2024
D. Welakuh et al., Non-perturbative mass renormalization effects in non-relativistic quantum electrodynamics, submitted to Physical Review Research (https://doi.org/10.48550/arXiv.2310.03213)
Y. Wang and K. Cho. Non-convolutional graph neural networks. NeurIPS 2024. arXiv:2408.00165, 2024
K. Takaba, A.J. Friedman, C.E Cavender, P. K. Behara, I. Pulido, M.M Henry, H. MacDermott-Opeskin, C. R Iacovella, A.M. Nagle, A. M. Payne, J.D. Chodera, and Y. Wang, Machine-learned molecular mechanics force fields from large- scale quantum chemical data. Chemical Science, 2024
Y. Wang, I. Pulido, K. Takaba, B. Kaminow, J. Scheen, L. Wang, and J.D. Chodera. Espalomacharge: Machine learning-enabled ultrafast partial charge assignment. The Journal of Physical Chemistry A, 128(20):4160–4167, 2024.
L. Wang, P.K. Behara, M.W. Thompson, T. Gokey, Y. Wang, J.R. Wagner, D.J. Cole, M.K. Gilson, M.R. Shirts, and D. L Mobley. The open force field initiative: Open software and open science for molecular modeling. B,The Journal of Physical Chemistry 2024
P. Eastman, R. Galvelis, et al.,. Openmm 8: Molecular dynamics simulation with machine learning potentials. The Journal of Physical Chemistry B, 128(1):109–116, 2024. doi: 10.1021/acs.jpcb.3c06662. URL https://doi.org/10.1021/acs.jpcb.3c06662. PMID: 38154096
Y. Wang, K. Takaba, et al., On the design space between molecular mechanics and machine learning force fields, 2024. URL https://arxiv.org/abs/2409.01931.
Y. Xu, D. Bian, et al., Envirodetanet: Pretrained e(3)-equivariant message-passing neural net- works with multi-level molecular representations for organic molecule spectra prediction. ChemRxiv, 2024. doi: 10.26434/chemrxiv-2024-scchg.
M. Retchin, Y. Wang, et al.,. Druggym: A testbed for the economics of autonomous drug discovery. bioRxiv, pages 2024–05, 2024.
H. Ni, S. Meng, et al.,. Harnessing earnings reports for stock predictions: A qlora-enhanced llm approach. arXiv:2408.06634
Y. Xu, H. Ni, et al., Molecular Dynamics and Machine Learning Unlock Possibilities in Beauty Design–A Perspective. arXiv preprint arXiv:2410.18101.
E. Skeens, F. Maschietto, M. Ramu*, S. Shillingford, E.J. Lolis, V.S. Batista, and G.P. Lisi, “Dynamic and structural insights into allosteric regulation on MKP5, a dual-specificity phosphatase.” bioRxiv (2024). *Co-first authors.
J.O. Richardson, J.E. Lawrence, and J.R. Mannouch “Nonadiabatic Dynamics with the Mapping Approach to Surface Hopping (MASH).” Submitted to Annu. Rev. Phys. Chem.
J.E. Lawrence, “Semiclassical instanton theory for reaction rates at any temperature: How a rigorous real-time derivation solves the crossover temperature problem.” J. Chem. Phys. 161, 184115 (2024). arXiv:2409.02820
J.E. Lawrence, J.R. Mannouch and J.O. Richardson, “A Size-Consistent Multi-State Mapping Approach to Surface Hopping.” J. Chem. Phys. 160, 244112 (2024).
J.E. Lawrence, I.M. Ansari, J.R. Mannouch, M.A. Manae, K. Asnaashari, A. Kelly and J.O. Richardson, “A MASH simulation of the photoexcited dynamics of cyclobutanone.” J. Chem. Phys. 160, 174306 (2024).
J. Gäding V. et al., The role of the water contact layer on hydration and transport at solid/liquid interfaces https://www.pnas.org/doi/abs/10.1073/pnas.2407877121
H. Kaur et al., Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies https://pubs.rsc.org/en/Content/ArticleLanding/2024/FD/D4FD00107A
J. Zhu, P. Guo, J. Zhang, S. Chen, et al., Superdiffusive Rotation of Interfacial Water on Noble Metal Surface https://pubs.acs.org/doi/full/10.1021/jacs.4c04588
Lan, J., Chergui, M. & Pasquarello, A. Dynamics of the charge transfer to solvent process in aqueous iodide. Nat Commun 15, 2544 (2024). https://doi.org/10.1038/s41467-024-46772-0
F. Hu, M.S. Chen, G.M. Rotskoff, M.W. Kanan, T.E. Markland, Accurate and Efficient Structure Elucidation from Routine One-Dimensional NMR Spectra Using Multitask Machine Learning, ACS Cent. Sci. 10, 11, 2162–2170 (2024).
