In the 1990s, she pioneered the concept of dynamic conformational ensembles with distinct conformational states indicators of protein and cell function, and of allosteric drugs actions.[6][7][8][9] She offered that the ability to perform biological function is determined by how stable, or populated, a protein is in its active (ON) conformation. She proposed that free energy landscapes are dynamic, offered that all conformations pre-exist and suggested “conformational selection and population shift” (as an alternative to the “induced fit” text-book model) to explain molecular mechanism of recognition and posited that population shift underlies allosteric regulation.[9] The concept of dynamic energy landscape that she introduced tacitly explains that strong activating mutations in cancer work by shifting the ensemble to spend more time in the active state. In 2000, she extended this pre-existing ensemble model to catalysis, and more recently to oncogenic transformation, contributing to extraordinary advancements in understanding structure and function.[10]
Nussinov has authored about 750 scientific papers with more than 51,000 citations as of 2023 and has given hundreds of invited talks.[11][12] Most recently, she has pioneered the connection, on the structural and cellular levels, of cancer and neurodevelopmental disorders asking How can same-gene mutations promote both cancer and developmental disorders?.[13]
A personal scientific overview of her biography has been published in 2018 as “Autobiography of Ruth Nussinov”.[14]
She was a postdoctoral fellow at the Weizmann Institute (1977-1980) and subsequently a visiting scientist at Berkeley and at Harvard. Nussinov was in the Computer Science Department of Tel Aviv University as a senior lecturer. She took a position in Tel Aviv University Medical School in 1984 as Associate Professor and was promoted to Professor in 1990, where she is now Professor Emeritus.[12]
In 1978, Nussinov published a dynamic programming algorithm for RNA secondary structure prediction, which has since been the leading method.[4] It has since been taught in bioinformatics and computational biology classes in Europe and the US, it is included in books, and exploited in multiple software packages.
In the 1990s Nussinov pioneered the role of dynamic conformational ensembles in function, with distinct conformational states and their propensities indicators of protein and cell phenotype, and of allosteric drugs actions.[6][7][8][9] She proposed that all conformations pre-exist, and the model of “conformational selection and population shift” as an alternative to “induced fit” to explain molecular recognition.[6] The concept that she introduced emphasized that all conformational states preexist, available for a range of ligands to bind, followed by re-equilibration (shift) of the ensemble. It also clarified how allosteric posttranslational modifications can work, and underscored that lipids, ions and water molecules can also act via allostery.[9] She also offered that all dynamic proteins are allosteric,[30] the role of allostery in disease, and how allosteric drugs work at the fundamental level. This paradigm has impacted the scientific community's views and strategies in allosteric drug design, biomolecular engineering, molecular evolution, and cell signaling. In line with Nussinov’s proposition, dynamic population shifts are now broadly recognized as the origin of allostery. It also explains the effects of allosteric, disease-related activating mutations.[31][32][33]
The new concepts that her group pioneered have changed the way biophysicists and structural biologists think about protein-ligand interactions and are now included in chemistry/biochemistry courses. The profound significance, and advance was also heralded in Science as innovating on the decades-old concepts, noting that "although textbooks have championed the induced fit mechanism for more than 50 years, data (especially NMR) unequivocally support the powerful paradigm for diverse biological processes".[34] The conformational selection/population shift mechanism is now widely established. As Nussinov and others have shown, the new paradigm helps unravel processes as diverse as signaling, catalysis, gene regulation, and aggregation in amyloid diseases, and recently, the mechanisms of activating mutationsincancer, and addressing the puzzling question of how same-gene mutations can promote both cancer and neurodevelopmental disorders.[35][13]