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Boro N, Alexandrino Fernandes P, Mukherjee AK. Computational analysis to comprehend the structure-function properties of fibrinolytic enzymes from Bacillus spp for their efficient integration into industrial applications. Heliyon 2024; 10:e33895. [PMID: 39055840 PMCID: PMC11269858 DOI: 10.1016/j.heliyon.2024.e33895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 06/28/2024] [Accepted: 06/28/2024] [Indexed: 07/28/2024] Open
Abstract
Background The fibrinolytic enzymes from Bacillus sp. are proposed as therapeutics in preventing thrombosis. Computational-based analyses of these enzymes' amino acid composition, basic physiological properties, presence of functional domain and motifs, and secondary and tertiary structure analyses can lead to developing a specific enzyme with improved catalytic activity and other properties that may increase their therapeutic potential. Methods The nucleotide sequences of fibrinolytic enzymes produced by the genus Bacillus and its corresponding protein sequences were retrieved from the NCBI database and aligned using the PRALINE programme. The varied physiochemical parameters and structural and functional analysis of the enzyme sequences were carried out with the ExPASy-ProtParam tool, MEME server, SOPMA, PDBsum tool, CYS-REC tool, SWISS-MODEL, SAVES servers, TMHMM program, GlobPlot, and peptide cutter software. The assessed in-silico data were compared with the published experimental results for validation. Results The alignment of sixty fibrinolytic serine protease enzymes (molecular mass 12-86 kDa) sequences showed 49 enzymes possess a conserved domain with a catalytic triad of Asp196, His242, and Ser569. The predicted instability and aliphatic indexes were 1.94-37.77, and 68.9-93.41, respectively, indicating high thermostability. The random coil means value suggested the predominance of this secondary structure in these proteases. A set of 50 amino acid residues representing motif 3 signifies the Peptidase S8/S53 domain that was invariably observed in 56 sequences. Additionally, 28 sequences have transmembrane helices, with two having the most disordered areas, and they pose 25 enzyme cleavage sites. A comparative analysis of the experimental work with the results of in-silico study put forward the characteristics of the enzyme sequences JF739176.1 and MF677779.1 to be considered when creating a potential mutant enzyme as these sequences are stable at high pH with thermostability and to exhibit αβ-fibrinogenase activity in both experimental and in-silico studies.
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Affiliation(s)
- Nitisha Boro
- Microbial Biotechnology and Protein Research Laboratory, Department of Molecular Biology and Biotechnology, Tezpur University, Tezpur, 784028, Assam, India
| | - Pedro Alexandrino Fernandes
- LAQV@REQUIMTE, Departamento de Química e Bioquímica, Faculdade De Ciências, Universidade do Porto, Rua Do Campo Alegre S/N, 4169-007, Porto, Portugal
| | - Ashis K. Mukherjee
- Microbial Biotechnology and Protein Research Laboratory, Department of Molecular Biology and Biotechnology, Tezpur University, Tezpur, 784028, Assam, India
- Microbial Biotechnology and Protein Research Laboratory, Division of Life Sciences, Institute of Advanced Studies in Science and Technology, Vigyan Path, Garchuk, Paschim Boragaon, Guwahati, 781035, Assam, India
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Lin P, Yan Y, Tao H, Huang SY. Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes. Nat Commun 2023; 14:4935. [PMID: 37582780 PMCID: PMC10427616 DOI: 10.1038/s41467-023-40426-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/21/2023] [Indexed: 08/17/2023] Open
Abstract
Membrane proteins are encoded by approximately a quarter of human genes. Inter-chain residue-residue contact information is important for structure prediction of membrane protein complexes and valuable for understanding their molecular mechanism. Although many deep learning methods have been proposed to predict the intra-protein contacts or helix-helix interactions in membrane proteins, it is still challenging to accurately predict their inter-chain contacts due to the limited number of transmembrane proteins. Addressing the challenge, here we develop a deep transfer learning method for predicting inter-chain contacts of transmembrane protein complexes, named DeepTMP, by taking advantage of the knowledge pre-trained from a large data set of non-transmembrane proteins. DeepTMP utilizes a geometric triangle-aware module to capture the correct inter-chain interaction from the coevolution information generated by protein language models. DeepTMP is extensively evaluated on a test set of 52 self-associated transmembrane protein complexes, and compared with state-of-the-art methods including DeepHomo2.0, CDPred, GLINTER, DeepHomo, and DNCON2_Inter. It is shown that DeepTMP considerably improves the precision of inter-chain contact prediction and outperforms the existing approaches in both accuracy and robustness.
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Affiliation(s)
- Peicong Lin
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Yumeng Yan
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Huanyu Tao
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
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Shakibaie MR, Modaresi F, Azizi O, Tadjrobehkar O, Ghaemi MM. Conformational changes in the AdeB transmembrane efflux pump by amphiphilic peptide Mastoparan-B, down-regulates expression of the adeB Gene and restores antibiotics Susceptibility.. [DOI: 10.1101/2023.01.03.522678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
ABSTRACTNo report exists on the role of Mastoparan B (MP-B) as an RND efflux pump inhibitor in multi-drug resistant (MDR)Acinetobacter baumannii. Here, we performed a series of in-silico experiments to predict the inhibition of the AdeB efflux pump by MP-B as a drug target agent. For this reason, an MDR strain ofA. baumanniiwas subjected to MICs against 12 antibiotics as well as MP-B. Expression of the adeB gene in the presence and absence of sub-MIC of MP-B was studied by qRT-PCR. It was found that MP-B had potent antimicrobial activity (MIC=1 μg/ml) associated with a 20-fold decrease in theadeB gene expression at the sub-MIC level. The stereochemical analysis using several automated servers confirmed that the AdeB protein is an inner membrane of the RND tripartite complex system with helix-turn-helix conformation and a pore rich in Phe, Ala, and Lys residue. Furthermore, 20 ligands were generated from the initial docked poses to create the correct protein-peptide complexes using the BioLiP pipeline. The pose showed high Z=1.2, C=1.41, TM=0.99, and RMSD=4.4 scores was selected for docking purposes. The molecular docking via AutoDock/Vina revealed that MP-B form H-bound with Val 499, Phe 454, Thr 474, Ser 461, Gly 465, and Tyr 468 residues of the AdeB helix-5 and caused a shift in the dihedral angle (Φ/Ψ) by distances of 9.0 Å, 9.3 Å, and 9.6 Å, respectively. This shift in folding was detected by AlphaFold 2 and influenced the overall druggability of the protein. From the above results, we concluded that MP-B can be a good candidate for bacterial efflux pump inhibition.
