2024
Portillo-Ledesma and T. Schlick, “Regulation of Chromatin Architecture by Protein Binding: Insights from Molecular Modeling”, Biophys. Rev. 16 (3): 331–343 (2024). Special Ascona meeting issue, Multiscale Simulations of DNA from Electrons to Nucleosome, W. Olson et al., Eds. doi: 10.1007/s12551-024-01195-5
Quarta and T. Schlick, “Riboswitch Distribution in the Human Gut Microbiome Reveals Common Metabo- lite Pathways”, J. Phys. Chem.: 128 (18): 4336–4343 (2024). Special Issue Devoted to Greg Voth. doi: 10.1021/acs/jpcb04c00267.
Dey, S. Yan, T. Schlick, and A. Laederach, “Abolished Frameshifting for Predicted Structure-stabilizing SARS-CoV-2 Mutants: Implications to alternative conformations and their statistical structural analyses”, RNA J. 30 (11): 1437–1450 (2024). doi: 10.1261/rna.080035.124. PMID: 39084880
Yan and T. Schlick, “Heterogeneous and Multiple Conformational Transition Pathways Between Pseudo- knots of the SARS-CoV-2 Frameshift Element”, Proc. Natl. Acad. Sci. USA, In Revision (2024).
R.Amaro, et al, “The need to implement FAIR principles in biomolecular simulations, Nat. Methods, In Revision (2024). https://arxiv.org/abs/2407.16584.
Lee, S. Yan, A. Dey, A. Laederach, and T. Schlick, “A Cascade of Conformational Switches in SARS-CoV-2 Frameshifting: Co-Regulation by Upstream and Downstream Elements”, Submitted (2024).
Li, S. Portillo-Ledesma, M. Janani, and T. Schlick, “Incorporating Multiscale Methylation Effects into Nucleosome-Resolution Chromatin Models for Simulating Mesoscale Fibers”, J. Chem. Phys., Special Issue on Chromatin Structure and Dynamics, B. Zhang and T. Schlick, Eds., In Revision (2024).
Hang, S. Portillo, and T. Schlick, “Regulation of Genome Architecture in Huntington’s Diseases”, Submitted (2024).
Newton, S. Yan, and T. Schlick “Structure-Altering Mutations and Conformational Landscapes of the HIV Frameshifting Element”, In Preparation (2024).
Zbib, J. and T. Schlick “Expanding the RNA-As-Graphs Motif Atlas to Viral RNAs”, In Preparation (2024).
Wang and T. Schlick, “How Large is the Universe of RNA-Like Motifs? A Clustering Analysis of RNA Graph Motifs Using Topological Descriptors”, In Preparation (2024).
T. Schlick, et al. “Molecular Dynamics Simulations of Biomolecules: Alive and Thriving in the Age of AI”, In Preparation as an Proc. Natl. Acad. Sci. USA Perspective” (2024).
2023
D. Welakuh et al., “Non-perturbative mass renormalization effects in non-relativistic quantum
electrodynamics” arXiv preprint arXiv:2310.03213 (2023).
D. Welakuh,“Cavity-induced modification of the Stark effect and control of molecular properties”
to be submitted (2023).
K. Tabata et al. Machine-learned molecular mechanics force fields from large-scale quantum chemical data. (arXiv:2307.07085, Under Review)
P. Eastman et al. OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials: J. Phys. Chem., Part B, in press
Y. Wang and T. Karaletsos, On the Stochasticity in Graph Neural Networks. (2023 Under Review)
Y. Litman, J. Lan, Y.Nagata, and D.M. Wilkins, Fully First-Principles Surface Spectroscopy with Machine Learning, J. Phys. Chem. Lett., 14, 36, 8175–8182 (2023)
P. Vindel-Zandbergen, D. Kedziera, M. Zoltowski, J. Klos, P. Zuchowski, P. M. Felker, F. Lique, and Z. Bačić, H 2 O-HCN complex: A new potential energy surface and intermolecular rovibrational states from rigorous quantum calculations, J. Chem. Phys. 159, 174302 (2023).
S. Xia, E. Chen and Y. Zhang, Integrated Molecular Modeling and Machine Learning for Drug Design, J. Chem. Theory Comput., 19, 7478 – 7495, 2023
A. D′Oliviera, X. Dai, S. Mottaghinia, E. P. Geissler, L. Etienne, Y. Zhang, and J. S. Mugridge, Recognition and Cleavage of Human tRNA Methyltransferase TRMT1 by the SARS-CoV-2 Main Protease, elife, 12, RP91168, 2023.
