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Abstract Background The order of genes in bacterial genomes is not random; for example, the products of genes belonging to an operon work together in the same pathway. The cotranslational assembly of protein complexes is deemed to conserve genomic neighborhoods even stronger than a common function. This is why a conserved genomic neighborhood can be utilized to predict, whether gene products form protein complexes. Results We were interested to assess the performance of a neighborhood-based classifier that analyzes a large number of genomes. Thus, we determined for the genes encoding the subunits of 494 experimentally verified hetero-dimers their local genomic context. In order to generate phylogenetically comprehensive genomic neighborhoods, we utilized the tools offered by the Enzyme Function Initiative. For each subunit, a sequence similarity network was generated and the corresponding genome neighborhood network was analyzed to deduce the most frequent gene product. This was predicted as interaction partner, if its abundance exceeded a threshold, which was the frequency giving rise to the maximal Matthews correlation coefficient. For the threshold of 16%, the true positive rate was 45%, the false positive rate 0.06%, and the precision 55%. For approximately 20% of the subunits, the interaction partner was not found in a neighborhood of ± 10 genes. Conclusions Our phylogenetically comprehensive analysis confirmed that complex formation is a strong evolutionary factor that conserves genome neighborhoods. On the other hand, for 55% of the cases analyzed here, classification failed. Either, the interaction partner was not present in a ± 10 gene window or was not the most frequent gene product.

Computational protein design has the ambitious goal of crafting novel proteins that address challenges in biology and medicine. To overcome these challenges, the computational protein modeling suite Rosetta has been tailored to address various protein design tasks. Recently, statistical methods have been developed that identify correlated mutations between residues in a multiple sequence alignment of homologous proteins. These subtle inter-dependencies in the occupancy of residue positions throughout evolution are crucial for protein function, but we found that three current Rosetta design approaches fail to recover these co-evolutionary couplings. Thus, we developed the Rosetta method ResCue (residue-coupling enhanced) that leverages co-evolutionary information to favor sequences which recapitulate correlated mutations, as observed in nature. To assess the protocols via recapitulation designs, we compiled a benchmark of ten proteins each represented by two, structurally diverse states. We could demonstrate that ResCue designed sequences with an average sequence recovery rate of 70%, whereas three other protocols reached not more than 50%, on average. Our approach had higher recovery rates also for functionally important residues, which were studied in detail. This improvement has only a minor negative effect on the fitness of the designed sequences as assessed by Rosetta energy. In conclusion, our findings support the idea that informing protocols with co-evolutionary signals helps to design stable and native-like proteins that are compatible with the different conformational states required for a complex function.

Rational protein design aims at the targeted modification of existing proteins. To reach this goal, software suites like Rosetta propose sequences to introduce the desired properties. Challenging design problems necessitate the representation of a protein by means of a structural ensemble. Thus, Rosetta multi-state design (MSD) protocols have been developed wherein each state represents one protein conformation. Computational demands of MSD protocols are high, because for each of the candidate sequences a costly three-dimensional (3D) model has to be created and assessed for all states. Each of these scores contributes one data point to a complex, design-specific energy landscape. As neural networks (NN) proved well-suited to learn such solution spaces, we integrated one into the framework Rosetta:MSF instead of the so far used genetic algorithm with the aim to reduce computational costs. As its predecessor, Rosetta:MSF:NN administers a set of candidate sequences and their scores and scans sequence space iteratively. During each iteration, the union of all candidate sequences and their Rosetta scores are used to re-train NNs that possess a design-specific architecture. The enormous speed of the NNs allows an extensive assessment of alternative sequences, which are ranked on the scores predicted by the NN. Costly 3D models are computed only for a small fraction of best-scoring sequences; these and the corresponding 3D-based scores replace half of the candidate sequences during each iteration. The analysis of two sets of candidate sequences generated for a specific design problem by means of a genetic algorithm confirmed that the NN predicted 3D-based scores quite well; the Pearson correlation coefficient was at least 0.95. Applying Rosetta:MSF:NN:enzdes to a benchmark consisting of 16 ligand-binding problems showed that this protocol converges ten-times faster than the genetic algorithm and finds sequences with comparable scores.

Abstract Background Large enzyme families may contain functionally diverse members that give rise to clusters in a sequence similarity network (SSN). In prokaryotes, the genome neighborhood of a gene-product is indicative of its function and thus, a genome neighborhood network (GNN) deduced for an SSN provides strong clues to the specific function of enzymes constituting the different clusters. The Enzyme Function Initiative ( http://enzymefunction.org/ ) offers services that compute SSNs and GNNs. Results We have implemented AGeNNT that utilizes these services, albeit with datasets purged with respect to unspecific protein functions and overrepresented species. AGeNNT generates refined GNNs (rGNNs) that consist of cluster-nodes representing the sequences under study and Pfam-nodes representing enzyme functions encoded in the respective neighborhoods. For cluster-nodes, AGeNNT summarizes the phylogenetic relationships of the contributing species and a statistic indicates how unique nodes and GNs are within this rGNN. Pfam-nodes are annotated with additional features like GO terms describing protein function. For edges, the coverage is given, which is the relative number of neighborhoods containing the considered enzyme function (Pfam-node). AGeNNT is available at https://github.com/kandlinf/agennt . Conclusions An rGNN is easier to interpret than a conventional GNN, which commonly contains proteins without enzymatic function and overly specific neighborhoods due to phylogenetic bias. The implemented filter routines and the statistic allow the user to identify those neighborhoods that are most indicative of a specific metabolic capacity. Thus, AGeNNT facilitates to distinguish and annotate functionally different members of enzyme families.