A. Khan, P. Vaish, Y. Pang, N. Kowshik, M.S. Chen, C.H. Batton, G.M. Rotskoff, J.W. Mullinax, B.K. Clark, B.M. Rubenstein, N.M. Tubman, Quantum Hardware-Enabled Molecular Dynamics via Transfer Learning, arXiv:2406.08554v1 (2024).
G. Zhang*, S. Martiniani, “Absorbing state dynamics of stochastic gradient descent”, arXiv preprint arxiv:2411.11834 (2024)
M. Casiulis*, A. Shih*, S. Martiniani, “Gyromorphs: a new class of functional disordered materials”, arXiv preprint arxiv:2410.09023 (2024)
P. Suryadevara*, M. Casiulis*, S. Martiniani, “Mirages in the Energy Landscape of Soft Sphere Packings”, arXiv preprint arXiv:2409.12113 (2024)
Y. Wang, K. Takaba, M.S. Chen, M. Wieder, Y. Xu, T. Zhu, J.Z.H. Zhang, A. Nagle, K. Yu, X. Wang, D.J. Cole, J.A. Rackers, K. Cho, J.G. Greener, P. Eastman, S. Martiniani, M.E. Tuckerman, “On the design space between molecular mechanics and machine learning force fields”, arXiv preprint arXiv:2409.01931 (2024)
M. Casiulis*, E. Arbel, Y. Lahini, S. Martiniani, N. Oppenheimer, M. Yah Ben Zion, “A geometric condition for robot-swarm cohesion and cluster-flock transition”, arXiv preprint arXiv:2409.04618 (2024)
T.V. Phan, S. Li, D. Ferreris, R. Morris, J. Bos, B. Guo, S. Martiniani, P. Chaikin, Y.G. Kevrekidis, R.H. Austin, “Social Physics of Bacteria: Avoidance of an Information Black Hole”, arXiv preprint arXiv:2401.16691 (2024)
S. Anand*, X. Ma, S. Guo, S. Martiniani†, X. Cheng†, “Transport and Energetics of Bacterial Rectification”, Proc. Natl. Acad. Sci., in print, arXiv:2308.08421
S. Rawat*, D. Heeger, S. Martiniani, “Unconditional stability of a recurrent neural circuit implementing divisive normalization”, Adv. Neural. Inf. Process. Syst. 38 (NeurIPS 2024), arxiv:2409.18946
S. Rawat*, S. Martiniani, “Element-wise and Recursive Solutions for the Power Spectral Density of Biological Stochastic Dynamical Systems at Fixed Points”, Phys. Rev. Res., 6, 043179 (2024)10. A. Shih*, M. Casiulis*, S. Martiniani, “Fast Generation of Spectrally-Shaped Disorder”, Phys. Rev. E 110, 034122 (2024)
E. Fuemmeler*, G. Wolfe*, A. Gupta*, J.A. Vita, E.B. Tadmor, S. Martiniani, “Advancing the ColabFit Exchange towards a Web-scale Data Source for Machine Learning Interatomic Potentials”, AI for Accelerated Materials Design – NeurIPS 2024, link
C. Gonzales, E. Fuemmeler*, E.B. Tadmor, S. Martiniani, S. Miret , “Benchmarking of Universal Machine Learning Interatomic Potentials for Structural Relaxation”, AI for Accelerated Materials Design – NeurIPS 2024, link
A. Gupta*, E.B. Tadmor, S. Martiniani, “KUSP: Python server for deploying ML interatomic potentials”, AI for Accelerated Materials Design – Vienna 2024, link
A. Pal*, S. Rawat*, D. Heeger, S. Martiniani, “Multi-stage Cortical Recurrent Circuit Implementing Normalization”. CCN Abstracts (2024), link.
S. Rawat*, D. Heeger, S. Martiniani, “A comprehensive large-scale model of primary visual cortex (V1)”. CCN Abstracts (2024), link.
G. Zhang*, S. Martiniani, “Neural manifold packing by stochastic gradient descent”, CCN Abstracts (2024), link.
S. Rawat*, D. Heeger, S. Martiniani, “A comprehensive large-scale model of primary visual cortex (V1)”. Cosyne Abstracts (2024), link.
W.J. Pena Ccoa, F. Mukadum, A. Ramon, G. Stirnemann, and G.M. Hocky. A direct computational assessment of vinculin-actin unbinding kinetics reveals catch bonding behavior, submitted. biorxiv:2024.10.10.617580 (2024).
S. Zang, S. Paul, C.W. Leung, M.S. Chen, T. Hueckel, G.M. Hocky, and S.Sacanna. Direct observation and control of non-classical crystallization pathways in binary colloidal systems. In review, doi:10.26434/chemrxiv-2024-rmhsr (2024).
