Two types of combined-isotopologue analysis have been performed on an extensive spectroscopic data set for ground-state N2 involving levels up to v=19, which is bound by half the well depth. Both a conventional Dunham-type analysis and a direct-potential-fit (DPF) analysis represent the data within (on average) the estimated experimental uncertainties. However, the Dunham-type parameters do not yield realistic predictions outside the range of the data used in the analysis, while the potential function obtained from the DPF treatment yields quantum mechanical accuracy over the data region and realistic predictions of the energies and properties of unobserved higher vibrational levels. Our DPF analysis also introduces a compact new analytic potential function form which incorporates the two leading inverse-power terms in the long-range potential.

Protein-protein interactions (PPI) serve an important role in both protein and cell function. They are difficult and time consuming to determine experimentally and thus benefit from in silico prediction methods. This thesis improves a high throughput, sequence-based protein-protein interaction prediction method called the protein-protein interaction engine (PIPE). The initial contribution was in developing comprehensive documentation on how to run and use PIPE. Next, a Python implementation of the scoring of PIPE was developed. A software framework was created for the systematic exploration of how physicochemical properties can improve PPI prediction. Subsequently, a sequence-based solvent accessibility approach was integrated with PIPE, improving PPI prediction recall by 0.9% from 73.8% to 74.7% at 90% precision. Finally, 166 different sequence-based physicochemical properties were generated using the ProtDCal software tool and were integrated with PIPE using the framework developed in this thesis. The best of these properties improved the recall of PIPE by 2% at 90% precision. This improvement was shown to be statistically significant and was confirmed on a larger test set including 10,000 protein pairs known to interact and 10,000 randomly selected pairs, assumed not to interact.

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