1
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Callea L, Caprai C, Bonati L, Giorgino T, Motta S. Self-organizing maps of unbiased ligand-target binding pathways and kinetics. J Chem Phys 2024; 161:135102. [PMID: 39360688 DOI: 10.1063/5.0225183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Accepted: 09/18/2024] [Indexed: 10/04/2024] Open
Abstract
The interpretation of ligand-target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, with the employment of both network-based approaches and Markov state models. We demonstrate the method by comprehensively mapping a large dataset of 640 μs of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand-target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design.
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Affiliation(s)
- Lara Callea
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, Milan 20126, Italy
| | - Camilla Caprai
- Department of Biosciences, University of Milan, via Celoria 26, Milan 20133, Italy
- National Research Council of Italy, Biophysics Institute (CNR-IBF), Via Celoria 26, Milan 20133, Italy
| | - Laura Bonati
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, Milan 20126, Italy
| | - Toni Giorgino
- National Research Council of Italy, Biophysics Institute (CNR-IBF), Via Celoria 26, Milan 20133, Italy
| | - Stefano Motta
- Department of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, Milan 20126, Italy
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2
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Cantero J, Ballesteros-Casallas A, Santos LHS, Paulino M, Pantano S. Pouring SIRAH on NAMD. J Phys Chem B 2024. [PMID: 39322588 DOI: 10.1021/acs.jpcb.4c03278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2024]
Abstract
Molecular dynamics (MD) simulations provide an invaluable platform for exploring the dynamics of complex biomolecular systems at atomic resolution. However, compatibility issues between force fields and MD software engines can limit the interoperability and transferability of simulations. This work demonstrates the successful use of the coarse-grained SIRAH force field on the widely used NAMD MD engine across a range of increasingly complex biomolecular systems. By leveraging NAMD's ability to read AMBER input files, SIRAH simulations can be run seamlessly on NAMD, including its recently released GPU-accelerated version, NAMD3. The benchmark systems demonstrate consistent results across AMBER, NAMD2, and NAMD3. Thus, these data highlight the enhanced simulation throughput achievable on GPU-accelerated desktop computers using all three engines along with SIRAH. Overall, this study expands the range of the SIRAH force field by utilizing advanced GPU computing resources and high-performance supercomputing facilities, which are particularly effective with NAMD.
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Affiliation(s)
- Jorge Cantero
- Área Bioinformática, Departamento DETEMA, Facultad de Química, Universidad de la República, General Flores 2124, Montevideo 11600, Uruguay
- Centro de Investigaciones Médicas, Facultad de Ciencias de la Salud, Universidad Nacional del Este, Panambi 101305, Paraguay
| | - Andrés Ballesteros-Casallas
- Área Bioinformática, Departamento DETEMA, Facultad de Química, Universidad de la República, General Flores 2124, Montevideo 11600, Uruguay
- Institut Pasteur de Montevideo, Mataojo 2020, Montevideo 11400, Uruguay
| | | | - Margot Paulino
- Área Bioinformática, Departamento DETEMA, Facultad de Química, Universidad de la República, General Flores 2124, Montevideo 11600, Uruguay
| | - Sergio Pantano
- Área Bioinformática, Departamento DETEMA, Facultad de Química, Universidad de la República, General Flores 2124, Montevideo 11600, Uruguay
- Institut Pasteur de Montevideo, Mataojo 2020, Montevideo 11400, Uruguay
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3
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Song P, Huang Q, Li W, Li M, Liu Z. Decomposition of Forces in Protein: Methodology and General Properties. J Chem Inf Model 2024. [PMID: 39262153 DOI: 10.1021/acs.jcim.4c00716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
In contrast to the central role played by the structure of biomolecules, the complementary force-based view has received little attention in past studies. Here, we proposed a simple method for the force decomposition of multibody interactions and provided some techniques to analyze and visualize the general behavior of forces in proteins. It was shown that atomic forces fluctuate at a magnitude of about 3000 pN, which is huge in the context of cell biology. Remarkably, the average scalar product between atomic force and displacement universally approximates -3kBT. This is smaller by an order of magnitude than the simple product of their fluctuation magnitudes due to the unexpectedly weak correlation between the directions of force and displacement. The pairwise forces are highly anisotropic, with elongated fluctuation ellipsoids. Residue-residue forces can be attractive or repulsive (despite being more likely to be attractive), forming some kind of tensegrity structure stabilized by a complicated network of forces. Being able to understand and predict the interaction network provides a basis for rational drug design and uncovering molecular recognition mechanisms.
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Affiliation(s)
- Pengbo Song
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Qiaojing Huang
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Wenyu Li
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Maodong Li
- Shenzhen Bay Laboratory, Shenzhen 518000, China
| | - Zhirong Liu
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- State Key Laboratory for Structural Chemistry of Unstable and Stable Species, Beijing National Laboratory for Molecular Sciences (BNLMS), Peking University, Beijing 100871, China
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4
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Meng Y, Peplowski L, Wu T, Cheng Z, Han L, Qiao J, Cheng Z, Zhou Z. Multi-method analysis revealed the mechanism of substrate selectivity in NHase: A gatekeeper residue at the activity center. Int J Biol Macromol 2024; 279:135426. [PMID: 39251006 DOI: 10.1016/j.ijbiomac.2024.135426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Revised: 08/27/2024] [Accepted: 09/06/2024] [Indexed: 09/11/2024]
Abstract
Recognizing the critical need to elucidate the molecular determinants of this selectivity offers a pathway to engineer enzymes with broader and more versatile catalytic capabilities. Through integrated methods including phylogenetic analysis, molecular docking, and structural analysis, we identified a pivotal amino acid residue, αTrp116, linking the substrate binding pocket and the active site of a NHase from Pseudonocardia thermophila JCM 3095 (PtNHase). This residue acts as a crucial determinant of substrate specificity within the NHase enzyme. The mutant αW116R modified the substrate specificity of PtNHase, significantly enhancing its catalytic efficiency towards aromatic substrates. The catalytic activity for aromatic compounds such as 3-Cyanopyridine was 14-fold that of the wild-type, whereas its activity for aliphatic substrates diminished to one-sixth. MD simulations revealed that replacing αTrp116 with Arg allowed aromatic nitrile substrates to achieve more favorable conformations within the active site. Based on the mutant αW116R, we further constructed a combinatorial variant Pt-4, tailored for aromatic substrates, which exhibited an enzyme activity 50 times that of the wild-type. These results highlight the critical influence of amino acid residues in the enzyme's active site on substrate specificity and offer fresh perspectives and approaches for the evolution of enzymes.
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Affiliation(s)
- Yiwei Meng
- Key Laboratory of Industrial Biotechnology (Ministry of Education), School of Biotechnology, Jiangnan University, Wuxi, Jiangsu, China
| | - Lukasz Peplowski
- Institute of Physics, Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University in Torun, Grudziadzka 5, 87-100 Torun, Poland
| | - Tong Wu
- Key Laboratory of Industrial Biotechnology (Ministry of Education), School of Biotechnology, Jiangnan University, Wuxi, Jiangsu, China
| | - Zhongyi Cheng
- Key Laboratory of Industrial Biotechnology (Ministry of Education), School of Biotechnology, Jiangnan University, Wuxi, Jiangsu, China
| | - Laichuang Han
- Key Laboratory of Industrial Biotechnology (Ministry of Education), School of Biotechnology, Jiangnan University, Wuxi, Jiangsu, China
| | - Jun Qiao
- Ningbo Institute of Marine Medicine, Peking University, China
| | - Zhongyi Cheng
- Key Laboratory of Industrial Biotechnology (Ministry of Education), School of Biotechnology, Jiangnan University, Wuxi, Jiangsu, China.
| | - Zhemin Zhou
- Key Laboratory of Industrial Biotechnology (Ministry of Education), School of Biotechnology, Jiangnan University, Wuxi, Jiangsu, China; Jiangnan University (Rugao) Food Biotechnology Research Institute, Rugao, Jiangsu, China.
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5
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Chen L, Mondal A, Perez A, Miranda-Quintana RA. Protein Retrieval via Integrative Molecular Ensembles (PRIME) through Extended Similarity Indices. J Chem Theory Comput 2024; 20:6303-6315. [PMID: 38978294 DOI: 10.1021/acs.jctc.4c00362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Molecular dynamics (MD) simulations are ideally suited to describe conformational ensembles of biomolecules such as proteins and nucleic acids. Microsecond-long simulations are now routine, facilitated by the emergence of graphical processing units. Clustering, which groups objects based on structural similarity, is typically used to process ensembles, leading to different states, their populations, and the identification of representative structures. A popular pipeline combines hierarchical clustering for clustering and selecting the cluster centroid as representative of the cluster. Here, we propose to improve on this approach, by developing a module-Protein Retrieval via Integrative Molecular Ensembles (PRIME), that consists of tools to improve the prediction of the representative in the most populated cluster using extended continuous similarity. PRIME is integrated with our Molecular Dynamics Analysis with N-ary Clustering Ensembles (MDANCE) package and can be used as a postprocessing tool for arbitrary clustering algorithms, compatible with several MD suites. PRIME predictions produced structures that when aligned to the experimental structure were better superposed (lower RMSD). A further benefit of PRIME is its linear scaling─rather than the traditional O(N2) traditionally associated with comparisons of elements in a set.
