Since 28 January 2020, Levitt, his Stanford group and an international team of volunteers have worked tirelessly on data analysis of COVID-19. While widely distributed, our work has been hidden from search engines due to failure of our 25-year old lab website at www.csb.stanford. Follow us on Twitter. Relevant links follow:
14 Mar. Analysis of China and Rest of the World. This is a key analysis full of Tables and Figures that is not easy to comprehend; it is the basis of all my subsequent work.
22 Mar. LA Times Interview with Joe Mozingo
Why this Nobel laureate predicts a quicker coronavirus recovery: ‘We’re going to be fine’
22 Mar. The Medium, a first post on the Excess Burden of Death associated with coronavirus. This is for saturation of infection, like that on the Diamond Princess which may lead to herd immunity.
16 Apr. Particularly clear Radio New Zealand interview with Dave Campbell:
'No evidence that Covid-19 is causing huge loss of life' (listen)
2 May. Lockdown TV in London, James Billot & Freddie Sayer. Nobel prize-winning scientist: the Covid-19 epidemic was never exponential
15 May. Three YouTube podcasts
COVID19 Never Grows Exponentially Michael Levitt 14May20
One of our team members Dr. Patrick Tam (retired Standard and Chartered Bank CIO living in Hong Kong) and his family have set up a remarkable website called Covibes.
Another early collaborator also from 7 Feb 2020 is Dr. Francesco Zonta at ShanghaiTech University. Francesco is heavily involved.
Now most of my group at Stanford are involved with COVID-19 as follows: Andrea Scaiewicz, Joao Rodrigues, and Frederic Poitevin.
I pioneered of computational biology setting up the conceptual and theoretical framework for a field that I am still actively involved in at all levels. More specifically, I still write and maintain computer programs of all types including large simulation packages and molecular graphics interfaces. I have also developed a high-level of expertise in Perl scripting, as well as in the advanced use of the Office Suite of programs (Word, Excel and PowerPoint), which is more important and rare than it may seem. My research focuses on three different but inter-related areas of research. First, we are interested in predicting the folding of a polypeptide chain into a protein with a unique native-structure with particular emphasis on how the hydrophobic forces affect the pathway. We expect hydrophobic interactions to energetically favor structure that are more native-like. In this way, the same stabilizing interactions that exist in the final folded state the search tractable. Second we are interested in predicting protein structure from sequence without regard for the process of folding. Such prediction relies on the well-established paradigms that similar protein sequences imply similar three-dimensional structures. We have focused on the hardest problem in homology modeling: the refinement of a near-native structure to make it more precisely like the actual native structure of protein. We have also focused on how the general similarity of all protein sequences resulting from their evolution from common ancestor sequence affects the nature of the protein universe. Third, we are focusing on mesoscale modeling of large macromolecular complexes such as RNA polymerase and the mammalian chaperonin. In this work, done in close collaboration with experimentalists, we use new morphing strategies combined with normal mode analysis in torsion angle space to overcome problems caused by the size and complexity of these critical, biomedically important systems. All this work depends on the way a molecular structure is represented in terms of the force-field that allows calculation of the potential energy of the system. We employ a very wide variety of such energy functions that extend from knowledge-based statistical potentials for a single interaction center per residue to quantum-mechanical force-fields that include inductive effects as well as polarization.