References

Parameters

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11. W. L. Jorgensen and J. D. Madura. Temperature and size dependence for Monte Carlo simulations of TIP4P water. Mol. Phys. 56, 1381-1392 (1985) (DOI)
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    Background

26. D. Chander, J. D. Weeks and H. C. Andersen. Van der Waals picture of liquids, solids, and phase transformations. Science 220, p787 (1983) (DOI)
27. M. O. Steinhauser. A molecular dynamics study on universal properties of polymer chains in different solvent qualities. Part 1. A review of linear chain properties. J. Chem. Phys. 122, 094901 (2005) (DOI)
28. D. Frenkel and B. Smit. Understanding Molecular Simulations, Second Edition: From Algorithms to Applications. Academic Press, San Diego, CA (2002)
29. J. E. Kohn, I. S. Millett, J. Jacob, B. Žagrović, T. M. Dillon, N. Cingel, R. S. Dothager, S. Seifert, P. Thiyagarajan, T. R. Sosnick, M. Z. Hasan, V. S. Pande, I. Ruczinski, S. Doniach, K. W. Plaxco. Random-coil behavior and the dimensions of chemically unfolded proteins. Proc. Natl. Acad. Sci. USA 101, 12491-12496 (2004) (DOI)
30. H. T. Tran and R. V. Pappu. Toward an accurate theoretical framework for describing ensembles for proteins under strongly denaturing conditions. Biophys. J. 91, 1868-1886 (2006) (DOI)
    
31. H. Qian and J. A. Schellman. Helix-coil theories: A comparative study for finite length polypeptides. J. Phys. Chem. 96, 3987-3994 (1992) (DOI)
    
32. H. R. Drew, R. M. Wing, T. Takano, C. Broka, S. Tanaka, K. Itakura, and R. E. Dickerson. Structure of a B-DNA dodecamer: conformation and dynamics. Proc. Natl. Acad. Sci. USA 78, 2179-2183 (1981) (PDF)
    
33. A. Vitalis, N. A. Baker, and J. A. McCammon. ISIM: A Program for Grand Canonical Monte Carlo Simulations of the Ionic Environment of Biomolecules. Mol. Simul. 30 (1), 45-61 (2004) (DOI)
    
34. D. Kofke and E. D. Glandt. Monte Carlo simulation of multicomponent equilibria in a semigrand canonical ensemble. Mol. Phys. 64 (6), 1105-1131 (1988) (DOI)
    
35. W. R. P. Scott, A. E. Mark, and W. F. van Gunsteren. On using time-averaging restraints in molecular dynamics simulation. J. Biomol. NMR 12 (4), 501-508 (1998) (DOI)
    
36. F. Wang and D. P. Landau. Efficient, multiple-range random walk algorithm to calculate the density of states. Phys. Rev. Lett., 86 (10), 2050-2053 (2001) (DOI)
    
37. F. Calvo. Sampling along reaction coordinates with the Wang-Landau method. Mol. Phys., 100 (21), 3421-3427 (2002) (DOI)
    
38. C. Zhou and R. N. Bhatt. Understanding and improving the Wang-Landau algorithm. Phys. Rev. E, 72 (2), 025701(R) (2005) (DOI)
    
39. R. E. Belardinelli and V. D. Pereyra. Fast algorithm to calculate density of states. Phys. Rev. E, 75 (4), 046701 (2007) (DOI)
    
40. A. Vitalis and A. Caflisch. Equilibrium sampling approach to the interpretation of electron density maps. Structure, 22 (1), 156-167 (2014) (DOI)
    
41. B. Roux. The calculation of the potential of mean force using computer simulations. Comp. Phys. Comm., 91 (1-3), 275-282 (1995) (DOI)
    
42. C. C. Lee, R. H. Walters, and R. M. Murphy. Reconsidering the mechanism of polyglutamine peptide aggregation. Biochemistry, 46 (44), 12810-12820 (2007) (DOI)
    
43. S. Chen, F. A. Ferrone, and R. Wetzel. Huntington's disease age-of-onset linked to polyglutamine aggregation nucleation. Proc. Natl. Acad. Sci. USA, 99 (18), 11884-11889 (2002) (DOI)
    
