Protein coevolution and physicochemical adaptation in the APAF-1/apoptosome: structural and functional implications

The apoptosome is involved in the mitochondrial pathway of apoptosis, consisting of APAF-1, caspase 9 and cytochrome c, forming a heptamer that activates effector caspases, causing cell death. This protein complex has also been characterized in Drosophila melanogaster (DARK) and Caenorhabditis elegans (CED-4). Here we present an evolutionary guided in silico characterization of the APAF-1/apoptosome. The evolutionary history of the apoptosome was determined, taking the possible orthologs of the APAF-1 version, and executing a protein coevolution and a positive selection analysis, to make structural and functional inferences and identify residues under destabilizing changes, respectively. Results suggests that the APAF-1/apoptosome is not unique to vertebrates, but also some basal invertebrates could possess orthologous copies. New possible versions were also detected in other taxa. Not all insects and other arthropods have the DARK version, just as not all nematodes have the CED-4 version. In the APAF-1 version, amino acid clusters with coevolution signal located in the interior, gave more insights on new potential interactions, allowing us to infer a more detailed model that includes allosterism, of how cytochrome c associates with β propellers during APAF-1 activation, as well as interactions essential for nucleotide exchange, activation of CASP9, the molecular timer and other pathways in mitochondria to induce apoptosis. Residues on the surface under destabilizing changes have guided the protein complex in adaptations necessary for conformational changes, interactions and folding.

Extracting the phylogenetic dimension of coevolution reveals hidden functional signal

Despite the structural and functional information contained in the statistical coupling between pairs of residues in a protein, coevolution associated with function is often obscured by artifactual signals such as genetic drift, which shapes a protein’s phylogenetic history and gives rise to concurrent variation between protein sequences that is not driven by selection for function. Here, we introduce a method for explicitly defining a phylogenetic dimension of coevolution signal, and demonstrate that coevolution can occur on multiple phylogenetic timescales within a single protein. Our method, Nested Coevolution (NC), can be applied as an extension to any coevolution metric. We use NC to demonstrate that poorly conserved residues can nonetheless have important roles in protein function. Moreover, NC improved structural-contact prediction over gold-standard coevolution-based methods, particularly in subsampled alignments with fewer sequences. NC also lowered the noise in detecting functional sectors of collectively coevolving residues. Sectors of coevolving residues identified after NC correction were more spatially compact and phylogenetically distinct from the rest of the protein, and strongly enriched for mutations that disrupt protein activity. Our conceptualization of the phylogenetic separation of coevolution represents an advance from previous pragmatic attempts to reduce phylogenetic artifacts in measurements of coevolution. Application of NC broadens the application of protein coevolution measurements, particularly to eukaryotic proteins with fewer naturally available sequences, and further elucidates relationships among protein evolution and genetic diseases.

A common root for coevolution and substitution rate variability in protein sequence evolution

We introduce a simple model that describes the average occurrence of point variations in a generic protein sequence. This model is based on the idea that mutations are more likely to be fixed at sites in contact with others that have mutated in the recent past. Therefore, we extend the usual assumptions made in protein coevolution by introducing a time dumping on the effect of a substitution on its surrounding and makes correlated substitutions happen in avalanches localized in space and time. The model correctly predicts the average correlation of substitutions as a function of their distance along the sequence. At the same time, it predicts an among-site distribution of the number of substitutions per site highly compatible with a negative binomial, consistently with experimental data. The promising outcomes achieved with this model encourage the application of the same ideas in the field of pairwise and multiple sequence alignment.