1
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Jain T, Mishra P, Kumar S, Panda G, Banerjee D. Molecular dissection studies of TAC1, a transcription activator of Candida drug resistance genes of the human pathogenic fungus Candida albicans. Front Microbiol 2023; 14:994873. [PMID: 37502396 PMCID: PMC10370356 DOI: 10.3389/fmicb.2023.994873] [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: 07/15/2022] [Accepted: 06/06/2023] [Indexed: 07/29/2023] Open
Abstract
The up-regulation of ABC transporters Cdr1p and Cdr2p that efflux antifungal azole drugs are a leading cause of Multi-Drug Resistance (MDR) in the white fungus Candida albicans. C. albicans was reported to infect patients following the recent Covid-19 pandemic after they were given steroids for recovery. Previously, the TAC1 gene was identified as the transcriptional activator of Candida drug resistance genes (CDR1 and CDR2) and has no known human homologs. This makes it a good target for the development of novel antifungals. We, therefore, carried out the molecular dissection study of TAC1 to understand the functional regulation of the ABC transporter genes (CDR1 and CDR2) under its control. The N-terminal DNA Binding Domain (DBD) of Tac1p interacts with the Drug Responsive Element (DRE) present in the upstream promoter region of CDR1 and CDR2 genes of C. albicans. The interaction between DBD and DRE recruits Tac1p to the promoter of CDR genes. The C-terminal Acidic Activation Domain (AAD) of Tac1p interacts with the TATA box Binding Protein (TBP) and thus recruits TBP to the TATA box of CDR1 and CDR2 genes. Taking a cue from a previous study involving a TAC1 deletion strain that suggested that Tac1p acts as a xenobiotic receptor, in this study, we identified that the Middle Homology Region (MHR) of Tac1p acts as a probable xenobiotic binding domain (XBD) which plays an important role in Candida drug resistance. In addition, we studied the role of Tac1p in the regulation of some lipid profiling genes and stress response genes since they also contain the DRE consensus sequence and found that some of them can respond to xenobiotic stimuli.
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Affiliation(s)
- Tushar Jain
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Lucknow, Uttar Pradesh, India
- CSIR-Central Drug Research Institute, Lucknow, Uttar Pradesh, India
| | - Pankaj Mishra
- CSIR-Central Drug Research Institute, Lucknow, Uttar Pradesh, India
| | - Sushil Kumar
- CSIR-Central Drug Research Institute, Lucknow, Uttar Pradesh, India
| | - Gautam Panda
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Lucknow, Uttar Pradesh, India
- CSIR-Central Drug Research Institute, Lucknow, Uttar Pradesh, India
| | - Dibyendu Banerjee
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Lucknow, Uttar Pradesh, India
- CSIR-Central Drug Research Institute, Lucknow, Uttar Pradesh, India
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2
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Mehrabiani KM, Cheng RR, Onuchic JN. Expanding Direct Coupling Analysis to Identify Heterodimeric Interfaces from Limited Protein Sequence Data. J Phys Chem B 2021; 125:11408-11417. [PMID: 34618469 DOI: 10.1021/acs.jpcb.1c07145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Direct coupling analysis (DCA) is a global statistical approach that uses information encoded in protein sequence data to predict spatial contacts in a three-dimensional structure of a folded protein. DCA has been widely used to predict the monomeric fold at amino acid resolution and to identify biologically relevant interaction sites within a folded protein. Going beyond single proteins, DCA has also been used to identify spatial contacts that stabilize the interaction in protein complex formation. However, extracting this higher order information necessary to predict dimer contacts presents a significant challenge. A DCA evolutionary signal is much stronger at the single protein level (intraprotein contacts) than at the protein-protein interface (interprotein contacts). Therefore, if DCA-derived information is to be used to predict the structure of these complexes, there is a need to identify statistically significant DCA predictions. We propose a simple Z-score measure that can filter good predictions despite noisy, limited data. This new methodology not only improves our prediction ability but also provides a quantitative measure for the validity of the prediction.
