A Design-for-Yield Algorithm to Assess and Improve the Structural and Energetic Robustness of Proteins and Drugs

Robustness is a property that pervades all aspects of nature. The ability of a system to adapt to perturbations due to internal and external agents, aging, wear, or to environmental changes is one of the driving forces of evolution. At the molecular level, understanding the robustness of a protein has a great impact on the

in-silico

design of polypeptide chains and drugs. The chance of computationally checking the ability of a protein to preserve its structure in the native state may lead to the design of new compounds that can work in a living cell more effectively. Inspired by the well known

robustness analysis framework

used in Electronic Design Automation, we introduce a formal definition of robustness for proteins and a dimensionless quantity, called

yield

, to quantify the robustness of a protein. Then, we introduce a new

robustness-centered

protein design algorithm called

Design-For-Yield

. The aim of the algorithm is to discover new conformations with a specific functionality and high yield values. We present extensive characterizations of the robustness properties of many peptides, proteins, and drugs. Finally, we apply the

DFY

algorithm on the

Crambin

protein (1CRN) and on the

Oxicitin

drug (DB00107). The obtained results confirm that the algorithm is able to discover a

Crambin-like

protein that is 23.61% more robust than the wild type. Concerning the Oxicitin drug a new protein sequence and the corresponding protein structure was discovered with an improved robustness of 3% at the global level.