Bibliography
Here is the complete list of the published papers connected to the eggs deposited in PLUMED-NEST:
[plumID:26.002] S. Aureli, N. Piasentin, T. Fröhlking, V. Rizzi, F. L. Gervasio, A Transferable and Robust Computational Framework for Class A GPCR Activation Free Energies. doi: 10.64898/2025.12.05.692536 (2025)
[plumID:25.030] F. Dietrich, M. Bellucci, M. Salvalaglio, Committor-Regularized Learning of Differentiable Collective Variables from Non-Differentiable Structural Descriptors. doi: 10.26434/chemrxiv-2025-8gqqj (2025)
[plumID:25.027] V. Bresciani, F. Rinaldi, P. Franco, S. Girotto, A. Cavalli, J. D. Langer, M. Masetti, M. Bernetti, Integrating computational and experimental biophysics reveals novel insights into the RAD51-BRC4 interaction. doi: 10.1101/2025.03.07.642044 (2025)
[plumID:25.026] J. A. Clark, G. Xue, C. Molteni, Deciphering the Molecular Mechanisms of Startle Disease: the Role of the N46K Mutation in the Glycine Receptor. doi: 10.26434/chemrxiv-2025-mr5rn (2025)
[plumID:25.022] S. K. Ethirajan, A. R. Kulkarni, Modeling Diffusion in Metal-Organic Frameworks using On-the-fly Probability Enhanced Sampling-based Machine Learning Potentials. doi: 10.26434/chemrxiv-2025-mg7qj-v2 (2025)
[plumID:25.021] M. Schulze, T. Khakhula, N. Piasentin, S. Aureli, V. Rizzi, F. L. Gervasio, All You Need Is Water: Converging Ligand Binding Simulations with Hydration Collective Variables. doi: 10.1101/2025.06.27.661893 (2025)
[plumID:25.020] S. Aureli, V. Rizzi, N. Piasentin, F. L. Gervasio, Revealing Water-Mediated Activation Mechanisms in the Beta 1-Adrenergic Receptor via OneOPES-Enhanced Free Energy Landscapes. doi: 10.1101/2025.03.03.641221 (2025)
[plumID:25.019] V. Rizzi, M. Heritier, N. Piasentin, S. Aureli, F. L. Gervasio, The Arch from the Stones: Understanding Protein Folding Energy Landscapes via Bio-inspired Collective Variables. doi: 10.1101/2025.05.28.656575 (2025)
[plumID:25.016] S. Sen, S. E. Hoff, T. I. Morozova, V. Schnapka, M. Bonomi, Advancing in silico drug design with Bayesian refinement of AlphaFold models. doi: 10.1101/2025.06.25.661454 (2025)
[plumID:25.014] V. Schnapka, T. I. Morozova, S. Sen, M. Bonomi, Atomic resolution ensembles of intrinsically disordered proteins with Alphafold. doi: 10.1101/2025.06.18.660298 (2025)
[plumID:25.012] S. Das, U. Raucci, E. Trizio, P. Kang, R. P. P. Neves, M. J. Ramos, M. Parrinello, A Machine Learning-Driven, Probability-Based Approach to Enzyme Catalysis. ACS Catalysis 15, 9785–9792 (2025)
[plumID:25.011] M. Fortino, G. Schifino, M. Salvalaglio, A. Pietropaolo, Stepwise kinetics of the early-stage nucleation in chiral perovskites via ab initio molecular dynamics and free-energy calculations. Nanoscale 17, 5823–5828 (2025)
[plumID:25.009] K.-T. Chan, M. L. Berrens, Z. Chen, C. W. McCurdy, C. Anastasio, D. Donadio, Revealing the photochemical pathways of nitrate in water through first-principles simulations. The Journal of Chemical Physics 162 (2025)
[plumID:25.008] J. Hanni, D. Ray, Data efficient learning of molecular slow modes from nonequilibrium metadynamics. The Journal of Chemical Physics 162 (2025)
[plumID:25.006] S. Bhusal, O. Valsson, Characterizing the conformational ensemble of PROTAC degraders in solutions via atomistic simulations. doi: 10.26434/chemrxiv-2025-bxf47 (2025)
[plumID:25.005] V. Tiwari, T. Karmakar, Atomistic Details of Nanocluster Formation from Machine-Learned-Potential-Based Simulations. Nano Letters 25, 5940–5946 (2025)
[plumID:25.003] S. Chatterjee, D. Ray, Acceleration with Interpretability: A Surrogate Model-Based Collective Variable for Enhanced Sampling. Journal of Chemical Theory and Computation 21, 1561–1571 (2025)
[plumID:24.035] S. Perego, L. Bonati, Data efficient machine learning potentials for modeling catalytic reactivity via active learning and enhanced sampling. npj Computational Materials 10 (2024)
[plumID:24.033] M. Milanesi, Z. F. Brotzakis, M. Vendruscolo, Transient interactions between the fuzzy coat and the cross-β core of brain-derived Aβ42 filaments. Science Advances 11 (2025)
[plumID:24.032] T. Fröhlking, V. Rizzi, S. Aureli, F. L. Gervasio, DeepLNE++ leveraging knowledge distillation for accelerated multi-state path-like collective variables. The Journal of Chemical Physics 161 (2024)
[plumID:24.031] T. Fröhlking, L. Bonati, V. Rizzi, F. L. Gervasio, Deep learning path-like collective variable for enhanced sampling molecular dynamics. The Journal of Chemical Physics 160 (2024)
[plumID:24.030] D. Patra, J. Paul, U. Rai, A. P. S., M. V. Deshmukh, Conformational Plasticity in dsRNA-Binding Domains Drives Functional Divergence in RNA Recognition. Journal of the American Chemical Society 147, 17088–17100 (2025)
[plumID:24.029] D. Ray, V. Rizzi, Enhanced Sampling with Suboptimal Collective Variables: Reconciling Accuracy and Convergence Speed. Journal of Chemical Theory and Computation 21, 58–69 (2024)
[plumID:24.028] S. Di Marco, J. Aupič, G. Bussi, A. Magistrato, All-Atom Simulations Elucidate the Molecular Mechanism Underlying RNA–Membrane Interactions. Nano Letters 25, 4628–4635 (2025)