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Zhang Q, Zhu J, Ju F, Kong L, Sun S, Zheng WM, Bu D. ISSEC: inferring contacts among protein secondary structure elements using deep object detection. BMC Bioinformatics 2020; 21:503. [PMID: 33153432 PMCID: PMC7643357 DOI: 10.1186/s12859-020-03793-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 09/30/2020] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND The formation of contacts among protein secondary structure elements (SSEs) is an important step in protein folding as it determines topology of protein tertiary structure; hence, inferring inter-SSE contacts is crucial to protein structure prediction. One of the existing strategies infers inter-SSE contacts directly from the predicted possibilities of inter-residue contacts without any preprocessing, and thus suffers from the excessive noises existing in the predicted inter-residue contacts. Another strategy defines SSEs based on protein secondary structure prediction first, and then judges whether each candidate SSE pair could form contact or not. However, it is difficult to accurately determine boundary of SSEs due to the errors in secondary structure prediction. The incorrectly-deduced SSEs definitely hinder subsequent prediction of the contacts among them. RESULTS We here report an accurate approach to infer the inter-SSE contacts (thus called as ISSEC) using the deep object detection technique. The design of ISSEC is based on the observation that, in the inter-residue contact map, the contacting SSEs usually form rectangle regions with characteristic patterns. Therefore, ISSEC infers inter-SSE contacts through detecting such rectangle regions. Unlike the existing approach directly using the predicted probabilities of inter-residue contact, ISSEC applies the deep convolution technique to extract high-level features from the inter-residue contacts. More importantly, ISSEC does not rely on the pre-defined SSEs. Instead, ISSEC enumerates multiple candidate rectangle regions in the predicted inter-residue contact map, and for each region, ISSEC calculates a confidence score to measure whether it has characteristic patterns or not. ISSEC employs greedy strategy to select non-overlapping regions with high confidence score, and finally infers inter-SSE contacts according to these regions. CONCLUSIONS Comprehensive experimental results suggested that ISSEC outperformed the state-of-the-art approaches in predicting inter-SSE contacts. We further demonstrated the successful applications of ISSEC to improve prediction of both inter-residue contacts and tertiary structure as well.
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Affiliation(s)
- Qi Zhang
- Key Lab of Intelligent Information Processing, Big Data Academy, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
- School of Computer Science, University of Chinese Academy of Sciences, Beijing, China
| | - Jianwei Zhu
- Key Lab of Intelligent Information Processing, Big Data Academy, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
- School of Computer Science, University of Chinese Academy of Sciences, Beijing, China
| | - Fusong Ju
- Key Lab of Intelligent Information Processing, Big Data Academy, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
- School of Computer Science, University of Chinese Academy of Sciences, Beijing, China
| | - Lupeng Kong
- Key Lab of Intelligent Information Processing, Big Data Academy, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
- School of Computer Science, University of Chinese Academy of Sciences, Beijing, China
| | - Shiwei Sun
- Key Lab of Intelligent Information Processing, Big Data Academy, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China
- School of Computer Science, University of Chinese Academy of Sciences, Beijing, China
| | - Wei-Mou Zheng
- Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing, 100190, China
| | - Dongbo Bu
- Key Lab of Intelligent Information Processing, Big Data Academy, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Computer Science, University of Chinese Academy of Sciences, Beijing, China.
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Fang C, Jia Y, Hu L, Lu Y, Wang H. IMPContact: An Interhelical Residue Contact Prediction Method. BIOMED RESEARCH INTERNATIONAL 2020; 2020:4569037. [PMID: 32309431 PMCID: PMC7140131 DOI: 10.1155/2020/4569037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Accepted: 03/09/2020] [Indexed: 11/17/2022]
Abstract
As an important category of proteins, alpha-helix transmembrane proteins (αTMPs) play an important role in various biological activities. Because the solved αTMP structures are inadequate, predicting the residue contacts among the transmembrane segments of an αTMP exhibits the basis of protein fold, which can be used to further discover more protein functions. A few efforts have been devoted to predict the interhelical residue contact using machine learning methods based on the prior knowledge of transmembrane protein structure. However, it is still a challenge to improve the prediction accuracy, while the deep learning method provides an opportunity to utilize the structural knowledge in a different insight. For this purpose, we proposed a novel αTMP residue-residue contact prediction method IMPContact, in which a convolutional neural network (CNN) was applied to recognize those interhelical contacts in a TMP using its specific structural features. There were four sequence-based TMP-specific features selected to descript a pair of residues, namely, evolutionary covariation, predicted topology structure, residue relative position, and evolutionary conservation. An up-to-date dataset was used to train and test the IMPContact; our method achieved better performance compared to peer methods. In the case studies, IHRCs in the regular transmembrane helixes were better predicted than in the irregular ones.
