1
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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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2
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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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3
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Mondal A, Lenz S, MacCallum JL, Perez A. Hybrid computational methods combining experimental information with molecular dynamics. Curr Opin Struct Biol 2023; 81:102609. [PMID: 37224642 DOI: 10.1016/j.sbi.2023.102609] [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: 02/05/2023] [Revised: 04/12/2023] [Accepted: 04/23/2023] [Indexed: 05/26/2023]
Abstract
A goal of structural biology is to understand how macromolecules carry out their biological roles by identifying their metastable states, mechanisms of action, pathways leading to conformational changes, and the thermodynamic and kinetic relationships between those states. Integrative modeling brings structural insights into systems where traditional structure determination approaches cannot help. We focus on the synergies and challenges of integrative modeling combining experimental data with molecular dynamics simulations.
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Affiliation(s)
- Arup Mondal
- Quantum Theory Project, Department of Chemistry, University of Florida, Leigh, UK. https://twitter.com/@amondal_chem
| | - Stefan Lenz
- Department of Chemistry, University of Calgary, 2500 University Drive, Canada
| | - Justin L MacCallum
- Department of Chemistry, University of Calgary, 2500 University Drive, Canada. https://twitter.com/@jlmaccal
| | - Alberto Perez
- Quantum Theory Project, Department of Chemistry, University of Florida, Leigh, UK.
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4
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Li EH, Spaman LE, Tejero R, Janet Huang Y, Ramelot TA, Fraga KJ, Prestegard JH, Kennedy MA, Montelione GT. Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2023; 352:107481. [PMID: 37257257 PMCID: PMC10659763 DOI: 10.1016/j.jmr.2023.107481] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 05/08/2023] [Accepted: 05/15/2023] [Indexed: 06/02/2023]
Abstract
Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open-source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15N-1H residual dipolar coupling data. For these nine small (70-108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research.
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Affiliation(s)
- Ethan H Li
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
| | - Laura E Spaman
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
| | - Roberto Tejero
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
| | - Yuanpeng Janet Huang
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
| | - Theresa A Ramelot
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
| | - Keith J Fraga
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
| | - James H Prestegard
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA.
| | - Michael A Kennedy
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056, USA.
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
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5
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Chang L, Mondal A, MacCallum JL, Perez A. CryoFold 2.0: Cryo-EM Structure Determination with MELD. J Phys Chem A 2023; 127:3906-3913. [PMID: 37084537 DOI: 10.1021/acs.jpca.3c01731] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2023]
Abstract
Cryo-electron microscopy data are becoming more prevalent and accessible at higher resolution levels, leading to the development of new computational tools to determine the atomic structure of macromolecules. However, while existing tools adapted from X-ray crystallography are suitable for the highest-resolution maps, new tools are needed for lower-resolution levels and to account for map heterogeneity. In this article, we introduce CryoFold 2.0, an integrative physics-based approach that combines Bayesian inference and the ability to handle multiple data sources with the molecular dynamics flexible fitting (MDFF) approach to determine the structures of macromolecules by using cryo-EM data. CryoFold 2.0 is incorporated into the MELD (modeling employing limited data) plugin, resulting in a pipeline that is more computationally efficient and accurate than running MELD or MDFF alone. The approach requires fewer computational resources and shorter simulation times than the original CryoFold, and it minimizes manual intervention. We demonstrate the effectiveness of the approach on eight different systems, highlighting its various benefits.
