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Quantifying the Structural and Energetic Consequences of EXOSC3 S1 Domain Variants from a Comparative Assessment of λ-Dynamics with Two Charge-Changing Perturbation Strategies

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Barron, Monica P., Wijeratne, H.R. Sagara, Runnebohm, Avery M., Caric, Katelyn M., Mosley, Amber L., Vilseck, Jonah Z.·2025·bioRxiv (Cold Spring Harbor Laboratory)
DOI 10.1101/2025.06.30.662390PMC PMC12236710
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Abstract

Charge-changing perturbations are notoriously dificult to investigate with alchemical free energy calculations. The routine use of periodic boundary conditions and electrostatic approximations, such as particle-mesh Ewald (PME), may produce finite-size efect errors that become non-negligible as a perturbation changes a simulation cell’s net charge away from zero. Two prevalent strategies exist to correct for these errors: the analytic correction (AC) and co-alchemical ion (CI) methods. Both correction schemes have been found to produce comparable relative free energy results for small molecule perturbations, but these methods have not been compared using λ-dynamics (λD) free energy calculations or for protein side chain mutations. Recently, we investigated relative folding and binding free energies (ΔΔ G s) of a series of EXOSC3 variants involved in a rare neurodegenerative disorder, including D132A, G135R, and G191D charge-change perturbations, with a simplified AC scheme in λD. In this study, these perturbations are reevaluated with the CI scheme for comparison with AC to identify the best correction strategy for λD. The collected AC- and CI-corrected ΔΔ G s show excellent agreement with a mean unsigned error of 0.4 kcal/mol. However, reduced sampling proficiency and increased dificulties of evaluating multisite perturbations with the CI method suggest that a simplified AC approach may be more generalizable for future λD calculations. Previously, the use of the CI approach with λD has been limited due to a lack of infrastructure available to users to simplify its more involved setup procedure. This study introduces an automated workflow for implementation of the CI approach with λD, laying the foundation for future comparisons between charge-change correction schemes. These studies facilitated analysis of the λD trajectories to identify structural changes within EXOSC3 and the RNA exosome complex that clearly rationalize the calculated ΔΔ G s for the D132A, G135R, G191C, and G191D EXOSC3 variants, providing insight into potential disease-causing mechanisms of EXOSC3 modifications.

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  • The change in charge accompanying the EXOSC3 G19D perturbation was accounted for using a post-simulation analytic correction (AC).

    “The change in charge accompanying the EXOSC3 G19D perturbation was accounted for using a post-simulation analytic correction (AC)75.”

    From EXOSC3 G191 Variants Trigger System-Wide Recalibration of RNA Processing Machinery (2026) · cites this paper as [75]

    The Methods section (“Analytic correction,” page unavailable) says a post-simulation AC was applied to account for charge-changing EXOSC3 perturbations. The paper identifies the relevant aspartate variant as G191D in the Discussion, not G19D; the cited passages do not support the residue number in the claim. (LLM, conf=0.88)