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Affiliation(s)
- Chao Fang
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Yajie Jia
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
- Institute of Computational Biology, Northeast Normal University, Changchun 130117, China
| | - Lihong Hu
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Yinghua Lu
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
- Department of Computer Science, College of Humanities & Sciences of Northeast Normal University, Changchun 130117, China
| | - Han Wang
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
- Institute of Computational Biology, Northeast Normal University, Changchun 130117, China
- Department of Computer Science, College of Humanities & Sciences of Northeast Normal University, Changchun 130117, China
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6
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Barreto CAV, Baptista SJ, Preto AJ, Matos-Filipe P, Mourão J, Melo R, Moreira I. Prediction and targeting of GPCR oligomer interfaces. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2020; 169:105-149. [PMID: 31952684 DOI: 10.1016/bs.pmbts.2019.11.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
GPCR oligomerization has emerged as a hot topic in the GPCR field in the last years. Receptors that are part of these oligomers can influence each other's function, although it is not yet entirely understood how these interactions work. The existence of such a highly complex network of interactions between GPCRs generates the possibility of alternative targets for new therapeutic approaches. However, challenges still exist in the characterization of these complexes, especially at the interface level. Different experimental approaches, such as FRET or BRET, are usually combined to study GPCR oligomer interactions. Computational methods have been applied as a useful tool for retrieving information from GPCR sequences and the few X-ray-resolved oligomeric structures that are accessible, as well as for predicting new and trustworthy GPCR oligomeric interfaces. Machine-learning (ML) approaches have recently helped with some hindrances of other methods. By joining and evaluating multiple structure-, sequence- and co-evolution-based features on the same algorithm, it is possible to dilute the issues of particular structures and residues that arise from the experimental methodology into all-encompassing algorithms capable of accurately predict GPCR-GPCR interfaces. All these methods used as a single or a combined approach provide useful information about GPCR oligomerization and its role in GPCR function and dynamics. Altogether, we present experimental, computational and machine-learning methods used to study oligomers interfaces, as well as strategies that have been used to target these dynamic complexes.
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Affiliation(s)
- Carlos A V Barreto
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Salete J Baptista
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, CTN, LRS, Portugal
| | - António José Preto
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Pedro Matos-Filipe
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal
| | - Joana Mourão
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Institute for Interdisciplinary Research, University of Coimbra, Coimbra, Portugal
| | - Rita Melo
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Centro de Ciências e Tecnologias Nucleares, Instituto Superior Técnico, Universidade de Lisboa, CTN, LRS, Portugal
| | - Irina Moreira
- Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal; Science and Technology Faculty, University of Coimbra, Coimbra, Portugal.
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Muncanovic D, Justesen MH, Preisler SS, Pedersen PA. Characterization of Hailey-Hailey Disease-mutants in presence and absence of wild type SPCA1 using Saccharomyces cerevisiae as model organism. Sci Rep 2019; 9:12442. [PMID: 31455819 PMCID: PMC6712213 DOI: 10.1038/s41598-019-48866-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Accepted: 08/12/2019] [Indexed: 01/07/2023] Open
Abstract
Hailey-Hailey disease is an autosomal genetic disease caused by mutations in one of the two ATP2C1 alleles encoding the secretory pathway Ca2+/Mn2+-ATPase, hSPCA1. The disease almost exclusively affects epidermis, where it mainly results in acantholysis of the suprabasal layers. The etiology of the disease is complex and not well understood. We applied a yeast based complementation system to characterize fourteen disease-causing ATP2C1 missense mutations in presence or absence of wild type ATP2C1 or ATP2A2, encoding SERCA2. In our yeast model system, mutations in ATP2C1 affected Mn2+ transport more than Ca2+ transport as twelve out of fourteen mutations were unable to complement Mn2+ sensitivity while thirteen out of fourteen to some extent complemented the high Ca2+requirement. Nine out of fourteen mutations conferred a cold sensitive complementation capacity. In absence of a wild type ATP2C1 allele, twelve out of fourteen mutations induced an unfolded protein response indicating that in vivo folding of hSPCA1 is sensitive to disease causing amino acid substitutions and four of the fourteen mutations caused the hSPCA1 protein to accumulate in the vacuolar membrane. Co-expression of either wild type ATP2C1 or ATP2A2 prevented induction of the unfolded protein response and hSPCA1 mis-localization.
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Affiliation(s)
- Daniel Muncanovic
- Department of Biology, August Krogh Building, University of Copenhagen, Universitetsparken 13, 2100, Copenhagen, OE, Denmark
| | - Mette Heberg Justesen
- Department of Biology, August Krogh Building, University of Copenhagen, Universitetsparken 13, 2100, Copenhagen, OE, Denmark
| | - Sarah Spruce Preisler
- Department of Biology, August Krogh Building, University of Copenhagen, Universitetsparken 13, 2100, Copenhagen, OE, Denmark
| | - Per Amstrup Pedersen
- Department of Biology, August Krogh Building, University of Copenhagen, Universitetsparken 13, 2100, Copenhagen, OE, Denmark.
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Wang S, Li Z, Yu Y, Xu J. Folding Membrane Proteins by Deep Transfer Learning. Cell Syst 2017; 5:202-211.e3. [PMID: 28957654 PMCID: PMC5637520 DOI: 10.1016/j.cels.2017.09.001] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2017] [Revised: 06/01/2017] [Accepted: 08/29/2017] [Indexed: 01/02/2023]
Abstract
Computational elucidation of membrane protein (MP) structures is challenging partially due to lack of sufficient solved structures for homology modeling. Here, we describe a high-throughput deep transfer learning method that first predicts MP contacts by learning from non-MPs and then predicts 3D structure models using the predicted contacts as distance restraints. Tested on 510 non-redundant MPs, our method has contact prediction accuracy at least 0.18 better than existing methods, predicts correct folds for 218 MPs, and generates 3D models with root-mean-square deviation (RMSD) less than 4 and 5 Å for 57 and 108 MPs, respectively. A rigorous blind test in the continuous automated model evaluation project shows that our method predicted high-resolution 3D models for two recent test MPs of 210 residues with RMSD ∼2 Å. We estimated that our method could predict correct folds for 1,345-1,871 reviewed human multi-pass MPs including a few hundred new folds, which shall facilitate the discovery of drugs targeting at MPs.
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Affiliation(s)
- Sheng Wang
- Toyota Technological Institute at Chicago, Chicago, IL 60637, USA; Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA; Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Zhen Li
- Toyota Technological Institute at Chicago, Chicago, IL 60637, USA; Department of Computer Science, University of Hong Kong, Hong Kong
| | - Yizhou Yu
- Department of Computer Science, University of Hong Kong, Hong Kong
| | - Jinbo Xu
- Toyota Technological Institute at Chicago, Chicago, IL 60637, USA.