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Affiliation(s)
- Kareem M Mehrabiani
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.,Systems, Synthetic, and Physical Biology, Rice University, Houston, Texas 77005, United States
| | - Ryan R Cheng
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States
| | - José N Onuchic
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.,Systems, Synthetic, and Physical Biology, Rice University, Houston, Texas 77005, United States.,Department of Physics & Astronomy, Rice University, Houston, Texas 77005, United States.,Department of Chemistry, Rice University, Houston, Texas 77005, United States.,Department of Biosciences, Rice University, Houston, Texas 77005, United States
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3
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Ma YW, Lin TY, Tsai MY. Fibril Surface-Dependent Amyloid Precursors Revealed by Coarse-Grained Molecular Dynamics Simulation. Front Mol Biosci 2021; 8:719320. [PMID: 34422910 PMCID: PMC8378332 DOI: 10.3389/fmolb.2021.719320] [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: 06/02/2021] [Accepted: 07/26/2021] [Indexed: 01/05/2023] Open
Abstract
Amyloid peptides are known to self-assemble into larger aggregates that are linked to the pathogenesis of many neurodegenerative disorders. In contrast to primary nucleation, recent experimental and theoretical studies have shown that many toxic oligomeric species are generated through secondary processes on a pre-existing fibrillar surface. Nucleation, for example, can also occur along the surface of a pre-existing fibril—secondary nucleation—as opposed to the primary one. However, explicit pathways are still not clear. In this study, we use molecular dynamics simulation to explore the free energy landscape of a free Abeta monomer binding to an existing fibrillar surface. We specifically look into several potential Abeta structural precursors that might precede some secondary events, including elongation and secondary nucleation. We find that the overall process of surface-dependent events can be described at least by the following three stages: 1. Free diffusion 2. Downhill guiding 3. Dock and lock. And we show that the outcome of adding a new monomer onto a pre-existing fibril is pathway-dependent, which leads to different secondary processes. To understand structural details, we have identified several monomeric amyloid precursors over the fibrillar surfaces and characterize their heterogeneity using a probability contact map analysis. Using the frustration analysis (a bioinformatics tool), we show that surface heterogeneity correlates with the energy frustration of specific local residues that form binding sites on the fibrillar structure. We further investigate the helical twisting of protofilaments of different sizes and observe a length dependence on the filament twisting. This work presents a comprehensive survey over the properties of fibril growth using a combination of several openMM-based platforms, including the GPU-enabled openAWSEM package for coarse-grained modeling, MDTraj for trajectory analysis, and pyEMMA for free energy calculation. This combined approach makes long-timescale simulation for aggregation systems as well as all-in-one analysis feasible. We show that this protocol allows us to explore fibril stability, surface binding affinity/heterogeneity, as well as fibrillar twisting. All these properties are important for understanding the molecular mechanism of surface-catalyzed secondary processes of fibril growth.
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Affiliation(s)
- Yuan-Wei Ma
- Department of Chemistry, Tamkang University, New Taipei City, Taiwan
| | - Tong-You Lin
- Department of Chemistry, Tamkang University, New Taipei City, Taiwan
| | - Min-Yeh Tsai
- Department of Chemistry, Tamkang University, New Taipei City, Taiwan
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4
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Lu W, Bueno C, Schafer NP, Moller J, Jin S, Chen X, Chen M, Gu X, Davtyan A, de Pablo JJ, Wolynes PG. OpenAWSEM with Open3SPN2: A fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations. PLoS Comput Biol 2021; 17:e1008308. [PMID: 33577557 PMCID: PMC7906472 DOI: 10.1371/journal.pcbi.1008308] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 02/25/2021] [Accepted: 01/09/2021] [Indexed: 01/28/2023] Open
Abstract
We present OpenAWSEM and Open3SPN2, new cross-compatible implementations of coarse-grained models for protein (AWSEM) and DNA (3SPN2) molecular dynamics simulations within the OpenMM framework. These new implementations retain the chemical accuracy and intrinsic efficiency of the original models while adding GPU acceleration and the ease of forcefield modification provided by OpenMM’s Custom Forces software framework. By utilizing GPUs, we achieve around a 30-fold speedup in protein and protein-DNA simulations over the existing LAMMPS-based implementations running on a single CPU core. We showcase the benefits of OpenMM’s Custom Forces framework by devising and implementing two new potentials that allow us to address important aspects of protein folding and structure prediction and by testing the ability of the combined OpenAWSEM and Open3SPN2 to model protein-DNA binding. The first potential is used to describe the changes in effective interactions that occur as a protein becomes partially buried in a membrane. We also introduced an interaction to describe proteins with multiple disulfide bonds. Using simple pairwise disulfide bonding terms results in unphysical clustering of cysteine residues, posing a problem when simulating the folding of proteins with many cysteines. We now can computationally reproduce Anfinsen’s early Nobel prize winning experiments by using OpenMM’s Custom Forces framework to introduce a