[plumID:24.027] A. J. Pettitt, V. K. Shukla, A. M. Figueiredo, L. S. Newton, S. McCarthy, A. B. Tabor, G. T. Heller, C. D. Lorenz, D. F. Hansen, An integrative characterization of proline cis and trans conformers in a disordered peptide. Biophysical Journal 123, 3798–3811 (2024)
[plumID:24.026] T. F. D. Silva, G. Bussi, Characterizing RNA Oligomers Using Stochastic Titration Constant-pH Metadynamics Simulations. Journal of Chemical Information and Modeling 65, 3568–3580 (2025)
[plumID:24.025] S. Das, U. Raucci, R. P. P. Neves, M. J. Ramos, M. Parrinello, Correlating enzymatic reactivity for different substrates using transferable data-driven collective variables. Proceedings of the National Academy of Sciences 121 (2024)
[plumID:24.024] P. Febrer Martinez, V. Rizzi, S. Aureli, F. L. Gervasio, Host–Guest Binding Free Energies à la Carte: An Automated OneOPES Protocol. Journal of Chemical Theory and Computation 20, 10275–10287 (2024)
[plumID:24.023] S. Aureli, F. Bellina, V. Rizzi, F. L. Gervasio, Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study. Journal of Chemical Information and Modeling 64, 8595–8603 (2024)
[plumID:24.022] D. Ray, Integrating path sampling with enhanced sampling for rare-event kinetics. The Journal of Chemical Physics 161 (2024)
[plumID:24.020] Z. Zou, D. Wang, P. Tiwary, A graph neural network-state predictive information bottleneck (GNN-SPIB) approach for learning molecular thermodynamics and kinetics. Digital Discovery 4, 211–221 (2025)
[plumID:24.019] M. Zhang, H. Wu, Y. Wang, Enhanced Sampling of Biomolecular Slow Conformational Transitions Using Adaptive Sampling and Machine Learning. Journal of Chemical Theory and Computation 20, 8569–8582 (2024)
[plumID:24.017] M. Karrenbrock, A. Borsatto, V. Rizzi, D. Lukauskis, S. Aureli, F. Luigi Gervasio, Absolute Binding Free Energies with OneOPES. The Journal of Physical Chemistry Letters 15, 9871–9880 (2024)
[plumID:24.016] E. Posani, P. Janoš, D. Haack, N. Toor, M. Bonomi, A. Magistrato, G. Bussi, Ensemble refinement of mismodeled cryo-EM RNA structures using all-atom simulations. Nature Communications 16 (2025)
[plumID:24.014] S. Yang, J. Nam, J. C. B. Dietschreit, R. Gómez-Bombarelli, Learning Collective Variables with Synthetic Data Augmentation through Physics-Inspired Geodesic Interpolation. Journal of Chemical Theory and Computation 20, 6559–6568 (2024)
[plumID:24.013] A. Bjola, M. Salvalaglio, Estimating Free-Energy Surfaces and Their Convergence from Multiple, Independent Static and History-Dependent Biased Molecular-Dynamics Simulations with Mean Force Integration. Journal of Chemical Theory and Computation 20, 5418–5427 (2024)
[plumID:24.012] V. Piomponi, M. Krepl, J. Sponer, G. Bussi, Molecular Simulations to Investigate the Impact of N6-Methylation in RNA Recognition: Improving Accuracy and Precision of Binding Free Energy Prediction. The Journal of Physical Chemistry B 128, 8896–8907 (2024)
[plumID:24.011] P. Kang, E. Trizio, M. Parrinello, Computing the committor with the committor to study the transition state ensemble. Nature Computational Science 4, 451–460 (2024)
[plumID:24.008] K. A. Croney, J. McCarty, Exploring Product Release from Yeast Cytosine Deaminase with Metadynamics. The Journal of Physical Chemistry B 128, 3102–3112 (2024)
[plumID:24.007] A. Borsatto, E. Gianquinto, V. Rizzi, F. L. Gervasio, SWISH-X, an Expanded Approach to Detect Cryptic Pockets in Proteins and at Protein–Protein Interfaces. Journal of Chemical Theory and Computation 20, 3335–3348 (2024)
[plumID:24.006] S. Zhong, Z. Shi, B. Zhang, Z. Wen, L. Chen, Homogeneous water vapor condensation with a deep neural network potential model. The Journal of Chemical Physics 160 (2024)
[plumID:24.005] J. Rydzewski, T. Gökdemir, Learning Markovian dynamics with spectral maps. The Journal of Chemical Physics 160 (2024)
[plumID:24.004] Z. Zou, P. Tiwary, Enhanced Sampling of Crystal Nucleation with Graph Representation Learnt Variables. The Journal of Physical Chemistry B 128, 3037–3045 (2024)
[plumID:24.002] X. Wang, M. J. Servis, Using Metadynamics to Reveal Extractant Conformational Free Energy Landscapes. The Journal of Physical Chemistry B 128, 263–272 (2023)
[plumID:24.001] D. Ray, S. Das, U. Raucci, Kinetic View of Enzyme Catalysis from Enhanced Sampling QM/MM Simulations. Journal of Chemical Information and Modeling 64, 3953–3958 (2024)
[plumID:24.000] F. Mambretti, U. Raucci, M. Yang, M. Parrinello, How Does Structural Disorder Impact Heterogeneous Catalysts? The Case of Ammonia Decomposition on Non-stoichiometric Lithium Imide. ACS Catalysis 14, 1252–1256 (2024)
[plumID:23.046] G. C. A. da Hora, M. Oh, J. D. M. Nguyen, J. M. J. Swanson, One Descriptor to Fold Them All: Harnessing Intuition and Machine Learning to Identify Transferable Lasso Peptide Reaction Coordinates. The Journal of Physical Chemistry B 128, 4063–4075 (2024)
[plumID:23.045] V. M. D’Amore, P. Conflitti, L. Marinelli, V. Limongelli, Minute-timescale free-energy calculations reveal a pseudo-active state in the adenosine A2A receptor activation mechanism. Chem 10, 3678–3698 (2024)
