 Hello, my name is Diego Cavallero and I'm a graduate student in the physics department at Yale University. I work with Cory O'Hern and Lynn Regan using computational tools to study protein conformations. Today, I'm going to be talking about our recently featured article in Protein Science, Intrinsic Alpha Helical and Beta Sheet Confirmation Preferences, a computational case study of Allen. We currently do not have an understanding of what the intrinsic conformational preferences for proteins are. This is important because we want to achieve a predictive understanding of protein conformations in order to develop software that can accurately describe the shapes that proteins take. Sterics and stereochemical constraints are the foundation of any molecular interaction. Any molecular dynamics force field that wants to accurately recapitulate protein structures has to take them into account correctly. To capture the role that sterics play, we have built a molecular dynamics force field where non-bonded atoms are treated as repulsive spheres. We use this force field to study our model system, the alanine dipeptide. Alanine is one of the simplest amino acids, making it ideally suited to study peptide intrinsic backbone conformations. The backbone of the alanine dipeptide is described by two rotational angles, the phi and psi dihedral angles. The position in phi-size space that an amino acid takes is what we call its backbone conformation. Generally speaking, there are two major regions in phi-size space that a dipeptide can explore, the so-called alpha helix and beta sheet regions. We discover that our force field is well suited to study transitions between different backbone conformations. For example, the alanine can transition between beta sheets and alpha helices. In this animation, we can see a beta sheet to alpha helix transition for a sample alanine dipeptide. On the right-hand side, the red cross shows its initial position in the beta sheet region. On the left, we have the space field model of the dipeptide, as it moves from one state to another. The black dots show the time evolution of the system in phi-size space. Running these kind of simulations until thermal equilibration is reached allows us to predict the relative abundances of the different backbone conformations. Furthermore, the force field is very well suited to study the mechanism through which beta sheets to alpha helix transitions occur. You can explain this using this model. If you imagine that this is the dipeptide where the phi-size rotations are analogous to these kind of rotations, one can see that in general, if this were a space-filling model, the system is quite packed together. For a phi-size rotation to not be impeded, one has to relieve the clashes that would prevent the transition from occurring. This relief occurs via the opening of what's called the tau angle, which is the backbone angle here described with the green bonds. If the tau is small, the phi-size rotations are impaired. However, if the tau is large, such as in this exaggerated example, phi-size rotations are much easier. Our force field recapitulates this kind of effect, meaning that whenever the tau angle opens up, you're essentially allowing phi inside to transition between the different conformations. However, when the tau angle is closed, these transitions are not allowed. Our predicted beta sheet to alpha helix relative abundances, as well as the tau mechanism that describes transitions between these two backbone conformations, can be seen in crystal structures as well as in experiments that report dipeptide conformations. We believe our results demonstrate the power of Hartzphere models to reveal fundamental features of protein structure.