multi-body disulfide bonding term that prevents unphysical clustering. Our protein-DNA simulations show that the binding landscape is funneled towards structures that are quite similar to those found using experiments. In summary, this paper provides a simulation tool for the molecular biophysics community that is both easy to use and sufficiently efficient to simulate large proteins and large protein-DNA systems that are central to many cellular processes. These codes should facilitate the interplay between molecular simulations and cellular studies, which have been hampered by the large mismatch between the time and length scales accessible to molecular simulations and those relevant to cell biology. The cell’s most important pieces of machinery are large complexes of proteins often along with nucleic acids. From the ribosome, to CRISPR-Cas9, to transcription factors and DNA-wrangling proteins like the SMC-Kleisins, these complexes allow organisms to replicate and enable cells to respond to environmental cues. Computer simulation is a key technology that can be used to connect physical theories with biological reality. Unfortunately, the time and length scales accessible to molecular simulation have not kept pace with our ambition to study the cell’s molecular factories. Many simulation codes also unfortunately remain effectively locked away from the user community who need to modify them as more of the underlying physics is learned. In this paper, we present OpenAWSEM and Open3SPN2, two new easy-to-use and easy to modify implementations of efficient and accurate coarse-grained protein and DNA simulation forcefields that can now be run hundreds of times faster than before, thereby making studies of large biomolecular machines more facile.
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Affiliation(s)
- Wei Lu
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, United States of America
- Department of Physics, Rice University, Houston, Texas, United States of America
| | - Carlos Bueno
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, United States of America
- Department of Chemistry, Rice University, Houston, Texas, United States of America
| | - Nicholas P. Schafer
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, United States of America
- Department of Chemistry, Rice University, Houston, Texas, United States of America
- Schafer Science, LLC, Houston, Texas United States of America
| | - Joshua Moller
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois, United States of America
- Argonne National Laboratory, Lemont, Illinois, United States of America
| | - Shikai Jin
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, United States of America
- Department of Biosciences, Rice University, Houston, Texas, United States of America
| | - Xun Chen
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, United States of America
- Department of Chemistry, Rice University, Houston, Texas, United States of America
| | - Mingchen Chen
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, United States of America
| | - Xinyu Gu
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, United States of America
- Department of Chemistry, Rice University, Houston, Texas, United States of America
| | - Aram Davtyan
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, United States of America
| | - Juan J. de Pablo
- Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois, United States of America
- Argonne National Laboratory, Lemont, Illinois, United States of America
| | - Peter G. Wolynes
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, United States of America
- Department of Chemistry, Rice University, Houston, Texas, United States of America
- Department of Physics, Rice University, Houston, Texas, United States of America
- Department of Biosciences, Rice University, Houston, Texas, United States of America
- * E-mail:
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5
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Chen M, Chen X, Jin S, Lu W, Lin X, Wolynes PG. Protein Structure Refinement Guided by Atomic Packing Frustration Analysis. J Phys Chem B 2020; 124:10889-10898. [PMID: 32931278 DOI: 10.1021/acs.jpcb.0c06719] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Recent advances in machine learning, bioinformatics, and the understanding of the folding problem have enabled efficient predictions of protein structures with moderate accuracy, even for targets where there is little information from templates. All-atom molecular dynamics simulations provide a route to refine such predicted structures, but unguided atomistic simulations, even when lengthy in time, often fail to eliminate incorrect structural features that would prevent the structure from becoming more energetically favorable owing to the necessity of making large scale motions and to overcoming energy barriers for side chain repacking. In this study, we show that localizing packing frustration at atomic resolution by examining the statistics of the energetic changes that occur when the local environment of a site is changed allows one to identify the most likely locations of incorrect contacts. The global statistics of atomic resolution frustration in structures that have been predicted using various algorithms provide strong indicators of structural quality when tested over a database of 20 targets from previous CASP experiments. Residues that are more correctly located turn out to be more minimally frustrated than more poorly positioned sites. These observations provide a diagnosis of both global and local quality of predicted structures and thus can be used as guidance in all-atom refinement simulations of the 20 targets. Refinement simulations guided by atomic packing frustration turn out to be quite efficient and significantly improve the quality of the structures.
