportunistic yeast pathogens arose multiple times in the Saccharomycetes class, including the recently emerged, multidrug-resistant (MDR) Candida auris. We show that homologs of a known yeast adhesin family in Candida albicans, the Hyr/Iff-like (Hil) family, are enriched in distinct clades of Candida species as a result of multiple, independent expansions. Following gene duplication, the tandem repeat-rich region in these proteins diverged extremely rapidly and generated large variations in length and β-aggregation potential, both of which are known to directly affect adhesion. The conserved N-terminal effector domain was predicted to adopt a β-helical fold followed by an α-crystallin domain, making it structurally similar to a group of unrelated bacterial adhesins. Evolutionary analyses of the effector domain in C. auris revealed relaxed selective constraint combined with signatures of positive selection, suggesting functional diversification after gene duplication. Lastly, we found the Hil family genes to be enriched at chromosomal ends, which likely contributed to their expansion via ectopic recombination and break-induced replication. Combined, these results suggest that the expansion and diversification of adhesin families generate variation in adhesion and virulence within and between species and are a key step toward the emergence of fungal pathogens.

Contact-Assisted Threading in Low-Homology Protein Modeling

The ability to successfully predict the three-dimensional structure of a protein from its amino acid sequence has made considerable progress in the recent past. The progress is propelled by the improved accuracy of deep learning-based inter-residue contact map predictors coupled with the rising growth of protein sequence databases. Contact map encodes interatomic interaction information that can be exploited for highly accurate prediction of protein structures via contact map threading even for the query proteins that are not amenable to direct homology modeling. As such, contact-assisted threading has garnered considerable research effort. In this chapter, we provide an overview of existing contact-assisted threading methods while highlighting the recent advances and discussing some of the current limitations and future prospects in the application of contact-assisted threading for improving the accuracy of low-homology protein modeling.

Bioinformatics-based Characterization of the Sequence Variability of Zika Virus Polyprotein and Envelope Protein (E)

Background:

Zika virus, which is widely spread and infects humans through the bites of Aedes albopictus and Aedes aegypti female mosquitoes, represents a serious global health issue.

Objective:

The objective of the present study is to computationally characterize Zika virus polyproteins (UniProt Name: PRO_0000443018 [residues 1-3423], PRO_0000445659 [residues 1-3423] and PRO_0000435828 [residues 1-3419]) and their envelope proteins using their physico-chemical properties.

Methods:

To achieve this, the Polarity Index Method (PIM) profile and the Protein Intrinsic Disorder Predisposition (PIDP) profile of 3 main groups of proteins were evaluated: structural proteins extracted from specific Databases, Zika virus polyproteins, and their envelope proteins (E) extracted from UniProt Database. Once the PIM profile of the Zika virus envelope proteins (E) was obtained and since the Zika virus polyproteins were also identified with this profile, the proteins defined as “reviewed proteins” extracted from the UniProt Database were searched for the similar PIM profile. Finally, the difference between the PIM profiles of the Zika virus polyproteins and their envelope proteins (E) was tested using 2 non-parametric statistical tests.

Results:

It was found and tested that the PIM profile is an efficient discriminant that allows obtaining a “computational fingerprint” of each Zika virus polyprotein from its envelope protein (E).

Conclusion:

PIM profile represents a computational tool, which can be used to effectively discover Zika virus polyproteins from Databases, from their envelope proteins (E) sequences.

3D structures inferred from cDNA clones identify the CD1D-Restricted γδ T cell receptor in dromedaries

The Camelidae species occupy an important immunological niche within the humoral as well as cell mediated immune response. Although recent studies have highlighted that the somatic hypermutation (SHM) shapes the T cell receptor gamma (TRG) and delta (TRD) repertoire in Camelus dromedarius, it is still unclear how γδ T cells use the TRG/TRD receptors and their respective variable V-GAMMA and V-DELTA domains to recognize antigen in an antibody-like fashion. Here we report about 3D structural analyses of the human and dromedary γδ T cell receptor. First, we have estimated the interaction energies at the interface within the human crystallized paired TRG/TRD chains and quantified interaction energies within the same human TRG/TRD chains in complex with the CD1D, an RPI-MH1-LIKE antigen presenting glycoprotein. Then, we used the human TRG/TRD-CD1D complex as template for the 3D structure of the dromedary TRG/TRD-CD1D complex and for guiding the 3D human/dromedary comparative analysis. The choice of mutated TRG alternatively combined with mutated TRD cDNA clones originating from the spleen of one single dromedary was crucial to quantify the strength of the interactions at the protein-protein interface between the paired C. dromedarius TRG and TRD V-domains and between the C. dromedarius TRG/TRD V-domains and CD1D G-domains. Interacting amino acids located in the V-domain Complementarity Determining Regions (CDR) and Framework Regions (FR) according to the IMGT unique numbering for V-domains were identified. The resulting 3D dromedary TRG V-GAMMA combined with TRD V-DELTA protein complexes allowed to deduce the most stable gamma/delta chains pairings and to propose a candidate CD1D-restricted γδ T cell receptor complex.

Identification of novel salt tolerance-associated proteins from the secretome of Enterococcus faecalis

The ability of bacteria to adapt to the external environment is fundamental for their survival. A halotolerant microorganism Enterococcus faecalis able to grow under high salt stress conditions was isolated in the present study. The SDS-PAGE analysis of the secretome showed a protein band with a molecular weight of 28 kDa, gradually increased with an increase in salt concentration, and the highest intensity was observed at 15% salt stress condition. LC-MS/MS analysis of this particular band identified fourteen different proteins, out of which nine proteins were uncharacterized. Further, the function of uncharacterized proteins was predicted based on structure-function relationship using a reverse template search approach deciphering uncharacterized protein into type III polyketide synthases, stress-induced protein-1, Eed-h3k79me3, ba42 protein, 3-methyladenine DNA glycosylase, Atxa protein, membrane-bound respiratory hydrogenase, type-i restriction-modification system methylation subunit and ManxA. STRING network analysis further a showed strong association among the proteins. The processes predicted involvement of these proteins in signal transduction, ions transport, synthesis of the protective layer, cellular homeostasis and regulation of gene expression and different metabolic pathways. Thus, the fourteen proteins identified in the secretome play an essential role in maintaining cellular homeostasis in E. faecalis under high-salinity stress. This may represent a novel and previously unreported strategy by E. faecalis to maintain their normal growth and physiology under high salinity conditions.

I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction

Most proteins in cells are composed of multiple folding units (or domains) to perform complex functions in a cooperative manner. Relative to the rapid progress in single-domain structure prediction, there are few effective tools available for multi-domain protein structure assembly, mainly due to the complexity of modeling multi-domain proteins, which involves higher degrees of freedom in domain-orientation space and various levels of continuous and discontinuous domain assembly and linker refinement. To meet the challenge and the high demand of the community, we developed I-TASSER-MTD to model the structures and functions of multi-domain proteins through a progressive protocol that combines sequence-based domain parsing, single-domain structure folding, inter-domain structure assembly and structure-based function annotation in a fully automated pipeline. Advanced deep-learning models have been incorporated into each of the steps to enhance both the domain modeling and inter-domain assembly accuracy. The protocol allows for the incorporation of experimental cross-linking data and cryo-electron microscopy density maps to guide the multi-domain structure assembly simulations. I-TASSER-MTD is built on I-TASSER but substantially extends its ability and accuracy in modeling large multi-domain protein structures and provides meaningful functional insights for the targets at both the domain- and full-chain levels from the amino acid sequence alone.