[plumID:23.044] F. M. Steudel, E. Ubasart, L. Leanza, M. Pujals, T. Parella, G. M. Pavan, X. Ribas, M. von Delius, Synthesis of C60/[10]CPP‐Catenanes by Regioselective, Nanocapsule‐Templated Bingel Bis‐Addition. Angewandte Chemie International Edition 62 (2023)
[plumID:23.043] L. Gigli, A. Goscinski, M. Ceriotti, G. A. Tribello, Modeling the ferroelectric phase transition in barium titanate with DFT accuracy and converged sampling. Physical Review B 110 (2024)
[plumID:23.041] S. E. Hoff, F. E. Thomasen, K. Lindorff-Larsen, M. Bonomi, Accurate model and ensemble refinement using cryo-electron microscopy maps and Bayesian inference. PLOS Computational Biology 20, e1012180 (2024)
[plumID:23.040] R. Capelli, G. Piccini, Atomistic Insights into Hydrogen-Bonded Supramolecular Capsule Self-Assembly Dynamics. The Journal of Physical Chemistry C 128, 635–641 (2023)
[plumID:23.039] E. R. Beyerle, P. Tiwary, Thermodynamically Optimized Machine-Learned Reaction Coordinates for Hydrophobic Ligand Dissociation. The Journal of Physical Chemistry B 128, 755–767 (2024)
[plumID:23.038] J. C. Calderón, P. Ibrahim, D. Gobbo, F. L. Gervasio, T. Clark, Determinants of Neutral Antagonism and Inverse Agonism in the β2-Adrenergic Receptor. Journal of Chemical Information and Modeling 64, 2045–2057 (2024)
[plumID:23.037] X. Qi, J. Pfaendtner, High-Throughput Computational Screening of Solid-Binding Peptides. Journal of Chemical Theory and Computation 20, 2959–2968 (2024)
[plumID:23.036] R. Wang, S. Mehdi, Z. Zou, P. Tiwary, Is the Local Ion Density Sufficient to Drive NaCl Nucleation from the Melt and Aqueous Solution? The Journal of Physical Chemistry B 128, 1012–1021 (2024)
[plumID:23.035] J. C. Calderón, E. Plut, M. Keller, C. Cabrele, O. Reiser, F. L. Gervasio, T. Clark, Extended Metadynamics Protocol for Binding/Unbinding Free Energies of Peptide Ligands to Class A G-Protein-Coupled Receptors. Journal of Chemical Information and Modeling 64, 205–218 (2023)
[plumID:23.034] R. Zhao, Z. Zou, J. D. Weeks, P. Tiwary, Quantifying the Relevance of Long-Range Forces for Crystal Nucleation in Water. Journal of Chemical Theory and Computation 19, 9093–9101 (2023)
[plumID:23.033] P. Pokorná, V. Mlýnský, G. Bussi, J. Šponer, P. Stadlbauer, Molecular dynamics simulations reveal the parallel stranded d(GGGA)3GGG DNA quadruplex folds via multiple paths from a coil-like ensemble. International Journal of Biological Macromolecules 261, 129712 (2024)
[plumID:23.032] H. Hradiská, M. Kurečka, J. Beránek, G. Tedeschi, V. Višňovský, A. Křenek, V. Spiwok, Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics. The Journal of Physical Chemistry B 128, 903–913 (2024)
[plumID:23.031] F. P. Panei, P. Gkeka, M. Bonomi, Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN. Nature Communications 15 (2024)
[plumID:23.030] D. Ray, M. Parrinello, Data-driven classification of ligand unbinding pathways. Proceedings of the National Academy of Sciences 121 (2024)
[plumID:23.029] F. Ballabio, C. Paissoni, M. Bollati, M. de Rosa, R. Capelli, C. Camilloni, Accurate and Efficient SAXS/SANS Implementation Including Solvation Layer Effects Suitable for Molecular Simulations. Journal of Chemical Theory and Computation 19, 8401–8413 (2023)
[plumID:23.028] M. Yang, U. Raucci, M. Parrinello, Reactant-induced dynamics of lithium imide surfaces during the ammonia decomposition process. Nature Catalysis 6, 829–836 (2023)
[plumID:23.027] A. R. Finney, M. Salvalaglio, Properties of aqueous electrolyte solutions at carbon electrodes: effects of concentration and surface charge on solution structure, ion clustering and thermodynamics in the electric double layer. Faraday Discussions 249, 334–362 (2024)
[plumID:23.026] F. M. Dietrich, X. R. Advincula, G. Gobbo, M. A. Bellucci, M. Salvalaglio, Machine Learning Nucleation Collective Variables with Graph Neural Networks. Journal of Chemical Theory and Computation 20, 1600–1611 (2023)
[plumID:23.025] M. D. Polêto, J. A. Lemkul, Differences in Conformational Sampling and Intrinsic Electric Fields Drive Ion Binding in Telomeric and TERRA G-Quadruplexes. Journal of Chemical Information and Modeling 63, 6851–6862 (2023)
[plumID:23.024] N. S. M. Herringer, S. Dasetty, D. Gandhi, J. Lee, A. L. Ferguson, Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multimolecular and Solvent-Inclusive Collective Variables. Journal of Chemical Theory and Computation 20, 178–198 (2023)
[plumID:23.023] K. Torkelson, N. Y. Naser, X. Qi, Z. Li, W. Yang, K. Pushpavanam, C.-L. Chen, F. Baneyx, J. Pfaendtner, Rational Design of Novel Biomimetic Sequence-Defined Polymers for Mineralization Applications. Chemistry of Materials 36, 786–794 (2024)
[plumID:23.022] L. Bonati, E. Trizio, A. Rizzi, M. Parrinello, A unified framework for machine learning collective variables for enhanced sampling simulations: mlcolvar. The Journal of Chemical Physics 159 (2023)
[plumID:23.021] L. Leanza, C. Perego, L. Pesce, M. Salvalaglio, M. von Delius, G. M. Pavan, Into the dynamics of rotaxanes at atomistic resolution. Chemical Science 14, 6716–6729 (2023)
