The DOI matches its Crossref record.
A second molecular biology revolution? The energy landscapes of biomolecular function
Abstract
A graphical abstract is available for this content
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“biomolecules are dynamical objects that are always interconverting between a variety of structures with varying energies,166 and that this is the origin of allosteric mechanisms.”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [166]
In the citing paper
“Biomolecules must be described statistically, not statically.”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [166]
Citations
Cited by
Sorted by citations
Wei, G., Xi, W., Nussinov, R., & Ma, B. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00562 · Cited by 436
Self-citationRas Conformational Ensembles, Allostery, and Signaling
Lu, S., Jang, H., Muratcioglu, S., Gursoy, A., Keskin, O., Nussinov, R., & Zhang, J. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00542 · Cited by 355
Self-citationProtein Folding and Mechanisms of Proteostasis
Díaz‐Villanueva, J. F., Díaz-Molina, R. J., & García‐González, V. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms160817193 · Cited by 354
“For instance, establishment of preformed enzyme complexes for cell function has been proposed as a mechanism used by cells to rapidly respond to homeostasis unbalances [2,3], whereas pathways that regulate the conservation of protein folding have a key role [4].”
BackgroundPrinciples and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics
Maximova, T., Moffatt, R., Ma, B., Nussinov, R., & Shehu, A. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004619 · Cited by 214
Self-citationEvolutionary drivers of thermoadaptation in enzyme catalysis
Nguyen, V., Wilson, C., Hoemberger, M., Stiller, J. B., Agafonov, R. V., Kutter, S., English, J., Theobald, D. L., & Kern, D. (2016) · Science · doi:10.1126/science.aah3717 · Cited by 201
“folding (23), an idea that suggests a unified view of protein folding that includes conformational transitions within the folded ensemble (32, 33).”
BackgroundAlphaFold, Artificial Intelligence (AI), and Allostery
Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04346 · Cited by 190
“166,175 Static descriptions were the norm for decades.”
BackgroundSelf-citationIntroduction to Protein Ensembles and Allostery
Nussinov, R. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00283 · Cited by 136
Self-citationNussinov, R., Jang, H., Tsai, C., & Cheng, F. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006658 · Cited by 134
Self-citationLigand Binding Thermodynamics in Drug Discovery: Still a Hot Tip?
Geschwindner, S., Ulander, J., & Johansson, P. (2015) · Journal of Medicinal Chemistry · doi:10.1021/jm501511f · Cited by 124
Large-Scale Conformational Changes and Protein Function: Breaking the in silico Barrier
Orellana, L. (2019) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2019.00117 · Cited by 124
“…complete understanding of equilibrium dynamics requires sampling both the structure space available and the underlying free-energy landscape (FEL; Frauenfelder et al., 1991; Zhuravlev and Papoian, 2010a; Nussinov and Wolynes, 2014; Röder et al., 2019) along its relevant dimensions ( Figures 1B,C ).”
BackgroundDiscovery of hidden allosteric sites as novel targets for allosteric drug design
Lu, S., Ji, M., Ni, D., & Zhang, J. (2017) · Drug Discovery Today · doi:10.1016/j.drudis.2017.10.001 · Cited by 119
Protein conformational ensembles in function: roles and mechanisms
Nussinov, R., Liu, Y., Zhang, W., & Jang, H. (2023) · RSC Chemical Biology · doi:10.1039/d3cb00114h · Cited by 122
Self-citationProtein ensembles link genotype to phenotype
Nussinov, R., Tsai, C., & Jang, H. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006648 · Cited by 102
Self-citationExploring biomolecular energy landscapes
Joseph, J. A., Röder, K., Chakraborty, D., Mantell, R. G., & Wales, D. J. (2017) · Chemical Communications · doi:10.1039/c7cc02413d · Cited by 96
AlphaFold, allosteric, and orthosteric drug discovery: Ways forward
Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103551 · Cited by 96
Self-citationStructure‐Based Drug Discovery with Deep Learning**
Özçelik, R., van Tilborg, D., Jiménez-Luna, J., & Grisoni, F. (2023) · ChemBioChem · doi:10.1002/cbic.202200776 · Cited by 71
An Information-Theory-Based Approach for Optimal Model Reduction of Biomolecules
Giulini, M., Menichetti, R., Shell, M. S., & Potestio, R. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00676 · Cited by 67
“[100] R.”