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Affiliation(s)
- Liwei Chang
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Arup Mondal
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
| | - Justin L MacCallum
- Department of Chemistry, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Alberto Perez
- Department of Chemistry and Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States
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6
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Li EH, Spaman L, Tejero R, Huang YJ, Ramelot TA, Fraga KJ, Prestegard JH, Kennedy MA, Montelione GT. Blind Assessment of Monomeric AlphaFold2 Protein Structure Models with Experimental NMR Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.22.525096. [PMID: 36712039 PMCID: PMC9882346 DOI: 10.1101/2023.01.22.525096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Recent advances in molecular modeling of protein structures are changing the field of structural biology. AlphaFold-2 (AF2), an AI system developed by DeepMind, Inc., utilizes attention-based deep learning to predict models of protein structures with high accuracy relative to structures determined by X-ray crystallography and cryo-electron microscopy (cryoEM). Comparing AF2 models to structures determined using solution NMR data, both high similarities and distinct differences have been observed. Since AF2 was trained on X-ray crystal and cryoEM structures, we assessed how accurately AF2 can model small, monomeric, solution protein NMR structures which (i) were not used in the AF2 training data set, and (ii) did not have homologous structures in the Protein Data Bank at the time of AF2 training. We identified nine open source protein NMR data sets for such "blind" targets, including chemical shift, raw NMR FID data, NOESY peak lists, and (for 1 case) 15 N- 1 H residual dipolar coupling data. For these nine small (70 - 108 residues) monomeric proteins, we generated AF2 prediction models and assessed how well these models fit to these experimental NMR data, using several well-established NMR structure validation tools. In most of these cases, the AF2 models fit the NMR data nearly as well, or sometimes better than, the corresponding NMR structure models previously deposited in the Protein Data Bank. These results provide benchmark NMR data for assessing new NMR data analysis and protein structure prediction methods. They also document the potential for using AF2 as a guiding tool in protein NMR data analysis, and more generally for hypothesis generation in structural biology research. Highlights AF2 models assessed against NMR data for 9 monomeric proteins not used in training.AF2 models fit NMR data almost as well as the experimentally-determined structures. RPF-DP, PSVS , and PDBStat software provide structure quality and RDC assessment. RPF-DP analysis using AF2 models suggests multiple conformational states.
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Affiliation(s)
- Ethan H. Li
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Laura Spaman
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Roberto Tejero
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Yuanpeng Janet Huang
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Theresa A. Ramelot
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Keith J. Fraga
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - James H. Prestegard
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602 USA
| | - Michael A. Kennedy
- Department of Chemistry and Biochemistry, Miami University, Oxford, OH 45056 USA
| | - Gaetano T. Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
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7
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Ślusarz R, Lubecka EA, Czaplewski C, Liwo A. Improvements and new functionalities of UNRES server for coarse-grained modeling of protein structure, dynamics, and interactions. Front Mol Biosci 2022; 9:1071428. [PMID: 36589235 PMCID: PMC9794589 DOI: 10.3389/fmolb.2022.1071428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 11/29/2022] [Indexed: 12/15/2022] Open
Abstract
In this paper we report the improvements and extensions of the UNRES server (https://unres-server.chem.ug.edu.pl) for physics-based simulations with the coarse-grained UNRES model of polypeptide chains. The improvements include the replacement of the old code with the recently optimized one and adding the recent scale-consistent variant of the UNRES force field, which performs better in the modeling of proteins with the β and the α+β structures. The scope of applications of the package was extended to data-assisted simulations with restraints from nuclear magnetic resonance (NMR) and chemical crosslink mass-spectroscopy (XL-MS) measurements. NMR restraints can be input in the NMR Exchange Format (NEF), which has become a standard. Ambiguous NMR restraints are handled without expert intervention owing to a specially designed penalty function. The server can be used to run smaller jobs directly or to prepare input data to run larger production jobs by using standalone installations of UNRES.
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Affiliation(s)
- Rafał Ślusarz
- Faculty of Chemistry, University of Gdańsk, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Emilia A. Lubecka
- Faculty of Electronics, Telecommunication and Informatics, Gdańsk University of Technology, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Cezary Czaplewski
- Faculty of Chemistry, University of Gdańsk, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland
| | - Adam Liwo
- Faculty of Chemistry, University of Gdańsk, Fahrenheit Union of Universities in Gdańsk, Gdańsk, Poland,*Correspondence: Adam Liwo,
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8
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Hu K, Lee W, Montelione GT, Sgourakis NG, Vögeli B. Editorial: Computational approaches for interpreting experimental data and understanding protein structure, dynamics and function relationships. Front Mol Biosci 2022; 9:1018149. [PMID: 36262477 PMCID: PMC9576191 DOI: 10.3389/fmolb.2022.1018149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/15/2022] [Indexed: 11/16/2022] Open
Affiliation(s)
- Kaifeng Hu
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Woonghee Lee
- Department of Chemistry, University of Colorado Denver, Denver, CO, United States
- *Correspondence: Woonghee Lee,
| | - Gaetano T. Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States
| | - Nikolaos G. Sgourakis
- Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, and Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Beat Vögeli
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
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9
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Lubecka EA, Liwo A. A coarse-grained approach to NMR-data-assisted modeling of protein structures. J Comput Chem 2022; 43:2047-2059. [PMID: 36134668 DOI: 10.1002/jcc.27003] [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: 03/25/2022] [Revised: 08/03/2022] [Accepted: 09/05/2022] [Indexed: 11/06/2022]
Abstract
The ESCASA algorithm for analytical estimation of proton positions from coarse-grained geometry developed in our recent work has been implemented in modeling protein structures with the highly coarse-grained UNRES model of polypeptide chains (two sites per residue) and nuclear magnetic resonance (NMR) data. A penalty function with the shape of intersecting gorges was applied to treat ambiguous distance restraints, which automatically selects consistent restraints. Hamiltonian replica exchange molecular dynamics was used to carry out the conformational search. The method was tested with both unambiguous and ambiguous restraints producing good-quality models with GDT_TS from 7.4 units higher to 14.4 units lower than those obtained with the CYANA or MELD software for protein-structure determination from NMR data at the all-atom resolution. The method can thus be applied in modeling the structures of flexible proteins, for which extensive conformational search enabled by coarse-graining is more important than high modeling accuracy.
