Ruth Nussinov, Ph.D.
- Center for Cancer Research
- National Cancer Institute
- Building 542, Rm 603
- Frederick, MD 21702-1201
Nussinov pioneered the “conformational selection and population shift” (1999) as an alternative to the “induced fit” text-book model to explain the molecular mechanism of recognition and postulated that population shift underlies allosteric regulation. She proposed that all conformations pre-exist and extended this pre-existing ensemble model to catalysis (2000). In 1999 she also postulated that in both allosteric and non-allosteric binding, the deceptively rigid snapshot images of protein motion observed by crystallography can be explained by the presence of low barriers around the bottom of the funnel and shifts in chemical equilibrium in favor of the bound states. She further posited that activation by oncogenic mutations mimic the activation mechanism of the wild-type protein. Experimental data over two decades supported these fundamental concepts, and as evidenced in the literature, by now a vast number of experimental and computational studies take them as established facts. Nussinov further applied these concepts to unravel the structural mechanisms of allosteric drug action and to formulate determinants of agonist/antagonist drug design. More recently, her group unraveled the detailed oncogenic activation of PI3K, B-Raf, and PTEN and the structural basis for oncogenic Ras signaling, also confirmed by experiments. Her pioneering work has contributed to extraordinary advances in understanding the conformational behaviors of biological macromolecules, and their uncontrolled actions in disease.
Dr. Nussinov served as the Editor-in-Chief of PLOS Computational Biology and is currently Editor-in-Chief of Current Opinion in Structural Biology. She is an elected Fellow of the Biophysical Society, the International Society of Computational Biology (ISCB), the American Physical Society (APS), and the American Institute for Medical and Biological Engineering (AIMBE). She was recognized by multiple Domestic and International Awards. She has been designated twice a Highly Cited Researcher (ranking among the top 3000 researchers or 1% across all fields according to Thomson Reuters Essential Science Indicators, http://highlycited.com/ "earning them the mark of exceptional impact"). Most of her publications are conceptualized and driven by her group.
A personal scientific overview of her biography has been published in
http://www.sciencedirect.com/science/article/pii/S0165614717301293?via%3Dihub Trends Pharmacol Sci. 2017 Sep;38(9):761-763. doi: 10.1016/j.tips.2017.06.008.
Autobiography of Ruth Nussinov.
Nussinov R. J Phys Chem B. 2021 Jul 1;125(25):6735-6739. doi:10.1021/acs.jpcb.1c04719
Areas of Expertise
1) protein structure, dynamics and function 2) signaling 3) allosteric drugs 4) cellular networks 5) key oncogenic proteins 6) computational biology
An Overview of Our On-Going Research
Our group is interested in cancer research. Our work distinguishes itself by its conceptual depth, breadth, and innovative view; and its underlying powerful idea of the dynamic energy landscape, that is, that biomolecules are dynamical objects that are always interconverting between a variety of structures with varying energies. We tackle significant questions that can be addressed by structural computations, and conceptual questions that cannot. Recently, we extended our cancer focus to neurodevelopmental disorders and cancer. We took up puzzling questions such as How can same-gene mutations promote both cancer and developmental disorders? (Nussinov R, Tsai CJ, Jang H. Sci Adv. 2022 Jan 14;8(2):eabm2059. doi: 10.1126/sciadv.abm2059), and why those with neurodevelopmental disorders have a high risk of cancer. We proposed that cell type–specific expression of the mutant protein, and of other proteins in the respective pathway, timing of activation (during embryonic development or sporadic emergence), and the absolute number of molecules that the mutations activate, alone or in combination, are pivotal in determining the pathological phenotypes—cancer and (or) developmental disorders. We further proposed that Neurodevelopmental disorders, immunity, and cancer are connected (Nussinov R, Tsai CJ, Jang H. iScience. 2022 May 30;25(6):104492. doi: 10.1016/j.isci.2022.104492). The immune and nervous systems co-evolve as the embryo develops. Immunity can release cytokines leading to activation of MAPK signaling in neural cells. In specific embryonic brain cell types, dysregulated signaling that results from germline or embryonic mutations can promote changes in chromatin organization and gene accessibility, and thus expression levels of essential genes in neurodevelopment. In cancer, dysregulated signaling can emerge from sporadic somatic mutations during human life.
