Accurately clustering biological sequences in linear time by relatedness sorting

Exploring plant protein functions through structure-based clustering

The upsurge in new plant protein sequences has far outpaced experimental functional characterization efforts. Prediction of protein function based on sequence homology often falls short when dealing with proteins that have low sequence similarity. Artificial intelligence (AI) programs, such as AlphaFold, have transformed computational protein structure prediction with remarkable accuracy. By leveraging the availability of predicted structures for nearly all protein sequences, clustering proteins based on their similarity in structural features has become a powerful tool for function annotation and discovery. Structure-based protein clustering enables the identification of distant evolutionary relationships and novel protein families, and offers an effective strategy for exploring plant protein functions, bridging the gap between sequence data and function annotation while also assisting in protein design.

Deciphering bacterial protein functions with innovative computational methods

Bacteria colonize every niche on Earth and play key roles in many environmental and host-associated processes. The sequencing revolution revealed the remarkable bacterial genetic and proteomic diversity and the genomic content of cultured and uncultured bacteria. However, deciphering functions of novel proteins remains a high barrier, often preventing the deep understanding of microbial life and its interaction with the surrounding environment. In recent years, exciting new bioinformatic tools, many of which are based on machine learning, facilitate the challenging task of gene and protein function discovery in the era of big genomics data, leading to the generation of testable hypotheses for bacterial protein functions. The new tools allow prediction of protein structures and interactions and allow sensitive and efficient sequence- and structure-based searching and clustering. Here, we summarize some of these recent tools which revolutionize modern microbiology research, along with examples for their usage, emphasizing the user-friendly, web-based ones. Adoption of these capabilities by experimentalists and computational biologists could save resources and accelerate microbiology research.

Tandem Repeats Provide Evidence for Convergent Evolution to Similar Protein Structures

Homology is a key concept underpinning the comparison of sequences across organisms. Sequence-level homology is based on a statistical framework optimized over decades of work. Recently, computational protein structure prediction has enabled large-scale homology inference beyond the limits of accurate sequence alignment. In this regime, it is possible to observe nearly identical protein structures lacking detectable sequence similarity. In the absence of a robust statistical framework for structure comparison, it is largely assumed similar structures are homologous. However, it is conceivable that matching structures could arise through convergent evolution, resulting in analogous proteins without shared ancestry. Large databases of predicted structures offer a means of determining whether analogs are present among structure matches. Here, I find that a small subset (∼2.6%) of Foldseek clusters lack sequence-level support for homology, including ∼1% of strong structure matches with template modeling score ≥ 0.5. This result by itself does not imply these structure pairs are nonhomologous, since their sequences could have diverged beyond the limits of recognition. Yet, strong matches without sequence-level support for homology are enriched in structures with predicted repeats that could induce spurious matches. Some of these structural repeats are underpinned by sequence-level tandem repeats in both matching structures. I show that many of these tandem repeat units have genealogies inconsistent with their corresponding structures sharing a common ancestor, implying these highly similar structure pairs are analogous rather than homologous. This result suggests caution is warranted when inferring homology from structural resemblance alone in the absence of sequence-level support for homology.

Many purported pseudogenes in bacterial genomes are bona fide genes

BACKGROUND

Microbial genomes are largely comprised of protein coding sequences, yet some genomes contain many pseudogenes caused by frameshifts or internal stop codons. These pseudogenes are believed to result from gene degradation during evolution but could also be technical artifacts of genome sequencing or assembly.

RESULTS

Using a combination of observational and experimental data, we show that many putative pseudogenes are attributable to errors that are incorporated into genomes during assembly. Within 126,564 publicly available genomes, we observed that nearly identical genomes often substantially differed in pseudogene counts. Causal inference implicated assembler, sequencing platform, and coverage as likely causative factors. Reassembly of genomes from raw reads confirmed that each variable affects the number of putative pseudogenes in an assembly. Furthermore, simulated sequencing reads corroborated our observations that the quality and quantity of raw data can significantly impact the number of pseudogenes in an assembler dependent fashion. The number of unexpected pseudogenes due to internal stops was highly correlated (R2 = 0.96) with average nucleotide identity to the ground truth genome, implying relative pseudogene counts can be used as a proxy for overall assembly correctness. Applying our method to assemblies in RefSeq resulted in rejection of 3.6% of assemblies due to significantly elevated pseudogene counts. Reassembly from real reads obtained from high coverage genomes showed considerable variability in spurious pseudogenes beyond that observed with simulated reads, reinforcing the finding that high coverage is necessary to mitigate assembly errors.

CONCLUSIONS

Collectively, these results demonstrate that many pseudogenes in microbial genome assemblies are actually genes. Our results suggest that high read coverage is required for correct assembly and indicate an inflated number of pseudogenes due to internal stops is indicative of poor overall assembly quality.