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Affiliation(s)
- Lexin Chen
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
- Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Arup Mondal
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
- Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Alberto Perez
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
- Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Ramón Alain Miranda-Quintana
- Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States
- Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
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6
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Stepanenko D, Wang Y, Simmerling C. Assessing pH-Dependent Conformational Changes in the Fusion Peptide Proximal Region of the SARS-CoV-2 Spike Glycoprotein. Viruses 2024; 16:1066. [PMID: 39066230 PMCID: PMC11281432 DOI: 10.3390/v16071066] [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: 05/14/2024] [Revised: 06/26/2024] [Accepted: 06/28/2024] [Indexed: 07/28/2024] Open
Abstract
One of the entry mechanisms of the SARS-CoV-2 coronavirus into host cells involves endosomal acidification. It has been proposed that under acidic conditions, the fusion peptide proximal region (FPPR) of the SARS-CoV-2 spike glycoprotein acts as a pH-dependent switch, modulating immune response accessibility by influencing the positioning of the receptor binding domain (RBD). This would provide indirect coupling of RBD opening to the environmental pH. Here, we explored this possible pH-dependent conformational equilibrium of the FPPR within the SARS-CoV-2 spike glycoprotein. We analyzed hundreds of experimentally determined spike structures from the Protein Data Bank and carried out pH-replica exchange molecular dynamics to explore the extent to which the FPPR conformation depends on pH and the positioning of the RBD. A meta-analysis of experimental structures identified alternate conformations of the FPPR among structures in which this flexible regions was resolved. However, the results did not support a correlation between the FPPR conformation and either RBD position or the reported pH of the cryo-EM experiment. We calculated pKa values for titratable side chains in the FPPR region using PDB structures, but these pKa values showed large differences between alternate PDB structures that otherwise adopt the same FPPR conformation type. This hampers the comparison of pKa values in different FPPR conformations to rationalize a pH-dependent conformational change. We supplemented these PDB-based analyses with all-atom simulations and used constant-pH replica exchange molecular dynamics to estimate pKa values in the context of flexibility and explicit water. The resulting titration curves show good reproducibility between simulations, but they also suggest that the titration curves of the different FPPR conformations are the same within the error bars. In summary, we were unable to find evidence supporting the previously published hypothesis of an FPPR pH-dependent equilibrium: neither from existing experimental data nor from constant-pH MD simulations. The study underscores the complexity of the spike system and opens avenues for further exploration into the interplay between pH and SARS-CoV-2 viral entry mechanisms.
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Affiliation(s)
- Darya Stepanenko
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; (D.S.); (Y.W.)
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA
| | - Yuzhang Wang
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; (D.S.); (Y.W.)
- Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA
| | - Carlos Simmerling
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA; (D.S.); (Y.W.)
- Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA
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7
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Pan T, Dun C, Jin S, Miller MD, Kyrillidis A, Phillips GN. CrysFormer: Protein structure determination via Patterson maps, deep learning, and partial structure attention. STRUCTURAL DYNAMICS (MELVILLE, N.Y.) 2024; 11:044701. [PMID: 39148510 PMCID: PMC11326852 DOI: 10.1063/4.0000252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 07/10/2024] [Indexed: 08/17/2024]
Abstract
Determining the atomic-level structure of a protein has been a decades-long challenge. However, recent advances in transformers and related neural network architectures have enabled researchers to significantly improve solutions to this problem. These methods use large datasets of sequence information and corresponding known protein template structures, if available. Yet, such methods only focus on sequence information. Other available prior knowledge could also be utilized, such as constructs derived from x-ray crystallography experiments and the known structures of the most common conformations of amino acid residues, which we refer to as partial structures. To the best of our knowledge, we propose the first transformer-based model that directly utilizes experimental protein crystallographic data and partial structure information to calculate electron density maps of proteins. In particular, we use Patterson maps, which can be directly obtained from x-ray crystallography experimental data, thus bypassing the well-known crystallographic phase problem. We demonstrate that our method, CrysFormer, achieves precise predictions on two synthetic datasets of peptide fragments in crystalline forms, one with two residues per unit cell and the other with fifteen. These predictions can then be used to generate accurate atomic models using established crystallographic refinement programs.
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Affiliation(s)
- Tom Pan
- Department of Computer Science, Rice University, Houston, Texas 77005, USA
| | - Chen Dun
- Department of Computer Science, Rice University, Houston, Texas 77005, USA
| | - Shikai Jin
- Department of BioSciences, Rice University, Houston, Texas 77005, USA
| | - Mitchell D Miller
- Department of BioSciences, Rice University, Houston, Texas 77005, USA
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8
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Caparotta M, Perez A. Advancing Molecular Dynamics: Toward Standardization, Integration, and Data Accessibility in Structural Biology. J Phys Chem B 2024; 128:2219-2227. [PMID: 38418288 DOI: 10.1021/acs.jpcb.3c04823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2024]
Abstract
Molecular dynamics (MD) simulations have become a valuable tool in structural biology, offering insights into complex biological systems that are difficult to obtain through experimental techniques alone. The lack of available data sets and structures in most published computational work has limited other researchers' use of these models. In recent years, the emergence of online sharing platforms and MD database initiatives favor the deposition of ensembles and structures to accompany publications, favoring reuse of the data sets. However, the lack of uniform metadata collection, formats, and what data are deposited limits the impact and its use by different communities that are not necessarily experts in MD. This Perspective highlights the need for standardization and better resource sharing for processing and interpreting MD simulation results, akin to efforts in other areas of structural biology. As the field moves forward, we will see an increase in popularity and benefits of MD-based integrative approaches combining experimental data and simulations through probabilistic reasoning, but these too are limited by uniformity in experimental data availability and choices on how the data are modeled that are not trivial to decipher from papers. Other fields have addressed similar challenges comprehensively by establishing task forces with different degrees of success. The large scope and number of communities to represent the breadth of types of MD simulations complicates a parallel approach that would fit all. Thus, each group typically decides what data and which format to upload on servers like Zenodo. Uploading data with FAIR (findable, accessible, interoperable, reusable) principles in mind including optimal metadata collection will make the data more accessible and actionable by the community. Such a wealth of simulation data will foster method development and infrastructure advancements, thus propelling the field forward.
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Affiliation(s)
- Marcelo Caparotta
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
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9
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Liu Z. Accelerating Kinetics with Time-Reversal Path Sampling. Molecules 2023; 28:8147. [PMID: 38138635 PMCID: PMC10745403 DOI: 10.3390/molecules28248147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 12/07/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
In comparison to numerous enhanced sampling methods for equilibrium thermodynamics, accelerating simulations for kinetics and nonequilibrium statistics are relatively rare and less effective. Here, we derive a time-reversal path sampling (tRPS) method based on time reversibility to accelerate simulations for determining the transition rates between free-energy basins. It converts the difficult uphill path sampling into an easy downhill problem. This method is easy to implement, i.e., forward and backward shooting simulations with opposite initial velocities are conducted from random initial conformations within a transition-state region until they reach the basin minima, which are then assembled to give the distribution of transition paths efficiently. The effects of tRPS are demonstrated using a comparison with direct simulations of protein folding and unfolding, where tRPS is shown to give results consistent with direct simulations and increase the efficiency by up to five orders of magnitude. This approach is generally applicable to stochastic processes with microscopic reversibility, regardless of whether the variables are continuous or discrete.
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Affiliation(s)
- Zhirong Liu
- Beijing National Laboratory for Molecular Sciences (BNLMS), College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
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10
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Alcantara J, Chiu K, Bickel JD, Rizzo RC, Simmerling C. Rapid Rescoring and Refinement of Ligand-Receptor Complexes Using Replica Exchange Molecular Dynamics with a Monte Carlo Pose Reservoir. J Chem Theory Comput 2023; 19:7934-7945. [PMID: 37831619 DOI: 10.1021/acs.jctc.3c00345] [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] [Indexed: 10/15/2023]
Abstract
Virtual screening (VS) involves generation of poses for a library of ligands and ranking using simplified energy functions and limited flexibility. Top-scored poses are used to rank and prioritize ligands. Here, we adapt the reservoir replica exchange molecular dynamics (res-REMD) method to rerank poses generated through VS. REMD simulations are carried out but with occasional Monte Carlo jumps to alternate VS-generated poses using a Metropolis criterion. The simulations converge within 10 ns for all systems, generating populations of alternate poses in the context of fully flexible ligand and protein side chains. The protocol is applied to four model protein-ligand complexes, where DOCK resulted in two successes and two scoring failures. In all four systems, the most populated cluster from the final ensemble exhibits high similarity to the crystallographic pose with ligand RMSD values under 2.0 Å. Both DOCK failures were rescued. For one DOCK success, the protocol identified the correct pose but also sampled an alternate pose at equal probability. Opportunities for future improvements and extensions are discussed.
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Affiliation(s)
- Juan Alcantara
- Department of Chemistry, Stony Brook University, Stony Brook 11794, United States
- Laufer Center for Physical & Quantitative Biology, Stony Brook University, Stony Brook 11794, United States
| | - Kelley Chiu
- Department of Computer Science, Stony Brook University, Stony Brook 11794, United States
| | - John D Bickel
- Department of Chemistry, Stony Brook University, Stony Brook 11794, United States
| | - Robert C Rizzo
- Department of Chemistry, Stony Brook University, Stony Brook 11794, United States
- Laufer Center for Physical & Quantitative Biology, Stony Brook University, Stony Brook 11794, United States
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook 11794, United States
| | - Carlos Simmerling
- Department of Chemistry, Stony Brook University, Stony Brook 11794, United States
- Laufer Center for Physical & Quantitative Biology, Stony Brook University, Stony Brook 11794, United States
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook 11794, United States
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11
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Zhang XE, Liu C, Dai J, Yuan Y, Gao C, Feng Y, Wu B, Wei P, You C, Wang X, Si T. Enabling technology and core theory of synthetic biology. SCIENCE CHINA. LIFE SCIENCES 2023; 66:1742-1785. [PMID: 36753021 PMCID: PMC9907219 DOI: 10.1007/s11427-022-2214-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/04/2022] [Indexed: 02/09/2023]
Abstract
Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling technology of synthetic biology, including synthesis and assembly of a genome, DNA storage, gene editing, molecular evolution and de novo design of function proteins, cell and gene circuit engineering, cell-free synthetic biology, artificial intelligence (AI)-aided synthetic biology, as well as biofoundries. We also introduce the concept of quantitative synthetic biology, which is guiding synthetic biology towards increased accuracy and predictability or the real rational design. We conclude that synthetic biology will establish its disciplinary system with the iterative development of enabling technologies and the maturity of the core theory.
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Affiliation(s)
- Xian-En Zhang
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Chenli Liu
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Junbiao Dai
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Yingjin Yuan
- Frontiers Science Center for Synthetic Biology and Key Laboratory of Systems Bioengineering (Ministry of Education), School of Chemical Engineering and Technology, Tianjin University, Tianjin, 300072, China.
| | - Caixia Gao
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yan Feng
- State Key Laboratory of Microbial Metabolism, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Bian Wu
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ping Wei
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
| | - Chun You
- Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.
| | - Xiaowo Wang
- Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Bioinformatics Division, Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Tong Si
- Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China.
- Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
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12
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Pinto ÉSM, Krause MJ, Dorn M, Feltes BC. The nucleotide excision repair proteins through the lens of molecular dynamics simulations. DNA Repair (Amst) 2023; 127:103510. [PMID: 37148846 DOI: 10.1016/j.dnarep.2023.103510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 04/07/2023] [Accepted: 04/23/2023] [Indexed: 05/08/2023]
Abstract
Mutations that affect the proteins responsible for the nucleotide excision repair (NER) pathway can lead to diseases such as xeroderma pigmentosum, trichothiodystrophy, Cockayne syndrome, and Cerebro-oculo-facio-skeletal syndrome. Hence, understanding their molecular behavior is needed to elucidate these diseases' phenotypes and how the NER pathway is organized and coordinated. Molecular dynamics techniques enable the study of different protein conformations, adaptable to any research question, shedding light on the dynamics of biomolecules. However, as important as they are, molecular dynamics studies focused on DNA repair pathways are still becoming more widespread. Currently, there are no review articles compiling the advancements made in molecular dynamics approaches applied to NER and discussing: (i) how this technique is currently employed in the field of DNA repair, focusing on NER proteins; (ii) which technical setups are being employed, their strengths and limitations; (iii) which insights or information are they providing to understand the NER pathway or NER-associated proteins; (iv) which open questions would be suited for this technique to answer; and (v) where can we go from here. These questions become even more crucial considering the numerous 3D structures published regarding the NER pathway's proteins in recent years. In this work, we tackle each one of these questions, revising and critically discussing the results published in the context of the NER pathway.
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Affiliation(s)
| | - Mathias J Krause
- Institute for Applied and Numerical Mathematics, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | - Márcio Dorn
- Center for Biotechnology, Federal University of Rio Grande do Sul, RS, Brazil; Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil; National Institute of Science and Technology - Forensic Science, Porto Alegre, RS, Brazil
| | - Bruno César Feltes
- Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
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13
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Parui S, Robertson JC, Somani S, Tresadern G, Liu C, Dill KA. MELD-Bracket Ranks Binding Affinities of Diverse Sets of Ligands. J Chem Inf Model 2023; 63:2857-2865. [PMID: 37093848 DOI: 10.1021/acs.jcim.3c00243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Affinity ranking of structurally diverse small-molecule ligands is a challenging problem with important applications in structure-based drug discovery. Absolute binding free energy methods can model diverse ligands, but the high computational cost of the current methods limits application to data sets with few ligands. We recently developed MELD-Bracket, a Molecular Dynamics method for efficient affinity ranking of ligands [ JCTC 2022, 18 (1), 374-379]. It utilizes a Bayesian framework to guide sampling to relevant regions of phase space, and it couples this with a bracket-like competition on a pool of ligands. Here we find that 6-competitor MELD-Bracket can rank dozens of diverse ligands that have low structural similarity and different net charges. We benchmark it on four protein systems─PTB1B, Tyk2, BACE, and JAK3─having varied modes of interactions. We also validated 8-competitor and 12-competitor protocols. The MELD-Bracket protocols presented here may have the appropriate balance of accuracy and computational efficiency to be suitable for ranking diverse ligands from typical drug discovery campaigns.
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Affiliation(s)
- Sridip Parui
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - James C Robertson
- Janssen Research and Development, Spring House, Pennsylvania 19477, United States
| | - Sandeep Somani
- Janssen Research and Development, Spring House, Pennsylvania 19477, United States
| | - Gary Tresadern
- Janssen Research and Development, Turnhoutseweg 30, Beerse B-2340, Belgium
| | - Cong Liu
- Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States
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14
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Kasavajhala K, Simmerling C. Exploring the Transferability of Replica Exchange Structure Reservoirs to Accelerate Generation of Ensembles for Alternate Hamiltonians or Protein Mutations. J Chem Theory Comput 2023; 19:1931-1944. [PMID: 36861842 PMCID: PMC10658647 DOI: 10.1021/acs.jctc.3c00005] [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] [Indexed: 03/03/2023]
Abstract
Generating precise ensembles is commonly a prerequisite to understand the energetics of biological processes using Molecular Dynamics (MD) simulations. Previously, we have shown how unweighted reservoirs built from high temperature MD simulations can accelerate convergence of Boltzmann-weighted ensembles by at least 10× with the Reservoir Replica Exchange MD (RREMD) method. Therefore, in this work, we explore whether an unweighted structure reservoir generated with one Hamiltonian (solute force field plus solvent model) can be reused to quickly generate accurately weighted ensembles for Hamiltonians other than the one that was used to generate the reservoir. We also extended this methodology to rapidly estimate the effects of mutations on peptide stability by using a reservoir of diverse structures obtained from wild-type simulations. These results suggest that structures generated via fast methods such as coarse-grained models or structures predicted by Rosetta or deep learning approaches could be integrated into a reservoir to accelerate generation of ensembles using more accurate representations.
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Affiliation(s)
- Koushik Kasavajhala
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Carlos Simmerling
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
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15
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Rachitskii P, Kruglov I, Finkelstein AV, Oganov AR. Protein structure prediction using the evolutionary algorithm USPEX. Proteins 2023. [PMID: 36780132 DOI: 10.1002/prot.26478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 11/08/2022] [Accepted: 02/06/2023] [Indexed: 02/14/2023]
Abstract
Protein structure prediction is one of major problems of modern biophysics: current attempts to predict the tertiary protein structure from amino acid sequence are successful mostly when the use of big data and machine learning allows one to reduce the "prediction problem" to the "problem of recognition". Compared with recent successes of deep learning, classical predictive methods lag behind in their accuracy for the prediction of stable conformations. Therefore, in this work we extended the evolutionary algorithm USPEX to predict protein structure based on global optimization starting with the amino acid sequence. Moreover, we compared frequently used force fields for the task of protein structure prediction. Protein structure relaxation and energy calculations were performed using Tinker (with several different force fields) and Rosetta (with REF2015 force field) codes. To create new protein structure models in the USPEX algorithm, we developed novel variation operators. The test of the new method on seven proteins having (for simplicity) no cis-proline (with ω ≈ 0°) residues, and a length of up to 100 residues, revealed that our algorithm predicts tertiary structures of proteins with high accuracy. The comparison of the final potential energies of the predicted protein structures obtained using the USPEX and the Rosetta Abinitio approach showed that in most cases the developed algorithm found structures with close or even lower energy (Amber/Charmm/Oplsaal) and scoring function (REF2015). While USPEX has clearly demonstrated its ability to find very deep energy minima, our study showed that the existing force fields are not sufficiently accurate for accurate blind prediction of protein structures without further experimental verification.
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Affiliation(s)
| | - Ivan Kruglov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia.,Dukhov Research Institute of Automatics (VNIIA), Moscow, Russia
| | - Alexei V Finkelstein
- Institute of Protein Research of the Russian Academy of Sciences, Moscow, Russia.,Biology Department of the Lomonosov Moscow State University, Moscow, Russia.,Biotechnology Department of the Lomonosov Moscow State University, Moscow, Russia
| | - Artem R Oganov
- Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Moscow, Russia
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16
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Brodmerkel MN, De Santis E, Uetrecht C, Caleman C, Marklund EG. Stability and conformational memory of electrosprayed and rehydrated bacteriophage MS2 virus coat proteins. Curr Res Struct Biol 2022; 4:338-348. [PMID: 36440379 PMCID: PMC9685359 DOI: 10.1016/j.crstbi.2022.10.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 09/23/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022] Open
Abstract
Proteins are innately dynamic, which is important for their functions, but which also poses significant challenges when studying their structures. Gas-phase techniques can utilise separation and a range of sample manipulations to transcend some of the limitations of conventional techniques for structural biology in crystalline or solution phase, and isolate different states for separate interrogation. However, the transfer from solution to the gas phase risks affecting the structures, and it is unclear to what extent different conformations remain distinct in the gas phase, and if resolution in silico can recover the native conformations and their differences. Here, we use extensive molecular dynamics simulations to study the two distinct conformations of dimeric capsid protein of the MS2 bacteriophage. The protein undergoes notable restructuring of its peripheral parts in the gas phase, but subsequent simulation in solvent largely recovers the native structure. Our results suggest that despite some structural loss due to the experimental conditions, gas-phase structural biology techniques provide meaningful data that inform not only about the structures but also conformational dynamics of proteins.
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Affiliation(s)
- Maxim N. Brodmerkel
- Department of Chemistry - BMC, Uppsala University, Box 576, Uppsala, 75123, Sweden
| | - Emiliano De Santis
- Department of Chemistry - BMC, Uppsala University, Box 576, Uppsala, 75123, Sweden
- Department of Physics and Astronomy, Uppsala University, Box 516, Uppsala, 75120, Sweden
| | - Charlotte Uetrecht
- Leibniz Institute of Virology (LIV), Hamburg, 20251, Germany
- Centre for Structural Systems Biology (CSSB), Deutsches Elektronen-Synchrotron, DESY, Notkestrasse 85, Hamburg, 22607, Germany
- School of Life Sciences, University of Siegen, Siegen, Germany
| | - Carl Caleman
- Department of Physics and Astronomy, Uppsala University, Box 516, Uppsala, 75120, Sweden
- Center for Free-Electron Laser Science, DESY, Notkestrasse 85, Hamburg, 22607, Germany
| | - Erik G. Marklund
- Department of Chemistry - BMC, Uppsala University, Box 576, Uppsala, 75123, Sweden
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17
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Schnee P, Choudalakis M, Weirich S, Khella MS, Carvalho H, Pleiss J, Jeltsch A. Mechanistic basis of the increased methylation activity of the SETD2 protein lysine methyltransferase towards a designed super-substrate peptide. Commun Chem 2022; 5:139. [PMID: 36697904 PMCID: PMC9814698 DOI: 10.1038/s42004-022-00753-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 10/07/2022] [Indexed: 01/28/2023] Open
Abstract
Protein lysine methyltransferases have important regulatory functions in cells, but mechanisms determining their activity and specificity are incompletely understood. Naturally, SETD2 introduces H3K36me3, but previously an artificial super-substrate (ssK36) was identified, which is methylated >100-fold faster. The ssK36-SETD2 complex structure cannot fully explain this effect. We applied molecular dynamics (MD) simulations and biochemical experiments to unravel the mechanistic basis of the increased methylation of ssK36, considering peptide conformations in solution, association of peptide and enzyme, and formation of transition-state (TS) like conformations of the enzyme-peptide complex. We observed in MD and FRET experiments that ssK36 adopts a hairpin conformation in solution with V35 and K36 placed in the loop. The hairpin conformation has easier access into the active site of SETD2 and it unfolds during the association process. Peptide methylation experiments revealed that introducing a stable hairpin conformation in the H3K36 peptide increased its methylation by SETD2. In MD simulations of enzyme-peptide complexes, the ssK36 peptide approached TS-like structures more frequently than H3K36 and distinct, substrate-specific TS-like structures were observed. Hairpin association, hairpin unfolding during association, and substrate-specific catalytically competent conformations may also be relevant for other PKMTs and hairpins could represent a promising starting point for SETD2 inhibitor development.