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Affiliation(s)
- Emilia A Lubecka
- Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Gdańsk, Poland
| | - Adam Liwo
- Faculty of Chemistry, University of Gdańsk, Gdańsk, Poland
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10
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Tejero R, Huang YJ, Ramelot TA, Montelione GT. AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures. Front Mol Biosci 2022; 9:877000. [PMID: 35769913 PMCID: PMC9234698 DOI: 10.3389/fmolb.2022.877000] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracies rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15N-1H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology.
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Affiliation(s)
- Roberto Tejero
- Departamento de Química Física, Universidad de Valencia, Valencia, Spain
| | - Yuanpeng Janet Huang
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States
| | - Theresa A. Ramelot
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States
| | - Gaetano T. Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, NY, United States
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11
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Martin-Malpartida P, Arrastia-Casado S, Farrera-Sinfreu J, Lucas R, Fischer H, Fischer B, Eaton DC, Tzotzos S, Macias MJ. Conformational ensemble of the TNF-derived peptide solnatide in solution. Comput Struct Biotechnol J 2022; 20:2082-2090. [PMID: 35601958 PMCID: PMC9079168 DOI: 10.1016/j.csbj.2022.04.031] [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: 03/21/2022] [Revised: 04/21/2022] [Accepted: 04/21/2022] [Indexed: 12/12/2022] Open
Abstract
Tumor necrosis factor (TNF) is a homotrimer that has two spatially distinct binding regions, three lectin-like domains (LLD) at the TIP of the protein and three basolaterally located receptor-binding sites, the latter of which are responsible for the inflammatory and cell death-inducing properties of the cytokine. Solnatide (a.k.a. TIP peptide, AP301) is a 17-mer cyclic peptide that mimics the LLD of human TNF which activates the amiloride-sensitive epithelial sodium channel (ENaC) and, as such, recapitulates the capacity of TNF to enhance alveolar fluid clearance, as demonstrated in numerous preclinical studies. TNF and solnatide interact with glycoproteins and these interactions are necessary for their trypanolytic and ENaC-activating activities. In view of the crucial role of ENaC in lung liquid clearance, solnatide is currently being evaluated as a novel therapeutic agent to treat pulmonary edema in patients with moderate-to-severe acute respiratory distress syndrome (ARDS), as well as severe COVID-19 patients with ARDS. To facilitate the description of the functional properties of solnatide in detail, as well as to further target-docking studies, we have analyzed its folding properties by NMR. In solution, solnatide populates a set of conformations characterized by a small hydrophobic core and two electrostatically charged poles. Using the structural information determined here and also that available for the ENaC protein, we propose a model to describe solnatide interaction with the C-terminal domain of the ENaCα subunit. This model may serve to guide future experiments to validate specific interactions with ENaCα and the design of new solnatide analogs with unexplored functionalities.
