1
|
Sheikhzadeh A, Safaei M, Fadaei Naeini V, Baghani M, Foroutan M, Baniassadi M. Multiscale modeling of unfolding and bond dissociation of rubredoxin metalloprotein. J Mol Graph Model 2024; 129:108749. [PMID: 38442439 DOI: 10.1016/j.jmgm.2024.108749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 02/20/2024] [Accepted: 02/20/2024] [Indexed: 03/07/2024]
Abstract
Mechanical properties of proteins that have a crucial effect on their operation. This study used a molecular dynamics simulation package to investigate rubredoxin unfolding on the atomic scale. Different simulation techniques were applied, and due to the dissociation of covalent/hydrogen bonds, this protein demonstrates several intermediate states in force-extension behavior. A conceptual model based on the cohesive finite element method was developed to consider the intermediate damages that occur during unfolding. This model is based on force-displacement curves derived from molecular dynamics results. The proposed conceptual model is designed to accurately identify bond rupture points and determine the associated forces. This is achieved by conducting a thorough comparison between molecular dynamics and cohesive finite element results. The utilization of a viscoelastic cohesive zone model allows for the consideration of loading rate effects. This rate-dependent model can be further developed and integrated into the multiscale modeling of large assemblies of metalloproteins, providing a comprehensive understanding of mechanical behavior while maintaining a reduced computational cost.
Collapse
Affiliation(s)
- Aliakbar Sheikhzadeh
- School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran; Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, T6G 2V4, Canada
| | - Mohammad Safaei
- School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Vahid Fadaei Naeini
- Division of Machine Elements, Luleå University of Technology, Luleå, SE-97187, Sweden
| | - Mostafa Baghani
- School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
| | - Masumeh Foroutan
- Department of Physical Chemistry, School of Chemistry, College of Science, University of Tehran, Tehran, Iran.
| | - Majid Baniassadi
- School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran; University of Strasbourg, CNRS, ICUBE Laboratory, Strasbourg, France.
| |
Collapse
|
2
|
Krupa MA, Krupa P. Free-Docking and Template-Based Docking: Physics Versus Knowledge-Based Docking. Methods Mol Biol 2024; 2780:27-41. [PMID: 38987462 DOI: 10.1007/978-1-0716-3985-6_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational cost while providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model.
Collapse
Affiliation(s)
- Magdalena A Krupa
- Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland
| | - Paweł Krupa
- Institute of Physics, Polish Academy of Sciences, Warsaw, Poland.
| |
Collapse
|
3
|
Rule-Based Pruning and In Silico Identification of Essential Proteins in Yeast PPIN. Cells 2022; 11:cells11172648. [PMID: 36078056 PMCID: PMC9454873 DOI: 10.3390/cells11172648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/18/2022] [Accepted: 08/22/2022] [Indexed: 11/25/2022] Open
Abstract
Proteins are vital for the significant cellular activities of living organisms. However, not all of them are essential. Identifying essential proteins through different biological experiments is relatively more laborious and time-consuming than the computational approaches used in recent times. However, practical implementation of conventional scientific methods sometimes becomes challenging due to poor performance impact in specific scenarios. Thus, more developed and efficient computational prediction models are required for essential protein identification. An effective methodology is proposed in this research, capable of predicting essential proteins in a refined yeast protein–protein interaction network (PPIN). The rule-based refinement is done using protein complex and local interaction density information derived from the neighborhood properties of proteins in the network. Identification and pruning of non-essential proteins are equally crucial here. In the initial phase, careful assessment is performed by applying node and edge weights to identify and discard the non-essential proteins from the interaction network. Three cut-off levels are considered for each node and edge weight for pruning the non-essential proteins. Once the PPIN has been filtered out, the second phase starts with two centralities-based approaches: (1) local interaction density (LID) and (2) local interaction density with protein complex (LIDC), which are successively implemented to identify the essential proteins in the yeast PPIN. Our proposed methodology achieves better performance in comparison to the existing state-of-the-art techniques.
