Submitted work
1*. Bhumika Singh(g), Arup Mondal (g), Kari Gaalswyk, Justin L MacCallum,and Alberto Perez “MELD-Adapt: On-the-Fly Belief Updating in Integrative Molecular Dynamics”, Under review, JCTC.
2.* Reza Esmaeeli (g), Bhumika Singh (g) , Elizabeth T. Sebastian (u), Yisel Martínez-Noa (g) and Alberto Perez “Enabling a new generation of molecular modelers through virtual reality” Under Review in Journal of Chemical Education.
3. EMD-consortium. “Outcomes of the EMDataResource Cryo-EM Ligand Modeling Challenge”. Accepted Nature Methods.
4. RNA-puzzles consortium. “”RNA-Puzzles Round V: The blind predictions of 23 RNA structures” Under Review.
5. Chen, Lexin; Mondal, Arup; Perez, Alberto; Miranda-Quintana, Ramon “Protein Retrieval via Integrative Molecular Ensembles (PRIME) through extended similarity indices”. Under Review
At UF
83. Kang, W.-Y., Mondal, A., 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. et al. A Computational Pipeline for Accurate Prioritization of Protein‐Protein Binding Candidates in High‐Throughput Protein Libraries. Angew. Chem. Int. Ed. e202405767 (2024) doi:10.1002/anie.202405767.
81. Chang, L., Mondal, A., Singh, B., Martínez‐Noa, Y. & Perez, A. Revolutionizing peptide‐based drug discovery: Advances in the post‐AlphaFold era. Wiley Interdiscip. Rev.: Comput. Mol. Sci. 14, (2024).
80. Caparotta, M. & Perez, A. Advancing Molecular Dynamics: Toward Standardization, Integration, and Data Accessibility in Structural Biology. J. Phys. Chem. B (2024) doi:10.1021/acs.jpcb.3c04823.
79. Caparotta, M. & Perez, A. When MELD Meets GaMD: Accelerating Biomolecular Landscape Exploration. J. Chem. Theory Comput. 19, 8743–8750 (2023).
78. H Akl, B Emison, X Zhao, A Mondal, A Perez, PD Dixit. GENERALIST: A latent space based generative model for protein sequence families. PLOS Comput. Biol.19, e1011655 (2023).
77. Chang, L., Mondal, A., Singh, B., Martínez‐Noa, Y. & Perez, A. Revolutionizing peptide‐based drug discovery: Advances in the post‐AlphaFold era. Wiley Interdiscip. Rev.: Comput. Mol. Sci. (2023) doi:10.1002/wcms.1693.
76. Mondal, A., Lenz, S., MacCallum, J. L. & Perez, A. Hybrid computational methods combining experimental information with molecular dynamics. Curr Opin Struc Biol 81, 102609 (2023).
75. Nguyen, L. T. et al. Engineering highly thermostable Cas12b via de novo structural analyses for one-pot detection of nucleic acids. Cell Reports Medicine 4, 101037 (2023).
74. Chang, L., Mondal, A., MacCallum, J. L. & Perez, A. CryoFold 2.0: Cryo-EM Structure Determination with MELD. J Phys Chem (2023) doi:10.1021/acs.jpca.3c01731.
73. Lang, L., Frontera, A., Perez, A. & Bauzá, A. Computational Study of Driving Forces in ATSP, PDIQ, and P53 Peptide Binding: CO···CO Tetrel Bonding Interactions at Work. J Chem Inf Model (2023) doi:10.1021/acs.jcim.3c00024.
72. Mondal, A. et al. Structure Determination of Challenging Protein–Peptide Complexes Combining NMR Chemical Shift Data and Molecular Dynamics Simulations. J Chem Inf Model (2023) doi:10.1021/acs.jcim.2c01595.
71. Liu, Q. & Perez, A. Assessing a computational pipeline to identify binding motifs to the α2β1 integrin. Front Chem 11, 1107400 (2023).
70. Jones, S. J. & Perez, A. Molecular Modeling of Self-Assembling Peptides. Acs Appl Bio Mater (2023) doi:10.1021/acsabm.2c00921.
69. Reza Esmaeeli, Antonio Bauzá, Alberto Perez, Structural predictions of protein–DNA binding: MELD-DNA, Nucleic Acids Research, 2023;, gkad013, https://doi.org/10.1093/nar/gkad013
68. Chang, L. & Perez, A. Ranking Peptide Binders by Affinity with AlphaFold. Angew. Chem. Int. Ed. 62, e202213362 (2023).
