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Kravchenko A, de Vries SJ, Smaïl-Tabbone M, Chauvot de Beauchene I. HIPPO: HIstogram-based Pseudo-POtential for scoring protein-ssRNA fragment-based docking poses. BMC Bioinformatics 2024; 25:129. [PMID: 38532339 DOI: 10.1186/s12859-024-05733-6] [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: 05/25/2023] [Accepted: 03/06/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND The RNA-Recognition motif (RRM) is a protein domain that binds single-stranded RNA (ssRNA) and is present in as much as 2% of the human genome. Despite this important role in biology, RRM-ssRNA interactions are very challenging to study on the structural level because of the remarkable flexibility of ssRNA. In the absence of atomic-level experimental data, the only method able to predict the 3D structure of protein-ssRNA complexes with any degree of accuracy is ssRNA'TTRACT, an ssRNA fragment-based docking approach using ATTRACT. However, since ATTRACT parameters are not ssRNA-specific and were determined in 2010, there is substantial opportunity for enhancement. RESULTS Here we present HIPPO, a composite RRM-ssRNA scoring potential derived analytically from contact frequencies in near-native versus non-native docking models. HIPPO consists of a consensus of four distinct potentials, each extracted from a distinct reference pool of protein-trinucleotide docking decoys. To score a docking pose with one potential, for each pair of RNA-protein coarse-grained bead types, each contact is awarded or penalised according to the relative frequencies of this contact distance range among the correct and incorrect poses of the reference pool. Validated on a fragment-based docking benchmark of 57 experimentally solved RRM-ssRNA complexes, HIPPO achieved a threefold or higher enrichment for half of the fragments, versus only a quarter with the ATTRACT scoring function. In particular, HIPPO drastically improved the chance of very high enrichment (12-fold or higher), a scenario where the incremental modelling of entire ssRNA chains from fragments becomes viable. However, for the latter result, more research is needed to make it directly practically applicable. Regardless, our approach already improves upon the state of the art in RRM-ssRNA modelling and is in principle extendable to other types of protein-nucleic acid interactions.
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Affiliation(s)
- Anna Kravchenko
- Université de Lorraine, CNRS, Inria, LORIA, 54000, Nancy, France
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2
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Liu X, Duan Y, Hong X, Xie J, Liu S. Challenges in structural modeling of RNA-protein interactions. Curr Opin Struct Biol 2023; 81:102623. [PMID: 37301066 DOI: 10.1016/j.sbi.2023.102623] [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: 01/31/2023] [Revised: 05/14/2023] [Accepted: 05/16/2023] [Indexed: 06/12/2023]
Abstract
In the past few years, the number of RNA-binding proteins (RBP) and RNA-RBP interactions has increased significantly. Here, we review recent developments in the methodology for protein-RNA and protein-protein complex structure modeling with deep learning and co-evolution, as well as discuss the challenges and opportunities for building a reliable approach for protein-RNA complex structure modelling. Protein Data bank (PDB) and Cross-linking immunoprecipitation (CLIP) data could be combined together and used to infer 2D geometry of protein-RNA interactions by deep learning.
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Affiliation(s)
- Xudong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Yingtian Duan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Xu Hong
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Juan Xie
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Shiyong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
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3
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Zeng C, Jian Y, Vosoughi S, Zeng C, Zhao Y. Evaluating native-like structures of RNA-protein complexes through the deep learning method. Nat Commun 2023; 14:1060. [PMID: 36828844 PMCID: PMC9958188 DOI: 10.1038/s41467-023-36720-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 02/14/2023] [Indexed: 02/26/2023] Open
Abstract
RNA-protein complexes underlie numerous cellular processes, including basic translation and gene regulation. The high-resolution structure determination of the RNA-protein complexes is essential for elucidating their functions. Therefore, computational methods capable of identifying the native-like RNA-protein structures are needed. To address this challenge, we thus develop DRPScore, a deep-learning-based approach for identifying native-like RNA-protein structures. DRPScore is tested on representative sets of RNA-protein complexes with various degrees of binding-induced conformation change ranging from fully rigid docking (bound-bound) to fully flexible docking (unbound-unbound). Out of the top 20 predictions, DRPScore selects native-like structures with a success rate of 91.67% on the testing set of bound RNA-protein complexes and 56.14% on the unbound complexes. DRPScore consistently outperforms existing methods with a roughly 10.53-15.79% improvement, even for the most difficult unbound cases. Furthermore, DRPScore significantly improves the accuracy of the native interface interaction predictions. DRPScore should be broadly useful for modeling and designing RNA-protein complexes.