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9
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Xiong D, Mao W, Gong H. Predicting the helix-helix interactions from correlated residue mutations. Proteins 2017; 85:2162-2169. [PMID: 28833538 DOI: 10.1002/prot.25370] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2017] [Revised: 08/03/2017] [Accepted: 08/13/2017] [Indexed: 12/30/2022]
Abstract
Helix-helix interactions are crucial in the structure assembly, stability and function of helix-rich proteins including many membrane proteins. In spite of remarkable progresses over the past decades, the accuracy of predicting protein structures from their amino acid sequences is still far from satisfaction. In this work, we focused on a simpler problem, the prediction of helix-helix interactions, the results of which could facilitate practical protein structure prediction by constraining the sampling space. Specifically, we started from the noisy 2D residue contact maps derived from correlated residue mutations, and utilized ridge detection to identify the characteristic residue contact patterns for helix-helix interactions. The ridge information as well as a few additional features were then fed into a machine learning model HHConPred to predict interactions between helix pairs. In an independent test, our method achieved an F-measure of ∼60% for predicting helix-helix interactions. Moreover, although the model was trained mainly using soluble proteins, it could be extended to membrane proteins with at least comparable performance relatively to previous approaches that were generated purely using membrane proteins. All data and source codes are available at http://166.111.152.91/Downloads.html or https://github.com/dpxiong/HHConPred.
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Affiliation(s)
- Dapeng Xiong
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China
- Beijing Innovation Center of Structural Biology, Tsinghua University, Beijing, China
| | - Wenzhi Mao
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China
- Beijing Innovation Center of Structural Biology, Tsinghua University, Beijing, China
| | - Haipeng Gong
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China
- Beijing Innovation Center of Structural Biology, Tsinghua University, Beijing, China
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Venko K, Roy Choudhury A, Novič M. Computational Approaches for Revealing the Structure of Membrane Transporters: Case Study on Bilitranslocase. Comput Struct Biotechnol J 2017; 15:232-242. [PMID: 28228927 PMCID: PMC5312651 DOI: 10.1016/j.csbj.2017.01.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Revised: 01/19/2017] [Accepted: 01/20/2017] [Indexed: 11/23/2022] Open
Abstract
The structural and functional details of transmembrane proteins are vastly underexplored, mostly due to experimental difficulties regarding their solubility and stability. Currently, the majority of transmembrane protein structures are still unknown and this present a huge experimental and computational challenge. Nowadays, thanks to X-ray crystallography or NMR spectroscopy over 3000 structures of membrane proteins have been solved, among them only a few hundred unique ones. Due to the vast biological and pharmaceutical interest in the elucidation of the structure and the functional mechanisms of transmembrane proteins, several computational methods have been developed to overcome the experimental gap. If combined with experimental data the computational information enables rapid, low cost and successful predictions of the molecular structure of unsolved proteins. The reliability of the predictions depends on the availability and accuracy of experimental data associated with structural information. In this review, the following methods are proposed for in silico structure elucidation: sequence-dependent predictions of transmembrane regions, predictions of transmembrane helix–helix interactions, helix arrangements in membrane models, and testing their stability with molecular dynamics simulations. We also demonstrate the usage of the computational methods listed above by proposing a model for the molecular structure of the transmembrane protein bilitranslocase. Bilitranslocase is bilirubin membrane transporter, which shares similar tissue distribution and functional properties with some of the members of the Organic Anion Transporter family and is the only member classified in the Bilirubin Transporter Family. Regarding its unique properties, bilitranslocase is a potentially interesting drug target.
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Affiliation(s)
- Katja Venko
- Department of Cheminformatics, National Institute of Chemistry, Ljubljana, Slovenia
| | - A Roy Choudhury
- Department of Cheminformatics, National Institute of Chemistry, Ljubljana, Slovenia
| | - Marjana Novič
- Department of Cheminformatics, National Institute of Chemistry, Ljubljana, Slovenia
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Zhang H, Huang Q, Bei Z, Wei Y, Floudas CA. COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming. Proteins 2016; 84:332-48. [PMID: 26756402 DOI: 10.1002/prot.24979] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Revised: 11/19/2015] [Accepted: 12/10/2015] [Indexed: 12/28/2022]
Abstract
In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/.
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Affiliation(s)
- Huiling Zhang
- Centre for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Qingsheng Huang
- Centre for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Zhendong Bei
- Center for Cloud Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Yanjie Wei
- Centre for High Performance Computing, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Christodoulos A Floudas
- Department of Chemical Engineering, Texas A&M University, College Station, Texas, 77843.,Texas A&M Energy Institute, Texas A&M University, College Station, Texas, 77843
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12
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Márquez-Chamorro AE, Asencio-Cortés G, Santiesteban-Toca CE, Aguilar-Ruiz JS. Soft computing methods for the prediction of protein tertiary structures: A survey. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.06.024] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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13
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Choudhury AR, Sikorska E, van den Boom J, Bayer P, Popenda Ł, Szutkowski K, Jurga S, Bonomi M, Sali A, Zhukov I, Passamonti S, Novič M. Structural Model of the Bilitranslocase Transmembrane Domain Supported by NMR and FRET Data. PLoS One 2015; 10:e0135455. [PMID: 26291722 PMCID: PMC4546402 DOI: 10.1371/journal.pone.0135455] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Accepted: 07/22/2015] [Indexed: 11/19/2022] Open
Abstract
We present a 3D model of the four transmembrane (TM) helical regions of bilitranslocase (BTL), a structurally uncharacterized protein that transports organic anions across the cell membrane. The model was computed by considering helix-helix interactions as primary constraints, using Monte Carlo simulations. The interactions between the TM2 and TM3 segments have been confirmed by Förster resonance energy transfer (FRET) spectroscopy and nuclear magnetic resonance (NMR) spectroscopy, increasing our confidence in the model. Several insights into the BTL transport mechanism were obtained by analyzing the model. For example, the observed cis-trans Leu-Pro peptide bond isomerization in the TM3 fragment may indicate a key conformational change during anion transport by BTL. Our structural model of BTL may facilitate further studies, including drug discovery.
