Algorithm of OMA for large-scale orthology inference

Since the publication of our article (Roth, Gonnet, and Dessimoz: BMC Bioinformatics 2008 9: 518), we have noticed several errors, which we correct in the following

A unique view of SARS-COV-2 through the lens of ORF8 protein

Immune evasion is one of the unique characteristics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) attributed to its ORF8 protein. This protein modulates the adaptive host immunity through down-regulation of MHC-1 (Major Histocompatibility Complex) molecules and innate immune responses by surpassing the host\u27s interferon-mediated antiviral response. To understand the host\u27s immune perspective in reference to the ORF8 protein, a comprehensive study of the ORF8 protein and mutations possessed by it have been performed. Chemical and structural properties of ORF8 proteins from different hosts, such as human, bat, and pangolin, suggest that the ORF8 of SARS-CoV-2 is much closer to ORF8 of Bat RaTG13-CoV than to that of Pangolin-CoV. Eighty-seven mutations across unique variants of ORF8 in SARS-CoV-2 can be grouped into four classes based on their predicted effects (Hussain et al., 2021) [1]. Based on the geo-locations and timescale of sample collection, a possible flow of mutations was built. Furthermore, conclusive flows of amalgamation of mutations were found upon sequence similarity analyses and consideration of the amino acid conservation phylogenies. Therefore, this study seeks to highlight the uniqueness of the rapidly evolving SARS-CoV-2 through the ORF8

Accounting For Alignment Uncertainty in Phylogenomics

Uncertainty in multiple sequence alignments has a large impact on phylogenetic analyses. Little has been done to evaluate the quality of individual positions in protein sequence alignments, which directly impact the accuracy of phylogenetic trees. Here we describe ZORRO, a probabilistic masking program that accounts for alignment uncertainty by assigning confidence scores to each alignment position. Using the BALIBASE database and in simulation studies, we demonstrate that masking by ZORRO significantly reduces the alignment uncertainty and improves the tree accuracy

Molecular Dynamics Simulation of the Complex PBP-2x with Drug Cefuroxime to Explore the Drug Resistance Mechanism of Streptococcus suis R61

Drug resistance of Streptococcus suis strains is a worldwide problem for both humans and pigs. Previous studies have noted that penicillin-binding protein (PBPs) mutation is one important cause of β-lactam antibiotic resistance. In this study, we used the molecular dynamics (MD) method to study the interaction differences between cefuroxime (CES) and PBP2x within two newly sequenced Streptococcus suis: drug-sensitive strain A7, and drug-resistant strain R61. The MM-PBSA results proved that the drug bound much more tightly to PBP2x in A7 (PBP2x-A7) than to PBP2x in R61 (PBP2x-R61). This is consistent with the evidently different resistances of the two strains to cefuroxime. Hydrogen bond analysis indicated that PBP2x-A7 preferred to bind to cefuroxime rather than to PBP2x-R61. Three stable hydrogen bonds were formed by the drug and PBP2x-A7, while only one unstable bond existed between the drug and PBP2x-R61. Further, we found that the Gln569, Tyr594, and Gly596 residues were the key mutant residues contributing directly to the different binding by pair wise energy decomposition comparison. By investigating the binding mode of the drug, we found that mutant residues Ala320, Gln553, and Thr595 indirectly affected the final phenomenon by topological conformation alteration. Above all, our results revealed some details about the specific interaction between the two PBP2x proteins and the drug cefuroxime. To some degree, this explained the drug resistance mechanism of Streptococcus suis and as a result could be helpful for further drug design or improvement

Bradyrhizobium elkanii nod regulon: insights through genomic analysis

Abstract A successful symbiotic relationship between soybean [Glycine max (L.) Merr.] and Bradyrhizobium species requires expression of the bacterial structural nod genes that encode for the synthesis of lipochitooligosaccharide nodulation signal molecules, known as Nod factors (NFs). Bradyrhizobium diazoefficiens USDA 110 possesses a wide nodulation gene repertoire that allows NF assembly and modification, with transcription of the nodYABCSUIJnolMNOnodZ operon depending upon specific activators, i.e., products of regulatory nod genes that are responsive to signaling molecules such as flavonoid compounds exuded by host plant roots. Central to this regulatory circuit of nod gene expression are NodD proteins, members of the LysR-type regulator family. In this study, publicly available Bradyrhizobium elkanii sequenced genomes were compared with the closely related B. diazoefficiens USDA 110 reference genome to determine the similarities between those genomes, especially with regards to the nod operon and nod regulon. Bioinformatics analyses revealed a correlation between functional mechanisms and key elements that play an essential role in the regulation of nod gene expression. These analyses also revealed new genomic features that had not been clearly explored before, some of which were unique for some B. elkanii genomes