67. Chang, L., Mondal, A. & Perez, A. Towards rational computational peptide design. Frontiers Bioinform2, 1046493 (2022). DOI: 10.3389/fbinf.2022.1046493
66. Mondal, A., Chang, L., & Perez, A. (2022). Modeling peptide-protein complexes: Docking, simulations, and machine learning. QRB Discovery, 1-54. doi:10.1017/qrd.2022.14
65. Chang, L. & Perez, A. Deciphering the Folding Mechanism of Proteins G and L and Their Mutants. J Am Chem Soc144, 14668–14677 (2022). https://pubs.acs.org/doi/10.1021/jacs.2c04488
64. Esmaeeli, R., Andal, B. & Perez, A. Searching for Low Probability Opening Events in a DNA Sliding Clamp. Life12, 261 (2022). https://doi.org/10.3390/life12020261
63. Chang, L., Perez, A. & Miranda-Quintana, R. A. Improving the analysis of biological ensembles through extended similarity measures. Phys Chem Chem Phys24, 444–451 (2021). https://doi.org/10.1039/D1CP04019G
62.Rosa, G. da et al. Sequence-dependent structural properties of B-DNA: what have we learned in 40 years? Biophysical Rev 1–11 (2021) https://doi.org/10.1007/s12551-021-00893-8.
61. Mondal, A., Perez. A. Simultaneous assignment and structure determination of proteins from sparsely labeled NMR dataset. Frontiers Mol Biosci 8, 1105 (2021). https://doi.org/10.3389/fmolb.2021.774394
60. Esmaeeli, Reza; Piña, María; Frontera, Antonio; Perez, Alberto ; Bauza, Antonio. On the importance of anion-π interactions in RNA GAAA and GGAG tetraloops: A combined MD and QM study. J. Chem. Theory Comput. 2021, 17, 10, 6624–6633 https://doi.org/10.1021/acs.jctc.1c00756
59. Kryshtafovych, A. et al. Modeling SARS-CoV2 proteins in the CASP-commons experiment. Proteins, https://doi.org/10.1002/prot.26231.
58. Shekhar, M. et al. CryoFold: Determining protein structures and data-guided ensembles from cryo-EM density maps, Matter, 2021, https://doi.org/10.1016/j.matt.2021.09.004
57. 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, 451-470 (2021). https://doi.org/10.3389/fmolb.2021.681617
56. Lawson, C. L. et al. Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge. Nat Methods 1–9 (2021) https://doi.org/10.1038/s41592-020-01051-w.
55. Lang, L. & Perez, A. Binding Ensembles of p53-MDM2 Peptide Inhibitors by Combining Bayesian Inference and Atomistic Simulations. Molecules26, 198 (2021). https://doi.org/10.3390/molecules26010198
54. Liu, C., Brini, E., Perez, A. & Dill, K. A. Computing Ligands Bound to Proteins Using MELD-Accelerated MD. J Chem Theory Comput16, 6377–6382 (2020). https://doi.org/10.1021/acs.jctc.0c00543
53. Robertson, J., Nassar, R., Liu, C., Brini, E., Dill, K., Perez, A. (2019). NMR‐assisted protein structure prediction with MELDxMD. Proteins 36(1), D402. https://dx.doi.org/10.1002/prot.25788
52. Perez, A., Gaalswyk, K., Jaroniec, C. P., and MacCallum, J. L. High Accuracy Protein Structures from Minimal Sparse Paramagnetic Solid-State NMR Restraints. Angewandte Chemie, 58(20), 6564-6568. (2019). https://doi.org/10.1002/anie.201811895
Prior to UF
51. Ignatov, M., Liu, C., Alekseenko, A., Sun, Z., Padhorny, D., Kotelnikov, S., et al. . Monte Carlo on the manifold and MD refinement for binding pose prediction of protein-ligand complexes: 2017 D3R Grand Challenge. Journal of Computer-Aided Molecular Design, 33(1), 119-127.(2019)
50. Robertson, J. C., Perez, A., and Dill, K. MELD x MD Folds Nonthreadables, Giving Native Structures and Populations. J Chem Theory Comput, 14(12), 6734-6740. (2018)
49. Balaceanu, A., Perez, A., Dans, P. D., and Orozco, M. (2018). Allosterism and signal transfer in DNA. Nucleic Acids Research, 308(15), 7554–7565.
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. 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).
46. Sahu, S. et al. Regulation of the activity of the promoter of RNA-induced silencing, C3PO. Protein Science 20, 90–1818 (2017).