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Affiliation(s)
| | | | - Johannes van den Boom
- Institute for Structural and Medicinal Biochemistry, Center for Medical Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Peter Bayer
- Institute for Structural and Medicinal Biochemistry, Center for Medical Biotechnology, University of Duisburg-Essen, Essen, Germany
| | - Łukasz Popenda
- NanoBioMedical Center, Adam Mickiewicz University, Poznań, Poland
| | - Kosma Szutkowski
- NanoBioMedical Center, Adam Mickiewicz University, Poznań, Poland
- Faculty of Physics, Adam Mickiewicz University, Poznań, Poland
| | - Stefan Jurga
- NanoBioMedical Center, Adam Mickiewicz University, Poznań, Poland
- Faculty of Physics, Adam Mickiewicz University, Poznań, Poland
| | - Massimiliano Bonomi
- Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco, California, United States of America
| | - Andrej Sali
- Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, and California Institute for Quantitative Biosciences, University of California, San Francisco, California, United States of America
| | - Igor Zhukov
- NanoBioMedical Center, Adam Mickiewicz University, Poznań, Poland
- Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Warsaw, Poland
- * E-mail: (MN); (SP); (IZ)
| | - Sabina Passamonti
- Department of Life Sciences, University of Trieste, Trieste, Italy
- * E-mail: (MN); (SP); (IZ)
| | - Marjana Novič
- National Institute of Chemistry, Hajdrihova 19, Ljubljana, Slovenia
- * E-mail: (MN); (SP); (IZ)
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14
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Esque J, Urbain A, Etchebest C, de Brevern AG. Sequence-structure relationship study in all-α transmembrane proteins using an unsupervised learning approach. Amino Acids 2015; 47:2303-22. [PMID: 26043903 DOI: 10.1007/s00726-015-2010-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Accepted: 05/15/2015] [Indexed: 01/28/2023]
Abstract
Transmembrane proteins (TMPs) are major drug targets, but the knowledge of their precise topology structure remains highly limited compared with globular proteins. In spite of the difficulties in obtaining their structures, an important effort has been made these last years to increase their number from an experimental and computational point of view. In view of this emerging challenge, the development of computational methods to extract knowledge from these data is crucial for the better understanding of their functions and in improving the quality of structural models. Here, we revisit an efficient unsupervised learning procedure, called Hybrid Protein Model (HPM), which is applied to the analysis of transmembrane proteins belonging to the all-α structural class. HPM method is an original classification procedure that efficiently combines sequence and structure learning. The procedure was initially applied to the analysis of globular proteins. In the present case, HPM classifies a set of overlapping protein fragments, extracted from a non-redundant databank of TMP 3D structure. After fine-tuning of the learning parameters, the optimal classification results in 65 clusters. They represent at best similar relationships between sequence and local structure properties of TMPs. Interestingly, HPM distinguishes among the resulting clusters two helical regions with distinct hydrophobic patterns. This underlines the complexity of the topology of these proteins. The HPM classification enlightens unusual relationship between amino acids in TMP fragments, which can be useful to elaborate new amino acids substitution matrices. Finally, two challenging applications are described: the first one aims at annotating protein functions (channel or not), the second one intends to assess the quality of the structures (X-ray or models) via a new scoring function deduced from the HPM classification.
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Affiliation(s)
- Jérémy Esque
- INSERM, U 1134, DSIMB, 75739, Paris, France.,Univ. Paris Diderot, Sorbonne Paris Cité UMR-S 1134, 75739, Paris, France.,Institut National de la Transfusion Sanguine (INTS), 75739, Paris, France.,Laboratoire d'Excellence GR-Ex, 75739, Paris, France.,Laboratoire d'Ingénierie des Fonctions Moléculaire (IFM), ISIS, UMR 7006, 67000, Strasbourg, France.,Department of Integrative Structural Biology, INSERM U964, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), 67404, Illkirch, France.,UMR7104, Centre National de la Recherche Scientifique (CNRS), 67404, Illkirch, France.,Université de Strasbourg, 67404, Illkirch, France
| | - Aurélie Urbain
- Institut Jean-Pierre Bourgin, INRA, UMR 1318, 78026, Versailles, France
| | - Catherine Etchebest
- INSERM, U 1134, DSIMB, 75739, Paris, France.,Univ. Paris Diderot, Sorbonne Paris Cité UMR-S 1134, 75739, Paris, France.,Institut National de la Transfusion Sanguine (INTS), 75739, Paris, France.,Laboratoire d'Excellence GR-Ex, 75739, Paris, France
| | - Alexandre G de Brevern
- INSERM, U 1134, DSIMB, 75739, Paris, France. .,Univ. Paris Diderot, Sorbonne Paris Cité UMR-S 1134, 75739, Paris, France. .,Institut National de la Transfusion Sanguine (INTS), 75739, Paris, France. .,Laboratoire d'Excellence GR-Ex, 75739, Paris, France.
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15
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Solution NMR Structure Determination of Polytopic α-Helical Membrane Proteins: A Guide to Spin Label Paramagnetic Relaxation Enhancement Restraints. Methods Enzymol 2015; 557:329-48. [PMID: 25950972 DOI: 10.1016/bs.mie.2014.12.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Solution nuclear magnetic resonance structures of polytopic α-helical membrane proteins require additional restraints beyond the traditional Nuclear Overhauser Effect (NOE) restraints. Several methods have been developed and this review focuses on paramagnetic relaxation enhancement (PRE). Important aspects of spin labeling, PRE measurements, structure calculations, and structural quality are discussed.
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16
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Abstract
Recent advances in identifying residue-residue contacts from large multiple sequence alignments have enabled impressive gains to be made in the field of protein structure prediction. In this chapter, we discuss these advances and provide a step-by-step guide to applying the latest tools to the de novo modelling of alpha-helical transmembrane proteins. As a practical example, we demonstrate the process of building an accurate 3D model of a G protein-coupled receptor, correctly orientated in the membrane, using only its primary protein sequence.
