pmi/restraints/proteomics.py

"""@namespace IMP.pmi.restraints.proteomics
Restraints for handling various kinds of proteomics data.
"""

from __future__ import print_function
import IMP
import IMP.core
import IMP.algebra
import IMP.atom
import IMP.container
import IMP.pmi
import IMP.pmi.tools
import IMP.pmi.output
import numpy
import math
import math
import sys
import warnings


class ConnectivityRestraint(object):

    '''
    generate a connectivity restraint between domains
    setting up the restraint
    example:
    sel1 = IMP.atom.Selection(root_hier, molecule="Rpb3",
                              residue_indexes=range(1,100))
    sel2 = IMP.atom.Selection(root_hier, molecule="Rpb4",
                              residue_indexes=range(1,100))
    cr=restraints.ConnectivityRestraint((sel1, sel2), label='CR1')
    cr.add_to_model()
    Multistate support =No
    Resolution=Yes
    '''

    def __init__(self, domains, kappa=10.0, resolution=None, label="None"):
        self.weight = 1.0
        self.kappa = kappa
        self.label = label

        cr = IMP.atom.create_connectivity_restraint(
            domains, self.kappa, self.label)
        self.m = cr.get_model()
        self.rs = IMP.RestraintSet(self.m, label)
        self.rs.add_restraint(cr)

    def set_label(self, label):
        self.label = label
        self.rs.set_name(label)
        for r in self.rs.get_restraints():
            r.set_name(label)

    def add_to_model(self):
        IMP.pmi.tools.add_restraint_to_model(self.m, self.rs)

    def get_restraint(self):
        return self.rs

    def get_restraints(self):
        rlist = []
        for r in self.rs.get_restraints():
            rlist.append(IMP.core.PairRestraint.get_from(r))
        return rlist

    def set_weight(self, weight):
        self.weight = weight
        self.rs.set_weight(weight)

    def get_output(self):
        output = {}
        score = self.weight * self.rs.unprotected_evaluate(None)
        output["_TotalScore"] = str(score)
        output["ConnectivityRestraint_" + self.label] = str(score)
        return output

#


class CompositeRestraint(object):

    '''
    handleparticles a list of particles
    compositeparticles is a list of list of particles
    '''

    def __init__(self, handle_particles, composite_particles, cut_off=5.0,
                 lam=1.0, plateau=0.0, resolution=None, label="None"):
        # composite particles: all particles beside the handle
        self.label = label

        hs = IMP.pmi.tools.input_adaptor(handle_particles, resolution,
                                         flatten=True)
        self.handleparticles = [h.get_particle() for h in hs]
        self.m = self.handleparticles[0].get_model()
        self.rs = IMP.RestraintSet(self.m, 'cr')

        self.compositeparticles = []
        compositeparticle_list = []
        for cp in composite_particles:
            hs = IMP.pmi.tools.input_adaptor(cp, resolution, flatten=True)
            tmplist = [h.get_particle() for h in hs]
            compositeparticle_list.append(tmplist)
            self.compositeparticles += tmplist

        ln = IMP.pmi.CompositeRestraint(self.m, self.handleparticles, cut_off,
            lam, True, plateau)

        for ps in compositeparticle_list:
            # composite particles is a list of list of particles
            ln.add_composite_particle(ps)

        self.rs.add_restraint(ln)

    def set_label(self, label):
        self.label = label

    def get_handle_particles(self):
        return self.handleparticles

    def get_composite_particles(self):
        return self.compositeparticles

    def get_restraint(self):
        return self.rs

    def add_to_model(self):
        IMP.pmi.tools.add_restraint_to_model(self.m, self.rs)

    def get_output(self):
        output = {}
        score = self.rs.unprotected_evaluate(None)
        output["_TotalScore"] = str(score)
        output["CompositeRestraint_" + self.label] = str(score)
        return output