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Affiliation(s)
- Mingchen Chen
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States
| | - Xun Chen
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.,Department of Chemistry, Rice University, Houston, Texas 77005, United States
| | - Shikai Jin
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.,Department of Biosciences, Rice University, Houston, Texas 77005, United States
| | - Wei Lu
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.,Department of Physics and Astronomy, Rice University, Houston, Texas 77030, United States
| | - Xingcheng Lin
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Peter G Wolynes
- Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, United States.,Department of Chemistry, Rice University, Houston, Texas 77005, United States.,Department of Biosciences, Rice University, Houston, Texas 77005, United States.,Department of Physics and Astronomy, Rice University, Houston, Texas 77030, United States
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6
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Jin S, Miller MD, Chen M, Schafer NP, Lin X, Chen X, Phillips GN, Wolynes PG. Molecular-replacement phasing using predicted protein structures from AWSEM-Suite. IUCRJ 2020; 7:1168-1178. [PMID: 33209327 PMCID: PMC7642774 DOI: 10.1107/s2052252520013494] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 10/07/2020] [Indexed: 06/11/2023]
Abstract
The phase problem in X-ray crystallography arises from the fact that only the intensities, and not the phases, of the diffracting electromagnetic waves are measured directly. Molecular replacement can often estimate the relative phases of reflections starting with those derived from a template structure, which is usually a previously solved structure of a similar protein. The key factor in the success of molecular replacement is finding a good template structure. When no good solved template exists, predicted structures based partially on templates can sometimes be used to generate models for molecular replacement, thereby extending the lower bound of structural and sequence similarity required for successful structure determination. Here, the effectiveness is examined of structures predicted by a state-of-the-art prediction algorithm, the Associative memory, Water-mediated, Structure and Energy Model Suite (AWSEM-Suite), which has been shown to perform well in predicting protein structures in CASP13 when there is no significant sequence similarity to a solved protein or only very low sequence similarity to known templates. The performance of AWSEM-Suite structures in molecular replacement is discussed and the results show that AWSEM-Suite performs well in providing useful phase information, often performing better than I-TASSER-MR and the previous algorithm AWSEM-Template.
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Affiliation(s)
- Shikai Jin
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, USA
- Department of Biosciences, Rice University, Houston, Texas, USA
| | | | - Mingchen Chen
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, USA
| | - Nicholas P. Schafer
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, USA
- Department of Chemistry, Rice University, Houston, Texas, USA
| | - Xingcheng Lin
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Xun Chen
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, USA
- Department of Chemistry, Rice University, Houston, Texas, USA
| | - George N. Phillips
- Department of Biosciences, Rice University, Houston, Texas, USA
- Department of Chemistry, Rice University, Houston, Texas, USA
| | - Peter G. Wolynes
- Center for Theoretical Biological Physics, Rice University, Houston, Texas, USA
- Department of Biosciences, Rice University, Houston, Texas, USA
- Department of Chemistry, Rice University, Houston, Texas, USA
- Department of Physics, Rice University, Houston, Texas, USA
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7
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Jin S, Contessoto VG, Chen M, Schafer NP, Lu W, Chen X, Bueno C, Hajitaheri A, Sirovetz BJ, Davtyan A, Papoian GA, Tsai MY, Wolynes PG. AWSEM-Suite: a protein structure prediction server based on template-guided, coevolutionary-enhanced optimized folding landscapes. Nucleic Acids Res 2020; 48:W25-W30. [PMID: 32383764 PMCID: PMC7319565 DOI: 10.1093/nar/gkaa356] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 04/19/2020] [Accepted: 04/28/2020] [Indexed: 12/19/2022] Open
Abstract
