Our Science – Nussinov Website

Ruth Nussinov, Ph.D.

CCR Nanobiology Program Head, Computational Structural Biology Group Senior Investigator (Contr) Center for Cancer Research National Cancer Institute Building 469, Room 149 P.O. Box B Frederick, MD 21702-1201 Phone:   301-846-5579 Fax:   301-846-5598 E-Mail:   ruthnu@helix.nih.gov

Biography

Dr. Ruth Nussinov is a Professor in the Department of Human Genetics, School of Medicine, Tel Aviv University, Tel Aviv, 69978 Israel, and a Senior Principal Scientist and Principal Investigator at the National Cancer Institute. She has received her B. Sc degree in Microbiology from the University of Washington (Seattle, Washington) and her Ph.D. in Biochemistry from Rutgers University (NJ). She was a Fellow at the Weizmann Institute, and a visiting scientist at the chemistry department at Berkeley and at the Biochemistry department at Harvard. She joined the Medical School at Tel Aviv University in 1985 as an Associate Professor. In 1990 she became a Full Professor. Her association with the NIH started in 1983, first with the NICHHD and since 1985 with the NCI. Currently, she has a large group of graduate students in Tel Aviv, in collaboration with Prof. H. Wolfson, from the School of Computer Science. Additionally, she has a group at the NCI. She is an author of over 440 scientific papers. Dr. Nussinov's 1978 paper proposed the dynamic programming algorithm for RNA secondary structure prediction (e.g. in wikipedia/encyclopedia/course lectures, etc.)

http://en.wikipedia.org/wiki/RNA_structure;
http://openwetware.org/wiki/Wikiomics:RNA_secondary_structure_prediction
http://wiki.case.edu/images/c/c7/Present.pdf;
http://www.nationmaster.com/encyclopedia/RNA-structure;
http://www.ibi.vu.nl/teaching/masters/prot_struc/2008/ps-lec12-2008.pdf;

first by maximizing the number of base pairs (SIAM, 35: 68-82, 1978) and later introducing the so-called 'energy rules' into the algorithm (Proc Natl Acad Sci U S A. 77: 6903-13, 1980; See also Biological sequence analysis (Durbin, Eddy, Krogh & Mitchison, Cambridge University Press, 1998). This algorithm is still the leading method for secondary structure prediction of RNA and is taught in classes. Dr. Nussinov was also a pioneer in DNA sequence analysis (e.g., see Time Warps, String Edits, and Macromolecules, by David Sankoff, Joseph Kruskal, CSLI Press, 1999) with numerous publications on recurrence of nucleotide patterns already in the early 1980's. Most notably, Ruth Nussinov's seminal papers - later often ignored or cited perfunctorily - presented clear evidence that reading-frame-independent preferences for nucleotide motifs may explain the occurrence of several codons and the Synonymous Codon (SC) usage in the human and viral genes available then [5], [6] ,[7]. (Antezana MA, Jordan IK. Highly conserved regimes of neighbor-base-dependent mutation generated the background primary-structural heterogeneities along vertebrate chromosomes. PLoS ONE. 2008 May 14;3(5):e2145). Until 1990 her papers addressed RNA and DNA sequence and structure and nucleic acid-protein interactions. In 1990 she switched to proteins. Currently her research focuses on computational studies of protein folding, binding and protein function. She addresses Structural Systems Biology, and develops concepts and strategies toward nanobiology, in nanostructure design. Her research is inter-disciplinary, with a strong component of computer science.

Dr. Nussinov serves as a Editor-in-Chief in PLoS Computational Biology; and as Editor in J. Biological chemistry (JBC), Physical Biology, Proteins, Biophysical J., BMC Bioinformatics, and other journals. She also serves as a long term member on the NIH Study Section MSFD. Dr. Nussinov is a frequent speaker in international and domestic conferences and gives numerous invited talks in academic institutions.

Dr. Nussinov was a recipient of the Biophysical Society Fellow Award 'for her extraordinary contributions to advances in computational biology on both nucleic acids and proteins.'

Research

Computational Structural Biology: Protein Structure, Function and Protein-Protein Interactions, Dynamic Conformational Ensembles; Mechanisms of Protein Allostery; Mechanisms in Biomolecular Recognition

Our work is concept-driven. We proposed the model of Conformational Selection and Population Shift as an alternative to the Induced fit model to explain molecular recognition. Experimental data, particularly coming from NMR, increasingly support our proposition: Two main hypotheses have been advanced to explain these changes. 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 (1). In the 'conformational selection' model, it is assumed that, prior to the binding interaction, the unliganded protein exists as an ensemble of 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 (2). Although biochemistry textbooks have championed the induced fit mechanism for more than 50 years, there is now growing support for the additional binding mechanism' (quoted from Boehr DD, Wright PE. Biochemistry. How do proteins interact? Science. 2008 Jun 13;320(5882):1429-30). Conformational selection has by now been observed for protein-ligand, protein-protein, protein-DNA, protein-RNA and RNA-ligand interactions. These data support a new molecular recognition paradigm for processes as diverse as signaling, catalysis, gene regulation, and protein aggregation in disease, which has the potential to significantly impact our views and strategies in drug design, biomolecular engineering and molecular evolution. The role of dynamic conformational ensembles in biomolecular recognition Boehr DD, Nussinov R, Wright, PE. Nature Chemical Biology 2009 Nov;5(11):789-96.