Allostery: Allosteric Cancer Drivers and Innovative Allosteric Drugs
Nussinov, R., Zhang, M., Maloney, R., Liu, Y., Tsai, C.-J., & Jang, H. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167569 · Cited by 66
Self-citationIntrinsic protein disorder in oncogenic KRAS signaling
Nussinov, R., Jang, H., Tsai, C., Liao, T.-J., Li, S., Fushman, D., & Zhang, J. (2017) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-017-2564-3 · Cited by 58
Self-citationPrecision medicine review: rare driver mutations and their biophysical classification
Nussinov, R., Jang, H., Tsai, C., & Cheng, F. (2019) · Biophysical Reviews · doi:10.1007/s12551-018-0496-2 · Cited by 56
“The shift of the ensemble can also explain the propagation of signaling, i.e., Ballo-networks^ (Nussinov et al. 2011; Szilagyi et al. 2013).”
BackgroundSelf-citationDoes Ras Activate Raf and PI3K Allosterically?
Nussinov, R., Tsai, C., & Jang, H. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.01231 · Cited by 55
Self-citationThe structural basis for cancer treatment decisions
Nussinov, R., Jang, H., & Tsai, C. (2014) · Oncotarget · doi:10.18632/oncotarget.2439 · Cited by 51
“com/oncotarget the complex dynamic circuitry, and population shifts in response to changing conditions, computational structural biology may foment and chart new territory [153].”
BackgroundSelf-citationInducing high activity of a thermophilic enzyme at ambient temperatures by directed evolution
Li, G., Maria‐Solano, M. A., Romero‐Rivera, A., et al. (2017) · Chemical Communications · doi:10.1039/c7cc05377k · Cited by 48
Nussinov, R., Zhang, M., Tsai, C., Liao, T.-J., Fushman, D., & Jang, H. (2018) · Biophysical Reviews · doi:10.1007/s12551-018-0461-0 · Cited by 47
“Macromolecules are not solid rocks; they are flexible, visiting multiple possible conformations (Nussinov and Wolynes 2014).”
BackgroundSelf-citationAllostery, and how to define and measure signal transduction
Nussinov, R., Tsai, C., & Jang, H. (2022) · Biophysical Chemistry · doi:10.1016/j.bpc.2022.106766 · Cited by 46
Self-citationBanerjee, A., Jang, H., Nussinov, R., & Gaponenko, V. (2015) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2015.11.010 · Cited by 42
Self-citationMa, B., & Nussinov, R. (2016) · Nature Chemical Biology · doi:10.1038/nchembio.2212 · Cited by 36
Self-citationAkhter, N., & Shehu, A. (2018) · Molecules · doi:10.3390/molecules23010216 · Cited by 37
Autoinhibition can identify rare driver mutations and advise pharmacology
Nussinov, R., Tsai, C., & Jang, H. (2019) · The FASEB Journal · doi:10.1096/fj.201901341r · Cited by 35
Self-citationOptimal Coarse-Grained Site Selection in Elastic Network Models of Biomolecules
Diggins, P. M., Liu, C., Deserno, M., & Potestio, R. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00654 · Cited by 33
Explicit Characterization of the Free Energy Landscape of pKID–KIX Coupled Folding and Binding
Chong, S., Im, H., & Ham, S. (2019) · ACS Central Science · doi:10.1021/acscentsci.9b00200 · Cited by 37
TRAF3 signaling: Competitive binding and evolvability of adaptive viral molecular mimicry
Guven‐Maiorov, E., Keskin, Ö., Gürsoy, A., Waes, C. V., Chen, Z., Tsai, C., & Nussinov, R. (2016) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2016.05.021 · Cited by 34
“Proteins exist as dynamic ensembles of states [28]; not as rigid statues [29].”
Self-citationGenerative deep learning for macromolecular structure and dynamics
Hoseini, P., Zhao, L., & Shehu, A. (2020) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2020.11.012 · Cited by 34
Mechanism of activation and the rewired network: New drug design concepts
Nussinov, R., Zhang, M., Maloney, R., Tsai, C., Yavuz, B. R., Tunçbağ, N., & Jang, H. (2021) · Medicinal Research Reviews · doi:10.1002/med.21863 · Cited by 30
Self-citationAre Parallel Proliferation Pathways Redundant?