#
class AmbiguousCompositeRestraint(object):

    '''
    this restraint allows ambiguous cross-linking between multiple copies
    excluding between symmetric copies
    It allows name ambiguity
    '''

    def __init__(self, root_hier, restraints_file, cut_off=5.0, lam=1.0,
                 plateau=0.01, resolution=None, label="None"):
        self.weight = 1.0
        self.m = root_hier.get_model()
        self.rs = IMP.RestraintSet(self.m, 'data')
        self.label = "None"
        self.pairs = []

        self.outputlevel = "low"
        self.cut_off = cut_off
        self.lam = lam
        self.plateau = plateau

        fl = IMP.pmi.tools.open_file_or_inline_text(restraints_file)

        for line in fl:

            tokens = line.split()
            # skip character
            if (tokens[0] == "#"):
                continue
            r1 = int(tokens[2])
            c1 = tokens[0]
            r2 = int(tokens[3])
            c2 = tokens[1]

            ps1 = IMP.atom.Selection(root_hier, resolution=resolution,
                                     molecule=c1, residue_index=r1)
            ps1 = ps1.get_selected_particles()
            hrc1 = [p.get_name() for p in ps1]
            def nosym_subset(ps):
                return [p for p in ps if not IMP.pmi.Symmetric.get_is_setup(p)
                        or IMP.pmi.Symmetric(p).get_symmetric() == 0]
            ps1nosym = nosym_subset(ps1)
            hrc1nosym = [p.get_name() for p in ps1nosym]

            if len(ps1) == 0:
                warnings.warn(
                    "AmbiguousCompositeRestraint: residue %d of chain %s "
                    "is not there" % (r1, c1), IMP.pmi.StructureWarning)
                continue

            ps2 = IMP.atom.Selection(root_hier, resolution=resolution,
                                     molecule=c2, residue_index=r2)
            ps2 = ps2.get_selected_particles()
            hrc2 = [p.get_name() for p in ps2]
            ps2nosym = nosym_subset(ps2)
            hrc2nosym = [p.get_name() for p in ps2nosym]

            if len(ps2) == 0:
                warnings.warn(
                    "AmbiguousCompositeRestraint: residue %d of chain %s "
                    "is not there" % (r2, c2), IMP.pmi.StructureWarning)
                continue

            cr = IMP.pmi.CompositeRestraint(
                self.m, ps1nosym, self.cut_off, self.lam, True, self.plateau)
            cr.add_composite_particle(ps2)

            self.rs.add_restraint(cr)
            self.pairs.append(
                (ps1nosym,
                 hrc1nosym,
                 c1,
                 r1,
                 ps2,
                 hrc2,
                 c2,
                 r2,
                 cr))

            cr = IMP.pmi.CompositeRestraint(
                self.m, ps1, self.cut_off, self.lam, True, self.plateau)
            cr.add_composite_particle(ps2nosym)

            self.rs.add_restraint(cr)
            self.pairs.append(
                (ps1,
                 hrc1,
                 c1,
                 r1,
                 ps2nosym,
                 hrc2nosym,
                 c2,
                 r2,
                 cr))

    def plot_restraint(
        self,
        maxdist=100,
            npoints=100):

        p1 = IMP.Particle(self.m)
        p2 = IMP.Particle(self.m)
        d1 = IMP.core.XYZR.setup_particle(p1)
        d2 = IMP.core.XYZR.setup_particle(p2)
        cr = IMP.pmi.CompositeRestraint(
            self.m,
            [p1],
            self.cut_off,
            self.lam,
            True,
            self.plateau)
        cr.add_composite_particle([p2])
        dists = []
        scores = []
        for i in range(npoints):
            d2.set_coordinates(
                IMP.algebra.Vector3D(maxdist / npoints * float(i), 0, 0))
            dists.append(IMP.core.get_distance(d1, d2))
            scores.append(cr.unprotected_evaluate(None))
        IMP.pmi.output.plot_xy_data(dists, scores)

    def set_label(self, label):
        self.label = label
        self.rs.set_name(label)
        for r in self.rs.get_restraints():
            r.set_name(label)

    def add_to_model(self):
        IMP.pmi.tools.add_restraint_to_model(self.m, self.rs)