[plumID:23.020] M. Fortino, G. Schifino, D. Vitone, F. Arnesano, A. Pietropaolo, The stepwise dissociation of the Zn(II)-bound Atox1 homodimer and its energetic asymmetry. Inorganica Chimica Acta 550, 121452 (2023)
[plumID:23.019] R. Bobrovs, L. Drunka, I. Kanepe, A. Jirgensons, A. Caflisch, M. Salvalaglio, K. Jaudzems, Exploring the Binding Pathway of Novel Nonpeptidomimetic Plasmepsin V Inhibitors. Journal of Chemical Information and Modeling 63, 6890–6899 (2023)
[plumID:23.018] K. J. Lachowski, H. T. Chiang, K. Torkelson, W. Zhou, S. Zhang, J. Pfaendtner, L. D. Pozzo, Anisotropic Gold Nanomaterial Synthesis Using Peptide Facet Specificity and Timed Intervention. Langmuir 39, 15878–15888 (2023)
[plumID:23.017] S. Das, U. Raucci, R. P. P. Neves, M. J. Ramos, M. Parrinello, How and When Does an Enzyme React? Unraveling α-Amylase Catalytic Activity with Enhanced Sampling Techniques. ACS Catalysis 13, 8092–8098 (2023)
[plumID:23.016] J. C. Calderón, P. Ibrahim, D. Gobbo, F. L. Gervasio, T. Clark, Activation/Deactivation Free-Energy Profiles for the β2-Adrenergic Receptor: Ligand Modes of Action. Journal of Chemical Information and Modeling 63, 6332–6343 (2023)
[plumID:23.015] V. Tiwari, T. Karmakar, Understanding Molecular Aggregation of Ligand-Protected Atomically-Precise Metal Nanoclusters. The Journal of Physical Chemistry Letters 14, 6686–6694 (2023)
[plumID:23.014] D. Di Marino, P. Conflitti, S. Motta, V. Limongelli, Structural basis of dimerization of chemokine receptors CCR5 and CXCR4. Nature Communications 14 (2023)
[plumID:23.013] I. Kolossváry, W. Sherman, Comprehensive Approach to Simulating Large Scale Conformational Changes in Biological Systems Utilizing a Path Collective Variable and New Barrier Restraint. The Journal of Physical Chemistry B 127, 5214–5229 (2023)
[plumID:23.012] I. Kolossváry, W. Sherman, Comprehensive Approach to Simulating Large Scale Conformational Changes in Biological Systems Utilizing a Path Collective Variable and New Barrier Restraint. The Journal of Physical Chemistry B 127, 5214–5229 (2023)
[plumID:23.010] N. Saleh, P. Ibrahim, G. Saladino, F. L. Gervasio, T. Clark, An Efficient Metadynamics-Based Protocol To Model the Binding Affinity and the Transition State Ensemble of G-Protein-Coupled Receptor Ligands. Journal of Chemical Information and Modeling 57, 1210–1217 (2017)
[plumID:23.009] D. Ray, E. Trizio, M. Parrinello, Deep learning collective variables from transition path ensemble. The Journal of Chemical Physics 158 (2023)
[plumID:23.008] F. Pesce, K. Lindorff-Larsen, Combining Experiments and Simulations to Examine the Temperature-Dependent Behavior of a Disordered Protein. The Journal of Physical Chemistry B 127, 6277–6286 (2023)
[plumID:23.007] S. Alamdari, J. Pfaendtner, Origins of Conformational Heterogeneity in Peptoid Helices Formed by Chiral N-1-Phenylethyl Sidechains. The Journal of Physical Chemistry B 127, 6163–6170 (2023)
[plumID:23.006] M. Lüking, D. van der Spoel, J. Elf, G. A. Tribello, Can molecular dynamics be used to simulate biomolecular recognition? The Journal of Chemical Physics 158 (2023)
[plumID:23.005] J. C. Calderón, P. Ibrahim, D. Gobbo, F. L. Gervasio, T. Clark, General Metadynamics Protocol To Simulate Activation/Deactivation of Class A GPCRs: Proof of Principle for the Serotonin Receptor. Journal of Chemical Information and Modeling 63, 3105–3117 (2023)
[plumID:23.004] P. M. Piaggi, T. E. Gartner, R. Car, P. G. Debenedetti, Melting curves of ice polymorphs in the vicinity of the liquid–liquid critical point. The Journal of Chemical Physics 159 (2023)
[plumID:23.003] W.-T. Hsu, V. Piomponi, P. T. Merz, G. Bussi, M. R. Shirts, Alchemical Metadynamics: Adding Alchemical Variables to Metadynamics to Enhance Sampling in Free Energy Calculations. Journal of Chemical Theory and Computation 19, 1805–1817 (2023)
[plumID:23.002] J. Lam, F. Pietrucci, Critical comparison of general-purpose collective variables for crystal nucleation. Physical Review E 107 (2023)
[plumID:23.001] S. L. Bore, F. Paesani, Realistic phase diagram of water from “first principles” data-driven quantum simulations. Nature Communications 14 (2023)
[plumID:23.000] G. H. Zerze, P. M. Piaggi, P. G. Debenedetti, A Computational Study of RNA Tetraloop Thermodynamics, Including Misfolded States. The Journal of Physical Chemistry B 125, 13685–13695 (2021)
[plumID:22.045] P. Falkenstein, Z. Zhao, A. Di Pede-Mattatelli, G. Künze, M. Sommer, C. Sonnendecker, W. Zimmermann, F. Colizzi, J. Matysik, C. Song, On the Binding Mode and Molecular Mechanism of Enzymatic Polyethylene Terephthalate Degradation. ACS Catalysis 13, 6919–6933 (2023)
[plumID:22.044] A. R. Finney, M. Salvalaglio, A variational approach to assess reaction coordinates for two-step crystallization. The Journal of Chemical Physics 158 (2023)
[plumID:22.043] K. Rahimi, P. M. Piaggi, G. H. Zerze, Comparison of On-the-Fly Probability Enhanced Sampling and Parallel Tempering Combined with Metadynamics for Atomistic Simulations of RNA Tetraloop Folding. The Journal of Physical Chemistry B 127, 4722–4732 (2023)