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Key Words
- AP301 peptide
- ARDS, Acute Respiratory Distress Syndrome
- AlphaFold applications
- Alveolar fluid clearance
- Amiloride-sensitive epithelial sodium channel
- Amphipathic helix
- ENaC
- ENaC, Amiloride-sensitive Epithelial Sodium/Channel
- HPLC, High Performance Liquid Chromatography
- HSQC, Heteronuclear Single Quantum Coherence
- LLD, Lectin-Like Domains
- MARCKS, Myristoylated Alanine-Rich C Kinase Substrate
- NMR, Nuclear Magnetic Resonance
- NOESY, Nuclear Overhauser Effect Spectroscopy
- PIP2, Phosphatidylinositol Bisphosphate
- Peptide NMR
- Pulmonary edema
- Solnatide structure
- TIP peptide
- TM, Transmembrane Regions
- TNF, Tumor Necrosis Factor
- TOCSY, Total Correlation Spectroscopy
- Tumor necrosis factor
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Affiliation(s)
- Pau Martin-Malpartida
- Institute for Research in Biomedicine, The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, Barcelona 08028, Spain
| | | | | | - Rudolf Lucas
- Vascular Biology Center, Dept of Pharmacology and Toxicology, Medical College of Georgia at Augusta University, Augusta, GA, USA
| | - Hendrik Fischer
- APEPTICO Forschung und Entwicklung GmbH, Mariahilferstraße 136, 1150 Vienna, Austria
| | - Bernhard Fischer
- APEPTICO Forschung und Entwicklung GmbH, Mariahilferstraße 136, 1150 Vienna, Austria
| | - Douglas C. Eaton
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Susan Tzotzos
- APEPTICO Forschung und Entwicklung GmbH, Mariahilferstraße 136, 1150 Vienna, Austria
| | - Maria J. Macias
- Institute for Research in Biomedicine, The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, Barcelona 08028, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain
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12
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Mondal A, Perez A. Simultaneous Assignment and Structure Determination of Proteins From Sparsely Labeled NMR Datasets. Front Mol Biosci 2021; 8:774394. [PMID: 34912846 PMCID: PMC8667806 DOI: 10.3389/fmolb.2021.774394] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 10/25/2021] [Indexed: 11/29/2022] Open
Abstract
Sparsely labeled NMR samples provide opportunities to study larger biomolecular assemblies than is traditionally done by NMR. This requires new computational tools that can handle the sparsity and ambiguity in the NMR datasets. The MELD (modeling employing limited data) Bayesian approach was assessed to be the best performing in predicting structures from sparsely labeled NMR data in the 13th edition of the Critical Assessment of Structure Prediction (CASP) event—and limitations of the methodology were also noted. In this report, we evaluate the nature and difficulty in modeling unassigned sparsely labeled NMR datasets and report on an improved methodological pipeline leading to higher-accuracy predictions. We benchmark our methodology against the NMR datasets provided by CASP 13.
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Affiliation(s)
- Arup Mondal
- The Quantum Theory Project, Department of Chemistry, University of Florida, Gainesville, FL, United States
| | - Alberto Perez
- The Quantum Theory Project, Department of Chemistry, University of Florida, Gainesville, FL, United States
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13
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Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J. Critical assessment of methods of protein structure prediction (CASP)-Round XIV. Proteins 2021; 89:1607-1617. [PMID: 34533838 PMCID: PMC8726744 DOI: 10.1002/prot.26237] [Citation(s) in RCA: 225] [Impact Index Per Article: 75.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 07/28/2021] [Indexed: 01/14/2023]
Abstract
Critical assessment of structure prediction (CASP) is a community experiment to advance methods of computing three-dimensional protein structure from amino acid sequence. Core components are rigorous blind testing of methods and evaluation of the results by independent assessors. In the most recent experiment (CASP14), deep-learning methods from one research group consistently delivered computed structures rivaling the corresponding experimental ones in accuracy. In this sense, the results represent a solution to the classical protein-folding problem, at least for single proteins. The models have already been shown to be capable of providing solutions for problematic crystal structures, and there are broad implications for the rest of structural biology. Other research groups also substantially improved performance. Here, we describe these results and outline some of the many implications. Other related areas of CASP, including modeling of protein complexes, structure refinement, estimation of model accuracy, and prediction of inter-residue contacts and distances, are also described.
