psico.fitting

2011 Thomas Holder, MPI for Developmental Biology

License: BSD-2-Clause

Functions

`alignwithanymethod`(mobile, target[, ...])	Align copies of mobile to target with several alignment methods
`dyndom`(mobile, target[, window, domain, ...])	DynDom wrapper
`dyndom_parse_info`(filename[, selection, ...])
`extra_fit`([selection, reference, method, ...])	Like "intra_fit", but for multiple objects instead of multiple states.
`gdt_ts`(mobile, target[, cutoffs, quiet, _self])	Global Distance Test Total Score (GDT_TS)
`get_rmsd_func`()	API only.
`intra_boxfit`([selection, center, _self])	Center selection in simulation box.
`intra_center`([selection, state, _self])	Center frames on a selection.
`intra_promix`(selection[, K, prefix, ...])	Finds rigid segments in a multi-state object.
`intra_theseus`(selection[, state, cov, ...])	Fits all states of an object to an atom selection with maximum likelihood.
`intra_xfit`(selection[, load_b, cycles, ...])	Weighted superposition of all states of an object to the intermediate structure over all states.
`local_rms`(mobile, target[, window, ...])	"local_rms" computes the C-alpha RMS fit within a sliding window along the backbone.
`promix`(mobile, target[, K, prefix, ...])	Finds rigid segments in two objects with different conformation.
`prosmart`(mobile, target[, mobile_state, ...])	ProSMART wrapper.
`theseus`(mobile, target[, match, cov, ...])	Structural superposition of two molecules with maximum likelihood.
`tmalign`(mobile, target[, mobile_state, ...])	TMalign wrapper.
`usalign`(mobile, target[, mobile_state, ...])	USalign wrapper.
`xfit`(mobile, target[, mobile_state, ...])	Weighted superposition of the model in the first selection on to the model in the second selection.

Classes

MatchMaker(mobile, target, match, *[, ...])

API only.

class psico.fitting.MatchMaker(mobile, target, match, *, autodelete=True, _self=...)[source]

Bases: object

API only. Matches two atom selections and provides two matched subselections with equal atom count. May involve temporary objects or named selections which will be automatically deleted.

Arguments

mobile = string: first atom selection

target = string: second atom selection

match = string: method how to match atoms

none: (dummy)

in: match atoms by “in” operator

like: match atoms by “like” operator

align: match atoms by cmd.align (without refinement)

super: match atoms by cmd.super (without refinement)

<name of alignment object>: use given alignment

Result

Properties “mobile” and “target” hold the matched subselections as selection strings.

align(mobile, target, match)[source]: Align mobile to target using the alignment method given by “match”

check()[source]

from_alignment(mobile, target, aln_obj)[source]: Use alignment given by “aln_obj” (name of alignment object)

psico.fitting.alignwithanymethod(mobile, target, methods=None, async_=1, quiet=1, *, _self=..., **kwargs)[source]: Align copies of mobile to target with several alignment methods

Arguments

mobile = string: atom selection

target = string: atom selection

methods = string: space separated list of PyMOL commands which take arguments “mobile” and “target” (in any order) {default: align super cealign tmalign theseus}

psico.fitting.dyndom(mobile, target, window=5, domain=20, ratio=1.0, exe='', transform=1, quiet=1, mobile_state=1, target_state=1, match='align', preserve=0, *, _self=...)[source]

DynDom wrapper

DynDom is a program to determine domains, hinge axes and hinge bending residues in proteins where two conformations are available.

http://fizz.cmp.uea.ac.uk/dyndom/

Usage

dyndom mobile, target [, window [, domain [, ratio ]]]

psico.fitting.dyndom_parse_info(filename, selection='(all)', quiet=0, *, _self=...)[source]

psico.fitting.extra_fit(selection='(all)', reference=None, method='align', zoom=1, quiet=0, _self=..., **kwargs)[source]: Like “intra_fit”, but for multiple objects instead of multiple states.

Arguments

selection = string: atom selection of multiple objects {default: all}

reference = string: reference object name {default: first object in selection}

method = string: alignment method (command that takes “mobile” and “target” arguments, like “align”, “super”, “cealign” {default: align}

… extra arguments are passed to “method”

See Also

alignto, cmd.util.mass_align, align_all.py from Robert Campbell

psico.fitting.gdt_ts(mobile, target, cutoffs='1 2 4 8', quiet=1, *, _self=...)[source]: Global Distance Test Total Score (GDT_TS)

psico.fitting.get_rmsd_func()[source]: API only. Returns a function that uses either numpy (fast) or chempy.cpv (slow) to calculate the rmsd fit of two nx3 arrays.

psico.fitting.intra_boxfit(selection='polymer', center=(0.5, 0.5, 0.5), *, _self=...)[source]: Center selection in simulation box.

Arguments

selection = str: atom selection to center {default: polymer}

center = list-of-3-floats: Target position in fractional space {default: [0.5, 0.5, 0.5]}

psico.fitting.intra_center(selection: str = 'polymer', state: int = -1, *, _self=...)[source]: Center frames on a selection. Like intra_fit, but without rotation.