[plumID:22.042] H. Wang, S. R. A. Rizvi, D. Dong, J. Lou, Q. Wang, W. Sopipong, Y. Su, F. Najar, P. K. Agarwal, F. Kozielski, S. Haider, Emerging variants of SARS-CoV-2 NSP10 highlight strong functional conservation of its binding to two non-structural proteins, NSP14 and NSP16. doi: 10.7554/elife.87884.2 (2023)
[plumID:22.041] M. Invernizzi, A. Krämer, C. Clementi, F. Noé, Skipping the Replica Exchange Ladder with Normalizing Flows. The Journal of Physical Chemistry Letters 13, 11643–11649 (2022)
[plumID:22.040] M. Wieczór, P. K. Tang, M. Orozco, P. Cossio, From Closed to Open: Omicron Mutations Increase Interdomain Interactions and Reduce Epitope Exposure. SSRN Electronic Journal
[plumID:22.039] Z. Zou, E. R. Beyerle, S.-T. Tsai, P. Tiwary, Driving and characterizing nucleation of urea and glycine polymorphs in water. Proceedings of the National Academy of Sciences 120 (2023)
[plumID:22.038] U. Raucci, D. M. Sanchez, T. J. Martínez, M. Parrinello, Enhanced Sampling Aided Design of Molecular Photoswitches. Journal of the American Chemical Society 144, 19265–19271 (2022)
[plumID:22.037] B. Ashwood, M. S. Jones, A. L. Ferguson, A. Tokmakoff, Disruption of energetic and dynamic base pairing cooperativity in DNA duplexes by an abasic site. Proceedings of the National Academy of Sciences 120 (2023)
[plumID:22.036] Z. Zhang, J. Šponer, G. Bussi, V. Mlýnský, P. Šulc, C. R. Simmons, N. Stephanopoulos, M. Krepl, Atomistic Picture of Opening–Closing Dynamics of DNA Holliday Junction Obtained by Molecular Simulations. Journal of Chemical Information and Modeling 63, 2794–2809 (2023)
[plumID:22.035] M. A. Roesgaard, J. E. Lundsgaard, E. A. Newcombe, N. L. Jacobsen, F. Pesce, E. E. Tranchant, S. Lindemose, A. Prestel, R. Hartmann-Petersen, K. Lindorff-Larsen, B. B. Kragelund, Deciphering the Alphabet of Disorder—Glu and Asp Act Differently on Local but Not Global Properties. Biomolecules 12, 1426 (2022)
[plumID:22.034] I. J. Sugden, N. F. Francia, T. Jensen, C. S. Adjiman, M. Salvalaglio, Rationalising the difference in crystallisability of two sulflowers using efficient in silico methods. CrystEngComm 24, 6830–6838 (2022)
[plumID:22.033] I. Kolossváry, W. Sherman, Comprehensive Approach to Simulating Large Scale Conformational Changes in Biological Systems Utilizing a Path Collective Variable and New Barrier Restraint. The Journal of Physical Chemistry B 127, 5214–5229 (2023)
[plumID:22.032] I. Kolossváry, W. Sherman, Comprehensive Approach to Simulating Large Scale Conformational Changes in Biological Systems Utilizing a Path Collective Variable and New Barrier Restraint. The Journal of Physical Chemistry B 127, 5214–5229 (2023)
[plumID:22.031] D. Ray, N. Ansari, V. Rizzi, M. Invernizzi, M. Parrinello, Rare Event Kinetics from Adaptive Bias Enhanced Sampling. Journal of Chemical Theory and Computation 18, 6500–6509 (2022)
[plumID:22.030] Y. Wang, L. Herron, P. Tiwary, From data to noise to data for mixing physics across temperatures with generative artificial intelligence. Proceedings of the National Academy of Sciences 119 (2022)
[plumID:22.029] L. A. Chi, S. Asgharpour, J. Correa-Basurto, C. R. Bandala, M. Martínez-Archundia, Unveiling the G4-PAMAM capacity to bind and protect Ang-(1-7) bioactive peptide by molecular dynamics simulations. Journal of Computer-Aided Molecular Design 36, 653–675 (2022)
[plumID:22.028] I. L. Grothaus, G. Bussi, L. Colombi Ciacchi, Exploration, Representation, and Rationalization of the Conformational Phase Space of N-Glycans. Journal of Chemical Information and Modeling 62, 4992–5008 (2022)
[plumID:22.027] S. Temmam, X. Montagutelli, C. Herate, F. Donati, B. Regnault, M. Attia, E. Baquero Salazar, D. Chretien, L. Conquet, G. Jouvion, J. Pipoli Da Fonseca, T. Cokelaer, F. Amara, F. Relouzat, T. Naninck, J. Lemaitre, N. Derreudre‐Bosquet, Q. Pascal, M. Bonomi, T. Bigot, S. Munier, F. A. Rey, R. Le Grand, S. van der Werf, M. Eloit, SARS‐CoV‐2‐related bat virus behavior in human‐relevant models sheds light on the origin of COVID‐19. EMBO reports 24 (2023)
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[plumID:20.004] L. Bonati, V. Rizzi, M. Parrinello, Data-Driven Collective Variables for Enhanced Sampling. The Journal of Physical Chemistry Letters 11, 2998–3004 (2020)
[plumID:20.002] L. S. M. Evenseth, R. Ocello, M. Gabrielsen, M. Masetti, M. Recanatini, I. Sylte, A. Cavalli, Exploring Conformational Dynamics of the Extracellular Venus flytrap Domain of the GABAB Receptor: A Path-Metadynamics Study. Journal of Chemical Information and Modeling 60, 2294–2303 (2020)
[plumID:20.001] B. M. Sala, T. Le Marchand, G. Pintacuda, C. Camilloni, A. Natalello, S. Ricagno, Conformational Stability and Dynamics in Crystals Recapitulate Protein Behavior in Solution. Biophysical Journal 119, 978–988 (2020)
[plumID:20.000] R. Capelli, A. Bochicchio, G. Piccini, R. Casasnovas, P. Carloni, M. Parrinello, Chasing the Full Free Energy Landscape of Neuroreceptor/Ligand Unbinding by Metadynamics Simulations. Journal of Chemical Theory and Computation 15, 3354–3361 (2019)
