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Department of Chemistry College of Liberal Arts and Sciences

What We Do

Core Research Areas

Five interconnected pillars — from foundational physics to AI-powered discovery and large-scale data infrastructure.

AI-Driven Molecular Discovery

Machine Learning · AlphaFold · Drug Design

We leverage AI — including competitive binding assays built on AlphaFold2 — to identify novel peptide binders, estimate protein folding pathways from sequence alone, and design miniprotein candidates targeting BET proteins and BRD3. Our hybrid AI/physics pipelines operate at scales impossible with simulation alone.

Molecular Dynamics & Enhanced Sampling

MD · Markov State Models · Energy Landscapes

Biomolecular events like folding and binding happen at timescales too fast for experiments and too slow for conventional computation. We develop enhanced sampling methods and Markov State Models to map complex energy landscapes and extract mechanistic insight with atomic precision.

Integrative Structural Biology (MELD)

NMR · Cryo-EM · MELD · Structure Determination

Our co-developed framework MELD (Modeling Employing Limited Data) combines sparse, noisy experimental data from NMR, cryo-EM, and cross-linking mass spectrometry with molecular simulations — resolving biomolecular structures that neither approach could achieve alone.

Peptide-Based Drug Discovery

Binding Affinity · Kinetics · Undruggable Targets

Peptides can engage protein surfaces that small molecules cannot. We have built pipelines for peptide hit discovery, lead optimization, binding affinity prediction, and kinetics characterization — including novel motifs experimentally validated against BET protein ET domains.

Nucleic Acid Dynamics & Gene Regulation

DNA · Transcription Factors · ABC Consortium

We study how transcription factors read DNA sequences through direct contacts and shape complementarity. As part of the Ascona B-DNA Consortium, we generate state-of-the-art simulation datasets for all possible DNA n-mers to understand the structural basis of gene regulation.

Latest Work

Most Recent Publications

A selection of recent high-impact work from the group.

J. Phys. Chem. B · 2026

Ensemble Sensitivity to Chemical Modifications in Free and Bound Macrocyclic Peptides

Modifying macrocyclic peptides can dramatically alter their structural ensembles without changing their bound shapes. This behavior makes them exceptionally difficult to model due to complex sampling and force-field limitations. Ultimately, the paper demonstrates that differences in binding often stem from subtle ensemble shifts rather than distinct geometric changes.

Macrocycles · Peptides

J. Phys. Chem. B · 2026

How Well Do Molecular Dynamics Force Fields Model Peptides: A Systematic Benchmark across Diverse Folding Behaviors

This study benchmarks 11 popular and emerging force fields against a diverse set of 12 linear peptides to evaluate their stability and folding behavior. The analysis reveals that no single force field performs optimally across all systems, with many exhibiting significant biases between structural order and disorder. Ultimately, these findings provide practical guidance for peptide modeling and establish a rigorous framework for future force field validation.

Force Fields · Molecular Simulations

ChemPhysChem · 2026

Beyond Classical Force Fields: Physics-Driven Assessment of the Grappa Machine-Learned Force Field on the FoldBind Dataset

This work evaluates the machine-learned force field Grappa on the newly introduced FoldBind dataset of 18 folding and binding systems. By utilizing the MELD framework to accelerate conformational exploration, the study tests Grappa’s ability to accurately stabilize native protein and peptide structures. Ultimately, this physics-driven assessment establishes a rigorous new benchmark for validating future AI-driven force fields.

Machine Learning · Force Fields

P u b l i c a t i o n s

102. 1.Martínez-Noa, Y., Chen, W.-T., Li, C.-Y., Bruner, S. D. & Perez, A. Ensemble Sensitivity to Chemical Modifications in Free and Bound Macrocyclic Peptides. J. Phys. Chem. B 130, 4314–4324 (2026).

101. Singh, B., Martínez-Noa, Y. & Perez, A. How Well Do Molecular Dynamics Force Fields Model Peptides: A Systematic Benchmark across Diverse Folding Behaviors. J. Phys. Chem. B 130, 4344–4357 (2026). 

