Metal preferences and metallation

Abstract

The metal binding preferences of most metalloproteins do not match their metal requirements. Thus, metallation of an estimated 30% of metalloenzymes is aided by metal delivery systems, with ∼ 25% acquiring preassembled metal cofactors. The remaining ∼ 70% are presumed to compete for metals from buffered metal pools. Metallation is further aided by maintaining the relative concentrations of these pools as an inverse function of the stabilities of the respective metal complexes. For example, magnesium enzymes always prefer to bind zinc, and these metals dominate the metalloenzymes without metal delivery systems. Therefore, the buffered concentration of zinc is held at least a million-fold below magnesium inside most cells.

Keywords: Copper; Iron; Irving-Williams Series; Manganese; Metal Sensors; Metallochaperone; Metalloenzymes; Nickel; Polydisperse Buffer; Zinc.

FIGURE 1.

Metallation is governed by metal availability for MncA and CucA. a, Mn(II)-MncA global fold. b, Cu(II)-CucA global fold. Both proteins adopt a cupin architecture, with MncA composed of two cupin domains. c, MncA N-terminal Mn(II)-binding site. d, CucA Cu(II)-binding site. Both proteins coordinate their metals with identical ligand sets, with a water molecule in the open coordination position (this position is occupied by acetate in the C-terminal Mn(II)-binding site of MncA). MncA and CucA both prefer to bind copper rather than manganese in vitro, but MncA folds and traps manganese in the metal-regulated environment of the cytoplasm. Protein Data Bank (PDB) codes: 2VQA and 2XL7.

FIGURE 2.

Correlation between buffered set points and metal sensor affinities. Shown are graphical representations of estimated intracellular buffered metal concentrations (gray bars) for magnesium, manganese, iron, cobalt, nickel, copper, and zinc (62, 65, 66, 72, 73, 90, 91) and correlation with K_Metal of cytosolic metal sensors for their cognate metal, including Fur (92), RcnR (93), NikR (94), CueR (89), Zur (88), and ZntR (88), from E. coli (red circles); the M-box riboswitch (95), MntR (96), Fur (96), CsoR (97), and Zur (98), from B. subtilis (blue triangles); CoaR (84), InrS (83), Zur (85), and ZiaR (85) from Synechocystis PCC 6803 (green diamonds); and CsoR (99) and CzrA (100) from Staphylococcus aureus (purple squares). It is hypothesized that K_Metal of metal sensors maintains the set points for buffered metal concentrations as an inverse function of the Irving-Williams series.

FIGURE 3.

Relative affinity, relative access, and relative allostery in a complement of metal sensors influences the metals detected in vivo. a, calculated fractional occupancy of InrS, Zur, ZiaR, and CoaR with Ni(II), Zn(II), and Co(II) as the concentration of these elements changes: Fractional occupancy: θ= [Metal]_buffered/(K_Metal + [Metal]_buffered) using published K_Metal (83–85). b, fractional occupancy of specific DNA (top) with apo- (dashed) and zinc-InrS (solid) and (bottom), apo- (dashed) and zinc-ZiaR (solid), as a function of protein concentration. Coupling free energy: ΔG_C = −RTln(K_DNA2/K_DNA1). The simulated curves were generated using published K_DNA values (85), [DNA] = 10 nm. The selective detection of nickel correlates with relative nickel affinity, of zinc with relative ΔG_C for zinc, but a major kinetic contribution (channeling) is invoked for cobalt.

FIGURE 4.

Associative ligand exchange with a polydisperse buffer. i, the transfer of zinc from InrS to ZiaR via a dissociative release of zinc from InrS to a hydrated state. ii, the transfer of zinc from InrS to ZiaR by (potentially swift) associative ligand exchange via a partly (x) zinc-saturated number of ligands (y) of a polydisperse buffer (L).