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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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2
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Sharifi F, Sharifi I, Babaei Z, Alahdin S, Afgar A. Bioinformatics evaluation of anticancer properties of GP63 protein-derived peptides on MMP2 protein of melanoma cancer. J Pathol Inform 2023; 14:100190. [PMID: 36700237 PMCID: PMC9867975 DOI: 10.1016/j.jpi.2023.100190] [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: 11/07/2022] [Revised: 01/09/2023] [Accepted: 01/09/2023] [Indexed: 01/13/2023] Open
Abstract
Background GP63, also known as Leishmanolysin, is a multifunctional virulence factor abundant on the surface of Leishmania spp. small peptides with anticancer capabilities that are selective and toxic to cancer cells are known as anticancer peptides. We aimed to demonstrate the activity of GP63 and its anticancer properties on melanoma using a range of in silico tools and screening methods to identify predicted and designed anticancer peptides. Methods Various in silico modeling methodologies are used to establish the three-dimensional (3D) structure of GP63. Refinement and re-evaluation of the modeled structures and the built models' quality evaluated using the different docking used to find the interacting amino acids between MMP2 and GP63 and its anticancer peptides. AntiCP2.0 is used for screening anticancer peptides. 2D interaction plots of protein-ligand complexes evaluated by Protein-Ligand Interaction Profiler server. It is for the first time that used anticancer peptides of GP63 and the predicted and designed peptides. Results We used 3 peptides of GP63 based on the AntiCP 2.0 server with scores of 0.63, 0.53, and 0.49, and common peptides of GP63/MMP2 (continues peptide: mean the completely selected peptide after docking with non-anticancer effect, predicted with 0.58 score and designed peptides with 0.47 and 0.45 scores by AntiCP 2.0 server). Conclusions The antileishmanial and anticancer peptide research topics exemplify the multidisciplinary nature of peptide research. The advancement of therapeutics targeting cancer and/or Leishmania requires an interconnected research strategy shown in this work.
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Key Words
- ACPs, anticancer peptides
- Anticancer
- CASTp, Computed Atlas of Surface Topography of proteins
- CL, cutaneous leishmaniasis
- GP63, Glycoprotein 63
- In silico
- Leishmania
- Leishmanolysin
- MD, molecular dynamics
- MMPs, matrix metalloproteases
- MSP, major surface protease
- Matrix metalloproteases
- PDB, Protein Data Bank
- PLIP, Protein–Ligand Interaction Profiler
- Peptide
- Protein–Ligand Interaction Profiler
- ROS, reactive oxygen species formation
- SVM, Support Vector Machine
- VL, visceral leishmaniasis
- kNN, k-Nearest Neighbors
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Affiliation(s)
- Fatemeh Sharifi
- Research Center of Tropical and Infectious Diseases, Kerman University of Medical Sciences, Kerman, Iran
| | - Iraj Sharifi
- Leishmaniasis Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Zahra Babaei
- Leishmaniasis Research Center, Kerman University of Medical Sciences, Kerman, Iran
| | - Sodabeh Alahdin
- Leishmaniasis Research Center, Kerman University of Medical Sciences, Kerman, Iran,Student Research Committee, Kerman University of Medical Sciences, Kerman, Iran
| | - Ali Afgar
- Research Center for Hydatid Disease in Iran, Kerman University of Medical Sciences, Kerman, Iran,Corresponding author.
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3
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Pavan M, Bassani D, Sturlese M, Moro S. Investigating RNA-protein recognition mechanisms through supervised molecular dynamics (SuMD) simulations. NAR Genom Bioinform 2022; 4:lqac088. [PMID: 36458023 PMCID: PMC9706429 DOI: 10.1093/nargab/lqac088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 10/20/2022] [Accepted: 11/09/2022] [Indexed: 12/03/2022] Open
Abstract
Ribonucleic acid (RNA) plays a key regulatory role within the cell, cooperating with proteins to control the genome expression and several biological processes. Due to its characteristic structural features, this polymer can mold itself into different three-dimensional structures able to recognize target biomolecules with high affinity and specificity, thereby attracting the interest of drug developers and medicinal chemists. One successful example of the exploitation of RNA's structural and functional peculiarities is represented by aptamers, a class of therapeutic and diagnostic tools that can recognize and tightly bind several pharmaceutically relevant targets, ranging from small molecules to proteins, making use of the available structural and conformational freedom to maximize the complementarity with their interacting counterparts. In this scientific work, we present the first application of Supervised Molecular Dynamics (SuMD), an enhanced sampling Molecular Dynamics-based method for the study of receptor-ligand association processes in the nanoseconds timescale, to the study of recognition pathways between RNA aptamers and proteins, elucidating the main advantages and limitations of the technique while discussing its possible role in the rational design of RNA-based therapeutics.