100. Ranaweera, I. & Perez, A. Beyond Classical Force Fields: Physics‐Driven Assessment of the Grappa Machine‐Learned Force Field on the FoldBind Dataset. ChemPhysChem 27, e202500815 (2026).

99. Alvarez, G. et al. Loop Plasticity Drives Paralog-Specific Recognition in BET ET Domains. J. Chem. Inf. Model. 66, 4685–4695 (2026). 

98. Chun Kit Chan,Jonathan Nguyen,Corey F Hryc,Chitrak Gupta,Kevin Redding,William Dowhan,Matthew L Baker,Alberto Perez,Eugenia Mileykovskaya,Abhishek Singharoy. Transient protein structure guides surface diffusion pathways for electron transport in membrane supercomplexes. Nat. Commun. (2026) doi:10.1038/s41467-025-67110-y. 

97. Silva, N. D. & Perez, A. MDZip: Neural Compression of Molecular Dynamics Trajectories for Scalable Storage and Ensemble Reconstruction. J. Phys. Chem. B (2025) doi:10.1021/acs.jpcb.5c05348.

96. Sage Nelson,Jokent Gaza,Seyednima Ajayebi,Ronald Masse,Raymond Pho,Cianna Scutero,Samantha Martinusen,Lawton Long,Amor Menezes,Alberto Perez,Carl Denard. PERRC: Protease Engineering with Reactant Residence Time Control. ACS Synth. Biol. 14, 2241–2253 (2025).

95. Amaro, R. E. et al. The need to implement FAIR principles in biomolecular simulations. Nat. Methods 22, 641–645 (2025).

94. Chang, L. & Perez, A. Rapid estimation of protein folding pathways from sequence alone using AlphaFold2. Nat. Commun. 17, 170 (2025).

93. Gaza, J., Roth, M. J., Montelione, G. T. & Perez, A. Hybrid AI/physics pipeline for miniprotein binder prioritization: application to the BRD3 ET domain. Chem. Commun. 61, 19028–19031 (2025).

92. Chen, L., Santos, J. B. W., Gaza, J.(g), Perez, A. & Miranda-Quintana, R. A. Hierarchical Extended Linkage Method (HELM)’s Deep Dive into Hybrid Clustering Strategies. J. Chem. Inf. Model. 65, 6209–6220 (2025).

91. Perez, D. H.; Mondal, A.(g); Xu, W.; Baredes, V.; Connell, H. E.; Perez, A.*; Butcher, R. A. ATP allosterically regulates an acyl-CoA oxidase. Nat. Commun. 16, 7318 (2025).

90. Silva, N. D. (g) & Perez, A.* Predicting rare DNA conformations via dynamical graphical models: a case study of the B→A transition. Nucleic Acids Res. 53, gkaf601 (2025).

89. Amaro, R. E. et al. The need to implement FAIR principles in biomolecular simulations. Nat. Methods 22, 641–645 (2025).

88. Bu, F., Adam, Y., Adamiak, R. W., Antczak, M., de Aquino, B. R. H., Badepally, N. G., Batey, R. T., Baulin, E. F., Boinski, P., Boniecki, M. J., Bujnicki, J. M., Carpenter, K. A., Chacon, J., Chen, S.-J., Chiu, W., Cordero, P., Das, N. K., Das, R., Dawson, W. K., DiMaio, F., Ding, F., Dock-Bregeon, A.-C., Dokholyan, N. V., Dror, R. O., Dunin-Horkawicz, S., Eismann, S., Ennifar, E., Esmaeeli, R. (g), Amiri Farsani, M., Ferré-D’Amaré, A. R., Geniesse, C., Ghanim, G. E., Guzman, H. V., Hood, I. V., Huang, L., Jain, D. S., Jaryani, F., Jin, L., Joshi, A., Karelina, M., Kieft, J. S., Kladwang, W., Kmiecik, S., Koirala, D., Kollmann, M., Kretsch, R. C., Kurciński, M., Li, J., Li, S., Magnus, M., Masquida, B., Moafinejad, S. N., Mondal, A. (g), Mukherjee, S., Nguyen, T. H. D., Nikolaev, G., Nithin, C., Nye, G., Jeyeram, I. P. N. P., Perez, A., Pham, P., Piccirilli, J. A., Pilla, S. P., Pluta, R., Poblete, S., Ponce-Salvatierra, A., Popenda, M., Popenda, L., Pucci, F., Rangan, R., Ray, A., Ren, A., Sarzynska, J., Sha, C. M., Stefaniak, F., Su, Z., Suddala, K. C., Szachniuk, M., Townshend, R., Trachman, R. J., Wang, J., Wang, W., Watkins, A., Wirecki, T. K., Xiao, Y., Xiong, P., Xiong, Y., Yang, J., Yesselman, J. D., Zhang, J., Zhang, Y., Zhang, Z., Zhou, Y., Zok, T., Zhang, D., Zhang, S., Żyła, A., Westhof, E. & Miao, Z. RNA-Puzzles Round V: blind predictions of 23 RNA structures. Nat. Methods 22, 399–411 (2025).

