Ruth Nussinov, Ph.D.
Ruth  Nussinov, Ph.D.
Senior Investigator (Contr)
Head, Computational Structural Biology Section

Ruth Nussinov’s algorithm for the prediction of RNA secondary structure is still the leading method. She proposed ‘Conformational Selection and Population Shift’ as an alternative to the textbook ‘Induced-Fit’ model in molecular recognition. Her recent studies unveiled the key role of allostery under normal conditions and in disease and the principles of allosteric drug discovery. She uncovered the structural basis for cancer signaling, and its mechanistic principles; predicted GTP-dependent K-Ras dimer structures and suggested that K-Ras4B dimerizes via two distinct interfaces and explained the consequences for Raf’s activation and MAPK signaling; elucidated calmodulin’s role in KRAS-driven adenocarcinomas; the critical role of oncogenic KRAS in the initiation of cancer through deregulation of the G1 cell cycle, and proposed a new view of Ras isoforms. This new view argues for multiple signaling states of palmitoylated Ras isoforms, such as K-Ras4A. This view questions the completeness and accuracy of small GTPase Ras isoform statistics in different cancer types and calls for reevaluation of concepts and protocols. Importantly, the multiple signaling states also call for reconsideration of oncogenic Ras therapeutics.

Dr. Nussinov serves as the Editor-in-Chief of PLOS Computational Biology and she is an elected Fellow of the Biophysical Society. She is a Highly Cited Researcher (ranking among the top 3000 researchers or 1% across all fields according to Thomson Reuters Essential Science Indicators, "earning them the mark of exceptional impact").

Areas of Expertise
1) protein structure, dynamics and function 2) signaling 3) allosteric drugs 4) cellular networks 5) key oncogenic proteins 6) computational biology

Contact Info

Ruth Nussinov, Ph.D.
Center for Cancer Research
National Cancer Institute
Building 542, Rm 603
Frederick, MD 21702-1201
Ph: 301-846-5579

Conformational Selection and Population Shift

Inspired by the milestone concept of the free energy landscape (1991), we proposed in 1999 the model of “conformational selection and population shift” as an alternative to the “induced fit” model to explain molecular recognition. According to the “induced fit” hypothesis, the initial interaction between a protein and a binding partner induces a conformational change in the protein through a stepwise process. In our “conformational selection” model it is assumed that, prior to binding, the unliganded protein exists as an ensemble of all possible conformations in dynamic equilibrium. The binding partner interacts preferentially with a weakly populated, higher-energy conformation, causing the equilibrium to shift in favor of the selected conformation. This conformation then becomes the major conformation in the complex. Importantly, there cannot be a conformational selection without a population shift. As we explained then, population shift, or the redistribution of the pre-existing conformational ensemble of the macromolecule, is the origin of allostery. A recent paper in Science noted, “Although biochemistry textbooks have championed the induced fit mechanism for more than 50 years, there is now growing support for the additional [conformational selection and population shift] binding mechanism” (quoted from Boehr DD and Wright PE.  How do proteins interact? Science 320: 1429-30, 2008).

Conformational selection has by now been observed for protein-ligand, protein-protein, protein-DNA, protein-RNA and RNA-ligand interactions. These data support the powerful new molecular recognition paradigm for processes as diverse as signaling, catalysis, gene regulation, and protein aggregation in disease. The “conformational selection and population shift” model has significantly impacted views and strategies in allosteric drug discovery, biomolecular engineering and molecular evolution. Some early references for our proposed model:  

  1. Folding funnels, binding funnels, and protein function. Tsai CJ, Kumar S, Ma B, Nussinov R. Protein Sci. 8: 1181-90, 1999.
  2. Folding funnels and binding mechanisms. Ma B, Kumar S, Tsai CJ, Nussinov R. Protein Eng. 12: 713-20, 1999.
  3. Folding and binding cascades: shifts in energy landscapes. Tsai CJ, Ma B, Nussinov R. Proc Natl Acad Sci U S A. 96:  9970-72, 1999.
  4. Folding and binding cascades: dynamic landscapes and population shifts. Kumar S, Ma B, Tsai CJ, Sinha N, Nussinov R. Protein Sci. 9: 10-9, 2000.
  5. Multiple diverse ligands binding at a single protein site: a matter of pre-existing populations. Ma B, Shatsky M, Wolfson HJ, Nussinov R. Protein Sci. 11: 184-97, 2002. 