[plumID:19.083] V. Rizzi, D. Mendels, E. Sicilia, M. Parrinello, Blind Search for Complex Chemical Pathways Using Harmonic Linear Discriminant Analysis. Journal of Chemical Theory and Computation 15, 4507–4515 (2019)
[plumID:19.082] V. Rizzi, D. Polino, E. Sicilia, N. Russo, M. Parrinello, The Onset of Dehydrogenation in Solid Ammonia Borane: An Ab Initio Metadynamics Study. Angewandte Chemie International Edition 58, 3976–3980 (2019)
[plumID:19.081] P. M. Piaggi, M. Parrinello, Calculation of phase diagrams in the multithermal-multibaric ensemble. The Journal of Chemical Physics 150 (2019)
[plumID:19.080] K. M. Bal, A. Bogaerts, E. C. Neyts, Ensemble-Based Molecular Simulation of Chemical Reactions under Vibrational Nonequilibrium. The Journal of Physical Chemistry Letters 11, 401–406 (2019)
[plumID:19.077] J. Sampath, A. Kullman, R. Gebhart, G. Drobny, J. Pfaendtner, Molecular recognition and specificity of biomolecules to titanium dioxide from molecular dynamics simulations. npj Computational Materials 6 (2020)
[plumID:19.076] S. Assenza, A. S. Sassi, R. Kellner, B. Schuler, P. De Los Rios, A. Barducci, Efficient conversion of chemical energy into mechanical work by Hsp70 chaperones. eLife 8 (2019)
[plumID:19.075] T. Giorgino, PYCV: a PLUMED 2 Module Enabling the Rapid Prototyping of Collective Variables in Python. Journal of Open Source Software 4, 1773 (2019)
[plumID:19.074] F. Colizzi, C. Perez-Gonzalez, R. Fritzen, Y. Levy, M. F. White, J. C. Penedo, G. Bussi, Asymmetric base-pair opening drives helicase unwinding dynamics. Proceedings of the National Academy of Sciences 116, 22471–22477 (2019)
[plumID:19.073] L. Kollias, D. C. Cantu, V. Glezakou, R. Rousseau, M. Salvalaglio, On the Role of Enthalpic and Entropic Contributions to the Conformational Free Energy Landscape of MIL‐101(Cr) Secondary Building Units. Advanced Theory and Simulations 3 (2020)
[plumID:19.072] S. Reißer, S. Zucchelli, S. Gustincich, G. Bussi, Conformational ensembles of an RNA hairpin using molecular dynamics and sparse NMR data. Nucleic Acids Research 48, 1164–1174 (2019)
[plumID:19.071] V. Marinova, M. Salvalaglio, Time-independent free energies from metadynamics via mean force integration. The Journal of Chemical Physics 151 (2019)
[plumID:19.070] S. Bottaro, P. J. Nichols, B. Vögeli, M. Parrinello, K. Lindorff-Larsen, Integrating NMR and simulations reveals motions in the UUCG tetraloop. Nucleic Acids Research 48, 5839–5848 (2020)
[plumID:19.069] V. Marinova, G. P. F. Wood, I. Marziano, M. Salvalaglio, Solvent Dynamics and Thermodynamics at the Crystal–Solution Interface of Ibuprofen. Crystal Growth & Design 19, 6534–6541 (2019)
[plumID:19.068] M. Invernizzi, M. Parrinello, Rethinking Metadynamics: From Bias Potentials to Probability Distributions. The Journal of Physical Chemistry Letters 11, 2731–2736 (2020)
[plumID:19.067] J. Rydzewski, R. Jakubowski, W. Nowak, H. Grubmüller, Kinetics of Huperzine A Dissociation from Acetylcholinesterase via Multiple Unbinding Pathways. Journal of Chemical Theory and Computation 14, 2843–2851 (2018)
[plumID:19.066] J. Rydzewski, O. Valsson, Finding multiple reaction pathways of ligand unbinding. The Journal of Chemical Physics 150 (2019)
[plumID:19.065] W. Chen, A. L. Ferguson, Molecular enhanced sampling with autoencoders: On‐the‐fly collective variable discovery and accelerated free energy landscape exploration. Journal of Computational Chemistry 39, 2079–2102 (2018)
[plumID:19.064] J. Sampath, J. Pfaendtner, Amphiphilic peptide binding on crystalline vs. amorphous silica from molecular dynamics simulations. Molecular Physics 117, 3642–3650 (2019)
[plumID:19.063] M. Bernetti, M. Masetti, M. Recanatini, R. E. Amaro, A. Cavalli, An Integrated Markov State Model and Path Metadynamics Approach To Characterize Drug Binding Processes. Journal of Chemical Theory and Computation 15, 5689–5702 (2019)
[plumID:19.062] J. Smith, P. McMullen, Z. Yuan, J. Pfaendtner, S. Jiang, Elucidating Molecular Design Principles for Charge-Alternating Peptides. Biomacromolecules 21, 435–443 (2019)
[plumID:19.061] E. Jackson, M. Miklitz, Q. Song, G. A. Tribello, K. E. Jelfs, Computational Evaluation of the Diffusion Mechanisms for C8 Aromatics in Porous Organic Cages. The Journal of Physical Chemistry C 123, 21011–21021 (2019)
[plumID:19.060] L. Bonati, Y.-Y. Zhang, M. Parrinello, Neural networks-based variationally enhanced sampling. Proceedings of the National Academy of Sciences 116, 17641–17647 (2019)
[plumID:19.058] V. Santolini, G. A. Tribello, K. E. Jelfs, Predicting solvent effects on the structure of porous organic molecules. Chemical Communications 51, 15542–15545 (2015)
[plumID:19.057] C. Paissoni, A. Jussupow, C. Camilloni, Determination of Protein Structural Ensembles by Hybrid-Resolution SAXS Restrained Molecular Dynamics. Journal of Chemical Theory and Computation 16, 2825–2834 (2020)
[plumID:19.056] J. Rydzewski, maze: Heterogeneous ligand unbinding along transient protein tunnels. Computer Physics Communications 247, 106865 (2020)