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Affiliation(s)
- Matteo Pavan
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Davide Bassani
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Mattia Sturlese
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences University of Padova, via Marzolo 5, 35131 Padova, Italy
| | - Stefano Moro
- To whom correspondence should be addressed. Tel: +39 0498275704; Fax: +39 0498275366;
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4
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Bheemireddy S, Sandhya S, Srinivasan N, Sowdhamini R. Computational tools to study RNA-protein complexes. Front Mol Biosci 2022; 9:954926. [PMID: 36275618 PMCID: PMC9585174 DOI: 10.3389/fmolb.2022.954926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 09/20/2022] [Indexed: 11/19/2022] Open
Abstract
RNA is the key player in many cellular processes such as signal transduction, replication, transport, cell division, transcription, and translation. These diverse functions are accomplished through interactions of RNA with proteins. However, protein–RNA interactions are still poorly derstood in contrast to protein–protein and protein–DNA interactions. This knowledge gap can be attributed to the limited availability of protein-RNA structures along with the experimental difficulties in studying these complexes. Recent progress in computational resources has expanded the number of tools available for studying protein-RNA interactions at various molecular levels. These include tools for predicting interacting residues from primary sequences, modelling of protein-RNA complexes, predicting hotspots in these complexes and insights into derstanding in the dynamics of their interactions. Each of these tools has its strengths and limitations, which makes it significant to select an optimal approach for the question of interest. Here we present a mini review of computational tools to study different aspects of protein-RNA interactions, with focus on overall application, development of the field and the future perspectives.
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Affiliation(s)
- Sneha Bheemireddy
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Sankaran Sandhya
- Department of Biotechnology, Faculty of Life and Allied Health Sciences, M.S. Ramaiah University of Applied Sciences, Bengaluru, India
- *Correspondence: Sankaran Sandhya, ; Ramanathan Sowdhamini,
| | | | - Ramanathan Sowdhamini
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
- National Centre for Biological Sciences, TIFR, GKVK Campus, Bangalore, India
- Institute of Bioinformatics and Applied Biotechnology, Bangalore, India
- *Correspondence: Sankaran Sandhya, ; Ramanathan Sowdhamini,
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5
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Jahantigh H, Ahmadi N, Lovreglio P, Stufano A, Enayatkhani M, Shahbazi B, Ahmadi K. Repurposing antiviral drugs against HTLV-1 protease by molecular docking and molecular dynamics simulation. J Biomol Struct Dyn 2022:1-10. [PMID: 35612907 DOI: 10.1080/07391102.2022.2078411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Human T-cell leukemia virus type I (HTLV-1) belongs to the delta retrovirus family and the etiological agent of adult T-cell leukemia (ATL(. While the current HTLV-1 therapy, relies on using Zidovudine plus IFN-γ, there is no FDA approved drugs against it. In silico drug repurposing is a fast and accurate way for screening US-FDA approved drugs to find a therapeutic option for the HTLV-1 infection. So that, this research aims to analyze a dataset of approved antiviral drugs as a potential prospect for an anti-viral drug against HTLV-1 infection. Molecular docking simulation was performed to identify interactions of the antiviral drugs with the key residues in the HTLV-1 protease binding site. Then, molecular dynamics simulation was also performed for the potential protein-ligand complexes to confirm the stable behavior of the ligands inside the binding pocket. The best docking scores with the target was found to be Simeprevir, Atazanavir, and Saquinavir compounds which indicate that these drugs can firmly bind to the HTLV-1 protease. The MD simulation confirmed the stability of Simeprevir-protease, Atazanavir-Protease, and Saquinavir-Protease interactions. Clearly, these compounds should be further evaluated in experimental assays and clinical trials to confirm their actual activity against HTLV-1 infection.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Hamidreza Jahantigh
- Interdisciplinary Department of Medicine - Section of Occupational Medicine, University of Bari, Bari, Italy.,Animal Health and Zoonosis PhD Course, Department of Veterinary Medicine, University of Bari, Bari, Italy
| | - Nahid Ahmadi