Recent examples of the significant conceptual questions that we undertook include:
- Anticancer drug resistance: An update and perspective (Nussinov R, Tsai CJ, Jang H. Drug Resist Updat. 2021 Dec;59:100796. doi: 10.1016/j.drup.2021.100796). Drug resistance mutations can occur in the same protein or in different proteins; as well as in the same pathway or in parallel pathways, bypassing the intercepted signaling. The dilemma that the clinical oncologist is facing is that not all the genomic alterations as well as alterations in the tumor microenvironment that facilitate cancer cell proliferation are known, and neither are the alterations that are likely to promote metastasis. For example, the common K-RasG12C driver mutation emerges in different cancers. Most occur in NSCLC, but some occur, albeit to a lower extent, in colorectal cancer and pancreatic ductal carcinoma. The responses to K-RasG12C inhibitors are variable and fall into three categories, (i) new point mutations in K-Ras, or multiple copies of KRAS G12C which lead to higher expression level of the mutant protein; (ii) mutations in genes other than KRAS; (iii) original cancer transitioning to other cancer(s). Here we discuss the molecular mechanisms underlying drug resistance while focusing on those emerging to common targeted cancer drivers. We also address questions of why cancers with a common driver mutation are unlikely to evolve a common drug resistance mechanism, and whether one can predict the likely mechanisms that the tumor cell may develop. These vastly important and tantalizing questions in drug discovery, and broadly in precision medicine, are the focus of our present review. We end with our perspective, which calls for target combinations to be selected and prioritized with the help of the emerging massive computer power which enables artificial intelligence (AI), and the increased gathering of data to overcome its insatiable needs.
- Allostery, and how to define and measure signal transduction (Nussinov R, Tsai CJ, Jang H. Biophys Chem. 2022 Apr;283:106766. doi: 10.1016/j.bpc.2022.106766). Here we ask: What is productive signaling? How to define it, how to measure it, and most of all, what are the parameters that determine it? Further, what determines the strength of signaling from an upstream to a downstream node in a specific cell? These questions have either not been considered or not entirely resolved. The requirements for the signal to propagate downstream to activate (repress) transcription have not been considered either. Yet, the questions are pivotal to clarify, especially in diseases such as cancer where determination of signal propagation can point to cell proliferation and to emerging drug resistance, and to neurodevelopmental disorders, such as RASopathy, autism, attention-deficit/hyperactivity disorder (ADHD), and cerebral palsy. Here we propose a framework for signal transduction from an upstream to a downstream node addressing these questions. Defining cellular processes, experimentally measuring them, and devising powerful computational AI-powered algorithms that exploit the measurements, are essential for quantitative science.
- Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs (Nussinov R, Zhang M, Maloney R, Liu Y, Tsai CJ, Jang H. J Mol Biol. 2022 Apr;167569. doi: 10.1016/j.jmb.2022.167569). Here, we discuss the principles of allosteric activating mutations, propagation downstream of the signals that they prompt, and allosteric drugs, with examples from the Ras signaling network. We focus on Abl kinase where mutations shift the landscape toward the active, imatinib binding-incompetent conformation, likely resulting in the high affinity ATP outcompeting drug binding. Recent pharmacological innovation extends to allosteric inhibitor (GNF-5)-linked PROTAC, targeting Bcr-Abl1 myristoylation site, and broadly, allosteric heterobifunctional degraders that destroy targets, rather than inhibiting them. Designed chemical linkers in bifunctional degraders can connect the allosteric ligand that binds the target protein and the E3 ubiquitin ligase warhead anchor. The physical properties and favored conformational state of the engineered linker can precisely coordinate the distance and orientation between the target and the recruited E3. Allosteric PROTACs, noncompetitive molecular glues, and bitopic ligands, with covalent links of allosteric ligands and orthosteric warheads, increase the effective local concentration of productively oriented and placed ligands. Through covalent chemical or peptide linkers, allosteric drugs can collaborate with competitive drugs, degrader anchors, or other molecules of choice, driving innovative drug discovery.