C.L Vizcarra, R. Trainor, A.R. McDonald, C. Richardson, D. Potoyan, J.A. Nash, B.Lundgren, T.Luchko, G.M. Hocky, J.J. Foley IV, T.J. Atherton, and G.Y. Stokes. An interdisciplinary effort to integrate coding into science courses. Nat Comput Sci (online) (2024).
N. Mazzaferro, S. Sasmal, P. Cossio, and G.M. Hocky. Good rates from bad coordinates: the exponential average time-dependent rate approach. J. Chem. Theory Comput. 20 (14), 5901-5912 (2024).
Y. Xie, T. Shu, T. Liu, M. Spindler, J. Mahamid, G.M. Hocky, D, Gresham, and L.J. Holt. Polysome collapse and RNA condensation fluidize the cytoplasm. Mol. Cell, 84 (14), 2698-2716e9 (2024).
T. Shu, G. Mitra, J. Alberts, M. Viana, E.Levy, G.M. Hocky, and L.J. Holt. Mesoscale molecular assembly is favored by the active, crowded cytoplasm. PRX Life, 2, 033001 (2024)
S. Zang, A.W. Hauser, S. Paul, G.M. Hocky, and S. Sacanna. Enabling three-dimensional real space analysis of ionic colloidal crystallization. Nat. Mater., 23, 1131-1137 (2024).
S. Sasmal, T. Pal, G.M. Hocky, and M. McCullagh. Quantifying. Unbiased Conformational Ensembles from Biased Simulations Using ShapeGMM. J. Chem. Theory Comput., 20 (9), 3492–3502 (2024).
K. Liu, G.M. Rotskoff, E. Vanden-Eijnden, and G.M. Hocky. Computing equilibrium free energies through a nonequilibrium quench. J. Chem. Phys., 160, 034109 (2024)
Y. Singh and G.M. Hocky. Improved prediction of molecular response to pulling by combining force tempering with replica exchange methods. J. Phys. Chem. B, 128, 706-715 (2024)
I. Simkó, P.M. Felker, and Z. Bačić, H2O trimer: Rigorous 12D quantum calculations of intermolecular vibrational states, tunneling splittings, and low-frequency spectrum, accepted in the J. Chem. Phys. [DOI: 10.26434/chemrxiv-2024-tn6qm].
P.M. Felker, I. Simkó, and Z. Bačić, Intermolecular Bending States and Tunneling Splittings of Water Trimer from Rigorous 9D Quantum Calculations: I. Methodology, Energy Levels, and Low-Frequency Spectrum, J. Phys. Chem. A 128, 8170 (2024).
I. Simkó, P.M. Felker, and Z. Bačić, HCl trimer: HCl-stretch excited intramolecular and intermolecular vibrational states from 12D fully coupled quantum calculations employing contracted intra- and intermolecular bases, J. Chem. Phys. 160, 164304 (2024).
J. Li, P. Vindel-Zandbergen, J. Li, P.M. Felker, and Z. Bačić, HF trimer: A new accurate full-dimensional potential energy surface and rigorous 12D quantum calculations of vibrational states, J. Phys. Chem. A 128, 9707 (2024).
D. M. Prado, A. Robledo, K. Hightower, A. Jahng, B. Doherty, K. Poling, M. E. Tuckerman, C. Burda, “Breakthrough conductivity enhancement in deep eutectic solvents via Grotthuss-type proton transport”, Adv. Materials Interfaces 2024, 2400508 (2024).
N. Galanakis, M. E. Tuckerman, “Rapid prediction of molecular crystal structures using simple topological and physical descriptors”, Nature Comm. 15, 9757 (2024).
Chao Han, Dongdong Zhang, Song Xia, and Yingkai Zhang: “Accurate Prediction of NMR Chemical Shifts: Integrating DFT Calculations with Three-Dimensional Graph Neural Networks.” Journal of Chemical Theory and Computation, 20, 5250–5258, 2024.
Nathan Soper, Isabelle Yardumian, Eric Chen, Chao Yang, Samantha Ciervo, Aaron Oom, Ludovic Desvignes, Mark Mulligan, Yingkai Zhang, and Tania Lupoli: “A Repurposed Drug Interferes with Nucleic Acid to Inhibit the Dual Activities of Coronavirus Nsp13.” ACS Chemical Biology, 19, 1593–1603, 2024.
Jianping Li, Ampon Sae Her, Alida Besch, Belen Ramirez-Cordero, Maureen Crames, James R. Banigan, Casey Mueller, William M. Marsiglia, Yingkai Zhang, Nathaniel J. Traaseth: “Dynamics Underlie the Drug Recognition Mechanism by the Efflux Transporter EmrE.” Nature Communications, 15, 4537, 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)
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