Research Repository

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

Nicosia, Giuseppe and Stracquadanio, Giovanni (2009) A Design-for-Yield Algorithm to Assess and Improve the Structural and Energetic Robustness of Proteins and Drugs. In: UNSPECIFIED, ? - ?.

Full text not available from this repository.


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. © 2009 Springer Berlin Heidelberg.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Published proceedings: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 13 Feb 2017 14:35
Last Modified: 23 Sep 2022 18:43

Actions (login required)

View Item View Item