    def get_hierarchies(self):
        return self.prot

    def get_restraint_sets(self):
        return self.rs

    def get_restraint(self):
        return self.rs

    def set_output_level(self, level="low"):
            # this might be "low" or "high"
        self.outputlevel = level

    def set_weight(self, weight):
        self.weight = weight
        self.rs.set_weight(weight)

    def get_output(self):
        # content of the cross-link database pairs
        # self.pairs.append((p1,p2,dr,r1,c1,r2,c2))
        output = {}
        score = self.weight * self.rs.unprotected_evaluate(None)
        output["_TotalScore"] = str(score)
        output["AmbiguousCompositeRestraint_Score_" + self.label] = str(score)
        for n, p in enumerate(self.pairs):

            ps1 = p[0]
            hrc1 = p[1]
            c1 = p[2]
            r1 = p[3]
            ps2 = p[4]
            hrc2 = p[5]
            c2 = p[6]
            r2 = p[7]
            cr = p[8]
            for n1, p1 in enumerate(ps1):
                name1 = hrc1[n1]

                for n2, p2 in enumerate(ps2):
                    name2 = hrc2[n2]
                    d1 = IMP.core.XYZR(p1)
                    d2 = IMP.core.XYZR(p2)
                    label = str(r1) + ":" + name1 + "_" + str(r2) + ":" + name2
                    output["AmbiguousCompositeRestraint_Distance_" +
                           label] = str(IMP.core.get_distance(d1, d2))

            label = str(r1) + ":" + c1 + "_" + str(r2) + ":" + c2
            output["AmbiguousCompositeRestraint_Score_" +
                   label] = str(self.weight * cr.unprotected_evaluate(None))

        return output


#
class SimplifiedPEMAP(object):

    def __init__(self, root_hier, restraints_file, expdistance, strength,
                 resolution=None):
        self.m = root_hier.get_model()
        self.rs = IMP.RestraintSet(self.m, 'data')
        self.label = "None"
        self.pairs = []

        self.outputlevel = "low"
        self.expdistance = expdistance
        self.strength = strength

        fl = IMP.pmi.tools.open_file_or_inline_text(restraints_file)

        for line in fl:

            tokens = line.split()
            # skip character
            if (tokens[0] == "#"):
                continue
            r1 = int(tokens[2])
            c1 = tokens[0]
            r2 = int(tokens[3])
            c2 = tokens[1]
            pcc = float(tokens[4])

            ps1 = IMP.atom.Selection(root_hier, resolution=resolution,
                                     molecule=c1, residue_index=r1,
                                     copy_index=0)
            ps1 = ps1.get_selected_particles()
            if len(ps1) == 0:
                warnings.warn(
                    "SimplifiedPEMAP: residue %d of chain %s is not there "
                    "(w/ %d %s)" % (r1, c1, r2, c2), IMP.pmi.StructureWarning)
                continue
            if len(ps1) > 1:
                warnings.warn(
                    "SimplifiedPEMAP: residue %d of chain %s selected "
                    "multiple particles" % (r1, c1), IMP.pmi.StructureWarning)
                continue

            ps2 = IMP.atom.Selection(root_hier, resolution=resolution,
                                     molecule=c2, residue_index=r2,
                                     copy_index=0)
            ps2 = ps2.get_selected_particles()
            if len(ps2) == 0:
                warnings.warn(
                    "SimplifiedPEMAP: residue %d of chain %s is not there "
                    "(w/ %d %s)" % (r1, c1, r2, c2), IMP.pmi.StructureWarning)
                continue
            if len(ps2) > 1:
                warnings.warn(
                    "SimplifiedPEMAP: residue %d of chain %s selected "
                    "multiple particles" % (r2, c2), IMP.pmi.StructureWarning)
                continue

            p1 = ps1[0]
            p2 = ps2[0]

            # This is harmonic potential for the pE-MAP data
            upperdist = self.get_upper_bond(pcc)
            limit = 0.5 * self.strength * 15.0 ** 2 + 10.0
            hub = IMP.core.TruncatedHarmonicUpperBound(
                upperdist, self.strength, 15, limit)