44. A. Blondel and M. Karplus. New formulation for derivatives of torsion angles and improper torsion angles in molecular mechanics: Elimination of singularities. J. Comput. Chem., 17 (9), 1131-1142 (1996) (DOI)
    
45. D. Hamelberg, J. Mongan, and J. A. McCammon. Accelerated molecular dynamics: A promising and efficient simulation method for biomolecules. J. Chem. Phys. 120 (24), 11919-11929 (2004) (DOI)
46. C. Esposito and A. Vitalis. Precise estimation of transfer free energies for ionic species between similar media. Phys. Chem. Chem. Phys. 20 (42), 27003-27010 (2018) (DOI)
    

    Random seeds

47. W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P. Flannery. Numerical Recipes in Fortran 90: The Art of Parallel Scientific Computing, Volume 2 of Fortran Numerical Recipes, Second Edition. Cambridge University Press, New York (1996)
48. G. Marsaglia and W. W. Tsang. 2000. A simple method for generating gamma variables. ACM Trans. Math. Softw. 26, 363-372 (2000) (DOI)
    

    Monte Carlo methods and Metropolis algorithm

49. N. Metropolis, A. W. Rosenbluth, N. M. Rosenbluth, A. N. Teller, and E. Teller. Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1087-1092 (1953) (DOI)
50. A. Vitalis and R. V. Pappu. Methods for Monte Carlo simulations of biomacromolecules. Annu. Rep. Comput. Chem. 5, 49-76 (2009) (DOI).
51. D. Frenkel and B. Smit. Understanding Molecular Simulations, Second Edition: From Algorithms to Applications. Academic Press, San Diego, CA (2002)
    

    Molecular, Langevin, and Brownian dynamics

52. M. Karplus and J. A. McCammon. Molecular dynamics simulations of biomolecules. Nat. Struct. Biol. 9, 646- 652 (2002) (DOI)
53. J. M. Haile. Molecular dynamics simulation: Elementary methods. John Wiley and Sons, New York,  NY (1992)
54. D. L. Ermak and J. A. McCammon. Brownian dynamics with hydrodynamic interactions. J. Chem. Phys. 69, 1352-1360 (1978) (DOI).
55. W. F. van Gunsteren and H. J. C. Berendsen. Algorithms for Brownian dynamics. Mol. Phys. 45, 637-647 (1982) (DOI)
56. S. He and H. A. Scheraga. Macromolecular conformational dynamics in torsional angle space. J. Chem. Phys. 108 , 271-300 (1998) (DOI)
57. M. G. Paterlini and D. M. Ferguson. Constant temperature simulations using the Langevin equation with velocity Verlet integration. Chem. Phys. 236, 243-252 (1998) (DOI)
58. R. D. Skeel and J. A. Izaguirre. An impulse integrator for Langevin dynamics. Mol. Phys. 100, 3885-3891 (2002) (DOI)
59. B. Hess, H. Bekker, H. J. C. Berendsen and J. G. E. M. Fraaije. LINCS: A linear constraint solver for molecular simulations. J. Comput. Chem. 18 (12), 1463-1472 (1997) (DOI)
60. J. P. Ryckaert, G. Ciccotti, and H. J. C. Berendsen. Numerical integration of the Cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comp. Phys. 23, 327-341 (1977) (DOI)
61. S. Miyamoto and P. A. Kollman. Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models. J. Comput. Chem. 13 (8), 952-962 (1992) (DOI)
62. H. C. Andersen. Rattle: A “velocity” version of the shake algorithm for molecular dynamics calculations. J. Comp. Phys. 52 (1), 24-34 (1983) (DOI)
63. P. Gonnet. P-SHAKE: A quadratically convergent SHAKE in O(N²). J. Comp. Phys. 220 (2), 740-750 (2007) (DOI)
64. E. Vanden-Eijnden and G. Ciccotti. Second-order integrators for Langevin equations with holonomic constraints. Chem. Phys. Lett. 429 (1-3), 310-316 (2006) (DOI)
65. A. K. Mazur. Quasi-Hamiltonian equations of motion for internal coordinate molecular dynamics of polymers. J. Comput. Chem. 18 (11), 1354-1364 (1997) (DOI)
66. J. Chen, W. Im, C. L. Brooks III. Application of torsion angle molecular dynamics for efficient sampling of protein conformations. J. Comput. Chem. 26 (15), 1565-1578 (2005) (DOI)
67. A. Vitalis and R. V. Pappu. A simple molecular mechanics integrator in mixed rigid body and dihedral angle space. J. Chem. Phys., 141 (3), 034105 (2014) (DOI)
68. M. Bacci, A. Vitalis, and A. Caflisch. A molecular simulation protocol to avoid sampling redundancy and discover new states. Biochim. Biophys. Acta, 1850 (5), 889-902 (2015) (DOI)
69. N. Arizumi and S. D. Bond. On the estimation and correction of discretization error in molecular dynamics averages. Appl. Num. Math., 62 (12), 1938-1953 (2012) (DOI)
    