- Department of Pharmaceutical Chemistry, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Piero Lovreglio
- Interdisciplinary Department of Medicine - Section of Occupational Medicine, University of Bari, Bari, Italy
| | - Angela Stufano
- Interdisciplinary Department of Medicine - Section of Occupational Medicine, University of Bari, Bari, Italy
| | - Maryam Enayatkhani
- Molecular Medicine Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Behzad Shahbazi
- Molecular Medicine Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Khadijeh Ahmadi
- Infectious and Tropical Diseases Research Center, Hormozgan Health Institute, Hormozgan University of Medical Sciences, Bandar Abbas, Iran
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6
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Sarnowski CP, Bikaki M, Leitner A. Cross-linking and mass spectrometry as a tool for studying the structural biology of ribonucleoproteins. Structure 2022; 30:441-461. [PMID: 35366400 DOI: 10.1016/j.str.2022.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 02/03/2022] [Accepted: 03/01/2022] [Indexed: 11/17/2022]
Abstract
Cross-linking and mass spectrometry (XL-MS) workflows represent an increasingly popular technique for low-resolution structural studies of macromolecular complexes. Cross-linking reactions take place in the solution state, capturing contact sites between components of a complex that represent the native, functionally relevant structure. Protein-protein XL-MS protocols are widely adopted, providing precise localization of cross-linking sites to single amino acid positions within a pair of cross-linked peptides. In contrast, protein-RNA XL-MS workflows are evolving rapidly and differ in their ability to localize interaction regions within the RNA sequence. Here, we review protein-protein and protein-RNA XL-MS workflows, and discuss their applications in studies of protein-RNA complexes. The examples highlight the complementary value of XL-MS in structural studies of protein-RNA complexes, where more established high-resolution techniques might be unable to produce conclusive data.
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Affiliation(s)
- Chris P Sarnowski
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, 8093 Zurich, Switzerland; Systems Biology PhD Program, University of Zürich and ETH Zürich, Zurich, Switzerland
| | - Maria Bikaki
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, 8093 Zurich, Switzerland
| | - Alexander Leitner
- Institute of Molecular Systems Biology, Department of Biology, ETH Zürich, 8093 Zurich, Switzerland.
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7
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Multitasking Na+/Taurocholate Cotransporting Polypeptide (NTCP) as a Drug Target for HBV Infection: From Protein Engineering to Drug Discovery. Biomedicines 2022; 10:biomedicines10010196. [PMID: 35052874 PMCID: PMC8773476 DOI: 10.3390/biomedicines10010196] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/10/2022] [Accepted: 01/13/2022] [Indexed: 02/05/2023] Open
Abstract
Hepatitis B virus (HBV) infections are among the major public health concerns worldwide with more than 250 million of chronically ill individuals. Many of them are additionally infected with the Hepatitis D virus, a satellite virus to HBV. Chronic infection frequently leads to serious liver diseases including cirrhosis and hepatocellular carcinoma, the most common type of liver cancer. Although current antiviral therapies can control HBV replication and slow down disease progress, there is an unmet medical need to identify therapies to cure this chronic infectious disease. Lately, a noteworthy progress in fighting against HBV has been made by identification of the high-affinity hepatic host receptor for HBV and HDV, namely Na+/taurocholate cotransporting polypeptide (NTCP, gene symbol SLC10A1). Next to its primary function as hepatic uptake transporter for bile acids, NTCP is essential for the cellular entry of HBV and HDV into hepatocytes. Due to this high-ranking discovery, NTCP has become a valuable target for drug development strategies for HBV/HDV-infected patients. In this review, we will focus on a newly predicted three-dimensional NTCP model that was generated using computational approaches and discuss its value in understanding the NTCP’s membrane topology, substrate and virus binding taking place in plasma membranes. We will review existing data on structural, functional, and biological consequences of amino acid residue changes and mutations that lead to loss of NTCP’s transport and virus receptor functions. Finally, we will discuss new directions for future investigations aiming at development of new NTCP-based HBV entry blockers that inhibit HBV tropism in human hepatocytes.
