We have been awarded an competing renewal R01 grant from the NIH entitled "Computational Structure-Based Protein Design." This continues our NIH funded project for years 9-12. More
April 23, 2018 at 10:30 AM, TTIC.
Abstract: Computational protein design is a transformative field with
exciting prospects for advancing both basic science and translational
medical research. New algorithms blend discrete and continuous
mathematics to address the challenges of creating designer proteins. I
will discuss recent progress in this area and some interesting open
problems.
I will motivate this talk by discussing how, by using continuous representations within a discrete optimization framework, broadly-neutralizing anti-HIV-1 antibodies were computationally designed that are now being tested in humans (clinical trial NCT03015181). These continuous representations model the flexibility and dynamics of biological macromolecules, which are an important structural determinant of function.
However, reconstruction of biomolecular dynamics from experimental observables requires the determination of a conformational probability distribution. These distributions are not fully constrained by the limited information from experiments, making the problem ill-posed in the sense of Hadamard. The ill-posed nature of the problem comes from the fact that it has no unique solution. Multiple or even an infinite number of solutions may exist. To avoid the ill-posed nature, the problem must be regularized by making (hopefully reasonable) assumptions.
I will present new ways to both represent and visualize correlated inter-domain protein motions (See Figure). We use Bingham distributions, based on a quaternion fit to circular moments of a physics-based quadratic form. To find the optimal solution for the distribution, we designed an efficient, provable branch-and-bound algorithm that exploits the structure of analytical solutions to the trigonometric moment problem. Hence, continuous conformational PDFs can be determined directly from NMR measurements. The representation works especially well for multi-domain systems with broad conformational distributions.
Ultimately, this method has parallels to other branches of computer science that balance discrete and continuous representations, including physical geometric algorithms, robotics, computer vision, and robust optimization. I will advocate for using continuous distributions for protein modeling, and describe future work and open problems.
One way HIV hides from the immune system is by continuously changing the shape of a surface molecule where powerful antibodies could potentially bind to stop infection. This shape-shifting of the so-called viral spike helps conceal key antibody-binding sites and instead exposes other sites on the virus that lure minimally effective antibodies.
In previous work, scientists found that an effective HIV vaccine that teaches the immune system to neutralize the virus should be based on a particular form of the viral spike called the closed, pre-fusion configuration. In the new research, we report the stabilization of a protein that maintains this very configuration and confirm that it allows binding of effective antibodies but not ineffective ones.
In addition, while the viral spike typically changes shape in the presence of a common immune-cell receptor called CD4, the newly stabilized protein does not. This is critical because the protein needs to stay in the closed, pre-fusion configuration to elicit potent antibodies that broadly neutralize HIV.
More molecular work remains to overcome other hurdles to eliciting HIV antibodies that could stop most strains of the virus from causing infection.
Ivelin was also selected to receive a 2011 NIAID Merit Award. This award recognizes the meritorious achievements and accomplishments of NIAID employees.
What else happened lately?