    Mixed molecular dynamics/Monte Carlo

70. F. Guarnieri and W. C. Still. A Rapidly Convergent Simulation Method: Mixed Monte Carlo/Stochastic Dynamics. J. Comput. Chem. 15, 1302-1310 (1994) (DOI)
71. M. O. Steinhauser. A molecular dynamics study on universal properties of polymer chains in different solvent qualities. Part I. A review of linear chain properties. J. Chem. Phys. 122, 094901 (2005) (DOI)
    

    Minimizers

72. J. Nocedal. Updating Quasi-Newton Matrices with Limited Storage. Mathematics of Computation 35, 773-782 (1980) (PDF).
73. C. G. Broyden. The Convergence of a Class of Double-rank Minimization Algorithms 1. General Considerations. IMA J. Appl. Math. 6 (1), 76-90 (1970) (DOI)
74. D. F. Shanno. Conditioning of quasi-Newton methods for function minimization. Math. Comput. 24 (111), 647-656 (1970) (PDF)
75. R. Fletcher. A New Approach to Variable Metric Algorithms. The Computer Journal 13 (3), 317-322 (1970) (DOI)
76. D. Goldfarb. A family of variable-metric methods derived by variational means. Math. Comput. 24, 23-26 (1970) (DOI)
77. R. Fletcher and C. M. Reeves. Function minimization by conjugate gradients. The Computer Journal 7 (2), 149-155 (1964) (DOI)
78. E. Polak and G. Ribiere. Note sur la convergence de méthodes de directions conjuguées. Revue Française d’Informatique et de Recherche Opérationnelle, 16, 35-43 (1969). (PDF)
    

    Periodic boundary conditions

79. G. Makov and M. C. Payne. Periodic boundary conditions in ab initio calculations. Phys. Rev. B 51, 4014-4022 (1995) (DOI)
    

    Particle-mesh Ewald summation

80. T. Darden, D. York and L. Pedersen. Particle mesh Ewald-an NlogN method for Ewald sums in large systems. J. Chem. Phys 98, 10089–10092 (1993) (DOI)
81. U. Essmann, L. Perera, M.L. Berkowitz, T. Darden, H. Lee and L. Pedersen. A smooth particle mesh Ewald method. J. Chem. Phys 103, 8577–8593 (1995) (DOI)
82. H. G. Petersen. Accuracy and efficiency of the particle mesh Ewald method.  J. Chem. Phys. 103, 3668-3679 (1995) (DOI)
83. P. P. Ewald. Die Berechnung optischer und elektrostatischer Gitterpotentiale. Annalen der Physik 369 (3), 253-287 (1921) (DOI)
    

    Spherical-cutoff methods

84. P. J. Steinbach and B. R. Brooks. New spherical-cutoff methods for long-range forces in macromolecular simulation. J. Comput. Chem. 15, 667-683 (1994) (DOI)
85. R. Garcia-Pelayo. Distribution of distance in the spheroid. Journal of Physics A: Mathematical and General 38, 3475-3482 (2005) (DOI)
    

    Generalized reaction-field methods

86. I. G. Tironi, R. Sperb, P. E. Smith, and W. F. van Gunsteren. A generalized reaction field method for molecular dynamics simulations.  J. Chem. Phys. 102, 5451-5459 (1995). (DOI)
    

