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Trofimov YA, Krylov NA, Minakov AS, Nadezhdin KD, Neuberger A, Sobolevsky AI, Efremov RG. Dynamic molecular portraits of ion-conducting pores characterize functional states of TRPV channels. Commun Chem 2024; 7:119. [PMID: 38824263 PMCID: PMC11144267 DOI: 10.1038/s42004-024-01198-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 05/06/2024] [Indexed: 06/03/2024] Open
Abstract
Structural biology is solving an ever-increasing number of snapshots of ion channel conformational ensembles. Deciphering ion channel mechanisms, however, requires understanding the ensemble dynamics beyond the static structures. Here, we present a molecular modeling-based approach characterizing the ion channel structural intermediates, or their "dynamic molecular portraits", by assessing water and ion conductivity along with the detailed evaluation of pore hydrophobicity and residue packing. We illustrate the power of this approach by analyzing structures of few vanilloid-subfamily transient receptor potential (TRPV) channels. Based on the pore architecture, there are three major states that are common for TRPVs, which we call α-closed, π-closed, and π-open. We show that the pore hydrophobicity and residue packing for the open state is most favorable for the pore conductance. On the contrary, the α-closed state is the most hydrophobic and always non-conducting. Our approach can also be used for structural and functional classification of ion channels.
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Affiliation(s)
- Yury A Trofimov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | - Nikolay A Krylov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia
| | | | - Kirill D Nadezhdin
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | - Arthur Neuberger
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | - Alexander I Sobolevsky
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA
| | - Roman G Efremov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow, Russia.
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2
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Liang S, Zhang C, Zhu M. Ab Initio Prediction of 3-D Conformations for Protein Long Loops with High Accuracy and Applications to Antibody CDRH3 Modeling. J Chem Inf Model 2023; 63:7568-7577. [PMID: 38018130 DOI: 10.1021/acs.jcim.3c01051] [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: 11/30/2023]
Abstract
Residue-level potentials of mean force were widely used for protein backbone refinements to avoid simultaneous sampling of side-chain conformations. The interaction energy between the reduced side chains and backbone atoms was not considered explicitly. In this study, we developed novel methods to calculate the residue-atom interaction energy in combination with atomic and residue-level terms. The parameters were optimized step by step to remove the overcounting or overlap problem between different energy terms. The mixing energy functions were then used to evaluate the generated backbone conformations at the initial sampling stage of protein loop modeling (OSCAR-loop), including the interaction energy between the reduced loop residues and full atoms of the protein framework. The accuracies of top-ranked decoys were 1.18 and 2.81 Å for 8-residue and 12-residue loops, respectively. We then selected diverse decoys for side-chain modeling, backbone refinement, and energy minimization. The procedure was repeated multiple times to select one prediction with the lowest energy. Consequently, we obtained an accuracy of 0.74 Å for a prevailing test set of 12-residue loops, compared with >1.4 Å reported by other researchers. The OSCAR-loop was also effective for modeling the H3 loops of antibody complementary determining regions (CDRs) in the crystal environment. The prediction accuracy of OSCAR-loop (1.74 Å) was better than the accuracy of the Rosetta NGK method (3.11 Å) or those achieved by deep learning methods (>2.2 Å) for the CDRH3 loops of 49 targets in the Rosetta antibody benchmark. The performance of OSCAR-loop in a model environment was also discussed.