- Mechanism of activation and the rewired network: New drug design concepts (Nussinov R, Zhang M, Maloney R, Tsai CJ, Yavuz BR, Tuncbag N, Jang H. Med Res Rev. 2022 Mar;42(2):770-799. doi: 10.1002/med.21863). Precision oncology benefits from effective early phase drug discovery decisions. Recently, drugging inactive protein conformations has shown impressive successes, raising the cardinal questions of which targets can profit and what are the principles of the active/inactive protein pharmacology. Cancer driver mutations have been established to mimic the protein activation mechanism. We suggest that the decision whether to target an inactive (or active) conformation should largely rest on the protein mechanism of activation. We next discuss the recent identification of double (multiple) same-allele driver mutations and their impact on cell proliferation and suggest that like single driver mutations, double drivers also mimic the mechanism of activation. We further suggest that the structural perturbations of double (multiple) in cis mutations may reveal new surfaces/pockets for drug design. Finally, we underscore the preeminent role of the cellular network which is deregulated in cancer. Our structure-based review and outlook updates the traditional Mechanism of Action, informs decisions, and calls attention to the intrinsic activation mechanism of the target protein and the rewired tumor-specific network, ushering innovative considerations in precision medicine.
- Inhibition of Nonfunctional Ras (Nussinov R, Jang H, Gursoy A, Keskin O, Gaponenko V. Cell Chem Biol. 2021 Feb 18;28(2):121-133. doi: 10.1016/j.chembiol.2020.12.012). Intuitively, functional states should be targeted, not nonfunctional ones. So why could drugging the inactive K-Ras4BG12C work -but drugging the inactive kinase will likely not? The reason is the distinct oncogenic mechanisms. Kinase driver mutations work by stabilizing the active state and/or destabilizing the inactive state. Either way, oncogenic kinases are mostly in the active state. Ras driver mutations work by quelling its deactivation mechanisms, GTP hydrolysis, and nucleotide exchange. Covalent inhibitors that bind to the inactive GDP-bound K-Ras4BG12C conformation can thus work. By contrast, in kinases, allosteric inhibitors work by altering the active-site conformation to favor orthosteric drugs. From the translational standpoint this distinction is vital: it expedites effective pharmaceutical development and extends the drug classification based on the mechanism of action. Collectively, here we postulate that drug action relates to blocking the mechanism of activation, not to whether the protein is in the active or inactive state.
Examples of recent significant questions that can be addressed by structural computations that we took up include:
- The mechanism of activation of MEK1 by B-Raf and KSR1 (Maloney RC, Zhang M, Liu Y, Jang H, Nussinov R. Cell Mol Life Sci. 2022 May 4;79(5):281. doi: 10.1007/s00018-022-04296-0). MEK1 interactions with B-Raf and KSR1 are key steps in Ras/Raf/MEK/ERK signaling. Despite this, vital mechanistic details of how these execute signal transduction are still enigmatic. Among these is why, despite B-Raf and KSR1 kinase domains similarity, the B-Raf/MEK1 and KSR1/MEK1 complexes have distinct contributions to MEK1 activation, and broadly, what is KSR1's role. We postulate that if KSR1 were to adopt an active configuration with an extended A-loop as seen in other protein kinases, then the MEK1 P-rich loop would extend in a similar manner, as seen in the active B-Raf/MEK1 heterodimer. This would result in highly flexible MEK1 A-loop, and KSR1 functioning as an active, B-Raf-like, kinase.
- The mechanism of Raf activation through dimerization (Zhang M, Maloney R, Jang H, Nussinov R. Chem Sci. 2021 Nov 18;12(47):15609-15619. doi: 10.1039/d1sc03444h). Despite its clinical importance, fundamental questions, such as how the side-to-side dimerization promotes the OFF-to-ON transition of Raf's kinase domain and how the fully activated ON-state kinase domain is stabilized in the dimer for Raf signaling, remain unanswered. Herein, we decipher an atomic-level mechanism of Raf activation through dimerization, clarifying this enigma. This work provides atomic level insight into critical steps in Raf activation and outlines a new venue for drug discovery against Raf dimerization.
