PC_ali: a tool for improved multiple alignments and evolutionary inference based on a hybrid protein sequence and structure similarity score

MOTIVATION

Evolutionary inference depends crucially on the quality of multiple sequence alignments (MSA), which is problematic for distantly related proteins. Since protein structure is more conserved than sequence, it seems natural to use structure alignments for distant homologs. However, structure alignments may not be suitable for inferring evolutionary relationships.

RESULTS

Here we examined four protein similarity measures that depend on sequence and structure (fraction of aligned residues, sequence identity, fraction of superimposed residues, and contact overlap), finding that they are intimately correlated but none of them provides a complete and unbiased picture of conservation in proteins. Therefore, we propose the new hybrid protein sequence and structure similarity score PC_sim based on their main principal component. The corresponding divergence measure PC_div shows the strongest correlation with divergences obtained from individual similarities, suggesting that it infers accurate evolutionary divergences. We developed the program PC_ali that constructs protein MSAs either de novo or modifying an input MSA, using a similarity matrix based on PC_sim. The program constructs a starting MSA based on the maximal cliques of the graph of these PAs and it refines it through progressive alignments along the tree reconstructed with PC_div. Compared with eight state-of-the-art multiple structure or sequence alignment tools, PC_ali achieves higher or equal aligned fraction and structural scores, sequence identity higher than structure aligners although lower than sequence aligners, highest score PC_sim, and highest similarity with the MSAs produced by other tools and with the reference MSA Balibase.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ugobas/PC_ali.

Leveraging genomic information to predict environmental preferences of bacteria

Genomic information is now available for a broad diversity of bacteria, including uncultivated taxa. However, we have corresponding knowledge on environmental preferences (i.e. bacterial growth responses across gradients in oxygen, pH, temperature, salinity, and other environmental conditions) for a relatively narrow swath of bacterial diversity. These limits to our understanding of bacterial ecologies constrain our ability to predict how assemblages will shift in response to global change factors, design effective probiotics, or guide cultivation efforts. We need innovative approaches that take advantage of expanding genome databases to accurately infer the environmental preferences of bacteria and validate the accuracy of these inferences. By doing so, we can broaden our quantitative understanding of the environmental preferences of the majority of bacterial taxa that remain uncharacterized. With this perspective, we highlight why it is important to infer environmental preferences from genomic information and discuss the range of potential strategies for doing so. In particular, we highlight concrete examples of how both cultivation-independent and cultivation-dependent approaches can be integrated with genomic data to develop predictive models. We also emphasize the limitations and pitfalls of these approaches and the specific knowledge gaps that need to be addressed to successfully expand our understanding of the environmental preferences of bacteria.