Predicting the structure of protein cavities created by mutation

Abstract

To assist in the efficient design of protein cavities, we have developed a minimization strategy that can predict with accuracy the fate of cavities created by mutation. We first modelled, under different conditions, the structures of six T4 lysozyme and cytochrome c peroxidase mutants of known crystal structure (where long, hydrophobic, buried side chains have been replaced by shorter ones) by minimizing the virtual structures derived from the corresponding wild-type co-ordinates. An unconstrained pathway together with an all-atom atom representation and a steepest descent minimization yielded modelled structures with lower root mean square deviations (r.m.s.d) from the crystal structures than other conditions. To test whether the method developed was generally applicable to other mutations of the kind, we have then modelled eighteen additional T4 lysozyme, barnase and cytochrome c peroxidase mutants of known crystal structure. The models of both cavity expanding and cavity collapsing mutants closely fit their crystal structures (average r.m.s.d. 0.33 +/- 0.25 A, with only one poorer prediction: L121A). The structure of protein cavities generated by mutation can thus be confidently simulated by energy minimization regardless of the tendency of the cavity to collapse or to expand. We think this is favoured by the fact that the typical response observed in these proteins to cavity-creating mutations is to experience only a limited rearrangement.

Structure-Based Engineering of Internal Cavities in Coiled-Coil Peptides,

“…enzyme specificities, creating novel binding sites, and studying protein stability (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12), but despite the diverse roles of natural cavities in biology, high-resolution structural investigations of model cavities in the aqueous milieu are rare. We believe studying designed cavities within synthetic peptides may provide models for understanding natural binding sites as well as designing novel receptors or biocatalysts.…”

Design of Ligand Binding to an Engineered Protein Cavity Using Virtual Screening and Thermal Up-shift Evaluation

“…For each conformation, fittings are carried out in ligand orientations selected using the non-mass-weighted principle moment of inertia [29], and the corresponding docking scores are computed. The docking was optimised using a training set of 6 L99A T4 lysozyme binders (ethylbenzene, o-ethyltoluene, benzofuran, indole, toluene, and n-butylbenzene) and six non-binders (benzylalcohol, t-butylbenzene, heptanol, camphene, mesytilene, azulene), and the L99A mutant structure modelled with a method previously described [24]. Atom charges were assigned with the Gasteiger-Marsili charging algorithm [30].…”

“…Partial charges were defined in protein and ligands using the CVFF force field. As only small accommodation changes of the residues near the protein cavity are expected [20,24], the ''bulk'' of the receptor was taken as rigid while the residues with atoms at the cavity surface and the ligand were considered flexible, which significantly saves computation time. Non-bonded interactions were calculated using a grid-based approach developed by Luty et al [39].…”

“…Cavity containing mutants of Anabaena PCC 7119 flavodoxin were designed using a method previously developed in our laboratory [24]. Percentages of volume reduction of the modelled cavities were calculated as 100…”

“…Crystallographic analysis of the protein response to cavity creating mutations [20] has revealed that, in many cases, the residues surrounding the cavity only experience slight readjustments. In addition, it has been recently shown that the structure of cavity-bearing mutant proteins can be precisely modelled from the corresponding wild type structures using simple procedures [24]. It seems therefore that protein cavities are suitable scenarios for designing ligand binding sites with specific characteristics not shared by solvent exposed binding sites.…”