- The mechanism of full activation of tumor suppressor PTEN at the phosphoinositide-enriched membrane (Jang H, Smith IN, Eng C, Nussinov R. iScience. 2021 Apr 17;24(5):102438. doi: 10.1016/j.isci.2021.102438). Tumor suppressor PTEN, the second most highly mutated protein in cancer, dephosphorylates signaling lipid PIP3 produced by PI3Ks. Excess PIP3 promotes cell proliferation. The mechanism at the membrane of this pivotal phosphatase is unknown hindering drug discovery. Exploiting explicit solvent simulations, we tracked full-length PTEN trafficking from the cytosol to the membrane. We followed it localizing on microdomains enriched in signaling lipids, as PI3K does, and observed PIP3 allosterically unfolding the N-terminal PIP2 binding domain, positioning it favorably for the polybasic motif interaction with PIP2. Finally, we determined PTEN catalytic action at the membrane, all in line with experimental observations, deciphering the mechanisms of how PTEN anchors to the membrane and restrains cancer.
- The mechanism of PI3Kα activation at the atomic level (Zhang M, Jang H, Nussinov R. Chem Sci. 2019 Feb 20;10(12):3671-3680. doi: 10.1039/c8sc04498h). PI3K lipid kinases phosphorylate PIP2 to PIP3 in the PI3K/Akt/mTOR pathway to regulate cellular processes. They are frequently mutated in cancer. Here we determine the PI3Kα activation mechanism at the atomic level. Unlike protein kinases where the substrate abuts the ATP, crystal structures indicate that in PI3Kα, the distance between the γ phosphate of the ATP and the PIP2 lipid substrate is over 6 Å, much too far for the phosphoryl transfer, raising the question of how catalysis is executed. The mechanism that we uncovered not only explains how oncogenic mutations promote PI3Kα activation by facilitating nSH2 release, or nSH2-release-induced, allosteric motions; it also offers an innovative, PI3K isoform-specific drug discovery principle. Rather than competing with nanomolar range ATP in the ATP-binding pocket and contending with ATP pocket conservation and massive binding targets, this mechanism suggests blocking the PI3Kα sequence-specific cavity between the ATP-binding pocket and the substrate binding site. Targeting isoform-specific residues in the cavity may prevent PIP2 phosphorylation.
Below we provide further details, including some of our earlier powerful contributions, and our recent and on-going work.
An Overview of Our Earlier Research
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:
- Folding funnels, binding funnels, and protein function. Tsai CJ, Kumar S, Ma B, Nussinov R. Protein Sci. 8: 1181-90, 1999.
- Folding funnels and binding mechanisms. Ma B, Kumar S, Tsai CJ, Nussinov R. Protein Eng. 12: 713-20, 1999.
- 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.
- 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.
- 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. … 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 proteins 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.
Ras and its major pathways, MAPK and PI3K/PTEN/AKT/PDK1/mTOR in tumor proliferation. We took up the challenging problem of the mechanisms of the Ras protein, particularly focusing on K-Ras4B-driven cancers. We addressed key questions, including Ras regulation at the membrane, signaling through its major (MAPK and PI3K/AKT) pathways, mechanisms of its oncogenic mutations, inhibition, and more. Examples include - Does Ras’ disordered hypervariable region (HVR) have a role beyond membrane anchoring? Does Ras dimerize? If so, why and how? How is Ras regulated at the membrane and what are the mechanisms of Ras signaling at the membrane? What is the dynamical organization of Ras/Raf assemblies at the membrane? Within this framework, we further took up the critical, though overlooked, role of calmodulin in KRAS-driven adenocarcinomas (The Key Role of Calmodulin in KRAS-Driven Adenocarcinomas. Nussinov R, Muratcioglu S, Tsai CJ, Jang H, Gursoy A, Keskin O. Mol Cancer Res. 13(9): 1265-73, 2015), for which we won an award.
Best of AACR journals
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. 2016 Jan 1;76(1):18-23). 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.