Z. Zhao, J. Du, Y. Du, Y. Gao, M. Yu, Y. Zhang, H. Fang, and X. Hou, Deciphering the Allosteric Activation Mechanism of SIRT6 Using Molecular Dynamics Simulations, J. Chem. Inf. Model., 63, 5896 – 5902, 2023.
Mao, A., Chen, C., Portillo-Ledesma, S., and Schlick, T., Effect of Single-Residue Mutations on CTCF Binding to DNA: Insights from Molecular Dynamics Simulations, Intl. J. Mol. Sci. 24 (7): 6395 (2023).
Portillo-Ledesma, S., Chung, W., Hoffman, J., and Schlick, T., Regulation of Chromatin Architecture by Transcription Factor Binding eLife 12:RP91320
Li , Z., and Schlick, T., “Hi-BDiSCO: Folding 3D Structures from Hi-C Data Using Brownian Dynamics”, Nuc. Acids Res., 25: 52 (2): 583–599 (2024).
Schlick, T., and Yan, S., “Modeling and Simulating RNA: Combining Structural, Dynamic, and Evolutionary Perspectives for Coronavirus Applications.” In: Yanez, Manuel and Boyd, Russell J. (eds.) Comprehensive Computational Chemistry, vol. 3, pp. 886–894. Oxford: Elsevier B978-0-12-821978-2.00118-5
Li, Z., and Schlick, T., Hi-BDiSCO: folding 3D mesoscale genome structures from Hi-C data using brownian dynamics, Nucleic Acids Research, 2023, gkad1121
Li, Z., Portillo-Ledesma, S., and Schlick, T., Techniques for and challenges in reconstructing 3D genome structures from 2D chromosome conformation capture data. Current Opinion in Cell Biology, 83, 102209 (2023)
Li, Z., Portillo-Ledesma, S., and Schlick, T. Brownian dynamics simulations of mesoscale chromatin fibers. Biophys. J. 122, 2884-2897 (2023)
Shih, A., Casiulis, M., and Martiniani, S., Fast Generation of Spectrally-Shaped Disorder. arXiv:2305.15693 [cond-mat.stat-mech] (2023)
S. Anand, X. Ma, S. Guo, S. Martiniani, and X. Cheng, Bacteria through obstacles: Unifying fluxes, entropy production, and extractable work in living active matter. arXiv preprint arXiv:2308.08421, 2023.
S. Rawat, and S. Martiniani, Explicit rational function solutions for the power spectral density of stochastic linear time-invariant systems. arXiv preprint arXiv:2305.19890, 2023.
S. Rawat, D.J. Heeger, and S. Martiniani. Coherence influences the dimensionality of communication subspaces. in Cosyne Abstracts 2023. 2023.
A.W. Golinski, Z.D. Schmitz, G.H. Nielsen, B. Johnson, D. Saha, S. Appiah, B.J. Hackel, and S. Martiniani, Predicting and interpreting protein developability via transfer of convolutional sequence representation. ACS Synthetic Biology, 2023. 12(9): p. 2600.
C. Anzivino, M. Casiulis, T. Zhang, A.S. Moussa, S. Martiniani, and A. Zaccone, Estimating random close packing in polydisperse and bidisperse hard spheres via an equilibrium model of crowding. The Journal of chemical physics, 2023. 158(4).
J.A. Vita, E.G. Fuemmeler, A. Gupta, G.P. Wolfe, A.Q. Tao, R.S. Elliott, S. Martiniani, and E.B. Tadmor, Colabfit exchange: Open-access datasets for data-driven interatomic potentials. The Journal of chemical physics, 2023. 159(15).
S. Martiniani and M. Casiulis, When you can’t count, sample! Computable entropies beyond equilibrium from basin volumes. Papers in Physics, 2023. 15: p. 150001.
S. Martiniani, Bit-propelled active matter. Journal Club for Condensed Matter Physics, 2023.
M. Casiulis, Active particles push the boundaries of two-dimensional solids. Physics, 2023. 16: p. 146.
M. Kilgour, J. Rogal, M. E. Tuckerman. Geometric Deep Learning for Molecular Crystal Structure Prediction, Jour Chem Th and Comp 19:4743 (2023).
N. Naleem, C. R. A. Abreu, K. Warmuz, M. C. Tong, S. Kirmizialtin, M. E. Tuckerman. An exploration of machine learning models for the determination of reaction coordinates associated with conformational transitions, Jour Chem Phys 149:034102 (2023).
S. Bajpai, B. K. Petkov, M. C. Tong, C. R. A. Abreu, N. N. Nair, M. E. Tuckerman. An interoperable implementation of collective-variable based enhanced sampling methods in extended phase space within the OpenMM package, Jour Comp Chem 44:2166 (2023).
