Prediction of Protein Sites and Physicochemical Properties Related to Functional Specificity

Specificity Determining Positions (SDPs) are protein sites responsible for functional specificity within a family of homologous proteins. These positions are extracted from a family’s multiple sequence alignment and complement the fully conserved positions as predictors of functional sites. SDP analysis is now routinely used for locating these specificity-related sites in families of proteins of biomedical or biotechnological interest with the aim of mutating them to switch specificities or design new ones. There are many different approaches for detecting these positions in multiple sequence alignments. Nevertheless, existing methods report the potential SDP positions but they do not provide any clue on the physicochemical basis behind the functional specificity, which has to be inferred a-posteriori by manually inspecting these positions in the alignment. In this work, a new methodology is presented that, concomitantly with the detection of the SDPs, automatically provides information on the amino-acid physicochemical properties more related to the change in specificity. This new method is applied to two different multiple sequence alignments of homologous of the well-studied RasH protein representing different cases of functional specificity and the results discussed in detail.

Discovery of a Kojibiose Hydrolase by Analysis of Specificity-Determining Correlated Positions in Glycoside Hydrolase Family 65

The Glycoside Hydrolase Family 65 (GH65) is an enzyme family of inverting a-glucoside phosphorylases and hydrolases that currently contains 10 characterized enzyme specificities. However, its sequence diversity has never been studied in detail. Here, an in-silico analysis of correlated mutations was performed, revealing specificity-determining positions that facilitate annotation of the family’s phylogenetic tree. By searching these positions for amino acid motifs that do not match those found in previously characterized enzymes from GH65, several clades that may harbor new functions could be identified. Three enzymes from across these regions were expressed in E. coli and their substrate profile was mapped. One of those enzymes, originating from the bacterium Mucilaginibacter mallensis, was found to hydrolyze kojibiose and a-1,2-oligoglucans with high specificity. We propose kojibiose glucohydrolase as the systematic name and kojibiose hydrolase or kojibiase as the short name for this new enzyme. This work illustrates a convenient strategy for mapping the natural diversity of enzyme families and smartly mining the ever-growing number of available sequences in the quest for novel specificities.

Predicting the Specificity- Determining Positions of Receptor Tyrosine Kinase Axl

Owing to its clinical significance, modulation of functionally relevant amino acids in protein-protein complexes has attracted a great deal of attention. To this end, many approaches have been proposed to predict the partner-selecting amino acid positions in evolutionarily close complexes. These approaches can be grouped into sequence-based machine learning and structure-based energy-driven methods. In this work, we assessed these methods’ ability to map the specificity-determining positions of Axl, a receptor tyrosine kinase involved in cancer progression and immune system diseases. For sequence-based predictions, we used SDPpred, Multi-RELIEF, and Sequence Harmony. For structure-based predictions, we utilized HADDOCK refinement and molecular dynamics simulations. As a result, we observed that (i) sequence-based methods overpredict partner-selecting residues of Axl and that (ii) combining Multi-RELIEF with HADDOCK-based predictions provides the key Axl residues, covered by the extensive molecular dynamics simulations. Expanding on these results, we propose that a sequence-structure-based approach is necessary to determine specificity-determining positions of Axl, which can guide the development of therapeutic molecules to combat Axl misregulation.

Predicting the Specificity-Determining Positions of Paralogous Complexes

ABSTRACTDue to its clinical relevance, modulation of functionally relevant amino acids in protein-protein complexes has attracted a great deal of attention. To this end, many approaches have been proposed to predict partner-selecting, i.e., specificity-determining positions in evolutionarily close complexes. These approaches can be grouped into sequence-based machine learning and structure-based energy-driven methods. In this work, we assessed these methods’ ability to map the specificity-determining positions of Axl, a receptor tyrosine kinase involved in cancer progression and immune-related diseases. For this, we used three sequence-based predictors – SDPred, Multi-RELIEF, and Sequence Harmony – and a structure-based approach by utilizing HADDOCK and extensive molecular dynamics simulations. As a result, we show that (i) sequence-based methods overpredict the number of specificity-determining positions for Axl complexes and that (ii) combining sequence-based approaches with HADDOCK provides the most coherent set of predictions. Our work lays out a critical study on the comparative performance specificity-determining position predictors. It also presents a combined sequence-structure-based approach, which can guide the development of therapeutic molecules capable of combatting Axl misregulation in different types of diseases.

Is the Bombali virus pathogenic in humans?