Examples of additional works on K-Ras 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. 290:28887, 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. 290:9465, 2015). We revealed the structural basis of oncogenic mutations in K-Ras4B (The Structural Basis of Oncogenic Mutations G12, G13 and Q61 in Small GTPase K-Ras4B. Lu S, Jang H, Nussinov R, Zhang J. Sci Rep. 6:21949, 2016). We also showed that in principle PDEδ should be able to bind and translocate all Ras isoforms, not only K-Ras4B. Earlier data suggested that PDEδ extracts K-Ras4B and N-Ras from the plasma membrane (PM); but surprisingly not K-Ras4A (PDEδ Binding to Ras Isoforms Provides a Route to Proper Membrane Localization. Muratcioglu S, Jang H, Gursoy A, Keskin O, Nussinov R. J Phys Chem B. 121:5917, 2017). Earlier analysis of the crystal structures advanced that the presence of large/charged residues adjacent to the farnesylated site precludes the PDEδ interaction. We showed that PDEδ can bind to farnesylated K-Ras4A and N-Ras like K-Ras4B - albeit not as strongly. We explained that the weaker binding to PDEδ, coupled with stronger anchoring of K-Ras4A in the membrane (but not of electrostatically neutral N-Ras), can explain the observation of why PDEδ is unable to effectively extract K-Ras4A. We thus proposed that farnesylated Ras isoforms can bind PDEδ to fulfil the required PM enrichment, and argued that the different environments - PM versus solution - can resolve apparent puzzling Ras observations. These are novel insights that would not be expected based on the crystal structures alone provide an elegant rationale for previously puzzling observations of the differential effects of PDEδ on farnesylated Ras family proteins.
Autoinhibition is an effective mechanism that guards proteins against spurious activation. Despite its ubiquity, the distinct organizations of the autoinhibited states and their release mechanisms differ. Signaling is most responsive to the cell environment only if a small shift in the equilibrium is required to switch the system from an inactive (occluded) to an active (exposed) state. Ras signaling follows this paradigm. This underscores the challenge in drug (inhibitor) discovery to exploit and enhance autoinhibited states. We took up autoinhibition and release mechanisms at the membrane, and pointed to the ramifications to drug discovery. We considered Ras, and its upstream and downstream signaling, activation, and the Ras superfamily in this light, altogether providing a broad outlook of the principles and complexities of autoinhibition (Autoinhibition in Ras effectors Raf, PI3Kα, and RASSF5: a comprehensive review underscoring the challenges in pharmacological intervention. Nussinov R, Zhang M, Tsai CJ, Liao TJ, Fushman D, Jang H. Biophys Rev. 10:1263, 2018) and more.
We determined the autoinhibition and activation mechanisms of PI3K (PI3K Driver Mutations: A Biophysical Membrane-Centric Perspective. Zhang M, Jang H, Nussinov R. Cancer Res. 81:237, 2021; Structural Features that Distinguish Inactive and Active PI3K Lipid Kinases. Zhang M, Jang H, Nussinov R. J Mol Biol. 432:5849, 2020; The structural basis for Ras activation of PI3Kα lipid kinase. Zhang M, Jang H, Nussinov R. Phys Chem Chem Phys. 21:12021, 2019; The mechanism of PI3Kα activation at the atomic level. Zhang M, Jang H, Nussinov R. Chem Sci. 10:3671, 2019), the autoinhibition and activation of B-Raf (The mechanism of Raf activation through dimerization. Zhang M, Maloney R, Jang H, Nussinov R. Chem Sci. 12:15609, 2021; B-Raf autoinhibition in the presence and absence of 14-3-3. Zhang M, Jang H, Li Z, Sacks DB, Nussinov R. Structure. 29:768, 2021), activation by the V600E mutant (The mechanism of activation of monomeric B-Raf V600E. Maloney RC, Zhang M, Jang H, Nussinov R. Comput Struct Biotechnol J. 19:3349, 2021), the Ras-Raf assembly (The quaternary assembly of KRas4B with Raf-1 at the membrane. Jang H, Zhang M, Nussinov R. Comput Struct Biotechnol J. 18:737, 2020), the activation mechanism of PTEN (The mechanism of full activation of tumor suppressor PTEN at the phosphoinositide-enriched membrane. Jang H, Smith IN, Eng C, Nussinov R. iScience, 24:102438, 2021), the role of KSR1 (The mechanism of activation of MEK1 by B-Raf and KSR1. Maloney RC, Zhang M, Liu Y, Jang H, Nussinov R. Cell Mol Life Sci. 79:281, 2022), and more, all within the conformational framework that guides our work. We further proposed that some phosphorylation sites are ‘passenger’ sites (Phosphorylation and Driver Mutations in PI3Kα and PTEN Autoinhibition. Nussinov R, Zhang M, Tsai CJ, Jang H. Mol Cancer Res. 19:543, 2021), and more.