45. Perez, A., Morrone, J. A. & Dill, K. Accelerating physical simulations of proteins by leveraging external knowledge. WIREs Comput Mol Sci 125, e1309 (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. Morrone, J. A., Perez, A., MacCallum, J. & Dill, K. Computed Binding of Peptides to Proteins with MELD-Accelerated Molecular Dynamics. J Chem Theory Comput 13, 870–876 (2017).
42. Perez, A., Morrone, J. A., Brini, E., MacCallum, J. L. & Dill, K. Blind protein structure prediction using accelerated free-energy simulations. Science Advances 2, e1601274–e1601274 (2016).
41. Perez, A., Morrone, J. A., Simmerling, C. & Dill, K. Advances in free-energy-based simulations of protein folding and ligand binding. Curr. Opin. Struct. Biol. 36, 25–31 (2016).
40. Ivani, I. et al. Parmbsc1: a refined force field for DNA simulations. Nature Methods 13, 55–58 (2016).
39. 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).
38. Toledo, A., Perez, A., Coleman, J. L. & Benach, J. L. The lipid raft proteome of Borrelia burgdorferi. Proteomics 15, 3662–3675 (2015).
37. 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).
36. Perez, A., MacCallum, J. L. & Dill, K. Accelerating molecular simulations of proteins using Bayesian inference on weak information. Proc. Natl. Acad. Sci. U. S. A. 112, 11846–11851 (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. MacCallum, J. L., Perez, A. & Dill, K. Determining protein structures by combining semireliable data with atomistic physical models by Bayesian inference. Proc. Natl. Acad. Sci. U. S. A. 112, 6985–6990 (2015).
33. 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).
32. Perez, A. et al. Extracting representative structures from protein conformational ensembles. Proteins 82, 2671–2680 (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. et al. Impact of methylation on the physical properties of DNA. Biophys. J. 102, 2140–2148 (2012).
25. Perez, A., Luque, F. J. & Orozco, M. Frontiers in molecular dynamics simulations of DNA. Acc. Chem. Res. 45, 196–205 (2012).
24. MacCallum, J. L. et al. Assessment of protein structure refinement in CASP9. Proteins 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. Meyer, T. et al. MoDEL (Molecular Dynamics Extended Library): a database of atomistic molecular dynamics trajectories. Structure 18, 1399–1409 (2010).
21. Faustino, I., Perez, A. & Orozco, M. Toward a consensus view of duplex RNA flexibility. Biophys. J. 99, 1876–1885 (2010).
20. Perez, A. & Orozco, M. Real-time atomistic description of DNA unfolding. Angew. Chem. Int. Ed. Engl. 49, 4805–4808 (2010).
19. 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).
18. 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).
17. 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).
16. Gros, J. et al. 8-Amino guanine accelerates tetramolecular G-quadruplex formation. Chem. Commun. (Camb.) 2926–2928 (2008). doi:10.1039/b801221k
15. Orozco, M., Noy, A. & Perez, A. Recent advances in the study of nucleic acid flexibility by molecular dynamics. Curr. Opin. Struct. 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. Perez, A., Luque, F. J. & Orozco, M. Dynamics of B-DNA on the microsecond time scale. J. Am. Chem. Soc. 129, 14739–14745 (2007).
12. 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).
11. Rueda, M. et al. A consensus view of protein dynamics. Proc. Natl. Acad. Sci. U.S.A. 104, 796–801 (2007).
10. Goñi, J. R., Perez, A., Torrents, D. & Orozco, M. Determining promoter location based on DNA structure first-principles calculations. Genome Biol. 8, R263 (2007).
9. Noy, A., Perez, A., Laughton, C. A. & Orozco, M. Theoretical study of large conformational transitions in DNA: the BA conformational change in water and ethanol/water. Nucleic Acids Res. 35, 3330–3338 (2007).
8. Meyer, T. et al. Essential Dynamics: A Tool for Efficient Trajectory Compression and Management. J Chem Theory Comput 2, 251–258 (2006).
7. Noy, A. et al. Data mining of molecular dynamics trajectories of nucleic acids. J. Biomol. Struct. Dyn. 23, 447–456 (2006).
6. Perez, A. et al. Exploring the Essential Dynamics of B-DNA. J Chem Theory Comput 1, 790–800 (2005).
5. Perez, A. et al. Are the hydrogen bonds of RNA (AU) stronger than those of DNA (AT)? A quantum mechanics study. Chemistry 11, 5062–5066 (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., Javier Luque, F. & 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., P rez, A., Noy, A. & Luque, F. J. Theoretical methods for the simulation of nucleic acids. Chem Soc Rev 32, 350 (2003).