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Affiliation(s)
- Philipp Schnee
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Michel Choudalakis
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Sara Weirich
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Mina S Khella
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany.,Biochemistry Department, Faculty of Pharmacy, Ain Shams University, African Union Organization Street, Abbassia, Cairo, 11566, Egypt
| | - Henrique Carvalho
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany
| | - Jürgen Pleiss
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany.
| | - Albert Jeltsch
- Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 31, 70569, Stuttgart, Germany.
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18
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Dissecting the stability determinants of a challenging de novo protein fold using massively parallel design and experimentation. Proc Natl Acad Sci U S A 2022; 119:e2122676119. [PMID: 36191185 PMCID: PMC9564214 DOI: 10.1073/pnas.2122676119] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Designing entirely new protein structures remains challenging because we do not fully understand the biophysical determinants of folding stability. Yet, some protein folds are easier to design than others. Previous work identified the 43-residue ɑββɑ fold as especially challenging: The best designs had only a 2% success rate, compared to 39 to 87% success for other simple folds [G. J. Rocklin et al., Science 357, 168-175 (2017)]. This suggested the ɑββɑ fold would be a useful model system for gaining a deeper understanding of folding stability determinants and for testing new protein design methods. Here, we designed over 10,000 new ɑββɑ proteins and found over 3,000 of them to fold into stable structures using a high-throughput protease-based assay. NMR, hydrogen-deuterium exchange, circular dichroism, deep mutational scanning, and scrambled sequence control experiments indicated that our stable designs fold into their designed ɑββɑ structures with exceptional stability for their small size. Our large dataset enabled us to quantify the influence of universal stability determinants including nonpolar burial, helix capping, and buried unsatisfied polar atoms, as well as stability determinants unique to the ɑββɑ topology. Our work demonstrates how large-scale design and test cycles can solve challenging design problems while illuminating the biophysical determinants of folding.
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19
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Aramyan S, McGregor K, Sandeep S, Haczku A. SP-A binding to the SARS-CoV-2 spike protein using hybrid quantum and classical in silico modeling and molecular pruning by Quantum Approximate Optimization Algorithm (QAOA) Based MaxCut with ZDOCK. Front Immunol 2022; 13:945317. [PMID: 36189278 PMCID: PMC9519185 DOI: 10.3389/fimmu.2022.945317] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 08/10/2022] [Indexed: 11/18/2022] Open
Abstract
The pulmonary surfactant protein A (SP-A) is a constitutively expressed immune-protective collagenous lectin (collectin) in the lung. It binds to the cell membrane of immune cells and opsonizes infectious agents such as bacteria, fungi, and viruses through glycoprotein binding. SARS-CoV-2 enters airway epithelial cells by ligating the Angiotensin Converting Enzyme 2 (ACE2) receptor on the cell surface using its Spike glycoprotein (S protein). We hypothesized that SP-A binds to the SARS-CoV-2 S protein and this binding interferes with ACE2 ligation. To study this hypothesis, we used a hybrid quantum and classical in silico modeling technique that utilized protein graph pruning. This graph pruning technique determines the best binding sites between amino acid chains by utilizing the Quantum Approximate Optimization Algorithm (QAOA)-based MaxCut (QAOA-MaxCut) program on a Near Intermediate Scale Quantum (NISQ) device. In this, the angles between every neighboring three atoms were Fourier-transformed into microwave frequencies and sent to a quantum chip that identified the chemically irrelevant atoms to eliminate based on their chemical topology. We confirmed that the remaining residues contained all the potential binding sites in the molecules by the Universal Protein Resource (UniProt) database. QAOA-MaxCut was compared with GROMACS with T-REMD using AMBER, OPLS, and CHARMM force fields to determine the differences in preparing a protein structure docking, as well as with Goemans-Williamson, the best classical algorithm for MaxCut. The relative binding affinity of potential interactions between the pruned protein chain residues of SP-A and SARS-CoV-2 S proteins was assessed by the ZDOCK program. Our data indicate that SP-A could ligate the S protein with a similar affinity to the ACE2-Spike binding. Interestingly, however, the results suggest that the most tightly-bound SP-A binding site is localized to the S2 chain, in the fusion region of the SARS-CoV-2 S protein, that is responsible for cell entry Based on these findings we speculate that SP-A may not directly compete with ACE2 for the binding site on the S protein, but interferes with viral entry to the cell by hindering necessary conformational changes or the fusion process.
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Affiliation(s)
- Sona Aramyan
- If and Only If (Iff) Technologies, Pleasanton, CA, United States
| | - Kirk McGregor
- If and Only If (Iff) Technologies, Pleasanton, CA, United States
| | - Samarth Sandeep
- If and Only If (Iff) Technologies, Pleasanton, CA, United States
- *Correspondence: Samarth Sandeep, ; Angela Haczku,
| | - Angela Haczku
- University of California (UC) Davis Lung Center Pulmonary, Critical Care and Sleep Division, Department of Medicine, School of Medicine, University of California, Davis, CA, United States
- *Correspondence: Samarth Sandeep, ; Angela Haczku,
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20
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Matsubara D, Kasahara K, Dokainish HM, Oshima H, Sugita Y. Modified Protein-Water Interactions in CHARMM36m for Thermodynamics and Kinetics of Proteins in Dilute and Crowded Solutions. Molecules 2022; 27:molecules27175726. [PMID: 36080494 PMCID: PMC9457699 DOI: 10.3390/molecules27175726] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/30/2022] [Accepted: 08/30/2022] [Indexed: 11/16/2022] Open
Abstract
Proper balance between protein-protein and protein-water interactions is vital for atomistic molecular dynamics (MD) simulations of globular proteins as well as intrinsically disordered proteins (IDPs). The overestimation of protein-protein interactions tends to make IDPs more compact than those in experiments. Likewise, multiple proteins in crowded solutions are aggregated with each other too strongly. To optimize the balance, Lennard-Jones (LJ) interactions between protein and water are often increased about 10% (with a scaling parameter, λ = 1.1) from the existing force fields. Here, we explore the optimal scaling parameter of protein-water LJ interactions for CHARMM36m in conjunction with the modified TIP3P water model, by performing enhanced sampling MD simulations of several peptides in dilute solutions and conventional MD simulations of globular proteins in dilute and crowded solutions. In our simulations, 10% increase of protein-water LJ interaction for the CHARMM36m cannot maintain stability of a small helical peptide, (AAQAA)3 in a dilute solution and only a small modification of protein-water LJ interaction up to the 3% increase (λ = 1.03) is allowed. The modified protein-water interactions are applicable to other peptides and globular proteins in dilute solutions without changing thermodynamic properties from the original CHARMM36m. However, it has a great impact on the diffusive properties of proteins in crowded solutions, avoiding the formation of too sticky protein-protein interactions.
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Affiliation(s)
- Daiki Matsubara
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Hyogo, Japan
| | - Kento Kasahara
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Hyogo, Japan
- Division of Chemical Engineering, Graduate School of Engineering Science, Osaka University, Toyonaka 560-8531, Osaka, Japan
| | - Hisham M. Dokainish
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako 351-0198, Saitama, Japan
| | - Hiraku Oshima
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Hyogo, Japan
| | - Yuji Sugita
- Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Hyogo, Japan
- Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako 351-0198, Saitama, Japan
- Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe 650-0047, Hyogo, Japan
- Correspondence: ; Tel.: +81-48-462-1407
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21
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Lam K, Kasavajhala K, Gunasekera S, Simmerling C. Accelerating the Ensemble Convergence of RNA Hairpin Simulations with a Replica Exchange Structure Reservoir. J Chem Theory Comput 2022; 18:3930-3947. [PMID: 35502992 PMCID: PMC10658646 DOI: 10.1021/acs.jctc.2c00065] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
RNA is a key participant in many biological processes, but studies of RNA using computer simulations lag behind those of proteins, largely due to less-developed force fields and the slow dynamics of RNA. Generating converged RNA ensembles for force field development and other studies remains a challenge. In this study, we explore the ability of replica exchange molecular dynamics to obtain well-converged conformational ensembles for two RNA hairpin systems in an implicit solvent. Even for these small model systems, standard REMD remains computationally costly, but coupling to a pre-generated structure library using the reservoir REMD approach provides a dramatic acceleration of ensemble convergence for both model systems. Such precise ensembles could facilitate RNA force field development and validation and applications of simulation to more complex RNA systems. The advantages and remaining challenges of applying R-REMD to RNA are investigated in detail.
