Macromolecular Modeling Blog ™

Please submit a resume and cover letter to hr@adimab.com.

Description

Adimab is looking for scientists with experience in computational protein design to join its Computational Biology group. Successful candidates will work on developing novel computational algorithms and software for rational structure-based design and optimization of antibody molecules and libraries. The computational effort will be closely linked to experimental sequence and binding information obtained from Adimab’s validated, high-throughput antibody discovery platform.

Qualifications

• Ph.D. in Biophysics, Biochemistry or related fields
• Previous academic or biotech industry experience in developing and employing protein design and/or docking algorithms and software
• Strong programming experience in C++ , C or Python
• Strong intuitive grasp of protein structure and function

About Adimab

Adimab’s integrated antibody discovery and optimization platform provides unprecedented speed from antigen to purified, full-length human IgGs. Adimab offers fundamental advantages by delivering diverse panels of therapeutically relevant antibodies that meet the most aggressive standards for affinity, epitope coverage, species cross-reactivity and expressability. Adimab enables its partners to rapidly expand their biologics pipelines through a broad spectrum of technology access arrangements.

For more information, visit http://www.adimab.com.

Adimab is located in New Hampshire’s beautiful Upper Connecticut River Valley, just minutes from Dartmouth College, Dartmouth Medical School, the Tuck School of Business, the Thayer School of Engineering, and Dartmouth-Hitchcock Medical Center. Adimab offers individually tailored compensation packages that are comprised of competitive salary, bonus, and equity components in addition to a world class working environment in a newly constructed lab.

PyRosetta Book

The Gray Lab at Johns Hopkins University has just released PyRosetta, a Python-based interactive platform for accessing the objects and algorithms within the Rosetta protein structure prediction suite.

In addition to the code, the Gray Lab has put together a book that leads the reader through basics of protein structure and energetics to applications in folding, refinement, docking and design. The focus is on enabling users to write custom scripts, so it includes material on Rosetta fundamentals and the appendices have a list of PyRosetta commands and a breakdown of the input files. The book was beta-tested by students during a course at JHU. The course is a series of workshops that teach how to measure and manipulate protein conformations, calculate energies in low- and high-resolution representations, fold proteins from sequence, model variable regions of proteins (loops), dock proteins or small molecules, design protein sequences, and build custom protocols for operations tailored to particular biomolecular applications

The book can be purchased through Lulu:

http://www.lulu.com/content/paperback-book/the-pyrosetta-interactive-platform-for-protein-structure-prediciton-and-design-a-set-of-educational-modules/7187010

or downloaded for free as pdf chapters from http://www.pyrosetta.org under the Tutorial link.