[plumID:19.054] G. Piccini, M. Parrinello, Accurate Quantum Chemical Free Energies at Affordable Cost. The Journal of Physical Chemistry Letters 10, 3727–3731 (2019)
[plumID:19.053] E. Baldi, M. Ceriotti, G. A. Tribello, Extracting the interfacial free energy and anisotropy from a smooth fluctuating dividing surface. Journal of Physics: Condensed Matter 29, 445001 (2017)
[plumID:19.052] B. Cheng, G. A. Tribello, M. Ceriotti, The Gibbs free energy of homogeneous nucleation: From atomistic nuclei to the planar limit. The Journal of Chemical Physics 147 (2017)
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[plumID:19.050] J. Klug, C. Triguero, M. G. Del Pópolo, G. A. Tribello, Using Intrinsic Surfaces To Calculate the Free-Energy Change When Nanoparticles Adsorb on Membranes. The Journal of Physical Chemistry B 122, 6417–6422 (2018)
[plumID:19.049] G. A. Tribello, F. Giberti, G. C. Sosso, M. Salvalaglio, M. Parrinello, Analyzing and Driving Cluster Formation in Atomistic Simulations. Journal of Chemical Theory and Computation 13, 1317–1327 (2017)
[plumID:19.048] G. Mattedi, F. Deflorian, J. S. Mason, C. de Graaf, F. L. Gervasio, Understanding Ligand Binding Selectivity in a Prototypical GPCR Family. Journal of Chemical Information and Modeling 59, 2830–2836 (2019)
[plumID:19.046] Z. F. Brotzakis, V. Limongelli, M. Parrinello, Accelerating the Calculation of Protein–Ligand Binding Free Energy and Residence Times Using Dynamically Optimized Collective Variables. Journal of Chemical Theory and Computation 15, 743–750 (2018)
[plumID:19.045] M. De La Pierre, P. Raiteri, A. G. Stack, J. D. Gale, Uncovering the Atomistic Mechanism for Calcite Step Growth. Angewandte Chemie International Edition 56, 8464–8467 (2017)
[plumID:19.043] G. Piccini, D. Mendels, M. Parrinello, Metadynamics with Discriminants: A Tool for Understanding Chemistry. Journal of Chemical Theory and Computation 14, 5040–5044 (2018)
[plumID:19.042] D. Mendels, G. Piccini, M. Parrinello, Collective Variables from Local Fluctuations. The Journal of Physical Chemistry Letters 9, 2776–2781 (2018)
[plumID:19.041] C. M. Mao, J. Sampath, K. G. Sprenger, G. Drobny, J. Pfaendtner, Molecular Driving Forces in Peptide Adsorption to Metal Oxide Surfaces. Langmuir 35, 5911–5920 (2019)
[plumID:19.040] L. Hovan, F. Comitani, F. L. Gervasio, Defining an Optimal Metric for the Path Collective Variables. Journal of Chemical Theory and Computation 15, 25–32 (2018)
[plumID:19.039] V. Limongelli, M. Bonomi, M. Parrinello, Funnel metadynamics as accurate binding free-energy method. Proceedings of the National Academy of Sciences 110, 6358–6363 (2013)
[plumID:19.038] A. Achour, L. Broggini, X. Han, R. Sun, C. Santambrogio, J. Buratto, C. Visentin, A. Barbiroli, C. M. G. De Luca, P. Sormanni, F. Moda, A. De Simone, T. Sandalova, R. Grandori, C. Camilloni, S. Ricagno, Biochemical and biophysical comparison of human and mouse beta‐2 microglobulin reveals the molecular determinants of low amyloid propensity. The FEBS Journal 287, 546–560 (2019)
[plumID:19.037] R. Drori, C. Li, C. Hu, P. Raiteri, A. L. Rohl, M. D. Ward, B. Kahr, A Supramolecular Ice Growth Inhibitor. Journal of the American Chemical Society 138, 13396–13401 (2016)
[plumID:19.036] D. Meral, D. Provasi, M. Filizola, An efficient strategy to estimate thermodynamics and kinetics of G protein-coupled receptor activation using metadynamics and maximum caliber. The Journal of Chemical Physics 149 (2018)
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[plumID:19.032] F. Pietrucci, A. M. Saitta, Formamide reaction network in gas phase and solution via a unified theoretical approach: Toward a reconciliation of different prebiotic scenarios. Proceedings of the National Academy of Sciences 112, 15030–15035 (2015)
[plumID:19.031] S. Pipolo, M. Salanne, G. Ferlat, S. Klotz, A. M. Saitta, F. Pietrucci, Navigating at Will on the Water Phase Diagram. Physical Review Letters 119 (2017)
[plumID:19.030] M. Lelimousin, V. Limongelli, M. S. P. Sansom, Conformational Changes in the Epidermal Growth Factor Receptor: Role of the Transmembrane Domain Investigated by Coarse-Grained MetaDynamics Free Energy Calculations. Journal of the American Chemical Society 138, 10611–10622 (2016)
[plumID:19.029] P. Marinelli, S. Navarro, R. Graña-Montes, M. Bañó-Polo, M. R. Fernández, E. Papaleo, S. Ventura, A single cysteine post-translational oxidation suffices to compromise globular proteins kinetic stability and promote amyloid formation. Redox Biology 14, 566–575 (2018)
[plumID:19.028] Y. Wang, J. M. L. Ribeiro, P. Tiwary, Past–future information bottleneck for sampling molecular reaction coordinate simultaneously with thermodynamics and kinetics. Nature Communications 10 (2019)
[plumID:19.026] G. C. Sosso, T. F. Whale, M. A. Holden, P. Pedevilla, B. J. Murray, A. Michaelides, Unravelling the origins of ice nucleation on organic crystals. Chemical Science 9, 8077–8088 (2018)
[plumID:19.025] T. Löhr, A. Jussupow, C. Camilloni, Metadynamic metainference: Convergence towards force field independent structural ensembles of a disordered peptide. The Journal of Chemical Physics 146 (2017)