    Thermostats

87. H. J. C. Berendsen, J. P. M. Postma. W. F. van Gunsteren, A. Dinola, and J. R. Haak. Molecular-Dynamics with Coupling to an External Bath. J. Chem. Phys. 81, 3684–3690 (1984) (DOI)
88. H. C. Andersen. Molecular dynamics at constant pressure and/or temperature. J. Chem. Phys. 72, 2384-2393 (1980) (DOI)
89. G. Bussi, D. Donadio, and M. Parrinello. Canonical sampling through velocity-rescaling. J. Chem. Phys. 126, p014101 (2007) (DOI)
90. S. C. Harvey, R. K.-Z. Tan, and T. E. Cheatham III. The flying ice cube: Velocity rescaling in molecular dynamics leads to violation of energy equipartition. J. Comput. Chem. 19 (7) 726-740 (1998) (DOI)
    
91. M. Lingenheil, R. Denschlag, R. Reichold and P. Tavan. The “hot-solvent/cold-solute” problem revisited. J. Chem. Theory Comput. 19 (7) 1293-1306 (2008) (DOI)
    
92. G. J. Martyna, D. J. Tobias, and M. L. Klein. Constant pressure molecular dynamics algorithms. J. Chem. Phys. 101 (5), 4177-4189 (1995) (DOI)
93. H. A. Stern. Simple algorithm for isobaric-isothermal molecular dynamics. J. Comput. Chem. 25 (5), 749-761 (2004) (DOI)
94. S. Nosé. A molecular dynamics method for simulations in the canonical ensemble. Mol. Phys. 52 (2), 255-268 (1984) (DOI)
95. W. G. Hoover. Canonical dynamics: Equilibrium phase space distributions. Phys. Rev. A 31 (3), 1695-1697 (1985) (DOI)
    

    Ring pucker

96. H. Sklenar, D. Wustner, and R. Rohs. Using Internal and Collective Variables in Monte Carlo Simulations of Nucleic Acid Structures: Chain Breakage/Closure Algorithm and Associated Jacobians. J. Comput. Chem. 27, 309-315 (2006). (DOI)
    

    Concerted rotation

97. G. Favrin, A. Irbäck, and F. Sjunnesson. Monte Carlo update for chain molecules: Biased Gaussian steps in torsional space. J. Chem. Phys. 114, 8154-8158 (2001). (DOI).
98. A. R. Dinner. Local deformations of polymers with nonplanar rigid main-chain internal coordinates. J. Comput. Chem. 21, 1132-1144 (2000). (DOI)
99. J. P. Ulmschneider and W. L. Jorgensen. Monte Carlo backbone sampling for polypeptides with variable bond angles and dihedral angles using concerted rotations and Gaussian bias. J. Chem. Phys. 118, 4261-4271 (2002). (DOI)
100. L. R. Dodd, T. D. Boone, and D. N. Theodorou. A concerted rotation algorithm for atomistic Monte Carlo simulation of polymer melts and glasses. Mol. Phys. 78, 961–96 (1993) (DOI)
     

     Analysis of secondary structure segments

101. G.N. Ramachandran, C. Ramakrishnan, and V. Sasisekharan. Stereochemistry of polypeptide chain configurations. J. Mol. Biol. 7, 95-99 (1963).
     

     NetCDF file format

102. Official website for Unidata's NetCDF format (link)
     

     Protein and nucleic acid geometries
     

103. R. A. Engh and R. Huber. Accurate bond and angle parameters for X-ray protein structure refinement. Acta Cryst. A47, 392-400 (1991) (DOI)
     
104. G. Parkinson, J. Vojtechovsky, L. Clowney, A. T. Brünger, and H. M. Berman. New parameters for the refinement of nucleic acid-containing structures. Acta Cryst., D52, 57-64 (1996) (DOI)
     

     WCA potential

105. J. D. Weeks, D. Chandler, and H. C. Andersen. Role of Repulsive Forces in Determining the Equilibrium Structure of Simple Liquids. J. Chem. Phys. 54, 5237 (1971). (DOI).
106. T. Boublik. The Gaussian overlap model again. Mol. Phys. 67, 1327-1336 (1989). (DOI)
     