A new perspective on the evolution and diversity of the genus Amdoparvovirus (family Parvoviridae) through genetic characterization, structural homology modeling, and phylogenetics

Amdoparvoviruses (genus Amdoparvovirus, family Parvoviridae) are primarily viruses of carnivorans, but recent studies have indicated that their host range might also extend to rodents and chiropterans. While their classification is based on the full sequence of the major nonstructural protein (NS1), several studies investigating amdoparvoviral diversity have been focused on partial sequences, leading to difficulties in accurately determining species demarcations and leaving several viruses unclassified. In this study, while reporting the complete genomic sequence of a novel amdoparvovirus identified in an American mink (British Columbia amdoparvovirus, BCAV), we studied the phylogenetic relationships of all amdoparvovirus-related sequences and provide a comprehensive reevaluation of their diversity and evolution. After excluding recombinant sequences, phylogenetic and pairwise sequence identity analyses allowed us to define fourteen different viruses, including the five currently classified species, BCAV, and four additional viruses that fulfill the International Committee on Taxonomy of Viruses criteria to be classified as species. We show that the group of viruses historically known as Aleutian mink disease virus (species Carnivore amdoparvovirus 1) should be considered as a cluster of at least four separate viral species that have been co-circulating in mink farms, facilitating the occurrence of inter-species recombination. Genome organization, splicing donor and acceptor sites, and protein sequence motifs were surprisingly conserved within the genus. The sequence of the major capsid protein virus protein 2 (VP2) was significantly more conserved between and within species compared to NS1, a phenomenon possibly linked to antibody-dependent enhancement (ADE). Homology models suggest a remarkably high degree of conservation of the spikes located near the icosahedral threefold axis of the capsid, comprising the surface region associated with ADE. A surprisingly high number of divergent amino acid positions were found in the luminal threefold and twofold axes of the capsid, regions of hitherto unknown function. We emphasize the importance of complete genome analyses and, given the marked phylogenetic inconsistencies across the genome, advise to obtain the complete coding sequences of divergent strains. Further studies on amdoparvovirus biology and structure as well as epidemiological and virus discovery investigations are required to better characterize the ecology and evolution of this important group of viruses.

Exome Sequencing Identifies Genetic Variants Associated with Extreme Manifestations of the Cardiovascular Phenotype in Marfan Syndrome

Marfan Syndrome (MFS) is an autosomal dominant condition caused by variants in the fibrillin-1 (FBN1) gene. Cardinal features of MFS include ectopia lentis (EL), musculoskeletal features and aortic root aneurysm and dissection. Although dissection of the ascending aorta is the main cause of mortality in MFS, the clinical course differs considerably in age of onset and severity, even among individuals who share the same causative variant, suggesting the existence of additional genetic variants that modify the severity of the cardiovascular phenotype in MFS. We recruited MFS patients and classified them into severe (n = 8) or mild aortic phenotype (n = 14) according to age of presentation of the first aorta-related incident. We used Exome Sequencing to identify the genetic variants associated with the severity of aortic manifestations and we performed linkage analysis where suitable. We found five genes associated with severe aortic phenotype and three genes that could be protective for this phenotype in MFS. These genes regulate components of the extracellular matrix, TGFβ pathway and other signaling pathways that are involved in the maintenance of the ECM or angiogenesis. Further studies will be required to understand the functional effect of these variants and explore novel, personalized risk management and, potentially, therapies for these patients.

Modeling SARS-CoV-2 spike/ACE2 protein-protein interactions for predicting the binding affinity of new spike variants for ACE2, and novel ACE2 structurally related human protein targets, for COVID-19 handling in the 3PM context

Aims

The rapid spread of new SARS-CoV-2 variants has highlighted the crucial role played in the infection by mutations occurring at the SARS-CoV-2 spike receptor binding domain (RBD) in the interactions with the human ACE2 receptor. In this context, it urgently needs to develop new rapid tools for quickly predicting the affinity of ACE2 for the SARS-CoV-2 spike RBD protein variants to be used with the ongoing SARS-CoV-2 genomic sequencing activities in the clinics, aiming to gain clues about the transmissibility and virulence of new variants, to prevent new outbreaks and to quickly estimate the severity of the disease in the context of the 3PM.

Methods

In our study, we used a computational pipeline for calculating the interaction energies at the SARS-CoV-2 spike RBD/ACE2 protein-protein interface for a selected group of characterized infectious variants of concern/interest (VoC/VoI). By using our pipeline, we built 3D comparative models of the SARS-CoV-2 spike RBD/ACE2 protein complexes for the VoC B.1.1.7-United Kingdom (carrying the mutations of concern/interest N501Y, S494P, E484K at the RBD), P.1-Japan/Brazil (RBD mutations: K417T, E484K, N501Y), B.1.351-South Africa (RBD mutations: K417N, E484K, N501Y), B.1.427/B.1.429-California (RBD mutations: L452R), the B.1.141 (RBD mutations: N439K), and the recent B.1.617.1-India (RBD mutations: L452R; E484Q) and the B.1.620 (RBD mutations: S477N; E484K). Then, we used the obtained 3D comparative models of the SARS-CoV-2 spike RBD/ACE2 protein complexes for predicting the interaction energies at the protein-protein interface.

Results

Along SARS-CoV-2 mutation database screening and mutation localization analysis, it was ascertained that the most dangerous mutations at VoC/VoI spike proteins are located mainly at three regions of the SARS-CoV-2 spike "boat-shaped" receptor binding motif, on the RBD domain. Notably, the P.1 Japan/Brazil variant present three mutations, K417T, E484K, N501Y, located along the entire receptor binding motif, which apparently determines the highest interaction energy at the SARS-CoV-2 spike RBD/ACE2 protein-protein interface, among those calculated. Conversely, it was also observed that the replacement of a single acidic/hydrophilic residue with a basic residue (E484K or N439K) at the "stern" or "bow" regions, of the boat-shaped receptor binding motif on the RBD, appears to determine an interaction energy with ACE2 receptor higher than that observed with single mutations occurring at the "hull" region or with other multiple mutants. In addition, our pipeline allowed searching for ACE2 structurally related proteins, i.e., THOP1 and NLN, which deserve to be investigated for their possible involvement in interactions with the SARS-CoV-2 spike protein, in those tissues showing a low expression of ACE2, or as a novel receptor for future spike variants. A freely available web-tool for the in silico calculation of the interaction energy at the SARS-CoV-2 spike RBD/ACE2 protein-protein interface, starting from the sequences of the investigated spike and/or ACE2 variants, was made available for the scientific community at: https://www.mitoairm.it/covid19affinities.