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Affiliation(s)
- Kenneth Lam
- Molecular and Cellular Biology, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Koushik Kasavajhala
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Sarah Gunasekera
- Program in Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Carlos Simmerling
- Molecular and Cellular Biology, Stony Brook University, Stony Brook, New York 11794, United States
- Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
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22
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Kim S, Swanson JMJ, Voth GA. Computational Studies of Lipid Droplets. J Phys Chem B 2022; 126:2145-2154. [PMID: 35263109 PMCID: PMC8957551 DOI: 10.1021/acs.jpcb.2c00292] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/26/2022] [Indexed: 02/05/2023]
Abstract
Lipid droplets (LDs) are intracellular organelles whose primary function is energy storage. Known to emerge from the endoplasmic reticulum (ER) bilayer, LDs have a unique structure with a core consisting of neutral lipids, triacylglycerol (TG) or sterol esters (SE), surrounded by a phospholipid (PL) monolayer and decorated by proteins that come and go throughout their complex lifecycle. In this Feature Article, we review recent developments in computational studies of LDs, a rapidly growing area of research. We highlight how molecular dynamics (MD) simulations have provided valuable molecular-level insight into LD targeting and LD biogenesis. Additionally, we review the physical properties of TG from different force fields compared with experimental data. Possible future directions and challenges are discussed.
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Affiliation(s)
- Siyoung Kim
- Pritzker
School of Molecular Engineering, University
of Chicago, Chicago, Illinois 60637, United States
| | - Jessica M. J. Swanson
- Department
of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States
| | - Gregory A. Voth
- Department
of Chemistry, Chicago Center for Theoretical Chemistry, James Franck
Institute, and Institute for Biophysical Dynamics, University of Chicago, Chicago, Illinois 60637, United States
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23
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Abstract
Proteins have dynamic structures that undergo chain motions on time scales spanning from picoseconds to seconds. Resolving the resultant conformational heterogeneity is essential for gaining accurate insight into fundamental mechanistic aspects of the protein folding reaction. The use of high-resolution structural probes, sensitive to population distributions, has begun to enable the resolution of site-specific conformational heterogeneity at different stages of the folding reaction. Different states populated during protein folding, including the unfolded state, collapsed intermediate states, and even the native state, are found to possess significant conformational heterogeneity. Heterogeneity in protein folding and unfolding reactions originates from the reduced cooperativity of various kinds of physicochemical interactions between various structural elements of a protein, and between a protein and solvent. Heterogeneity may arise because of functional or evolutionary constraints. Conformational substates within the unfolded state and the collapsed intermediates that exchange at rates slower than the subsequent folding steps give rise to heterogeneity on the protein folding pathways. Multiple folding pathways are likely to represent distinct sequences of structure formation. Insight into the nature of the energy barriers separating different conformational states populated during (un)folding can also be obtained by resolving heterogeneity.
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Affiliation(s)
- Sandhya Bhatia
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bengaluru 560065, India.,Indian Institute of Science Education and Research, Pune 411008, India
| | - Jayant B Udgaonkar
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bengaluru 560065, India.,Indian Institute of Science Education and Research, Pune 411008, India
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24
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Nassar R, Brini E, Parui S, Liu C, Dignon GL, Dill KA. Accelerating Protein Folding Molecular Dynamics Using Inter-Residue Distances from Machine Learning Servers. J Chem Theory Comput 2022; 18:1929-1935. [PMID: 35133832 PMCID: PMC9281603 DOI: 10.1021/acs.jctc.1c00916] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Recently, predicting the native structures of proteins has become possible using computational molecular physics (CMP)─physics-based force fields sampled with proper statistics─but only for small proteins. Algorithms with better scaling are needed. We describe ML x MELD x MD, a molecular dynamics (MD) method that inputs residue contacts derived from machine learning (ML) servers into MELD, a Bayesian accelerator that preserves detailed-balance statistics. Contacts are derived from trRosetta-predicted distance histograms (distograms) and are integrated into MELD's atomistic MD as spatial restraints through parametrized potential functions. In the CASP14 blind prediction event, ML x MELD x MD predicted 13 native structures to better than 4.5 Å error, including for 10 proteins in the range of 115-250 amino acids long. Also, the scaling of simulation time vs protein length is much better than unguided MD: tsim ∼ e0.023N for ML x MELD x MD vs tsim ∼ e0.168N for MD alone. This shows how machine learning information can be leveraged to advance physics-based modeling of proteins.
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Affiliation(s)
- Roy Nassar
- Laufer
Center for Physical and Quantitative Biology, Stony Brook University, Stony
Brook, New York 11794, United States
- Department
of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Emiliano Brini
- Laufer
Center for Physical and Quantitative Biology, Stony Brook University, Stony
Brook, New York 11794, United States
| | - Sridip Parui
- Laufer
Center for Physical and Quantitative Biology, Stony Brook University, Stony
Brook, New York 11794, United States
| | - Cong Liu
- Laufer
Center for Physical and Quantitative Biology, Stony Brook University, Stony
Brook, New York 11794, United States
- Department
of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
| | - Gregory L. Dignon
- Laufer
Center for Physical and Quantitative Biology, Stony Brook University, Stony
Brook, New York 11794, United States
| | - Ken A. Dill
- Laufer
Center for Physical and Quantitative Biology, Stony Brook University, Stony
Brook, New York 11794, United States
- Department
of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States
- Department
of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States
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25
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Fang R, Hon J, Zhou M, Lu Y. An empirical energy landscape reveals mechanism of proteasome in polypeptide translocation. eLife 2022; 11:71911. [PMID: 35050852 PMCID: PMC8853663 DOI: 10.7554/elife.71911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 01/17/2022] [Indexed: 11/15/2022] Open
Abstract
The ring-like ATPase complexes in the AAA+ family perform diverse cellular functions that require coordination between the conformational transitions of their individual ATPase subunits (Erzberger and Berger, 2006; Puchades et al., 2020). How the energy from ATP hydrolysis is captured to perform mechanical work by these coordinated movements is unknown. In this study, we developed a novel approach for delineating the nucleotide-dependent free-energy landscape (FEL) of the proteasome’s heterohexameric ATPase complex based on complementary structural and kinetic measurements. We used the FEL to simulate the dynamics of the proteasome and quantitatively evaluated the predicted structural and kinetic properties. The FEL model predictions are consistent with a wide range of experimental observations in this and previous studies and suggested novel mechanistic features of the proteasomal ATPases. We find that the cooperative movements of the ATPase subunits result from the design of the ATPase hexamer entailing a unique free-energy minimum for each nucleotide-binding status. ATP hydrolysis dictates the direction of substrate translocation by triggering an energy-dissipating conformational transition of the ATPase complex. In cells, many biological processes are carried out by large complexes made up of different proteins. These macromolecules act like miniature machines, flexing and moving their various parts to perform their cellular roles. One such complex is the 26S proteasome, which is responsible for recycling other proteins in the cell. The proteasome consists of approximately 31 subunits, including a ring of six ATPase enzymes that provide the complex with the energy it needs to mechanically unfold proteins. To understand how the proteasome and other large complexes work, researchers need to be able to monitor how their structure changes over time. These dynamics are challenging to probe directly with experiments, but can be assessed using computer simulations which track the movement of individual molecules and atoms. However, currently available computer systems do not have enough power to simulate the dynamics of large protein assemblies, like the 26S proteasome: for example, it would take longer than a thousand years to model how each atom in the complex moves over a timescale in which a biological change would happen (roughly 100ms). Here, Fang, Hon et al. have developed a new approach to simulate the structural dynamics of the proteasome’s ring of ATPase enzymes. Different known structures of the proteasome were used to identify the range of possible movements and shapes the complex can make. Fang, Hon et al. then used this data to calculate the energy level of each structure – also known as the ‘free energy landscape’ – and the rate of transition between them. This made it possible to simulate how the different ATPase enzymes move within the ring under a wide range of conditions. The simulated ATPase movements predicted how the proteasome machine would behave during various tasks, including degrading other proteins. Fan, Hon et al. carefully examined these predictions and found that they were consistent with experimental observations, validating their new simulation method. This work demonstrates the feasibility of simulating the actions of a large protein complex based on its free energy landscape. The results offer important insights into the functional mechanics of the 26S proteasome and related protein machines. Further work may help to simplify this process so the approach can be used to investigate the dynamics of other protein assemblies.
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Affiliation(s)
- Rui Fang
- Department of Systems Biology, Harvard Medical School, Boston, United States
| | - Jason Hon
- Department of Systems Biology, Harvard Medical School, Boston, United States
| | - Mengying Zhou
- Department of Systems Biology, Harvard Medical School, Boston, United States
| | - Ying Lu
- Department of Systems Biology, Harvard Medical School, Boston, United States
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26
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Liu C, Brini E, Dill KA. Accelerating Molecular Dynamics Enrichments of High-Affinity Ligands for Proteins. J Chem Theory Comput 2021; 18:374-379. [PMID: 34877865 DOI: 10.1021/acs.jctc.1c00855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Molecular docking algorithms are used to seek the most active compounds from a pool of ligands. In principle, molecular dynamics (MD) simulations with accurate physical potentials and sampling could yield better enrichments, but they are computationally expensive. Here, we describe a method called MELD-Bracket that utilizes biased replica exchange ladders in MD in order to compete different ligands against each other within a fast bracket style "binding tournament". MELD-Bracket finds best-binders rapidly when ligands are well separated in their binding affinities.
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Affiliation(s)
- Cong Liu
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States.,Department of Chemistry, Stony Brook University, Stony Brook, New York 11790, United States
| | - Emiliano Brini
- School of Chemistry and Materials Science, Rochester Institute of Technology, Rochester, New York 14623, United States
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States.,Department of Chemistry, Stony Brook University, Stony Brook, New York 11790, United States.,Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11790, United States
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27
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Hashem S, Macaluso V, Nottoli M, Lipparini F, Cupellini L, Mennucci B. From crystallographic data to the solution structure of photoreceptors: the case of the AppA BLUF domain. Chem Sci 2021; 12:13331-13342. [PMID: 34777752 PMCID: PMC8528011 DOI: 10.1039/d1sc03000k] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/01/2021] [Indexed: 12/28/2022] Open
Abstract
Photoreceptor proteins bind a chromophore, which, upon light absorption, modifies its geometry or its interactions with the protein, finally inducing the structural change needed to switch the protein from an inactive to an active or signaling state. In the Blue Light-Using Flavin (BLUF) family of photoreceptors, the chromophore is a flavin and the changes have been connected with a rearrangement of the hydrogen bond network around it on the basis of spectroscopic changes measured for the dark-to-light conversion. However, the exact conformational change triggered by the photoexcitation is still elusive mainly because a clear consensus on the identity not only of the light activated state but also of the dark one has not been achieved. Here, we present an integrated investigation that combines microsecond MD simulations starting from the two conflicting crystal structures available for the AppA BLUF domain with calculations of NMR, IR and UV-Vis spectra using a polarizable QM/MM approach. Thanks to such a combined analysis of the three different spectroscopic responses, a robust characterization of the structure of the dark state in solution is given together with the uncovering of important flaws of the most popular molecular mechanisms present in the literature for the dark-to-light activation.