87. Kang, W.-Y., Mondal, A.(p), Perez, A. & Prentice, B. M. Structural differentiation of protein charge state conformers via gas-phase ion/ion cross-linking mass spectrometry. Int. J. Mass Spectrom. 509, 117410 (2025).

86. Gaza, J. (g), Brini, E.*, MacCallum, J. L.*, Dill, K. A.* & Perez, A*. MELD in Action: Harnessing Data to Accelerate Molecular Dynamics. J. Chem. Inf. Model. (2025) doi:10.1021/acs.jcim.4c02108.

85. Chen, L; Mondal, A (g); Perez, A*; Miranda-Quintana, R* “Protein Retrieval via Integrative Molecular Ensembles (PRIME) through extended similarity indices”. Journal of Chemical Theory and Computation 20, 6303–6315 (2024).

84. Lawson, C. L., Kryshtafovych, A., Pintilie, G. D., Burley, S. K., Černý, J., Chen, V. B., Emsley, P., Gobbi, A., Joachimiak, A., Noreng, S., Prisant, M. G., Read, R. J., Richardson, J. S., Rohou, A. L., Schneider, B., Sellers, B. D., Shao, C., Sourial, E., Williams, C. I., Williams, C. J., Yang, Y., Abbaraju, V., Afonine, P. V., Baker, M. L., Bond, P. S., Blundell, T. L., Burnley, T., Campbell, A., Cao, R., Cheng, J., Chojnowski, G., Cowtan, K. D., DiMaio, F., Esmaeeli, R. (g), Giri, N., Grubmüller, H., Hoh, S. W., Hou, J., Hryc, C. F., Hunte, C., Igaev, M., Joseph, A. P., Kao, W. C., Kihara, D., Kumar, D., Lang, L. (g), Lin, S., Subramaniya, S. R. M. V., Mittal, S., Mondal, A. (G), Moriarty, N. W., Muenks, A., Murshudov, G. N., Nicholls, R. A., Olek, M., Palmer, C. M., Perez, A., Pohjolainen, E., Pothula, K. R., Rowley, C. N., Sarkar, D., Schäfer, L. U., Schlicksup, C. J., Schröder, G. F., Shekhar, M., Si, D., Singharoy, A., Sobolev, O. V., Terashi, G., Vaiana, A. C., Vedithi, S. C., Verburgt, J., Wang, X., Warshamanage, R., Winn, M. D., Weyand, S., Yamashita, K., Zhao, M., Schmid, M. F., Berman, H. M., Chiu, W. Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge. Nat. Methods 1–9 (2024) doi:10.1038/s41592-024-02321-7.

83. Kang, W.-Y., Mondal, A.,(g) Bonney, J. R., Perez, A. & Prentice, B. M. Structural Elucidation of Ubiquitin via Gas-Phase Ion/Ion Cross-Linking Reactions Using Sodium-Cationized Reagents Coupled with Infrared Multiphoton Dissociation. Anal. Chem. 96, 8518–8527 (2024).

82. Mondal, A. (g),  Singh, B. (g), Felkner, R.,  De Falco, A., Swapna, GVT, Montelione, G.,  Roth, M., Perez, A. A Computational Pipeline for Accurate Prioritization of Protein‐Protein Binding Candidates in High‐Throughput Protein Libraries. Angew. Chem. Int. Ed. 2024, e202405767.

