Exploring protein symmetry at the RCSB Protein Data Bank

Jumping through hoops: Structural rearrangements and accelerated mutation rates on Dendrodorididae (Mollusca: Nudibranchia) mitogenomes rumble their evolution

The systematics of the family Dendrodorididae, with only three valid genera, is a challenge for integrative taxonomists. Its members lack hard structures for morphological comparisons and their mitochondrial and nuclear markers provide contradictory phylogenetic signals, making phylogenetic reconstructions difficult. This molecular discordance has been hypothesized to be the result of nuclear pseudogenes or exogenous contamination. However, these hypotheses have not been tested. Here, we assembled the first genome drafts of seven Dendrodorididae species to investigate the evolutionary processes of this family. Two of the mitogenomes displayed an identical structural rearrangement involving the translocation of three coding genes and five tRNAs, described for the first time in nudibranchs. In addition, we found particularly high dN and dN/dS values and multiple insertions and deletions on the mitochondrial genes of smooth Dendrodoris. In contrast, nuclear single-copy ortholog genes showed no such mutational differences. Models of protein structures from mitochondrial genes are conserved, suggesting conserved functionality. Phylogenies using mitogenomic and nuclear data showed that species with rearranged mitogenomes form a clade, although Dendrodorididae relationships remained unresolved. The present study provides novel evidence for accelerated mutation rates in the mitogenomes of Dendrodorididae, which presumably have implications on respiratory adaptation, and highlights the importance of using genomic data to unveil rare evolutionary processes, crucial for correctly inferring phylogenies.

Compensatory mutations potentiate constructive neutral evolution by gene duplication

The functions of proteins generally depend on their assembly into complexes. During evolution, some complexes have transitioned from homomers encoded by a single gene to heteromers encoded by duplicate genes. This transition could occur without adaptive evolution through intermolecular compensatory mutations. Here, we experimentally duplicated and evolved a homodimeric enzyme to determine whether and how this could happen. We identified hundreds of deleterious mutations that inactivate individual homodimers but produce functional enzymes when coexpressed as duplicated proteins that heterodimerize. The structure of one such heteromer reveals how both losses of function are buffered through the introduction of asymmetry in the complex that allows them to subfunctionalize. Constructive neutral evolution can thus occur by gene duplication followed by only one deleterious mutation per duplicate.

CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2

Deep learning models, such as AlphaFold2 and RosettaFold, enable high-accuracy protein structure prediction. However, large protein complexes are still challenging to predict due to their size and the complexity of interactions between multiple subunits. Here we present CombFold, a combinatorial and hierarchical assembly algorithm for predicting structures of large protein complexes utilizing pairwise interactions between subunits predicted by AlphaFold2. CombFold accurately predicted (TM-score >0.7) 72% of the complexes among the top-10 predictions in two datasets of 60 large, asymmetric assemblies. Moreover, the structural coverage of predicted complexes was 20% higher compared to corresponding Protein Data Bank entries. We applied the method on complexes from Complex Portal with known stoichiometry but without known structure and obtained high-confidence predictions. CombFold supports the integration of distance restraints based on crosslinking mass spectrometry and fast enumeration of possible complex stoichiometries. CombFold's high accuracy makes it a promising tool for expanding structural coverage beyond monomeric proteins.

Accurate prediction by AlphaFold2 for ligand binding in a reductive dehalogenase and implications for PFAS (per- and polyfluoroalkyl substance) biodegradation

Despite the success of AlphaFold2 (AF2), it is unclear how AF2 models accommodate for ligand binding. Here, we start with a protein sequence from Acidimicrobiaceae TMED77 (T7RdhA) with potential for catalyzing the degradation of per- and polyfluoroalkyl substances (PFASs). AF2 models and experiments identified T7RdhA as a corrinoid iron-sulfur protein (CoFeSP) which uses a norpseudo-cobalamin (BVQ) cofactor and two Fe4S4 iron-sulfur clusters for catalysis. Docking and molecular dynamics simulations suggest that T7RdhA uses perfluorooctanoic acetate (PFOA) as a substrate, supporting the reported defluorination activity of its homolog, A6RdhA. We showed that AF2 provides processual (dynamic) predictions for the binding pockets of ligands (cofactors and/or substrates). Because the pLDDT scores provided by AF2 reflect the protein native states in complex with ligands as the evolutionary constraints, the Evoformer network of AF2 predicts protein structures and residue flexibility in complex with the ligands, i.e., in their native states. Therefore, an apo-protein predicted by AF2 is actually a holo-protein awaiting ligands.

Introduction to the special issue on symmetry in the life sciences - symmetry and asymmetry across biological scales

In this special issue of Emerging Topics in Life Sciences, we present a series of mini-reviews of some of the most exciting research involving the concept of symmetry. This research spans the biological sciences from proteins to ecosystems. The reviews examine protein and floral symmetry, primate brain and behavioral asymmetries, geometric morphometrics, and various fluctuating asymmetries.