Conclusion

In the context of the PPPM/3PM, the employment of the described pipeline through the provided webservice, together with the ongoing SARS-CoV-2 genomic sequencing, would help to predict the transmissibility of new variants sequenced from future patients, depending on SARS-CoV-2 genomic sequencing activities and on the specific amino acid replacement and/or on its location on the SARS-CoV-2 spike RBD, to put in play all the possible counteractions for preventing the most deleterious scenarios of new outbreaks, taking into consideration that a greater transmissibility has not to be necessarily related to a more severe manifestation of the disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-021-00267-w.

DisCovER: distance- and orientation-based covariational threading for weakly homologous proteins

Threading a query protein sequence onto a library of weakly homologous structural templates remains challenging, even when sequence-based predicted contact or distance information is used. Contact-assisted or distance-assisted threading methods utilize only the spatial proximity of the interacting residue pairs for template selection and alignment, ignoring their orientation. Moreover, existing threading methods fail to consider the neighborhood effect induced by the query-template alignment. We present a new distance- and orientation-based covariational threading method called DisCovER by effectively integrating information from inter-residue distance and orientation along with the topological network neighborhood of a query-template alignment. Our method first selects a subset of templates using standard profile-based threading coupled with topological network similarity terms to account for the neighborhood effect and subsequently performs distance- and orientation-based query-template alignment using an iterative double dynamic programming framework. Multiple large-scale benchmarking results on query proteins classified as weakly homologous from the continuous automated model evaluation experiment and from the current literature show that our method outperforms several existing state-of-the-art threading approaches, and that the integration of the neighborhood effect with the inter-residue distance and orientation information synergistically contributes to the improved performance of DisCovER. DisCovER is freely available at https://github.com/Bhattacharya-Lab/DisCovER.

TASOR epigenetic repressor cooperates with a CNOT1 RNA degradation pathway to repress HIV

The Human Silencing Hub (HUSH) complex constituted of TASOR, MPP8 and Periphilin recruits the histone methyl-transferase SETDB1 to spread H3K9me3 repressive marks across genes and transgenes in an integration site-dependent manner. The deposition of these repressive marks leads to heterochromatin formation and inhibits gene expression, but the underlying mechanism is not fully understood. Here, we show that TASOR silencing or HIV-2 Vpx expression, which induces TASOR degradation, increases the accumulation of transcripts derived from the HIV-1 LTR promoter at a post-transcriptional level. Furthermore, using a yeast 2-hybrid screen, we identify new TASOR partners involved in RNA metabolism including the RNA deadenylase CCR4-NOT complex scaffold CNOT1. TASOR and CNOT1 synergistically repress HIV expression from its LTR. Similar to the RNA-induced transcriptional silencing complex found in fission yeast, we show that TASOR interacts with the RNA exosome and RNA Polymerase II, predominantly under its elongating state. Finally, we show that TASOR facilitates the association of RNA degradation proteins with RNA polymerase II and is detected at transcriptional centers. Altogether, we propose that HUSH operates at the transcriptional and post-transcriptional levels to repress HIV proviral expression.

FEPS: A Tool for Feature Extraction from Protein Sequence

Machine learning has become one of the most popular choices for developing computational approaches in protein structural bioinformatics. The ability to extract features from protein sequence/structure often becomes one of the crucial steps for the development of machine learning-based approaches. Over the years, various sequence, structural, and physicochemical descriptors have been developed for proteins and these descriptors have been used to predict/solve various bioinformatics problems. Hence, several feature extraction tools have been developed over the years to help researchers to generate numeric features from protein sequences. Most of these tools have some limitations regarding the number of sequences they can handle and the subsequent preprocessing that is required for the generated features before they can be fed to machine learning methods. Here, we present Feature Extraction from Protein Sequences (FEPS), a toolkit for feature extraction. FEPS is a versatile software package for generating various descriptors from protein sequences and can handle several sequences: the number of which is limited only by the computational resources. In addition, the features extracted from FEPS do not require subsequent processing and are ready to be fed to the machine learning techniques as it provides various output formats as well as the ability to concatenate these generated features. FEPS is made freely available via an online web server as well as a stand-alone toolkit. FEPS, a comprehensive toolkit for feature extraction, will help spur the development of machine learning-based models for various bioinformatics problems.

A Helminth-Derived Chitinase Structurally Similar to Mammalian Chitinase Displays Immunomodulatory Properties in Inflammatory Lung Disease

Immunomodulation of airway hyperreactivity by excretory-secretory (ES) products of the first larval stage (L1) of the gastrointestinal nematode Trichuris suis is reported by us and others. Here, we aimed to identify the proteins accounting for the modulatory effects of the T. suis L1 ES proteins and studied six selected T. suis L1 proteins for their immunomodulatory efficacy in a murine OVA-induced allergic airway disease model. In particular, an enzymatically active T. suis chitinase mediated amelioration of clinical signs of airway hyperreactivity, primarily associated with suppression of eosinophil recruitment into the lung, the associated chemokines, and increased numbers of RELMα⁺ interstitial lung macrophages. While there is no indication of T. suis chitinase directly interfering with dendritic cell activation or antigen presentation to CD4 T cells, treatment of allergic mice with the worm chitinase influenced the hosts' own chitinase activity in the inflamed lung. The three-dimensional structure of the T. suis chitinase as determined by high-resolution X-ray crystallography revealed high similarities to mouse acidic mammalian chitinase (AMCase) but a unique ability of T. suis chitinase to form dimers. Our data indicate that the structural similarities between the parasite and host chitinase contribute to the disease-ameliorating effect of the helminth-derived chitinase on allergic lung inflammation.

Co-operative binding of SKP1, Cullin1 and Cullin7 to FBXW8 results in Cullin1-SKP1-FBXW8-Cullin7 functional complex formation that monitors cellular function of β-TrCP1

F-box protein FBXW8 is known to interact with scaffolding protein Cullin1 and Cullin7 to form SCF (SKP1, Cullin and F-box protein) complex. However, detail understanding about the importance of both Cullins for SCF-FBXW8 complex formation as well as its ubiquitin ligase activity remains elusive. Here, we show that, through in vitro and in vivo studies, Cullin1 and Cullin7 increase each other's binding to FBXW8 synergistically. Interestingly, absence of either Cullin results in abrogation of binding of other Cullin to FBXW8. Binding of SKP1 to FBXW8 also increases in the presence of both the Cullins. Thus, SKP1, Cullin1 and Cullin7 are essential to form Cullin1-SKP1-FBXW8-Cullin7 functional ubiquitin ligase complex. Further, using computational, mutational and biochemical analysis, we found that Cullin1 binds to N-terminus of FBXW8 through SKP1 while Cullin7 associates with C-terminus of FBXW8 to form Cullin1-SKP1-FBXW8-Cullin7 functional complex in a cooperative manner. Results showed that Cullin1-SKP1-FBXW8-Cullin7 complex plays a key role in maintaining the basal level expression of β-TrCP1. Moreover, Cullin1-SKP1-FBXW8-Cullin7 complex promotes cell migration by activating β-catenin via directing proteasomal degradation of β-TrCP1. Overall, our study reveals the intriguing molecular mechanism of assembly of SKP1, Cullin1, Cullin7 and FBXW8 to form Cullin1-SKP1-FBXW8-Cullin7 functional complex that control the function of β-TrCP1.