Nussinov, R., Tsai, C., & Jang, H. (2020) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2020.03.013 · Cited by 33
Self-citationInhibition of Nonfunctional Ras
Nussinov, R., Jang, H., Gürsoy, A., Keskin, Ö., & Gaponenko, V. (2021) · Cell chemical biology · doi:10.1016/j.chembiol.2020.12.012 · Cited by 29
“Both types of proteins can be considered from the mechanistic free energy landscape standpoint, which epitomizes the ‘second molecular biological revolution’ (Nussinov and Wolynes, 2014).”
BackgroundInfluentialSelf-citationPDEδ Binding to Ras Isoforms Provides a Route to Proper Membrane Localization
Muratcioğlu, S., Jang, H., Gürsoy, A., Keskin, Ö., & Nussinov, R. (2017) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.7b03035 · Cited by 30
Self-citationDistributions of experimental protein structures on coarse-grained free energy landscapes
Sankar, K., Liu, J., Wang, Y., & Jernigan, R. L. (2015) · The Journal of Chemical Physics · doi:10.1063/1.4937940 · Cited by 26
Advancements and Challenges in Computational Biology
Nussinov, R. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004053 · Cited by 24
“functionally relevant ensembles of threedimensional conformation [5].”
Self-citationModeling Binding with Large Conformational Changes: Key Points in Ensemble-Docking Approaches
Motta, S., & Bonati, L. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00125 · Cited by 21
Zaman, A. B., & Shehu, A. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2794-5 · Cited by 22
Limits in accuracy and a strategy of RNA structure prediction using experimental information
Wang, J., Williams, B., Chirasani, V. R., Krokhotin, A., Das, R., & Dokholyan, N. V. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz427 · Cited by 22
Allosteric Modalities for Membrane-Bound Receptors: Insights from Drug Hunting for Brain Diseases
Coughlin, Q., Hopper, A. T., Blanco, M.-J., Tirunagaru, V. G., Robichaud, A. J., & Doller, D. (2019) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b01651 · Cited by 22
Dynamic Protein Allosteric Regulation and Disease
Nussinov, R., Tsai, C.-J., & Jang, H. (2019) · Advances in Experimental Medicine and Biology · doi:10.1007/978-981-13-8719-7_2 · Cited by 18
Self-citationPhosphorylation-induced changes in the energetic frustration in human Tank binding kinase 1
Husain, S., Kumar, V., & Hassan, M. I. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.04.016 · Cited by 17
Tegner, J. N., Zenil, H., Kiani, N. A., Ball, G., & Gómez-Cabrero, D. (2016) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2016.0144 · Cited by 18
“effective potentials, free energy minimization, maximum entropy, computational mechanics, maximum calibre, motifs in biological networks, self-organization, compressed sensing, evolution as thermodynamics, algorithmic complexity and algorithmic laws of information [47,50–64].”
BackgroundWang, J. (2021) · Journal of Biological Physics · doi:10.1007/s10867-021-09586-5 · Cited by 17
Allostery in Disease: Anticancer Drugs, Pockets, and the Tumor Heterogeneity Challenge
Nussinov, R., Yavuz, B. R., & Jang, H. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169050 · Cited by 19
Self-citationDrug resistance and tumor heterogeneity: cells and ensembles
Nussinov, R., Yavuz, B. R., & Jang, H. (2025) · Biophysical Reviews · doi:10.1007/s12551-025-01320-y · Cited by 18
“Being unaware of this seminal work at the time, in 2014, Nussinov and Wolynes (2014) noted that even living things must conform to the laws of quantum mechanics and structural chemistry.”
Self-citationAn Energy Landscape Treatment of Decoy Selection in Template-Free Protein Structure Prediction
Akhter, N., Qiao, W., & Shehu, A. (2018) · Computation · doi:10.3390/computation6020039 · Cited by 15
“The energy landscape relates biologically-active conformations of a molecule to thermodynamic stability (and function) [22–25], and has been central to a better understanding of the relationship between protein structure, dynamics, and function [18,26].”