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Affiliation(s)
- Shide Liang
- Department of Computational Biology, 20n Bio Limited, Hangzhou 310018, P. R. China
- Department of Research and Development, Bio-Thera Solutions, Guangzhou 510530, P. R. China
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska, Lincoln, Nebraska 68588, United States
| | - Mingfu Zhu
- Department of Computational Biology, 20n Bio Limited, Hangzhou 310018, P. R. China
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3
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Newton MH, Zaman R, Mataeimoghadam F, Rahman J, Sattar A. Constraint Guided Beta-Sheet Refinement for Protein Structure Prediction. Comput Biol Chem 2022; 101:107773. [DOI: 10.1016/j.compbiolchem.2022.107773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/15/2022] [Accepted: 09/16/2022] [Indexed: 11/16/2022]
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Bufano M, Laffranchi M, Sozzani S, Raimondo D, Silvestri R, Coluccia A. Exploring
CCRL2
chemerin binding using accelerated molecular dynamics. Proteins 2022; 90:1714-1720. [PMID: 35437825 PMCID: PMC9543397 DOI: 10.1002/prot.26348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 03/08/2022] [Accepted: 04/13/2022] [Indexed: 12/21/2022]
Abstract
Chemokine (C–C motif) receptor‐like 2 (CCRL2), is a seven transmembrane receptor closely related to the chemokine receptors CCR1, CCR2, CCR3, and CCR5. Nevertheless, CCRL2 is unable to activate conventional G‐protein dependent signaling and to induce cell directional migration. The only commonly accepted CCRL2 ligand is the nonchemokine chemotactic protein chemerin (RARRES2). The chemerin binding to CCLR2 does induce leukocyte chemotaxis, yet, genetic targeting of CCRL2 was shown to modulate the inflammatory response in different experimental models. This mechanism was shown to be crucial for lung dendritic cell migration, neutrophil recruitment, and Natural Killer cell‐dependent immune surveillance in lung cancer. To gain more insight in the interactions involved in the CCRL2‐chemerin, the binding complexes were generated by protein–protein docking, then submitted to accelerated molecular dynamics. The obtained trajectories were inspected by principal component analyses followed by kernel density estimation to identify the ligand‐receptor regions most frequently involved in the binding. To conclude, the reported analyses led to the identification of the putative hot‐spot residues involved in CCRL2‐chemerin binding.
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Affiliation(s)
- Marianna Bufano
- Department of Drug Chemistry and Technologies Sapienza University of Rome, Laboratory affiliated to Istituto Pasteur Italia – Fondazione Cenci Bolognetti Rome
| | - Mattia Laffranchi
- Department of Molecular Medicine Sapienza University of Rome, Laboratory Affiliated to Istituto Pasteur Italia‐Fondazione Cenci Bolognetti Rome Italy
| | - Silvano Sozzani
- Department of Molecular Medicine Sapienza University of Rome, Laboratory Affiliated to Istituto Pasteur Italia‐Fondazione Cenci Bolognetti Rome Italy
| | - Domenico Raimondo
- Department of Molecular Medicine Sapienza University of Rome, Laboratory Affiliated to Istituto Pasteur Italia‐Fondazione Cenci Bolognetti Rome Italy
| | - Romano Silvestri
- Department of Drug Chemistry and Technologies Sapienza University of Rome, Laboratory affiliated to Istituto Pasteur Italia – Fondazione Cenci Bolognetti Rome
| | - Antonio Coluccia
- Department of Drug Chemistry and Technologies Sapienza University of Rome, Laboratory affiliated to Istituto Pasteur Italia – Fondazione Cenci Bolognetti Rome
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5
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Schwarz D, Georges G, Kelm S, Shi J, Vangone A, Deane CM. Co-evolutionary distance predictions contain flexibility information. Bioinformatics 2021; 38:65-72. [PMID: 34383892 DOI: 10.1093/bioinformatics/btab562] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 06/19/2021] [Accepted: 08/10/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Co-evolution analysis can be used to accurately predict residue-residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predicting binary contacts to predict distances between pairs of residues. These developments have significantly improved the accuracy of de novo prediction of static protein structures. With AlphaFold2 lifting the accuracy of some predicted protein models close to experimental levels, structure prediction research will move on to other challenges. One of those areas is the prediction of more than one conformation of a protein. Here, we examine the potential of residue-residue distance predictions to be informative of protein flexibility rather than simply static structure. RESULTS We used DMPfold to predict distance distributions for every residue pair in a set of proteins that showed both rigid and flexible behaviour. Residue pairs that were in contact in at least one reference structure were classified as rigid, flexible or neither. The predicted distance distribution of each residue pair was analysed for local maxima of probability indicating the most likely distance or distances between a pair of residues. We found that rigid residue pairs tended to have only a single local maximum in their predicted distance distributions while flexible residue pairs more often had multiple local maxima. These results suggest that the shape of predicted distance distributions contains information on the rigidity or flexibility of a protein and its constituent residues. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dominik Schwarz
- Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
| | - Guy Georges
- Department of Computational Engineering and Data Science, Large Molecule Research, Penzberg 82377, Germany
| | - Sebastian Kelm