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Affiliation(s)
- Chengwei Zeng
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China
| | - Yiren Jian
- Department of Computer Science, Dartmouth College, Hanover, NH, 03755, USA
| | - Soroush Vosoughi
- Department of Computer Science, Dartmouth College, Hanover, NH, 03755, USA
| | - Chen Zeng
- Department of Physics, The George Washington University, Washington, DC, 20052, USA
| | - Yunjie Zhao
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China.
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4
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Li H, Huang E, Zhang Y, Huang S, Xiao Y. HDOCK update for modeling protein-RNA/DNA complex structures. Protein Sci 2022; 31:e4441. [PMID: 36305764 PMCID: PMC9615301 DOI: 10.1002/pro.4441] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 09/05/2022] [Accepted: 09/06/2022] [Indexed: 11/05/2022]
Abstract
Protein-nucleic acid interactions are involved in various cellular processes. Therefore, determining the structures of protein-nucleic acid complexes can provide insights into the mechanisms of the interactions and thus guide the rational drug design to modulate these interactions. Due to the high cost and technical difficulties of solving complex structures experimentally, computational modeling such as molecular docking has been playing an important role in the study of molecular interactions. In order to make it easier for researchers to obtain biomolecular complex structures through molecular docking, we developed the HDOCK server for protein-protein and protein-RNA/DNA docking (accessed at http://hdock.phys.hust.edu.cn/). Since its first release in 2017, HDOCK has been widely used in the scientific community. As nucleic acids may include single-stranded (ss) RNA/DNA and double-stranded (ds) RNA/DNA, we now present an updated version of HDOCK, which offers new options for structural modeling of ssRNA, ssDNA, dsRNA, and dsDNA. We hope this update will better help the scientific community solve important biological problems, thereby advancing the field. In this article, we describe the general protocol of HDOCK with emphasis on the new functions on RNA/DNA modeling. Several application examples are also given to illustrate the usage of the new functions.
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Affiliation(s)
- Hao Li
- School of Physics, Huazhong University of Science and TechnologyWuhanHubeiChina
| | | | - Yi Zhang
- School of Physics, Huazhong University of Science and TechnologyWuhanHubeiChina
| | - Sheng‐You Huang
- School of Physics, Huazhong University of Science and TechnologyWuhanHubeiChina
| | - Yi Xiao
- School of Physics, Huazhong University of Science and TechnologyWuhanHubeiChina
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5
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Gaither J, Lin YH, Bundschuh R. RBPBind: Quantitative prediction of Protein-RNA interactions. J Mol Biol 2022; 434:167515. [DOI: 10.1016/j.jmb.2022.167515] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 02/21/2022] [Accepted: 02/22/2022] [Indexed: 10/19/2022]
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6
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3D Modeling of Non-coding RNA Interactions. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1385:281-317. [DOI: 10.1007/978-3-031-08356-3_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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7
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Hong X, Zheng J, Xie J, Tong X, Liu X, Song Q, Liu S, Liu S. RR3DD: an RNA global structure-based RNA three-dimensional structural classification database. RNA Biol 2021; 18:738-746. [PMID: 34663179 DOI: 10.1080/15476286.2021.1989200] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
