Detecting genetic gain and loss events in terms of protein domain: Method and implementation

Continuous gain and loss of genes are the primary driving forces of bacterial evolution and environmental adaptation. Studying bacterial evolution in terms of protein domain, which is the fundamental function and evolutionary unit of proteins, can provide a more comprehensive understanding of bacterial differentiation and phenotypic adaptation processes. Therefore, we proposed a phylogenetic tree-based method for detecting genetic gain and loss events in terms of protein domains. Specifically, the method focuses on a single domain to trace its evolution process or on multiple domains to investigate their co-evolution principles. This novel method was validated using 122 Shigella isolates. We found that the loss of a significant number of domains was likely the main driving force behind the evolution of Shigella, which could reduce energy expenditure and preserve only the most essential functions. Additionally, we observed that simultaneously gained and lost domains were often functionally related, which can facilitate and accelerate phenotypic evolutionary adaptation to the environment. All results obtained using our method agree with those of previous studies, which validates our proposed method.

Protein salvage and repurposing in evolution: Phospholipase D toxins are stabilized by a remodeled scrap of a membrane association domain

The glycerophosphodiester phosphodiesterase (GDPD)-like SMaseD/PLD domain family, which includes phospholipase D (PLD) toxins in recluse spiders and actinobacteria, evolved anciently in bacteria from the GDPD. The PLD enzymes retained the core (β/α)8 barrel fold of GDPD, while gaining a signature C-terminal expansion motif and losing a small insertion domain. Using sequence alignments and phylogenetic analysis, we infer that the C-terminal motif derives from a segment of an ancient bacterial PLAT domain. Formally, part of a protein containing a PLAT domain repeat underwent fusion to the C terminus of a GDPD barrel, leading to attachment of a segment of a PLAT domain, followed by a second complete PLAT domain. The complete domain was retained only in some basal homologs, but the PLAT segment was conserved and repurposed as the expansion motif. The PLAT segment corresponds to strands β7-β8 of a β-sandwich, while the expansion motif as represented in spider PLD toxins has been remodeled as an α-helix, a β-strand, and an ordered loop. The GDPD-PLAT fusion led to two acquisitions in founding the GDPD-like SMaseD/PLD family: (1) a PLAT domain that presumably supported early lipase activity by mediating membrane association, and (2) an expansion motif that putatively stabilized the catalytic domain, possibly compensating for, or permitting, loss of the insertion domain. Of wider significance, messy domain shuffling events can leave behind scraps of domains that can be salvaged, remodeled, and repurposed.

Evidence for the preferential reuse of sub-domain motifs in primordial protein folds

A comparison of protein backbones makes clear that not more than approximately 1400 different folds exist, each specifying the three-dimensional topology of a protein domain. Large proteins are composed of specific domain combinations and many domains can accommodate different functions. These findings confirm that the reuse of domains is key for the evolution of multi-domain proteins. If reuse was also the driving force for domain evolution, ancestral fragments of sub-domain size exist that are shared between domains possessing significantly different topologies. For the fully automated detection of putatively ancestral motifs, we developed the algorithm Fragstatt that compares proteins pairwise to identify fragments, that is, instantiations of the same motif. To reach maximal sensitivity, Fragstatt compares sequences by means of cascaded alignments of profile Hidden Markov Models. If the fragment sequences are sufficiently similar, the program determines and scores the structural concordance of the fragments. By analyzing a comprehensive set of proteins from the CATH database, Fragstatt identified 12 532 partially overlapping and structurally similar motifs that clustered to 134 unique motifs. The dissemination of these motifs is limited: We found only two domain topologies that contain two different motifs and generally, these motifs occur in not more than 18% of the CATH topologies. Interestingly, motifs are enriched in topologies that are considered ancestral. Thus, our findings suggest that the reuse of sub-domain sized fragments was relevant in early phases of protein evolution and became less important later on.