81. Chang, L.(g), Mondal, A. (g), Singh, B. (g), Martínez‐Noa, Y. (g) & Perez, A. Revolutionizing peptide‐based drug discovery: Advances in the post‐AlphaFold era. Wiley Interdiscip. Rev.: Comput. Mol. Sci. 14, (2024).

80. Caparotta, M.(p) & Perez, A. Advancing Molecular Dynamics: Toward Standardization, Integration, and Data Accessibility in Structural Biology. J. Phys. Chem. B 128, 2219–2227 (2024).

79. Singharoy, A., Pérez, A. & Chipot, C. Biophysics at the dawn of exascale computers. J. 122, E1–E2 (2023).

78. Mondal, A.(g), Swapna, G. V. T., Hao, J., Ma, L., Roth, M. J., Montelione, G. T. & Perez, A. Structure Determination of Challenging Protein–Peptide Complexes Combining NMR Chemical Shift Data and Molecular Dynamics Simulations. J. Chem. Inf. Model. 63, 2058–2072 (2023).

77. Lang, L. (g), Frontera, A., Perez, A. & Bauzá, A. Computational Study of Driving Forces in ATSP, PDIQ, and P53 Peptide Binding: C=O···C=O Tetrel Bonding Interactions at Work. J. Chem. Inf. Model. 63, 3018–3029 (2023).

76. Chang, L. (g), Mondal, A. (g), MacCallum, J. L. & Perez, A. CryoFold 2.0: Cryo-EM Structure Determination with MELD. J. Phys. Chem. A 127, 3906–3913 (2023).

75. Nguyen, L., Rananaware, S.R., Yang, L.G., Macaluso, N.C., Ocana-Ortiz, J.E., Meister, K.S., Pizzano, B.L.M, Sandoval, L.S.W., Hautamaki, R.C., Fang, Z.R., Joseph, S.M., Shoemaker, G.M., Carman, D.R., Chang, L.(g), Rakestraw, N.R., Zachary, J.F., Guerra, S., Perez, A. Jain, P.K. Engineering highly thermostable Cas12b via de novo structural analyses for one-pot detection of nucleic acids. Cell Rep. Med. 4, 101037 (2023).

74. Mondal, A. (g), Lenz, S., MacCallum, J. L. & Perez, A. Hybrid computational methods combining experimental information with molecular dynamics. Curr. Opin. Struct. Biol. 81, 102609 (2023).

73. Caparotta, M. (p) & Perez, A. When MELD Meets GaMD: Accelerating Biomolecular Landscape Exploration. J. Chem. Theory Comput. 19, 8743–8750 (2023).

72. Akl H, Emison B, Zhao X, Mondal A (g), Perez A, Dixit PD. GENERALIST: A latent space based generative model for protein sequence families. PLOS Comput. Biol. 19, e1011655 (2023).

71. Liu, Q. (g) & Perez, A. Assessing a computational pipeline to identify binding motifs to the α2β1 integrin. Front Chem 11, 1107400 (2023).

70. Jones, S. J. (g) & Perez, A. Molecular Modeling of Self-Assembling Peptides. Acs Appl Bio Mater (2023) doi:10.1021/acsabm.2c00921.

69. Chang, L. (g) & Perez, A. Ranking Peptide Binders by Affinity with AlphaFold. Angew. Chem. Int. Ed. 62, e202213362 (2023).

68.  Esmaeeli, R.,(g) Bauzá, A.(p) & Perez, A. Structural predictions of protein–DNA binding: MELD-DNA. Nucleic Acids Res 2021.06.24.449809 (2023) doi:10.1093/nar/gkad013.

67. Chang, L.(g) & Perez, A. Deciphering the Folding Mechanism of Proteins G and L and Their Mutants. J Am Chem Soc 144, 14668–14677 (2022).

66. Mondal, A.(g), Chang, L.(g) & Perez, A. Modelling peptide–protein complexes: docking, simulations and machine learning. Qrb Discov 3, 1–54 (2022).