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8
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Wei J, Chen S, Zong L, Gao X, Li Y. Protein-RNA interaction prediction with deep learning: structure matters. Brief Bioinform 2022; 23:bbab540. [PMID: 34929730 PMCID: PMC8790951 DOI: 10.1093/bib/bbab540] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/14/2021] [Accepted: 11/22/2021] [Indexed: 12/11/2022] Open
Abstract
Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Because of the limitation of the previous database, especially the lack of protein structure data, most of the existing computational methods rely heavily on the sequence data, with only a small portion of the methods utilizing the structural information. Recently, AlphaFold has revolutionized the entire protein and biology field. Foreseeably, the protein-RNA interaction prediction will also be promoted significantly in the upcoming years. In this work, we give a thorough review of this field, surveying both the binding site and binding preference prediction problems and covering the commonly used datasets, features and models. We also point out the potential challenges and opportunities in this field. This survey summarizes the development of the RNA-binding protein-RNA interaction field in the past and foresees its future development in the post-AlphaFold era.
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Affiliation(s)
- Junkang Wei
- Department of Computer Science and Engineering (CSE), The Chinese
University of Hong Kong (CUHK), 999077, Hong Kong SAR, China
| | - Siyuan Chen
- Computational Bioscience Research Center (CBRC),
King Abdullah University of Science and Technology (KAUST),
23955-6900, Thuwal, Saudi Arabia
| | - Licheng Zong
- Department of Computer Science and Engineering (CSE), The Chinese
University of Hong Kong (CUHK), 999077, Hong Kong SAR, China
| | - Xin Gao
- Computational Bioscience Research Center (CBRC),
King Abdullah University of Science and Technology (KAUST),
23955-6900, Thuwal, Saudi Arabia
| | - Yu Li
- Department of Computer Science and Engineering (CSE), The Chinese
University of Hong Kong (CUHK), 999077, Hong Kong SAR, China
- The CUHK Shenzhen Research Institute, Hi-Tech Park, 518057,
Shenzhen, China
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9
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Feng Y, Zhang K, Wu Q, Huang SY. NLDock: a Fast Nucleic Acid-Ligand Docking Algorithm for Modeling RNA/DNA-Ligand Complexes. J Chem Inf Model 2021; 61:4771-4782. [PMID: 34468128 DOI: 10.1021/acs.jcim.1c00341] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Nucleic acid-ligand interactions play an important role in numerous cellular processes such as gene function expression and regulation. Therefore, nucleic acids such as RNAs have become more and more important drug targets, where the structural determination of nucleic acid-ligand complexes is pivotal for understanding their functions and thus developing therapeutic interventions. Molecular docking has been a useful computational tool in predicting the complex structure between molecules. However, although a number of docking algorithms have been developed for protein-ligand interactions, only a few docking programs were presented for nucleic acid-ligand interactions. Here, we have developed a fast nucleic acid-ligand docking algorithm, named NLDock, by implementing our intrinsic scoring function ITScoreNL for nucleic acid-ligand interactions into a modified version of the MDock program. NLDock was extensively evaluated on four test sets and compared with five other state-of-the-art docking algorithms including AutoDock, DOCK 6, rDock, GOLD, and Glide. It was shown that our NLDock algorithm obtained a significantly better performance than the other docking programs in binding mode predictions and achieved the success rates of 73%, 36%, and 32% on the largest test set of 77 complexes for local rigid-, local flexible-, and global flexible-ligand docking, respectively. In addition, our NLDock approach is also computationally efficient and consumed an average of as short as 0.97 and 2.08 min for a local flexible-ligand docking job and a global flexible-ligand docking job, respectively. These results suggest the good performance of our NLDock in both docking accuracy and computational efficiency.
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Affiliation(s)
- Yuyu Feng
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Keqiong Zhang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Qilong Wu
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
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10
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González-Alemán R, Chevrollier N, Simoes M, Montero-Cabrera L, Leclerc F. MCSS-Based Predictions of Binding Mode and Selectivity of Nucleotide Ligands. J Chem Theory Comput 2021; 17:2599-2618. [PMID: 33764770 DOI: 10.1021/acs.jctc.0c01339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Computational fragment-based approaches are widely used in drug design and discovery. One of their limitations is the lack of performance of docking methods, mainly the scoring functions. With the emergence of fragment-based approaches for single-stranded RNA ligands, we analyze the performance in docking and screening powers of an MCSS-based approach. The performance is evaluated on a benchmark of protein-nucleotide complexes where the four RNA residues are used as fragments. The screening power can be considered the major limiting factor for the fragment-based modeling or design of sequence-selective oligonucleotides. We show that the MCSS sampling is efficient even for such large and flexible fragments. Hybrid solvent models based on some partial explicit representations improve both the docking and screening powers. Clustering of the n best-ranked poses can also contribute to a lesser extent to better performance. A detailed analysis of molecular features suggests various ways to optimize the performance further.