Collapse
|
4
|
Co NT, Li MS, Krupa P. Computational Models for the Study of Protein Aggregation. Methods Mol Biol 2022; 2340:51-78. [PMID: 35167070 DOI: 10.1007/978-1-0716-1546-1_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Protein aggregation has been studied by many groups around the world for many years because it can be the cause of a number of neurodegenerative diseases that have no effective treatment. Obtaining the structure of related fibrils and toxic oligomers, as well as describing the pathways and main factors that govern the self-organization process, is of paramount importance, but it is also very difficult. To solve this problem, experimental and computational methods are often combined to get the most out of each method. The effectiveness of the computational approach largely depends on the construction of a reasonable molecular model. Here we discussed different versions of the four most popular all-atom force fields AMBER, CHARMM, GROMOS, and OPLS, which have been developed for folded and intrinsically disordered proteins, or both. Continuous and discrete coarse-grained models, which were mainly used to study the kinetics of aggregation, are also summarized.
Collapse
Affiliation(s)
- Nguyen Truong Co
- Institute of Physics, Polish Academy of Sciences, Warsaw, Poland
| | - Mai Suan Li
- Institute of Physics, Polish Academy of Sciences, Warsaw, Poland
- Institute for Computational Science and Technology, Ho Chi Minh City, Vietnam
| | - Pawel Krupa
- Institute of Physics, Polish Academy of Sciences, Warsaw, Poland.
| |
Collapse
|
5
|
Karczyńska AS, Ziȩba K, Uciechowska U, Mozolewska MA, Krupa P, Lubecka EA, Lipska AG, Sikorska C, Samsonov SA, Sieradzan AK, Giełdoń A, Liwo A, Ślusarz R, Ślusarz M, Lee J, Joo K, Czaplewski C. Improved Consensus-Fragment Selection in Template-Assisted Prediction of Protein Structures with the UNRES Force Field in CASP13. J Chem Inf Model 2020; 60:1844-1864. [PMID: 31999919 PMCID: PMC7588044 DOI: 10.1021/acs.jcim.9b00864] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
![]()
The method for protein-structure
prediction, which combines the
physics-based coarse-grained UNRES force field with knowledge-based
modeling, has been developed further and tested in the 13th Community
Wide Experiment on the Critical Assessment of Techniques for Protein
Structure Prediction (CASP13). The method implements restraints from
the consensus fragments common to server models. In this work, the
server models to derive fragments have been chosen on the basis of
quality assessment; a fully automatic fragment-selection procedure
has been introduced, and Dynamic Fragment Assembly pseudopotentials
have been fully implemented. The Global Distance Test Score (GDT_TS),
averaged over our “Model 1” predictions, increased by
over 10 units with respect to CASP12 for the free-modeling category
to reach 40.82. Our “Model 1” predictions ranked 20
and 14 for all and free-modeling targets, respectively (upper 20.2%
and 14.3% of all models submitted to CASP13 in these categories, respectively),
compared to 27 (upper 21.1%) and 24 (upper 18.9%) in CASP12, respectively.
For oligomeric targets, the Interface Patch Similarity (IPS) and Interface
Contact Similarity (ICS) averaged over our best oligomer models increased
from 0.28 to 0.36 and from 12.4 to 17.8, respectively, from CASP12
to CASP13, and top-ranking models of 2 targets (H0968 and T0997o)
were obtained (none in CASP12). The improvement of our method in CASP13
over CASP12 was ascribed to the combined effect of the overall enhancement
of server-model quality, our success in selecting server models and
fragments to derive restraints, and improvements of the restraint
and potential-energy functions.