Motivation The potential of the Bombali virus, a novel Ebolavirus, to cause disease in humans remains unknown. We have previously identified potential determinants of Ebolavirus pathogenicity in humans by analysing the amino acid positions that are differentially conserved (specificity determining positions; SDPs) between human pathogenic Ebolaviruses and the non-pathogenic Reston virus. Here, we include the many Ebolavirus genome sequences that have since become available into our analysis and investigate the amino acid sequence of the Bombali virus proteins at the SDPs that discriminate between human pathogenic and non-human pathogenic Ebolaviruses. Results The use of 1408 Ebolavirus genomes (196 in the original analysis) resulted in a set of 166 SDPs (reduced from 180), 146 (88%) of which were retained from the original analysis. This indicates the robustness of our approach and refines the set of SDPs that distinguish human pathogenic Ebolaviruses from Reston virus. At SDPs, Bombali virus shared the majority of amino acids with the human pathogenic Ebolaviruses (63.25%). However, for two SDPs in VP24 (M136L, R139S) that have been proposed to be critical for the lack of Reston virus human pathogenicity because they alter the VP24-karyopherin interaction, the Bombali virus amino acids match those of Reston virus. Thus, Bombali virus may not be pathogenic in humans. Supporting this, no Bombali virus-associated disease outbreaks have been reported, although Bombali virus was isolated from fruit bats cohabitating in close contact with humans, and anti-Ebolavirus antibodies that may indicate contact with Bombali virus have been detected in humans. Availability and implementation Data files are available from https://github.com/wasslab/EbolavirusSDPsBioinformatics2019. Supplementary information Supplementary data are available at Bioinformatics online.

Substrate binding and specificity appear as major forces in the functional diversification of eqolisins

BackgroundEqolisins are rare acid proteases found in archaea, bacteria and fungi. Certain fungi secrete acids as part of their lifestyle and interestingly these also have many eqolisin paralogs, up to nine paralogs have been recorded. This suggests functional redundancy and diversification, which was the subject of the research we performed and describe here.ResultsWe identified eqolisin homologs by means of iterative HMMER analysis of the NR database. The identified sequences were scrutinized for which we defined novel hallmarks, identified by molecular dynamics simulations of mutants of highly conserved positions, using the structure of an eqolisin that was crystallized in the presence of a transition state inhibitor. Four conserved glycines were shown to be required for functionality. A substitution of W67F is shown to be accompanied by the L105W substitution. Molecular dynamics shows that the W67 binds to the substrate via a π-π stacking and a salt bridge, the latter being stronger in a virtual W67F/L105W double mutant of the resolved structure of Scytalido-carboxyl peptidase-B (PDB ID: 2IFW)). Additional likely fatal mutants are discussed.Upon sequence scrutiny we obtained a set of 233 sequences that in all likelihood lack false positives. This was used to reconstruct a Bayesian phylogenetic tree. We identified 14 putative specificity determining positions (SDPs) of which four are explained by mere structural explanations and nine seem to correspond to functional diversification related wit substrate binding ans specificity. A first sub-network of SDPs is related to substrate specificity whereas the second sub-network seems to affect the dynamics of three loops that are involved in substrate binding.HighlightsEqolisins are acid proteases found in prokaryotes and fungi only.The recently co-evolved W67F-L105W substitutions promote substrate bindingTwo Specificity Determining Networks, SDN1 and 2, were identifiedSDN1 has four Specificity Determining Positions involved in substrate specificitySDN2 has five Specificity Determining Positions involved in loop-substrate dynamics

Functional Diversification of Tripeptidyl Peptidase and Endopeptidase Sedolisins in Fungi

Sedolisins are acid proteases that are related to the basic subtilisins. They have been identified in all three superkingdoms but are not ubiquitous, although fungi that secrete acids as part of their lifestyle can have up to six paralogs. Both tripeptidyl peptidase (TPP) and endopeptidase activity have been identified and it has been suggested that these correspond to separate subfamilies.We studied eukaryotic sedolisins by computational analysis. A maximum likelihood tree shows three major clades of which two contain only fungal sequences. One fungal clade contains all known TPPs whereas the other contains the endosedolisins. We identified four cluster specific inserts (CSIs) in endosedolisins, of which CSIs 1, 3 and 4 appear as solvent exposed according to structure modeling. Part of CSI2 is exposed but a short stretch forms a novel and partially buried α-helix that induces a conformational change near the binding pocket. We also identified a total of 12 specificity determining positions (SDPs) divided over three SDP sub-networks. The major SDP network contains eight directly connected SDPs and modeling of virtual mutants suggests a key role for the W307A or F307A substitution. This substitution is accompanied by a group of four SDPs that physically interact at the interface of the catalytic domain and the enzyme’s prosegment. Modeling of virtual mutants suggests these SDPs are indeed required to compensate the conformational change induced by CSI2 and the A307. The additional major network SDPs as well as the two small SDP networks appear to be linked to this major substitution, all together explaining the hypothesized functional diversification of fungal sedolisins.HighlightsThere are two sedolisin subfamilies in fungi: tripeptidyl peptidases and endopeptidasesFunctional Diversification of fungal sedolisins led to a conformational change in the pocketFunctional Diversification centers around buried SDP307SDP307 is aromatic in TPPs and Alanine in endosedolisinsAdditional SDPs affect the interaction between core and chaperone-like prosegment