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  1. [1]Morris R., Black K. A., Stollar E. J., Uncovering protein function: from classification to complexes. Essays in Biochemistry 2022, 66, 255– 285. 35946411 PMC9400073
  2. [2]Gavi A-C., Proteome survey reveals modularity of the yeast cell machinery. Nature 2006, 440, 631– 636. 16429126
  3. [3]Cierpicki T., Grembeck J., Targeting protein-protein interactions in hematologic malignancies. Annu. Rev. Pathol. Mech. Dis. 2025, 20, 275– 301. 39854187
  4. [4]Greenblatt J. F., Alberts B. M., Krogan N. J., Discovery and significance of protein-protein interactions in health and disease. Cell 2024, 187, 6501– 6517. 39547210 PMC11874950
  5. [5]Haakonsen D. L., Heider M., Ingersoll A. J., Vodehnal K., Witus S. R., Uenaka T., Wernig M., Rapé M., Stress response silencing by an E3 ligase mutated in neurodegeneration. Nature 2024, 626, 874– 880. 38297121 PMC10881396
  6. [6]Ferreira C. R., Rahman S., Keller M., Zschocke J., An international classification of inherited metabolic disorders (ICIMD). J. Inherit. Metab. Dis. 2021, 44, 164– 177. 33340416 PMC9021760
  7. [7]Liu N., Olson E. N., CRISPR modeling and correction of cardiovascular disease. Circulation Research 2022, 130, 1827– 1850. 35679361 PMC9202442
  8. [8]Cheng F., Zhao J., Wang Y., Lu W., Liu Z., Zhou Y., Martin W. R., Wang R., Huang J., Hao T., Comprehensive characterization of protein-protein interactions perturbed by disease mutations. Nature Genetics 2021, 53, 342– 353. 33558758 PMC8237108
  9. [9]Jemimah S., Gromiha M. M., Insights into changes in binding affinity caused by disease mutations in protein-protein complexes. Computers in Biology and Medicine 2020. 123, 103829. 32768037
  10. [10]Mitchell P., Petfalski E., Shevchenko A., Mann M., Tollervey D. The exosome: a conserved eukaryotic RNA processing complex containing multiple 3′→5′ exoribonucleases. Cell, 1997, 91(4), 457– 466. 9390555
  11. [11]Makino D. L., Baumgärtner M., Conti E. Crystal structure of an RNA-bound 11-subunit eukaryotic exosome complex. Nature, 2013, 495(7439), 70– 75. 23376952
  12. [12]Allmang C., Petfalski E., Podtelejnikov A., Mann M., Tollervey D., Mitchell P. The yeast exosome and human PM–Scl are related complexes of 3′→ 5′ exonucleases. Genes & development, 1999, 13(16), 2148– 2158. 10465791 PMC316947
  13. [13]Makino D. L., Schuch B., Stegmann E., Baumgärtner M., Basquin C., Conti E. RNA degradation paths in a 12-subunit nuclear exosome complex. Nature, 2015, 524(7563), 54– 58. 26222026
  14. [14]Wasmuth E. V., Januszyk K., Lima C. D. Structure of an Rrp6–RNA exosome complex bound to poly (A) RNA. Nature, 2014, 511(7510), 435– 439. 25043052 PMC4310248
  15. [15]Bonneau F., Basquin J., Ebert J., Lorentzen E., Conti E. The yeast exosome functions as a macromolecular cage to channel RNA substrates for degradation. 2019, Cell, 139(3), 547– 559. 19879841
  16. [17]Du Y., An W., Zhu X., Sun Q., Qi J., Ye K. Cryo-EM structure of 90S small ribosomal subunit precursors in transition states. Science, 2020, 369(6510), 1477– 1481. 32943522
  17. [18]Schuller J. M., Falk S., Fromm L., Hurt E., Conti E. Structure of the nuclear exosome captured on a maturing preribosome. Science, 2018, 360(6385), 219– 222. 29519915