X-linked juvenile retinoschisis (XLRS) is a hereditary retinal dystrophy, caused by mutations in the RS1 gene which encodes the secreted protein retinoschisin. In recent years, several molecules have been proposed to interact with retinoschisin, including the retinal Na/K-ATPase, L-voltage gated Ca2+ channels, and specific sugars. We recently showed that the retinal Na/K-ATPase consisting of subunits ATP1A3 and ATP1B2 is essential for anchoring retinoschisin to plasma membranes and identified the glycosylated ATP1B2 subunit as the direct interaction partner for retinoschisin. We now aimed to precisely map the retinoschisin binding domain(s) in ATP1B2. In general, retinoschisin binding was not affected after selective elimination of individual glycosylation sites via site-directed mutagenesis as well as after full enzymatic deglycosylation of ATP1B2. Applying the interface prediction tool PresCont, two putative protein-protein interaction patches ("patch I" and "patch II") consisting each of four hydrophobic amino acid stretches on the ATP1B2 surface were identified. These were consecutively altered by site-directed mutagenesis. Functional assays with the ATP1B2 patch mutants identified patch II and, specifically, the associated amino acid at position 240 (harboring a threonine in ATP1B2) as crucial for retinoschisin binding to ATP1B2. These and previous results led us to suggest an induced-fit binding mechanism for the interaction between retinoschisin and the Na/K-ATPase, which is dependent on threonine 240 in ATP1B2 allowing the accommodation of hyperflexible retinoschisin spikes by the associated protein-protein interaction patch on ATP1B2.

Computational protein design (CPD) is a powerful technique to engineer existing proteins or to design novel ones that display desired properties. Rosetta is a software suite including algorithms for computational modeling and analysis of protein structures and offers many elaborate protocols created to solve highly specific tasks of protein engineering. Most of Rosetta's protocols optimize sequences based on a single conformation (i. e. design state). However, challenging CPD objectives like multi-specificity design or the concurrent consideration of positive and negative design goals demand the simultaneous assessment of multiple states. This is why we have developed the multi-state framework MSF that facilitates the implementation of Rosetta's single-state protocols in a multi-state environment and made available two frequently used protocols. Utilizing MSF, we demonstrated for one of these protocols that multi-state design yields a 15% higher performance than single-state design on a ligand-binding benchmark consisting of structural conformations. With this protocol, we designed de novo nine retro-aldolases on a conformational ensemble deduced from a (βα)8-barrel protein. All variants displayed measurable catalytic activity, testifying to a high success rate for this concept of multi-state enzyme design.

The packaging and organization of genomic DNA into chromatin represents an additional regulatory layer of gene expression, with specific nucleosome positions that restrict the accessibility of regulatory DNA elements. The mechanisms that position nucleosomes in vivo are thought to depend on the biophysical properties of the histones, sequence patterns, like phased di-nucleotide repeats and the architecture of the histone octamer that folds DNA in 1.65 tight turns. Comparative studies of human and P. falciparum histones reveal that the latter have a strongly reduced ability to recognize internal sequence dependent nucleosome positioning signals. In contrast, the nucleosomes are positioned by AT-repeat sequences flanking nucleosomes in vivo and in vitro. Further, the strong sequence variations in the plasmodium histones, compared to other mammalian histones, do not present adaptations to its AT-rich genome. Human and parasite histones bind with higher affinity to GC-rich DNA and with lower affinity to AT-rich DNA. However, the plasmodium nucleosomes are overall less stable, with increased temperature induced mobility, decreased salt stability of the histones H2A and H2B and considerable reduced binding affinity to GC-rich DNA, as compared with the human nucleosomes. In addition, we show that plasmodium histone octamers form the shortest known nucleosome repeat length (155bp) in vitro and in vivo. Our data suggest that the biochemical properties of the parasite histones are distinct from the typical characteristics of other eukaryotic histones and these properties reflect the increased accessibility of the P. falciparum genome.

Modern enzymes are highly optimized biocatalysts that process their substrates with extreme efficiency. Many enzymes catalyze more than one reaction; however, the persistence of such ambiguities, their consequences and evolutionary causes are largely unknown. As a paradigmatic case, we study the history of bi-functionality for a time span of approximately two billion years for the sugar isomerase HisA from histidine biosynthesis. To look back in time, we computationally reconstructed and experimentally characterized three HisA predecessors. We show that these ancient enzymes catalyze not only the HisA reaction but also the isomerization of a similar substrate, which is commonly processed by the isomerase TrpF in tryptophan biosynthesis. Moreover, we found that three modern-day HisA enzymes from Proteobacteria and Thermotogae also possess low TrpF activity. We conclude that this bi-functionality was conserved for at least two billion years, most likely without any evolutionary pressure. Although not actively selected for, this trait can become advantageous in the case of a gene loss. Such exaptation is exemplified by the Actinobacteria that have lost the trpF gene but possess the bi-functional HisA homolog PriA, which adopts the roles of both HisA and TrpF. Our findings demonstrate that bi-functionality can perpetuate in the absence of selection for very long time-spans.