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3.0
1.3
2.0
Modelling Algorithms
A collection of algorithms that can be useful in modelling
Rigid Blocks
RMSD is a typical measure for similarity of two structures. Given an atom atom
mapping between two structures, the minimum RMSD and the according superposition
can efficiently be calculated using an approach based on singular value
decomposition. This approach is problematic if there are very dissimilar regions
or when domain movement events occur. We can therefore implement an iterative
superposition. The two structures undergo an initial superposition. For every
iteration we then select a subset of atoms that are within a certain distance
threshold that serve as input for the next superposition. This iterative
superpostion typically converges to the largest common subpart but is
nondeterministic since it depends on the initial superposition.
The RigidBlocks algorithm is based on only the CA positions and performs this
iterative superposition multiple times by using a sliding window to select the
initial subset and gathers all unique results. These results can be very
similar and only differ by single positions. The algorithm therefore reduces
the amount of solutions by merging them based on a threshold of similarity.
The similarity is defined by the fraction of positions in solution A that are
also present in solution B. As a final result, the algorithm therefore detects
common rigid subsets of positions.

promod3.modelling. RigidBlocks (bb_list_one, bb_list_two[, window_length = 12, max_iterations=20, distance_thresh=3.0, cluster_thresh=0.9])
Performs the RigidBlock algorithm on given input
Parameters: 
 bb_list_one (
promod3.loop.BackboneList ) – First piece structural information from which CA
positions will be extracted
 bb_list_two (
promod3.loop.BackboneList ) – Second piece of structural information from which CA
positions will be extracted
 window_length (
int ) – Length of sliding window to generate initial subsets
 max_iterations (
int ) – Maximal numbers of iterations for every single
iterative superposition
 distance_thresh (
float ) – Maximal distance two CA positions can have to be
considered in the same rigid block and to select
the common subset for the next iteration of the
iterative superposition
 cluster_thresh (
float ) – Threshold of similarity to perform the final merging
of the solutions

Returns:  tuple with the first element being a
list of list defining the
indices of the common subsets (rigid blocks) relative
to the input promod3.loop.BackboneList objects
and the second element being a list of
ost.geom.Mat4 defining the transformations to
superpose the according positions in bb_list_one
onto bb_list_two


promod3.modelling. RigidBlocks (aln[, seq_one_idx=0, seq_two_idx=1, window_length = 12, max_iterations=20, distance_thresh=3.0, cluster_thresh=0.9])
Performs the RigidBlock algorithm on given input
Parameters: 
 aln (
ost.seq.AlignmentHandle ) – An alignment with attached ost.mol.EntityView
objects from which the positions are extracted
 seq_idx_one (
int ) – The idx of the first sequence from which the CA
positions will be extracted
 seq_idx_two (
int ) – The idx of the second sequence from which the CA
positions will be extracted
 window_length (
int ) – Length of sliding window to generate initial subsets
 max_iterations (
int ) – Maximal numbers of iterations for every single
iterative superposition
 distance_thresh (
float ) – Maximal distance two CA positions can have to be
considered in the same rigid block and to select
the common subset for the next iteration of the
iterative superposition
 cluster_thresh (
float ) – Threshold of similarity to perform the final merging
of the solutions

Returns:  tuple with the first element being a
list of list defining the
column indices of the common subsets (rigid blocks)
relative to the input ost.seq.AlignmentHandle
and the second element being a list of
ost.geom.Mat4 defining the transformations to
superpose the according positions from the first
sequence onto the second sequence.


promod3.modelling. RigidBlocks (pos_one, pos_two[, window_length = 12, max_iterations=20, distance_thresh=3.0, cluster_thresh=0.9])
Performs the RigidBlock algorithm on given input
Parameters: 
 pos_one (
ost.geom.Vec3List ) – First piece position information
 pos_two (
ost.geom.Vec3List ) – Second piece of position information
 window_length (
int ) – Length of sliding window to generate initial subsets
 max_iterations (
int ) – Maximal numbers of iterations for every single
iterative superposition
 distance_thresh (
float ) – Maximal distance two CA positions can have to be
considered in the same rigid block and to select
the common subset for the next iteration of the
iterative superposition
 cluster_thresh (
float ) – Threshold of similarity to perform the final merging
of the solutions