Molecular modelling of coat protein of the Groundnut bud necrosis tospovirus and its binding with Squalene as an antiviral agent: In vitro and in silico docking investigations

Bud blight disease caused by groundnut bud necrosis virus (GBNV) is a serious constraint in the cultivation of agricultural crops such as legumes, tomato, chilies, potato, cotton etc. Owing to the significant damage caused by GBNV, an attempt was made to identify suitable organic antiviral agents through molecular modelling of the nucleocapsid Coat Protein of GBNV; molecular docking and molecular dynamics that disclosed the interaction of the ligands viz., Squalene and Ganoderic acid-A with coat protein of GBNV. Invitro inhibitory effect of Squalene and Ganoderic acid-A was examined in comparison with different concentrations, against GBNV in cowpea plants under glasshouse condition. The different concentrations of Squalene (50, 100, 150, 250 and 500 ppm) tested in vitro resulted in reduction of lesion numbers (1.69 cm²) as well as reduced virus titre in co-inoculation spray. The present study suggests the antiviral activity of Squalene by effectively fitting into binding site of coat protein of GBNV with favourable hydrophilic as well as strong hydrophobic interactions thereby challenging and blocking the binding of viral replication RNA with coat protein and propagation. The present organic antiviral molecules will be helpful in development of suitable eco-friendly formulations to mitigate GBNV infection disease in plants.

Proteomic Tools for the Analysis of Cytoskeleton Proteins

Proteomic analyses have become an essential part of the toolkit of the molecular biologist, given the widespread availability of genomic data and open source or freely accessible bioinformatics software. Tools are available for detecting homologous sequences, recognizing functional domains, and modeling the three-dimensional structure for any given protein sequence, as well as for predicting interactions with other proteins or macromolecules. Although a wealth of structural and functional information is available for many cytoskeletal proteins, with representatives spanning all of the major subfamilies, the majority of cytoskeletal proteins remain partially or totally uncharacterized. Moreover, bioinformatics tools provide a means for studying the effects of synthetic mutations or naturally occurring variants of these cytoskeletal proteins. This chapter discusses various freely available proteomic analysis tools, with a focus on in silico prediction of protein structure and function. The selected tools are notable for providing an easily accessible interface for the novice while retaining advanced functionality for more experienced computational biologists.

N-acetylaspartate release by glutaminolytic ovarian cancer cells sustains protumoral macrophages

Glutaminolysis is known to correlate with ovarian cancer aggressiveness and invasion. However, how this affects the tumor microenvironment is elusive. Here, we show that ovarian cancer cells become addicted to extracellular glutamine when silenced for glutamine synthetase (GS), similar to naturally occurring GS-low, glutaminolysis-high ovarian cancer cells. Glutamine addiction elicits a crosstalk mechanism whereby cancer cells release N-acetylaspartate (NAA) which, through the inhibition of the NMDA receptor, and synergistically with IL-10, enforces GS expression in macrophages. In turn, GS-high macrophages acquire M2-like, tumorigenic features. Supporting this in␣vitro model, in silico data and the analysis of ascitic fluid isolated from ovarian cancer patients prove that an M2-like macrophage phenotype, IL-10 release, and NAA levels positively correlate with disease stage. Our study uncovers the unprecedented role of glutamine metabolism in modulating macrophage polarization in highly invasive ovarian cancer and highlights the anti-inflammatory, protumoral function of NAA.

Identification of a Family of Vibrio Type III Secretion System Effectors That Contain a Conserved Serine/Threonine Kinase Domain

Vibrio parahaemolyticus is a marine Gram-negative bacterium that is a leading cause of seafood-borne gastroenteritis. Pandemic strains of V. parahaemolyticus rely on a specialized protein secretion machinery known as the type III secretion system 2 (T3SS2) to cause disease. The T3SS2 mediates the delivery of effector proteins into the cytosol of infected cells, where they subvert multiple cellular pathways. Here, we identify a new T3SS2 effector protein encoded by VPA1328 (VP_RS21530) in V. parahaemolyticus RIMD2210633. Bioinformatic analysis revealed that VPA1328 is part of a larger family of uncharacterized T3SS effector proteins with homology to the VopG effector protein in Vibrio cholerae AM-19226. These VopG-like proteins are found in many but not all T3SS2 gene clusters and are distributed among diverse Vibrio species, including V. parahaemolyticus, V. cholerae, V. mimicus, and V. diabolicus and also in Shewanella baltica. Structure-based prediction analyses uncovered the presence of a conserved C-terminal kinase domain in VopG orthologs, similar to the serine/threonine kinase domain found in the NleH family of T3SS effector proteins. However, in contrast to NleH effector proteins, in tissue culture-based infections, VopG did not impede host cell death or suppress interleukin 8 (IL-8) secretion, suggesting a yet undefined role for VopG during V. parahaemolyticus infection. Collectively, our work reveals that VopG effector proteins, a new family of likely serine/threonine kinases, is widely distributed in the T3SS2 effector armamentarium among marine bacteria. IMPORTANCE Vibrio parahaemolyticus is the leading bacterial cause of seafood-borne gastroenteritis worldwide. The pathogen relies on a type III secretion system to deliver a variety of effector proteins into the cytosol of infected cells to subvert cellular function. In this study, we identified a novel Vibrio parahaemolyticus effector protein that is similar to the VopG effector of Vibrio cholerae. VopG-like effectors were found in diverse Vibrio species and contain a conserved serine/threonine kinase domain that bears similarity to the kinase domain in the enterohemorrhagic Escherichia coli (EHEC) and Shigella NleH effectors that manipulate host cell survival pathways and host immune responses. Together our findings identify a new family of Vibrio effector proteins and highlight the role of horizontal gene transfer events among marine bacteria in shaping T3SS gene clusters.

TopDomain: Exhaustive Protein Domain Boundary Metaprediction Combining Multisource Information and Deep Learning

Protein domains are independent, functional, and stable structural units of proteins. Accurate protein domain boundary prediction plays an important role in understanding protein structure and evolution, as well as for protein structure prediction. Current domain boundary prediction methods differ in terms of boundary definition, methodology, and training databases resulting in disparate performance for different proteins. We developed TopDomain, an exhaustive metapredictor, that uses deep neural networks to combine multisource information from sequence- and homology-based features of over 50 primary predictors. For this purpose, we developed a new domain boundary data set termed the TopDomain data set, in which the true annotations are informed by SCOPe annotations, structural domain parsers, human inspection, and deep learning. We benchmark TopDomain against 2484 targets with 3354 boundaries from the TopDomain test set and achieve F1 scores of 78.4% and 73.8% for multidomain boundary prediction within ±20 residues and ±10 residues of the true boundary, respectively. When examined on targets from CASP11-13 competitions, TopDomain achieves F1 scores of 47.5% and 42.8% for multidomain proteins. TopDomain significantly outperforms 15 widely used, state-of-the-art ab initio and homology-based domain boundary predictors. Finally, we implemented TopDomain_TMC, which accurately predicts whether domain parsing is necessary for the target protein.

