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Kornev AP, Weng JH, Maillard RA, Taylor SS. Gauging Dynamics-driven Allostery Using a New Computational Tool: A CAP Case Study. J Mol Biol 2024; 436:168395. [PMID: 38097109 PMCID: PMC10851786 DOI: 10.1016/j.jmb.2023.168395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/22/2023] [Accepted: 12/08/2023] [Indexed: 12/24/2023]
Abstract
In this study, we utilize Protein Residue Networks (PRNs), constructed using Local Spatial Pattern (LSP) alignment, to explore the dynamic behavior of Catabolite Activator Protein (CAP) upon the sequential binding of cAMP. We employed the Degree Centrality of these PRNs to investigate protein dynamics on a sub-nanosecond time scale, hypothesizing that it would reflect changes in CAP's entropy related to its thermal motions. We show that the binding of the first cAMP led to an increase in stability in the Cyclic-Nucleotide Binding Domain A (CNBD-A) and destabilization in CNBD-B, agreeing with previous reports explaining the negative cooperativity of cAMP binding in terms of an entropy-driven allostery. LSP-based PRNs also allow for the study of Betweenness Centrality, another graph-theoretical characteristic of PRNs, providing insights into global residue connectivity within CAP. Using this approach, we were able to correctly identify amino acids that were shown to be critical in mediating allosteric interactions in CAP. The agreement between our studies and previous experimental reports validates our method, particularly with respect to the reliability of Degree Centrality as a proxy for entropy related to protein thermal dynamics. Because LSP-based PRNs can be easily extended to include dynamics of small organic molecules, polynucleotides, or other allosteric proteins, the methods presented here mark a significant advancement in the field, positioning them as vital tools for a fast, cost-effective, and accurate analysis of entropy-driven allostery and identification of allosteric hotspots.
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Affiliation(s)
- Alexandr P Kornev
- Departmen of Pharmacology, University of California San Diego, La Jolla, CA 92093, USA.
| | - Jui-Hung Weng
- Departmen of Pharmacology, University of California San Diego, La Jolla, CA 92093, USA
| | - Rodrigo A Maillard
- Department of Chemistry, Georgetown University, Washington, DC 20007, USA
| | - Susan S Taylor
- Departmen of Pharmacology, University of California San Diego, La Jolla, CA 92093, USA; Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, USA
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2
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Nussinov R, Liu Y, Zhang W, Jang H. Cell phenotypes can be predicted from propensities of protein conformations. Curr Opin Struct Biol 2023; 83:102722. [PMID: 37871498 PMCID: PMC10841533 DOI: 10.1016/j.sbi.2023.102722] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/26/2023] [Accepted: 09/27/2023] [Indexed: 10/25/2023]
Abstract
Proteins exist as dynamic conformational ensembles. Here we suggest that the propensities of the conformations can be predictors of cell function. The conformational states that the molecules preferentially visit can be viewed as phenotypic determinants, and their mutations work by altering the relative propensities, thus the cell phenotype. Our examples include (i) inactive state variants harboring cancer driver mutations that present active state-like conformational features, as in K-Ras4BG12V compared to other K-Ras4BG12X mutations; (ii) mutants of the same protein presenting vastly different phenotypic and clinical profiles: cancer and neurodevelopmental disorders; (iii) alterations in the occupancies of the conformational (sub)states influencing enzyme reactivity. Thus, protein conformational propensities can determine cell fate. They can also suggest the allosteric drugs efficiency.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel; Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD 21702, USA.
| | - Yonglan Liu
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD 21702, USA
| | - Wengang Zhang
- Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD 21702, USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA; Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD 21702, USA
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3
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Kumar A, Kaynak BT, Dorman KS, Doruker P, Jernigan RL. Predicting allosteric pockets in protein biological assemblages. Bioinformatics 2023; 39:btad275. [PMID: 37115636 PMCID: PMC10185404 DOI: 10.1093/bioinformatics/btad275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 02/06/2023] [Accepted: 03/09/2023] [Indexed: 04/29/2023] Open
Abstract
MOTIVATION Allostery enables changes to the dynamic behavior of a protein at distant positions induced by binding. Here, we present APOP, a new allosteric pocket prediction method, which perturbs the pockets formed in the structure by stiffening pairwise interactions in the elastic network across the pocket, to emulate ligand binding. Ranking the pockets based on the shifts in the global mode frequencies, as well as their mean local hydrophobicities, leads to high prediction success when tested on a dataset of allosteric proteins, composed of both monomers and multimeric assemblages. RESULTS Out of the 104 test cases, APOP predicts known allosteric pockets for 92 within the top 3 rank out of multiple pockets available in the protein. In addition, we demonstrate that APOP can also find new alternative allosteric pockets in proteins. Particularly interesting findings are the discovery of previously overlooked large pockets located in the centers of many protein biological assemblages; binding of ligands at these sites would likely be particularly effective in changing the protein's global dynamics. AVAILABILITY AND IMPLEMENTATION APOP is freely available as an open-source code (https://github.com/Ambuj-UF/APOP) and as a web server at https://apop.bb.iastate.edu/.