Computational Design of a PDZ Domain Peptide Inhibitor that Rescues CFTR Activity

The cystic fibrosis transmembrane conductance regulator (CFTR) is an epithelial chloride channel mutated in patients with cystic fibrosis (CF). The most prevalent CFTR mutation, ΔF508, blocks folding in the endoplasmic reticulum. Recent work has shown that some ΔF508-CFTR channel activity can be recovered by pharmaceutical modulators (“potentiators” and “correctors”), but ΔF508-CFTR can still be rapidly degraded via a lysosomal pathway involving the CFTR-associated ligand (CAL), which binds CFTR via a PDZ interaction domain. We present a study that goes from theory, to new structure-based computational design algorithms, to computational predictions, to biochemical testing and ultimately to epithelial-cell validation of novel, effective CAL PDZ inhibitors (called “stabilizers”) that rescue ΔF508-CFTR activity. To design the “stabilizers”, we extended our structural ensemble-based computational protein redesign algorithm to encompass protein-protein and protein-peptide interactions. The computational predictions achieved high accuracy: all of the top-predicted peptide inhibitors bound well to CAL. Furthermore, when compared to state-of-the-art CAL inhibitors, our design methodology achieved higher affinity and increased binding efficiency. The designed inhibitor with the highest affinity for CAL (kCAL01) binds six-fold more tightly than the previous best hexamer (iCAL35), and 170-fold more tightly than the CFTR C-terminus. We show that kCAL01 has physiological activity and can rescue chloride efflux in CF patient-derived airway epithelial cells. Since stabilizers address a different cellular CF defect from potentiators and correctors, our inhibitors provide an additional therapeutic pathway that can be used in conjunction with current methods

Extent of Structural Asymmetry in Homodimeric Proteins: Prevalence and Relevance

Most homodimeric proteins have symmetric structure. Although symmetry is known to confer structural and functional advantage, asymmetric organization is also observed. Using a non-redundant dataset of 223 high-resolution crystal structures of biologically relevant homodimers, we address questions on the prevalence and significance of asymmetry. We used two measures to quantify global and interface asymmetry, and assess the correlation of several molecular and structural parameters with asymmetry. We have identified rare cases (11/223) of biologically relevant homodimers with pronounced global asymmetry. Asymmetry serves as a means to bring about 2∶1 binding between the homodimer and another molecule; it also enables cellular signalling arising from asymmetric macromolecular ligands such as DNA. Analysis of these cases reveals two possible mechanisms by which possible infinite array formation is prevented. In case of homodimers associating via non-topologically equivalent surfaces in their tertiary structures, ligand-dependent mechanisms are used. For stable dimers binding via large surfaces, ligand-dependent structural change regulates polymerisation/depolymerisation; for unstable dimers binding via smaller surfaces that are not evolutionarily well conserved, dimerisation occurs only in the presence of the ligand. In case of homodimers associating via interaction surfaces with parts of the surfaces topologically equivalent in the tertiary structures, steric hindrance serves as the preventive mechanism of infinite array. We also find that homodimers exhibiting grossly symmetric organization rarely exhibit either perfect local symmetry or high local asymmetry. Binding of small ligands at the interface does not cause any significant variation in interface asymmetry. However, identification of biologically relevant interface asymmetry in grossly symmetric homodimers is confounded by the presence of similar small magnitude changes caused due to artefacts of crystallisation. Our study provides new insights regarding accommodation of asymmetry in homodimers

Predicting DNA-Binding Specificities of Eukaryotic Transcription Factors

Today, annotated amino acid sequences of more and more transcription factors (TFs) are readily available. Quantitative information about their DNA-binding specificities, however, are hard to obtain. Position frequency matrices (PFMs), the most widely used models to represent binding specificities, are experimentally characterized only for a small fraction of all TFs. Even for some of the most intensively studied eukaryotic organisms (i.e., human, rat and mouse), roughly one-sixth of all proteins with annotated DNA-binding domain have been characterized experimentally. Here, we present a new method based on support vector regression for predicting quantitative DNA-binding specificities of TFs in different eukaryotic species. This approach estimates a quantitative measure for the PFM similarity of two proteins, based on various features derived from their protein sequences. The method is trained and tested on a dataset containing 1 239 TFs with known DNA-binding specificity, and used to predict specific DNA target motifs for 645 TFs with high accuracy