Mycobacterial crypto-AcpM as a tool to investigate the consequence of drug binding on its key FAS II partner enzyme HadAB

Background Mycobacterial FASII pathway is governed by the Protein-Protein Interaction mediated dynamics existent between Acyl Carrier Protein and its partner enzymes. The dehydratase HadAB, involved in the third step of FASII synthesis has remained a key target of drugs like Thiacetazone (TAC) and its consequence on AcpM binding is yet to be deciphered. Owing to the transient nature of these interactions, analysing their implications as a drug target has been exhausting. Methods In this context, we have developed an in vitro method to study the effect of thiocarbamide-containing compounds, TAC and SPA0355 (a thiourea analogue) against mycobacterial HadAB. Additionally, by utilizing crypto-ACP (NBD-tagged Acyl Carrier Protein) as a tool of our choice, we attempted at exploring the effect of TAC and SPA0355 on mycobacterial HadAB. Results SPA0355 behaves at par with TAC and undergoes activation in the presence of monooxygenase EthA thus, bringing about a covalent modification in HadA subunit of HadAB. The crypto-ACP method provides insights into the altered substrate housing capability in HadAB associated with the impediment of its AcpM mediated functionality; an outcome attributed to the repercussions associated with the binding of the aforementioned thiourea compounds. Conclusion This investigation has assisted in unveiling a two-step mechanism undertaken by AcpM for interacting with its corresponding partner protein during acyl chain transfer. General significance This study highlights the alterations brought about by drug binding in the interplay between ACP and HadAB. Additionally, this work for the first time establishes the role of SPA0355 as a promising drug candidate against dehydratase HadAB.

Anti-oomycetes and immunostimulatory activity of natural plant extract compounds against Saprolegnia spp.: Molecular docking and in-vitro studies

This study aimed to investigate the effectiveness of five natural plant extract compounds Curcumin (CUR); Eugenol (EUG), Cinnamaldehyde (CIN), Stigmasterol (ST) and Morin (MOR), on two species of Saprolegnia; Saprolegnia parasitica and S. australis. Selective compounds were screened for the minimum inhibitory concentration, first for anti-oomycetes activity and then mycelium growth inhibition, spore germination inhibition and colonisation test. Nitric oxide production and myeloperoxidase activity of the compounds were tested in head kidney leukocytes of rainbow trout, Oncorhynchus mykiss to assess the immunostimulatory potential. Molecular docking of effective compounds was carried out with effector proteins of S. parasitica to investigate the target binding sites. Among all, CUR could completely inhibit zoospore production and significantly (p ≤ .05) inhibit hyphal growth at 16 mg l^-1 against S. parasitica and S. australis. CIN at the concentration of 50 mg l^-1 completely inhibited hyphal growth of both Saprolegnia spp., although the zoospore production of S. parasitica and S. australis was reduced at 25 mg l^-1 and 10 mg l^-1. In the case of EUG, significant inhibition of the hyphal growth and germination of S. parasitica zoospores was observed at 50 mg l^-1. ST and MOR did not show antioomycetes activity. The molecular docking results were consistent with in vitro studies, possibly due to the binding with the vital proteins (Plasma membrane ATPase, V-type proton ATPase, TKL protein kinase, Host targeting protein 1) of S. parasitica and ultimately inhibiting their activity. CUR and CIN showed increased nitric oxide production at the highest concentration of 250 and 256 mg l^-1 but the value was not significant (p ≤ .05) with control. CUR showed significantly higher peroxidase activity (p ≤ .05) at a concentration of 256 mg l^-1 though values were significantly similar with concentration from 16 to 128 mg l^-1. The nitric oxide and total peroxidase activity of rainbow trout leukocytes in the case of CIN showed a significant difference only at 250 mg l^-1 against the control. The results conclude that CUR, CIN showed the better anti-Saprolegnia activity and could be used as phyto-additives in aquaculture. Among all, the inclusion of CUR as phyto-additives will provide additional immunostimulatory activity.

Data-Driven Ensemble Docking to Map Molecular Interactions of Steroid Analogs with Hepatic Organic Anion Transporting Polypeptides

Hepatic organic anion transporting polypeptides—OATP1B1, OATP1B3, and OATP2B1—are expressed at the basolateral membrane of hepatocytes, being responsible for the uptake of a wide range of natural substrates and structurally unrelated pharmaceuticals. Impaired function of hepatic OATPs has been linked to clinically relevant drug–drug interactions leading to altered pharmacokinetics of administered drugs. Therefore, understanding the commonalities and differences across the three transporters represents useful knowledge to guide the drug discovery process at an early stage. Unfortunately, such efforts remain challenging because of the lack of experimentally resolved protein structures for any member of the OATP family. In this study, we established a rigorous computational protocol to generate and validate structural models for hepatic OATPs. The multistep procedure is based on the systematic exploration of available protein structures with shared protein folding using normal-mode analysis, the calculation of multiple template backbones from elastic network models, the utilization of multiple template conformations to generate OATP structural models with various degrees of conformational flexibility, and the prioritization of models on the basis of enrichment docking. We employed the resulting OATP models of OATP1B1, OATP1B3, and OATP2B1 to elucidate binding modes of steroid analogs in the three transporters. Steroid conjugates have been recognized as endogenous substrates of these transporters. Thus, investigating this data set delivers insights into mechanisms of substrate recognition. In silico predictions were complemented with in vitro studies measuring the bioactivity of a compound set on OATP expressing cell lines. Important structural determinants conferring shared and distinct binding patterns of steroid analogs in the three transporters have been identified. Overall, this comparative study provides novel insights into hepatic OATP-ligand interactions and selectivity. Furthermore, the integrative computational workflow for structure-based modeling can be leveraged for other pharmaceutical targets of interest.

Protein Structure Prediction: Conventional and Deep Learning Perspectives

Protein structure prediction is a way to bridge the sequence-structure gap, one of the main challenges in computational biology and chemistry. Predicting any protein's accurate structure is of paramount importance for the scientific community, as these structures govern their function. Moreover, this is one of the complicated optimization problems that computational biologists have ever faced. Experimental protein structure determination methods include X-ray crystallography, Nuclear Magnetic Resonance Spectroscopy and Electron Microscopy. All of these are tedious and time-consuming procedures that require expertise. To make the process less cumbersome, scientists use predictive tools as part of computational methods, using data consolidated in the protein repositories. In recent years, machine learning approaches have raised the interest of the structure prediction community. Most of the machine learning approaches for protein structure prediction are centred on co-evolution based methods. The accuracy of these approaches depends on the number of homologous protein sequences available in the databases. The prediction problem becomes challenging for many proteins, especially those without enough sequence homologs. Deep learning methods allow for the extraction of intricate features from protein sequence data without making any intuitions. Accurately predicted protein structures are employed for drug discovery, antibody designs, understanding protein-protein interactions, and interactions with other molecules. This article provides a review of conventional and deep learning approaches in protein structure prediction. We conclude this review by outlining a few publicly available datasets and deep learning architectures currently employed for protein structure prediction tasks.

Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading

Sequence-based protein homology detection has emerged as one of the most sensitive and accurate approaches to protein structure prediction. Despite the success, homology detection remains very challenging for weakly homologous proteins with divergent evolutionary profile. Very recently, deep neural network architectures have shown promising progress in mining the coevolutionary signal encoded in multiple sequence alignments, leading to reasonably accurate estimation of inter-residue interaction maps, which serve as a rich source of additional information for improved homology detection. Here, we summarize the latest developments in protein homology detection driven by inter-residue interaction map threading. We highlight the emerging trends in distant-homology protein threading through the alignment of predicted interaction maps at various granularities ranging from binary contact maps to finer-grained distance and orientation maps as well as their combination. We also discuss some of the current limitations and possible future avenues to further enhance the sensitivity of protein homology detection.

Computational structure prediction provides a plausible mechanism for electron transfer by the outer membrane protein Cyc2 from Acidithiobacillus ferrooxidans

Cyc2 is the key protein in the outer membrane of Acidithiobacillus ferrooxidans that mediates electron transfer between extracellular inorganic iron and the intracellular central metabolism. This cytochrome c is specific for iron and interacts with periplasmic proteins to complete a reversible electron transport chain. A structure of Cyc2 has not yet been characterized experimentally. Here we describe a structural model of Cyc2, and associated proteins, to highlight a plausible mechanism for the ferrous iron electron transfer chain. A comparative modeling protocol specific for trans membrane beta barrel (TMBB) proteins in acidophilic conditions (pH ~ 2) was applied to the primary sequence of Cyc2. The proposed structure has three main regimes: Extracellular loops exposed to low-pH conditions, a TMBB, and an N-terminal cytochrome-like region within the periplasmic space. The Cyc2 model was further refined by identifying likely iron and heme docking sites. This represents the first computational model of Cyc2 that accounts for the membrane microenvironment and the acidity in the extracellular matrix. This approach can be used to model other TMBBs which can be critical for chemolithotrophic microbial growth.

The T Cell Receptor (TRB) Locus in Tursiops truncatus: From Sequence to Structure of the Alpha/Beta Heterodimer in the Human/Dolphin Comparison

The bottlenose dolphin (Tursiops truncatus) belongs to the Cetartiodactyla and, similarly to other cetaceans, represents the most successful mammalian colonization of the aquatic environment. Here we report a genomic, evolutionary, and expression study of T. truncatus T cell receptor beta (TRB) genes. Although the organization of the dolphin TRB locus is similar to that of the other artiodactyl species, with three in tandem D-J-C clusters located at its 3' end, its uniqueness is given by the reduction of the total length due essentially to the absence of duplications and to the deletions that have drastically reduced the number of the germline TRBV genes. We have analyzed the relevant mature transcripts from two subjects. The simultaneous availability of rearranged T cell receptor α (TRA) and TRB cDNA from the peripheral blood of one of the two specimens, and the human/dolphin amino acids multi-sequence alignments, allowed us to calculate the most likely interactions at the protein interface between the alpha/beta heterodimer in complex with major histocompatibility class I (MH1) protein. Interacting amino acids located in the complementarity-determining region according to IMGT numbering (CDR-IMGT) of the dolphin variable V-alpha and beta domains were identified. According to comparative modelization, the atom pair contact sites analysis between the human MH1 grove (G) domains and the T cell receptor (TR) V domains confirms conservation of the structure of the dolphin TR/pMH.

Protein domain identification methods and online resources

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

Protein structure analysis of the interactions between SARS-CoV-2 spike protein and the human ACE2 receptor: from conformational changes to novel neutralizing antibodies

The recent severe acute respiratory syndrome, known as Coronavirus Disease 2019 (COVID-19) has spread so much rapidly and severely to induce World Health Organization (WHO) to declare a state of emergency over the new coronavirus SARS-CoV-2 pandemic. While several countries have chosen the almost complete lock-down for slowing down SARS-CoV-2 spread, the scientific community is called to respond to the devastating outbreak by identifying new tools for diagnosis and treatment of the dangerous COVID-19. With this aim, we performed an in silico comparative modeling analysis, which allows gaining new insights into the main conformational changes occurring in the SARS-CoV-2 spike protein, at the level of the receptor-binding domain (RBD), along interactions with human cells angiotensin-converting enzyme 2 (ACE2) receptor, that favor human cell invasion. Furthermore, our analysis provides (1) an ideal pipeline to identify already characterized antibodies that might target SARS-CoV-2 spike RBD, aiming to prevent interactions with the human ACE2, and (2) instructions for building new possible neutralizing antibodies, according to chemical/physical space restraints and complementary determining regions (CDR) mutagenesis of the identified existing antibodies. The proposed antibodies show in silico high affinity for SARS-CoV-2 spike RBD and can be used as reference antibodies also for building new high-affinity antibodies against present and future coronaviruses able to invade human cells through interactions of their spike proteins with the human ACE2. More in general, our analysis provides indications for the set-up of the right biological molecular context for investigating spike RBD-ACE2 interactions for the development of new vaccines, diagnostic kits, and other treatments based on the targeting of SARS-CoV-2 spike protein.

Targeting mitochondrial metabolite transporters in Penicillium expansum for reducing patulin production

There is an increasing need of alternative treatments to control fungal infection and consequent mycotoxin accumulation in harvested fruits and vegetables. Indeed, only few biological targets of antifungal agents have been characterized and can be used for limiting fungal spread from decayed fruits/vegetables to surrounding healthy ones during storage. On this concern, a promising target of new antifungal treatments may be represented by mitochondrial proteins due to some species-specific functions played by mitochondria in fungal morphogenesis, drug resistance and virulence. One of the most studied mycotoxins is patulin produced by several species of Penicillium and Aspergillus genera. Patulin is toxic to many biological systems including bacteria, higher plants and animalia. Although precise biochemical mechanisms of patulin toxicity in humans are not completely clarified, its high presence in fresh and processed apple fruits and other apple-based products makes necessary developing a strategy for limiting its presence/accumulation. Patulin biosynthetic pathway consists of an enzymatic cascade, whose first step is represented by the synthesis of 6-methylsalicylic acid, obtained from the condensation of one acetyl-CoA molecule with three malonyl-CoA molecules. The most abundant acetyl-CoA precursor is represented by citrate produced by mitochondria. In the present investigation we report about the possibility to control patulin production through the inhibition of mitochondrial/peroxisome transporters involved in the export of acetyl-CoA precursors from mitochondria and/or peroxisomes, with specific reference to the predicted P. expansum mitochondrial Ctp1p, DTC, Sfc1p, Oac1p and peroxisomal PXN carriers.