  18. [19]Gerlach P., Schuller J. M., Bonneau F., Basquin J., Reichelt P., Falk S., Conti E. Distinct and evolutionary conserved structural features of the human nuclear exosome complex. Elife, 2018, 7, e38686. 30047866 PMC6072439
  19. [20]Keidel A., Kögel A., Reichelt P., Kowalinski E., Schäfer I. B., Conti E. Concerted structural rearrangements enable RNA channeling into the cytoplasmic Ski238-Ski7-exosome assembly. Molecular Cell, 2023, 83(22), 4093– 4105. 37879335 PMC10659929
  20. [21]Kögel A., Keidel A., Loukeri M. J., Kuhn C. C., Langer L. M., Schäfer I. B., Conti E. Structural basis of mRNA decay by the human exosome–ribosome supercomplex. Nature, 2024, 635(8037), 237– 242. 39385025 PMC11540850
  21. [22]Müller J. S., Giunta M., Horvath R. Exosomal protein deficiencies: how abnormal RNA metabolism results in childhood-onset neurological diseases. Journal of neuromuscular diseases, 2(s2), 2015, S31– S37. PMC4845884 27127732
  22. [23]Wolin S. L., Maquat L. E. Cellular RNA surveillance in health and disease. Science, 2019, 366(6467), 822– 827. 31727827 PMC6938259
  23. [24]Tomecki R., Drazkowska K., Kobylecki K., Tudek A. SKI complex: a multifaceted cytoplasmic RNA exosome cofactor in mRNA metabolism with links to disease, developmental processes, and antiviral responses. Wiley Interdisciplinary Reviews: RNA, 2023, 14(6), e1795. 37384835
  24. [25]Wan J., Yourshaw M., Mamsa H., Rudnik-Schöneborn S., Menezes M. P., Hong J. E., Mutations in the RNA exosome component gene EXOSC3 cause pontocerebellar hypoplasia and spinal motor neuron degeneration. Nature genetics, 2012, 44(6), 704– 708. 22544365 PMC3366034
  25. [26]Slavotinek A., Misceo D., Htun S., Mathisen L., Frengen E., Foreman M., Biallelic variants in the RNA exosome gene EXOSC5 are associated with developmental delays, short stature, cerebellar hypoplasia and motor weakness. Human molecular genetics, 2020, 29(13), 2218– 2239. 32504085 PMC7399534
  26. [27]Di Donato N., Neuhann T., Kahlert A. K., Klink B., Hackmann K., Neuhann I., Mutations in EXOSC2 are associated with a novel syndrome characterised by retinitis pigmentosa, progressive hearing loss, premature ageing, short stature, mild intellectual disability and distinctive gestalt. Journal of medical genetics, 2016, 53(6), 419– 425. 26843489
  27. [28]Laugwitz L., Buchert R., Groeschel S., Riess A., Grimmel M., Beck-Wödl S., Pontocerebellar hypoplasia type 11: Does the genetic defect determine timing of cerebellar pathology?. European Journal of Medical Genetics, 2020, 63(7), 103938. 32360255
  28. [29]van Dijk T., Baas F., Barth P. G., Poll-The B. T. What’s new in pontocerebellar hypoplasia? An update on genes and subtypes. Orphanet Journal of Rare Diseases, 2018, 13, 1– 16. 29903031 PMC6003036
  29. [30]Rudnik-Schöneborn S., Senderek J., Jen J. C., Houge G., Seeman P., Puchmajerová A., Pontocerebellar hypoplasia type 1: clinical spectrum and relevance of EXOSC3 mutations. Neurology, 2013, 80(5), 438– 446. 23284067 PMC3590055
  30. [31]Eggens V. R., Barth P. G., Niermeijer J. M. F., Berg J. N., Darin N., Dixit A., EXOSC3 mutations in pontocerebellar hypoplasia type 1: novel mutations and genotype-phenotype correlations. 2014, Orphanet journal of rare diseases, S, 1– 10. PMC3928094 24524299
