Integrating experiments and computational modeling is critical for understanding the structure and dynamics of biomolecules. Beyond providing validation for experimental results, computational modeling, that incorporates accurate physical models and enhanced sampling methods, can provide insight into the mechanisms underlying experimental observations. I will present four projects where experiments and computational modeling were used together, to understand mechanisms underlying the structure and dynamics of biomolecules. The first project involves using enhanced sampling to improve the efficiency of calculating the hydration free energies of small molecules using a polarizable force field. These predictions are compared with a conventional free energy method, and excellent agreement is found between the methods. The second project involves using atomic molecular dynamics simulations to determine the molecular mechanism underlying the ability of nanosensor to detect point-mutations in a DNA sequence. By analyzing the nearest-neighbor hydrogen bonding profile, from simulations of the nanosensor, a molecular mechanism was proposed to explain the experimental data. The third project involves the incorporation of non-canonical hydrogen bonding in a RNA coarse-grained model in order to improve 3D structure prediction. This new model is applied to study the sequence-dependent stability of several RNAs including RNA G-quadruplexes. The final project involves the development of a new single-molecule assay to measure local transitions in nucleic acid structures using ultrashort DNA tethers. This project involves collaboration with an experimental biochemistry group to design the DNA tethers and to prepare single-molecule samples. All projects involve the development of new methods to understand the 3D structure and dynamics of biomolecules