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Affiliation(s)
- Roy González-Alemán
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Saclay, Gif-sur-Yvette F-91198, France.,Laboratorio de Química Computacional y Teórica (LQCT), Facultad de Química, Universidad de La Habana, 10400 La Habana, Cuba
| | - Nicolas Chevrollier
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Saclay, Gif-sur-Yvette F-91198, France
| | - Manuel Simoes
- CPC Manufacturing Analytics, 67000 Strasbourg, France
| | - Luis Montero-Cabrera
- Laboratorio de Química Computacional y Teórica (LQCT), Facultad de Química, Universidad de La Habana, 10400 La Habana, Cuba
| | - Fabrice Leclerc
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Saclay, Gif-sur-Yvette F-91198, France
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11
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Feng Y, Huang SY. ITScore-NL: An Iterative Knowledge-Based Scoring Function for Nucleic Acid-Ligand Interactions. J Chem Inf Model 2020; 60:6698-6708. [PMID: 33291885 DOI: 10.1021/acs.jcim.0c00974] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Nucleic acid-ligand complexes underlie numerous cellular processes, such as gene function expression and regulation, in which their three-dimensional structures are important to understand their functions and thus to develop therapeutic interventions. Given the high cost and technical difficulties in experimental methods, computational methods such as molecular docking have been actively used to investigate nucleic acid-ligand interactions in which an accurate scoring function is crucial. However, because of the limited number of experimental nucleic acid-ligand binding data and structures, the scoring function development for nucleic acid-ligand interactions falls far behind that for protein-protein and protein-ligand interactions. Here, based on our statistical mechanics-based iterative approach, we have developed an iterative knowledge-based scoring function for nucleic acid-ligand interactions, named as ITScore-NL, by explicitly including stacking and electrostatic potentials. Our ITScore-NL scoring function was extensively evaluated for its ability in the binding mode and binding affinity predictions on three diverse test sets and compared with state-of-the-art scoring functions. Overall, ITScore-NL obtained significantly better performance than the other 12 scoring functions and predicted near-native poses with rmsd ≤ 1.5 Å for 71.43% of the cases when the top three binding modes were considered and a good correlation of R = 0.64 in binding affinity prediction on the large test set of 77 nucleic acid-ligand complexes. These results suggested the accuracy of ITScore-NL and the necessity of explicitly including stacking and electrostatic potentials.
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Affiliation(s)
- Yuyu Feng
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
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12
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Wang K, Hu G, Wu Z, Su H, Yang J, Kurgan L. Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type. Int J Mol Sci 2020; 21:E6879. [PMID: 32961749 PMCID: PMC7554811 DOI: 10.3390/ijms21186879] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Revised: 09/15/2020] [Accepted: 09/17/2020] [Indexed: 02/07/2023] Open
Abstract
With close to 30 sequence-based predictors of RNA-binding residues (RBRs), this comparative survey aims to help with understanding and selection of the appropriate tools. We discuss past reviews on this topic, survey a comprehensive collection of predictors, and comparatively assess six representative methods. We provide a novel and well-designed benchmark dataset and we are the first to report and compare protein-level and datasets-level results, and to contextualize performance to specific types of RNAs. The methods considered here are well-cited and rely on machine learning algorithms on occasion combined with homology-based prediction. Empirical tests reveal that they provide relatively accurate predictions. Virtually all methods perform well for the proteins that interact with rRNAs, some generate accurate predictions for mRNAs, snRNA, SRP and IRES, while proteins that bind tRNAs are predicted poorly. Moreover, except for DRNApred, they confuse DNA and RNA-binding residues. None of the six methods consistently outperforms the others when tested on individual proteins. This variable and complementary protein-level performance suggests that users should not rely on applying just the single best dataset-level predictor. We recommend that future work should focus on the development of approaches that facilitate protein-level selection of accurate predictors and the consensus-based prediction of RBRs.
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Affiliation(s)
- Kui Wang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China; (K.W.); (Z.W.); (H.S.); (J.Y.)
| | - Gang Hu
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin 300071, China;
| | - Zhonghua Wu
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China; (K.W.); (Z.W.); (H.S.); (J.Y.)
| | - Hong Su
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China; (K.W.); (Z.W.); (H.S.); (J.Y.)
| | - Jianyi Yang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China; (K.W.); (Z.W.); (H.S.); (J.Y.)