BackgroundBergman, M. T., Zhang, W., Liu, Y., Jang, H., & Nussinov, R. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c03243 · Cited by 14
Self-citationGarland, J. M. (2017) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2017.06.004 · Cited by 13
Kolimi, N., Pabbathi, A., Saikia, N., Ding, F., Sanabria, H., & Alper, J. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c02424 · Cited by 12
Examining a Thermodynamic Order Parameter of Protein Folding
Chong, S., & Ham, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-25406-8 · Cited by 11
Grieco, A., Quereda‐Moraleda, I., & Martin-Garcia, J. M. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14090909 · Cited by 15
Open Structural Data in Precision Medicine
Nussinov, R., Jang, H., Nir, G., Tsai, C., & Cheng, F. (2022) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-122220-012951 · Cited by 12
Self-citationBerezovsky, I. N., & Nussinov, R. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169407 · Cited by 12
Self-citationBivalent molecular mimicry by ADP protects metal redox state and promotes coenzyme B 12 repair
Gouda, H., Mascarenhas, R., Ruetz, M., Yaw, M., & Banerjee, R. V. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2220677120 · Cited by 8
Sample-Based Models of Protein Energy Landscapes and Slow Structural Rearrangements
Maximova, T., Zhang, Z., Carr, D. B., et al. (2017) · Journal of Computational Biology · doi:10.1089/cmb.2017.0158 · Cited by 10
“…selection/population shift principle (Boehr et al., 2009) that has been reported to regulate the structure-function relationship in proteins (Nussinov and Wolynes, 2014), mutations change the probability (which is related to their energetics) with which structures are populated at…”
Unsupervised and Supervised Learning over the Energy Landscape for Protein Decoy Selection
Akhter, N., Chennupati, G., Kabir, K. L., Djidjev, H. N., & Shehu, A. (2019) · Biomolecules · doi:10.3390/biom9100607 · Cited by 10
“The energy landscape exposes the relationship between molecular structure, changes to structure, and biological activity or function [5].”
BackgroundFrom mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes
Qiao, W., Akhter, N., Fang, X., Maximova, T., Plaku, E., & Shehu, A. (2018) · BMC Genomics · doi:10.1186/s12864-018-5024-z · Cited by 9
Mechanical Unfolding Response of Proteins
Popa, I., & Berkovich, R. (2023) · ACS in focus · doi:10.1021/acsinfocus.7e7015 · Cited by 9
Physics-Grounded Evaluation to Guide Accurate Biomolecular PredictionPreprint
Lyu, N., Du, S., Shao, Q., Yang, Z. J., Ma, J., & Herschlag, D. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.30.662466 · Cited by 10
“Moreover, ensemble generation is a rapidly growing frontier of predictive models 47–50 , an important direction as ensembles provide the states and probabilities that are ultimately needed to provide quantitative prediction of function 51–53 (Fig.”
How residence time works in allosteric drugs
Nussinov, R., & Jang, H. (2025) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2025.103149 · Cited by 10
Self-citationKabir, K. L., Akhter, N., & Shehu, A. (2019) · Journal of Bioinformatics and Computational Biology · doi:10.1142/s0219720019400146 · Cited by 9
Energy landscapes in molecular biology: History, principles, and perspectives
Nussinov, R., Regev, C., & Jang, H. (2026) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583526100134 · Cited by 8
“The landscape concept has not only brought forth new theoretical interpretive tools, and inspired new experimental approaches, but bore clinical fruit (Nussinov and Wolynes, 2014), leading to next-generation drug discovery (Nussinov et al. , 2025c).”
InfluentialSelf-citationUsing subpopulation EAs to map molecular structure landscapes
Zaman, A. B., Jong, K. A. D., & Shehu, A. (2019) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3321707.3321777 · Cited by 7
“Deep and narrow minima constitute structural states with few but very low-energy structures, whereas shallow and broad minima constitute (more heterogeneous) states with many but not as low-energy structures [25].”
BackgroundUsing Sequence-Predicted Contacts to Guide Template-free Protein Structure Prediction
Zaman, A. B., Parthasarathy, P. V., & Shehu, A. (2019) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3307339.3342175 · Cited by 7
“…under physical principles demonstrated in theory and experiment that the three-dimensional/tertiary structures comprising the stable, long-time populated state(s) observed in the wet laboratory [2] reside in deep and broad basins of the potential energy function summing up atomic interactions [15].”