Computational Methods for the Elucidation of Protein Structure and Interactions

Biologists are increasingly aware of the importance of protein structure in revealing function. The computational tools now exist which allow researchers to model unknown proteins simply on the basis of their primary sequence. However, for the non-specialist bioinformatician, there is a dazzling array of terminology, acronyms, and competing computer software available for this process. This review is intended to highlight the key stages of computational protein structure prediction, as well as explain the reasons behind some of the procedures and list some established workarounds for common pitfalls. Thereafter follows a review of five one-stop servers for start-to-finish structure prediction.

Targeting Penicillium expansum GMC Oxidoreductase with High Affinity Small Molecules for Reducing Patulin Production

Simple Summary

With the urgent necessity of potential treatments for limiting mycotoxin production and postharvest fungal rots, we propose a combined in silico/in vitro/in vivo strategy for the rapid and effective identification of bioactive small molecules, chosen among a chemical library hosting approved drugs and phytochemicals, to be used after harvest. The molecular target of our analysis was the GMC oxidoreductase from Penicillium expansum involved in the biosynthesis of patulin, a mycotoxin that can contaminate many foods, especially fruits and fruit-based products. The employed in silico/in vitro/in vivo assays described in our study proved the effectiveness of our strategy and in particular of two small molecules, 6-hydroxycoumarin (structurally related to umbelliferon, an already characterized patulin synthase inhibitor) and meticrane (an already approved drug) in reducing patulin accumulation. Our findings highly recommend the mentioned ligands to be subjected to further analysis for being used in the next future in place of other more toxic compounds, in postharvest treatments based on dipping or drenching methods.

Abstract

Flavine adenine dinucleotide (FAD) dependent glucose methanol choline oxidoreductase (GMC oxidoreductase) is the terminal key enzyme of the patulin biosynthetic pathway. GMC oxidoreductase catalyzes the oxidative ring closure of (E)-ascladiol to patulin. Currently, no protein involved in the patulin biosynthesis in Penicillium expansum has been experimentally characterized or solved by X-ray diffraction. Consequently, nothing is known about P. expansum GMC oxidoreductase substrate-binding site and mode of action. In the present investigation, a 3D comparative model for P. expansum GMC oxidoreductase has been described. Furthermore, a multistep computational approach was used to identify P. expansum GMC oxidoreductase residues involved in the FAD binding and in substrate recognition. Notably, the obtained 3D comparative model of P. expansum GMC oxidoreductase was used for performing a virtual screening of a chemical/drug library, which allowed to predict new GMC oxidoreductase high affinity ligands to be tested in in vitro/in vivo assays. In vitro assays performed in presence of 6-hydroxycoumarin and meticrane, among the highly affinity predicted binders, confirmed a dose-dependent inhibition (17–81%) of patulin production by 6-hydroxycoumarin (10 µM–1 mM concentration range), whereas the approved drug meticrane inhibited patulin production by 43% already at 10 µM. Furthermore, 6-hydroxycoumarin and meticrane caused a 60 and 41% reduction of patulin production, respectively, in vivo on apples at 100 µg/wound.

Differences in Metformin and Thiamine Uptake between Human and Mouse Organic Cation Transporter 1: Structural Determinants and Potential Consequences for Intrahepatic Concentrations

The most commonly used oral antidiabetic drug, metformin, is a substrate of the hepatic uptake transporter OCT1 (gene name SLC22A1). However, OCT1 deficiency leads to more pronounced reductions of metformin concentrations in mouse than in human liver. Similarly, the effects of OCT1 deficiency on the pharmacokinetics of thiamine were reported to differ between human and mouse. Here, we compared the uptake characteristics of metformin and thiamine between human and mouse OCT1 using stably transfected human embryonic kidney 293 cells. The affinity for metformin was 4.9-fold lower in human than in mouse OCT1, resulting in a 6.5-fold lower intrinsic clearance. Therefore, the estimated liver-to-blood partition coefficient is only 3.34 in human compared with 14.4 in mouse and may contribute to higher intrahepatic concentrations in mice. Similarly, the affinity for thiamine was 9.5-fold lower in human than in mouse OCT1. Using human-mouse chimeric OCT1, we showed that simultaneous substitution of transmembrane helices TMH2 and TMH3 resulted in the reversal of affinity for metformin. Using homology modeling, we suggest several explanations, of which a different interaction of Leu155 (human TMH2) compared with Val156 (mouse TMH2) with residues in TMH3 had the strongest experimental support. In conclusion, the contribution of human OCT1 to the cellular uptake of thiamine and especially of metformin may be much lower than that of mouse OCT1. This may lead to an overestimation of the effects of OCT1 on hepatic concentrations in humans when using mouse as a model. In addition, comparative analyses of human and mouse orthologs may help reveal mechanisms of OCT1 transport. SIGNIFICANCE STATEMENT: OCT1 is a major hepatic uptake transporter of metformin and thiamine, but this study reports strong differences in the affinity for both compounds between human and mouse OCT1. Consequently, intrahepatic metformin concentrations could be much higher in mice than in humans, impacting metformin actions and representing a strong limitation of using rodent animal models for predictions of OCT1-related pharmacokinetics and efficacy in humans. Furthermore, OCT1 transmembrane helices TMH2 and TMH3 were identified to confer the observed species-specific differences in metformin affinity.

Glufosinate constrains synchronous and metachronous metastasis by promoting anti-tumor macrophages

Glutamine synthetase (GS) generates glutamine from glutamate and controls the release of inflammatory mediators. In macrophages, GS activity, driven by IL10, associates to the acquisition of M2-like functions. Conditional deletion of GS in macrophages inhibits metastasis by boosting the formation of anti-tumor, M1-like, tumor-associated macrophages (TAMs). From this basis, we evaluated the pharmacological potential of GS inhibitors in targeting metastasis, identifying glufosinate as a specific human GS inhibitor. Glufosinate was tested in both cultured macrophages and on mice bearing metastatic lung, skin and breast cancer. We found that glufosinate rewires macrophages toward an M1-like phenotype both at the primary tumor and metastatic site, countering immunosuppression and promoting vessel sprouting. This was also accompanied to a reduction in cancer cell intravasation and extravasation, leading to synchronous and metachronous metastasis growth inhibition, but no effects on primary tumor growth. Glufosinate treatment was well-tolerated, without liver and brain toxicity, nor hematopoietic defects. These results identify GS as a druggable enzyme to rewire macrophage functions and highlight the potential of targeting metabolic checkpoints in macrophages to treat cancer metastasis.