  31. [32]Schwabova J., Brozkova D. S., Petrak B., Mojzisova M., Pavlickova K., Haberlova J., Homozygous EXOSC3 mutation c. 92G→ C, p. G31A is a founder mutation causing severe pontocerebellar hypoplasia type 1 among the Czech Roma. Journal of neurogenetics, 2013, 27(4), 163– 169. 23883322
  32. [33]Ivanov I., Atkinson D., Litvinenko I., Angelova L., Andonova S., Mumdjiev H., Pontocerebellar hypoplasia type 1 for the neuropediatrician: Genotype–phenotype correlations and diagnostic guidelines based on new cases and overview of the literature. European Journal of Paediatric Neurology, 2018, 22(4), 674– 681. 29656927
  33. [34]Landrum M. J., Lee J. M., Benson M., Brown G. R., Chao C., Chitipiralla S., ClinVar: improving access to variant interpretations and supporting evidence. Nucleic acids research, 2018, 46(D1), D1062– D1067. 29165669 PMC5753237
  34. [36]Fasken M. B., Losh J. S., Leung S. W., Brutus S., Avin B., Vaught J. C., Insight into the RNA exosome complex through modeling pontocerebellar hypoplasia type 1b disease mutations in yeast. Genetics, 2017, 205(1), 221– 237. 27777260 PMC5223504
  35. [37]Gillespie A., Gabunilas J., Jen J. C., Chanfreau G. F. Mutations of EXOSC3/Rrp40p associated with neurological diseases impact ribosomal RNA processing functions of the exosome in S. cerevisiae. RNA, 2017, 23(4), 466– 472. 28053271 PMC5340910
  36. [38]Runnebohm A. M., Wijeratne H. R. S., Barron M. P., Smith-Kinnaman W. R., Rooney J. D., Peck Justice S. A., Cureton L. A., Holland A., Ghalei H., Pelletier S., Doud E. H., Vilseck J. Z., Mosley A. L. EXOSC3 S1-domain variants implicated in PCH1b alter RNA exosome cap subunit abundance and thermal stability disrupting rRNA processing and targeting of AU-rich mRNA. bioRxiv, 2025, 05.31.657176.
  37. [41]Barron M. P., Vilseck J. Z. A λ-dynamics investigation of insulin Wakayama and other A3 variant binding affinities to the insulin receptor. J. Chem. Inf. Model. 2025, 64, 5657– 5670. PMC11268370 38963805
  38. [42]Yeh C., Hayes R. L. Predicting thermodynamic stability at protein G sites with deleterious mutations using λ-dynamics with competitive screening. J. Phys. Chem. Lett. 2025, 16, 3206– 3211. 40115984 PMC11973915
  39. [43]Kong X., Brooks C. L. III λ-dynamics: A new approach to free energy calculations. The Journal of chemical physics 1996, 105(6), 2414– 2423.
  40. [44]Guo Z., Brooks C. L. III Efficient and flexible algorithm for free energy calculations using the λ-dynamics approach. J. Phys. Chem. B 1998, 102, 2032– 2036.
  41. [45]Hayes R. L., Cervantes L. F., Santos J. C. A., Samadi A., Vilseck J. Z., Brooks C. L. III How to sample dozens of substituents per site with λ dynamics. J. Chem. Theory Comput. 2024, 20, 6098– 6110. 38976796 PMC11270746
  42. [46]Hayes R. L., Armacost K. A., Vilseck J. Z., Brooks C. L. III Adaptive landscape flattening accelerates sampling of alchemical space in Multisite λ Dynamics. The Journal of Physical Chemistry B. 2017, 121(15), 3626– 3635. 28112940 PMC5824625
  43. [47]Hayes R. L., Vilseck J. Z., Brooks C. L. III Approaching protein design with multisite λ dynamics: Accurate and scalable mutational folding free energies in T4 lysozyme. Protein Science 2018, 27(11), 1910– 1922. 30175503 PMC6225981
  44. [48]Robo M. T., Hayes R. L., Ding X., Pulawski B., Vilseck J. Z., Fast free energy estimates from λ-dynamics with bias-updated Gibbs sampling. Nat. Commun. 2023, 14, 8515. 38129400 PMC10740020