Collapse
Affiliation(s)
| | - Karolina Ziȩba
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Urszula Uciechowska
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Magdalena A Mozolewska
- Institute of Computer Science, Polish Academy of Sciences, ul. Jana Kazimierza 5, Warsaw PL-02668, Poland
| | - Paweł Krupa
- Institute of Physics, Polish Academy of Sciences, Aleja Lotników 32/46, Warsaw PL-02668, Poland
| | - Emilia A Lubecka
- Institute of Informatics, Faculty of Mathematics, Physics, and Informatics, University of Gdańsk, Wita Stwosza 57, Gdańsk 80-308, Poland
| | - Agnieszka G Lipska
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Celina Sikorska
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Sergey A Samsonov
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Adam K Sieradzan
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland.,School of Computational Sciences, Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu, Seoul 130-722, Republic of Korea
| | - Artur Giełdoń
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Adam Liwo
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland.,School of Computational Sciences, Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu, Seoul 130-722, Republic of Korea
| | - Rafał Ślusarz
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Magdalena Ślusarz
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Jooyoung Lee
- School of Computational Sciences, Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu, Seoul 130-722, Republic of Korea
| | - Keehyoung Joo
- Center for Advanced Computation, Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu, Seoul 130-722, Republic of Korea
| | - Cezary Czaplewski
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| |
Collapse
|
6
|
Recent Advances in Coarse-Grained Models for Biomolecules and Their Applications. Int J Mol Sci 2019; 20:ijms20153774. [PMID: 31375023 PMCID: PMC6696403 DOI: 10.3390/ijms20153774] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 07/28/2019] [Accepted: 07/30/2019] [Indexed: 12/23/2022] Open
Abstract
Molecular dynamics simulations have emerged as a powerful tool to study biological systems at varied length and timescales. The conventional all-atom molecular dynamics simulations are being used by the wider scientific community in routine to capture the conformational dynamics and local motions. In addition, recent developments in coarse-grained models have opened the way to study the macromolecular complexes for time scales up to milliseconds. In this review, we have discussed the principle, applicability and recent development in coarse-grained models for biological systems. The potential of coarse-grained simulation has been reviewed through state-of-the-art examples of protein folding and structure prediction, self-assembly of complexes, membrane systems and carbohydrates fiber models. The multiscale simulation approaches have also been discussed in the context of their emerging role in unravelling hierarchical level information of biosystems. We conclude this review with the future scope of coarse-grained simulations as a constantly evolving tool to capture the dynamics of biosystems.
Collapse
|
7
|
A structural model of the immune checkpoint CD160-HVEM complex derived from HDX-mass spectrometry and molecular modeling. Oncotarget 2019; 10:536-550. [PMID: 30728903 PMCID: PMC6355189 DOI: 10.18632/oncotarget.26570] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2018] [Accepted: 12/10/2018] [Indexed: 11/25/2022] Open
Abstract
CD160 is a T cell coinhibitory molecule that interacts with the herpes virus entry mediator (HVEM) on antigen-presenting cells to provide an inhibitory signal to T cells. To date, the structure of CD160 and its complex with HVEM are unknown. Here, we have identified the fragments of CD160 interacting with HVEM using ELISA tests, hydrogen/deuterium studies, affinity chromatography and mass spectrometry (MS). By combining hydrogen/deuterium exchange and mass spectrometry (HDX-MS) we obtained key information about the tertiary structure of CD160, predicting the 3D structure of the CD160–HVEM complex. Our results provide insights into the molecular architecture of this complex, serving as a useful basis for designing inhibitors for future immunotherapies.
