No abstract

Although virtual and augmented reality (VR and AR) techniques have been used extensively in specialized laboratories, only recently did they become affordable, reaching wider consumer markets. With increased availability, it is timely to examine the roles that VR and AR may play in teaching structural biology and in experiencing complex data sets such as macromolecular structures. This guide is suitable for those teachers of structural biology who do not have a deep knowledge of information technologies. This study focuses on three questions: 1) How can teachers of structural biology produce and disseminate VR/AR‐ready educational material with established and user‐friendly software tools?; 2) What are the positive and negative experiences reported by test participants when performing identical learning tasks in the VR and AR environments?; and 3) How do the test participants perceive prerecorded narration during VR/AR exploration? © 2018 International Union of Biochemistry and Molecular Biology, 47(1):16–24, 2018.

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Low-frequency vibrations are crucial for protein structure and function, but only a few experimental techniques can shine light on them. The main challenge when addressing protein dynamics in the terahertz domain is the ubiquitous water that exhibit strong absorption. In this paper, we observe the protein atoms directly using X-ray crystallography in bovine trypsin at 100 K while irradiating the crystals with 0.5 THz radiation alternating on and off states. We observed that the anisotropy of atomic displacements increased upon terahertz irradiation. Atomic displacement similarities developed between chemically related atoms and between atoms of the catalytic machinery. This pattern likely arises from delocalized polar vibrational modes rather than delocalized elastic deformations or rigid-body displacements. The displacement correlation between these atoms were detected by a hierarchical clustering method, which can assist the analysis of other ultra-high resolution crystal structures. These experimental and analytical tools provide a detailed description of protein dynamics to complement the structural information from static diffraction experiments.

A biomolecular ensemble exhibits different responses to a temperature gradient depending on its diffusion properties. MicroScale Thermophoresis technique exploits this effect and is becoming a popular technique for analyzing interactions of biomolecules in solution. When comparing affinities of related compounds, the reliability of the determined thermodynamic parameters often comes into question. The thermophoresis binding curves can be assessed by Bayesian inference, which provides a probability distribution for the dissociation constant of the interacting partners. By applying Bayesian machine learning principles, binding curves can be autonomously analyzed without manual intervention and without introducing subjective bias by outlier rejection. We demonstrate the Bayesian inference protocol on the known survivin:borealin interaction and on the putative protein-protein interactions between human survivin and two members of the human Shugoshin-like family (hSgol1 and hSgol2). These interactions were identified in a protein microarray binding assay against survivin and confirmed by MicroScale Thermophoresis.