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
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Zheng J, Hong X, Xie J, Tong X, Liu S. P3DOCK: a protein-RNA docking webserver based on template-based and template-free docking. Bioinformatics 2020; 36:96-103. [PMID: 31173056 DOI: 10.1093/bioinformatics/btz478] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 05/24/2019] [Accepted: 06/04/2019] [Indexed: 01/02/2023] Open
Abstract
MOTIVATION The main function of protein-RNA interaction is to regulate the expression of genes. Therefore, studying protein-RNA interactions is of great significance. The information of three-dimensional (3D) structures reveals that atomic interactions are particularly important. The calculation method for modeling a 3D structure of a complex mainly includes two strategies: free docking and template-based docking. These two methods are complementary in protein-protein docking. Therefore, integrating these two methods may improve the prediction accuracy. RESULTS In this article, we compare the difference between the free docking and the template-based algorithm. Then we show the complementarity of these two methods. Based on the analysis of the calculation results, the transition point is confirmed and used to integrate two docking algorithms to develop P3DOCK. P3DOCK holds the advantages of both algorithms. The results of the three docking benchmarks show that P3DOCK is better than those two non-hybrid docking algorithms. The success rate of P3DOCK is also higher (3-20%) than state-of-the-art hybrid and non-hybrid methods. Finally, the hierarchical clustering algorithm is utilized to cluster the P3DOCK's decoys. The clustering algorithm improves the success rate of P3DOCK. For ease of use, we provide a P3DOCK webserver, which can be accessed at www.rnabinding.com/P3DOCK/P3DOCK.html. An integrated protein-RNA docking benchmark can be downloaded from http://rnabinding.com/P3DOCK/benchmark.html. AVAILABILITY AND IMPLEMENTATION www.rnabinding.com/P3DOCK/P3DOCK.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jinfang Zheng
- 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
| | - Xiaoxue Tong
- 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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Vakser IA. Challenges in protein docking. Curr Opin Struct Biol 2020; 64:160-165. [PMID: 32836051 DOI: 10.1016/j.sbi.2020.07.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 06/19/2020] [Accepted: 07/11/2020] [Indexed: 11/30/2022]
Abstract
Current developments in protein docking aim at improvement of applicability, accuracy and utility of modeling macromolecular complexes. The challenges include the need for greater emphasis on protein docking to molecules of different types, proper accounting for conformational flexibility upon binding, new promising methodologies based on residue co-evolution and deep learning, affinity prediction, and further development of fully automated docking servers. Importantly, new developments increasingly focus on realistic modeling of protein interactions in vivo, including crowded environment inside a cell, which involves multiple transient encounters, and propagating the system in time. This opinion paper offers the author's perspective on these challenges in structural modeling of protein interactions and the future of protein docking.
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Affiliation(s)
- Ilya A Vakser
- Computational Biology Program and Department of Molecular Biosciences, The University of Kansas, Lawrence, KS 66045, USA.
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15
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He J, Tao H, Huang SY. Protein-ensemble-RNA docking by efficient consideration of protein flexibility through homology models. Bioinformatics 2020; 35:4994-5002. [PMID: 31086984 DOI: 10.1093/bioinformatics/btz388] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2019] [Revised: 04/28/2019] [Accepted: 05/03/2019] [Indexed: 12/18/2022] Open
Abstract
MOTIVATION Given the importance of protein-ribonucleic acid (RNA) interactions in many biological processes, a variety of docking algorithms have been developed to predict the complex structure from individual protein and RNA partners in the past decade. However, due to the impact of molecular flexibility, the performance of current methods has hit a bottleneck in realistic unbound docking. Pushing the limit, we have proposed a protein-ensemble-RNA docking strategy to explicitly consider the protein flexibility in protein-RNA docking through an ensemble of multiple protein structures, which is referred to as MPRDock. Instead of taking conformations from MD simulations or experimental structures, we obtained the multiple structures of a protein by building models from its homologous templates in the Protein Data Bank (PDB). RESULTS Our approach can not only avoid the reliability issue of structures from MD simulations but also circumvent the limited number of experimental structures for a target protein in the PDB. Tested on 68 unbound-bound and 18 unbound-unbound protein-RNA complexes, our MPRDock/DITScorePR considerably improved the docking performance and achieved a significantly higher success rate than single-protein rigid docking whether pseudo-unbound templates are included or not. Similar improvements were also observed when combining our ensemble docking strategy with other scoring functions. The present homology model-based ensemble docking approach will have a general application in molecular docking for other interactions. AVAILABILITY AND IMPLEMENTATION http://huanglab.phys.hust.edu.cn/mprdock/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jiahua He