We further proposed conformational selection and population shift in protein disorder (Structured disorder and conformational selection. Tsai CJ, Ma B, Sham YY, Kumar S, Nussinov R. Proteins  44:  418-27, 2001).

In 1999 we also proposed that the energy landscape and the “conformational selection and population shift” model are related to protein function:  “Here we extend the utility of the concept of folding funnels, relating them to biological mechanisms and function..." (Ref. 1). “Whereas previously we have successfully utilized the folding funnels concept to rationalize binding mechanisms... here we further extend the concept of folding funnels, illustrating its utility in explaining enzyme pathways, multimolecular associations, and allostery. This extension is based on the recognition that funnels are not stationary; rather, they are dynamic, depending on the physical or binding conditions.” (Ref.  4). We suggested that the energy landscape and the minima at the bottom of the funnel explain multiple binding events in signaling (Ref. 3):  “For each such [binding] event, the population around the bottom of the corresponding (folding or binding) funnel serves as the repertoire of potentially available molecules for the following binding event in the chain. As in the case of the conformers present around the bottom of the folding funnel, here, too, it is not the conformer with the highest population times that will bind in the following step. Rather, it is the conformer whose structure in the current bound stage is most favorable for the next binding event.”

In 2004 we suggested that all proteins are allosteric (Is allostery an intrinsic property of all dynamic proteins? Gunasekaran K, Ma B, Nussinov R. Proteins 57: 433-43, 2004) and, more recently, we provided the unified mechanistic underpinnings of allostery (A unified view of "how allostery works". Tsai CJ, Nussinov R. PLoS Comput Biol. 10: e1003394, 2014), the underappreciated role of allostery in the cell (The underappreciated role of allostery in the cellular network. Nussinov R, Tsai CJ, Ma B. Annu Rev Biophys. 42: 169-89, 2013), the principles of allosteric interactions in cell signaling (Principles of allosteric interactions in cell signaling. Nussinov R, Tsai CJ, Liu J. J Am Chem Soc. 136(51): 17692-701, 2014), its role in disease (Allostery in disease and in drug discovery. Nussinov R, Tsai CJ. Cell 153: 293-305, 2013), and, over the years, the design principles of allosteric drugs (e.g., Unraveling structural mechanisms of allosteric drug action. Nussinov R, Tsai CJ. Trends Pharmacol Sci. 35: 256-64, 2014; The design of covalent allosteric drugs. Nussinov R, Tsai CJ. Annu Rev Pharmacol Toxicol. 55: 249-67 2014).

Preexisting conformational ensembles in catalysis.  By 2000 we had also extended our pre-existing ensembles view to enzyme catalysis:  “The widely accepted view of enzymatic catalysis holds that there is tight binding of the substrate to the transition-state structure, lowering the activation energy. This picture may, however, be oversimplified. The real meaning of a transition state is a surface, not a single saddle point on the potential energy surface. In a reaction with a “loose” transition-state structure, the entire transition-state region, rather than a single saddle point, contributes to reaction kinetics. Consequently, here we explore the validity of such a model, namely, the enzymatic modulation of the transition-state surface. We examine its utility in explaining enzyme catalysis. We analyze the possibility that instead of optimizing binding to a well-defined transition-state structure, enzymes are optimized by evolution to bind efficiently with a transition-state ensemble, and with a broad range of activated conformations. For enzyme catalysis, the key issue is still transition state (ensemble) stabilization. The source of the catalytic power is the modulation of the transition state. However, our definition of the transition state is the entire transition-state surface rather than just a single well-defined structure. This view of the transition-state ensemble is consistent with the nature of the protein molecule, as embodied and depicted in the protein energy landscape of folding and binding funnels.” (Transition-state ensemble in enzyme catalysis: possibility, reality, or necessity? Ma B, Kumar S, Tsai CJ, Hu Z, Nussinov R. J Theor Biol. 203:  383-97, 2000). More recently, we argued that conformational transitions may involve conformational selection and induced fit, which can be viewed as a special case in the catalytic network (Enzyme dynamics point to stepwise conformational selection in catalysis. Ma B, Nussinov R. Curr Opin Chem Biol. 14: 652-9, 2010).