     Implicit Solvent Models

107. A. Vitalis and R. V. Pappu. ABSINTH: A new continuum solvation model for simulations of polypeptides in aqueous solutions. J. Comput. Chem. 30, 673-699 (2009). (DOI)
108. Z. A. Arnon, A. Vitalis, A. Levin, T. C. T. Michaels, A. Caflisch, T. P. J. Knowles, L. Adler-Abramovich, and E. Gazit. Dynamic microfluidic control of supramolecular peptide self-assembly. Nat. Commun. 7, 13190 (2016). (DOI)
109. J.-M. Choi, and R. V. Pappu. Improvements to the ABSINTH Force Field for Proteins Based on Experimentally Derived Amino Acid Specific Backbone Conformational Statistics. J. Chem. Theor. Comput. 15(2), 1367-1382 (2019). (DOI)
110. J.-R. Marchand, T. Knehans, A. Caflisch, and A. Vitalis. An ABSINTH-based protocol for predicting binding affinities between proteins and small molecules. J. Chem. Inf. Model. XX, XXXX-XXXX (2020). (DOI)
111. T. Lazaridis and M. Karplus. Effective energy function for proteins in solution. Prot. Struct. Func. Bioinf. 35 (2), 133-152 (1999) (DOI)
     

     DSSP

112. W. Kabsch and C. Sander. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22, 2577-2637 (1983). (website).
     

     Replica exchange Methodology

113. R. H. Swendsen and J. S. Wang. Replica Monte Carlo simulation of spin glasses. Physical Review Letters 57, 2607-2609 (1986). (DOI)
114. Y. Sugita and Y. Okamoto. Replica-exchange molecular dynamics method for protein folding. Chem. Phys. Lett. 314 (1-2), 141-151 (1999) (DOI)
115. Y. Okamoto. Generalized-ensemble algorithms: enhanced sampling techniques for Monte Carlo and molecular dynamics simulations. J Mol. Graphics and Modelling, 22 (5), 425-439 (2004) (DOI)
     

     MPI refs

116. Official website of OpenMPI (link)
     

     Special references for certain output and analysis features

117. B. Žagrović. Helical signature motif in the fiber diffraction patterns of random walk chains. Mol. Phys. 105 (10), 1299-1306 (2007) (DOI)
118. F. Avbelj and R. L. Baldwin. Role of backbone solvation and electrostatics in generating preferred peptide backbone conformations: Distributions of phi. Proc. Natl. Acad. Sci. USA 100 (10), 5742-5747 (2003) (DOI)
119. T. Zhang, R. Ramakrishnan, and M. Livny. BIRCH: An Efficient Data Clustering Method for Very Large Databases. SIGMOD '96 Proceedings (DOI)
     
120. A. Vitalis and A. Caflisch. Efficient Construction of Mesostate Networks from Molecular Dynamics Trajectories. J. Chem. Theory Comput. 8 (3), 1108-1120 (2012) (DOI)
121. N. Blöchliger, A. Vitalis, and A. Caflisch. A scalable algorithm to order and annotate continuous observations reveals the metastable states visited by dynamical systems. Comput. Phys. Comm. 184 (11), 2446-2453 (2013) (DOI)
122. O. Borůvka. "O jistém problému minimálním" and "Příspěvek k řešení otázky ekonomické stavby elektrovodních sítí". translated in Discr. Math. 233 (1–3), 3–36 (2001) (DOI of translation)
123. V. Jarník. "O jistém problému minimálním". Práce Moravské Přírodovědecké Společnosti, 6, 57–63 (1930) or R. C. Prim. Shortest connection networks and some generalizations. Bell System Technical Journal, 36, 1389–1401 (1957).
124. J. B. Kruskal. On the Shortest Spanning Subtree of a Graph and the Traveling Salesman Problem. Proc. Amer. Math. Soc., 7 (1), 48–50 (1956) (DOI)
125. W. Humphrey, A. Dalke, and K. Schulten. VMD - Visual Molecular Dynamics. J. Molec. Graphics 14, 33-38 (1996) (DOI, webpage)
126. E. F. Pettersen, T. D. Goddard, C. C. Huang, G. S. Couch, D. M. Greenblatt, E. C. Meng, and T. E. Ferrin. UCSF Chimera - A visualization system for exploratory research and analysis. J. Comput. Chem. 25 (13), 1605-1612 (2004) (DOI, webpage)
127. M. Senne, B. Trendelkamp-Schroer, A. S. J. S. Mey, C. Schütte, and F. Noé. EMMA: A Software Package for Markov Model Building and Analysis. J. Chem. Theory Comput. 8 (7), 2223-2238 (2012) (DOI)
128. S. V. Krivov and M. Karplus. One-dimensional free-energy profiles of complex systems: progress variables that perserve the barriers. J. Phys. Chem. B 110 (25), 12689-12698 (2006) (DOI)
129. M. Seeber, A. Felline, F. Raimondi, S. Muff, R. Friedman, F. Rao, A. Caflisch, and F. Fanelli. Wordom: a user-friendly program for the analysis of molecular structures, trajectories, and free energy surfaces. J. Comput. Chem. 32 (6), 1183–1194 (2011) (DOI)
130. S. Lifson and A. Roig. On the theory of helix-coil transition in polypeptides. J. Chem. Phys. 34 (6), 1963-1974 (1961) (DOI)
131. I. T. Joliffe. Principal Component Analysis, Second Edition, Springer Verlag, New York (2002) (DOI)
132. L. Molgedey and H. G. Schuster. Separation of a mixture of independent signals using time delayed correlations. Phys. Rev. Lett. 72 (23), 3634-3637 (1994) (DOI)
     
