The scripts and input files that accompany this demo can be found in the
demos/public directory of the Rosetta weekly releases.
KEYWORDS: DESIGN GENERAL
If you want to run supercharge now, the application is called 'supercharge' in
Here are four examples:
// Rosetta-mode, positive-charge, fixed surface cutoff and input ref energies
$> $ROSETTA3/bin/supercharge.default.macosgccrelease @rosetta_inputs/options1
// Rosetta-mode, negative-charge, fixed surface cutoff and target net charge
$> $ROSETTA3/bin/supercharge.default.macosgccrelease @rosetta_inputs/options2
// AvNAPSA-mode, negative-charge, target net charge
$> $ROSETTA3/bin/supercharge.default.macosgccrelease @rosetta_inputs/options3
// AvNAPSA-mode, positive-charge, fixed surface cutoff
$> $ROSETTA3/bin/supercharge.default.macosgccrelease @rosetta_inputs/options4
Rosetta-mode and AvNAPSA-mode are explained below...
Reengineering protein surfaces to have high net charge, called supercharging, can improve reversibility of unfolding by preventing aggregation of partially unfolded states. Aggregation is a common obstacle for use of proteins in biotechnology and medicine. Additionally, highly cationic proteins and peptides are capable of nonviral cell entry, and highly anionic proteins are filtered by kidneys more slowly than neutral or cationic proteins.
Optimal positions for incorporation of charged side chains should be determined, as numerous mutations and accumulation of like-charges can also destabilize the native state. A previously demonstrated approach deterministically mutates flexible polar residues (amino acids DERKNQ) with the fewest average neighboring atoms per side chain atom (AvNAPSA: Lawrence MS, Phillips KJ, Liu DR, 2007, Supercharging proteins can impart unusual resilience, JACS). Our approach uses Rosetta-based energy calculations to choose the surface mutations. Both automated approaches for supercharging are implemented in this online server.
There are two automated approaches, Rosetta supercharge (Rsc) and AvNAPSA supercharge (Asc)
AvNAPSA supercharge philosophy (Asc): mutate the most exposed polar residues to minimize structural change or destabilization. Only DE-RK-NQ residues can be mutated.
Rosetta supercharge philosophy (Rsc): mutate residue positions that preserve and/or add favorable surface interactions. Hydrophobic and small polar surface residues can also be mutated.
AvNAPSA drawbacks: mutating surface polar residues can eliminate hydrogen bonds. Helix capping, edge-strand interaction, and loop stabilization all result from surface hydrogen bonds. Furthermore, this automated protocol mutates N to D and Q to E, but N and Q sometimes act simultaneously as a donor and acceptor for hydrogen bonds.
Rosetta drawbacks: mutating less-exposed positions can lead to better computed energies, but mistakes at these positions can be destabilizing. AvNAPSA favors charge swaps, so Rosetta requires more mutations to accomplish the same net charge.
The AvNAPSA approach varies net charge by adjusting the surface cutoff. The Rosetta approach varies net charge by adjusting reference energies of the positive or negatively charged residues.
The supercharge server can run in four different modes: -AvNAPSA with a target net charge -AvNAPSA with a surface cutoff -Rosetta with a surface cutoff and target net charge -Rosetta with a surface cutoff and input reference energies for charged residue types
What does AvNAPSA stand for: average number of neighboring atoms per sidechain atom. This is a value that measures the extent of burial/accessibility. It's similar to the residue neighbors by distance that Rosetta typically uses to define the surface, but it's on the atom-level rather than residue-level. AvNAPSA-mode calculates an AvNAPSA value for every residue. 'surface_atom_cutoff' indicates the cutoff AvNAPSA value that defines surface residues. AvNAPSA values of 50-150 are typical for surface residues. AvNAPSA values >150 are typical for core residues. A surface_atom_cutoff of 100 will lead to moderate supercharging. A surface_atom_cutoff of 150 will lead to heavier supercharging.
