Fitting Loops Into Gaps¶

CCD¶

The ProMod3 implementation of the cyclic coordinate descent first superposes the n-stem of the input loop with the provided n-stem positions. In every iteration of the algorithm, we loop over all residues of the loop and find the ideal phi/psi angles to minimize the RMSD between the c-stem and the target c-stem positions. Iterations continue until a c-stem RMSD threshold is reached or the number of iterations hits a limit. By performing CCD, unfavorable backbone dihedral pairs can be introduced. It is therefore optionally possible to use torsion samplers to guide the iterative process. In this case, the algorithm calculates the probability of observing the dihedral pair before and after performing the phi/psi update. If the fraction after/before is smaller than a uniform random number in the range [0,1[, the proposed dihedral pair gets rejected. Please note, that this increases the probability of non-convergence.

class promod3.modelling.CCD¶

Class, that sets up everything you need to perform a particular loop closing action.

CCD(sequence, n_stem, c_stem, torsion_sampler, max_steps, rmsd_cutoff, seed)¶

All runs with this CCD object will be with application of torsion samplers to avoid moving into unfavourable regions of the backbone dihedrals.

Parameters:

• sequence (str) – Sequence of the backbones to be closed

• n_stem (ost.mol.ResidueHandle) – Residue defining the n_stem. If the residue before n_stem doesn’t exist, the torsion sampler will use a default residue (ALA) and and phi angle (-1.0472) to evaluate the first angle.

• c_stem (ost.mol.ResidueHandle) – Residue defining the c_stem. If the residue after c_stem doesn’t exist, the torsion sampler will use a default residue (ALA) and psi angle (-0.7854) to evaluate the last angle.

• torsion_sampler (TorsionSampler / list of TorsionSampler) – To extract probabilities for the analysis of the backbone dihedrals. Either a list of torsion samplers (one for for every residue of the loop to be closed) or a single one (used for all residues).

• max_steps (int) – Maximal number of iterations

• rmsd_cutoff (float) – The algorithm stops as soon as the c_stem of the loop to be closed has RMSD below the c_stem

• seed (int) – Seed of random number generator to decide whether new phi/psi pair should be accepted.

Raises:

RuntimeError if a list of torsion samplers is given with inconsistent length regarding the sequence. Another requirement is that all backbone atoms of the stems must be present.

CCD(n_stem, c_stem, max_steps, rmsd_cutoff)¶

All runs with this CCD object will be without application of torsion samplers. This is faster but might lead to weird backbone dihedral pairs.

Parameters:

• n_stem (ost.mol.ResidueHandle) – Residue defining the n_stem

• c_stem (ost.mol.ResidueHandle) – Residue defining the c_stem

• max_steps (int) – Maximal number of iterations

• rmsd_cutoff (float) – The algorithm stops as soon as the c_stem of the loop to be closed has RMSD below the given c_stem

Close(bb_list)¶

Closes given bb_list with the settings set at initialization.

Parameters:

bb_list (BackboneList) – Loop to be closed

Returns:

Whether rmsd_cutoff has been reached

Return type:

bool

Raises:

RuntimeError if the CCD object has been initialized with TorsionSampler support and the length of the bb_list is not consistent with the initial sequence.

KIC¶

The kinematic closure leads to a fragmentation of the loop based on given pivot residues. It then calculates possible relative orientations of these fragments by considering constraints of bond length and bond angles at these pivot residues. Due to the internal mathematical formalism, up to 16 solutions can be found for a given loop closing problem.

class promod3.modelling.KIC¶

Class, that sets up everything you need to perform a particular loop closing action.

KIC(n_stem, c_stem)¶

All runs of this KIC closing object will adapt the input loops to these stem residues.

Parameters:

• n_stem – Residue describing the stem towards n_ter

• c_stem – Residue describing the stem towards c_ter

Close(bb_list, pivot_one, pivot_two, pivot_three)¶

Applies KIC algorithm, so that the output loops match the given stem residues

Parameters:

• bb_list (BackboneList) – Loop to be closed

• pivot_one (int) – Index of first pivot residue

• pivot_two (int) – Index of second pivot residue

• pivot_three (int) – Index of third pivot residue

Returns:

List of closed loops (maximum of 16 entries)

Return type:

list of BackboneList

Raises:

RuntimeError in case of invalid pivot indices.