The three-dimensional (3D) structure of RNA usually plays an important role in the recognition with RNA-binding protein. Along with the discovering of RNAs, several RNA databases are developed to study the functions of RNA based on sequence, secondary structure, local 3D structural motif and global structure. Based on RNA function and structure, different RNAs are classified and stored in SCOR and DARTS, respectively. The classification of RNA structures is useful in RNA structure prediction and function annotation. However, the SCOR and DARTS are not updated any more. In this study, we present an RNA classification database RR3DD based on RNA fold with the global 3D structural similarity. The RR3DD includes 13,601 RNA chains from PDB and mmCIF format structures which are classified into 780 RNA folds. The RNA chains from PDB and mmCIF format structures are aligned and clustered into 675 and 220 RNA folds, respectively. By analysing the RNA structure in RR3DD, we find that there are 11 clusters with more than 50 members. These clusters include rRNAs, riboswitches, tRNAs and so on. By mapping RR3DD into Rfam, we found that some RNAs without annotation by Rfam can be annotated through structural alignment. For example, we analysed tRNAs and found that tRNA were successfully grouped in RR3DD for which Rfam did not classify them into one family. Finally, we provide a web interface of RR3DD offering functions of browsing RR3DD, annotating RNA 3D structure and finding templates for RNA homology modelling.
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Affiliation(s)
- Xu Hong
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Jinfang Zheng
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Juan Xie
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoxue Tong
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Xudong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
| | - Qi Song
- Key Laboratory of Fermentation Engineering (Ministry of Education, Hubei University of Technology, Wuhan, China
| | - Sen Liu
- Key Laboratory of Fermentation Engineering (Ministry of Education, Hubei University of Technology, Wuhan, China
| | - Shiyong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, China
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8
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Philip M, Chen T, Tyagi S. A Survey of Current Resources to Study lncRNA-Protein Interactions. Noncoding RNA 2021; 7:ncrna7020033. [PMID: 34201302 PMCID: PMC8293367 DOI: 10.3390/ncrna7020033] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 05/28/2021] [Accepted: 06/07/2021] [Indexed: 12/15/2022] Open
Abstract
Phenotypes are driven by regulated gene expression, which in turn are mediated by complex interactions between diverse biological molecules. Protein-DNA interactions such as histone and transcription factor binding are well studied, along with RNA-RNA interactions in short RNA silencing of genes. In contrast, lncRNA-protein interaction (LPI) mechanisms are comparatively unknown, likely directed by the difficulties in studying LPI. However, LPI are emerging as key interactions in epigenetic mechanisms, playing a role in development and disease. Their importance is further highlighted by their conservation across kingdoms. Hence, interest in LPI research is increasing. We therefore review the current state of the art in lncRNA-protein interactions. We specifically surveyed recent computational methods and databases which researchers can exploit for LPI investigation. We discovered that algorithm development is heavily reliant on a few generic databases containing curated LPI information. Additionally, these databases house information at gene-level as opposed to transcript-level annotations. We show that early methods predict LPI using molecular docking, have limited scope and are slow, creating a data processing bottleneck. Recently, machine learning has become the strategy of choice in LPI prediction, likely due to the rapid growth in machine learning infrastructure and expertise. While many of these methods have notable limitations, machine learning is expected to be the basis of modern LPI prediction algorithms.
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Affiliation(s)
- Melcy Philip
- School of Biological Sciences, Monash University, 25 Rainforest Walk, Clayton, VIC 3800, Australia; (M.P.); (T.C.)
| | - Tyrone Chen
- School of Biological Sciences, Monash University, 25 Rainforest Walk, Clayton, VIC 3800, Australia; (M.P.); (T.C.)
| | - Sonika Tyagi
- School of Biological Sciences, Monash University, 25 Rainforest Walk, Clayton, VIC 3800, Australia; (M.P.); (T.C.)