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Huanyu Tao
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Sheng-You Huang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
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Roel-Touris J, Bonvin AM. Coarse-grained (hybrid) integrative modeling of biomolecular interactions. Comput Struct Biotechnol J 2020; 18:1182-1190. [PMID: 32514329 PMCID: PMC7264466 DOI: 10.1016/j.csbj.2020.05.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 04/23/2020] [Accepted: 05/06/2020] [Indexed: 12/23/2022] Open
Abstract
The computational modeling field has vastly evolved over the past decades. The early developments of simplified protein systems represented a stepping stone towards establishing more efficient approaches to sample intricated conformational landscapes. Downscaling the level of resolution of biomolecules to coarser representations allows for studying protein structure, dynamics and interactions that are not accessible by classical atomistic approaches. The combination of different resolutions, namely hybrid modeling, has also been proved as an alternative when mixed levels of details are required. In this review, we provide an overview of coarse-grained/hybrid models focusing on their applicability in the modeling of biomolecular interactions. We give a detailed list of ready-to-use modeling software for studying biomolecular interactions allowing various levels of coarse-graining and provide examples of complexes determined by integrative coarse-grained/hybrid approaches in combination with experimental information.
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Yan Y, Tao H, He J, Huang SY. The HDOCK server for integrated protein–protein docking. Nat Protoc 2020; 15:1829-1852. [DOI: 10.1038/s41596-020-0312-x] [Citation(s) in RCA: 288] [Impact Index Per Article: 72.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Accepted: 02/03/2020] [Indexed: 12/27/2022]
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18
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Koukos P, Bonvin A. Integrative Modelling of Biomolecular Complexes. J Mol Biol 2020; 432:2861-2881. [DOI: 10.1016/j.jmb.2019.11.009] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2019] [Revised: 11/12/2019] [Accepted: 11/13/2019] [Indexed: 12/31/2022]
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Nithin C, Mukherjee S, Bahadur RP. A structure-based model for the prediction of protein-RNA binding affinity. RNA (NEW YORK, N.Y.) 2019; 25:1628-1645. [PMID: 31395671 PMCID: PMC6859855 DOI: 10.1261/rna.071779.119] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2019] [Accepted: 08/05/2019] [Indexed: 05/28/2023]
Abstract
Protein-RNA recognition is highly affinity-driven and regulates a wide array of cellular functions. In this study, we have curated a binding affinity data set of 40 protein-RNA complexes, for which at least one unbound partner is available in the docking benchmark. The data set covers a wide affinity range of eight orders of magnitude as well as four different structural classes. On average, we find the complexes with single-stranded RNA have the highest affinity, whereas the complexes with the duplex RNA have the lowest. Nevertheless, free energy gain upon binding is the highest for the complexes with ribosomal proteins and the lowest for the complexes with tRNA with an average of -5.7 cal/mol/Å2 in the entire data set. We train regression models to predict the binding affinity from the structural and physicochemical parameters of protein-RNA interfaces. The best fit model with the lowest maximum error is provided with three interface parameters: relative hydrophobicity, conformational change upon binding and relative hydration pattern. This model has been used for predicting the binding affinity on a test data set, generated using mutated structures of yeast aspartyl-tRNA synthetase, for which experimentally determined ΔG values of 40 mutations are available. The predicted ΔGempirical values highly correlate with the experimental observations. The data set provided in this study should be useful for further development of the binding affinity prediction methods. Moreover, the model developed in this study enhances our understanding on the structural basis of protein-RNA binding affinity and provides a platform to engineer protein-RNA interfaces with desired affinity.
