Post-translational modification: nature's escape from genetic imprisonment and the basis for dynamic information encoding

Abstract

We discuss protein post-translational modification (PTM) from an information processing perspective. PTM at multiple sites on a protein creates a combinatorial explosion in the number of potential 'mod-forms', or global patterns of modification. Distinct mod-forms can elicit distinct downstream responses, so that the overall response depends partly on the effectiveness of a particular mod-form to elicit a response and partly on the stoichiometry of that mod-form in the molecular population. We introduce the 'mod-form distribution'-the relative stoichiometries of each mod-form-as the most informative measure of a protein's state. Distinct mod-form distributions may summarize information about distinct cellular and physiological conditions and allow downstream processes to interpret this information accordingly. Such information 'encoding' by PTMs may facilitate evolution by weakening the need to directly link upstream conditions to downstream responses. Mod-form distributions provide a quantitative framework in which to interpret ideas of 'PTM codes' that are emerging in several areas of biology, as we show by reviewing examples of ion channels, GPCRs, microtubules, and transcriptional co-regulators. We focus particularly on examples other than the well-known 'histone code', to emphasize the pervasive use of information encoding in molecular biology. Finally, we touch briefly on new methods for measuring mod-form distributions.

Figure 1. Reversible phosphorylation as information processing

a. A single phosphorylated site on a substrate is dynamically regulated by a forward kinase, E, and a reverse phosphatase, F. Not shown are the donor, ATP, its hydrolysis products, ADP and Pi, and the background metabolic pathways that maintain the ATP “voltage” (see Figure 3a). b. The state of the population of substrate molecules is summarised by the relative stoichiometry of the phosphorylated state, denoted U, and defined by the fraction shown. Note that the denominator may have more contributions than just the free unphosphorylated and phosphorylated states, since, depending on the enzyme mechanisms, substrate may also be bound in enzyme-substrate complexes. c. The steady-state level of U is shown as a function of the relative amounts of kinase and phosphatase. This is a hypothetical, but typical, illustration; the quantitative details depend on the enzyme mechanisms (114). The value of U contains information about the relative amounts of kinase and phosphatase, which can be sensed and utilised by downstream processes. The response curve can exhibit increasing steepness, from nearly hyperbolic (black) to strongly sigmoidal (blue), as the amount of substrate is increased (40), allowing the information processing characteristics to be regulated.

Figure 2. Occurrence of experimentally detected PTMs, as curated from SwissProt, taken from (58, Figure 2A)

Figure 3. Metabolic and polypeptide PTMs

The biochemical details may differ depending on the modification; see (106) for more details. a. Metabolic PTMs. Note that lysine deacetylation by the sirtuins uses NAD⁺ and releases acetyl-ADP-ribose rather than acetate. b. Polypeptide PTMs. Ubiquitin-like modifiers are synthesised by gene transcription, which, in the case of ubiquitin, yields tandem repeats or fusion proteins. These must be proteolytically cleaved prior to being used for PTM (14). E2 enzymes can sometimes modify substrates independently of E3s; E2 and E3 enzymes often collaborate and E4 elongation factors can join in (46). Assembly of polymeric chains is not fully understood and ubiquitin chains may be preformed prior to substrate ligation (46).

Figure 4. Simple PTMs

The chemistry of those PTMs above the double line in Table 1, which exhibit a small, limited number of modifications, is shown, with the modifications to each residue in red. Chemical formulas were drawn in BKChem, an open source utility.

Figure 5. Complex PTMs

The chemistry of those PTMs below the double line in Table 1, which exhibit potentially unlimited numbers of modifications, is summarised, as in Figure 4. The human ubiquitin sequence was obtained from PDB 1UBI, along with the secondary structure assignment through DSSP. The PDB entries of the ubiquitin structures are 1UBI for the monomer, 1AAR for the Lys48 dimer and 2JF5 for the Lys63 dimer. The structures were oriented and annotated in Open Source PyMol 1.2.X.

Figure 6. Mod-form distributions

a. Cartoon depiction of a hypothetical substrate with 3 sites of modification; site 1 is ubiquitinated with a chain of up to two monomers; sites 2 and 3 are phosphorylated. b. There are 12 = 3 × 2 × 2 global patterns of modification, enumerated as shown. c. A hypothetical mod-form distribution, showing the proportions in the population of each of the 12 mod-forms, following the numbering used in b. The mod-form distribution can be viewed as a probability distribution, which gives, for each mod-form, the probability of finding a substrate molecule in that mod-form. The vertical scale has been omitted to focus on qualitative aspects. d. In current practice, only limited information may be available. The separate phosphoryl- and ubiquityl-modifications calculated from c are shown, with the phosphoryl-modifications given as site-specific stoichiometries (the proportion of unphosphorylated substrate and of substrate phosphorylated on each site). Such summaries lose considerable information compared to the underlying mod-form distribution, making it harder to infer correlations between modification states and downstream responses.

Figure 7. Encoding of information by mod-form distributions

a. A bow-tie architecture describes the behaviour of many of the examples discussed here. Distinct physiological and cellular states on the left can be represented (“encoded”) by distinct mod-form distributions of a single substrate at the centre of the bow-tie (“fan-in”). Each mod-form distribution can then orchestrate its own mix of downstream cellular processes, as on the right (“fan-out”). Figure 1 of (75) reflects a similar architecture for the particular case of SRC-3. The mod-form distribution plays a central role here as the quantitative representation of the encoded information. b. The mod-form distribution can also affect the behaviour of the substrate itself, as in the example of ion channels. This is shown here by the hypothetical dose-response kinetics on the right, whose characteristics—threshold sensitivity, steepness, saturation level, latency, etc—may be modulated by changes in the mod-form distribution.

Figure 8. Measurement of a four-site phosphoryl-form distribution

The phosphoryl-form distribution of the MAP kinase Erk2 is shown, after in-vitro preparation to generate four sites of phosphorylation, two canonical ones, Thr and Tyr, on the activation loop and two novel ones, both Ser, on the N-terminal tail. Their approximate positions are marked by yellow Ps on the ribbon diagram of PDB-2ERK in the inset. Note, in particular, that these residues are not all on the same tryptic peptide. A combination of peptide-based and protein-based mass spectrometry, confirmed by nuclear magnetic resonance spectroscopy, was used to measure the relative stoichiometry of the sixteen potential phosphoryl-forms, not all of which could be individually determined. Adapted from Figure 6B of (90), with the inset created in Open Source PyMol 1.2.X.