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[plumID:19.023] A. Gil-Ley, G. Bussi, Enhanced Conformational Sampling Using Replica Exchange with Collective-Variable Tempering. Journal of Chemical Theory and Computation 11, 1077–1085 (2015)
[plumID:19.022] H. Chen, H. Fu, X. Shao, C. Chipot, W. Cai, ELF: An Extended-Lagrangian Free Energy Calculation Module for Multiple Molecular Dynamics Engines. Journal of Chemical Information and Modeling 58, 1315–1318 (2018)
[plumID:19.021] G. M. Hocky, T. Dannenhoffer-Lafage, G. A. Voth, Coarse-Grained Directed Simulation. Journal of Chemical Theory and Computation 13, 4593–4603 (2017)
[plumID:19.020] M. Bernetti, M. Masetti, F. Pietrucci, M. Blackledge, M. R. Jensen, M. Recanatini, L. Mollica, A. Cavalli, Structural and Kinetic Characterization of the Intrinsically Disordered Protein SeV NTAIL through Enhanced Sampling Simulations. The Journal of Physical Chemistry B 121, 9572–9582 (2017)
[plumID:19.019] Y. Wang, O. Valsson, P. Tiwary, M. Parrinello, K. Lindorff-Larsen, Frequency adaptive metadynamics for the calculation of rare-event kinetics. The Journal of Chemical Physics 149 (2018)
[plumID:19.018] C. Cozza, M. Bonomi, A. Pietropaolo, A Versatile Computational Strategy To Characterize the Free-Energy Landscape of Excited States in Oligofluorenes. Journal of Chemical Theory and Computation 14, 5441–5445 (2018)
[plumID:19.017] R. Capelli, P. Carloni, M. Parrinello, Exhaustive Search of Ligand Binding Pathways via Volume-Based Metadynamics. The Journal of Physical Chemistry Letters 10, 3495–3499 (2019)
[plumID:19.016] P. Lucaioli, E. Nauha, I. Gimondi, L. S. Price, R. Guo, L. Iuzzolino, I. Singh, M. Salvalaglio, S. L. Price, N. Blagden, Serendipitous isolation of a disappearing conformational polymorph of succinic acid challenges computational polymorph prediction. CrystEngComm 20, 3971–3977 (2018)
[plumID:19.015] V. Marinova, G. P. F. Wood, I. Marziano, M. Salvalaglio, Dynamics and Thermodynamics of Ibuprofen Conformational Isomerism at the Crystal/Solution Interface. Journal of Chemical Theory and Computation 14, 6484–6494 (2018)
[plumID:19.014] L. Kollias, D. C. Cantu, M. A. Tubbs, R. Rousseau, V.-A. Glezakou, M. Salvalaglio, Molecular Level Understanding of the Free Energy Landscape in Early Stages of Metal–Organic Framework Nucleation. Journal of the American Chemical Society 141, 6073–6081 (2019)
[plumID:19.013] A. Cesari, S. Bottaro, K. Lindorff-Larsen, P. Banáš, J. Šponer, G. Bussi, Fitting Corrections to an RNA Force Field Using Experimental Data. Journal of Chemical Theory and Computation 15, 3425–3431 (2019)
[plumID:19.012] C. Paissoni, A. Jussupow, C. Camilloni, Martini bead form factors for nucleic acids and their application in the refinement of protein–nucleic acid complexes against SAXS data. Journal of Applied Crystallography 52, 394–402 (2019)
[plumID:19.011] T. Giorgino, How to differentiate collective variables in free energy codes: Computer-algebra code generation and automatic differentiation. Computer Physics Communications 228, 258–263 (2018)
[plumID:19.010] B. Weber, M. Hora, P. Kazman, C. Göbl, C. Camilloni, B. Reif, J. Buchner, The Antibody Light-Chain Linker Regulates Domain Orientation and Amyloidogenicity. Journal of Molecular Biology 430, 4925–4940 (2018)
[plumID:19.009] S. Bottaro, P. Banáš, J. Šponer, G. Bussi, Free Energy Landscape of GAGA and UUCG RNA Tetraloops. The Journal of Physical Chemistry Letters 7, 4032–4038 (2016)
[plumID:19.007] L. Eshun-Wilson, R. Zhang, D. Portran, M. V. Nachury, D. B. Toso, T. Löhr, M. Vendruscolo, M. Bonomi, J. S. Fraser, E. Nogales, Effects of α-tubulin acetylation on microtubule structure and stability. Proceedings of the National Academy of Sciences 116, 10366–10371 (2019)
[plumID:19.005] G. T. Heller, F. A. Aprile, M. Bonomi, C. Camilloni, A. De Simone, M. Vendruscolo, Sequence Specificity in the Entropy-Driven Binding of a Small Molecule and a Disordered Peptide. Journal of Molecular Biology 429, 2772–2779 (2017)
[plumID:19.004] M. Bonomi, C. Camilloni, A. Cavalli, M. Vendruscolo, Metainference: A Bayesian inference method for heterogeneous systems. Science Advances 2 (2016)
[plumID:19.003] S. Vahidi, Z. A. Ripstein, M. Bonomi, T. Yuwen, M. F. Mabanglo, J. B. Juravsky, K. Rizzolo, A. Velyvis, W. A. Houry, M. Vendruscolo, J. L. Rubinstein, L. E. Kay, Reversible inhibition of the ClpP protease via an N-terminal conformational switch. Proceedings of the National Academy of Sciences 115 (2018)
[plumID:19.002] M. Bonomi, R. Pellarin, M. Vendruscolo, Simultaneous Determination of Protein Structure and Dynamics Using Cryo-Electron Microscopy. Biophysical Journal 114, 1604–1613 (2018)
[plumID:19.001] V. Mlýnský, G. Bussi, Molecular Dynamics Simulations Reveal an Interplay between SHAPE Reagent Binding and RNA Flexibility. The Journal of Physical Chemistry Letters 9, 313–318 (2018)
[plumID:19.000] M. Invernizzi, M. Parrinello, Making the Best of a Bad Situation: A Multiscale Approach to Free Energy Calculation. Journal of Chemical Theory and Computation 15, 2187–2194 (2019)