  45. [49]Vilseck J. Z., Sohail N., Hayes R. L., Brooks C. L. III, Overcoming challenging substituent perturbations with multisite λ-dynamics: A case study targeting β-secretase 1. J. Phys. Chem. Lett. 2019, 10, 4875– 4880. 31386370 PMC7015761
  46. [50]Chen W., Deng Y., Russell E., Wu Y., Abel R., Wang L. Accurate calculation of relative binding free energies between ligands with different net charges. J. Chem. Theory. Comput., 2018, 14(12), 6346– 6358. 30375870
  47. [52]Rocklin G. J., Mobley D. L., Dill K. A., Hünenberger P. H. Calculating the binding free energies of charged species based on explicit-solvent simulations employing lattice-sum methods: an accurate correction scheme for electrostatic finite-size effects. J. Chem. Phys. 2013, 139, 184103. 24320250 PMC3838431
  48. [53]Rief M. M., Oostenbrink C. Net charge changes in the calculation of relative ligand-binding free energies via classical atomistic molecular dynamics simulations. J. Comput. Chem. 2014, 35, 227– 243. 24249099 PMC4237198
  49. [54]Kastenholz M. A., Hünenberger P. H. Computation of methodology-independent ionic solvation free energies from molecular simulations. II. The hydration free energy of the sodium cation. J. Chem. Phys., 2006, 124(22). 16784292
  50. [55]Petrov D., Perthold J. W., Oostenbrink C., de Groot B. L., Gapsys V. Guidelines for free-energy calculations involving charge changes. Journal of Chemical Theory and Computation, 2024, 20(2), 914– 925. 38164763 PMC10809403
  51. [56]Lin Y-L., Aleksandrov A., Simonson T., Roux B. An overview of electrostatic free energy computations for solutions and proteins. J. Chem. Theory Comput. 2014, 10, 2690– 2709. 26586504
  52. [57]Öhlknecht C., Lier B., Petrov D., Fuchs J., Oostenbrink C. Correcting electrostatic artifacts due to net-charge changes in the calculation of ligand binding free energies. J. Comput. Chem. 2019, 41, 986– 999. 31930547
  53. [58]Hayes R. L., Nixon C. F., Marqusee S., Brooks III, Selection C. L. pressures on evolution of ribonuclease H explored with rigorous free-energy-based design. Proc. Natl. Acad. Sci. U.S.A. 2024, 121, e2312029121. 38194446 PMC10801872
  54. [59]Huang Y., Chen W., Wallace J. A., Shen J. All-atom continuous constant pH molecular dynamics with particle mesh Ewald and titratable water. J. Chem. Theory Comput. 2016, 12, 5411– 5421. 27709966 PMC5713900
  55. [60]Buslaev P., Aho N., Jansen A., Bauer P., Hess B., Groenhof G. Best practices in constant pH MD simulations: Accuracy and sampling. J. Chem. Theory Comput. 2022, 18, 6134– 6147. 36107791 PMC9558372
  56. [61]Hanquier J. N., Sanders K., Berryhill C. A., Sahoo F. K., Hudmon A., Vilseck J. Z., Cornett E. M. Identification of nonhistone substrates of the lysine methyltransferase PRDM9. J. Biol. Chem. 2023, 299(5), 104651. 36972790 PMC10164904
  57. [62]Brooks B. R., Brooks C. L. III, MacKerell A. D. Jr., Nilsson L., Petrella R. J., Roux B., Won Y., Archontis G., Bartel C., Boresch S.,, CHARMM: The biomolecular simulation program. J. Comput. Chem. 2009, 30, 1545– 1614. 19444816 PMC2810661
  58. [63]Brooks B. R., Bruccoleri R. E., Olafson B. D., States D. J., Swaminathan S., Karplus M., CHARMM: A program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem. 1983, 4, 187– 217.