            # This is harmonic for the X-link
            #hub= IMP.core.TruncatedHarmonicBound(17.0,self.strength,upperdist+15,limit)

            df = IMP.core.SphereDistancePairScore(hub)
            dr = IMP.core.PairRestraint(self.m, df, (p1, p2))
            self.rs.add_restraint(dr)
            self.pairs.append((p1, p2, dr, r1, c1, r2, c2))

            # Lower-bound restraint
            lowerdist = self.get_lower_bond(pcc)
            limit = 0.5 * self.strength * 15.0 ** 2 + 10.0
            hub2 = IMP.core.TruncatedHarmonicLowerBound(
                lowerdist, self.strength, 15, limit)

            # This is harmonic for the X-link
            #hub2= IMP.core.TruncatedHarmonicBound(17.0,self.strength,upperdist+15,limit)

            df2 = IMP.core.SphereDistancePairScore(hub2)
            dr2 = IMP.core.PairRestraint(self.m, df2, (p1, p2))
            self.rs.add_restraint(dr2)
            self.pairs.append((p1, p2, dr2, r1, c1, r2, c2))

    def get_upper_bond(self, pearsoncc):
        # return (pearsoncc-1.)/-0.0075
        return (pearsoncc - .5) / (-0.005415)

    def get_lower_bond(self, pearsoncc):
        return (pearsoncc - 1.) / -0.0551

    def set_label(self, label):
        self.label = label

    def add_to_model(self):
        IMP.pmi.tools.add_restraint_to_model(self.m, self.rs)

    def get_hierarchies(self):
        return self.prot

    def get_restraint_sets(self):
        return self.rs

    def set_output_level(self, level="low"):
            # this might be "low" or "high"
        self.outputlevel = level

    def get_output(self):
        # content of the cross-link database pairs
        # self.pairs.append((p1,p2,dr,r1,c1,r2,c2))
        output = {}
        score = self.rs.unprotected_evaluate(None)
        output["_TotalScore"] = str(score)
        output["SimplifiedPEMAP_Score_" + self.label] = str(score)
        for i in range(len(self.pairs)):

            p0 = self.pairs[i][0]
            p1 = self.pairs[i][1]
            crosslinker = 'standard'
            ln = self.pairs[i][2]
            resid1 = self.pairs[i][3]
            chain1 = self.pairs[i][4]
            resid2 = self.pairs[i][5]
            chain2 = self.pairs[i][6]

            label = str(resid1) + ":" + chain1 + "_" + \
                str(resid2) + ":" + chain2
            output["SimplifiedPEMAP_Score_" + crosslinker + "_" +
                   label] = str(ln.unprotected_evaluate(None))

            d0 = IMP.core.XYZ(p0)
            d1 = IMP.core.XYZ(p1)
            output["SimplifiedPEMAP_Distance_" +
                   label] = str(IMP.core.get_distance(d0, d1))

        return output


class SetupConnectivityNetworkRestraint(object):

    '''
    generates and wraps a IMP.pmi.ConnectivityRestraint between domains
    example:
    cr=restraints.ConnectivityNetworkRestraint(simo,["CCC",(1,100,"TTT"),(100,150,"AAA")])
    cr.add_to_model()
    cr.set_label("CR1")

    Multistate support =No
    Selection type=selection tuple
    Resolution=Yes
    '''

    def __init__(self, objects, kappa=10.0, resolution=1.0, label="None"):

        self.weight = 1.0
        self.kappa = kappa
        self.label = label
        if self.label == "None":
            self.label = str(selection_tuples)

        hiers=[]

        for obj in objects:
            hiers.append(IMP.pmi.tools.input_adaptor(obj,
                                                resolution,
                                                flatten=True))
            self.m=hiers[0][0].get_model()

