Evolution: a guide to perturb protein function and networks

Abstract

Protein interactions give rise to networks that control cell fate in health and disease; selective means to probe these interactions are therefore of wide interest. We discuss here Evolutionary Tracing (ET), a comparative method to identify protein functional sites and to guide experiments that selectively block, recode, or mimic their amino acid determinants. These studies suggest, in principle, a scalable approach to perturb individual links in protein networks.

Figure 1. The Evolutionary Trace Method

(a) A fragment of a multiple sequence alignment and the evolutionary tree of a protein family are displayed. ET considers, for each sequence position, every branch and sub branch from root to tip. The variation pattern of a rank 1 residue is complete invariance, since this correlates perfectly with the whole tree viewed as a single branch (red). The variation pattern at rank 2, when the tree is split into its first two branches, is variation between these two branches but invariance within each one (green). The relevant pattern of variation at rank 3 is, likewise, invariance within all first three branches but variation between the two nearest ones (blue). Thus, every position gets a rank–the earliest tree node after which it varies no further [21]. (b) A drawback is that sequence errors, gaps, or insertions arise ever more frequently as sequence space grows. The heuristic requirement for absolute invariance within branches can be relaxed by summing entropy terms over the sub-branches (g), weighted by the nodes (n) at which they occur in a tree with N leaves. This more robust hybrid phylogenetic-entropy approach called rvET for real-value ET [22] produces a non-integer rank of evolutionary importance (ρ_i) for each residue (i) making up the query protein. The frequency of amino acid a in the MSA column for residue i, in sub-branch g, is $f_{ia}^g$. This more robust hybrid phylogenetic-entropy approach produces non-integer rank of evolutionary importance (ρ_i) and is called rvET for real-value ET [22]. (c) When trace residues are mapped onto the structure, they typically cluster (red) whereas randomly picked amino acids do not (blue) [55]. Large clustering Z-scores imply the analysis is reliable and the ET site likely to be functionally relevant. (d) The resolution of functional site discovery is a parameter under operator control, it modifies the percentile rank coverage, or the threshold on a heat map of the structure where evolutionary importance decreases from red to blue, illustrated by a trace of the GDP-bound form of the Ras-family GTPase Ran bound to the ribbon form of nuclear transport factor 2 (PDB:1a2K, [93]). Public ET servers are available [23,24].

Figure 2. Determinants of allosteric specificity in psychoactive receptors

A stereo view of the β2-adrenergic receptor structure bound to carazolol (yellow) (PDB:2rh1 [94]) provides a homology model for bioamine receptors, including the D2R and 5HT2AR dopamine and serotonin receptors. Four top-ranked amino acids (red), that are part of a putative allosteric pathway identified by ET, were swapped into D2R from their cognates in 5HT2AR and this conferred significant serotonin responsiveness to the mutants even though none had increased affinity to serotonin (red). Two of the four also had decreased dopamine responsiveness with either no change or with a paradoxical increase in dopamine affinity. Although distant from the ligand binding site, these residues are closely associated to other top-ranked ET residues in the putative allosteric pathway that are invariant between D2DR and 5HT2AR (C_α atoms of residues within 5 Å, gray). Together, these top-ranked residues link the toggle switch (top, cyan residue) and the NPxxY motif (lower cyan residues) that are generic GPCR mediators of activation; and they also surround a pocket of structural waters (blue spheres). Like rekeying the tumblers of a lock, the exchange of these residues shifted the sensitivity of the allosteric pathway from one ligand to another, independently of changes to binding affinity. These allosteric specificity determinants are consistent with the pharmacology of ligand-biased signaling [81].

Figure 3. Structure function prediction based on evolutionary 3D templates

(a) Evolutionary Trace rankings of residue importance for Mycobacterium tuberculosis v1626 (PDB 1sd5, chain A) are represented as a heat map of the structure’s surface (red, most important; purple, least important). Based on these rankings, the most important residues are mapped onto the structure (green ribbons) to identify a solvent-accessible cluster of 10 ET residues (red and yellow spheres). The C_α coordinates of the top six residues (red) are used as the template and searched against a database of annotated target structures. (b) A conceptual diagram of ETA heuristic filtering shows proteins as large circles, with color representing functions and templates matches as smaller circles. ETA first discards matched sites if they are not themselves evolutionary important. Red matches indicate importance and pass the filter (arrows), while white matches are unimportant and do not pass this filter (flat-headed line). ETA then examines match reciprocity, with one-way (single-headed arrows) matches rejected, and reciprocal matches (double-handed arrows) accepted. Finally, ETA requires that a predicted function achieve a vote plurality; here, after all filters are used, the two proteins with the “blue” function represent the majority of the matches. ETA would therefore predict that the query protein (question mark) has the blue function. Public ETA servers are available at http://mammoth.bcm.tmc.edu [89].