- Computer-Aided Drug Design, UCB Pharma, Slough SL1 3WE, UK
| | - Jiye Shi
- Computer-Aided Drug Design, UCB Pharma, Slough SL1 3WE, UK
| | - Anna Vangone
- Department of Computational Engineering and Data Science, Large Molecule Research, Penzberg 82377, Germany
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Liu J, Zhao KL, He GX, Wang LJ, Zhou XG, Zhang GJ. A de novo protein structure prediction by iterative partition sampling, topology adjustment and residue-level distance deviation optimization. Bioinformatics 2021; 38:99-107. [PMID: 34459867 DOI: 10.1093/bioinformatics/btab620] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Revised: 07/23/2021] [Accepted: 08/25/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION With the great progress of deep learning-based inter-residue contact/distance prediction, the discrete space formed by fragment assembly cannot satisfy the distance constraint well. Thus, the optimal solution of the continuous space may not be achieved. Designing an effective closed-loop continuous dihedral angle optimization strategy that complements the discrete fragment assembly is crucial to improve the performance of the distance-assisted fragment assembly method. RESULTS In this article, we proposed a de novo protein structure prediction method called IPTDFold based on closed-loop iterative partition sampling, topology adjustment and residue-level distance deviation optimization. First, local dihedral angle crossover and mutation operators are designed to explore the conformational space extensively and achieve information exchange between the conformations in the population. Then, the dihedral angle rotation model of loop region with partial inter-residue distance constraints is constructed, and the rotation angle satisfying the constraints is obtained by differential evolution algorithm, so as to adjust the spatial position relationship between the secondary structures. Finally, the residue distance deviation is evaluated according to the difference between the conformation and the predicted distance, and the dihedral angle of the residue is optimized with biased probability. The final model is generated by iterating the above three steps. IPTDFold is tested on 462 benchmark proteins, 24 FM targets of CASP13 and 20 FM targets of CASP14. Results show that IPTDFold is significantly superior to the distance-assisted fragment assembly method Rosetta_D (Rosetta with distance). In particular, the prediction accuracy of IPTDFold does not decrease as the length of the protein increases. When using the same FastRelax protocol, the prediction accuracy of IPTDFold is significantly superior to that of trRosetta without orientation constraints, and is equivalent to that of the full version of trRosetta. AVAILABILITYAND IMPLEMENTATION The source code and executable are freely available at https://github.com/iobio-zjut/IPTDFold. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jun Liu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Kai-Long Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Guang-Xing He
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Liu-Jing Wang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Xiao-Gen Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109-2218, USA
| | - Gui-Jun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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7
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Guo L, Jiang Q, Jin X, Liu L, Zhou W, Yao S, Wu M, Wang Y. A Deep Convolutional Neural Network to Improve the Prediction of Protein Secondary Structure. Curr Bioinform 2020. [DOI: 10.2174/1574893615666200120103050] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Protein secondary structure prediction (PSSP) is a fundamental task in
bioinformatics that is helpful for understanding the three-dimensional structure and biological
function of proteins. Many neural network-based prediction methods have been developed for
protein secondary structures. Deep learning and multiple features are two obvious means to improve
prediction accuracy.
Objective:
To promote the development of PSSP, a deep convolutional neural network-based
method is proposed to predict both the eight-state and three-state of protein secondary structure.
Methods:
In this model, sequence and evolutionary information of proteins are combined as multiple
input features after preprocessing. A deep convolutional neural network with no pooling layer and
connection layer is then constructed to predict the secondary structure of proteins. L2 regularization,
batch normalization, and dropout techniques are employed to avoid over-fitting and obtain better
prediction performance, and an improved cross-entropy is used as the loss function.
Results:
Our proposed model can obtain Q3 prediction results of 86.2%, 84.5%, 87.8%, and 84.7%,
respectively, on CullPDB, CB513, CASP10 and CASP11 datasets, with corresponding Q8
prediction results of 74.1%, 70.5%, 74.9%, and 71.3%.
Conclusion:
We have proposed the DCNN-SS deep convolutional-network-based PSSP method,
and experimental results show that DCNN-SS performs competitively with other methods.
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Affiliation(s)
- Lin Guo
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Qian Jiang
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Xin Jin
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Lin Liu
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Wei Zhou
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Shaowen Yao
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Min Wu
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
| | - Yun Wang
- School of Software, Yunnan University, Kunming, China; 2School of Information, Yunnan Normal University, Kunming, China
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