Vargas Lab

Our work focusses on developing new image processing methods in cryo-electron microscopy
(cryo-EM) to obtain high-resolution 3D reconstructions of macromolecules as proteins or virus.

Cryo-EM is a structural biology technique that uses the electron microscope to reconstruct
biomolecular assemblies. This technique is living a revolution in its capacity to obtain atomic resolution
3D reconstructions of macromolecules. However, the extraordinary potential of cryo-EM
is presently limited by important factors: several steps in the pipeline can only be performed by
highly experienced researchers, biological complexes are typically conformationally
heterogeneous, and there are not reliable validation methods to evaluate the obtained structures.
Developing algorithms that remove these bottlenecks is critical for cryo-EM to unfold its
tremendous potential as a research tool. Our work aims to develop these methods to transform
cryo-EM into a reliable, high-throughput and high-resolution technique. We are developers of
of Scipion package [1] and actively collaborate with strong research groups
in Spain [2], Canada [3], United States [4-5] and Mexico. A list of recent publications can be
seen from [7].

[1] http://scipion.i2pc.es/
[2] http://i2pc.es/about-i2pc/
[3] https://www.huy-bui.lab.mcgill.ca/
[4] https://www.mclellanlab.org/lab-members
[5] https://sites.google.com/uw.edu/dais-uw
[6] https://orcid.org/0000-0001-7519-6106

Our research program focuses on the development of computational methods for cryo-electron microscopy (cryo-EM). Many of our current developments are taking advantage of current advances in Artificial intelligence and deep learning. Obtaining atomic-resolution macromolecular maps requires deep expertise on cryo-EM. The quality of the data collected depends highly on the expertise of the operator running the microscope. Currently, only few institutions around the world have the required know-how. In addition, appropriate computational tools to analyze challenging cryo-EM samples are still underdeveloped. Then, samples that exhibit high flexibility or multiple conformations are difficult to reconstruct at atomic-resolution. Additionally, current computational methods cannot fully validate resulting cryo-EM structures. Our research is leading the transformation of cryo-EM into a reliable, high-throughput and high-resolution technique that is accessible to the broad scientific community. In this area, We are pursuing the following aims:

Aim 1.1 Developing a robust automated data collection pipeline.

Aim 1.2 Extracting high-resolution information from heterogeneous samples.

Aim 1.3. Developing reliable structure validation methods.

This work will provide computational tools for structural biologists to advance their research programs by allowing them to extract critical information from their cryo-EM data. High-throughput tools will support groups with moderate expertise in cryo-EM to use this technique at its full potential. Novel validation approaches will prevent mistakes and publication of incorrect structures. Finally, innovative methods to analyze the conformational variability will provide new and essential information to infer the macromolecular functions.

- PhD in computer science, physics or computational biology, or a related field of science.
- Experience in image processing and data science.
- Knowledge in Python programming (Numpy, Matplotlib, Pandas).
- Experience working in Linux envi