Some references for our proposed model:
(1) Tsai CJ, Kumar S, Ma B, Nussinov R. Protein Science, 8:1181-1190, 1999, Folding funnels, binding funnels, and protein function;
(2) Ma B, Kumar S, Tsai CJ, Nussinov R. Folding funnels and binding mechanisms Protein Engineering 12:713-720, 1999;
(3) Tsai CJ, Ma B, Nussinov R. Folding and binding cascades: shifts in energy landscapes. Proc Natl Acad Sci USA 96(18):9970-9972, 1999;
(4) Kumar S, Ma B, Tsai CJ, Sinha N, Nussinov R. Folding and binding cascades: dynamic landscapes and population shifts. Protein Sci. 2000 Jan;9(1):10-9;
(5) Ma B, Shatsky M, Wolfson HJ, Nussinov R. Multiple diverse ligands binding at a single protein site: a matter of pre-existing populations. Protein Sci. 2002 Feb;11(2):184-97).

We further proposed conformational selection and population shift in protein disorder (Tsai CJ, Ma B, Sham YY, Kumar S, Nussinov R. Proteins 2001 44(4):418-27. Structured disorder and conformational selection). These are some of the concepts developed by the group which are increasingly accepted and substantiated by the academic community.

In 1999 we further proposed that the Conformational Selection and Population Shift model and the energy landscapes are related to protein function 'Here we extend the utility of the concept of folding funnels, relating them to biological mechanisms and function... The bottoms of the funnels reflect the extent of the flexibility of the proteins. Rugged floors imply a range of conformational isomers, which may be close on the energy landscape. Rather than undergoing an induced fit binding mechanism, the conformational ensembles around the rugged bottoms argue that the conformers, which are most complementary to the ligand, will bind to it with the equilibrium shifting in their favor.' (ref 1 above). '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 3).

We suggested that the energy landscape and the minima at the bottom of the funnel explain multiple binding events in signaling (1999): '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. This is a general phenomenon that holds uniformly (5, 14) whether in allostery (15), molecular communication, or signal transduction.'

Mechanistically, allostery is based on the same principles. Allostery is based on an ensemble of conformational states which are shifted following an allosteric event.

(1) Gunasekaran K, Ma B, Nussinov R. Is allostery an intrinsic property of all dynamic proteins? Proteins. 2004;57(3):433-43.

Most recently, we provided a general mechanistic overview and proposed a classification scheme of allosteric mechanisms:

(2) Tsai CJ, del Sol A, Nussinov R.Allostery: Absence of a Change in Shape Does Not Imply that Allostery Is Not at Play. J. Mol. Biol. 2008; 378(1), 1-11.

(3) Tsai CJ, del Sol A, Nussinov R. Protein allostery, signal transmission and dynamics: a classification scheme of allosteric mechanisms. Mol. BioSyst., 2009, 5, 207-216.

Moreover, Nussinov and her colleagues have also proposed that the origin of allosteric functional modulation is the multiple pre-existing pathways with signal amplification in the cell:

(4) del Sol A, Tsai CJ, Ma B, Nussinov R. The origin of allosteric functional modulation: multiple pre-existing pathways. Structure. 2009 Aug 12;17(8):1042-50.

(5) Allosteric effects in the marginally stable von Hippel-Lindau tumor
suppressor protein and allostery-based rescue mutant design. Liu J, Nussinov R. Proc Natl Acad Sci USA. 2008 Jan 22;105(3):901-6.

In 2002 we further suggested that conformational selection also applies in self assembly in amyloid formation (Ma B, Nussinov R. Molecular dynamics simulations of alanine rich beta-sheet oligomers: Insight into amyloid formation. Protein Sci. 2002 Oct;11(10):2335-50. These are some of the concepts developed by the group which are increasingly accepted and substantiated by the academic community.

Conformational selection and population shift also takes place in protein folding:

Protein folding: binding of conformationally fluctuating building blocks via population selection. Tsai CD, Ma B, Kumar S, Wolfson H, Nussinov R. Crit Rev Biochem Mol Biol. 2001;36(5):399-433.

Binding and folding: in search of intramolecular chaperone-like building block fragments. Ma B, Tsai CJ, Nussinov R. Protein Eng. 2000 13(9):617-27.

Intra-molecular chaperone: the role of the N-terminal in conformational selection and kinetic control. Tsai CJ, Ma B, Nussinov R. Phys Biol. 2009 Feb 4;6(1):13001.