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Affiliation(s)
- Chandran Nithin
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Sunandan Mukherjee
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Ranjit Prasad Bahadur
- Computational Structural Biology Lab, Department of Biotechnology, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
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20
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Dovrolis N, Filidou E, Kolios G. Systems biology in inflammatory bowel diseases: on the way to precision medicine. Ann Gastroenterol 2019; 32:233-246. [PMID: 31040620 PMCID: PMC6479645 DOI: 10.20524/aog.2019.0373] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Accepted: 02/25/2019] [Indexed: 02/07/2023] Open
Abstract
Inflammatory bowel diseases (IBD) are chronic and recurrent inflammatory disorders of the gastrointestinal tract. The elucidation of their etiopathology requires complex and multiple approaches. Systems biology has come to fulfill this need in approaching the pathogenetic mechanisms of IBD and its etiopathology, in a comprehensive way, by combining data from different scientific sources. In combination with bioinformatics and network medicine, it uses principles from computer science, mathematics, physics, chemistry, biology, medicine and computational tools to achieve its purposes. Systems biology utilizes scientific sources that provide data from omics studies (e.g., genomics, transcriptomics, etc.) and clinical observations, whose combined analysis leads to network formation and ultimately to a more integrative image of disease etiopathogenesis. In this review, we analyze the current literature on the methods and the tools utilized by systems biology in order to cover an innovative and exciting field: IBD-omics.
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Affiliation(s)
- Nikolas Dovrolis
- Laboratory of Pharmacology, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
| | - Eirini Filidou
- Laboratory of Pharmacology, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
| | - George Kolios
- Laboratory of Pharmacology, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
- Correspondence to: Prof. George Kolios, MD PhD, Laboratory of Pharmacology, Faculty of Medicine, Democritus University of Thrace, Dragana, Alexandroupolis, 68100, Greece, e-mail:
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21
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Computational approaches to macromolecular interactions in the cell. Curr Opin Struct Biol 2019; 55:59-65. [PMID: 30999240 DOI: 10.1016/j.sbi.2019.03.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2018] [Accepted: 03/08/2019] [Indexed: 12/15/2022]
Abstract
Structural modeling of a cell is an evolving strategic direction in computational structural biology. It takes advantage of new powerful modeling techniques, deeper understanding of fundamental principles of molecular structure and assembly, and rapid growth of the amount of structural data generated by experimental techniques. Key modeling approaches to principal types of macromolecular assemblies in a cell already exist. The main challenge, along with the further development of these modeling approaches, is putting them together in a consistent, unified whole cell model. This opinion piece addresses the fundamental aspects of modeling macromolecular assemblies in a cell, and the state-of-the-art in modeling of the principal types of such assemblies.
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Special Issue: Computational Analysis of RNA Structure and Function. Genes (Basel) 2019; 10:genes10010055. [PMID: 30654585 PMCID: PMC6357010 DOI: 10.3390/genes10010055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Accepted: 01/08/2019] [Indexed: 01/18/2023] Open
Abstract
RNA structure often plays a key role in determining the function of non-coding and coding transcripts [...].
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Ezat AA, Elshemey WM. A comparative study of the efficiency of HCV NS3/4A protease drugs against different HCV genotypes using in silico approaches. Life Sci 2018; 217:176-184. [PMID: 30528183 DOI: 10.1016/j.lfs.2018.12.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 11/21/2018] [Accepted: 12/03/2018] [Indexed: 02/06/2023]
Abstract
AIMS To investigate the efficacy of Direct Acting Antivirals (DAAs) in the treatment of different Hepatitis C Virus (HCV) genotypes. MAIN METHODS Homology modeling is used to predict the 3D structures of different genotypes while molecular docking is employed to predict genotype - drug interactions (Binding Mode) and binding free energy (Docking Score). KEY FINDINGS Simeprevir (TMC435) and to a lesser degree MK6325 are the best drugs among the studied drugs. The predicted affinity of drugs against genotype 1a is always better than other genotypes. P2-P4 macrocyclic drugs show better performance against genotypes 2, 3 and 5. Macrocyclic drugs are better than linear drugs. SIGNIFICANCE HCV is one of the major health problems worldwide. Until the discovery of DAAs, HCV treatment faced many failures. DAAs target key functional machines of the virus life cycle and shut it down. NS3/4A protease is an important target and several drugs have been designed to inhibit its functions. There are several NS3/4A protease drugs approved by Food and Drug Administration (FDA). Unfortunately, the virus exhibits resistance against these drugs. This study is significant in elucidating that no one drug is able to treat different genotypes with the same efficiency. Therefore, treatment should be prescribed based on the HCV genotype.
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Affiliation(s)
- Ahmed A Ezat
- Biophysics Department, Faculty of Science, Cairo University, 12613 Giza, Egypt.
| | - Wael M Elshemey
- Biophysics Department, Faculty of Science, Cairo University, 12613 Giza, Egypt
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