Biology subjects

ten Have, A.

Publications and source records attributed to ten Have, A..

3 recordsLinked to original sources

bioRxiv2017

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.\n\nResultsWe 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 {pi}-{pi} 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.\n\nUpon 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.\n\nHighlightsEqolisins are acid proteases found in prokaryotes and fungi only.\n\nThe recently co-evolved W67F-L105W substitutions promote substrate binding\n\nTwo Specificity Determining Networks, SDN1 and 2, were identified\n\nSDN1 has four Specificity Determining Positions involved in substrate specificity\n\nSDN2 has five Specificity Determining Positions involved in loop-substrate dynamics

bioinformatics↗

bioRxiv2017

Functional Diversification of Tripeptidylpeptidase 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.\n\nWe 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 -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 enzymes 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.\n\nHighlightsThere are two sedolisin subfamilies in fungi: tripeptidyl peptidases and endopeptidases\nFunctional Diversification of fungal sedolisins led to a conformational change in the pocket\nFunctional Diversification centers around buried SDP307\nSDP307 is aromatic in TPPs and Alanine in endosedolisins\nAdditional SDPs affect the interaction between core and chaperone-like prosegment

bioinformatics↗

bioRxiv2017

HMMER Cut-off Threshold Tool (HMMERCTTER): Supervised Classification of Superfamily Protein Sequences with a reliable Cut-off Threshold

Protein superfamilies can be divided into subfamilies of proteins with different functional characteristics. Their sequences can be classified hierarchically, which is part of sequence function assignation. Typically, there are no clear subfamily hallmarks that would allow pattern-based function assignation by which this task is mostly achieved based on the similarity principle. This is hampered by the lack of a score cut-off that is both sensitive and specific.\n\nHMMER Cut-off Threshold Tool (HMMERCTTER) adds a reliable cut-off threshold to the popular HMMER. Using a high quality superfamily phylogeny, it clusters a set of training sequences such that the cluster-specific HMMER profiles show 100% precision and recall (P&R), thereby generating a specific threshold as inclusion cut-off. Profiles and threshold are then used as classifiers to screen a target dataset. Iterative inclusion of novel sequences to groups and the corresponding HMMER profiles results in high sensitivity while specificity is maintained by imposing 100% P&R. In three presented case studies of protein superfamilies, classification of large datasets with 100% P&R was achieved with over 95% coverage. Limits and caveats are presented and explained.\n\nHMMERCTTER is a promising protein superfamily sequence classifier provided high quality training datasets are used. It provides a decision support system that aids in the difficult task of sequence function assignation in the twilight zone of sequence similarity. A package containing source code and full dataset will be deposited at Github and is available for reviewers at: https://www.dropbox.com/s/aacao6ggcak30bg/Repo.tar.gz?dl=0\n\nAuthor summaryThe enormous amount of genome sequences made available in the last decade provide new challenges for scientists. An important step in genome sequence processing is function assignation of the encoded protein sequences, typically based on the similarity principle: The more similar sequences are, the more likely they encode the same function. However, evolution generated many protein superfamilies that consist of various subfamilies with different functional characteristics, such as substrate specificity, optimal activity conditions or the catalyzed reaction. The classification of superfamily sequences to their respective subfamilies can be performed based on similarity but since the different subfamilies also remain similar, it requires a reliable similarity score cut-off.\n\nWe present a tool that clusters training sequences and describes them in profiles that identify cluster members with higher similarity scores than non-cluster members, i.e. with 100% precision and recall. This defines a score cut-off threshold. Profiles and thresholds are then used to classify other sequences. Classified sequences are included in the profiles in order to improve sensitivity while maintaining specificity by imposing 100% precision and recall. Results on three case studies show that the tool can correctly classify complex superfamilies with over 95% coverage.\n\nHMMERCTTER is meant as a decision support system for the expert biologist rather than the computational biologist.

bioinformatics↗