- Monash eResearch Centre, Monash University, Clayton, VIC 3800, Australia
- Department of Infectious Disease, Monash University (Alfred Campus), 85 Commercial Road, Melbourne, VIC 3004, Australia
- Correspondence:
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9
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Sui B, Chen D, Liu W, Wu Q, Tian B, Li Y, Hou J, Liu S, Xie J, Jiang H, Luo Z, Lv L, Huang F, Li R, Zhang C, Tian Y, Cui M, Zhou M, Chen H, Fu ZF, Zhang Y, Zhao L. A novel antiviral lncRNA, EDAL, shields a T309 O-GlcNAcylation site to promote EZH2 lysosomal degradation. Genome Biol 2020; 21:228. [PMID: 32873321 PMCID: PMC7465408 DOI: 10.1186/s13059-020-02150-9] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 08/18/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The central nervous system (CNS) is vulnerable to viral infection, yet few host factors in the CNS are known to defend against invasion by neurotropic viruses. Long noncoding RNAs (lncRNAs) have been revealed to play critical roles in a wide variety of biological processes and are highly abundant in the mammalian brain, but their roles in defending against invasion of pathogens into the CNS remain unclear. RESULTS We report here that multiple neurotropic viruses, including rabies virus, vesicular stomatitis virus, Semliki Forest virus, and herpes simplex virus 1, elicit the neuronal expression of a host-encoded lncRNA EDAL. EDAL inhibits the replication of these neurotropic viruses in neuronal cells and rabies virus infection in mouse brains. EDAL binds to the conserved histone methyltransferase enhancer of zest homolog 2 (EZH2) and specifically causes EZH2 degradation via lysosomes, reducing the cellular H3K27me3 level. The antiviral function of EDAL resides in a 56-nt antiviral substructure through which its 18-nt helix-loop intimately contacts multiple EZH2 sites surrounding T309, a known O-GlcNAcylation site. EDAL positively regulates the transcription of Pcp4l1 encoding a 10-kDa peptide, which inhibits the replication of multiple neurotropic viruses. CONCLUSIONS Our findings show that a neuronal lncRNA can exert an effective antiviral function via blocking a specific O-GlcNAcylation that determines EZH2 lysosomal degradation, rather than the traditional interferon-dependent pathway.
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Affiliation(s)
- Baokun Sui
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Preventive Veterinary Medicine of Hubei Province, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Dong Chen
- Center for Genome analysis, ABLife Inc., Wuhan, 430075, China
- Center for Genome analysis and Laboratory for Genome Regulation and Human Health, ABLife Inc., Wuhan, 430075, China
| | - Wei Liu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Preventive Veterinary Medicine of Hubei Province, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Qiong Wu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Preventive Veterinary Medicine of Hubei Province, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Bin Tian
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Preventive Veterinary Medicine of Hubei Province, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yingying Li
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Preventive Veterinary Medicine of Hubei Province, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jing Hou
- Center for Genome analysis, ABLife Inc., Wuhan, 430075, China
- Center for Genome analysis and Laboratory for Genome Regulation and Human Health, ABLife Inc., Wuhan, 430075, China
| | - Shiyong Liu
- School of Physics, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Juan Xie
- School of Physics, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Hao Jiang
- Key Laboratory of Marine Drugs, Ministry of Education, School of Medicine and Pharmacy, Shandong Provincial Key Laboratory of Glycoscience and Glycotechnology, Ocean University of China, Qingdao, 266003, China
| | - Zhaochen Luo
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Preventive Veterinary Medicine of Hubei Province, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Lei Lv
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Preventive Veterinary Medicine of Hubei Province, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Fei Huang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Preventive Veterinary Medicine of Hubei Province, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ruiming Li
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Preventive Veterinary Medicine of Hubei Province, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chengguang Zhang
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Preventive Veterinary Medicine of Hubei Province, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yuling Tian
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Preventive Veterinary Medicine of Hubei Province, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Min Cui
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Preventive Veterinary Medicine of Hubei Province, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ming Zhou
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Preventive Veterinary Medicine of Hubei Province, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Huanchun Chen
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Preventive Veterinary Medicine of Hubei Province, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
| | - Zhen F Fu
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China
- Key Laboratory of Preventive Veterinary Medicine of Hubei Province, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China
- Department of Pathology, University of Georgia, Athens, GA, 30602, USA
| | - Yi Zhang
- Center for Genome analysis, ABLife Inc., Wuhan, 430075, China.
