Highly significant improvement of protein sequence alignments with AlphaFold2.

MOTIVATION: Protein sequence alignments are essential to structural, evolutionary and functional analysis, but their accuracy is often limited by sequence similarity unless molecular structures are available. Protein structures predicted at experimental grade accuracy, as achieved by AlphaFold2, could therefore have a major impact on sequence analysis. RESULTS: Here, we find that multiple sequence alignments estimated on AlphaFold2 predictions are almost as accurate as alignments estimated on experimental structures and significantly closer to the structural reference than sequence-based alignments. We also show that AlphaFold2 structural models of relatively low quality can be used to obtain highly accurate alignments. These results suggest that, besides structure modeling, AlphaFold2 encodes higher-order dependencies that can be exploited for sequence analysis. AVAILABILITY AND IMPLEMENTATION: All data, analyses and results are available on Zenodo (https://doi.org/10.5281/zenodo.7031286). The code and scripts have been deposited in GitHub (https://github.com/cbcrg/msa-af2-nf) and the various containers in (https://cloud.sylabs.io/library/athbaltzis/af2/alphafold, https://hub.docker.com/r/athbaltzis/pred). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TFforge utilizes large-scale binding site divergence to identify transcriptional regulators involved in phenotypic differences.

Changes in gene regulation are important for phenotypic and in particular morphological evolution. However, it remains challenging to identify the transcription factors (TFs) that contribute to differences in gene regulation and thus to phenotypic differences between species. Here, we present TFforge (Transcription Factor forward genomics), a computational method to identify TFs that are involved in the loss of phenotypic traits. TFforge screens an input set of regulatory genomic regions to detect TFs that exhibit a significant binding site divergence signature in species that lost a particular phenotypic trait. Using simulated data of modular and pleiotropic regulatory elements, we show that TFforge can identify the correct TFs for many different evolutionary scenarios. We applied TFforge to available eye regulatory elements to screen for TFs that exhibit a significant binding site decay signature in subterranean mammals. This screen identified interacting and co-binding eye-related TFs, and thus provides new insights into which TFs likely contribute to eye degeneration in these species. TFforge has broad applicability to identify the TFs that contribute to phenotypic changes between species, and thus can help to unravel the gene-regulatory differences that underlie phenotypic evolution.

REforge Associates Transcription Factor Binding Site Divergence in Regulatory Elements with Phenotypic Differences between Species.

Elucidating the genomic determinants of morphological differences between species is key to understanding how morphological diversity evolved. While differences in cis-regulatory elements are an important genetic source for morphological evolution, it remains challenging to identify regulatory elements involved in phenotypic differences. Here, we present Regulatory Element forward genomics (REforge), a computational approach that detects associations between transcription factor binding site divergence in putative regulatory elements and phenotypic differences between species. By simulating regulatory element evolution in silico, we show that this approach has substantial power to detect such associations. To validate REforge on real data, we used known binding motifs for eye-related transcription factors and identified significant binding site divergence in vision-impaired subterranean mammals in 1% of all conserved noncoding elements. We show that these genomic regions are significantly enriched in regulatory elements that are specifically active in mouse eye tissues, and that several of them are located near genes, which are required for eye development and photoreceptor function and are implicated in human eye disorders. Thus, our genome-wide screen detects widespread divergence of eye-regulatory elements and highlights regulatory regions that likely contributed to eye degeneration in subterranean mammals. REforge has broad applicability to detect regulatory elements that could be involved in many other phenotypes, which will help to reveal the genomic basis of morphological diversity.