FAS: assessing the similarity between proteins using multi-layered feature architectures

MOTIVATION

Protein sequence comparison is a fundamental element in the bioinformatics toolkit. When sequences are annotated with features such as functional domains, transmembrane domains, low complexity regions or secondary structure elements, the resulting feature architectures allow better informed comparisons. However, many existing schemes for scoring architecture similarities cannot cope with features arising from multiple annotation sources. Those that do fall short in the resolution of overlapping and redundant feature annotations.

RESULTS

Here, we introduce FAS, a scoring method that integrates features from multiple annotation sources in a directed acyclic architecture graph. Redundancies are resolved as part of the architecture comparison by finding the paths through the graphs that maximize the pair-wise architecture similarity. In a large-scale evaluation on more than 10 000 human-yeast ortholog pairs, architecture similarities assessed with FAS are consistently more plausible than those obtained using e-values to resolve overlaps or leaving overlaps unresolved. Three case studies demonstrate the utility of FAS on architecture comparison tasks: benchmarking of orthology assignment software, identification of functionally diverged orthologs, and diagnosing protein architecture changes stemming from faulty gene predictions. With the help of FAS, feature architecture comparisons can now be routinely integrated into these and many other applications.

AVAILABILITY AND IMPLEMENTATION

FAS is available as python package: https://pypi.org/project/greedyFAS/.

Structural evolution drives diversification of the large LRR-RLK gene family

●Cells are continuously exposed to chemical signals that they must discriminate between and respond to appropriately. In embryophytes, the leucine-rich repeat receptor-like kinases (LRR-RLKs) are signal receptors critical in development and defense. LRR-RLKs have diversified to hundreds of genes in many plant genomes. Although intensively studied, a well-resolved LRR-RLK gene tree has remained elusive. ●To resolve the LRR-RLK gene tree, we developed an improved gene discovery method based on iterative hidden Markov model searching and phylogenetic inference. We used this method to infer complete gene trees for each of the LRR-RLK subclades and reconstructed the deepest nodes of the full gene family. ●We discovered that the LRR-RLK gene family is even larger than previously thought, and that protein domain gains and losses are prevalent. These structural modifications, some of which likely predate embryophyte diversification, led to misclassification of some LRR-RLK variants as members of other gene families. Our work corrects this misclassification. ●Our results reveal ongoing structural evolution generating novel LRR-RLK genes. These new genes are raw material for the diversification of signaling in development and defense. Our methods also enable phylogenetic reconstruction in any large gene family.

Evolution of Protein Domain Architectures

This chapter reviews current research on how protein domain architectures evolve. We begin by summarizing work on the phylogenetic distribution of proteins, as this will directly impact which domain architectures can be formed in different species. Studies relating domain family size to occurrence have shown that they generally follow power law distributions, both within genomes and larger evolutionary groups. These findings were subsequently extended to multi-domain architectures. Genome evolution models that have been suggested to explain the shape of these distributions are reviewed, as well as evidence for selective pressure to expand certain domain families more than others. Each domain has an intrinsic combinatorial propensity, and the effects of this have been studied using measures of domain versatility or promiscuity. Next, we study the principles of protein domain architecture evolution and how these have been inferred from distributions of extant domain arrangements. Following this, we review inferences of ancestral domain architecture and the conclusions concerning domain architecture evolution mechanisms that can be drawn from these. Finally, we examine whether all known cases of a given domain architecture can be assumed to have a single common origin (monophyly) or have evolved convergently (polyphyly). We end by a discussion of some available tools for computational analysis or exploitation of protein domain architectures and their evolution.

Genomic identification and functional analysis of essential genes in Caenorhabditis elegans

Background

Essential genes are required for an organism’s viability and their functions can vary greatly, spreading across many pathways. Due to the importance of essential genes, large scale efforts have been undertaken to identify the complete set of essential genes and to understand their function. Studies of genome architecture and organization have found that genes are not randomly disturbed in the genome.

Results

Using combined genetic mapping, Illumina sequencing, and bioinformatics analyses, we successfully identified 44 essential genes with 130 lethal mutations in genomic regions of C. elegans of around 7.3 Mb from Chromosome I (left). Of the 44 essential genes, six of which were genes not characterized previously by mutant alleles, let-633/let-638 (B0261.1), let-128 (C53H9.2), let-511 (W09C3.4), let-162 (Y47G6A.18), let-510 (Y47G6A.19), and let-131 (Y71G12B.6). Examine essential genes with Hi-C data shows that essential genes tend to cluster within TAD units rather near TAD boundaries. We have also shown that essential genes in the left half of chromosome I in C. elegans function in enzyme and nucleic acid binding activities during fundamental processes, such as DNA replication, transcription, and translation. From protein-protein interaction networks, essential genes exhibit more protein connectivity than non-essential genes in the genome. Also, many of the essential genes show strong expression in embryos or early larvae stages, indicating that they are important to early development.

Conclusions

Our results confirmed that this work provided a more comprehensive picture of the essential gene and their functional characterization. These genetic resources will offer important tools for further heath and disease research.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5251-3) contains supplementary material, which is available to authorized users.

Understanding enzyme function evolution from a computational perspective

In this review, we will explore recent computational approaches to understand enzyme evolution from the perspective of protein structure, dynamics and promiscuity. We will present quantitative methods to measure the size of evolutionary steps within a structural domain, allowing the correlation between change in substrate and domain structure to be assessed, and giving insights into the evolvability of different domains in terms of the number, types and sizes of evolutionary steps observed. These approaches will help to understand the evolution of new catalytic and non-catalytic functionality in response to environmental demands, showing potential to guide de novoenzyme design and directed evolution experiments.

Natural diversity of cellulases, xylanases, and chitinases in bacteria

BACKGROUND

Glycoside hydrolases (GH) targeting cellulose, xylan, and chitin are common in the bacterial genomes that have been sequenced. Little is known, however, about the architecture of multi-domain and multi-activity glycoside hydrolases. In these enzymes, combined catalytic domains act synergistically and thus display overall improved catalytic efficiency, making these proteins of high interest for the biofuel technology industry.

RESULTS

Here, we identify the domain organization in 40,946 proteins targeting cellulose, xylan, and chitin derived from 11,953 sequenced bacterial genomes. These bacteria are known to be capable, or to have the potential, to degrade polysaccharides, or are newly identified potential degraders (e.g., Actinospica, Hamadaea, Cystobacter, and Microbispora). Most of the proteins we identified contain a single catalytic domain that is frequently associated with an accessory non-catalytic domain. Regarding multi-domain proteins, we found that many bacterial strains have unique GH protein architectures and that the overall protein organization is not conserved across most genera. We identified 217 multi-activity proteins with at least two GH domains for cellulose, xylan, and chitin. Of these proteins, 211 have GH domains targeting similar or associated substrates (i.e., cellulose and xylan), whereas only six proteins target both cellulose and chitin. Fifty-two percent of multi-activity GHs are hetero-GHs. Finally, GH6, -10, -44 and -48 domains were mostly C-terminal; GH9, -11, -12, and -18 were mostly N-terminal; and GH5 domains were either N- or C-terminal.

CONCLUSION

We identified 40,946 multi-domain/multi-activity proteins targeting cellulase, chitinase, and xylanase in bacterial genomes and proposed new candidate lineages and protein architectures for carbohydrate processing that may play a role in biofuel production.