        #particles=[h.get_particle() for h in hiers]
        cr = ConnectivityNetworkRestraint(self.m)
        for hs in hiers:
            cr.add_particles([h.get_particle() for h in hs])
        self.rs = IMP.RestraintSet(self.m, label)
        self.rs.add_restraint(cr)

    def set_label(self, label):
        self.label = label
        self.rs.set_name(label)
        for r in self.rs.get_restraints():
            r.set_name(label)

    def add_to_model(self):
        IMP.pmi.tools.add_restraint_to_model(self.m, self.rs)

    def get_restraint(self):
        return self.rs

    def get_restraints(self):
        rlist = []
        for r in self.rs.get_restraints():
            rlist.append(IMP.core.PairRestraint.get_from(r))
        return rlist

    def set_weight(self, weight):
        self.weight = weight
        self.rs.set_weight(weight)

    def get_output(self):
        output = {}
        score = self.weight * self.rs.unprotected_evaluate(None)
        output["_TotalScore"] = str(score)
        output["ConnectivityNetworkRestraint_" + self.label] = str(score)
        return output


class ConnectivityNetworkRestraint(IMP.Restraint):

    '''
    a python restraint that computes the score for a composite of proteins
    Authors: G. Bouvier, R. Pellarin. Pasteur Institute.
    '''

    def __init__(self, m, slope=1.0, theta=0.0, plateau=0.0000000001, linear_slope=0.015):
        '''
        input a list of particles, the slope and theta of the sigmoid potential
        theta is the cutoff distance for a protein-protein contact
        '''
        # Import networkx here so that we don't introduce it as a dependency
        # for *every* proteomics restraint, only this one
        import networkx
        self.networkx = networkx
        IMP.Restraint.__init__(self, m, "ConnectivityNetworkRestraint %1%")
        self.slope = slope
        self.theta = theta
        self.linear_slope = linear_slope
        self.plateau = plateau
        self.particles_blocks = []
        self.particle_list = []

    def get_number_of_particle_blocks(self):
        return len(self.particles_blocks)

    def get_number_of_particles_for_block(self, block_index):
        return len(self.particles_blocks[block_index])

    def add_particles(self, particles):
        self.particles_blocks.append(particles)
        self.particle_list += particles

    def get_full_graph(self):
        '''
        get the full graph of distances between every particle pair
        '''
        import scipy.spatial
        pdist_array = numpy.array(
            IMP.pmi.get_list_of_bipartite_minimum_sphere_distance(self.particles_blocks))
        pdist_mat = scipy.spatial.distance.squareform(pdist_array)
        pdist_mat[pdist_mat < 0] = 0
        graph = self.networkx.Graph(pdist_mat)
        return graph

    def get_minimum_spanning_tree(self):
        """
        return the minimum spanning tree
        """
        graph = self.get_full_graph()
        graph = self.networkx.minimum_spanning_tree(graph)
        return graph

    def sigmoid(self, x):
        '''
        a sigmoid function that scores the probability of a contact
        between two proteins
        '''
        # return 1 - (x)**self.slope/ float(((x)**self.slope +
        # self.theta**self.slope))
        argvalue = (x - self.theta) / self.slope
        return 1.0 - (1.0 - self.plateau) / (1.0 + math.exp(-argvalue))

    def unprotected_evaluate(self, da):
        graph = self.get_minimum_spanning_tree()
        score = 0.0
        for e in graph.edges():
            dist = graph.get_edge_data(*e)['weight']
            prob = self.sigmoid(dist)
            score += -numpy.log(prob)
            score += self.linear_slope * dist
        return score

    def do_get_inputs(self):
        return self.particle_list


class FuzzyBoolean(object):

