Development and evaluation of a low cost IgG ELISA test based in RBD protein for COVID-19.

Serology tests for SARS-CoV-2 have proven to be important tools to fight against the COVID-19 pandemic. These serological tests can be used in low-income and remote areas for patient contact tracing, epidemiologic studies and vaccine efficacy evaluations. In this study, we used a semi-stable mammalian episomal expression system to produce high quantities of the receptor-binding domain-RBD of SARS-CoV-2 in a simple and very economical way. The recombinant antigen was tested in an in-house IgG ELISA for COVID-19 with a panel of human sera. A performance comparison of this serology test with a commercial test based on the full-length spike protein showed 100% of concordance between tests. Thus, this serological test can be an attractive and inexpensive option in scenarios of limited resources to face the COVID-19 pandemic.

Machine Learning Approaches for Protein⁻Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment.

Hot spots are the subset of interface residues that account for most of the binding free energy, and they play essential roles in the stability of protein binding. Effectively identifying which specific interface residues of protein⁻protein complexes form the hot spots is critical for understanding the principles of protein interactions, and it has broad application prospects in protein design and drug development. Experimental methods like alanine scanning mutagenesis are labor-intensive and time-consuming. At present, the experimentally measured hot spots are very limited. Hence, the use of computational approaches to predicting hot spots is becoming increasingly important. Here, we describe the basic concepts and recent advances of machine learning applications in inferring the protein⁻protein interaction hot spots, and assess the performance of widely used features, machine learning algorithms, and existing state-of-the-art approaches. We also discuss the challenges and future directions in the prediction of hot spots.

Review and comparative assessment of sequence-based predictors of protein-binding residues.

Understanding of molecular mechanisms that govern protein-protein interactions and accurate modeling of protein-protein docking rely on accurate identification and prediction of protein-binding partners and protein-binding residues. We review over 40 methods that predict protein-protein interactions from protein sequences including methods that predict interacting protein pairs, protein-binding residues for a pair of interacting sequences and protein-binding residues in a single protein chain. We focus on the latter methods that provide residue-level annotations and that can be broadly applied to all protein sequences. We compare their architectures, inputs and outputs, and we discuss aspects related to their assessment and availability. We also perform first-of-its-kind comprehensive empirical comparison of representative predictors of protein-binding residues using a novel and high-quality benchmark data set. We show that the selected predictors accurately discriminate protein-binding and non-binding residues and that newer methods outperform older designs. However, these methods are unable to accurately separate residues that bind other molecules, such as DNA, RNA and small ligands, from the protein-binding residues. This cross-prediction, defined as the incorrect prediction of nucleic-acid- and small-ligand-binding residues as protein binding, is substantial for all evaluated methods and is not driven by the proximity to the native protein-binding residues. We discuss reasons for this drawback and we offer several recommendations. In particular, we postulate the need for a new generation of more accurate predictors and data sets, inclusion of a comprehensive assessment of the cross-predictions in future studies and higher standards of availability of the published methods.

Functional Assessment of Residues in the Amino- and Carboxyl-Termini of Crustacean Hyperglycemic Hormone (CHH) in the Mud Crab Scylla olivacea Using Point-Mutated Peptides.

To assess functional importance of the residues in the amino- and carboxyl-termini of crustacean hyperglycemic hormone in the mud crab Scylla olivacea (Sco-CHH), both wild-type and point-mutated CHH peptides were produced with an amidated C-terminal end. Spectral analyses of circular dichroism, chromatographic retention time, and mass spectrometric analysis of the recombinant peptides indicate that they were close in conformation to native CHH and were produced with the intended substitutions. The recombinant peptides were subsequently used for an in vivo hyperglycemic assay. Two mutants (R13A and I69A rSco-CHH) completely lacked hyperglycemic activity, with temporal profiles similar to that of vehicle control. Temporal profiles of hyperglycemic responses elicited by 4 mutants (I2A, F3A, D12A, and D60A Sco-CHH) were different from that elicited by wild-type Sco-CHH; I2A was unique in that it exhibited significantly higher hyperglycemic activity, whereas the remaining 3 mutants showed lower activity. Four mutants (D4A, Q51A, E54A, and V72A rSco-CHH) elicited hyperglycemic responses with temporal profiles similar to those evoked by wild-type Sco-CHH. In contrast, the glycine-extended version of V72A rSco-CHH (V72A rSco-CHH-Gly) completely lost hyperglycemic activity. By comparing our study with previous ones of ion-transport peptide (ITP) and molt-inhibiting hormone (MIH) using deleted or point-mutated mutants, detail discussion is made regarding functionally important residues that are shared by both CHH and ITP (members of Group I of the CHH family), and those that discriminate CHH from ITP, and Group-I from Group-II peptides. Conclusions summarized in the present study provide insights into understanding of how functional diversification occurred within a peptide family of multifunctional members.