The accurate and reliable prediction of the 3D structures of proteins and their assemblies remains difficult even though the number of solved structures soars and prediction techniques improve. In this study, a free and open access web server, AWSEM-Suite, whose goal is to predict monomeric protein tertiary structures from sequence is described. The model underlying the server’s predictions is a coarse-grained protein force field which has its roots in neural network ideas that has been optimized using energy landscape theory. Employing physically motivated potentials and knowledge-based local structure biasing terms, the addition of homologous template and co-evolutionary restraints to AWSEM-Suite greatly improves the predictive power of pure AWSEM structure prediction. From the independent evaluation metrics released in the CASP13 experiment, AWSEM-Suite proves to be a reasonably accurate algorithm for free modeling, standing at the eighth position in the free modeling category of CASP13. The AWSEM-Suite server also features a front end with a user-friendly interface. The AWSEM-Suite server is a powerful tool for predicting monomeric protein tertiary structures that is most useful when a suitable structure template is not available. The AWSEM-Suite server is freely available at: https://awsem.rice.edu.
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Affiliation(s)
- Shikai Jin
- Department of Biosciences, Rice University, 6100 Main St, Houston, TX 77005, USA.,Center for Theoretical Biological Physics, Rice University, 6100 Main St, Houston, TX 77005, USA
| | - Vinicius G Contessoto
- Center for Theoretical Biological Physics, Rice University, 6100 Main St, Houston, TX 77005, USA
| | - Mingchen Chen
- Center for Theoretical Biological Physics, Rice University, 6100 Main St, Houston, TX 77005, USA
| | - Nicholas P Schafer
- Center for Theoretical Biological Physics, Rice University, 6100 Main St, Houston, TX 77005, USA
| | - Wei Lu
- Center for Theoretical Biological Physics, Rice University, 6100 Main St, Houston, TX 77005, USA.,Department of Physics, Rice University, 6100 Main St, Houston, TX 77005, USA
| | - Xun Chen
- Center for Theoretical Biological Physics, Rice University, 6100 Main St, Houston, TX 77005, USA.,Department of Chemistry, Rice University, 6100 Main St, Houston, TX 77005, USA
| | - Carlos Bueno
- Center for Theoretical Biological Physics, Rice University, 6100 Main St, Houston, TX 77005, USA
| | - Arya Hajitaheri
- Department of Computer Science, University of Houston, 4800 Calhoun Rd, Houston, TX 77004, USA
| | - Brian J Sirovetz
- Center for Theoretical Biological Physics, Rice University, 6100 Main St, Houston, TX 77005, USA
| | - Aram Davtyan
- Center for Theoretical Biological Physics, Rice University, 6100 Main St, Houston, TX 77005, USA
| | - Garegin A Papoian
- Department of Chemistry, University of Maryland, College Park, MD 20742, USA
| | - Min-Yeh Tsai
- Department of Chemistry, Tamkang University, 151 Yingzhuan Road, New Taipei City 25137, Taiwan
| | - Peter G Wolynes
- Department of Biosciences, Rice University, 6100 Main St, Houston, TX 77005, USA.,Center for Theoretical Biological Physics, Rice University, 6100 Main St, Houston, TX 77005, USA.,Department of Physics, Rice University, 6100 Main St, Houston, TX 77005, USA.,Department of Chemistry, Rice University, 6100 Main St, Houston, TX 77005, USA
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8
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Jin S, Chen M, Chen X, Bueno C, Lu W, Schafer NP, Lin X, Onuchic JN, Wolynes PG. Protein Structure Prediction in CASP13 Using AWSEM-Suite. J Chem Theory Comput 2020; 16:3977-3988. [PMID: 32396727 DOI: 10.1021/acs.jctc.0c00188] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Recently several techniques have emerged that significantly enhance the quality of predictions of protein tertiary structures. In this study, we describe the performance of AWSEM-Suite, an algorithm that incorporates template-based modeling and coevolutionary restraints with a realistic coarse-grained force field, AWSEM. With its roots in neural networks, AWSEM contains both physical and bioinformatical energies that have been optimized using energy landscape theory. AWSEM-Suite participated in CASP13 as a server predictor and generated reliable predictions for most targets. AWSEM-Suite ranked eighth in both the free-modeling category and the hard-to-model category and in one case provided the best submitted prediction. Here we critically discuss the prediction performance of AWSEM-Suite using several examples from different categories in CASP13. Structure prediction tests on these selected targets, two of them being hard-to-model targets, show that AWSEM-Suite can achieve high-resolution structure prediction after incorporating both template guidances and coevolutionary restraints even when homology is weak. For targets with reliable templates (template-easy category), introducing coevolutionary restraints sometimes damages the overall quality of the predictions. Free energy profile analyses demonstrate, however, that the incorporations of both of these evolutionarily informed terms effectively increase the funneling of the landscape toward native-like structures while still allowing sufficient flexibility to correct for discrepancies between the correct target structure and the provided guidance. In contrast to other predictors that are exclusively oriented toward structure prediction, the connection of AWSEM-Suite to a statistical mechanical basis and affiliated molecular dynamics and importance sampling simulations makes it suitable for functional explorations.