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Affiliation(s)
- Ambuj Kumar
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, United States
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, United States
| | - Burak T Kaynak
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, United States
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15232, United States
| | - Karin S Dorman
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, United States
- Department of Statistics, Iowa State University, Ames, IA 50011, United States
| | - Pemra Doruker
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15232, United States
| | - Robert L Jernigan
- Bioinformatics and Computational Biology Program, Iowa State University, Ames, IA 50011, United States
- Roy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011, United States
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Shahu L, Chowdhury SR, Lu HP. Single-Molecule Human Nucleosome Spontaneously Ruptures under the Stress of Compressive Force: A New Perspective on Gene Stability and Epigenetic Pathways. J Phys Chem B 2023; 127:37-44. [PMID: 36537668 DOI: 10.1021/acs.jpcb.2c04449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Force manipulation on the biological entities from living cells to protein molecules has revealed many mechanical details of cell biology from resolving folding and unfolding pathways to finding molecular interaction forces. A nucleosome is the basic repeating unit of chromatin where the histone octamer is wrapped by DNA, important for gene stability and regulation. How the inner side of the DNA gets accessed by other DNA binding molecules has been a puzzle that has been intensively studied and debated, important to epigenetics, gene stability, and regulations. Here we report our observation of spontaneous ruptures of human nucleosomes under pico-Newton (pN) compressive force. The amplitude of the compressive force, a squeezing rather than pulling force, involved in our experiment is tens of pN, which can be thermally available by biological force fluctuation at room temperature and under physiological conditions. This kind of structural rupture can loosen up the DNA around the histone, which in turn makes the DNA accessible to transcription and epigenetic modifications.
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Affiliation(s)
- Lalita Shahu
- Department of Chemistry, Center for Photochemical Sciences, Bowling Green State University, Bowling Green, Ohio 43403, United States
| | - S Roy Chowdhury
- Department of Chemistry, Center for Photochemical Sciences, Bowling Green State University, Bowling Green, Ohio 43403, United States
| | - H Peter Lu
- Department of Chemistry, Center for Photochemical Sciences, Bowling Green State University, Bowling Green, Ohio 43403, United States
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Zhang H, Kim S, Im W. Practical Guidance for Consensus Scoring and Force Field Selection in Protein-Ligand Binding Free Energy Simulations. J Chem Inf Model 2022; 62:6084-6093. [PMID: 36399655 PMCID: PMC9772090 DOI: 10.1021/acs.jcim.2c01115] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
The advances in ligand binding affinity prediction have been fostered by system generation tools and improved force fields (FFs). CHARMM-GUI Free Energy Calculator provides input and postprocessing scripts for AMBER-TI free energy calculations with various FFs. In this study, we used 12 different FF combinations (ff14SB and ff19SB for protein, GAFF2.2 and OpenFF for ligand, and TIP3P, TIP4PEW, and OPC for water) to calculate relative binding free energies (ΔΔGbind) for 80 alchemical transformations (among the JACS benchmark set) with different numbers of λ windows (12 or 21) and simulation times (1, 5, or 10 ns). Our results show that 12 λ windows and 5 ns simulation time for each window are sufficient to obtain reliable ΔΔGbind with 4 independent runs for the current benchmark set. While there is no statistically noticeable performance difference among 12 different FF combinations compared to the experimental values, a combination of ff14SB + GAFF2.2 + TIP3P FFs appears to be best with a mean unsigned error of 0.87 [0.69, 1.07] kcal/mol, a root-mean-square error of 1.22 [0.94, 1.50] kcal/mol, a Pearson's correlation of 0.64 [0.52, 0.76], a Spearman's correlation of 0.73 [0.58, 0.83], and a Kendell's correlation of 0.54 [0.42, 0.64]. This large-scale ΔΔGbind calculation study provides useful information about ΔΔGbind prediction with different AMBER FF combinations and presents valuable suggestions for FF selection in AMBER-TI ΔΔGbind calculations.
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Affiliation(s)
- Han Zhang
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, USA
| | - Seonghoon Kim
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul 02455, Republic of Korea
| | - Wonpil Im
- Departments of Biological Sciences, Chemistry, Bioengineering, and Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, USA,Corresponding Author:
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Wakefield AE, Kozakov D, Vajda S. Mapping the binding sites of challenging drug targets. Curr Opin Struct Biol 2022; 75:102396. [PMID: 35636004 PMCID: PMC9790766 DOI: 10.1016/j.sbi.2022.102396] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/20/2022] [Accepted: 04/25/2022] [Indexed: 02/03/2023]
Abstract
An increasing number of medically important proteins are challenging drug targets because their binding sites are too shallow or too polar, are cryptic and thus not detectable without a bound ligand or located in a protein-protein interface. While such proteins may not bind druglike small molecules with sufficiently high affinity, they are frequently druggable using novel therapeutic modalities. The need for such modalities can be determined by experimental or computational fragment based methods. Computational mapping by mixed solvent molecular dynamics simulations or the FTMap server can be used to determine binding hot spots. The strength and location of the hot spots provide very useful information for selecting potentially successful approaches to drug discovery.
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Affiliation(s)
- Amanda E. Wakefield
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215,Department of Chemistry, Boston University, Boston, Massachusetts 02215
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York, USA,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York, USA NY, USA
| | - Sandor Vajda
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts 02215,Department of Chemistry, Boston University, Boston, Massachusetts 02215
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From single-omics to interactomics: How can ligand-induced perturbations modulate single-cell phenotypes? ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 131:45-83. [PMID: 35871896 DOI: 10.1016/bs.apcsb.2022.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Cells suffer from perturbations by different stimuli, which, consequently, rise to individual alterations in their profile and function that may end up affecting the tissue as a whole. This is no different if we consider the effect of a therapeutic agent on a biological system. As cells are exposed to external ligands their profile can change at different single-omics levels. Detecting how these changes take place through different sequencing technologies is key to a better understanding of the effects of therapeutic agents. Single-cell RNA-sequencing stands out as one of the most common approaches for cell profiling and perturbation analysis. As a result, single-cell transcriptomics data can be integrated with other omics data sources, such as proteomics and epigenomics data, to clarify the perturbation effects and mechanism at the cell level. Appropriate computational tools are key to process and integrate the available information. This chapter focuses on the recent advances on ligand-induced perturbation and single-cell omics computational tools and algorithms, their current limitations, and how the deluge of data can be used to improve the current process of drug research and development.
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