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Affiliation(s)
- Timothy Nugent
- Bioinformatics Group, Department of Computer Science, University College London, Office: 8.11, Desk: 206, Gower Street, London, WC1E 6BT, UK,
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17
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Probabilistic grammatical model for helix-helix contact site classification. Algorithms Mol Biol 2013; 8:31. [PMID: 24350601 PMCID: PMC3892132 DOI: 10.1186/1748-7188-8-31] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2013] [Accepted: 11/28/2013] [Indexed: 11/25/2022] Open
Abstract
Background Hidden Markov Models power many state‐of‐the‐art tools in
the field of protein bioinformatics. While excelling in their tasks, these
methods of protein analysis do not convey directly information on
medium‐ and long‐range residue‐residue interactions. This
requires an expressive power of at least context‐free grammars.
However, application of more powerful grammar formalisms to protein analysis
has been surprisingly limited. Results In this work, we present a probabilistic grammatical framework for
problem‐specific protein languages and apply it to classification of
transmembrane helix‐helix pairs configurations. The core of the model
consists of a probabilistic context‐free grammar, automatically
inferred by a genetic algorithm from only a generic set of
expert‐based rules and positive training samples. The model was
applied to produce sequence based descriptors of four classes of
transmembrane helix‐helix contact site configurations. The highest
performance of the classifiers reached AUCROC of 0.70. The analysis of grammar parse trees revealed the ability
of representing structural features of helix‐helix contact sites. Conclusions We demonstrated that our probabilistic context‐free framework for
analysis of protein sequences outperforms the state of the art in the task
of helix‐helix contact site classification. However, this is achieved
without necessarily requiring modeling long range dependencies between
interacting residues. A significant feature of our approach is that grammar
rules and parse trees are human‐readable. Thus they could provide
biologically meaningful information for molecular biologists.
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Conserved-residue mutations in Wzy affect O-antigen polymerization and Wzz-mediated chain-length regulation in Pseudomonas aeruginosa PAO1. Sci Rep 2013; 3:3441. [PMID: 24309320 PMCID: PMC3854497 DOI: 10.1038/srep03441] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2013] [Accepted: 11/20/2013] [Indexed: 12/15/2022] Open
Abstract
O antigen (O-Ag) in many bacteria is synthesized via the Wzx/Wzy-dependent pathway in which Wzy polymerizes lipid-linked O-Ag subunits to modal lengths regulated by Wzz. Characterization of 83 site-directed mutants of Wzy from Pseudomonas aeruginosa PAO1 (WzyPa) in topologically-mapped periplasmic (PL) and cytoplasmic loops (CL) verified the functional importance of PL3 and PL5, with the former shown to require overall cationic properties. Essential Arg residues in the RX10G motifs of PL3 and PL5 were found to be conserved in putative homologues of WzyPa, as was the overall sequence homology between these two periplasmic loops in each protein. Amino acid substitutions in CL6 were found to alter Wzz-mediated O-antigen modality, with evidence suggesting that these changes may perturb the C-terminal WzyPa tertiary structure. Together, these data suggest that the catch-and-release mechanism of O-Ag polymerization is widespread among bacteria and that regulation of polymer length is affected by interaction of Wzz with Wzy.
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19
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Lai JS, Cheng CW, Lo A, Sung TY, Hsu WL. Lipid exposure prediction enhances the inference of rotational angles of transmembrane helices. BMC Bioinformatics 2013; 14:304. [PMID: 24112406 PMCID: PMC3854514 DOI: 10.1186/1471-2105-14-304] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2013] [Accepted: 10/01/2013] [Indexed: 11/12/2022] Open
Abstract
Background Since membrane protein structures are challenging to crystallize, computational approaches are essential for elucidating the sequence-to-structure relationships. Structural modeling of membrane proteins requires a multidimensional approach, and one critical geometric parameter is the rotational angle of transmembrane helices. Rotational angles of transmembrane helices are characterized by their folded structures and could be inferred by the hydrophobic moment; however, the folding mechanism of membrane proteins is not yet fully understood. The rotational angle of a transmembrane helix is related to the exposed surface of a transmembrane helix, since lipid exposure gives the degree of accessibility of each residue in lipid environment. To the best of our knowledge, there have been few advances in investigating whether an environment descriptor of lipid exposure could infer a geometric parameter of rotational angle. Results Here, we present an analysis of the relationship between rotational angles and lipid exposure and a support-vector-machine method, called TMexpo, for predicting both structural features from sequences. First, we observed from the development set of 89 protein chains that the lipid exposure, i.e., the relative accessible surface area (rASA) of residues in the lipid environment, generated from high-resolution protein structures could infer the rotational angles with a mean absolute angular error (MAAE) of 46.32˚. More importantly, the predicted rASA from TMexpo achieved an MAAE of 51.05˚, which is better than 71.47˚ obtained by the best of the compared hydrophobicity scales. Lastly, TMexpo outperformed the compared methods in rASA prediction on the independent test set of 21 protein chains and achieved an overall Matthew’s correlation coefficient, accuracy, sensitivity, specificity, and precision of 0.51, 75.26%, 81.30%, 69.15%, and 72.73%, respectively. TMexpo is publicly available at http://bio-cluster.iis.sinica.edu.tw/TMexpo. Conclusions TMexpo can better predict rASA and rotational angles than the compared methods. When rotational angles can be accurately predicted, free modeling of transmembrane protein structures in turn may benefit from a reduced complexity in ensembles with a significantly less number of packing arrangements. Furthermore, sequence-based prediction of both rotational angle and lipid exposure can provide essential information when high-resolution structures are unavailable and contribute to experimental design to elucidate transmembrane protein functions.
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Affiliation(s)
- Jhih-Siang Lai
- Institute of Information Science, Academia Sinica, Taipei, Taiwan.