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Affiliation(s)
| | | | - Xun Chen
- Department of Chemistry, Rice University, Houston, Texas 77005, United States
| | | | - Wei Lu
- Department of Physics, Rice University, Houston, Texas 77005, United States
| | | | - Xingcheng Lin
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - José N Onuchic
- Department of Chemistry, Rice University, Houston, Texas 77005, United States.,Department of Physics, Rice University, Houston, Texas 77005, United States
| | - Peter G Wolynes
- Department of Chemistry, Rice University, Houston, Texas 77005, United States.,Department of Physics, Rice University, Houston, Texas 77005, United States
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9
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Tollefson MR, Litman JM, Qi G, O'Connell CE, Wipfler MJ, Marini RJ, Bernabe HV, Tollefson WTA, Braun TA, Casavant TL, Smith RJH, Schnieders MJ. Structural Insights into Hearing Loss Genetics from Polarizable Protein Repacking. Biophys J 2019; 117:602-612. [PMID: 31327459 DOI: 10.1016/j.bpj.2019.06.030] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 06/10/2019] [Accepted: 06/25/2019] [Indexed: 12/21/2022] Open
Abstract
Hearing loss is associated with ∼8100 mutations in 152 genes, and within the coding regions of these genes are over 60,000 missense variants. The majority of these variants are classified as "variants of uncertain significance" to reflect our inability to ascribe a phenotypic effect to the observed amino acid change. A promising source of pathogenicity information is biophysical simulation, although input protein structures often contain defects because of limitations in experimental data and/or only distant homology to a template. Here, we combine the polarizable atomic multipole optimized energetics for biomolecular applications force field, many-body optimization theory, and graphical processing unit acceleration to repack all deafness-associated proteins and thereby improve average structure MolProbity score from 2.2 to 1.0. We then used these optimized wild-type models to create over 60,000 structures for missense variants in the Deafness Variation Database, which are being incorporated into the Deafness Variation Database to inform deafness pathogenicity prediction. Finally, this work demonstrates that advanced polarizable atomic multipole force fields are efficient enough to repack the entire human proteome.
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Affiliation(s)
- Mallory R Tollefson
- Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa; Molecular Otolaryngology & Renal Research Laboratories, Department of Otolaryngology-Head and Neck Surgery, University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | - Jacob M Litman
- Department of Biochemistry, University of Iowa, Iowa City, Iowa
| | - Guowei Qi
- Department of Biochemistry, University of Iowa, Iowa City, Iowa
| | - Claire E O'Connell
- Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa
| | - Matthew J Wipfler
- Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa
| | - Robert J Marini
- Molecular Otolaryngology & Renal Research Laboratories, Department of Otolaryngology-Head and Neck Surgery, University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | - Hernan V Bernabe
- Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa; Molecular Otolaryngology & Renal Research Laboratories, Department of Otolaryngology-Head and Neck Surgery, University of Iowa Hospitals and Clinics, Iowa City, Iowa
| | | | - Terry A Braun
- Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa
| | - Thomas L Casavant
- Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa
| | - Richard J H Smith
- Molecular Otolaryngology & Renal Research Laboratories, Department of Otolaryngology-Head and Neck Surgery, University of Iowa Hospitals and Clinics, Iowa City, Iowa.
| | - Michael J Schnieders
- Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa; Department of Biochemistry, University of Iowa, Iowa City, Iowa.