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Affiliation(s)
- Shaima Hashem
- Dipartimento di Chimica e Chimica Industriale, Università di Pisa Via G. Moruzzi 13 56124 Pisa Italy
| | - Veronica Macaluso
- Dipartimento di Chimica e Chimica Industriale, Università di Pisa Via G. Moruzzi 13 56124 Pisa Italy
| | - Michele Nottoli
- Dipartimento di Chimica e Chimica Industriale, Università di Pisa Via G. Moruzzi 13 56124 Pisa Italy
| | - Filippo Lipparini
- Dipartimento di Chimica e Chimica Industriale, Università di Pisa Via G. Moruzzi 13 56124 Pisa Italy
| | - Lorenzo Cupellini
- Dipartimento di Chimica e Chimica Industriale, Università di Pisa Via G. Moruzzi 13 56124 Pisa Italy
| | - Benedetta Mennucci
- Dipartimento di Chimica e Chimica Industriale, Università di Pisa Via G. Moruzzi 13 56124 Pisa Italy
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28
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Walker CC, Meek GA, Fobe TL, Shirts MR. Using a Coarse-Grained Modeling Framework to Identify Oligomeric Motifs with Tunable Secondary Structure. J Chem Theory Comput 2021; 17:6018-6035. [PMID: 34495659 DOI: 10.1021/acs.jctc.1c00528] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Coarse-grained modeling can be used to explore general theories that are independent of specific chemical detail. In this paper, we present cg_openmm, a Python-based simulation framework for modeling coarse-grained hetero-oligomers and screening them for structural and thermodynamic characteristics of cooperative secondary structures. cg_openmm facilitates the building of coarse-grained topology and random starting configurations, setup of GPU-accelerated replica exchange molecular dynamics simulations with the OpenMM software package, and features a suite of postprocessing thermodynamic and structural analysis tools. In particular, native contact analysis, heat capacity calculations, and free energy of folding calculations are used to identify and characterize cooperative folding transitions and stable secondary structures. In this work, we demonstrate the capabilities of cg_openmm on a simple 1-1 Lennard-Jones coarse-grained model, in which each residue contains 1 backbone and 1 side-chain bead. By scanning both nonbonded and bonded force-field parameter spaces at the coarse-grained level, we identify and characterize sets of parameters which result in the formation of stable helices through cooperative folding transitions. Moreover, we show that the geometries and stabilities of these helices can be tuned by manipulating the force-field parameters.
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Affiliation(s)
- Christopher C Walker
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Garrett A Meek
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Theodore L Fobe
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
| | - Michael R Shirts
- Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, Colorado 80309, United States
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29
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Nassar R, Dignon GL, Razban RM, Dill KA. The Protein Folding Problem: The Role of Theory. J Mol Biol 2021; 433:167126. [PMID: 34224747 PMCID: PMC8547331 DOI: 10.1016/j.jmb.2021.167126] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 06/21/2021] [Accepted: 06/26/2021] [Indexed: 10/20/2022]
Abstract
The protein folding problem was first articulated as question of how order arose from disorder in proteins: How did the various native structures of proteins arise from interatomic driving forces encoded within their amino acid sequences, and how did they fold so fast? These matters have now been largely resolved by theory and statistical mechanics combined with experiments. There are general principles. Chain randomness is overcome by solvation-based codes. And in the needle-in-a-haystack metaphor, native states are found efficiently because protein haystacks (conformational ensembles) are funnel-shaped. Order-disorder theory has now grown to encompass a large swath of protein physical science across biology.
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Affiliation(s)
- Roy Nassar
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA; Department of Chemistry, Stony Brook University, Stony Brook, NY, USA
| | - Gregory L Dignon
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Rostam M Razban
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA; Department of Chemistry, Stony Brook University, Stony Brook, NY, USA; Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA.
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30
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Borg AM, Baker JE. Contemporary biomedical engineering perspective on volitional evolution for human radiotolerance enhancement beyond low-earth orbit. Synth Biol (Oxf) 2021; 6:ysab023. [PMID: 34522784 PMCID: PMC8434797 DOI: 10.1093/synbio/ysab023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 07/15/2021] [Accepted: 09/01/2021] [Indexed: 11/14/2022] Open
Abstract
A primary objective of the National Aeronautics and Space Administration (NASA) is expansion of humankind's presence outside low-Earth orbit, culminating in permanent interplanetary travel and habitation. Having no inherent means of physiological detection or protection against ionizing radiation, humans incur capricious risk when journeying beyond low-Earth orbit for long periods. NASA has made large investments to analyze pathologies from space radiation exposure, emphasizing the importance of characterizing radiation's physiological effects. Because natural evolution would require many generations to confer resistance against space radiation, immediately pragmatic approaches should be considered. Volitional evolution, defined as humans steering their own heredity, may inevitably retrofit the genome to mitigate resultant pathologies from space radiation exposure. Recently, uniquely radioprotective genes have been identified, conferring local or systemic radiotolerance when overexpressed in vitro and in vivo. Aiding in this process, the CRISPR/Cas9 technique is an inexpensive and reproducible instrument capable of making limited additions and deletions to the genome. Although cohorts can be identified and engineered to protect against radiation, alternative and supplemental strategies should be seriously considered. Advanced propulsion and mild synthetic torpor are perhaps the most likely to be integrated. Interfacing artificial intelligence with genetic engineering using predefined boundary conditions may enable the computational modeling of otherwise overly complex biological networks. The ethical context and boundaries of introducing genetically pioneered humans are considered.
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Affiliation(s)
- Alexander M Borg
- Departments of Biomedical Engineering and Radiation Oncology, Wake Forest University, Winston-Salem, NC, USA
| | - John E Baker
- Radiation Biosciences Laboratory, Medical College of Wisconsin, Milwaukee, WI, USA
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31
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Greener JG, Jones DT. Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins. PLoS One 2021; 16:e0256990. [PMID: 34473813 PMCID: PMC8412298 DOI: 10.1371/journal.pone.0256990] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/19/2021] [Indexed: 11/26/2022] Open
Abstract
Finding optimal parameters for force fields used in molecular simulation is a challenging and time-consuming task, partly due to the difficulty of tuning multiple parameters at once. Automatic differentiation presents a general solution: run a simulation, obtain gradients of a loss function with respect to all the parameters, and use these to improve the force field. This approach takes advantage of the deep learning revolution whilst retaining the interpretability and efficiency of existing force fields. We demonstrate that this is possible by parameterising a simple coarse-grained force field for proteins, based on training simulations of up to 2,000 steps learning to keep the native structure stable. The learned potential matches chemical knowledge and PDB data, can fold and reproduce the dynamics of small proteins, and shows ability in protein design and model scoring applications. Problems in applying differentiable molecular simulation to all-atom models of proteins are discussed along with possible solutions and the variety of available loss functions. The learned potential, simulation scripts and training code are made available at https://github.com/psipred/cgdms.
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Affiliation(s)
- Joe G. Greener
- Department of Computer Science, University College London, London, United Kingdom
| | - David T. Jones
- Department of Computer Science, University College London, London, United Kingdom
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32
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Powers ET, Gierasch LM. The Proteome Folding Problem and Cellular Proteostasis. J Mol Biol 2021; 433:167197. [PMID: 34391802 DOI: 10.1016/j.jmb.2021.167197] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 08/04/2021] [Accepted: 08/04/2021] [Indexed: 12/16/2022]
Abstract
Stunning advances have been achieved in addressing the protein folding problem, providing deeper understanding of the mechanisms by which proteins navigate energy landscapes to reach their native states and enabling powerful algorithms to connect sequence to structure. However, the realities of the in vivo protein folding problem remain a challenge to reckon with. Here, we discuss the concept of the "proteome folding problem"-the problem of how organisms build and maintain a functional proteome-by admitting that folding energy landscapes are characterized by many misfolded states and that cells must deploy a network of chaperones and degradation enzymes to minimize deleterious impacts of these off-pathway species. The resulting proteostasis network is an inextricable part of in vivo protein folding and must be understood in detail if we are to solve the proteome folding problem. We discuss how the development of computational models for the proteostasis network's actions and the relationship to the biophysical properties of the proteome has begun to offer new insights and capabilities.
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Affiliation(s)
- Evan T Powers
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA 92037, USA.
| | - Lila M Gierasch
- Departments of Biochemistry & Molecular Biology and Chemistry, University of Massachusetts-Amherst, Amherst, MA 01003, USA.
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33
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Bouatta N, Sorger P, AlQuraishi M. Protein structure prediction by AlphaFold2: are attention and symmetries all you need? Acta Crystallogr D Struct Biol 2021; 77:982-991. [PMID: 34342271 PMCID: PMC8329862 DOI: 10.1107/s2059798321007531] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 07/21/2021] [Indexed: 11/11/2022] Open
Abstract
The functions of most proteins result from their 3D structures, but determining their structures experimentally remains a challenge, despite steady advances in crystallography, NMR and single-particle cryoEM. Computationally predicting the structure of a protein from its primary sequence has long been a grand challenge in bioinformatics, intimately connected with understanding protein chemistry and dynamics. Recent advances in deep learning, combined with the availability of genomic data for inferring co-evolutionary patterns, provide a new approach to protein structure prediction that is complementary to longstanding physics-based approaches. The outstanding performance of AlphaFold2 in the recent Critical Assessment of protein Structure Prediction (CASP14) experiment demonstrates the remarkable power of deep learning in structure prediction. In this perspective, we focus on the key features of AlphaFold2, including its use of (i) attention mechanisms and Transformers to capture long-range dependencies, (ii) symmetry principles to facilitate reasoning over protein structures in three dimensions and (iii) end-to-end differentiability as a unifying framework for learning from protein data. The rules of protein folding are ultimately encoded in the physical principles that underpin it; to conclude, the implications of having a powerful computational model for structure prediction that does not explicitly rely on those principles are discussed.