- Center for Genome analysis and Laboratory for Genome Regulation and Human Health, ABLife Inc., Wuhan, 430075, China.
| | - Ling Zhao
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan, 430070, China.
- Key Laboratory of Preventive Veterinary Medicine of Hubei Province, College of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China.
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10
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Kotelnikov S, Alekseenko A, Liu C, Ignatov M, Padhorny D, Brini E, Lukin M, Coutsias E, Dill KA, Kozakov D. Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4. J Comput Aided Mol Des 2019; 34:179-189. [PMID: 31879831 DOI: 10.1007/s10822-019-00257-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 11/19/2019] [Indexed: 12/25/2022]
Abstract
We describe a new template-based method for docking flexible ligands such as macrocycles to proteins. It combines Monte-Carlo energy minimization on the manifold, a fast manifold search method, with BRIKARD for complex flexible ligand searching, and with the MELD accelerator of Replica-Exchange Molecular Dynamics simulations for atomistic degrees of freedom. Here we test the method in the Drug Design Data Resource blind Grand Challenge competition. This method was among the best performers in the competition, giving sub-angstrom prediction quality for the majority of the targets.
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Affiliation(s)
- Sergei Kotelnikov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.,Innopolis University, Innopolis, Russia
| | - Andrey Alekseenko
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Cong Liu
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Chemistry, Stony Brook University, Stony Brook, NY, USA
| | - Mikhail Ignatov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.,Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Dzmitry Padhorny
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Emiliano Brini
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Mark Lukin
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Evangelos Coutsias
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.,Department of Chemistry, Stony Brook University, Stony Brook, NY, USA.,Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA
| | - Dima Kozakov
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA. .,Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA. .,Institute for Advanced Computational Sciences, Stony Brook University, Stony Brook, NY, USA.
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11
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Shashikala HBM, Chakravorty A, Alexov E. Modeling Electrostatic Force in Protein-Protein Recognition. Front Mol Biosci 2019; 6:94. [PMID: 31608289 PMCID: PMC6774301 DOI: 10.3389/fmolb.2019.00094] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Accepted: 09/11/2019] [Indexed: 12/25/2022] Open
Abstract
Electrostatic interactions are important for understanding molecular interactions, since they are long-range interactions and can guide binding partners to their correct binding positions. To investigate the role of electrostatic forces in molecular recognition, we calculated electrostatic forces between binding partners separated at various distances. The investigation was done on a large set of 275 protein complexes using recently developed DelPhiForce tool and in parallel, evaluating the total electrostatic force via electrostatic association energy. To accomplish the goal, we developed a method to find an appropriate direction to move one chain of protein complex away from its bound position and then calculate the corresponding electrostatic force as a function of separation distance. It is demonstrated that at large distances between the partners, the electrostatic force (magnitude and direction) is consistent among the protocols used and the main factors contributing to it are the net charge of the partners and their interfaces. However, at short distances, where partners form specific pair-wise interactions or de-solvation penalty becomes significant, the outcome depends on the precise balance of these factors. Based on the electrostatic force profile (force as a function of distance), we group the cases into four distinctive categories, among which the most intriguing is the case termed "soft landing." In this case, the electrostatic force at large distances is favorable assisting the partners to come together, while at short distance it opposes binding, and thus slows down the approach of the partners toward their physical binding.
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