Returns:  tuple with the first element being a
list of list defining the
indices of the common subsets (rigid blocks) relative
to the input ost.geom.Vec3List objects
and the second element being a list of
ost.geom.Mat4 defining the transformations to
superpose the according positions in pos_one
onto pos_two

De Novo Modelling
ProMod3 provides algorithms for sampling and fragment detection.
Here we provide an object, that facilitates fragment detection and caching,
as well as a convenient function to combine the functionalities into an
example pipeline.

class
promod3.modelling. FraggerHandle (sequence, profile=None, psipred_pred=None, fragment_length=9, fragments_per_position=100, rmsd_thresh=0.0, structure_db=None, torsion_sampler_coil=None, torsion_sampler_helix=None, torsion_sampler_extended=None)
Handler for Fragger objects linked to a
specific chain.
Tries to get the most accurate fragments given your input.
You can only provide a SEQRES, the returned fragments are
then searched by using sequence similarity as the only target value.
You can massively increase the accuracy of the found fragments by
providing a secondary structure prediction and / or sequence profile.
Following features influence the fragment search given your input:
 sequence:
 Sequence Similarity with BLOSUM62
 sequence, psipred_pred:
 Sequence Similarity with BLOSUM62
 Secondary Structure Agreement
 Secondary Structure Dependent Torsion Probabilities
 sequence, profile:
 Sequence Profile Score
 Structure Profile Score
 sequence, psipred_pred, profile:
 Secondary Structure Agreement
 Secondary Structure Dependent Torsion Probabilities
 Sequence Profile Score
 Structure Profile Score
The FraggerHandle internally uses the promod3.loop.FraggerMap for caching. You
can therefore request fragments for a certain position several times and the
search is performed only once. This also allows to save the FraggerHandle to
disk. When loading the FraggerHandle again, you need to provide all parameters
again. These parameters must be exactly the same than the ones you used when
initially constructing the FraggerHandle, especially the structure database.
Weird things are happening otherwise.

Get (frag_pos)
Get fragger for sequence at index frag_pos..frag_pos+frag_length1.
Parameters:  frag_pos (int ) – Startindex (note that sequenceindexing starts at 0) 
Returns:  A Fragger object. 
Raises:  ValueError if index outofbounds. 

GetList (pos_start=0, pos_end=1)
Get List of fraggers covering sequence indices pos_start..pos_end.
This will return an empty list if range is smaller than fragment_length.
Parameters: 
 pos_start (
int ) – Startindex (note that sequenceindexing starts at 0)
 pos_end (
int ) – Endindex or 1 if it should go to the sequenceend.

Returns:  A list of Fragger objects.

Raises:  ValueError if indices outofbounds.


LoadCached (filename)
Load fragger objects stored with SaveCached() .
Note that here we require that the same structure db is set as was
used when filename was saved.

SaveCached (filename)
Save cached fraggers.

promod3.modelling. GenerateDeNovoTrajectories (sequence, num_trajectories=200, avg_sampling_per_position=600, profile=None, psipred_prediction=None, fragment_handler=None, scorer=None, scorer_env=None, scoring_weights=None)
Example de novo modelling pipeline based on Fragment sampling and
backbone scoring. Take this as a starting point for more advanced
de novo procedures.
Parameters: 
 sequence (
str ) – The sequence you want to sample
 num_trajectories (
int ) – The number of sampling trajectories you
want to generate
 avg_sampling_per_position – Number of Monte Carlo sampling steps
the total number is:
len(sequence) * avg_sampling_per_position
 profile (
ost.seq.ProfileHandle ) – The sequence profile for sequence. This increases the
fragment search performance.
 psipred_prediction (
promod3.loop.PsipredPrediction ) – The psipred prediction for sequence. This
increases the fragment search performance
 fragment_handler (
promod3.modelling.FraggerHandle ) – You can provide already initialized fragments.
If you pass this parameter, profile and
psipred_prediction get neglected and do
not influence the fragment search, the
ones you initialized fragment_handler with
get used instead.
 scorer (
promod3.scoring.BackboneOverallScorer ) – Scorer doing the backbone scoring. If not provided, a
default one gets loaded with default objects with
following keys: clash, reduced, cb_packing, hbond, cbeta,
torsion and pairwise
 scorer_env (
promod3.scoring.BackboneScoreEnv ) – The scoring env that relates to scorer
This environment will be changed!
 scoring_weights (
dict ) – Linear weights for different scores. If not provided,
the output of ScoringWeights.GetWeights() is used.
Please note, that the weights must be consistent
with the keys of the scores in scorer