Arguments

selection = str: atom selection to center {default: polymer}

state = int: reference state {default: current state}

psico.fitting.intra_promix(selection, K=0, prefix=None, conformers=0, guide=1, quiet=1, async_=-1, _self=..., **kwargs)[source]

Finds rigid segments in a multi-state object.

Requires CSB, https://github.com/csb-toolbox/CSB

Arguments

selection = string: atom selection

K = integer: Number of segments {default: guess}

prefix = string: Prefix of named segment selections to make

Reference

Mixture models for protein structure ensembles Hirsch M, Habeck M. - Bioinformatics. 2008 Oct 1;24(19):2184-92

psico.fitting.intra_theseus(selection, state=1, cov=0, cycles=200, exe='theseus', preserve=0, quiet=1, *, _self=...)[source]

Fits all states of an object to an atom selection with maximum likelihood.

THESEUS: Maximum likelihood multiple superpositioning http://www.theseus3d.org

Arguments

selection = string: atoms to fit

state = integer: keep transformation of this state unchanged {default: 1}

cov = 0/1: 0 is variance weighting, 1 is covariance weighting (slower) {default: 0}

Arguments

selection = string: atom selection

load_b = 0 or 1: save -log(weights) into B-factor column {default: 0}

Note

Assumes all states to have identical number of CA-atoms.

Usage

local_rms mobile, target [, window ]

Arguments

mobile = string: object to assign b-factors and to visualize as putty cartoon

target = string: object to superimpose mobile to

window = integer: width of sliding window {default: 20}

match = string: in, like, align, none or the name of an alignment object {default: align}

in: match all atom identifiers (segi,chain,resn,resi,name)

like: match residue number (resi)

align: do a sequence alignment

none: assume same number of atoms in both selections

name of alignment object: take sequence alignment from object

Example

fetch 2x19 2xwu, async=0 remove not chain B or not polymer local_rms 2x19, 2xwu, 40

psico.fitting.promix(mobile, target, K=0, prefix=None, mobile_state=-1, target_state=-1, match='align', guide=1, quiet=1, async_=-1, _self=..., **kwargs)[source]

Finds rigid segments in two objects with different conformation.

Requires CSB, https://github.com/csb-toolbox/CSB

Arguments

mobile, target = string: atom selections

K = integer: Number of segments {default: guess}

prefix = string: Prefix of named segment selections to make

Reference

Mixture models for protein structure ensembles Hirsch M, Habeck M. - Bioinformatics. 2008 Oct 1;24(19):2184-92

psico.fitting.prosmart(mobile, target, mobile_state=1, target_state=1, exe='prosmart', transform=1, object=None, quiet=0, *, _self=...)[source]

ProSMART wrapper.

http://www2.mrc-lmb.cam.ac.uk/groups/murshudov/

psico.fitting.theseus(mobile, target, match='align', cov=0, cycles=200, mobile_state=1, target_state=1, exe='theseus', preserve=0, quiet=1, *, _self=...)[source]

Structural superposition of two molecules with maximum likelihood.

THESEUS: Maximum likelihood multiple superpositioning http://www.theseus3d.org

Arguments

mobile = string: atom selection for mobile atoms

target = string: atom selection for target atoms

match = string: in, like, align, none or the name of an alignment object (see “local_rms” help for details) {default: align}

cov = 0/1: 0 is variance weighting, 1 is covariance weighting (slower) {default: 0}

Arguments

mobile, target = string: atom selections

mobile_state, target_state = int: object states {default: 1}

args = string: Extra arguments like -d0 5 -L 100

exe = string: Path to TMalign (or TMscore, MMalign, USalign) executable {default: TMalign}

ter = 0/1: If ter=0, then ignore chain breaks because TMalign will stop at first TER record {default: 0}

psico.fitting.usalign(mobile: str, target: str, mobile_state: int = 1, target_state: int = 1, *, mm: int = 1, ter: int = 0, args: str = '', exe='USalign', _self=..., **_kwargs)[source]

USalign wrapper. See tmalign for details.

Reference: Zhang et al., Nature Methods 2022 19: 1109-1115 https://zhanggroup.org/US-align/

Note: PyMOL 3.2 adds a native usalign implementation

Arguments

mm = int: 1=multi-chain, 3=circularly permutated {default: 1}

ter = int: 3=stop at the first TER record {default: 0}

Example

fetch 1y39 2zjr usalign 1y39 & chain A+C, 2zjr & chain F+X, object=aln

psico.fitting.xfit(mobile, target, mobile_state=-1, target_state=-1, load_b=0, cycles=10, match='align', guide=1, seed=0, quiet=1, bfit=0, distribution='student', _self=...)[source]

Weighted superposition of the model in the first selection on to the model in the second selection. The weights are estimated with maximum likelihood.

The result should be very similar to “theseus”.

Requires CSB, https://github.com/csb-toolbox/CSB

Arguments

mobile = string: atom selection

target = string: atom selection

mobile_state = int: object state of mobile selection {default: current}

target_state = int: object state of target selection {default: current}

load_b = 0 or 1: save -log(weights) into B-factor column {default: 0}

psico.fitting

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