Synergistic transport of a fluorescent coumarin probe marks coumarins as pharmacological modulators of Organic anion-transporting polypeptide, OATP3A1

Organic anion-transporting polypeptide 3A1 (OATP3A1) is a membrane transporter mediating the cellular uptake of various hormones such as estrone-3-sulfate, prostaglandins E1 and E2 and thyroxine. OATP3A1 is widely expressed in the human body and its presence in tissue-blood barriers, neurons and muscle cells marks it as a potential pharmacological target. Herein we demonstrate that an otherwise membrane impermeant, zwitterionic fluorescent coumarin probe, bearing a sulfonate function is a potent substrate of human OATP3A1, thus readily transported into HEK-293-OATP3A1 cells allowing functional investigation and the screen of drug interactions of the OATP3A1 transporter. At the same time, dyes lacking either the sulfonate motif or the coumarin scaffold showed a dramatic decrease in affinity or even a complete loss of transport. Furthermore, we observed a distinct inhibition/activation pattern in the OATP3A1-mediated uptake of closely related fluorescent coumarin derivatives differing only in the presence of the sulfonate moiety. Additionally, we detected a synergistic effect between one of the probes tested and the endogenous OATP substrate estrone-3-sulfate. These data, together with docking results indicate the presence of at least two cooperative substrate binding sites in OATP3A1. Besides providing the first sensitive probe for testing OATP3A1 substrate/inhibitor interactions, our results also help to understand substrate recognition and transport mechanism of the poorly characterized OATP3A1. Moreover, coumarins are good candidates for OATP3A1-targeted drug delivery and as pharmacological modulators of OATP3A1.

A systematic, label-free method for identifying RNA-associated proteins in vivo provides insights into vertebrate ciliary beating machinery

Cell-type specific RNA-associated proteins are essential for development and homeostasis in animals. Despite a massive recent effort to systematically identify RNA-associated proteins, we currently have few comprehensive rosters of cell-type specific RNA-associated proteins in vertebrate tissues. Here, we demonstrate the feasibility of determining the RNA-associated proteome of a defined vertebrate embryonic tissue using DIF-FRAC, a systematic and universal (i.e., label-free) method. Application of DIF-FRAC to cultured tissue explants of Xenopus mucociliary epithelium identified dozens of known RNA-associated proteins as expected, but also several novel RNA-associated proteins, including proteins related to assembly of the mitotic spindle and regulation of ciliary beating. In particular, we show that the inner dynein arm tether Cfap44 is an RNA-associated protein that localizes not only to axonemes, but also to liquid-like organelles in the cytoplasm called DynAPs. This result led us to discover that DynAPs are generally enriched for RNA. Together, these data provide a useful resource for a deeper understanding of mucociliary epithelia and demonstrate that DIF-FRAC will be broadly applicable for systematic identification of RNA-associated proteins from embryonic tissues.

ZCWPW1 is recruited to recombination hotspots by PRDM9 and is essential for meiotic double strand break repair

During meiosis, homologous chromosomes pair and recombine, enabling balanced segregation and generating genetic diversity. In many vertebrates, double-strand breaks (DSBs) initiate recombination within hotspots where PRDM9 binds, and deposits H3K4me3 and H3K36me3. However, no protein(s) recognising this unique combination of histone marks have been identified. We identified Zcwpw1, containing H3K4me3 and H3K36me3 recognition domains, as having highly correlated expression with Prdm9. Here, we show that ZCWPW1 has co-evolved with PRDM9 and, in human cells, is strongly and specifically recruited to PRDM9 binding sites, with higher affinity than sites possessing H3K4me3 alone. Surprisingly, ZCWPW1 also recognises CpG dinucleotides. Male Zcwpw1 knockout mice show completely normal DSB positioning, but persistent DMC1 foci, severe DSB repair and synapsis defects, and downstream sterility. Our findings suggest ZCWPW1 recognition of PRDM9-bound sites at DSB hotspots is critical for synapsis, and hence fertility.

Sexual reproduction – that is, the combination of sex cells from two different individuals to produce an embryo – is one of the many mechanisms that have evolved to maintain genetic diversity. Most human cells contain 23 pairs of chromosomes, with each chromosome in a pair carrying either a paternal or maternal copy of the same gene. To form an embryo with the right number of chromosomes, each sex cell (the egg or sperm cell) must only contain one chromosome from each pair.

Sex cells are produced from parent cells containing two sets of paternal and maternal chromosomes: these cells then divide twice to form four sex cells which contain only one chromosome from each pair. Before the parent cell divides, a process known as ‘recombination’ takes place, which allows chromosomes in a pair to exchange bits of genetic information. This reshuffling ensures that each chromosome in a sex cell is unique. A protein called PRDM9 helps control which sections of genetic information are recombined by modifying proteins attached to the chromosomes, marking them as locations for exchange. The DNA at each of these sites is then broken and repaired using the genetic sequence of the chromosome it is paired with as a template, thus causing the two chromosomes to swap genes.

In 2019, a group of researchers found a set of genes in the testis of mice that are expressed at the same time as the gene for PRDM9. This suggested that another protein called ZCWPW1 is likely involved in recombination, but the precise role of this protein was unclear. To answer this question, Wells, Bitoun et al. – including many of the researchers involved in the 2019 study – examined human cells grown in the laboratory to determine where ZCWPW1 binds to in the chromosome.

This revealed that ZCWPW1 can be found at the same sites as PRDM9, which is responsible for bringing it there. Furthermore, cells from male mice lacking the gene for ZCWPW1 cannot complete the exchange of genetic information between chromosomes, meaning that the mice are infertile. As such, ZCWPW1 seems to connect location selection by PRDM9 to the DNA repair mechanisms needed for gene exchange between chromosomes.

Infertility is a significant issue for humans affecting as many as one in every six couples. Fertility is complex and many of the biological mechanisms involved are not fully understood. This work suggests that both PRDM9 and ZCWPW1 are key to the production of sex cells and may be worth investigating as factors that affect fertility in humans.

Molecular biology and structure of a novel penaeid shrimp densovirus elucidate convergent parvoviral host capsid evolution

The giant tiger prawn (Penaeus monodon) is a decapod crustacean widely reared for human consumption. Currently, viruses of two distinct lineages of parvoviruses (PVs, family Parvoviridae; subfamily Hamaparvovirinae) infect penaeid shrimp. Here, a PV was isolated and cloned from Vietnamese P. monodon specimens, designated Penaeus monodon metallodensovirus (PmMDV). This is the first member of a third divergent lineage shown to infect penaeid decapods. PmMDV has a transcription strategy unique among invertebrate PVs, using extensive alternative splicing and incorporating transcription elements characteristic of vertebrate-infecting PVs. The PmMDV proteins have no significant sequence similarity with other PVs, except for an SF3 helicase domain in its nonstructural protein. Its capsid structure, determined by cryoelectron microscopy to 3-Å resolution, has a similar surface morphology to Penaeus stylirostris densovirus, despite the lack of significant capsid viral protein (VP) sequence similarity. Unlike other PVs, PmMDV folds its VP without incorporating a βA strand and displayed unique multimer interactions, including the incorporation of a Ca²⁺ cation, attaching the N termini under the icosahedral fivefold symmetry axis, and forming a basket-like pentamer helix bundle. While the PmMDV VP sequence lacks a canonical phospholipase A2 domain, the structure of an EDTA-treated capsid, determined to 2.8-Å resolution, suggests an alternative membrane-penetrating cation-dependent mechanism in its N-terminal region. PmMDV is an observed example of convergent evolution among invertebrate PVs with respect to host-driven capsid structure and unique as a PV showing a cation-sensitive/dependent basket structure for an alternative endosomal egress.