Collapse
|
8
|
Blaszczyk M, Gront D, Kmiecik S, Kurcinski M, Kolinski M, Ciemny MP, Ziolkowska K, Panek M, Kolinski A. Protein Structure Prediction Using Coarse-Grained Models. SPRINGER SERIES ON BIO- AND NEUROSYSTEMS 2019. [DOI: 10.1007/978-3-319-95843-9_2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
|
9
|
Use of the UNRES force field in template-assisted prediction of protein structures and the refinement of server models: Test with CASP12 targets. J Mol Graph Model 2018; 83:92-99. [DOI: 10.1016/j.jmgm.2018.05.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 05/18/2018] [Accepted: 05/20/2018] [Indexed: 11/22/2022]
|
10
|
Sieradzan AK, Mozolewska MA. Extension of coarse-grained UNRES force field to treat carbon nanotubes. J Mol Model 2018; 24:121. [PMID: 29700628 PMCID: PMC5920012 DOI: 10.1007/s00894-018-3656-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 04/13/2018] [Indexed: 11/25/2022]
Abstract
Carbon nanotubes (CNTs) have recently received considerable attention because of their possible applications in various branches of nanotechnology. For their cogent application, knowledge of their interactions with biological macromolecules, especially proteins, is essential and computer simulations are very useful for such studies. Classical all-atom force fields limit simulation time scale and size of the systems significantly. Therefore, in this work, we implemented CNTs into the coarse-grained UNited RESidue (UNRES) force field. A CNT is represented as a rigid infinite-length cylinder which interacts with a protein through the Kihara potential. Energy conservation in microcanonical coarse-grained molecular dynamics simulations and temperature conservation in canonical simulations with UNRES containing the CNT component have been verified. Subsequently, studies of three proteins, bovine serum albumin (BSA), soybean peroxidase (SBP), and α-chymotrypsin (CT), with and without CNTs, were performed to examine the influence of CNTs on the structure and dynamics of these proteins. It was found that nanotubes bind to these proteins and influence their structure. Our results show that the UNRES force field can be used for further studies of CNT-protein systems with 3-4 order of magnitude larger timescale than using regular all-atom force fields. Graphical abstract Bovine serum albumin (BSA), soybean peroxidase (SBP), and α-chymotrypsin (CT), with and without CNTsᅟ.
Collapse
Affiliation(s)
- Adam K Sieradzan
- Faculty of Chemistry, University of Gdansk, ul. Wita Stwosza 63, 80-308, Gdansk, Poland
| | - Magdalena A Mozolewska
- Faculty of Chemistry, University of Gdansk, ul. Wita Stwosza 63, 80-308, Gdansk, Poland.
- Institute of Computer Science, Polish Academy of Sciences, ul. Jana Kazimierza 5, 01-248, Warsaw, Poland.
| |
Collapse
|
11
|
Karczyńska AS, Mozolewska MA, Krupa P, Giełdoń A, Liwo A, Czaplewski C. Prediction of protein structure with the coarse-grained UNRES force field assisted by small X-ray scattering data and knowledge-based information. Proteins 2017; 86 Suppl 1:228-239. [PMID: 29134679 DOI: 10.1002/prot.25421] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 11/09/2017] [Accepted: 11/10/2017] [Indexed: 11/09/2022]
Abstract
A new approach to assisted protein-structure prediction has been proposed, which is based on running multiplexed replica exchange molecular dynamics simulations with the coarse-grained UNRES force field with restraints derived from knowledge-based models and distance distribution from small angle X-ray scattering (SAXS) measurements. The latter restraints are incorporated into the target function as a maximum-likelihood term that guides the shape of the simulated structures towards that defined by SAXS. The approach was first verified with the 1KOY protein, for which the distance distribution was calculated from the experimental structure, and subsequently used to predict the structures of 11 data-assisted targets in the CASP12 experiment. Major improvement of the GDT_TS was obtained for 2 targets, minor improvement for other 2 while, for 6 target GDT_TS deteriorated compared with that calculated for predictions without the SAXS data, partly because of assuming a wrong multimeric state (for Ts866) or because the crystal conformation was more compact than the solution conformation (for Ts942). Particularly good results were obtained for Ts909, in which use of SAXS data resulted in the selection of a correctly packed trimer and, subsequently, increased the GDT_TS of monomer prediction. It was found that running simulations with correct oligomeric state is essential for the success in SAXS-data-assisted prediction.