BackgroundOn the Relationship Between Protein Stability, Thermostability, and Allosteric Signaling
Raechell, R., Tee, W.-V., Dong, B., Guarnera, E., & Berezovsky, I. N. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169537 · Cited by 11
From Pathogens to Cancer: Are Cancer Cells Evolved Mitochondrial Super Cells?
Balzanelli, M. G., Distratis, P., Lazzaro, R., Pham, V. H., Prete, R. D., Mosca, A., Inchingolo, F., Aityan, S. K., Santacroce, L., Nguyen, K. C. D., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13040813 · Cited by 7
Probing biological mechanisms with chemical tools
Kang, C., & Keller, T. H. (2020) · Pharmacological Research · doi:10.1016/j.phrs.2020.104656 · Cited by 6
Ma, B., Bai, G., Nussinov, R., Ding, J., & Wang, Y. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.1c00038 · Cited by 6
Self-citationWeight average approaches for predicting dynamical properties of biomolecules
Yagi, K., Re, S., Mori, T., & Sugita, Y. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.08.008 · Cited by 6
Decoy selection for protein structure prediction via extreme gradient boosting and ranking
Akhter, N., Chennupati, G., Djidjev, H. N., & Shehu, A. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3523-9 · Cited by 6
“features an ensemble of structural states near or far from the native state and an extensive collection of intermediate states that shape the multi-modal and multi-dimensional nature of the landscape [45]. The concept of a basin is connected to a local/focal minimum. A focal minimum in a landscape is surrounded by a b…”
BackgroundAllosteric changes in the conformational landscape of Src kinase upon substrate binding
Chong, S., Oshima, H., & Sugita, Y. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168871 · Cited by 5
Akhter, N., Hassan, L., Rajabi, Z., et al. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9161-7_8 · Cited by 5
First-passage times in complex energy landscapes: a case study with nonmuscle myosin II assembly
Bebon, R., & Schwarz, U. S. (2022) · New Journal of Physics · doi:10.1088/1367-2630/ac78fd · Cited by 4
“examples from chemistry and condensed matter physics include chemical kinetics [2, 3] or glass-forming systems [4–6], in a biophysical context, energy landscapes are central for explaining a wide range of biomolecular processes [7].”
BackgroundWang, J., & Wang, W. (2016) · Advances in Physics X · doi:10.1080/23746149.2016.1216329 · Cited by 5
Völker, J., Gindikin, V., & Breslauer, K. (2024) · Biomolecules · doi:10.3390/biom14121532 · Cited by 4
How do mutations and allosteric inhibitors modulate caspase-7 activity? A molecular dynamics study
Bingöl, E. N., Serçinoğlu, O., & Özbek, P. (2018) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2018.1517611 · Cited by 4
“Allosteric modulation can be observed when binding of a small molecule/ligand to a specific site affects the properties of a distant site in the protein (Greener & Sternberg, 2018; Guo & Zhou, 2016; Liu & Nussinov, 2016; Nussinov & Wolynes, 2014).”
BackgroundKabir, K. L., Akhter, N., & Shehu, A. (2019) · EPiC series in computing · doi:10.29007/tmgc · Cited by 4
Reconstruction and Decomposition of High-Dimensional Landscapes via Unsupervised Learning
Lei, J., Akhter, N., Qiao, W., et al. (2020) · ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) · doi:10.1145/3394486.3403300 · Cited by 3
“In particular, the energy landscape has improved our understanding of the relationship between protein structure, structure plasticity, and function [7, 23].”
BackgroundTandem Domains with Tuned Interactions Are a Powerful Biological Design Principle
Nussinov, R., & Tsai, C. (2015) · PLoS Biology · doi:10.1371/journal.pbio.1002306 · Cited by 3
“Structures are not rocks; and the only way to gain insight is to consider molecules as ensembles of multiple interconverting conformations, where the relative population of a certain conformation, or “state”, is dynamic [3].”
BackgroundSelf-citationEnergy Landscapes in Chemical Reactions and Transport
Weitzel, K. (2025) · ChemPhysChem · doi:10.1002/cphc.202400877 · Cited by 2
Protein aggregation in health and disease: A looking glass of two faces
de Andrade, G. C., Mota, M. F., Moreira-Ferreira, D. N., Silva, J. L., de Oliveira, G. A. P., & Marques, M. A. (2024) · Advances in protein chemistry and structural biology · doi:10.1016/bs.apcsb.2024.09.010 · Cited by 2
Qiao, W., Maximova, T., Plaku, E., & Shehu, A. (2017) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3107411.3107499 · Cited by 2
“Proteins switch between dierent shapes/structures to interact with dierent molecular partners [15].”