Protlego: A Python package for the analysis and design of chimeric proteins

Motivation

Duplication and recombination of protein fragments have led to the highly diverse protein space that we observe today. By mimicking this natural process, the design of protein chimeras via fragment recombination has proven experimentally successful and has opened a new era for the design of customizable proteins. The in silico building of structural models for these chimeric proteins, however, remains a manual task that requires a considerable degree of expertise and is not amenable for high-throughput studies. Energetic and structural analysis of the designed proteins often require the use of several tools, each with their unique technical difficulties and available in different programming languages or web servers.

Results

We implemented a Python package that enables automated, high-throughput design of chimeras and their structural analysis. First, it fetches evolutionarily conserved fragments from a built-in database (also available at fuzzle.uni-bayreuth.de). These relationships can then be represented via networks or further selected for chimera construction via recombination. Designed chimeras or natural proteins are then scored and minimized with the Charmm and Amber forcefields and their diverse structural features can be analyzed at ease. Here, we showcase Protlego’s pipeline by exploring the relationships between the P-loop and Rossmann superfolds, building and characterizing their offspring chimeras. We believe that Protlego provides a powerful new tool for the protein design community.

Availability and implementation

Protlego runs on the Linux platform and is freely available at (https://hoecker-lab.github.io/protlego/) with tutorials and documentation.

Supplementary information

Supplementary data are available at Bioinformatics online.

Identification and Analysis of Natural Building Blocks for Evolution-Guided Fragment-Based Protein Design

Natural evolution has generated an impressively diverse protein universe via duplication and recombination from a set of protein fragments that served as building blocks. The application of these concepts to the design of new proteins using subdomain-sized fragments from different folds has proven to be experimentally successful. To better understand how evolution has shaped our protein universe, we performed an all-against-all comparison of protein domains representing all naturally existing folds and identified conserved homologous protein fragments. Overall, we found more than 1000 protein fragments of various lengths among different folds through similarity network analysis. These fragments are present in very different protein environments and represent versatile building blocks for protein design. These data are available in our web server called F(old P)uzzle (fuzzle.uni-bayreuth.de), which allows to individually filter the dataset and create customized networks for folds of interest. We believe that our results serve as an invaluable resource for structural and evolutionary biologists and as raw material for the design of custom-made proteins.

Origins and structural properties of novel and de novo protein domains during insect evolution

Over long time scales, protein evolution is characterized by modular rearrangements of protein domains. Such rearrangements are mainly caused by gene duplication, fusion and terminal losses. To better understand domain emergence mechanisms we investigated 32 insect genomes covering a speciation gradient ranging from ~ 2 to ~ 390 mya. We use established domain models and foldable domains delineated by hydrophobic cluster analysis (HCA), which does not require homologous sequences, to also identify domains which have likely arisen de novo, that is, from previously noncoding DNA. Our results indicate that most novel domains emerge terminally as they originate from ORF extensions while fewer arise in middle arrangements, resulting from exonization of intronic or intergenic regions. Many novel domains rapidly migrate between terminal or middle positions and single- and multidomain arrangements. Young domains, such as most HCA-defined domains, are under strong selection pressure as they show signals of purifying selection. De novo domains, linked to ancient domains or defined by HCA, have higher degrees of intrinsic disorder and disorder-to-order transition upon binding than ancient domains. However, the corresponding DNA sequences of the novel domains of de novo origins could only rarely be found in sister genomes. We conclude that novel domains are often recruited by other proteins and undergo important structural modifications shortly after their emergence, but evolve too fast to be characterized by cross-species comparisons alone.

Clustering of multi-domain protein sequences

The overall function of a multi-domain protein is determined by the functional and structural interplay of its constituent domains. Traditional sequence alignment-based methods commonly utilize domain-level information and provide classification only at the level of domains. Such methods are not capable of taking into account the contributions of other domains in the proteins, and domain-linker regions and classify multi-domain proteins. An alignment-free protein sequence comparison tool, CLAP (CLAssification of Proteins) was previously developed in our laboratory to especially handle multi-domain protein sequences without a requirement of defining domain boundaries and sequential order of domains. Through this method we aim to achieve a biologically meaningful classification scheme for multi-domain protein sequences. In this article, CLAP-based classification has been explored on 5 datasets of multi-domain proteins and we present detailed analysis for proteins containing (1) Tyrosine phosphatase and (2) SH3 domain. At the domain-level CLAP-based classification scheme resulted in a clustering similar to that obtained from an alignment-based method. CLAP-based clusters obtained for full-length datasets were shown to comprise of proteins with similar functions and domain architectures. Our study demonstrates that multi-domain proteins could be classified effectively by considering full-length sequences without a requirement of identification of domains in the sequence.