Novel and unique domains in aminoacyl-tRNA synthetases from human fungal pathogens Aspergillus niger, Candida albicans and Cryptococcus neoformans.

BACKGROUND: Some species of fungi can cause serious human diseases, particularly to immuno-compromised individuals. Opportunistic fungal infections are a leading cause of mortality, and present an emerging challenge that requires development of new and effective therapeutics. Aminoacyl-tRNA synthetases (aaRSs) are indispensable components of cellular protein translation machinery and can be targeted for discovery of novel anti-fungal agents. RESULTS: Validation of aaRSs as potential drug targets in pathogenic microbes prompted us to investigate the genomic distribution of aaRSs within three fungi that infect humans - A. niger, C. albicans and C. neoformans. Hidden Markov Models were built for aaRSs and related proteins to search for homologues in these fungal genomes. Here, we provide a detailed and comprehensive annotation for 3 fungal genome aaRSs and their associated proteins. We delineate predicted localizations, subdomain architectures and prevalence of unusual motifs within these aaRSs. Several fungal aaRSs have unique domain appendages of unknown function e.g. A. niger AsxRS and C. neoformans TyrRS have additional domains that are absent from human homologs. CONCLUSIONS: Detailed comparisons of fungal aaRSs with human homologs suggest key differences that could be exploited for specific drug targeting. Our cataloging and structural analyses provide a comprehensive foundation for experimentally dissecting fungal aaRSs that may enable development of new anti-fungal agents.

Signatures of pleiotropy, economy and convergent evolution in a domain-resolved map of human-virus protein-protein interaction networks.

A central challenge in host-pathogen systems biology is the elucidation of general, systems-level principles that distinguish host-pathogen interactions from within-host interactions. Current analyses of host-pathogen and within-host protein-protein interaction networks are largely limited by their resolution, treating proteins as nodes and interactions as edges. Here, we construct a domain-resolved map of human-virus and within-human protein-protein interaction networks by annotating protein interactions with high-coverage, high-accuracy, domain-centric interaction mechanisms: (1) domain-domain interactions, in which a domain in one protein binds to a domain in a second protein, and (2) domain-motif interactions, in which a domain in one protein binds to a short, linear peptide motif in a second protein. Analysis of these domain-resolved networks reveals, for the first time, significant mechanistic differences between virus-human and within-human interactions at the resolution of single domains. While human proteins tend to compete with each other for domain binding sites by means of sequence similarity, viral proteins tend to compete with human proteins for domain binding sites in the absence of sequence similarity. Independent of their previously established preference for targeting human protein hubs, viral proteins also preferentially target human proteins containing linear motif-binding domains. Compared to human proteins, viral proteins participate in more domain-motif interactions, target more unique linear motif-binding domains per residue, and contain more unique linear motifs per residue. Together, these results suggest that viruses surmount genome size constraints by convergently evolving multiple short linear motifs in order to effectively mimic, hijack, and manipulate complex host processes for their survival. Our domain-resolved analyses reveal unique signatures of pleiotropy, economy, and convergent evolution in viral-host interactions that are otherwise hidden in the traditional binary network, highlighting the power and necessity of high-resolution approaches in host-pathogen systems biology.

Analysis of human CD36 gene sequence alterations in the oxidized low-density lipoprotein-binding region using denaturing high-performance liquid chromatography.

Denaturing high-performance liquid chromatography (DHPLC) has been employed as a prescreening tool to reduce the amount of DNA sequencing. It could be a simple and cost-effective screening method for mutations and polymorphisms in exons 4, 5, and 6 of the CD36 gene, which encode the protein region responsible for the removal of oxidized low-density lipoprotein. Genomic DNA was isolated from 306 Caucasian infants of Polish origin. Six single-nucleotide substitutions were detected by DHPLC and confirmed by direct sequencing. The A591T, G550A, and C572T alterations have not been described so far. Each of two nonsynonymous substitutions (Asp184Asn, Pro191Leu) was found in one subject (0.2% minor allele frequency). The results suggest that nonsynonymous alterations in the analyzed CD36 region are rare in Caucasians. DHPLC is a specific and cost-effective technique that may prove to be particularly useful for the identification of polymorphisms and mutations in the CD36 gene.