    '''
    Fully Ambiguous Restraint that can be built using boolean logic
    R. Pellarin. Pasteur Institute.
    '''

    def __init__(self, p1, operator=None, p2=None):
        '''
        input a list of particles, the slope and theta of the sigmoid potential
        theta is the cutoff distance for a protein-protein contact
        '''
        if isinstance(p1, FuzzyBoolean) and isinstance(p2, FuzzyBoolean):
            self.operations = [p1, operator, p2]
            self.value = None
        else:
            self.operations = []
            self.value = p1

    def __or__(self, FuzzyBoolean2):
        return FuzzyBoolean(self, self.or_, FuzzyBoolean2)

    def __and__(self, FuzzyBoolean2):
        return FuzzyBoolean(self, self.and_, FuzzyBoolean2)

    def and_(self, a, b):
        return a * b

    def or_(self, a, b):
        return 1.0 - (1.0 - a) * (1.0 - b)

    def evaluate(self):

        if len(self.operations) == 0:
            return self.value
        FuzzyBoolean1, op, FuzzyBoolean2 = self.operations

        return op(FuzzyBoolean1.evaluate(), FuzzyBoolean2.evaluate())


class FuzzyRestraint(IMP.Restraint):

    '''
    Fully Ambiguous Restraint that can be built using boolean logic
    R. Pellarin. Pasteur Institute.
    '''
    plateau = 0.00000000000001
    theta = 5.0
    slope = 2.0
    innerslope = 0.01

    def __init__(self, m, p1, p2, operator=None):
        '''
        input a list of particles, the slope and theta of the sigmoid potential
        theta is the cutoff distance for a protein-protein contact
        '''
        IMP.Restraint.__init__(self, m, "FuzzyRestraint %1%")
        self.m = m
        self.min = sys.float_info.min
        if isinstance(p1, FuzzyRestraint) and isinstance(p2, FuzzyRestraint):
            self.operations = [p1, operator, p2]
            self.particle_pair = None
        elif isinstance(p1, FuzzyRestraint) and p2 is None:
            self.operations = [p1, operator, None]
            self.particle_pair = None
        else:
            self.operations = []
            self.particle_pair = (p1, p2)

    def __or__(self, FuzzyRestraint2):
        return FuzzyRestraint(self.m, self, FuzzyRestraint2, self.or_)

    def __and__(self, FuzzyRestraint2):
        return FuzzyRestraint(self.m, self, FuzzyRestraint2, self.and_)

    def __invert__(self):
        return FuzzyRestraint(self.m, self, None, self.invert_)

    def and_(self, a, b):
        c = a + b
        return c

    def or_(self, a, b):
        c = math.exp(-a) + math.exp(-b) - math.exp(-a - b)
        return -math.log(c)

    def invert_(self, a):
        c = 1.0 - math.exp(-a)
        return -math.log(c)

    def evaluate(self):
        if len(self.operations) == 0:
            return self.distance()
        FuzzyRestraint1, op, FuzzyRestraint2 = self.operations

        if FuzzyRestraint2 is not None:
            return op(FuzzyRestraint1.evaluate(), FuzzyRestraint2.evaluate())
        else:
            return op(FuzzyRestraint1.evaluate())

    def distance(self):
        d1 = IMP.core.XYZ(self.particle_pair[0])
        d2 = IMP.core.XYZ(self.particle_pair[1])
        d = IMP.core.get_distance(d1, d2)
        argvalue = (d-self.theta)/self.slope
        return -math.log(1.0 -(1.0-self.plateau)/(1.0+math.exp(-argvalue)))+self.innerslope*d

    def add_to_model(self):
        IMP.pmi.tools.add_restraint_to_model(self.m, self)

    def unprotected_evaluate(self, da):
        return self.evaluate()

    def __str__(self):
        if len(self.operations) == 0:
            return str(self.particle_pair)
        FuzzyRestraint1, op, FuzzyRestraint2 = self.operations
        if FuzzyRestraint2 is not None:
            return str(FuzzyRestraint1) +str(op)+str(FuzzyRestraint2)
        else:
            return str(FuzzyRestraint1) +str(op)

    def do_get_inputs(self):
        if len(self.operations) == 0:
            return list(self.particle_pair)
        FuzzyRestraint1, op, FuzzyRestraint2 = self.operations
        if FuzzyRestraint2 is not None:
            return list(set(FuzzyRestraint1.do_get_inputs() +FuzzyRestraint2.do_get_inputs()))
        else:
            return list(set(FuzzyRestraint1.do_get_inputs()))