AvNAPSA_positive BOOL def(false); //run positive-charge AvNAPSA AvNAPSA_negative BOOL def(false); //run negative-charge AvNAPSA target_net_charge SIGNED_INT def(0); //residue positions will be mutated one at a time from most exposed to least exposed until target net charge is achieved surface_atom_cutoff UNSIGNED_INT def(100); // if you have no target net charge in mind, AvNAPSA will mutate all surface DE-RK-NQ residues on the surface, with this surface cutoff
surface_residue_cutoff UNSIGNED_INT def(16); //residues with <16 neighboring residues within 10 Å are considered part of the surface include_arg BOOL def(false); //use arginine in Rosetta supercharge include_lys BOOL def(false); //use lysine in Rosetta supercharge include_asp BOOL def(false); //use aspartate in Rosetta supercharge include_glu BOOL def(false); //use glutamate in Rosetta supercharge //the reference energies of the charged residue types will govern the net charge of Rosetta designs. Rosetta can choose between the allowed charged residue types and the native residue. More negative reference energies will result in more charge mutations. refweight_arg FLOAT def(-0.98); refweight_lys FLOAT def(-0.65); refweight_asp FLOAT def(-0.67); refweight_glu FLOAT def(-0.81); dont_mutate_glyprocys BOOL def(true); //glycine, proline, and cysteine often serve special structural roles in proteins dont_mutate_correct_charge BOOL def(true); //i.e., don’t mutate arginine to lysine dont_mutate_hbonded_sidechains BOOL def(true); //don’t mutate residues with sidechains forming a hydrogen bond pre_packminpack BOOL def(false); //Packrotamers is always done as the first step. This option will go one step further and run packrotamers, sidechain+backbone minimization, packrotamers on the input structure before performing the supercharge design step. nstruct UNSIGNED_INT def(1); //Monte Carlo sequence design of a protein surface is often convergent but it is still stochastic, multiple design runs can be performed if desired. target_net_charge UNSIGNED_INT def(0); //a target net charge can be achieved if desired, this is done in an automated way by incrementing/decrementing charged residue reference energies until the desired net charge results from the Monte Carlo design step.
surface_atom_cutoff UNSIGNED_INT def(100); // this is how AvNAPSA defines surface, can be used in either approach compare_energies BOOL def(false); //prints a full residue-by-residue energy analysis in the log file only_compare_mutated_residues BOOL def(false); //only includes mutated residues in the energy analysis resfile FILE; //this is how you can specify which residues to not mutate. Default setting must be ALLAA, and residue-by-residue settings should be NATAA, as shown below: ALLAA start 20 A NATAA 24 A NATAA 26 A NATAA Note: an input resfile is optional. However, every supercharge run generates an output resfile that governs the design run. The default of this output resfile is NATAA, which prevents core residues from mutating (see below). The input resfile is read first, the output resfile (see below) is read second, and this is why ALLAA must be the default for the input resfile. If the default were NATRO, for example, no design would occur!
As output, a log file, the residue file that governed the design run, and the output PDB are provided. First, the log file contains the exact Rosetta command line, the residue positions identified as located on the surface, a list of charged residues in the final sequence, the net charge, a list of mutations, text for a PyMOL selection to easily view the mutations in PyMOL, and optionally, a full energetic comparison of repacked native versus supercharged structures. Secondly, the Rosetta residue file indicates which residue positions could possibly mutate, and to what residue types. The third output file is the atomic coordinate file of the supercharged protein, in PDB format, and the naming of the output PDB is intended to facilitate self-documentation of the inputs for a given design run. For Rosetta designs, the name includes the final reference energies that were used and the final net charge, and for AvNAPSA designs, the name includes the net charge and the largest AvNAPSA value of the mutated residues.
This is what an output resfile looks like for AvNAPSA-positive supercharging, which always chooses lysine:
NATAA start 6 A PIKAA K 19 A PIKAA K 21 A PIKAA K 32 A PIKAA K 34 A PIKAA K 39 A PIKAA K
This is what an output resfile looks like for Rosetta positive supercharging, which allows choice between native and RK, and preserves h-bonds:
NATAA start 6 A PIKAA ERK 9 A PIKAA TRK 11 A PIKAA VRK 21 A PIKAA DRK 25 A PIKAA HRK 26 A NATAA #same charge 30 A NATAA #same charge 32 A NATRO #has sc hbond energy=-1.15844 38 A PIKAA TRK 39 A NATRO #has sc hbond energy=-1.33149 43 A PIKAA TRK 50 A NATRO #has sc hbond energy=-0.536622 52 A NATAA #same charge 76 A PIKAA DRK 77 A PIKAA HRK