Phenotypic innovation through recombination in genome-scale metabolic networks

Recombination is an important source of metabolic innovation, especially in prokaryotes, which have evolved the ability to survive on many different sources of chemical elements and energy. Metabolic systems have a well-understood genotype-phenotype relationship, which permits a quantitative and biochemically principled understanding of how recombination creates novel phenotypes. Here, we investigate the power of recombination to create genome-scale metabolic reaction networks that enable an organism to survive in new chemical environments. To this end, we use flux balance analysis, an experimentally validated computational method that can predict metabolic phenotypes from metabolic genotypes. We show that recombination is much more likely to create novel metabolic abilities than random changes in chemical reactions of a metabolic network. We also find that phenotypic innovation is more likely when recombination occurs between parents that are genetically closely related, phenotypically highly diverse, and viable on few rather than many carbon sources. Survival on a new carbon source preferentially involves reactions that are superessential, that is, essential in many metabolic networks. We validate our observations with data from 61 reconstructed prokaryotic metabolic networks. Our systematic and quantitative analysis of metabolic systems helps understand how recombination creates innovation.

Hybrid and rogue kinases encoded in the genomes of model eukaryotes

The highly modular nature of protein kinases generates diverse functional roles mediated by evolutionary events such as domain recombination, insertion and deletion of domains. Usually domain architecture of a kinase is related to the subfamily to which the kinase catalytic domain belongs. However outlier kinases with unusual domain architectures serve in the expansion of the functional space of the protein kinase family. For example, Src kinases are made-up of SH2 and SH3 domains in addition to the kinase catalytic domain. A kinase which lacks these two domains but retains sequence characteristics within the kinase catalytic domain is an outlier that is likely to have modes of regulation different from classical src kinases. This study defines two types of outlier kinases: hybrids and rogues depending on the nature of domain recombination. Hybrid kinases are those where the catalytic kinase domain belongs to a kinase subfamily but the domain architecture is typical of another kinase subfamily. Rogue kinases are those with kinase catalytic domain characteristic of a kinase subfamily but the domain architecture is typical of neither that subfamily nor any other kinase subfamily. This report provides a consolidated set of such hybrid and rogue kinases gleaned from six eukaryotic genomes-S.cerevisiae, D. melanogaster, C.elegans, M.musculus, T.rubripes and H.sapiens-and discusses their functions. The presence of such kinases necessitates a revisiting of the classification scheme of the protein kinase family using full length sequences apart from classical classification using solely the sequences of kinase catalytic domains. The study of these kinases provides a good insight in engineering signalling pathways for a desired output. Lastly, identification of hybrids and rogues in pathogenic protozoa such as P.falciparum sheds light on possible strategies in host-pathogen interactions.

A pluralistic account of homology: adapting the models to the data

Defining homologous genes is important in many evolutionary studies but raises obvious issues. Some of these issues are conceptual and stem from our assumptions of how a gene evolves, others are practical, and depend on the algorithmic decisions implemented in existing software. Therefore, to make progress in the study of homology, both ontological and epistemological questions must be considered. In particular, defining homologous genes cannot be solely addressed under the classic assumptions of strong tree thinking, according to which genes evolve in a strictly tree-like fashion of vertical descent and divergence and the problems of homology detection are primarily methodological. Gene homology could also be considered under a different perspective where genes evolve as “public goods,” subjected to various introgressive processes. In this latter case, defining homologous genes becomes a matter of designing models suited to the actual complexity of the data and how such complexity arises, rather than trying to fit genetic data to some a priori tree-like evolutionary model, a practice that inevitably results in the loss of much information. Here we show how important aspects of the problems raised by homology detection methods can be overcome when even more fundamental roots of these problems are addressed by analyzing public goods thinking evolutionary processes through which genes have frequently originated. This kind of thinking acknowledges distinct types of homologs, characterized by distinct patterns, in phylogenetic and nonphylogenetic unrooted or multirooted networks. In addition, we define “family resemblances” to include genes that are related through intermediate relatives, thereby placing notions of homology in the broader context of evolutionary relationships. We conclude by presenting some payoffs of adopting such a pluralistic account of homology and family relationship, which expands the scope of evolutionary analyses beyond the traditional, yet relatively narrow focus allowed by a strong tree-thinking view on gene evolution.