Protein-protein interactions.  We are very interested in protein-protein interactions. We proposed that protein folding and binding are similar processes with similar underlying mechanisms, and that the architectural motifs at protein-protein interfaces resemble those of single chain proteins (Protein-protein interfaces: architectures and interactions in protein-protein interfaces and in protein cores. Their similarities and differences. Tsai CJ, Lin SL, Wolfson HJ, Nussinov R. Crit Rev Biochem Mol Biol. 31: 127-52, 1996). This conceptual similarity, now universally accepted, led us to develop and apply similar biophysical and algorithmic approaches. In particular, it led us to propose in the 1990s and again around 2005 that interface structural similarity exists not only between homologous protein pairs; different protein fold-pairs can also interact via similar interface architectures and these architectures are similar to those observed in single chain proteins. This recognition inspired the idea that known three-dimensional (3D) interface architectures can be used as templates to identify interacting protein pairs independent of homology or global fold similarity. This has proven to be a powerful strategy, which is now followed for large-scale docking by a number of research groups.

Signaling pathways in cancer and inflammation. In line with the NCI mission, we are currently studying the Ras protein, particularly focusing on K-Ras4B, its highly oncogenic isoform, to address key questions in Ras regulation, signaling, K-Ras4B-driven adenocarcinomas, and mechanisms of its oncogenic mutations. In parallel, we aim to figure out mechanisms of signaling in cancer and inflammation pathways. We believe that a structure-based approach is powerful in uncovering cellular regulation. We construct the cancer and inflammation network, including alternative paths, to elucidate pathway regulation, cross-talk and mutational mechanisms. Beyond complementing experiments on the cellular and animal levels, a structure-based approach can further elevate the significance of these experiments by formulating testable mechanisms on the basic molecular level.

Our overarching vision is going beyond the NCI Cancer Genome Atlas. We aim to model cancer-related structural pathways to understand the genetics of cancer, how the mutations work, and forecast drug resistance effects. Eventually, the genetics of cancer translates into proteins and their interactions.

Ras and cancer: GTP-dependent K-Ras dimers, the role of calmodulin in KRAS-driven adenocarcinomas, the critical role of oncogenic KRAS in the initiation of cancer through deregulation of the G1 cell cycle, and a new view of Ras isoforms in cancers. We discovered the two major interfaces of GTP-dependent K-Ras dimers (GTP-Dependent K-Ras Dimerization. Muratcioglu S, Chavan TS, Freed BC, Jang H, Khavrutskii L, Freed RN, Dyba MA5, Stefanisko K, Tarasov SG, Gursoy A, Keskin O, Tarasova NI, Gaponenko V, Nussinov R. Structure 23(7): 1325-35, 2015). The first, highly populated β-sheet dimer interface is at the Switch I and effector binding regions, overlapping Raf’s, PI3K’s, RalGDS’ and additional effectors’ binding surfaces. This interface has to be inhibitory to such effectors. The second, helical interface also overlaps some effectors’ binding sites. This interface may promote Raf‘s activation. Our data reveal how Ras self-association can regulate effector binding and activity, and suggest that disruption of the helical dimer interface by drugs may abate Raf’s signaling in cancer.

We pointed out the overlooked critical role of calmodulin in KRAS-driven adenocarcinomas (The Key Role of Calmodulin in KRAS-Driven Adenocarcinomas. Muratcioglu S, Tsai C.-J., Jang H, Gursoy A, Keskin O, Nussinov R. Mol Cancer Res. 13(9): 1265-73, 2015). Calmodulin (CaM) selectively binds to GTP-bound K-Ras4B; but not to its isoforms. Cell proliferation and growth require the MAPK (Ras/Raf/MEK/ERK) and PI3Kα/Akt pathways. We proposed that Ca2+/CaM promote PI3K/Akt signaling, and suggest how. Ca2+/CaM involvement may explain puzzling observations like the elevated calcium levels in adenocarcinomas. We hypothesized that CaM recruits and helps activate PI3K at the membrane, and that this is the likely reason for Ca2+/CaM-dependence in adenocarcinomas. CaM can contribute to initiation/progression of ductal (pancreatic, colorectal, lung) cancers via both PI3Kα/Akt and Raf/MEK/ERK pathways. Blocking the K-Ras/MAPK pathway and CaM/PI3Kα binding in a K-Ras4B/CaM/PI3Kα trimer could be a promising adenocarcinoma-specific therapeutic strategy. We further illustrated the critical role of oncogenic KRAS in the initiation of cancer through deregulation of the G1 cell cycle (Principles of K-Ras effector organization and the role of oncogenic K-Ras in cancer initiation through G1 cell cycle deregulation. Nussinov R, Tsai C-J, Muratcioglu S, Jang H, Gursoy A, Keskin O. Expert Rev Proteomics 50(6): 669-82, 2015).