Relaxing Backbones¶

In many cases one wants to quickly relax a loop. This can be useful to close small gaps in the backbone or resolve the most severe clashes. The BackboneRelaxer internally sets up a topology for the input loop. Once setup, every loop of the same length and sequence can be relaxed by the relaxer.

class promod3.modelling.BackboneRelaxer(bb_list, ff, fix_nterm=True, fix_cterm=True)¶

Sets up a molecular mechanics topology for given bb_list. Every BackboneList of same length and sequence can then be relaxed. The parametrization is taken from ff, simply neglecting all interactions to non backbone atoms. The electrostatics get neglected completely by setting all charges to 0.0.

Parameters:

• bb_list (BackboneList) – Basis for topology creation

• ff (promod3.loop.ForcefieldLookup) – Source for parametrization

• fix_nterm (bool) – Whether N-terminal backbone positions (N, CA, CB) should be kept rigid during relaxation (C and O are left to move).

• fix_cterm (bool) – Whether C-terminal backbone positions (CA, CB, C, O) should be kept rigid during relaxation (N is left to move).

class promod3.modelling.BackboneRelaxer(bb_list, density, resolution, fix_nterm=True, fix_cterm=True)¶

Parameters:

• bb_list (BackboneList) – Basis for topology creation

• fix_nterm (bool) – Whether N-terminal backbone positions (N, CA, CB) should be kept rigid during relaxation.

• fix_cterm (bool) – Whether C-terminal backbone positions (CA, CB, C, O) should be kept rigid during relaxation.

Raises:

RuntimeError if size of bb_list is below 2

Run(bb_list, steps=100, stop_criterion=0.01)¶

Performs steepest descent on given BackboneList. The function possibly fails if there are particles (almost) on top of each other, resulting in NaN positions in the OpenMM system. The positions of bb_list are not touched in this case and the function returns an infinit potential energy. In Python you can check for that with float(“inf”).

Parameters:

• bb_list (BackboneList) – To be relaxed

• steps (int) – number of steepest descent steps

• stop_criterion (float) – If maximum force acting on a particle falls below that threshold, the relaxation aborts.

Returns:

Forcefield energy upon relaxation, infinity in case of OpenMM Error

Return type:

float

Raises:

RuntimeError if bb_list has not the same size or sequence as the initial one.

AddNRestraint(idx, pos, force_constant=100000)¶

Adds harmonic position restraint for nitrogen atom at specified residue

Parameters:

• idx (int) – Idx of residue

• pos (ost.geom.Vec3) – Restraint Position (in Angstrom)

• force_constant (float) – Force constant in kJ/mol/nm^2

Raises:

RuntimeError if idx is too large

AddCARestraint(idx, pos, force_constant=100000)¶

Adds harmonic position restraint for CA atom at specified residue

Parameters:

• idx (int) – Idx of residue

• pos (ost.geom.Vec3) – Restraint Position (in Angstrom)

• force_constant (float) – Force constant in kJ/mol/nm^2

Raises:

RuntimeError if idx is too large

AddCBRestraint(idx, pos, force_constant=100000)¶

Adds harmonic position restraint for CB atom at specified residue, doesn’t do anything if specified residue is a glycine

Parameters:

• idx (int) – Idx of residue

• pos (ost.geom.Vec3) – Restraint Position (in Angstrom)

• force_constant (float) – Force constant in kJ/mol/nm^2

Raises:

RuntimeError if idx is too large

AddCRestraint(idx, pos, force_constant=100000)¶

Adds harmonic position restraint for C atom at specified residue

Parameters:

• idx (int) – Idx of residue

• pos (ost.geom.Vec3) – Restraint Position (in Angstrom)

• force_constant (float) – Force constant in kJ/mol/nm^2

Raises:

RuntimeError if idx is too large

AddORestraint(idx, pos, force_constant=100000)¶

Adds harmonic position restraint for O atom at specified residue

Parameters:

• idx (int) – Idx of residue

• pos (ost.geom.Vec3) – Restraint Position (in Angstrom)

• force_constant (float) – Force constant in kJ/mol/nm^2

Raises:

RuntimeError if idx is too large

SetNonBondedCutoff(nonbonded_cutoff)¶

Defines cutoff to set for non bonded interactions. By default, all atom- pairs are compared which is the fastest for small backbones (e.g. in CloseSmallDeletions()). Otherwise, a cutoff around 5 is reasonable as we ignore electrostatics.

Parameters:

nonbonded_cutoff (float) – Cutoff to set for non bonded interactions. Negative means no cutoff.

GetNonBondedCutoff(nonbonded_cutoff)¶

Returns:

Cutoff for non bonded interactions.

Return type:

float

Relaxing All Atom Loops¶

After the reconstruction of loop sidechains with the Reconstruct() method of SidechainReconstructor, it may be desired to quickly relax the loop. The AllAtomRelaxer class takes care of that.

Example usage:

from ost import io
from promod3 import modelling, loop

# load example (has res. numbering starting at 1)
prot = io.LoadPDB('data/1CRN.pdb')
res_list = prot.residues
seqres_str = ''.join([r.one_letter_code for r in res_list])

# initialize AllAtom environment and sidechain reconstructor
env = loop.AllAtomEnv(seqres_str)
env.SetInitialEnvironment(prot)
sc_rec = modelling.SidechainReconstructor()
sc_rec.AttachEnvironment(env)

# "reconstruct" subset (res. num. 6..10) -> sidechains kept here
sc_result = sc_rec.Reconstruct(6, 5)
# setup sys creator
ff_lookup = loop.ForcefieldLookup.GetDefault()
mm_sys = loop.MmSystemCreator(ff_lookup)
relaxer = modelling.AllAtomRelaxer(sc_result, mm_sys)
# relax loop
pot_e = relaxer.Run(sc_result, 300, 0.1)
print("Potential energy after: %g" % pot_e)
# update environment with solution
env.SetEnvironment(sc_result.env_pos)
# store all positions of environment
io.SavePDB(env.GetAllAtomPositions().ToEntity(), 'aa_relax_test.pdb')

class promod3.modelling.AllAtomRelaxer(sc_data, mm_system_creator)¶

Setup relaxer for a given sidechain reconstruction result and a given MM system creator, which takes care of generating a simulation object.

N/C stems of loop are fixed if they are non-terminal.

Parameters:

• sc_data (SidechainReconstructionData) – Sidechain reconstruction result

• mm_system_creator (MmSystemCreator) – System creator to be used here

Run(sc_data, steps=100, stop_criterion=0.01)¶

Performs steepest descent for this system and writes updated positions into sc_data.env_pos.all_pos. The function possibly fails if there are particles (almost) on top of each other, resulting in NaN positions in the OpenMM system. The positions of sc_data.env_pos.all_pos are not touched in this case and the function returns an infinit potential energy. In Python you can check for that with float(“inf”).

Parameters:

• sc_data (SidechainReconstructionData) – Sidechain reconstruction result to be updated

• steps (int) – Number of steepest descent steps

• stop_criterion (float) – If maximum force acting on a particle falls below that threshold, the relaxation aborts.

Returns:

Potential energy after relaxation, infinity in case of OpenMM Error

Return type:

float

Raises:

RuntimeError if sc_data is incompatible with the one given in the constructor.

UpdatePositions(sc_data)¶

Resets simulation positions to a new set of positions. It is assumed that this sc_data object has the same amino acids as loops and surrounding and the same disulfid bridges as the one given in the constructor.

Parameters:

sc_data (SidechainReconstructionData) – Get new positions from sc_data.env_pos.all_pos

Raises:

RuntimeError if sc_data is incompatible with the one given in the constructor.

GetSystemCreator()¶

Returns:

MM system creator passed in the constructor

Return type:

MmSystemCreator