# -*- tab-width:2;indent-tabs-mode:t;show-trailing-whitespace:t;rm-trailing-spaces:t -*-
# vi: set ts=2 noet:
#
# (c) Copyright Rosetta Commons Member Institutions.
# (c) This file is part of the Rosetta software suite and is made available under license.
# (c) The Rosetta software is developed by the contributing members of the Rosetta Commons.
# (c) For more information, see http://www.rosettacommons.org. Questions about this can be
# (c) addressed to University of Washington UW TechTransfer, email: license@u.washington.edu.
library(ggplot2)
library(plyr)
feature_analyses <- c(feature_analyses, methods::new("FeaturesAnalysis",
id = "OHdonor",
author = "Matthew O'Meara",
brief_description = "",
feature_reporter_dependencies = c("HBondFeatures"),
run=function(self, sample_sources, output_dir, output_formats){
extract_features <- function(sample_sources, don_chem_type, acc_chem_type, xmin, xmax){
sele <-paste("
SELECT
geom.AHdist
FROM
hbond_geom_coords AS geom,
hbonds AS hb,
hbond_sites AS don, hbond_sites AS acc,
hbond_sites_pdb AS don_pdb, hbond_sites_pdb AS acc_pdb
WHERE
geom.struct_id = hb.struct_id AND geom.hbond_id = hb.hbond_id AND
don.struct_id = hb.struct_id AND don.site_id = hb.don_id AND
acc.struct_id = hb.struct_id AND acc.site_id = hb.acc_id AND
don.HBChemType = '", don_chem_type, "' AND
acc.HBChemType = '", acc_chem_type, "' AND
", xmin, " <= geom.AHdist AND
geom.AHdist <= ", xmax, " AND
don_pdb.struct_id = hb.struct_id AND don_pdb.site_id = hb.don_id AND
don_pdb.heavy_atom_temperature < 30 AND
acc_pdb.struct_id = hb.struct_id AND acc_pdb.site_id = hb.acc_id AND
acc_pdb.heavy_atom_temperature < 30;", sep="")
query_sample_sources(sample_sources, sele)
}
estimate_density <- function(f){
estimate_density_1d(
data = f,
ids = c("sample_source"),
variable = "AHdist",
weight_fun = radial_3d_normalization)
}
extract_model <- function(sample_sources, don_chem_type, acc_chem_type){
sele <-paste("
SELECT DISTINCT
ev.don_chem_type,
ev.acc_chem_type,
ev.separation AS seq_sep,
p.dimension,
p.xmin, p.xmax,
p.degree,
p.c_a, p.c_b, p.c_c, p.c_d, p.c_e, p.c_f, p.c_g, p.c_h, p.c_i, p.c_j, p.c_k
FROM
hbond_evaluation_types AS ev,
hbond_polynomial_1d AS p
WHERE
ev.don_chem_type = '", don_chem_type, "' AND
ev.acc_chem_type = '", acc_chem_type, "' AND
ev.separation = 'seq_sep_other' AND
ev.database_tag = p.database_tag AND ev.AHdist = p.name;", sep="")
query_sample_sources(sample_sources, sele)
}
extract_evaluate_models <- function(
sample_sources,
don_chem_type,
acc_chem_type,
xmin,
xmax,
sample_source="A-H Distance Term",
temperature = 10,
normalization = .03
) {
m_polys <- extract_model(
subset(sample_sources, !is.na(model)),
don_chem_type, acc_chem_type)
ddply(m_polys, .(sample_source), function(m){
poly <- get_1d_polynomial(m, don_chem_type, acc_chem_type)
x <- seq(xmin, xmax, length.out=300)
boltzmann_transform <- function(x, e, xmin, xmax) {
ifelse(x >= xmin & x <= xmax, normalization * exp(-e*temperature), NA)
}
data.frame(
x = x,
y = boltzmann_transform(x, predict(poly$poly, x), poly$xmin, poly$xmax),
sample_source = sample_source,
model = sample_sources[sample_sources$sample_source == m$sample_source, "model",],
counts = NA)
})
}
generate_plot <- function(
dens, model,
don_chem_type, acc_chem_type,
sample_sources, output_dir, output_formats) {
plot_id <- paste("OHdonor_AHdist", don_chem_type, acc_chem_type, sep="_")
ref_dens <- transform(subset(dens, reference), sample_source=NULL)
new_dens <- subset(dens, !reference)
# facet_labels <- data.frame(
# sample_source = sample_sources$sample_source,
# label=toupper(letters[1:nrow(sample_sources)]))
#
# facet_labels <- rbind(
# data.frame(sample_source = unique(model$sample_source)),
# data.frame(sample_source = unique(new_dens$sample_source)))
# facet_labels$label <- toupper(letters[1:nrow(facet_labels)])
#hack!
facet_labels <- data.frame(
sample_source = c("A-H Distance Term", "Relaxed Native Score12", "Relaxed Native NewHB", "Relaxed Native NewHB LJcorr"),
label = c("A", "B", "C", "D"))
p <- ggplot() +
geom_line(data=ref_dens, aes(x=x, y=y), colour="black", size=2) +
geom_line(data=model, aes(x=x, y=y, colour=model), size=1) +
geom_line(data=new_dens, aes(x=x, y=y, colour=model), size=2) +
geom_indicator(data=facet_labels, aes(indicator=label),
group=1, color="black", xpos=.03, ypos=.97, size=12) +
facet_wrap(~sample_source, ncol=2) +
scale_y_continuous("Feature Density") +
scale_x_continuous(
expression(paste('Acceptor -- Hydrogen Distance (', ring(A), ')'))) +
scale_colour_grey("HBond Potential", start=.4, end=.7) +
theme_bw() +
theme(legend.position = c(.36, .85))
save_plots(self, plot_id=plot_id, sample_sources, output_dir, output_formats)
}
##########################
xmin <- 1.5; xmax <- 2.2
don_chem_type <- "hbdon_HXL"; acc_chem_type <- "hbacc_PBA"
f <- extract_features(
sample_sources,
don_chem_type, acc_chem_type,
xmin, xmax)
#counts, e.g. to add to caption
print(ddply(f, .(sample_source), nrow))
dens <- estimate_density(f)
dens <- merge(dens, sample_sources[, c("sample_source", "reference", "model")])
model <- extract_evaluate_models(
sample_sources,
don_chem_type, acc_chem_type,
xmin, xmax)
generate_plot(
dens, model,
don_chem_type, acc_chem_type,
sample_sources, output_dir, output_formats)
})) # end FeaturesAnalysis
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