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20
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Sánchez-Muñoz OL, Strandberg E, Esteban-Martín E, Grage SL, Ulrich AS, Salgado J. Canonical azimuthal rotations and flanking residues constrain the orientation of transmembrane helices. Biophys J 2013; 104:1508-16. [PMID: 23561527 DOI: 10.1016/j.bpj.2013.02.030] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2012] [Revised: 01/31/2013] [Accepted: 02/20/2013] [Indexed: 12/22/2022] Open
Abstract
In biological membranes the alignment of embedded proteins provides crucial structural information. The transmembrane (TM) parts have well-defined secondary structures, in most cases α-helices and their orientation is given by a tilt angle and an azimuthal rotation angle around the main axis. The tilt angle is readily visualized and has been found to be functionally relevant. However, there exist no general concepts on the corresponding azimuthal rotation. Here, we show that TM helices prefer discrete rotation angles. They arise from a combination of intrinsic properties of the helix geometry plus the influence of the position and type of flanking residues at both ends of the hydrophobic core. The helical geometry gives rise to canonical azimuthal angles for which the side chains of residues from the two ends of the TM helix tend to have maximum or minimum immersion within the membrane. This affects the preferential position of residues that fall near hydrophobic/polar interfaces of the membrane, depending on their hydrophobicity and capacity to form specific anchoring interactions. On this basis, we can explain the orientation and dynamics of TM helices and make accurate predictions, which correspond well to the experimental values of several model peptides (including dimers), and TM segments of polytopic membrane proteins.
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21
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Yang J, Jang R, Zhang Y, Shen HB. High-accuracy prediction of transmembrane inter-helix contacts and application to GPCR 3D structure modeling. ACTA ACUST UNITED AC 2013; 29:2579-87. [PMID: 23946502 DOI: 10.1093/bioinformatics/btt440] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
MOTIVATION Residue-residue contacts across the transmembrane helices dictate the three-dimensional topology of alpha-helical membrane proteins. However, contact determination through experiments is difficult because most transmembrane proteins are hard to crystallize. RESULTS We present a novel method (MemBrain) to derive transmembrane inter-helix contacts from amino acid sequences by combining correlated mutations and multiple machine learning classifiers. Tested on 60 non-redundant polytopic proteins using a strict leave-one-out cross-validation protocol, MemBrain achieves an average accuracy of 62%, which is 12.5% higher than the current best method from the literature. When applied to 13 recently solved G protein-coupled receptors, the MemBrain contact predictions helped increase the TM-score of the I-TASSER models by 37% in the transmembrane region. The number of foldable cases (TM-score >0.5) increased by 100%, where all G protein-coupled receptor templates and homologous templates with sequence identity >30% were excluded. These results demonstrate significant progress in contact prediction and a potential for contact-driven structure modeling of transmembrane proteins. AVAILABILITY www.csbio.sjtu.edu.cn/bioinf/MemBrain/
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Affiliation(s)
- Jing Yang
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China, Department of Computational Medicine and Bioinformatics and Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
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22
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Mortality predicted accuracy for hepatocellular carcinoma patients with hepatic resection using artificial neural network. ScientificWorldJournal 2013; 2013:201976. [PMID: 23737707 PMCID: PMC3659648 DOI: 10.1155/2013/201976] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2013] [Accepted: 04/03/2013] [Indexed: 12/15/2022] Open
Abstract
The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation.
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23
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Aprodu I, Stănciuc N, Banu I, Bahrim G. Probing thermal behaviour of microbial transglutaminase with fluorescence and in silico methods. JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE 2013; 93:794-802. [PMID: 22836726 DOI: 10.1002/jsfa.5799] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2012] [Revised: 05/17/2012] [Accepted: 06/12/2012] [Indexed: 06/01/2023]
Abstract
BACKGROUND Knowledge of transglutaminase behaviour at thermal treatment allows efficient applications in food processing. The heat-induced conformational changes of microbial transglutaminase were studied by fluorescence spectroscopy and a molecular modelling approach. RESULTS The experimental results indicate the unfolding of transglutaminase in a single-phase reaction, at temperatures over 60 °C. The incidence of conformational changes is also supported by the increase of both intrinsic and 1-anilino-8-naphthalene sulfonate fluorescence intensity with temperature. Changes in the secondary and tertiary structure of transglutaminase were outlined after running molecular dynamics simulations at temperatures ranging from 25 °C to 80 °C. CONCLUSION The motif's particularities varied with the temperature, suggesting structural rearrangements of the protein, mainly in helices. The largest deviation from the structure equilibrated at 25 °C was observed at 80 °C.
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Affiliation(s)
- Iuliana Aprodu
- Dunarea de Jos University of Galati, Faculty of Food Science and Engineering, Galati, Romania
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25
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Morrill GA, Kostellow AB, Askari A. Caveolin-Na/K-ATPase interactions: role of transmembrane topology in non-genomic steroid signal transduction. Steroids 2012; 77:1160-8. [PMID: 22579740 DOI: 10.1016/j.steroids.2012.04.012] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2012] [Revised: 04/04/2012] [Accepted: 04/10/2012] [Indexed: 12/14/2022]
Abstract
Progesterone and its polar metabolite(s) trigger the meiotic divisions in the amphibian oocyte through a non-genomic signaling system at the plasma membrane. Published site-directed mutagenesis studies of ouabain binding and progesterone-ouabain competition studies indicate that progesterone binds to a 23 amino acid extracellular loop of the plasma membrane α-subunit of Na/K-ATPase. Integral membrane proteins such as caveolins are reported to form Na/K-ATPase-peptide complexes essential for signal transduction. We have characterized the progesterone-induced Na/K-ATPase-caveolin (CAV-1)-steroid 5α-reductase interactions initiating the meiotic divisions. Peptide sequence analysis algorithms indicate that CAV-1 contains two plasma membrane spanning helices, separated by as few as 1-2 amino acid residues at the cell surface. The CAV-1 scaffolding domain, reported to interact with CAV-1 binding (CB) motifs in signaling proteins, overlaps transmembrane (TM) helix 1. The α-subunit of Na/K-ATPase (10 TM helices) contains double CB motifs within TM-1 and TM-10. Steroid 5α-reductase (6 TM helices), an initial step in polar steroid formation, contains CB motifs overlapping TM-1 and TM-6. Computer analysis predicts that interaction between antipathic strands may bring CB motifs and scaffolding domains into close proximity, initiating allostearic changes. Progesterone binding to the α-subunit may thus facilitate CB motif:CAV-1 interaction, which in turn induces helix-helix interaction and generates both a signaling cascade and formation of polar steroids.