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10
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Abstract
Refining predicted protein structures with all-atom molecular dynamics simulations is one route to producing, entirely by computational means, structural models of proteins that rival in quality those that are determined by X-ray diffraction experiments. Slow rearrangements within the compact folded state, however, make routine refinement of predicted structures by unrestrained simulations infeasible. In this work, we draw inspiration from the fields of metallurgy and blacksmithing, where practitioners have worked out practical means of controlling equilibration by mechanically deforming their samples. We describe a two-step refinement procedure that involves identifying collective variables for mechanical deformations using a coarse-grained model and then sampling along these deformation modes in all-atom simulations. Identifying those low-frequency collective modes that change the contact map the most proves to be an effective strategy for choosing which deformations to use for sampling. The method is tested on 20 refinement targets from the CASP12 competition and is found to induce large structural rearrangements that drive the structures closer to the experimentally determined structures during relatively short all-atom simulations of 50 ns. By examining the accuracy of side-chain rotamer states in subensembles of structures that have varying degrees of similarity to the experimental structure, we identified the reorientation of aromatic side chains as a step that remains slow even when encouraging global mechanical deformations in the all-atom simulations. Reducing the side-chain rotamer isomerization barriers in the all-atom force field is found to further speed up refinement.
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11
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Structural and Dynamical Order of a Disordered Protein: Molecular Insights into Conformational Switching of PAGE4 at the Systems Level. Biomolecules 2019; 9:biom9020077. [PMID: 30813315 PMCID: PMC6406393 DOI: 10.3390/biom9020077] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 02/10/2019] [Accepted: 02/10/2019] [Indexed: 01/10/2023] Open
Abstract
Folded proteins show a high degree of structural order and undergo (fairly constrained) collective motions related to their functions. On the other hand, intrinsically disordered proteins (IDPs), while lacking a well-defined three-dimensional structure, do exhibit some structural and dynamical ordering, but are less constrained in their motions than folded proteins. The larger structural plasticity of IDPs emphasizes the importance of entropically driven motions. Many IDPs undergo function-related disorder-to-order transitions driven by their interaction with specific binding partners. As experimental techniques become more sensitive and become better integrated with computational simulations, we are beginning to see how the modest structural ordering and large amplitude collective motions of IDPs endow them with an ability to mediate multiple interactions with different partners in the cell. To illustrate these points, here, we use Prostate-associated gene 4 (PAGE4), an IDP implicated in prostate cancer (PCa) as an example. We first review our previous efforts using molecular dynamics simulations based on atomistic AWSEM to study the conformational dynamics of PAGE4 and how its motions change in its different physiologically relevant phosphorylated forms. Our simulations quantitatively reproduced experimental observations and revealed how structural and dynamical ordering are encoded in the sequence of PAGE4 and can be modulated by different extents of phosphorylation by the kinases HIPK1 and CLK2. This ordering is reflected in changing populations of certain secondary structural elements as well as in the regularity of its collective motions. These ordered features are directly correlated with the functional interactions of WT-PAGE4, HIPK1-PAGE4 and CLK2-PAGE4 with the AP-1 signaling axis. These interactions give rise to repeated transitions between (high HIPK1-PAGE4, low CLK2-PAGE4) and (low HIPK1-PAGE4, high CLK2-PAGE4) cell phenotypes, which possess differing sensitivities to the standard PCa therapies, such as androgen deprivation therapy (ADT). We argue that, although the structural plasticity of an IDP is important in promoting promiscuous interactions, the modulation of the structural ordering is important for sculpting its interactions so as to rewire with agility biomolecular interaction networks with significant functional consequences.
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