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Affiliation(s)
- Nazim Bouatta
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Peter Sorger
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA 02115, USA
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34
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Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A, Meyer C, Kohl SAA, Ballard AJ, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D. Highly accurate protein structure prediction with AlphaFold. Nature 2021; 596:583-589. [PMID: 34265844 PMCID: PMC8371605 DOI: 10.1038/s41586-021-03819-2] [Citation(s) in RCA: 16445] [Impact Index Per Article: 5481.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 07/12/2021] [Indexed: 02/07/2023]
Abstract
Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1-4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence-the structure prediction component of the 'protein folding problem'8-has been an important open research problem for more than 50 years9. Despite recent progress10-14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Martin Steinegger
- School of Biological Sciences, Seoul National University, Seoul, South Korea
- Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
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35
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Varghese A, Chaturvedi SS, Fields GB, Karabencheva-Christova TG. A synergy between the catalytic and structural Zn(II) ions and the enzyme and substrate dynamics underlies the structure-function relationships of matrix metalloproteinase collagenolysis. J Biol Inorg Chem 2021; 26:583-597. [PMID: 34228191 DOI: 10.1007/s00775-021-01876-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 05/28/2021] [Indexed: 10/20/2022]
Abstract
Matrix metalloproteinases (MMPs) are Zn(II) dependent endopeptidases involved in the degradation of collagen. Unbalanced collagen breakdown results in numerous pathological conditions, including cardiovascular and neurodegenerative diseases and tumor growth and invasion. Matrix metalloproteinase-1 (MMP-1) is a member of the MMPs family. The enzyme contains catalytic and structural Zn(II) ions. Despite many studies on the enzyme, there is little known about the synergy between the two Zn(II) metal ions and the enzyme and substrate dynamics in MMP-1 structure-function relationships. We performed a computational study of the MMP-1•triple-helical peptide (THP) enzyme•substrate complex to provide this missing insight. Our results revealed Zn(II) ions' importance in modulating the long-range correlated motions in the MMP-1•THP complex. Overall, our results reveal the importance of the catalytic Zn(II) and the role of the structural Zn(II) ion in preserving the integrity of the enzyme active site and the overall enzyme-substrate complex synergy with the dynamics of the enzyme and the substrate. Notably, both Zn(II) sites participate in diverse networks of long-range correlated motions that involve the CAT and HPX domains and the THP substrate, thus exercising a complex role in the stability and functionality of the MMP-1•THP complex. Both the Zn(II) ions have a distinct impact on the structural stability and dynamics of the MMP-1•THP complex. The study shifts the paradigm from the "local role" of the Zn(II) ions with knowledge about their essential role in the long-range dynamics and stability of the overall enzyme•substrate (ES) complex.
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Affiliation(s)
- Ann Varghese
- Department of Chemistry, Michigan Technological University, Houghton, MI, 49931, USA
| | - Shobhit S Chaturvedi
- Department of Chemistry, Michigan Technological University, Houghton, MI, 49931, USA
| | - Gregg B Fields
- Department of Chemistry and Biochemistry and I-HEALTH, Florida Atlantic University, Jupiter, FL, 33458, USA
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36
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Spoel D, Zhang J, Zhang H. Quantitative predictions from molecular simulations using explicit or implicit interactions. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1560] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- David Spoel
- Uppsala Center for Computational Chemistry, Science for Life Laboratory, Department of Cell and Molecular Biology Uppsala University Uppsala Sweden
| | - Jin Zhang
- Department of Chemistry Southern University of Science and Technology Shenzhen China
| | - Haiyang Zhang
- Department of Biological Science and Engineering, School of Chemistry and Biological Engineering University of Science and Technology Beijing Beijing China
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37
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Kim S, Voth GA. Physical Characterization of Triolein and Implications for Its Role in Lipid Droplet Biogenesis. J Phys Chem B 2021; 125:6874-6888. [PMID: 34139844 DOI: 10.1021/acs.jpcb.1c03559] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Lipid droplets (LDs) are neutral lipid-storing organelles surrounded by a phospholipid (PL) monolayer. At present, how LDs are formed in the endoplasmic reticulum (ER) bilayer is poorly understood. In this study, we present a revised all-atom (AA) triolein (TG) model, the main constituent of the LD core, and characterize its properties in a bilayer membrane to demonstrate the implications of its behavior in LD biogenesis. In bilayer simulations, TG resides at the surface, adopting PL-like conformations (denoted in this work as SURF-TG). Free energy sampling simulation results estimate the barrier for TG relocating from the bilayer surface to the bilayer center to be ∼2 kcal/mol in the absence of an oil lens. SURF-TG is able to modulate membrane properties by increasing PL ordering, decreasing bending modulus, and creating local negative curvature. The other neutral lipid, dioleoyl-glycerol (DAG), also reduces the membrane bending modulus and populates negative curvature regions. A phenomenological coarse-grained (CG) model is also developed to observe larger-scale SURF-TG-mediated membrane deformation. CG simulations confirm that TG nucleates between the bilayer leaflets at a critical concentration when SURF-TG is evenly distributed. However, when one monolayer contains more SURF-TG, the membrane bends toward the other leaflet, followed by TG nucleation if a concentration is higher than the critical threshold. The central conclusion of this study is that SURF-TG is a negative curvature inducer, as well as a membrane modulator. To this end, a model is proposed in which the accumulation of SURF-TG in the luminal leaflet bends the ER bilayer toward the cytosolic side, followed by TG nucleation.
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Affiliation(s)
- Siyoung Kim
- Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, United States
| | - Gregory A Voth
- Department of Chemistry, Chicago Center for Theoretical Chemistry, James Franck Institute, and Institute for Biophysical Dynamics, The University of Chicago, Chicago, Illinois 60637, United States
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38
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Yagi K, Ito S, Sugita Y. Exploring the Minimum-Energy Pathways and Free-Energy Profiles of Enzymatic Reactions with QM/MM Calculations. J Phys Chem B 2021; 125:4701-4713. [PMID: 33914537 PMCID: PMC10986901 DOI: 10.1021/acs.jpcb.1c01862] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Understanding molecular mechanisms of enzymatic reactions is of vital importance in biochemistry and biophysics. Here, we introduce new functions of hybrid quantum mechanical/molecular mechanical (QM/MM) calculations in the GENESIS program to compute the minimum-energy pathways (MEPs) and free-energy profiles of enzymatic reactions. For this purpose, an interface in GENESIS is developed to utilize a highly parallel electronic structure program, QSimulate-QM (https://qsimulate.com), calling it as a shared library from GENESIS. Second, algorithms to search the MEP are implemented, combining the string method (E et al. J. Chem. Phys. 2007, 126, 164103) with the energy minimization of the buffer MM region. The method implemented in GENESIS is applied to an enzyme, triosephosphate isomerase, which converts dihyroxyacetone phosphate to glyceraldehyde 3-phosphate in four proton-transfer processes. QM/MM-molecular dynamics simulations show performances of greater than 1 ns/day with the density functional tight binding (DFTB), and 10-30 ps/day with the hybrid density functional theory, B3LYP-D3. These performances allow us to compute not only MEP but also the potential of mean force (PMF) of the enzymatic reactions using the QM/MM calculations. The barrier height obtained as 13 kcal mol-1 with B3LYP-D3 in the QM/MM calculation is in agreement with the experimental results. The impact of conformational sampling in PMF calculations and the level of electronic structure calculations (DFTB vs B3LYP-D3) suggests reliable computational protocols for enzymatic reactions without high computational costs.
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Affiliation(s)
- Kiyoshi Yagi
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Shingo Ito
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Yuji Sugita
- Theoretical
Molecular Science Laboratory, RIKEN Cluster
for Pioneering Research, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
- Computational
Biophysics Research Team, RIKEN Center for
Computational Science, 7-1-26 minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
- Laboratory
for Biomolecular Function Simulation, RIKEN
Center for Biosystems Dynamics Research, 1-6-5 minatojima-Minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
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39
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Machado MR, Pantano S. Fighting viruses with computers, right now. Curr Opin Virol 2021; 48:91-99. [PMID: 33975154 DOI: 10.1016/j.coviro.2021.04.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/20/2021] [Accepted: 04/06/2021] [Indexed: 10/21/2022]
Abstract
The synergistic conjunction of various technological revolutions with the accumulated knowledge and workflows is rapidly transforming several scientific fields. Particularly, Virology can now feed from accurate physical models, polished computational tools, and massive computational power to readily integrate high-resolution structures into biological representations of unprecedented detail. That preparedness allows for the first time to get crucial information for vaccine and drug design from in-silico experiments against emerging pathogens of worldwide concern at relevant action windows. The present work reviews some of the main milestones leading to these breakthroughs in Computational Virology, providing an outlook for future developments in capacity building and accessibility to computational resources.
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Affiliation(s)
- Matías R Machado
- Biomolecular Simulations Group, Institut Pasteur de Montevideo, Mataojo 2020, Montevideo, 11400, Uruguay.
| | - Sergio Pantano
- Biomolecular Simulations Group, Institut Pasteur de Montevideo, Mataojo 2020, Montevideo, 11400, Uruguay.
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40
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Schlick T, Portillo-Ledesma S, Myers CG, Beljak L, Chen J, Dakhel S, Darling D, Ghosh S, Hall J, Jan M, Liang E, Saju S, Vohr M, Wu C, Xu Y, Xue E. Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field. Annu Rev Biophys 2021; 50:267-301. [PMID: 33606945 PMCID: PMC8105287 DOI: 10.1146/annurev-biophys-091720-102019] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners.