Quantification and functional analysis of modular protein evolution in a dense phylogenetic tree

Modularity is a hallmark of molecular evolution. Whether considering gene regulation, the components of metabolic pathways or signaling cascades, the ability to reuse autonomous modules in different molecular contexts can expedite evolutionary innovation. Similarly, protein domains are the modules of proteins, and modular domain rearrangements can create diversity with seemingly few operations in turn allowing for swift changes to an organism's functional repertoire. Here, we assess the patterns and functional effects of modular rearrangements at high resolution. Using a well resolved and diverse group of pancrustaceans, we illustrate arrangement diversity within closely related organisms, estimate arrangement turnover frequency and establish, for the first time, branch-specific rate estimates for fusion, fission, domain addition and terminal loss. Our results show that roughly 16 new arrangements arise per million years and that between 64% and 81% of these can be explained by simple, single-step modular rearrangement events. We find evidence that the frequencies of fission and terminal deletion events increase over time, and that modular rearrangements impact all levels of the cellular signaling apparatus and thus may have strong adaptive potential. Novel arrangements that cannot be explained by simple modular rearrangements contain a significant amount of repeat domains that occur in complex patterns which we term "supra-repeats". Furthermore, these arrangements are significantly longer than those with a single-step rearrangement solution, suggesting that such arrangements may result from multi-step events. In summary, our analysis provides an integrated view and initial quantification of the patterns and functional impact of modular protein evolution in a well resolved phylogenetic tree. This article is part of a Special Issue entitled: The emerging dynamic view of proteins: Protein plasticity in allostery, evolution and self-assembly.

Functional Diversity of the Schistosoma mansoni Tyrosine Kinases

Schistosoma mansoni, one of the causative agents of schistosomiasis, has a complex life cycle infecting over 200 million people worldwide. Such a successful and prolific parasite life cycle has been shown to be dependent on the adaptive interaction between the parasite and hosts. Tyrosine kinases (TKs) play a key role in signaling pathways as demonstrated by a large body of experimental work in eukaryotes. Furthermore, comparative genomics have allowed the identification of TK homologs and provided insights into the functional role of TKs in several biological systems. Finally, TK structural biology has provided a rational basis for obtaining selective inhibitors directed to the treatment of human diseases. This paper covers the important aspects of the phospho-tyrosine signaling network in S. mansoni, Caenorhabditis elegans, and humans, the main process of functional diversification of TKs, that is, protein-domain shuffling, and also discusses TKs as targets for the development of new anti-schistosome drugs.

Processes of fungal proteome evolution and gain of function: gene duplication and domain rearrangement

During evolution, organisms have gained functional complexity mainly by modifying and improving existing functioning systems rather than creating new ones ab initio. Here we explore the interplay between two processes which during evolution have had major roles in the acquisition of new functions: gene duplication and protein domain rearrangements. We consider four possible evolutionary scenarios: gene families that have undergone none of these event types; only gene duplication; only domain rearrangement, or both events. We characterize each of the four evolutionary scenarios by functional attributes. Our analysis of ten fungal genomes indicates that at least for the fungi clade, species significantly appear to gain complexity by gene duplication accompanied by the expansion of existing domain architectures via rearrangements. We show that paralogs gaining new domain architectures via duplication tend to adopt new functions compared to paralogs that preserve their domain architectures. We conclude that evolution of protein families through gene duplication and domain rearrangement is correlated with their functional properties. We suggest that in general, new functions are acquired via the integration of gene duplication and domain rearrangements rather than each process acting independently.