Collapse
Affiliation(s)
| | - Magdalena A Mozolewska
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk, 80-308, Poland.,Institute of Computer Science, Polish Academy of Sciences, ul. Jana Kazimierza 5, Warsaw, 01-248, Poland
| | - Paweł Krupa
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk, 80-308, Poland.,Institute of Physics, Polish Academy of Sciences, Aleja Lotników 32/46, Warsaw, PL-02668, Poland
| | - Artur Giełdoń
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk, 80-308, Poland
| | - Adam Liwo
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk, 80-308, Poland.,School of Computational Sciences, Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu, Seoul, 130-722, Republic of Korea
| | - Cezary Czaplewski
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk, 80-308, Poland
| |
Collapse
|
12
|
Karczyńska AS, Czaplewski C, Krupa P, Mozolewska MA, Joo K, Lee J, Liwo A. Ergodicity and model quality in template-restrained canonical and temperature/Hamiltonian replica exchange coarse-grained molecular dynamics simulations of proteins. J Comput Chem 2017; 38:2730-2746. [DOI: 10.1002/jcc.25070] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Revised: 07/10/2017] [Accepted: 09/01/2017] [Indexed: 01/22/2023]
Affiliation(s)
- Agnieszka S. Karczyńska
- Faculty of Chemistry; University of Gdańsk, ul. Wita Stwosza 63; Gdańsk 80-308 Poland
- Center for In Silico Protein Science; Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu; Seoul 02455 Republic of Korea
- School of Computational Sciences; Korea Institute for Advanced Study, 85 Hoegiro Dongdaemun-gu; Seoul 02455 Republic of Korea
| | - Cezary Czaplewski
- Faculty of Chemistry; University of Gdańsk, ul. Wita Stwosza 63; Gdańsk 80-308 Poland
| | - Paweł Krupa
- Faculty of Chemistry; University of Gdańsk, ul. Wita Stwosza 63; Gdańsk 80-308 Poland
- Institute of Physics, Polish Academy of Sciences, Aleja Lotników 32/46; Warsaw PL 02668 Poland
| | - Magdalena A. Mozolewska
- Faculty of Chemistry; University of Gdańsk, ul. Wita Stwosza 63; Gdańsk 80-308 Poland
- Institute of Computer Science, Polish Academy of Sciences, ul. Jana Kazimierza 5; Warsaw 01-248 Poland
| | - Keehyoung Joo
- School of Computational Sciences; Korea Institute for Advanced Study, 85 Hoegiro Dongdaemun-gu; Seoul 02455 Republic of Korea
- Center for Advanced Computation, Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu; Seoul 02455 Republic of Korea
| | - Jooyoung Lee
- Center for In Silico Protein Science; Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu; Seoul 02455 Republic of Korea
- School of Computational Sciences; Korea Institute for Advanced Study, 85 Hoegiro Dongdaemun-gu; Seoul 02455 Republic of Korea
- Center for Advanced Computation, Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu; Seoul 02455 Republic of Korea
| | - Adam Liwo
- Faculty of Chemistry; University of Gdańsk, ul. Wita Stwosza 63; Gdańsk 80-308 Poland
- Center for In Silico Protein Science; Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu; Seoul 02455 Republic of Korea
- School of Computational Sciences; Korea Institute for Advanced Study, 85 Hoegiro Dongdaemun-gu; Seoul 02455 Republic of Korea
| |
Collapse
|
13
|
Sieradzan AK, Jakubowski R. Introduction of steered molecular dynamics into UNRES coarse-grained simulations package. J Comput Chem 2017; 38:553-562. [DOI: 10.1002/jcc.24685] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Revised: 11/08/2016] [Accepted: 11/09/2016] [Indexed: 01/21/2023]
Affiliation(s)
- Adam K. Sieradzan
- Faculty of Chemistry; University of Gdańsk; Wita Stwosza 63 Gdańsk 80-308 Poland
| | - Rafał Jakubowski
- Faculty of Physics, Astronomy and Informatics, Institute of Physics, Nicolaus Copernicus University; Grudziadzka 5 Torun 87-100 Poland
| |
Collapse
|
14
|
Mozolewska MA, Krupa P, Zaborowski B, Liwo A, Lee J, Joo K, Czaplewski C. Use of Restraints from Consensus Fragments of Multiple Server Models To Enhance Protein-Structure Prediction Capability of the UNRES Force Field. J Chem Inf Model 2016; 56:2263-2279. [DOI: 10.1021/acs.jcim.6b00189] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
| | - Paweł Krupa
- Faculty
of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308 Gdańsk, Poland
| | | | - Adam Liwo
- Faculty
of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308 Gdańsk, Poland
- Center
for In Silico Protein Structure and School of Computational Sciences, Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu, Seoul 130-722, Republic of Korea
| | - Jooyoung Lee
- Center
for In Silico Protein Structure and School of Computational Sciences, Korea Institute for Advanced Study, 85 Hoegiro, Dongdaemun-gu, Seoul 130-722, Republic of Korea