  59. [64]Hynninen A.-P., Crowley M. F., New faster CHARMM molecular dynamics engine. J Comput Chem 2014, 35, 406– 413. 24302199 PMC3966901
  60. [65]Hayes R. L., Brooks III, A C. L. strategy for proline and glycine mutations to proteins with alchemical free energy calculations. J. Comput. Chem. 2021, 42, 1088– 1094. 33844328 PMC8628859
  61. [67]Wang R. Y.-R; Song Y.; Barad B. A.; Cheng Y.; Fraser J. S.; DiMaio F. Automated structure refinement of macromolecular assemblies from cryo-EM maps using Rosetta. eLife 2016, 5, e17219. 27669148 PMC5115868
  62. [69]Davis I. W.; Leaver-Fay A.; Chen V. B.; Block J. N.; Kapral G. J.; Wang X.; Murray L. W.; Arendall W. B. III.; Snoeyink J.; Richardson J. S.; Richardson D. C. MolProbity: All-atom contacts and structure validation for proteins and nucleic acids. Nucl. Acids Res. 2007, 35, W375– 383. 17452350 PMC1933162
  63. [70]Chen V. B.; Arendall W. B. III.; Headd J. J.; Keedy D. A.; Immormino R. M.; Kapral G. J.; Murray L. W.; Richardson J. S.; Richardson D. C. MolProbity: All-atom structure validation for macromolecular crystallography. Acta Crystallogr. D Biol. Crystallogr. 2010, 66, 12– 21. 20057044 PMC2803126
  64. [71]Williams C. J.; Headd J. J.; Moriarty N. W.; Prisant M. G.; Videau L. L.; Deis L. N.; Verma V.; Keedy D. A.; Hintze B. J.; Chen V. B.; Jain S.; Lewis S. M.; Arendall B. W. III.; Snoeyink J.; Adams P. D.; Lovell S. C.; Richardson J. S.; Richardson D. C. MolProbity: More and better reference data for improved all-atom structure validation. Protein Science 2018. 27, 293– 315. 29067766 PMC5734394
  65. [72]Word J. M., Lovell S. C., Richardson J. S., Richardson D. C., Asparagine and glutamine: Using hydrogen atom contacts in the choice of side-chain amide orientation. J. Mol. Biol. 1999, 285, 1735– 1747. 9917408
  66. [73]Sondergaard C. R.; Olsson M. H. M.; Rostkowski M.; Jensen J. H. Improved treatment of ligands and coupling effects in empirical calculation and rationalization of pKa values. J. Chem. Theory Comput. 2011, 7, 2284– 2295. 26606496
  67. [74]Olsson M. H. M.; Sondergaard C. R.; Rostkowski M.; Jensen J. H. PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J. Chem. Theory Comput. 2011, 7, 525– 537. 26596171
  68. [75]Webb B.; Sali A. Comparative Protein Structure Modeling Using Modeller. Curr Protoc Bioinformatics. 2016, 54, 5.6.1– 5.6.37. PMC5031415 27322406
  69. [76]Frenz B.; Walls A. C.; Egelman E. H.; Veesler D.; DiMaio F. RosettaES: a sampling strategy enabling automated interpretation of difficult cryo-EM maps. Nature Methods 2017, 14(8), 797– 800. 28628127 PMC6009829
  70. [77]Kappel K., Zhang K., Su Z., Watkins A. M., Kladwang W., Li S., Pintilie G., Topkar V. V., Rangan R., Zheludev I. N., Yesselman J. D., Chiu W., Das R. Accelerated cryo-EM-guided determination of three-dimensional RNA-only structures. Nat Methods 2020, 17, 699– 707. 32616928 PMC7386730
  71. [79]Lau B., Cheng J., Flemming D., La Venuta G., Berninghausen O., Beckmann R., Hurt E. Structure of the maturing 90S pre-ribosome in association with the RNA exosome. Molecular cell, 2021, 81(2), 293– 303. 33326748
  72. [80]Jorgensen W. L.; Chandrasekhar J.; Madura J. D.; Impey R. W.; Klein M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983, 79, 926– 935.