We also proposed a new view of Ras isoforms in cancers (A New View of Ras Isoforms in Cancers. Nussinov R, Tsai C-J, Chakrabarti M, Jang H. Cancer Res. [Epub ahead of print - Dec. 10], 2015).  We proposed that small GTPase K-Ras4A have a single state or two states, one resembling K-Ras4B and the other N-Ras. A recent study of K-Ras4A made the remarkable observation that even in the absence of the palmitoyl K-Ras4A can be active at the plasma membrane. Importantly, this suggests that K-Ras4A may exist in two distinct signaling states. In state 1 K-Ras4A is only farnesylated, like K-Ras4B; in state 2 farnesylated and palmitoylated, like N-Ras. The K-Ras4A hypervariable region (HVR) sequence is positively charged, in-between K-Ras4B and N-Ras. Taken together, this raises the possibility that the farnesylated but nonpalmitoylated state 1, like K-Ras4B, binds calmodulin and is associated with colorectal and other adenocarcinomas like lung cancer and PDAC (pancreatic ductal adenocarcinoma). On the other hand, state 2 may be associated with melanoma and other cancers where N-Ras is a major contributor, such as acute myeloid leukemia (AML). Importantly, H-Ras has two – single and double – palmitoylated states that may also serve distinct functional roles. The multiple signaling states of palmitoylated Ras isoforms question the completeness of small GTPase Ras isoform statistics in different cancer types and call for reevaluation of concepts and protocols. They may also call for reconsideration of oncogenic Ras therapeutics.

Additional recent works on KRas include GTP Binding and Oncogenic Mutations May Attenuate Hypervariable Region (HVR)-Catalytic Domain Interactions in Small GTPase KRAS4B, Exposing the Effector Binding Site (Lu S, Banerjee A, Jang H, Zhang J, Gaponenko V, Nussinov R. J Biol Chem. 290(48): 28887-900, 2015) and Mechanisms of Membrane Binding of Small GTPase K-Ras4B Farnesylated Hypervariable Region (Jang H, Abraham SJ, Chavan TS, Hitchinson B, Khavrutskii L, Tarasova NI, Nussinov R, Gaponenko V. J Biol Chem. 2015) and more.

Methods. Our research group includes a strong method development component. We have developed extremely efficient algorithms with unique capabilities for structural comparisons, motif detection, and docking. These algorithms are used for prediction of binding sites on protein surfaces, detection of residue hot spots, protein classification, and protein-protein and protein-small ligand docking. Development of new algorithms is ongoing.

Scientific Focus Areas:
Chemical Biology, Computational Biology, Molecular Pharmacology, Structural Biology, Systems Biology
  1. Nussinov R, Tsai CJ
    Trends Pharmacol Sci. 35(5): 256-64, 2014. [ Journal Article ]
  2. Tsai CJ, Nussinov R.
    PLoS Comput Biol. 10: e1003394, 2014. [ Journal Article ]
  3. Muratcioglu S, Chavan TS, Freed BC, Jang H, Khavrutskii L, Freed RN, Dyba MA5, Stefanisko K, Tarasov SG, Gursoy A, Keskin O, Tarasova NI, Gaponenko V, and Nussinov R.
    Structure. 23(7): 1325-35, 2015. [ Journal Article ]
  4. Muratcioglu S, Tsai C.-J., Jang H, Gursoy A, Keskin O, and Nussinov R.
    Mol Cancer Res. 13(9): 1265-73, 2015. [ Journal Article ]
  5. Nussinov R, Tsai C-J, Chakrabarti M, and Jang H.
    Cancer Res. [Epub ahead of print - Dec. 10], 2015. [ Journal Article ]

Dr. Ruth Nussinov is a senior investigator in the Cancer and Inflammation Program, CCR, and a professor emeritus in the Department of Human Genetics, School of Medicine, Tel Aviv University, Tel Aviv, Israel. She received her B.Sc. in microbiology from the University of Washington (Seattle, WA) and her Ph.D. in biochemistry from Rutgers University (New Brunswick, NJ). Her postdoctoral training included a fellowship at the Weizmann Institute, and two visiting scientist positions, one in the Chemistry Department at the University of California, Berkeley, and the other in the Biochemistry Department at Harvard University. Dr. Nussinov then joined the Medical School at Tel Aviv University in 1985 as an associate professor and in 1990 became a full professor. Her association with the NIH started in 1983, first with the National Institute of Child Health and Human Development and, since 1985, with the National Cancer Institute. At Tel Aviv University Dr. Nussinov directed a large group of graduate students at the Medical and Computer Science Schools in parallel to leading her research team at the NCI-CCR.  

