Kamal Al Nasr

About

Kamal Al Nasr is a leading researcher in computational structural biology, with a primary focus on protein structure modeling and loop closure problems. His work bridges robotics and bioinformatics, applying inverse kinematics algorithms—particularly Cyclic Coordinate Descent (CCD)—to solve complex protein backbone assembly challenges. Al Nasr’s most influential contribution is the development of the Forward-Backward Cyclic Coordinate Descent (FBCCD) method, which provides an effective, convergence-independent approach to closing gaps between constrained protein chain segments. This work, published in 2009, has garnered 13 citations and remains a foundational technique in the field. He further advanced the application of CCD beyond traditional loop closure, demonstrating its utility in building protein backbone models from cryo-EM images at medium resolutions, a breakthrough published in 2016 that also earned 13 citations. Al Nasr’s earlier work introduced a variant of CCD for assembling loops between helices, showcasing his sustained innovation. By adapting robotics algorithms to structural biology, Al Nasr has enabled more accurate and efficient protein modeling, impacting areas from drug design to molecular dynamics. His research continues to inspire interdisciplinary approaches to computational biology.

Research Focus

Key Achievements

Top Papers

1. 1
   An effective convergence independent loop closure method using Forward-Backward Cyclic Coordinate Descent

   13 citations · 2009
2. 2
   Constrained cyclic coordinate descent for cryo-EM images at medium resolutions: beyond the protein loop closure problem

   13 citations · 2016
3. 3
   An approximate robotics algorithm to assemble a loop between two helices

   2 citations · 2007

Key Collaborators

Contact & Links