Returns:  A promod3.loop.LoopCandidates object containing
num_trajectories elements for further processing

Motif Finder
Distinct spatial arrangements of atoms or functional groups are key for protein
function. For their detection, ProMod3 implements the MotifFinder algorithm
which is based on geometric hashing as described by Nussinov and Wolfson
[nussinov1991]. The algorithm consists of a learning stage, a detection stage
and a refinement stage.
Learning Stage: A motif (query) is represented by a set of coordinates. Triplets
(p1, p2, p3) of coordinates are selected that define triangles. For each
triangle one can define an orthogonal vector basis
(in our case v1 = norm(p2p1), v3 = norm(cross(v1,p3p1),
v2 = norm(cross(v1,v3)))). For each coordinate not in [p1,p2,p3], we add the
identity of the query/triangle as value to a hash map.
The corresponding key consists of discretized values describing the edge lengths
of the triangle, as well as the coordinate transformed into the triangle
specific orthogonal vector basis. That’s 6 numbers in total.
Detection Stage: The goal is to identify one or several subsets of target
coordinates that resemble an input query.
We first setup an accumulator containing a counter for each triangle observed
in the input query. We then iterate over each possible triangle with vertices
p1, p2 and p3 in the target coordinates. At the beginning of each iteration,
all counters in the accumulator are set to zero. Again, we build a vector basis
given that triangle and transform all coordinates not in [p1,p2,p3] into that
vector space. For each transformed coordinate we obtain a key for the query hash
map. If there is one or several values at that location in the hash map,
we increment the corresponding locations in the accumulator.
Once all coordinates are processed, we search for high counts in the
accumulator. Given N query coordinates, we keep a solution for further
refinement if count/(N3) >= hash_tresh. This is repeated until all
triangles in the target are processed. One key problem with this approach is
the discretization of floating point numbers that give raise to the hash map
keys. Two extremely close values might end up in different bins just because
they are close to the bin boundaries. For each of the 6 relevant numbers
we estimate the actual bin as well as the closest neighbouring bin. Processing
all possible combinations results in 64 hash map lookups instead of only one.
Refinement Stage: Every potential solution identified in the detection stage is
further refined based on the distance_thresh and refine_thresh parameters.
A potential solution found in the detection stage is a pair of triangles, one
in the query and one in the target, for which we find many matching coordinates
in their respective vector space. We start with a coordinate mapping based on
the triangle vertices from the query and the target (3 pairs).
This coordinate mapping is iteratively updated by estimating the minimum RMSD
superposition of the mapped query coordinates onto the target, apply that
superposition on the query, find the closest target coordinate for each
coordinate in the query and redo the mapping by including all pairs with
minimum distance < distance_thresh. Iteration stops if nothing changes
anymore. The solution is returned to the user if the final fraction of mapped
query coordinates is larger or equal refine_thresh.
The larger the mapping, the more accurate the superposition. As we start with
only the three triangle vertices, distance_thresh is doubled for the initial
iteration.
# Example script that loads protein structures that contain ATP and
# generates motif queries describing their binding pockets.
# In a second step, those pockets are matched against a protein
# structure that only contains an ATP analog. The ATP from every
# match is transformed and stored to disk for further processing.
from ost import io, geom, mol
from promod3 import modelling
files = ['data/1E2Q.pdb', 'data/1KO5.pdb', 'data/2IYW.pdb']
atp_list = list()
query_list = list()
for f in files:
prot = io.LoadPDB(f)
peptide_sel = prot.Select("peptide=true")
atp_sel = prot.Select("rname=ATP")
# generate a single query for each ATP pocket
for atp_idx, atp_r in enumerate(atp_sel.residues):
pocket_view = peptide_sel.CreateEmptyView()
for atp_at in atp_r.atoms:
close_at = peptide_sel.FindWithin(atp_at.GetPos(), 4.5)
for at in close_at:
r = at.handle.GetResidue()
add_flag = mol.INCLUDE_ATOMS  mol.CHECK_DUPLICATES
pocket_view.AddResidue(r, add_flag)
ca_positions = geom.Vec3List()
for res in pocket_view.residues:
ca_positions.append(res.FindAtom("CA").GetPos())
i = "%s_%i"%(f, atp_idx)
query = modelling.MotifQuery(ca_positions, i, 4.0, 9.0, 1.0)
query_list.append(query)
# create an entity from atp for later use
atp_view = prot.CreateEmptyView()
atp_view.AddResidue(atp_r, mol.INCLUDE_ATOMS)
atp_list.append(mol.CreateEntityFromView(atp_view, True))
# That's it, let's combine the single queries
full_query = modelling.MotifQuery(query_list)
prot = io.LoadPDB("data/1AKE.pdb")
peptide_sel = prot.Select("peptide=true")
ca_positions = geom.Vec3List()
for r in peptide_sel.residues:
ca_positions.append(r.FindAtom("CA").GetPos())
# search all matches, fetch the corresponding atps,
# transform them onto the 1ake binding site and dump them to disk
matches = modelling.FindMotifs(full_query, ca_positions)
for m_idx, m in enumerate(matches):
atp = atp_list[m.query_idx].Copy()
atp.EditXCS().ApplyTransform(m.mat)
io.SavePDB(atp, "m_%i.pdb"%(m_idx))