65. Esmaeeli, R.(g), Andal, B.(u) & Perez, A. Searching for Low Probability Opening Events in a DNA Sliding Clamp. Life 12, 261 (2022).

64. Chang, L.(g), Mondal, A.(g) & Perez, A. Towards rational computational peptide design. Frontiers Bioinform 2, 1046493 (2022).

63. Lang, L.(g) & Perez, A. Binding Ensembles of p53-MDM2 Peptide Inhibitors by Combining Bayesian Inference and Atomistic Simulations. Molecules 26, 198 (2021).

62. Perez, J. J., Perez, R. A. & Perez, A. Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering. Frontiers Mol Biosci 8, 681617 (2021).

61. Lawson, C.L., Kryshtafovych, A., Adams, P.D., Afonine, P.V., Baker, M.L., Barad, B.A., Bond, P., Burnley, T., Cao, R., Cheng, J., Chojnowski, G., Cowtan, K., Dill, K.A., DiMaio, F., Farrell, D.P., Fraser, J.S., Herzik, M.A., Hoh, S.W., Hou, J., Hung, L.W., Igaev, M., Joseph, A.P., Kihara, D., Kumar, D., Mittal, S., Monastyrskyy, B., Olek, M., Palmer, C.M., Patwardhan, A., Perez, A., Pfab, J., Pintilie, G.D., Richardson, J.S., Rosenthal, P.B., Sarkar, D., Sch√§fer, L.U., Schmid, M.F., Schafer, G.F., Shekhar, M., Si, D., Singharoy, A., Terashi, G., Terwilliger, T.C., Vaiana, A., Wang, L., Wang, Z., Wankowicz, S.A., Williams, C.J., Winn, M., Wu, T., Yu, X., Zhang, K., Berman, H.M., Chiu, W. Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge. Nat Methods 18, 156–164 (2021).

60. Mrinal Shekhar, Genki Terashi, Chitrak Gupta, Daipayan Sarkar, Gaspard Debussche, Nicholas J. Sisco, Jonathan Nguyen, Arup Mondal (g), John Vant, Petra Fromme, Wade D. Van Horn, Emad Tajkhorshid, Daisuke Kihara, Ken Dill, Alberto Perez, Abhishek Singharoy CryoFold: Determining protein structures and data-guided ensembles from cryo-EM density maps. Matter 4, 3195–3216 (2021).

59. Esmaeeli, R.(g), Piña, M. de las N., Frontera, A., Pérez, A. & Bauzá, A.(p) Importance of Anion−π Interactions in RNA GAAA and GGAG Tetraloops: A Combined MD and QM Study. J Chem Theory Comput 17, 6624–6633 (2021).

58. Chang, L.(g), Perez, A. & Miranda-Quintana, R. A. Improving the analysis of biological ensembles through extended similarity measures. Phys Chem Chem Phys 24, 444–451 (2022).

57. Kryshtafovych, A., Moult, J., Billings, W.M., Della Corte, D., Fidelis, K.,  Kwon, S. ,  Olechnovič, K.,  Seok, C.,  Venclovas, Č, Won, J., CASP‐COVID participants & .Modeling SARS‐CoV‐2 proteins in the CASP‐commons experiment. Proteins Struct Funct Bioinform 89, 1987–1996 (2021).