BackgroundBai, Y., Jiao, W., Vörster, J., & Parker, E. J. (2022) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2022.102789 · Cited by 2
“Enzyme catalysis is a structurally dynamic process consisting of multiple steps including substrate binding, transition state stabilization, and product release (1-4).”
BackgroundLin, R. L. F., Bellaiche, A., & Etchebest, C. (2025) · Biochimie · doi:10.1016/j.biochi.2025.10.007 · Cited by 1
Robust SH2 binding affinity indicates minimal SH3-to-SH2 communication in Grb2
Ventura, E. S., Felice, M. D., Toso, J., Pennacchietti, V., Toto, A., & Gianni, S. (2026) · Biology Direct · doi:10.1186/s13062-026-00729-z · Cited by 1
Reconstructing and mining protein energy landscape to understand disease
Qiao, W., Maximova, T., Fang, X., Plaku, E., & Shehu, A. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217619 · Cited by 1
“Obtaining detailed representations of protein energy landscapes is central to elucidating the structural dynamics that regulates protein function or dysfunction [2].”
BackgroundElucidating the Role of Wildtype and Variant FGFR2 Structural Dynamics in (Dys)Function and Disorder
Lian, Y., Bodian, D. L., & Shehu, A. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25084523 · Cited by 1
“By relying on the principle of conformation selection [27,28], we harness the structural diversity present among PDB-deposited tertiary structures of wildtype and variant forms of FGFR2 in various functional states.”
BackgroundKang, H.-S., & Sattler, M. (2018) · Emerging Topics in Life Sciences · doi:10.1042/etls20170090 · Cited by 1
Akhter, N., Lei, J., Qiao, W., et al. (2018) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621411 · Cited by 1
“their proximity and energetics [4].”
Antigen Binding Reshapes Antibody Energy Landscape and Conformation Dynamics
Kabir, K. L., Nussinov, R., Ma, B., & Shehu, A. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669830 · Cited by 1
Self-citationGolf-course and funnel energy landscapes: Protein folding concepts in martensites
Shankaraiah, N. (2017) · Physical review. E · doi:10.1103/physreve.95.063003
Frehse, G., & Althoff, M. (2017) · Institute of Science and Technology Austria
Out of one, many: Exploiting intrinsic motions to explore protein structure spaces
Morris, D., Maximova, T., Plaku, B., & Shehu, A. (2017) · IEEE International Conference on Computational Advances in Bio and Medical Sciences · doi:10.1109/iccabs.2017.8114290
“In principle, these issues can be addressed via a detailed reconstruction of the energy landscape in silico [2].”
BackgroundAttenuating dependence on structural data in computing protein energy landscapes
Morris, D., Maximova, T., Plaku, E., & Shehu, A. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2822-5
Discovering Differences and Similarities among Species Based on Numeric Features of microRNAsPreprint
Zhu, R., Zhang, Z., Xin, D., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/767046
“Almost all kinds of biology functional molecular have specific nucleotide composition or special molecular structure 39, 40, 41,42,43,44 .”
Background
Metadata
- Authors
- Nussinov, R., & Wolynes, P. G.
- Affiliations
- Tel Aviv University (IL)
- Center for Theoretical Biological Physics (US)
- Venue
- Physical Chemistry Chemical Physics
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2014
- Volume
- 16
- Issue
- 14
- Pages
- 6321
- ISSN
- 1463-9076 (ISSN-L)
- 1463-9084
- OpenAlex
- W2165550831
- Cited by
- 104
- References
- 0
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 6 · 2023: 6 · 2022: 7 · 2021: 7 · 2020: 6 · 2019: 21 · 2018: 10 · 2017: 13 · 2016: 9 · 2015: 6 · 2014: 1
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Physical Chemistry Chemical Physics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- energy landscape, biomolecular function, molecular biology, protein folding, conformational change
- MeSH terms
- DNA, Models, Molecular, Proteins, Quantum Theory, Thermodynamics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