class
promod3.modelling. MotifQuery (positions, identifier, min_triangle_edge_length, max_triangle_edge_length, bin_size)

class
promod3.modelling. MotifQuery (positions, identifier, min_triangle_edge_length, max_triangle_edge_length, bin_size, flags)

class
promod3.modelling. MotifQuery (query_list)
A single query or a container of queries.
The constructor performs the learning stage of a single query or combines
several queries, so they can be searched at once.
Parameters: 
 positions (
ost.geom.Vec3List ) – Coordinates of the query
 identifier (
str ) – Descriptor of the query
 min_triangle_edge_length (
float ) – To avoid the full O(n^3) hell, triangles
with any edge length below min_triangle_edge_length
are skipped
 max_triangle_edge_length (
float ) – Same as min_triangle_edge_length but
upper bound
 bin_size (
float ) – Bin size in A, relevant to generate hash map keys
 flags (
list of int ) – Flag in range [0,63] for every coordinate in positions.
They’re also added to the hash map keys (default: 0).
This means that additionally to having a matching
relative position, the coordinates must also have a
matching flag in the detection/refinement stage.
If not provided (in the query and in the search),
only coordinates matter.
 query_list (
list of MotifQuery ) – E pluribus unum


Save (filename)
Saves the query down to disk
Parameters:  filename (str ) – filename 

static
Load (filename)
Load query from disk
Parameters:  filename (str ) – filename 

GetPositions (query_idx)
Returns coordinates of specified query
Parameters:  query_idx (int ) – Query from which you want the positions 

GetIdentifiers ()
Returns a list of all query identifiers.

GetN ()
Returns the number of queries

class
promod3.modelling. MotifMatch
Object that holds information about a match found in FindMotifs()

query_idx
Index of matching query

mat
Transformation matrix to superpose matching query onto target

alignment
List of tuples which define matching pairs of query/target coordinates

promod3.modelling. FindMotifs (query, target_positions, hash_tresh=0.4, distance_thresh=1.0, refine_thresh=0.7, flags=list())
Performs the detection and refinement stages of the geometric hashing
algorithm.
Parameters: 
 query – Query to be searched
 target_positions – Coordinates of the target
 hash_thresh – Parameter relevant for detection stage
 distance_thresh – Parameter relevant for refinement stage
 refine_thresh – Parameter relevant for refinement stage
 flags – Equivalent to flags in
MotifQuery
constructor. If you didn’t provide anything there,
this can be ignored. Only the actual coordinates
matter in this case.

Returns:  All found matches

Return type:  list of MotifMatch


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