We also provided an innovative view of the allosteric pharmacology, from the conformational standpoint, for example (Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs. Nussinov R, Zhang M, Maloney R, Liu Y, Tsai CJ, Jang H. J Mol Biol. doi: 10.1016/j.jmb.2022.167569, 2022; Inhibition of Nonfunctional Ras. Nussinov R, Jang H, Gursoy A, Keskin O, Gaponenko V. Cell Chem Biol. 28:121, 2021).
Ras nanoclusters. We asked Is Nanoclustering essential for all oncogenic KRas pathways? Can it explain why wild-type KRas can inhibit its oncogenic variant? Nussinov R, Tsai CJ, Jang H. Semin Cancer Biol. 54:114, 2019). We proposed that nanoclustering (or dimerization, or oligomerization) is needed only for the Ras-driven MAPK pathway, but not the PI3K/AKT/mTOR, and that nanoclustering can explain why wild-type K-Ras can inhibit its oncogenic variant.
Thus, is nanoclustering essential for all oncogenic K-Ras pathways? We proposed that dimerization (nanoclustering) is needed only for activation of Raf/MAPK - but not PI3K. We explained that Raf’s activation requires dimerization of the catalytic domain. Thus, Raf’s activation requires Ras spatial proximity (whether as dimers oligomers, or nanoclusters). In contract, a monomeric Ras activates PI3K.
And why does wild-type Ras inhibit its oncogenic variants? And why K-Ras more so than N-Ras?
Early on, it was observed that wild-type human H-Ras gene can act as an onco-suppressor. Subsequent work revealed that in RAS-mutant cancers, wild-type Ras proteins can act as cancer suppressors, notably when the mutant and the wild-type are the same isoform. Shortly thereafter, it transpired that this is particularly the case for K-Ras. Wild-type K-Ras inhibited colony formation and tumor development in cell lines containing an actively expressed KRAS allele. Subsequent experiments confirmed and expanded on this apparent puzzling observation. Even though it was also shown to hold for other Ras isoforms, such as N-Ras, the trend was not as strong. How to explain these observations, coupled with potential pharmacological implications, fueled experiments searching for answers.
Thus, how then to explain the roles of wild-type Ras proteins with respect to their corresponding oncogenic mutants in human cancers? How can the wild-type K-Ras isoform act as a tumor suppressor in mutant KRAS-driven cancers? Why are the observations particularly robust for K-Ras, rather than N-Ras? And why a wild-type Ras isoform not suppress a mutant isoform of a different type? Further complicating the issue were observations supporting that the wild-type can promote tumorigenesis when the mutant and the wild-type belong to different isoforms, or that neither suppresses nor promotes cancer.
In the paper referenced above, we offer a simple clustering-centric interpretation, and a way to test it. Oncogenic Ras does not require an incoming signal from an RTK; wild-type Ras does. If a nanocluster of oncogenic Ras also contains inactive wild-type Ras, the MAPK signaling output of the nanocluster will be reduced. This can explain why wild-type K-Ras appears to quench, or suppress, cancer by inhibiting its oncogenic form. Why then will N-Ras have a more limited suppression effect than K-Ras? Consider the simple statistics: K-Ras is more abundant in cancer cells than N-Ras. And why would mixed isoforms – where the wild-type and mutant are not the same – not show this suppression effect? This is because nanoclusters tend to be homogeneous, which is understandable when we consider that the membrane preferences of distinct isoforms differ. The HVR of K-Ras4B favors loosely packed anionic membranes in the liquid-ordered phase; N-Ras can anchor easily into any lipid microdomain. The paper above has the relevant references.