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Affiliation(s)
- Tamar Schlick
- Department of Chemistry, New York University, New York, New York 10003, USA;
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA
- New York University-East China Normal University Center for Computational Chemistry, New York University Shanghai, Shanghai 200122, China
| | | | - Christopher G Myers
- Department of Chemistry, New York University, New York, New York 10003, USA;
| | - Lauren Beljak
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Justin Chen
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Sami Dakhel
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Daniel Darling
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Sayak Ghosh
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Joseph Hall
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Mikaeel Jan
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Emily Liang
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Sera Saju
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Mackenzie Vohr
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Chris Wu
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Yifan Xu
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Eva Xue
- College of Arts and Science, New York University, New York, New York 10003, USA
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41
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Schlick T, Portillo-Ledesma S. Biomolecular modeling thrives in the age of technology. NATURE COMPUTATIONAL SCIENCE 2021; 1:321-331. [PMID: 34423314 PMCID: PMC8378674 DOI: 10.1038/s43588-021-00060-9] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/22/2021] [Indexed: 12/12/2022]
Abstract
The biomolecular modeling field has flourished since its early days in the 1970s due to the rapid adaptation and tailoring of state-of-the-art technology. The resulting dramatic increase in size and timespan of biomolecular simulations has outpaced Moore's law. Here, we discuss the role of knowledge-based versus physics-based methods and hardware versus software advances in propelling the field forward. This rapid adaptation and outreach suggests a bright future for modeling, where theory, experimentation and simulation define three pillars needed to address future scientific and biomedical challenges.
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Affiliation(s)
- Tamar Schlick
- Department of Chemistry, New York University, New York, NY, USA
- Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
- New York University–East China Normal University Center for Computational Chemistry at New York University Shanghai, Shanghai, China
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42
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Nguyen PH, Ramamoorthy A, Sahoo BR, Zheng J, Faller P, Straub JE, Dominguez L, Shea JE, Dokholyan NV, De Simone A, Ma B, Nussinov R, Najafi S, Ngo ST, Loquet A, Chiricotto M, Ganguly P, McCarty J, Li MS, Hall C, Wang Y, Miller Y, Melchionna S, Habenstein B, Timr S, Chen J, Hnath B, Strodel B, Kayed R, Lesné S, Wei G, Sterpone F, Doig AJ, Derreumaux P. Amyloid Oligomers: A Joint Experimental/Computational Perspective on Alzheimer's Disease, Parkinson's Disease, Type II Diabetes, and Amyotrophic Lateral Sclerosis. Chem Rev 2021; 121:2545-2647. [PMID: 33543942 PMCID: PMC8836097 DOI: 10.1021/acs.chemrev.0c01122] [Citation(s) in RCA: 386] [Impact Index Per Article: 128.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Protein misfolding and aggregation is observed in many amyloidogenic diseases affecting either the central nervous system or a variety of peripheral tissues. Structural and dynamic characterization of all species along the pathways from monomers to fibrils is challenging by experimental and computational means because they involve intrinsically disordered proteins in most diseases. Yet understanding how amyloid species become toxic is the challenge in developing a treatment for these diseases. Here we review what computer, in vitro, in vivo, and pharmacological experiments tell us about the accumulation and deposition of the oligomers of the (Aβ, tau), α-synuclein, IAPP, and superoxide dismutase 1 proteins, which have been the mainstream concept underlying Alzheimer's disease (AD), Parkinson's disease (PD), type II diabetes (T2D), and amyotrophic lateral sclerosis (ALS) research, respectively, for many years.
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Affiliation(s)
- Phuong H Nguyen
- CNRS, UPR9080, Université de Paris, Laboratory of Theoretical Biochemistry, IBPC, Fondation Edmond de Rothschild, PSL Research University, Paris 75005, France
| | - Ayyalusamy Ramamoorthy
- Biophysics and Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109-1055, United States
| | - Bikash R Sahoo
- Biophysics and Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109-1055, United States
| | - Jie Zheng
- Department of Chemical & Biomolecular Engineering, The University of Akron, Akron, Ohio 44325, United States
| | - Peter Faller
- Institut de Chimie, UMR 7177, CNRS-Université de Strasbourg, 4 rue Blaise Pascal, 67000 Strasbourg, France
| | - John E Straub
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Laura Dominguez
- Facultad de Química, Departamento de Fisicoquímica, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
| | - Joan-Emma Shea
- Department of Chemistry and Biochemistry, and Department of Physics, University of California, Santa Barbara, California 93106, United States
| | - Nikolay V Dokholyan
- Department of Pharmacology and Biochemistry & Molecular Biology, Penn State University College of Medicine, Hershey, Pennsylvania 17033, United States
- Department of Chemistry, and Biomedical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Alfonso De Simone
- Department of Life Sciences, Imperial College London, London SW7 2AZ, U.K
- Molecular Biology, University of Naples Federico II, Naples 80138, Italy
| | - Buyong Ma
- Basic Science Program, Leidos Biomedical Research, Inc., Cancer and Inflammation Program, National Cancer Institute, Frederick, Maryland 21702, United States
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai, China
| | - Ruth Nussinov
- Basic Science Program, Leidos Biomedical Research, Inc., Cancer and Inflammation Program, National Cancer Institute, Frederick, Maryland 21702, United States
- Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Saeed Najafi
- Department of Chemistry and Biochemistry, and Department of Physics, University of California, Santa Barbara, California 93106, United States
| | - Son Tung Ngo
- Laboratory of Theoretical and Computational Biophysics & Faculty of Applied Sciences, Ton Duc Thang University, 33000 Ho Chi Minh City, Vietnam
| | - Antoine Loquet
- Institute of Chemistry & Biology of Membranes & Nanoobjects, (UMR5248 CBMN), CNRS, Université Bordeaux, Institut Européen de Chimie et Biologie, 33600 Pessac, France
| | - Mara Chiricotto
- Department of Chemical Engineering and Analytical Science, University of Manchester, Manchester M13 9PL, U.K
| | - Pritam Ganguly
- Department of Chemistry and Biochemistry, and Department of Physics, University of California, Santa Barbara, California 93106, United States
| | - James McCarty
- Chemistry Department, Western Washington University, Bellingham, Washington 98225, United States
| | - Mai Suan Li
- Institute for Computational Science and Technology, SBI Building, Quang Trung Software City, Tan Chanh Hiep Ward, District 12, Ho Chi Minh City 700000, Vietnam
- Institute of Physics, Polish Academy of Sciences, Al. Lotnikow 32/46, 02-668 Warsaw, Poland
| | - Carol Hall
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695-7905, United States
| | - Yiming Wang
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina 27695-7905, United States
| | - Yifat Miller
- Department of Chemistry and The Ilse Katz Institute for Nanoscale Science & Technology, Ben-Gurion University of the Negev, Be'er Sheva 84105, Israel
| | | | - Birgit Habenstein
- Institute of Chemistry & Biology of Membranes & Nanoobjects, (UMR5248 CBMN), CNRS, Université Bordeaux, Institut Européen de Chimie et Biologie, 33600 Pessac, France
| | - Stepan Timr
- CNRS, UPR9080, Université de Paris, Laboratory of Theoretical Biochemistry, IBPC, Fondation Edmond de Rothschild, PSL Research University, Paris 75005, France
| | - Jiaxing Chen
- Department of Pharmacology and Biochemistry & Molecular Biology, Penn State University College of Medicine, Hershey, Pennsylvania 17033, United States
| | - Brianna Hnath
- Department of Pharmacology and Biochemistry & Molecular Biology, Penn State University College of Medicine, Hershey, Pennsylvania 17033, United States
| | - Birgit Strodel
- Institute of Complex Systems: Structural Biochemistry (ICS-6), Forschungszentrum Jülich, 52425 Jülich, Germany
| | - Rakez Kayed
- Mitchell Center for Neurodegenerative Diseases, and Departments of Neurology, Neuroscience and Cell Biology, University of Texas Medical Branch, Galveston, Texas 77555, United States
| | - Sylvain Lesné
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Guanghong Wei
- Department of Physics, State Key Laboratory of Surface Physics, and Key Laboratory for Computational Physical Science, Multiscale Research Institute of Complex Systems, Fudan University, Shanghai 200438, China
| | - Fabio Sterpone
- CNRS, UPR9080, Université de Paris, Laboratory of Theoretical Biochemistry, IBPC, Fondation Edmond de Rothschild, PSL Research University, Paris 75005, France
| | - Andrew J Doig
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PT, U.K
| | - Philippe Derreumaux
- CNRS, UPR9080, Université de Paris, Laboratory of Theoretical Biochemistry, IBPC, Fondation Edmond de Rothschild, PSL Research University, Paris 75005, France
- Laboratory of Theoretical Chemistry, Ton Duc Thang University, 33000 Ho Chi Minh City, Vietnam
- Faculty of Pharmacy, Ton Duc Thang University, 33000 Ho Chi Minh City, Vietnam
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43
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March D, Bianco V, Franzese G. Protein Unfolding and Aggregation near a Hydrophobic Interface. Polymers (Basel) 2021; 13:polym13010156. [PMID: 33401542 PMCID: PMC7795562 DOI: 10.3390/polym13010156] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 12/22/2020] [Accepted: 12/24/2020] [Indexed: 01/29/2023] Open
Abstract
The behavior of proteins near interfaces is relevant for biological and medical purposes. Previous results in bulk show that, when the protein concentration increases, the proteins unfold and, at higher concentrations, aggregate. Here, we study how the presence of a hydrophobic surface affects this course of events. To this goal, we use a coarse-grained model of proteins and study by simulations their folding and aggregation near an ideal hydrophobic surface in an aqueous environment by changing parameters such as temperature and hydrophobic strength, related, e.g., to ions concentration. We show that the hydrophobic surface, as well as the other parameters, affect both the protein unfolding and aggregation. We discuss the interpretation of these results and define future lines for further analysis, with their possible implications in neurodegenerative diseases.
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Affiliation(s)
- David March
- Secció de Física Estadística i Interdisciplinària—Departament de Física de la Matèria Condensada, Facultat de Física, Universitat de Barcelona, Martí i Franquès 1, 08028 Barcelona, Spain;
| | - Valentino Bianco
- Chemical Physics Department, Faculty of Chemistry, Universidad Complutense de Madrid, Plaza de las Ciencias, Ciudad Universitaria, 28040 Madrid, Spain
- Correspondence: (V.B.); (G.F.)
| | - Giancarlo Franzese
- Secció de Física Estadística i Interdisciplinària—Departament de Física de la Matèria Condensada, Facultat de Física, Universitat de Barcelona, Martí i Franquès 1, 08028 Barcelona, Spain;
- Correspondence: (V.B.); (G.F.)
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