Moreover, already in 2000 we have also extended the 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, 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 just than 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. 2000 Apr 21;203(4):383-97.

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. NMR, X-ray crystallography, single-molecule FRET, and simulations clearly demonstrate that the free enzyme dynamics already encompass all the conformations necessary for substrate binding, preorganization, transition-state stabilization, and product release. Conformational selection and substate population shift at each step of the catalytic turnover can accommodate enzyme specificity and efficiency. Within such a framework, entropy can have a larger role in conformational dynamics than in direct energy transfer in dynamically promoted catalysis. Enzyme dynamics point to stepwise conformational selection in catalysis.
Ma B, Nussinov R. Curr Opin Chem Biol. 2010 PMID: 20822947


Recently we have further extended these conformational ensembles and molecular recognition views to address (1) a key mechanistic question in the first step in transcription initiation: How does a transcription factor select a specific DNA response element given the presence of degenerate sequences? The initiation of transcription is regulated by transcription factors (TFs) binding to DNA response elements (REs). How do TFs recognize specific binding sites among the many similar ones available in the genome? To date, this question has largely been viewed from the standpoint of DNA sequence variability and transcription factor binding affinity under steady-state conditions. Recent research has illustrated that even a single nucleotide substitution can alter the selective binding of TFs to coregulators, that prior binding events can lead to selective DNA binding, and that selectivity is influenced by the availability of binding sites in the genome. Recently, we combined structural insights with recent genomics screens to address the problem of TF-DNA interaction specificity. The emerging picture of selective binding site sequence recognition and TF activation let us to propose three major factors which are involved: the cellular network, protein and DNA as dynamic conformational ensembles and combinatorial assembly, that is, the tight packing of multiple TFs and coregulators on promters and enhancers DNA sequences. The classification of TF recognition mechanisms based on these factors impacts our understanding of how transcription initiation is regulated:

How do transcription factors select specific binding sites in the genome? Pan Y, Tsai CJ, Ma B, Nussinov R. Nat Struct Mol Biol. 2009 Nov;16(11):1118-20;

Mechanisms of transcription factor selectivity. Pan Y, Tsai CJ, Ma B, Nussinov R. Trends Genet. 2010 Feb;26(2):75-83.

Further, we put these view in the general context of (2) Why does binding of proteins to DNA or proteins to proteins not necessarily spell function? Studies of binding are often question: first, is the observed binding functional, and second, if it is, which function? Is it activation or repression? The first question relates to binding at different sites; the second relates to binding at similar sites. These questions apply to transcription factors binding to genomic DNA and to protein interaction domains binding to their partners. Recently, we explained that both can be understood in terms of allostery and the cellular (or in vitro) environment. The idea is simple yet powerful; it emphasizes the role of allostery in defining whether binding between transcription factors and (cognate or noncognate) DNA sequences will lead to function and to the type of function. Allosteric effects are the outcome of dynamically shifting populations; thus binding to even slightly different DNA sequences will lead to different transcription factor conformations that can be reflected in the binding sites to their co-regulators. Currently, allostery is not considered when trying to understand how binding phenomena determine the functional outcome. Allosteric effects can enhance the binding specificity in a function-oriented manner. We provided a biological rationale that considers cellular crowding effects. (Why does binding of proteins to DNA or proteins to proteins not necessarily spell function? Ma B, Tsai CJ, Pan Y, Nussinov R. ACS Chem Biol. 2010 Mar 19;5(3):265-72.)

Our research is computational. We focus on protein structures and their associations. In particular, we address the following areas: Protein folding, protein binding, and the inter-relationship between structure and function. We have developed the building block folding model. We study protein conformations, dynamics and function. In parallel, we investigate amyloid formation, and polymorphism; we model toxic Alzheimer and other amyloid ion channels and address the mechanism of amyloid toxicity. We are intensely interested in protein binding. We have derived nonredundant datasets of protein-protein interfaces and these are used to decipher the determinants of protein-protein associations; to construct the functional network and to add the time dimensionality into the systems biology maps. Using protein-protein interfaces we predict interactions in Systems Biology strategies. We are extremely interested in protein flexibility and its role in binding mechanisms and in protein function.

The group has a strong component of method development. It has 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 on- going. Our research is carried out with the notion also pioneered by the group, that protein folding and binding are similar processes with similar underlying mechanisms (1996; Protein-protein interfaces: architectures and interactions in protein-protein interfaces and in protein cores. Their similarities and differences). 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 1990's 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 3D interface architectures can be used as templates to identify interacting protein pairs independent of homology or global fold similarity.

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. 1996 Apr;31(2):127-52.

A dataset of protein-protein interfaces generated with a sequence-order-independent comparison technique. Tsai CJ, Lin SL, Wolfson HJ, Nussinov R. J Mol Biol. 1996 Jul 26;260(4):604-20.