  73. [81]Jo S.; Kim T.; Iyer V. G.; Im W. CHARMM-GUI: A web based graphical user interface for CHARMM. J. Comput. Chem. 2008, 29, 1859– 1865. 18351591
  74. [82]Best R. B.; Zhu X.; Shim J.; Lopes P. E. M.; Mittal J.; Feig M.; MacKerell A. D. Jr. Optimization of the additive CHARMM all atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ1 and χ2 dihedral angles. J. Chem. Theory Comput. 2012, 8, 3257– 3273. 23341755 PMC3549273
  75. [83]Denning E. J.; Priyakumar U. D.; Nilsson L.; MacKerell A. D. Jr. Impact of 2’-hydroxyl sampling on the conformational properties of RNA: Update of the CHARMM all-atom additive force field for RNA. J. Comput. Chem. 2011, 32, 1929– 1943. 21469161 PMC3082605
  76. [84]Foloppe N.; MacKerell A. D. Jr. All-Atom Empirical Force Field for Nucleic Acids: 1) Parameter Optimization Based on Small Molecule and Condensed Phase Macromolecular Target Data. J. Comput. Chem. 2000, 21, 86– 104.
  77. [86]Feller S. E., Zhang Y., Pastor R. W., and Brooks B. R. Constant pressure molecular dynamics simulation: the Langevin piston method. J. Chem. Phys. 1995, 103, 4613– 4621.
  78. [87]Ryckaert J.-P.; Ciccotti G.; Berendsen H. J. C. Numerical integration of the cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. J. Comput. Phys. 1977, 23, 327– 341.
  79. [88]Darden T.; York D.; Pedersen L. Particle mesh Ewald: an Nlog(N) method for Ewald sums in large systems. J. Chem. Phys. 1993, 98, 10089– 10092.
  80. [89]Essmann U.; Perera L.; Berkowitz M. L.; Darden T.; Lee H.; Pedersen L. G. A smooth particle mesh Ewald method. J. Chem. Phys. 1995, 103, 8577– 8593.
  81. [90]Steinbach P. J.; Brooks B. R. New spherical-cutoff methods for long-range forces in macromolecular simulation. J. Comput. Chem. 1994, 15, 667– 683.
  82. [91]Knight J. L., Brooks III, Multisite C. L. λ dynamics for simulated structure activity relationship studies. J. Chem. Theory Comput. 2011, 7, 2728– 2739. 22125476 PMC3223982
  83. [92]Kumar S.; Bouzida D.; Swendsen R. H.; Kollman P. A.; Rosenberg J. M. The weighted histogram analysis method for free energy calculations on biomolecules. I. The method. J. Comput. Chem. 1992, 13, 1011– 1021.
  84. [93]Dixit S. B.; Chipot C.; Can absolute free energies of association be estimated from molecular mechanical simulations? The biotin-streptavidin system revisited. J. Phys. Chem. A 2001, 105, 9795– 9799.
  85. [94]Comaniciu D., Meer P. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on pattern analysis and machine intelligence, 2002, 24(5), 603– 619.

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Authors
Barron, M. P., Wijeratne, H. R. S., Runnebohm, A. M., Caric, K. M., Mosley, A. L., & Vilseck, J. Z.
Affiliations
Indiana University Indianapolis (US)
Corresponding author
Vilseck, Jonah Z.
ORCID
Venue
bioRxiv (Cold Spring Harbor Laboratory)
Publisher
openRxiv
Published
2025-07-04
DOI
10.1101/2025.06.30.662390
PMC
PMC12236710
OpenAlex
W4412024076
Cited by
1
References
5
Citations by year
2026: 1
Type
posted content
Language
en
Open access
Licence
Locations
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • IUScholarWorks (Indiana University) (submittedVersion, cc-by-nc-nd)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
Funders
  • National Institute of Aging
  • National Institute of General Medicine
  • National Institute of Neurodegenerative Diseases and Stroke
  • National Cancer Institute
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Keywords
EXOSC3, alchemical free energy calculations, lambda-dynamics, finite-size effects, RNA exosome, neurodegenerative diseases, workflow automation

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