We further reviewed factors influencing Ras lateral diffusion, asking whether oncogenic Ras diffusion speed in the membrane is important for signaling response times and whether it affects ubiquitously all pathways. We suggested that if Ras expression is sufficiently high to dimerize (or form nanoclusters), signaling response of those pathways where dimers (or nanoclusters) are involved corresponds to the speed with which Ras molecules travel in the membrane. On average, the faster the rate at which Ras travels to dimerize, the shorter the time to MAPK signaling; but not PI3Kα. However, we argued that K-Ras speed may not play an important functional role because changes in mobility at this scale are unlikely to be significant. In line with this, despite the anchors' variability, lateral diffusion speeds of K-Ras and H-Ras are similar, as is that of Lck kinase; however, even though with similar anchor, Cdc42 mobility presents a different pattern, commensurate with its role in the positioning of the apical domain, suggesting that mobility evolved for function (Oncogenic KRas mobility in the membrane and signaling response. Semin Cancer Biol. 54:109, 2019).
Finally, we took up the Rap1 puzzle (The Mystery of Rap1 Suppression of Oncogenic Ras. Nussinov R, Jang H, Zhang M, Tsai CJ, Sablina AA. Trends Cancer. 6:369, 2020). Decades ago, Rap1, a small GTPase very similar to Ras, was observed to suppress oncogenic Ras phenotype, reverting its transformation. The proposed reason, persisting since, has been competition between Ras and Rap1 for a common target. Yet, none was found. There was also Rap1’s puzzling suppression of Raf-1 versus activation of B-Raf. Reemerging interest in Rap1 envisages capturing its Ras suppression action by inhibitors. Here, we review the literature and resolve the enigma. In vivo oncogenic Ras exists in isoform-distinct nanoclusters. The presence of Rap1 within the nanoclusters reduces the number of the clustered oncogenic Ras molecules, thus suppressing Raf-1 activation and MAPK signaling. Nanoclustering suggests that Rap1 suppression is Ras isoform dependent. Altogether, a potent Rap1-like inhibitor appears unlikely.
Predicting host-pathogen interactions on the structural level. We further proposed host-pathogen interactions (Pathogen mimicry of host protein-protein interfaces modulates immunity. Guven-Maiorov E, Tsai CJ, Ma B, Nussinov R. Semin Cell Dev Biol. 58:136, 2016) and have developed a structural, interface-based method to predict host-pathogen interactions (Prediction of Host-Pathogen Interactions for Helicobacter pylori by Interface Mimicry and Implications to Gastric Cancer. Guven-Maiorov E, Tsai CJ, Ma B, Nussinov R. J Mol Biol. 8:429, 2017). We applied it to all human oncoviruses and constructed a network of the ‘superorganism’ interactions. Recently, we further developed and optimized the method to predict human-microbiome protein-protein interactions focusing on crosstalk between host and microbial proteins (HMI-PRED: A Web Server for Structural Prediction of Host-Microbe Interactions Based on Interface Mimicry. Guven-Maiorov E, Hakouz A, Valjevac S, Keskin O, Tsai CJ, Gursoy A, Nussinov R. J Mol Biol, 432:3395, 2020). We aim to predict which proteins can interact and how, through a structure-based interface mimicry strategy. Efficient and reliable prediction of new interactions can allow identification of potential targets. Powerful protein-protein interaction prediction tools can map interactions and predict how pathogens can hijack signaling in the host cell, which can be tested by experiment. Available experimental structural data are scant, and the combinatorial landscape of host protein-pathogen interactions is vast. We are working to further enhance our server to include more interactions and modeled proteins with AI-adopted prediction methods.
Ruth Nussinov, Ph.D.
Dr. Ruth Nussinov is a senior investigator in the Laboratory of Cancer Immunometabolism, 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 elected a Biophysical Society Fellow “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; Elected Fellow of the American Physical Society (APS) “For extraordinary advancements in the understanding of the structure and function of biomacromolecules, an algorithm for predicting RNA secondary structure, and the Conformational Selection and Population Shift concept as an alternative to the Induced-Fit model in molecular recognition.” 2020; Elected Fellow, American Institute for Medical and Biological Engineering (AIMBE) College of Fellows (2021); A Festschript Special Issue in honor of Ruth Nussinov Achievements, ACS, Journal of Physical Chemistry, 2021. More in the CV.
Course Lecture/Reference Materials
RNA Structure Determination (Center for Integrative Bioinformatics, Vrije Universiteit Amsterdam)
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