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Affiliation(s)
- Andriy Kryshtafovych
- Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - Torsten Schwede
- University of Basel, Biozentrum & SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Maya Topf
- Centre for Structural Systems Biology, Leibniz-Institut für Experimentelle Virologie and Universit tsklinikum Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Krzysztof Fidelis
- Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA 95616, USA
| | - John Moult
- Institute for Bioscience and Biotechnology Research, 9600 Gudelsky Drive, Rockville, MD 20850, USA, Department of Cell Biology and Molecular Genetics, University of Maryland
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14
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Huang YJ, Zhang N, Bersch B, Fidelis K, Inouye M, Ishida Y, Kryshtafovych A, Kobayashi N, Kuroda Y, Liu G, LiWang A, Swapna GVT, Wu N, Yamazaki T, Montelione GT. Assessment of prediction methods for protein structures determined by NMR in CASP14: Impact of AlphaFold2. Proteins 2021; 89:1959-1976. [PMID: 34559429 PMCID: PMC8616817 DOI: 10.1002/prot.26246] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 09/09/2021] [Accepted: 09/14/2021] [Indexed: 12/26/2022]
Abstract
NMR studies can provide unique information about protein conformations in solution. In CASP14, three reference structures provided by solution NMR methods were available (T1027, T1029, and T1055), as well as a fourth data set of NMR‐derived contacts for an integral membrane protein (T1088). For the three targets with NMR‐based structures, the best prediction results ranged from very good (GDT_TS = 0.90, for T1055) to poor (GDT_TS = 0.47, for T1029). We explored the basis of these results by comparing all CASP14 prediction models against experimental NMR data. For T1027, NMR data reveal extensive internal dynamics, presenting a unique challenge for protein structure prediction methods. The analysis of T1029 motivated exploration of a novel method of “inverse structure determination,” in which an AlphaFold2 model was used to guide NMR data analysis. NMR data provided to CASP predictor groups for target T1088, a 238‐residue integral membrane porin, was also used to assess several NMR‐assisted prediction methods. Most groups involved in this exercise generated similar beta‐barrel models, with good agreement with the experimental data. However, as was also observed in CASP13, some pure prediction groups that did not use any NMR data generated models for T1088 that better fit the NMR data than the models generated using these experimental data. These results demonstrate the remarkable power of modern methods to predict structures of proteins with accuracies rivaling solution NMR structures, and that it is now possible to reliably use prediction models to guide and complement experimental NMR data analysis.
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Affiliation(s)
- Yuanpeng Janet Huang
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, USA
| | - Ning Zhang
- Department of Chemistry and Biochemistry, University of California, Merced, California, USA
| | - Beate Bersch
- Biomolecular NMR Spectroscopy Group, Institut de Biologie Structurale, UMD-5075, CNRS-CEA-UJF, Grenoble, France
| | | | - Masayori Inouye
- Department of Biochemistry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey, USA.,Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey, USA
| | - Yojiro Ishida
- Department of Biochemistry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey, USA.,Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey, USA
| | | | - Naohiro Kobayashi
- NMR Science and Development Division, RSC, RIKEN, Yokohama, Kanagawa, Japan
| | - Yutaka Kuroda
- Department of Biotechnology and Life Science, Graduate School of Engineering, Tokyo University of Agriculture and Technology (TUAT), Tokyo, Japan
| | - Gaohua Liu
- Nexomics Biosciences, Inc., Rocky Hill, New Jersey, USA
| | - Andy LiWang
- Department of Chemistry and Biochemistry, University of California, Merced, California, USA.,Center for Cellular and Biomolecular Machines and Health Sciences Research Institute, University of California, Merced, California, USA
| | - G V T Swapna
- Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey, USA
| | - Nan Wu
- College of Food and Bioengineering, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Toshio Yamazaki
- NMR Science and Development Division, RSC, RIKEN, Yokohama, Kanagawa, Japan
| | - Gaetano T Montelione
- Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, USA
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15
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Abstract
Every protein has a story-how it folds, what it binds, its biological actions, and how it misbehaves in aging or disease. Stories are often inferred from a protein's shape (i.e., its structure). But increasingly, stories are told using computational molecular physics (CMP). CMP is rooted in the principled physics of driving forces and reveals granular detail of conformational populations in space and time. Recent advances are accessing longer time scales, larger actions, and blind testing, enabling more of biology's stories to be told in the language of atomistic physics.
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Affiliation(s)
- Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, 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.,Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA
| | - Ken Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY 11794, USA. .,Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA.,Department of Physics and Astronomy, Stony Brook University, Stony Brook, New NY 11794, USA
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16
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Structural and Molecular Interaction Studies on Familial Hypercholesterolemia Causative PCSK9 Functional Domain Mutations Reveals Binding Affinity Alterations with LDLR. Int J Pept Res Ther 2020. [DOI: 10.1007/s10989-020-10121-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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