& CASP-COVID participants are all co-authors of the paper: AlphaFold team, Badri Adhikari, Recep Adiyaman, Joaquim AguirrePlans, Ivan Anishchenko, Minkyung Baek, David Baker, Frederico Baldassarre, Jacob Barger, Sutanu Bhattacharya, Debswapna Bhattacharya, Mor Bitton, Renzhi Cao, Jianlin Cheng, Charles Christoffer, Cezary Czaplewski, Zongyang Du, Arne Elofsson, Eshel Faraggi, Michael Feig, Narcis Fernandez-Fuentes, Nick Grishin, Sergei Grudinin, Zhiye Guo, Yuya Hanazono, Demis Hassabis, Bryce Hedelius, Lim Heo, Naozumi Hiranuma, Cassandra Hunt, Ilia Igashov, Takashi Ishida, Robert L. Jernigan, David Jones, John Jumper, Maria Kadukova, Shaun Kandathil, Chen Keasar, Daisuke Kihara, Lisa Kinch, Yasuomi Kiyota, Andrzej Kloczkowski, Pushmeet Kohli, Mateusz Kogut, Elodie Laine, Cade Lilley, Jian Liu, Adam Liwo, Emilia Lubecka, Arup Mondal (g), Connor J. Morris, Liam McGuffin, Alexis Molina, Tsukasa Nakamura, Baldo Oliva, Alberto Perez, Gabriele Pozzati, Daipayan Sarkar, Rin Sato, Torsten Schwede, Bikash Shrestha, Tomer Sidi, Gabriel Studer, Md Hossain Shuvo, Mayuko Takeda-Shitaka, Yuma Takei, Genki Terashi, Kentaro Tomii, Yuko Tsuchiya, Kathryn Tunyasuvunakool, Björn Wallner, Tianqi Wu, Jinbo Xu, Yu Yamamori, Jianyi Yang, Lisha Ye, Chengxin Zhang, Yang Zhang, Wei Zheng

56. Gabriela da Rosa, Leandro Grille, Victoria Calzada, Katya Ahmad, Juan Pablo Arcon, Federica Battistini, Genís Bayarri, Thomas Bishop, Paolo Carloni, Thomas Cheatham III, Rosana Collepardo-Guevara, Jacek Czub, Jorge R. Espinosa, Rodrigo Galindo-Murillo, Sarah A. Harris, Adam Hospital, Charles Laughton, John H. Maddocks, Agnes Noy, Modesto Orozco, Marco Pasi, Alberto Pérez, Daiva Petkevičiūtė-Gerlach, Rahul Sharma, Ran Sun, Pablo D. Dans Sequence-dependent structural properties of B-DNA: what have we learned in 40 years? Biophysical Rev 1–11 (2021) doi:10.1007/s12551-021-00893-8.

55. Mondal, A.(g) & Perez, A. Simultaneous Assignment and Structure Determination of Proteins From Sparsely Labeled NMR Datasets. Frontiers Mol Biosci 8, 774394 (2021).

54. Liu, C., Brini, E., Perez, A. & Dill, K. A. Computing Ligands Bound to Proteins Using MELD-Accelerated MD. J Chem Theory Comput 16, 6377–6382 (2020).

53. Perez, A., Gaalswyk, K., Jaroniec, C. P. & MacCallum, J. L. High Accuracy Protein Structures from Minimal Sparse Paramagnetic Solid-State NMR Restraints. Angewandte Chemie Int Ed 58, 6564–6568 (2019).

52. James C Robertson, Roy Nassar, Cong Liu, Emiliano Brini, Ken Dill, Alberto Perez. NMR‐assisted protein structure prediction with MELDxMD. Proteins Struct Funct Bioinform 36, D402 (2019).

51. Ignatov, M. et al. Monte Carlo on the manifold and MD refinement for binding pose prediction of protein-ligand complexes: 2017 D3R Grand Challenge. J Comput Aid Mol Des 33, 119–127 (2019).

50. Balaceanu, A., Pérez, A., Dans, P. D. & Orozco, M. Allosterism and signal transfer in DNA. Nucleic Acids Res 46, 7554–7565 (2018).

49. Robertson, J. C., Perez, A. & Dill, K. MELD × MD Folds Nonthreadables, Giving Native Structures and Populations. J Chem Theory Comput 14, 6734–6740 (2018).

48. Perez, A., Sittel, F., Stock, G. & Dill, K. MELD-Path Efficiently Computes Conformational Transitions, Including Multiple and Diverse Paths. J Chem Theory Comput 14, 2109–2116 (2018).

47. Perez, A., Morrone, J. A. & Dill, K. Accelerating physical simulations of proteins by leveraging external knowledge. Wiley Interdiscip Rev Comput Mol Sci 125, e1309 (2017).

46. Morrone, J. A., Perez, A., MacCallum, J. & Dill, K. A. Computed Binding of Peptides to Proteins with MELD-Accelerated Molecular Dynamics. J Chem Theory Comput 13, 870–876 (2017).