Dr. Nussinov's 1978 paper proposed the dynamic programming algorithm for RNA secondary structure prediction. To date, this algorithm is still the leading method and is taught in bioinformatics classes in universities across Europe and in the U.S. The algorithm and its extension are described in SIAM J Appl Math, 35(1): 68-82, 1978 and Proc Natl Acad Sci U S A 77: 6309-13, 1980 (see also Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids, Durbin R, Eddy SR, Krogh A, Mitchison G, eds., Cambridge University Press, 1998). Below are links to course lecture/reference materials.

Dr. Nussinov was also a pioneer in DNA sequence analysis (see Time Warps, String Edits, and Macromolecules:  The Theory and Practice of Sequence Comparison, by David Sankoff  and Joseph Kruskal, CSLI Press, 1999). In the early 1980s, she authored papers on the recurrence of nucleotide patterns, later becoming a trend in computational biology.

In 1990 Dr. Nussinov began to focus on proteins. In 1999, her NCI group proposed the model of 'conformational selection and population shift' as an alternative to ‘induced fit’ to explain molecular recognition. Biochemistry textbooks have championed the ‘induced fit’ mechanism for more than 50 years. The concept of conformational selection and population shift that her group introduced emphasized that all conformational states preexist, and that evolution has exploited them for function. This paradigm has impacted the scientific community's views and strategies in drug design, biomolecular engineering, and molecular evolution. Population shift is now broadly recognized as the origin of allostery, and thus signaling, across multimolecular complexes, pathways, and the entire cellular network. It also explains the effects of allosteric, disease-related mutations. The new concepts that we have contributed “have changed the way biophysicists and structural biologists think about protein folding, protein-protein interactions, and ligand binding” and is now included in a semester chemistry/biochemistry course in U.S. college so the students are “exposed to the depth and breadth of your work, which includes applications relevant to health such as cancer and inflammation, would be tremendously beneficial and inspiring to them”.

Her research team conducts studies of protein folding, binding and function. In particular, they investigate cancer-related proteins and pathways, aiming to figure out the mechanisms of signaling in cancer and inflammation pathways and oncogenic mutations. Dr. Nussinov’s research is interdisciplinary.

Dr. Nussinov is the author of more than 500 scientific papers and serves as editor-in-chief of PLoS Computational Biology and as an editor for the Journal of Biological Chemistry, Physical Biology, Proteins, BMC Bioinformatics, and other journals. Previously, she served two terms as editor for the Biophysical Journal.  Dr. Nussinov is also a long-term member of the NIH Macromolecular Structure and Function D (MFSD) Study Section.  She is a frequent speaker in colloquia and at international and domestic conferences and has given numerous invited talks at academic institutions around the world. She serves on site visit and grant review teams, and plays numerous roles in the scientific community.

Awards and Honors

In 2011, Dr. Nussinov was the recipient of the Biophysical Society Fellow Award “for her extraordinary contributions to advances in computational biology on both nucleic acids and proteins.” She was also elected a Fellow of the International Society of Computational Biology (ISCB) in 2013.

Course Lecture/Reference Materials

Wikipedia:  Nucleic Acid Structure

Wikiomics:  RNA Secondary Structure Prediction

RNA Structure Determination (Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam)



Name Position
Huili Lu Guest Researcher
Liang Xu Ph.D. Guest Researcher (Contr)
Bahar Delibas Ph.D. Guest Researcher (Contr)
David Fushman Ph.D. Guest Researcher (Contr)
Emine Guven-Maiorov Ph.D. Postdoctoral Fellow (Contr)
Hyunbum Jang Ph.D. Senior Computational Scientist (Contr)
Shuai Li Guest Researcher (Contr)
Tsung-Jen Liao Guest Researcher (Contr)
Buyong Ma Ph.D. Senior Computational Scientist (Contr)
Ece Salihe Ozbabacan Ph.D. Guest Researcher (Contr)
Ruxi Qi Guest Researcher (Contr)
Amarda Shehu Ph.D. Guest Researcher (Contr)
Chung-Jung Tsai Ph.D. Programmer / Analyst (Contr)
Mingzhen Zhang Guest Researcher (Contr)
Jun Zhao Ph.D. Postdoctoral Fellow (CRTA)