Gerald Weber1. 1. Departamento de Física, Universidade Federal Minas Gerais, Belo Horizonte-MG, Brazil.
Abstract
MOTIVATION: Free energy nearest-neighbour (NN) thermodynamics is widely used in DNA biochemistry, ranging from the calculation of melting temperatures to the prediction of secondary structures. Methods to calculate NN parameters require the knowledge of total sequence entropies and enthalpies, which are not always available. RESULTS: Here, we implement and test a new melting temperature optimization method where we obtain the NN parameters directly from the temperatures. In this way, we bypass the constraints imposed by total sequence entropies and enthalpies. This enabled us to calculate the missing NN entropies and enthalpies for some published datasets, including salt-dependent parameters. Also this allowed us to combine 281 sequences from different types of melting temperature data for which we derived a new set of NN parameters, which have a smaller uncertainty and an improved predictive power. AVAILABILITY AND IMPLEMENTATION: C++ source code and compiled binaries for several Linux distributions are available from https://sites.google.com/site/geraldweberufmg/vargibbs and from OpenSuse build service at https://build.opensuse.org/package/show/home:drgweber/VarGibbs. The software package contains scripts and data files to reproduce all results presented here.
MOTIVATION: Free energy nearest-neighbour (NN) thermodynamics is widely used in DNA biochemistry, ranging from the calculation of melting temperatures to the prediction of secondary structures. Methods to calculate NN parameters require the knowledge of total sequence entropies and enthalpies, which are not always available. RESULTS: Here, we implement and test a new melting temperature optimization method where we obtain the NN parameters directly from the temperatures. In this way, we bypass the constraints imposed by total sequence entropies and enthalpies. This enabled us to calculate the missing NN entropies and enthalpies for some published datasets, including salt-dependent parameters. Also this allowed us to combine 281 sequences from different types of melting temperature data for which we derived a new set of NN parameters, which have a smaller uncertainty and an improved predictive power. AVAILABILITY AND IMPLEMENTATION:C++ source code and compiled binaries for several Linux distributions are available from https://sites.google.com/site/geraldweberufmg/vargibbs and from OpenSuse build service at https://build.opensuse.org/package/show/home:drgweber/VarGibbs. The software package contains scripts and data files to reproduce all results presented here.