133. Y. Naritomi and S. Fuchigami. Slow dynamics in protein fluctuations revealed by time-structure based independent component analysis: The case of domain motions. J. Chem. Phys. 134 (6), 065101 (2011) (DOI)
134. N. Blöchliger, A. Caflisch, and A. Vitalis. Weighted distance functions improve analysis of high-dimensional data: Application to molecular dynamics simulations. J. Chem. Theory Comput. 11 (11), 5481-5492 (2015) (DOI)
135. J. H. Prinz, H. Wu, M. Sarich, B. Keller, M. Senne, M. Held, J. D. Chodera, C. Schütte, and F. Noé. Markov models of molecular kinetics: Generation and validation. J. Chem. Phys., 134 (17), 174105 (2011) (DOI)
136. F. Noé, C. Schütte, E. Vanden-Eijnden, L. Reich , L. and T. R. Weikl. Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations. Proc. Natl. Acad. Sci. USA, 106 (45), 19011-19016 (2009) (DOI)
137. A. Berezhkovskii, G. Hummer, and A. Szabo.. Reactive flux and folding pathways in network models of coarse-grained protein dynamics. J. Chem. Phys., 130 (20), 205102 (2009) (DOI)
138. G. R. Bowman, K. A. Beauchamp, G. Boxer, and V. Pande.. Progress and challenges in the automated construction of Markov state models for full protein systems. J. Chem. Phys., 131 (12), 124101 (2009) (DOI)
139. T. Zhou and A. Caflisch. Distribution of reciprocal of interatomic distances: A fast structural metric. J. Chem. Theory Comput., 8 (8), 2930-2937 (2012) (DOI)
140. M. Bacci, A. Caflisch, and A. Vitalis. On the removal of initial state bias from simulation data. J. Chem. Phys., 150 (10), 104105 (2019) (DOI)
141. A. Vitalis. An improved and parallel version of a scalable algorithm for analyzing time series data. ArXiv (June 2020) (ArXiv)
142. F. Cocina, A. Vitalis, and A. Caflisch. SAPPHIRE-based clustering. J. Chem. Theory Comput., 16 (10), 6383-6396 (2020) (DOI)
     

     Application examples using CAMPARI or its predecessors (list updated until ca. 2013)

143. H. T. Tran, X. Wang, and R. V. Pappu. Reconciling Observations of Sequence-Specific Conformational Propensities with the Generic Polymeric Behavior of Denatured Proteins. Biochemistry 44 (34), 11369-11380 (2005) (DOI)
144. H. T. Tran and R. V. Pappu. Toward an accurate theoretical framework for describing ensembles for proteins under strongly denaturing conditions. Biopys. J. 91, 1868-1886 (2006) (PDF)
     
