# -*- 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)
source("../hbond_geo_dim_scales.R")
feature_analyses <- c(feature_analyses, methods::new("FeaturesAnalysis",
id = "cosBAH_chem_type_with_rosetta_model",
author = "Matthew O'Meara",
brief_description = "",
feature_reporter_dependencies = c("HBondFeatures"),
run=function(self, sample_sources, output_dir, output_formats){
sele <-"
SELECT
geom.AHdist,
don.HBChemType AS don_chem_type, acc.HBChemType AS acc_chem_type
FROM
hbonds AS hb,
hbond_geom_coords AS geom,
hbond_sites AS don, hbond_sites AS acc
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;"
f <- query_sample_sources(sample_sources, sele)
sele <-"
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.separation = 'seq_sep_other' AND
ev.database_tag = p.database_tag AND ev.AHdist = p.name;"
polynomials <- query_sample_sources(sample_sources, sele)
xmin <- min(f$AHdist, polynomials$xmin)
xmax <- max(f$AHdist, polynomials$xmax)
dens <- estimate_density_1d(
f, c("sample_source", "acc_chem_type", "don_chem_type"),
"AHdist", weight_fun = radial_3d_normalization)
dens$y <- -log(dens$y)*.35
dens.model <-
expand.grid(
sample_source = sample_sources$sample_source[2],
acc_chem_type = levels(f$acc_chem_type),
don_chem_type = levels(f$don_chem_type))
dens.model <- ddply(dens.model,
.(sample_source, acc_chem_type, don_chem_type), function(df){
sample_source <- as.character(df$sample_source[1])
acc_chem_type <- as.character(df$acc_chem_type[1])
don_chem_type <- as.character(df$don_chem_type[1])
poly <- get_1d_polynomial(
polynomials, don_chem_type, acc_chem_type)
x <- seq(xmin, xmax, length.out=100)
z <- data.frame(x=x, y=predict(poly$poly, x), sample_source=factor("Rosetta Model"))
z$y <- ifelse(z$x >= poly$xmin & z$x <= poly$xmax, z$y, NA)
z
})
dens.model$counts <- NA
dens <- rbind(dens, dens.model)
# Order the plots better and give more descriptive labels
dens$don_chem_type_name <- factor(dens$don_chem_type,
levels = c("hbdon_IMD", "hbdon_IME", "hbdon_GDE", "hbdon_GDH",
"hbdon_AHX", "hbdon_HXL", "hbdon_IND", "hbdon_AMO", "hbdon_CXA", "hbdon_PBA"),
labels = c("dIMD: h", "dIME: h", "dGDE: r", "dGDH: r",
"dAHX: y", "dHXL: s,t", "dIND: w", "dAMO: k", "dCXA: n,q", "dPBA: bb"))
# Order the plots better and give more descriptive labels
dens$acc_chem_type_name <- factor(dens$acc_chem_type,
levels = c("hbacc_IMD", "hbacc_IME", "hbacc_AHX", "hbacc_HXL",
"hbacc_CXA", "hbacc_CXL", "hbacc_PBA"),
labels = c("aIMD: h", "aIME: h", "aAHX: y", "aHXL: s,t",
"aCXA: n,q", "aCXL: d,e", "aPBA: bb"))
dens <- dens[!is.na(dens$acc_chem_type_name) & !is.na(dens$don_chem_type_name),]
plot_id <- "hbond_AHdist_chem_type_with_parameters"
p <- ggplot(data=dens) + theme_bw() +
geom_line(aes(x, y, colour=sample_source)) +
geom_indicator(aes(indicator=counts, colour=sample_source, group=sample_source)) +
facet_grid(don_chem_type_name ~ acc_chem_type_name) +
ggtitle("HBond A-H Distance by Chemical Type, B-Factor < 30\nnormalized for equal weight per unit distance in density estimation") +
scale_y_continuous("Energy (arbitrary units)", limits=c(-.6,1.1), breaks=c(-.5,0, .5)) +
scale_x_continuous(expression(paste('Acceptor -- Proton Distance (', ring(A), ')')), limits=c(.5,3), breaks=c(1, 1.5, 2, 2.5))
if(nrow(sample_sources) <= 3){
p <- p + theme(legend.position="bottom", legend.direction="horizontal")
}
save_plots(self, plot_id, sample_sources, output_dir, output_formats)
})) # end FeaturesAnalysis
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