Yan, S., Zhu, Q., Hohl J., Dong, A., and Schlick, T., Evolution of Coronavirus Frameshifting Elements: Competing Stem Networks Explain Conservation and Variability, Proc. Nat. Acad. Sci., 120 (20) e2221324120 (2023)
Besch, A., Marsiglia, W.M., Mohammadi, M., Zhang, Y., and Traaseth, N.J., Gatekeeper mutations activate FGF receptor tyrosine kinases by destabilizing the autoinhibited state, Proc. Nat. Acad. Sci. 120 (8) e2213090120 (2023)
Xia, S., Zhang, D., and Zhang, Y., Multitask Deep Ensemble Prediction of Molecular Energetics in Solution: From Quantum Mechanics to Experimental Properties, J. Chem. Theory Comput., 19, 659 – 668 (2023)
Anzivino, C., Casiulis, M., Zhang, T., Moussa, A.S., Martiniani, S., and Zaccone, A., Estimating random close packing in polydisperse and bidisperse hard spheres via an equilibrium model of crowding, J. Chem. Phys. 158, 044901 (2023)
White, A.D., Hocky, G.M., et al.,Assessment of chemistry knowledge in large language models that generate code, Digital Discovery, 2, 368-376 (2023)
Wellawatte, G.P., Hocky, G.M., and White, A.D., Neural potentials of proteins extrapolate beyond training data, J. Chem. Phys., 159, 085103 (2023)
Mitra, G., Chang, C., McMullen, A., Puchall, D., Brujic, J., and Hocky, G.M., A Coarse-Grained Simulation Model for Self-Assembly of Liquid Droplets Featuring Explicit Mobile Binders. Soft Matter, 19, 4223-4236 (2023)
She, T., Mitra, G., Alberts, J., Viana, M., Levy, E., and Hocky, G.M., Mesoscale molecular assembly is favored by the active, crowded cytoplasm, biorXiv: 2023.09.19.558334. Submitted (2023)
Singh, Y., Hocky, G.M., and Nolan B.J., Molecular dynamics simulations support a multi-step Arp2/3 complex activation pathway,
J Biol Chem, 299(9):105169 (2023)
Singh, Y., and Hocky, G.M., Improved prediction of molecular response to pulling by combining force tempering with replica exchange methods, Submitted, arXiv:2310.12329 (2023)
Sasmal, S., McCullagh, M., and Hocky, G.M., Reaction Coordinates for Conformational Transitions using Linear Discriminant Analysis on Positions, J. Chem. Theor. Comput., 19, 14, 4427-4435 (2023)
Rostskoff, G.M., Vanden-Eijnden, E., and Hocky, G.M., Computing equilibrium free energies through a nonequilibrium quench
Kangxin Liu, In revision, arXiv:2309.05122 (2023)
Portillo-Ledesma, S., Li, Z., and Schlick, T., Genome modeling: From chromatin fibers to genes. Curr. Opin. Struc. Biol., Special issue on Theory and Simulation/ Computational Methods, 78, 102506 (2023)
2022
Guo, X. and Zhang, Y. CovBinderInPDB: a Structure-based Covalent Binder Database. J. Chem. Inf. Model., 62, 6057 – 6068 (2022)
Ro, S., Guo, B., Shih, A., Phan, T.V., Austinm, R.H., Levine, D., Chaikin, P.M. Chaikin, and Martiniani, S. Model-Free measurement of local entropy production and extractable work in active matter. Phys. Rev. Lett. 129, 220601 (2022)
Felker, P.M., and Bačić, Z. Intermolecular vibrational states of HF trimer from rigorous nine-dimensional quantum calculations: Strong coupling between intermolecular bending and stretching vibrations and the importance of the three-body interactions. J. Chem. Phys. 157, 194103 (2022)
Cossio, P., and Hocky, G.M. Catching actin proteins in action. Nature News & Views, Oct 26 (2022)
Hong, R.S., Mattei, A., Sheikh, A.Y., and Tuckerman, M.E. A data-driven and topological mapping approach for the a priori prediction of stable molecular crystalline hydrates, Proc. Nat. Acad. Sci. 119, e2204414119 (2022)
Schlick, T., Innovations in Biophysics: A Sampling of Ideas Celebrating Ned Seeman’s Legacy, Special volume dedicated to Ned Seeman, T. Schlick, Ed., Biophys. J. 121 (24) (2022).
Beasock, R.D., et al., Biomotors, Viral Assembly, and RNA Nanobiotechnology: Current Achievements and Future Directions, Comp. Struc. Biotech. J., 20: 6120–6137 (2022).