| | - Keehyoung Joo
- Center
for Advanced Computation, Korea Institute for Advanced Study, 85
Hoegiro, Dongdaemun-gu, Seoul 130-722, Republic of Korea
| | - Cezary Czaplewski
- Faculty
of Chemistry, University of Gdańsk, Wita Stwosza 63, 80-308 Gdańsk, Poland
| |
Collapse
|
15
|
Krupa P, Mozolewska MA, Wiśniewska M, Yin Y, He Y, Sieradzan AK, Ganzynkowicz R, Lipska AG, Karczyńska A, Ślusarz M, Ślusarz R, Giełdoń A, Czaplewski C, Jagieła D, Zaborowski B, Scheraga HA, Liwo A. Performance of protein-structure predictions with the physics-based UNRES force field in CASP11. Bioinformatics 2016; 32:3270-3278. [PMID: 27378298 DOI: 10.1093/bioinformatics/btw404] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 06/20/2016] [Indexed: 12/20/2022] Open
Abstract
Participating as the Cornell-Gdansk group, we have used our physics-based coarse-grained UNited RESidue (UNRES) force field to predict protein structure in the 11th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP11). Our methodology involved extensive multiplexed replica exchange simulations of the target proteins with a recently improved UNRES force field to provide better reproductions of the local structures of polypeptide chains. All simulations were started from fully extended polypeptide chains, and no external information was included in the simulation process except for weak restraints on secondary structure to enable us to finish each prediction within the allowed 3-week time window. Because of simplified UNRES representation of polypeptide chains, use of enhanced sampling methods, code optimization and parallelization and sufficient computational resources, we were able to treat, for the first time, all 55 human prediction targets with sizes from 44 to 595 amino acid residues, the average size being 251 residues. Complete structures of six single-domain proteins were predicted accurately, with the highest accuracy being attained for the T0769, for which the CαRMSD was 3.8 Å for 97 residues of the experimental structure. Correct structures were also predicted for 13 domains of multi-domain proteins with accuracy comparable to that of the best template-based modeling methods. With further improvements of the UNRES force field that are now underway, our physics-based coarse-grained approach to protein-structure prediction will eventually reach global prediction capacity and, consequently, reliability in simulating protein structure and dynamics that are important in biochemical processes. AVAILABILITY AND IMPLEMENTATION Freely available on the web at http://www.unres.pl/ CONTACT: has5@cornell.edu.
Collapse
Affiliation(s)
- Paweł Krupa
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Magdalena A Mozolewska
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Marta Wiśniewska
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Yanping Yin
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Yi He
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Adam K Sieradzan
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Robert Ganzynkowicz
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Agnieszka G Lipska
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Agnieszka Karczyńska
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Magdalena Ślusarz
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Rafał Ślusarz
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Artur Giełdoń
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Cezary Czaplewski
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | - Dawid Jagieła
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| | | | - Harold A Scheraga
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853-1301, USA
| | - Adam Liwo
- Faculty of Chemistry, University of Gdańsk, Wita Stwosza 63, Gdańsk 80-308, Poland
| |
Collapse
|
16
|
Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A. Coarse-Grained Protein Models and Their Applications. Chem Rev 2016; 116:7898-936. [DOI: 10.1021/acs.chemrev.6b00163] [Citation(s) in RCA: 555] [Impact Index Per Article: 69.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Sebastian Kmiecik
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Dominik Gront
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Michal Kolinski
- Bioinformatics
Laboratory, Mossakowski Medical Research Center of the Polish Academy of Sciences, Pawinskiego 5, 02-106 Warsaw, Poland
| | - Lukasz Wieteska
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
- Department
of Medical Biochemistry, Medical University of Lodz, Mazowiecka 6/8, 92-215 Lodz, Poland
| | | | - Andrzej Kolinski
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| |
Collapse
|