45. Li, J. et al. Expanding the repertoire of DNA shape features for genome-scale studies of transcription factor binding. Nucleic Acids Res 45, 12877–12887 (2017).

44. Morrone, J. A. et al. Molecular Simulations Identify Binding Poses and Approximate Affinities of Stapled α-Helical Peptides to MDM2 and MDMX. J Chem Theory Comput 13, 863–869 (2017).

43. Sahu, S. et al. Regulation of the activity of the promoter of RNA-induced silencing, C3PO. Protein Sci 20, 90–1818 (2017).

42. Perez, A., Morrone, J. A., Simmerling, C. & Dill, K. Advances in free-energy-based simulations of protein folding and ligand binding. Curr Opin Struc Biol 36, 25–31 (2016).

41. Perez, A., Morrone, J. A., Brini, E., MacCallum, J. L. & Dill, K. A. Blind protein structure prediction using accelerated free-energy simulations. Sci Adv 2, e1601274 (2016).

40. Ivani, I. et al. Parmbsc1: a refined force field for DNA simulations. Nat Methods 13, 55–58 (2016).

39. Perez, A., MacCallum, J. L. & Dill, K. A. Accelerating molecular simulations of proteins using Bayesian inference on weak information. P Natl Acad Sci Usa 112, 11846–51 (2015).

38. Perez, A., MacCallum, J. L., Coutsias, E. A. & Dill, K. Constraint methods that accelerate free-energy simulations of biomolecules. J Chem Phys 143, 243143 (2015).

37. MacCallum, J. L., Perez, A. & Dill, K. Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference. Proc National Acad Sci 112, 6985–6990 (2015).

36. Perez, A., MacCallum, J. L., Brini, E., Simmerling, C. & Dill, K. Grid-based backbone correction to the ff12SB protein force field for implicit-solvent simulations. J Chem Theory Comput 11, 4770–4779 (2015).

35. Nguyen, H., Perez, A., Bermeo, S. & Simmerling, C. Refinement of Generalized Born Implicit Solvation Parameters for Nucleic Acids and Their Complexes with Proteins. J Chem Theory Comput 11, 3714–3728 (2015).

34. Toledo, A., Perez, A., Coleman, J. L. & Benach, J. L. The lipid raft proteome of Borrelia burgdorferi. Proteomics 15, 3662–3675 (2015).

33. Perez, A. et al. Extracting representative structures from protein conformational ensembles. Proteins Struct Funct Bioinform 82, 2671–2680 (2014).

32. Pasi, M. et al. μABC: a systematic microsecond molecular dynamics study of tetranucleotide sequence effects in B-DNA. Nucleic Acids Res 42, 12272–12283 (2014).

31. Roy, A., Perez, A., Dill, K. & MacCallum, J. L. Computing the Relative Stabilities and the Per-Residue Components in Protein Conformational Changes. Structure 22, 168–175 (2013).

30. Candotti, M. et al. Exploring early stages of the chemical unfolding of proteins at the proteome scale. Plos Comput Biol 9, e1003393 (2013).

29. Dršata, T. et al. Structure, Stiffness and Substates of the Dickerson-Drew Dodecamer. J Chem Theory Comput 9, 707–721 (2013).

28. Dans, P. D., Perez, A., Faustino, I., Lavery, R. & Orozco, M. Exploring polymorphisms in B-DNA helical conformations. Nucleic Acids Res 40, 10668–10678 (2012).

27. Perez, A., Yang, Z., Bahar, I., Dill, K. & MacCallum, J. L. FlexE: Using elastic network models to compare models of protein structure. J Chem Theory Comput 8, 3985–3991 (2012).

26. Perez, A., Luque, F. J. & Orozco, M. Frontiers in molecular dynamics simulations of DNA. Accounts Chem Res 45, 196–205 (2012).

25. Perez, A. et al. Impact of methylation on the physical properties of DNA. Biophys J 102, 2140–2148 (2012).

24. MacCallum, J. L. et al. Assessment of protein structure refinement in CASP9. Proteins Struct Funct Bioinform 79 Suppl 10, 74–90 (2011).

