calculate.affinity: Calculate thermodynamic variables of oligonucleotides
In Santaris/affinity: Functions to do thermodynamics on oligonucleotides

Description Usage Arguments Details Value Author(s) References See Also Examples

For an input character vector of oligonucleotide sequences, this function calculates/predicts melting temperature (tm), Gibbs free energy of binding (dg), enthalpy (dh), entropy (ds), and the equilibrium constant (k), based on a nearest-neigbhbor model.

1	calculate.affinity(oligos, conc.total = 1e-05, temp.c.in = 37, parallel=F, cores=NA)

`oligos`	A character vector of oligonucleotide sequences in camel-case (uppercase: LNA, lowercase: DNA) in the 5 to 3 direction.
`conc.total`	A numeric, the total concentration (in moles per liter, M) of oligonucleotide and RNA target (assumed to be present 1:1, so that the concentration of each equals half the total concentration). This is used for calculating melting temperature (tm).
`temp.c.in`	A numeric, the temperature (in degrees celcius, C) at which the reaction takes place.
`parallel`	logical(T/F), should the computation be parallelized.
`cores`	integer, the number of cores used for parallel computation. If NA, use the maximal number of cores. This parameter is only relevant if parallel=T.

To calculate changes in thermodynamic state functions for the binding of oligo (O) to RNA target (R), written O+R = OR, a nearest neighbor model is used as described in SantaLucia et al., 1998. Specifically, For DNA/RNA binding, ds (entropy) and dh (enthalpy), all possible Watson-Crick dinucleotide pairs are taken from Sugimoto et al., 1995. Changing DNA to LNA can be treated as additions dds and ddh (McTigue et al. et al., 2004; Owczarzy et al, 2011), to dh and ds. Although, dds and ddh were determined from DNA/DNA and LNA/DNA binding, they are assumed to be similar for DNA/RNA and LNA/RNA binding, and used in this context here. The effect on tm (melting temperature) of a phosphorothiate backbone is estimated using data from Hashem et al., 1998. This estimate is from full-DNA oligos, but assumed to apply to LNA-modified DNA oligos as well. The nearest neighbor predictor for LNA-modified oligonucleotides as described here was used by Pedersen et al., 2014.

Returns a dataframe with one row for each oligonucleotide, and 6 columnns:

`tm`	The melting temperature (C), taken the phosphorothiate backbone into account.
`tm.unadj`	The melting temperature (C), not taking the phosphorothiate backbone into account.
`dg`	The Gibbs free energy of binding (cal/mol).
`dh`	The enthalpy (cal/mol).
`ds`	The entropy (cal/(mol*K)).
`k`	The equilibrium constant, k=[OR]/[O][R].

Peter Hagedorn and Morten Lindow

Hashem, G, Pham, L, Vaughan, MR, and Gray, DM (1998) Hybrid Oligomer Duplexes Formed with Phosphorothioate DNAs: CD Spectra and Melting Temperatures of S-DNA/RNA Hybrids Are Sequence-Dependent but Consistent with Similar Heteronomous Conformations. Biochemistry 37:61-72.

McTigue, PM, Peterson, RJ and Kahn, JD (2004). Sequence-dependent thermodynamic parameters for locked nucleic acid (LNA)-DNA duplex formation. Biochemistry 43: 5388-5405.

Owczarzy, R, You, Y, Groth, CL and Tataurov, AV (2011). Stability and mismatch discrimination of locked nucleic acid-DNA duplexes. Biochemistry 50: 9352-9367.

Pedersen, L, Hagedorn, P, Lindholm, MW, Lindow, M (2014). A Kinetic Model Explains Why Shorter and Less Affine Enzyme-recruiting Oligonucleotides Can Be More Potent. Molecular Therapy - Nucleic Acids, 3, e149.

SantaLucia, J (1998). A unified view of polymer, dumbbell, and oligonucleotide DNA nearest-neighbor thermodynamics. Proc Natl Acad Sci USA 95: 1460-1465.

Sugimoto, N, Nakano, S, Katoh, M, Matsumura, A, Nakamuta, H, Ohmichi, T et al. (1995). Thermodynamic parameters to predict stability of RNA/DNA hybrid duplexes. Biochemistry 34:11211-11216.

calc.dh.ds, calc.dg, calc.tm, calc.k.

data(publictm)

## select oligos from Stanton et al., 2012
i <-  grep("Stanton",publictm$IDstudy)

## calculate tm adjusted for PS linkages
predtm <- calculate.affinity(publictm$Sequence[i])$tm

## plot the results
plot(publictm$Measured.Tm[i], predtm, pch=16, xlab="Measured Tm", ylab="Predicted Tm")