Portillo-Ledesma, S., Wagley, M., and Schlick, T. Chromatin transitions triggered by LH density as epigenetic regulators of the genome. Nucleic Acids Research, 50, 10328–10342 (2022)
Felker, P.M., and Bačić, Z. Noncovalently bound molecular complexes beyond diatom–diatom systems: full-dimensional, fully coupled quantum calculations of rovibrational states. Phys. Chem. Chem. Phys. 24, 24655-24676 (2022)
Zhu, Q., Petingi, L., and Schlick, T. RNA-as-graphs motif atlas—Dual graph library of RNA modules and viral frameshifting-element applications. Int. J. Mol. Sci. 23, 9249 (2022)
Casiulis, M., and Martiniani, S. When you can’t count, sample! Computable entropies beyond equilibrium from basin volumes. arXiv preprint arXiv:2207.08241 (2022)
Yang, C., Chen, E.A., and Zhang, Y. Protein-Ligand docking in the machine learning era. Molecules, 27,4568 (2022)
Zelovich, T., et al. Non-Monotonic Temperature Dependence of Hydroxide Ion Diffusion in Anion Exchange Membranes.
Chemistry of Materials 34 (5), 2133-2145 (2022)
Yan, S., Zhu, Q., Jain, S., and Schlick, T. Length-dependent motions of SARS-CoV-2 frameshifting RNA pseudoknot and alternative conformations suggest avenues for frameshifting suppression. Nat Commun 13, 4284 (2022)
Ding, B., Narvaez-Ortiz, H.Y., Singh, Y., Hocky, G.M., Chowdhury, S., and Nolen, B. J., Structure of Arp23 complex at a branched actin filament junction resolved by single-particle cryo-EM Proc. Natl. Acad. Sci. 19, e2202723119 (2022)
Yang, C., and Zhang, Y. Delta machine learning to improve scoring-ranking-screening performances of protein-ligand scoring functions. J. Chem. Inf. Model., 62, 2696-2712 (2022)
Klem, H., Hocky, G.M., and McCullagh, M. Size-and-Shape Space Gaussian Mixture Models for Structural Clustering of Molecular Dynamics Trajectories. J. Chem. Theor. Comput., 18, 3218-3230 (2022)
Zhang, D., Xia, S., and Zhang, Y. Accurate prediction of aqueous solvation free energies using 3D atomic feature-based graph neural network with transfer learning. J. Chem. Inf. Model., 62, 1840-1848 (2022)
Hocky, G.M., and Pena Ccoa, W.J. Assessing models of force-dependent unbinding rates via infrequent metadynamics. J. Chem. Phys. 156, 125102 (2022)
Xu, M., Felker, P.M., and Bačić, Z. H2O inside the fullerene C60: Inelastic neutron scattering spectrum from rigorous quantum calculations. J. Chem. Phys. 156, 124101 (2022)
Lu, J., and Zhang, Y. Unified deep learning model for multi-task reaction predictions with explanation. J. Chem. Inf. Model., 62, 1376-1387 (2022)
Felker, P.M., and Bačić, Z. Intermolecular rovibrational states of the H2O–CO2 and D2O–CO2 van der Waals complexes. J. Chem. Phys. 156, 064301 (2022)
Hocky, G.M., and White, A.D. Natural Language Processing Models That Automate Programming Will Transform Chemistry Research and Teaching. Digital Discovery, available online (2022)
Fischer, C., Veprek, N., Peitsinis, Z., et al. De novo Design of SARS-CoV-2 Main Protease Inhibitor. Synlett, 33, 458 – 463 (2022).
Modell, A.E., Marrone III, F., Panigrahi, N.R., Zhang, Y., and Arora, P.S. Peptide tethering: Pocket-directed fragment screening for peptidomimetic inhibitor discovery. J. Am. Chem. Soc., 144, 1198-1204 (2022)
Ciccotti, G., Dellago, C., Ferrario, M. et al. Molecular simulations: past, present, and future (a Topical Issue in EPJB). Eur. Phys. J. B 95, 3 (2022).
Tuckerman, M.E. The curse of dimensionality loses its power. Nat Comput Sci 2, 6–7 (2022)
Spittle, S., Poe, D., Doherty, B. et al. Evolution of microscopic heterogeneity and dynamics in choline chloride-based deep eutectic solvents. Nat Commun 13, 219 (2022)
Metz, M.P., et al. Crystal Structure Predictions for 4-Amino-2,3,6-trinitrophenol Using a Tailor-Made First-Principles-Based Force Field Crystal Growth & Design 22 (2), 1182-1195 (2022)