23. Deniz, O. et al. Physical properties of naked DNA influence nucleosome positioning and correlate with transcription start and termination sites in yeast. Bmc Genomics 12, 489 (2011).

22. Lavery, R. et al. A systematic molecular dynamics study of nearest-neighbor effects on base pair and base pair step conformations and fluctuations in B-DNA. Nucleic Acids Res 38, 299–313 (2010).

21. Meyer, T. et al. MoDEL (Molecular Dynamics Extended Library): a database of atomistic molecular dynamics trajectories. Structure 18, 1399–1409 (2010).

20. Perez, A. & Orozco, M. Real-time atomistic description of DNA unfolding. Angewandte Chemie Int Ed 49, 4805–4808 (2010).

19. Faustino, I., Perez, A. & Orozco, M. Toward a consensus view of duplex RNA flexibility. Biophys J 99, 1876–1885 (2010).

18. Gros, J. et al. 8-Amino guanine accelerates tetramolecular G-quadruplex formation. Chem Commun 35, 2926–2928 (2008).

17. Goñi, J. R., Fenollosa, C., Perez, A., Torrents, D. & Orozco, M. DNAlive: a tool for the physical analysis of DNA at the genomic scale. Bioinformatics 24, 1731–1732 (2008).

16. Svozil, D. et al. Geometrical and electronic structure variability of the sugar-phosphate backbone in nucleic acids. J Phys Chem B 112, 8188–8197 (2008).

15. Orozco, M., Noy, A. & Perez, A. Recent advances in the study of nucleic acid flexibility by molecular dynamics. Curr Opin Struc Biol 18, 185–193 (2008).

14. Perez, A., Lankas, F., Luque, F. J. & Orozco, M. Towards a molecular dynamics consensus view of B-DNA flexibility. Nucleic Acids Res 36, 2379–2394 (2008).

13. Rueda, M. et al. A consensus view of protein dynamics. Proc National Acad Sci 104, 796–801 (2007).

12. Goñi, J. R., Perez, A., Torrents, D. & Orozco, M. Determining promoter location based on DNA structure first-principles calculations. Genome Biol 8, R263 (2007).

11. Perez, A., Luque, F. J. & Orozco, M. Dynamics of B-DNA on the microsecond time scale. J Am Chem Soc 129, 14739–14745 (2007).

10. Perez, A. et al. Refinement of the AMBER force field for nucleic acids: improving the description of alpha/gamma conformers. Biophys J 92, 3817–3829 (2007).

9. Noy, A., Perez, A., Laughton, C. A. & Orozco, M. Theoretical study of large conformational transitions in DNA: the B<–>A conformational change in water and ethanol/water. Nucleic Acids Res 35, 3330–3338 (2007).

8. Noy, A. et al. Data mining of molecular dynamics trajectories of nucleic acids. J Biomol Struct Dyn 23, 447–456 (2006).

7. Meyer, T. et al. Essential Dynamics:  A Tool for Efficient Trajectory Compression and Management. J Chem Theory Comput 2, 251–258 (2006).

6. Perez, A. et al. Are the hydrogen bonds of RNA (AU) stronger than those of DNA (AT)? A quantum mechanics study. Chem – European J 11, 5062–5066 (2005).

5. Perez, A. et al. Exploring the Essential Dynamics of B-DNA. J Chem Theory Comput 1, 790–800 (2005).

4. Noy, A., Perez, A., Márquez, M., Luque, F. J. & Orozco, M. Structure, recognition properties, and flexibility of the DNA.RNA hybrid. J Am Chem Soc 127, 4910–4920 (2005).

3. Noy, A., Perez, A., Lankas, F., Luque, F. J. & Orozco, M. Relative flexibility of DNA and RNA: a molecular dynamics study. J Mol Biol 343, 627–638 (2004).

2. Perez, A., Noy, A., Lankas, F., Luque, F. J. & Orozco, M. The relative flexibility of B-DNA and A-RNA duplexes: database analysis. Nucleic Acids Res 32, 6144–6151 (2004).

1. Orozco, M., Perez, A. , Noy, A. & Luque, F. J. Theoretical methods for the simulation of nucleic acids. Chem Soc Rev 32, 350 (2003).