145. A. Vitalis, X. Wang, and R. V. Pappu. Quantitative characterization of intrinsic disorder in polyglutamine: insights from analysis based on polymer theories. Biophys. J. 93 (6), 1923-1937 (2007) (DOI)
146. A. Vitalis, X. Wang, and R. V. Pappu. Atomistic Simulations of the Effects of Polyglutamine Chain Length and Solvent Quality on Conformational Equilibria and Spontaneous Homodimerization. J. Mol. Biol. 384 (1), 279-297 (2008) (DOI)
147. A. Vitalis, N. Lyle, and R. V. Pappu. Thermodynamics of β-Sheet Formation in Polyglutamine. Biophys. J. 97 (1), 303-311 (2009) (DOI)
148. T. E. Williamson, A. Vitalis, S. L. Crick, and R. V. Pappu. Modulation of Polyglutamine Conformations and Dimer Formation by the N-Terminus of Huntingtin. J. Mol. Biol. 396 (5), 1295-1309 (2010) (DOI)
149. M. A. Wyczalkowski, A. Vitalis, and R. V. Pappu. New Estimators for Calculating Solvation Entropy and Enthalpy and Comparative Assessments of Their Accuracy and Precision. J. Phys. Chem. B 114 (24), 8166-8180 (2010) (DOI)
150. A. H. Mao, S. L. Crick, A. Vitalis, C. L. Chicoine, and R. V. Pappu. Net charge per residue modulates conformational ensembles of intrinsically disordered proteins. Proc. Natl. Acad. Sci. USA 107 (18), 8183-8188 (2010) (DOI)
151. A. Vitalis and A. Caflisch. Micelle-Like Architecture of the Monomer Ensemble of Alzheimer’s Amyloid-β Peptide in Aqueous Solution and Its Implications for Aβ Aggregation. J. Mol. Biol. 403 (1), 148-165 (2010) (DOI)
152. R. Halfmann, S. Alberti, R. Krishnan, N. Lyle, C. W. O'Donnell, O. D. King, B. Berger, R. V. Pappu, and S. Lindquist. Opposing Effects of Glutamine and Asparagine Govern Prion Formation by Intrinsically Disordered Proteins. Mol. Cell 73, 72-84 (2011) (DOI)
153. A. Vitalis and A. Caflisch. 50 Years of Lifson–Roig Models: Application to Molecular Simulation Data. J. Chem. Theory Comput. 8 (1), 363-373 (2012) (DOI)
154. R. K. Das, S. L. Crick, and R. V. Pappu. N-Terminal Segments Modulate the α-Helical Propensities of the Intrinsically Disordered Basic Regions of bZIP Proteins. J. Mol. Biol. 416 (2), 287-299 (2012) (DOI)
155. A. Radhakrishnan, A. Vitalis, A. H. Mao, A. T. Steffen, and R. V. Pappu. Improved Atomistic Monte Carlo Simulations Demonstrate That Poly-l-Proline Adopts Heterogeneous Ensembles of Conformations of Semi-Rigid Segments Interrupted by Kinks. J. Phys. Chem. B 116 (23), 6862-6871(2012) (DOI)
156. R. Scalco and A. Caflisch. Ultrametricity in protein folding dynamics. J. Chem. Theory Comput. 8 (5), 1580-1588 (2012) (DOI)
157. W. Meng, N. Lyle, B. Luan, D. P. Raleigh, and R. V. Pappu. Experiments and simulations show how long-range contacts can form in expanded unfolded proteins with negligible secondary structure. Proc. Natl. Acad. Sci. USA 110 (6), 2123-2128 (2013) (DOI)
158. R. K. Das and R. V. Pappu. Conformations of intrinsically disordered proteins are influenced by linear sequence distributions of oppositely charged residues. Proc. Natl. Acad. Sci. USA 110 (33), 13392-13397 (2013) (DOI)
159. N. Lyle, R. K. Das, and R. V. Pappu. A quantitative measure for protein conformational heterogeneity. J. Chem. Phys. 139 (12), 121907 (2013) (DOI)
160. A. Magno, S. Steiner, and A. Caflisch. Mechanisms and kinetics of acetyl-lysine binding to bromodomains. J. Chem. Theory Comput. 9 (9), 4225-4232 (2013) (DOI)