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Affiliation(s)
- Gene A Morrill
- Department of Physiology & Biophysics, Albert Einstein College of Medicine, Bronx, NY 10461, USA.
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26
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Nugent T, Jones DT. Membrane protein structural bioinformatics. J Struct Biol 2012; 179:327-37. [DOI: 10.1016/j.jsb.2011.10.008] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2011] [Accepted: 10/25/2011] [Indexed: 10/15/2022]
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Abstract
Background Identifying the location of binding sites on proteins is of fundamental importance for a wide range of applications including molecular docking, de novo drug design, structure identification and comparison of functional sites. Structural genomic projects are beginning to produce protein structures with unknown functions. Therefore, efficient methods are required if all these structures are to be properly annotated. Lots of methods for finding binding sites involve 3D structure comparison. Here we design a method to find protein binding sites by direct comparison of protein 3D structures. Results We have developed an efficient heuristic approach for finding similar binding sites from the surface of given proteins. Our approach consists of three steps: local sequence alignment, protein surface detection, and 3D structures comparison. We implement the algorithm and produce a software package that works well in practice. When comparing a complete protein with all complete protein structures in the PDB database, experiments show that the average recall value of our approach is 82% and the average precision value of our approach is also significantly better than the existing approaches. Conclusions Our program has much higher recall values than those existing programs. Experiments show that all the existing approaches have recall values less than 50%. This implies that more than 50% of real binding sites cannot be reported by those existing approaches. The software package is available at http://sites.google.com/site/guofeics/bsfinder.
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Affiliation(s)
- Fei Guo
- Department of Computer Science, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong
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28
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Wang H, Zhang C, Shi X, Zhang L, Zhou Y. Improving transmembrane protein consensus topology prediction using inter-helical interaction. BIOCHIMICA ET BIOPHYSICA ACTA-BIOMEMBRANES 2012; 1818:2679-86. [PMID: 22683598 DOI: 10.1016/j.bbamem.2012.05.030] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2012] [Revised: 05/29/2012] [Accepted: 05/31/2012] [Indexed: 11/18/2022]
Abstract
Alpha helix transmembrane proteins (αTMPs) represent roughly 30% of all open reading frames (ORFs) in a typical genome and are involved in many critical biological processes. Due to the special physicochemical properties, it is hard to crystallize and obtain high resolution structures experimentally, thus, sequence-based topology prediction is highly desirable for the study of transmembrane proteins (TMPs), both in structure prediction and function prediction. Various model-based topology prediction methods have been developed, but the accuracy of those individual predictors remain poor due to the limitation of the methods or the features they used. Thus, the consensus topology prediction method becomes practical for high accuracy applications by combining the advances of the individual predictors. Here, based on the observation that inter-helical interactions are commonly found within the transmembrane helixes (TMHs) and strongly indicate the existence of them, we present a novel consensus topology prediction method for αTMPs, CNTOP, which incorporates four top leading individual topology predictors, and further improves the prediction accuracy by using the predicted inter-helical interactions. The method achieved 87% prediction accuracy based on a benchmark dataset and 78% accuracy based on a non-redundant dataset which is composed of polytopic αTMPs. Our method derives the highest topology accuracy than any other individual predictors and consensus predictors, at the same time, the TMHs are more accurately predicted in their length and locations, where both the false positives (FPs) and the false negatives (FNs) decreased dramatically. The CNTOP is available at: http://ccst.jlu.edu.cn/JCSB/cntop/CNTOP.html.
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Affiliation(s)
- Han Wang
- Jilin University, Changchun, China
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29
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Accurate de novo structure prediction of large transmembrane protein domains using fragment-assembly and correlated mutation analysis. Proc Natl Acad Sci U S A 2012; 109:E1540-7. [PMID: 22645369 DOI: 10.1073/pnas.1120036109] [Citation(s) in RCA: 140] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
A new de novo protein structure prediction method for transmembrane proteins (FILM3) is described that is able to accurately predict the structures of large membrane proteins domains using an ensemble of two secondary structure prediction methods to guide fragment selection in combination with a scoring function based solely on correlated mutations detected in multiple sequence alignments. This approach has been validated by generating models for 28 membrane proteins with a diverse range of complex topologies and an average length of over 300 residues with results showing that TM-scores > 0.5 can be achieved in almost every case following refinement using MODELLER. In one of the most impressive results, a model of mitochondrial cytochrome c oxidase polypeptide I was obtained with a TM-score > 0.75 and an rmsd of only 5.7 Å over all 514 residues. These results suggest that FILM3 could be applicable to a wide range of transmembrane proteins of as-yet-unknown 3D structure given sufficient homologous sequences.
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Islam ST, Fieldhouse RJ, Anderson EM, Taylor VL, Keates RAB, Ford RC, Lam JS. A cationic lumen in the Wzx flippase mediates anionic O-antigen subunit translocation in Pseudomonas aeruginosa PAO1. Mol Microbiol 2012; 84:1165-76. [PMID: 22554073 PMCID: PMC3412221 DOI: 10.1111/j.1365-2958.2012.08084.x] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Heteropolymeric B-band O-antigen (O-Ag) biosynthesis in Pseudomonas aeruginosa PAO1 follows the Wzy-dependent pathway, beginning with translocation of undecaprenyl pyrophosphate-linked anionic O-Ag subunits (O units) from the inner to the outer leaflets of the inner membrane (IM). This translocation is mediated by the integral IM flippase Wzx. Through experimentally based and unbiased topological mapping, our group previously observed that Wzx possesses many charged and aromatic amino acid residues within its 12 transmembrane segments (TMS). Herein, site-directed mutagenesis targeting 102 residues was carried out on the TMS and loops of Wzx, followed by assessment of each construct's ability to restore B-band O-Ag production, identifying eight residues important for flippase function. The importance of various charged and aromatic residues was highlighted, predominantly within the TMS of the protein, revealing functional ‘hotspots’ within the flippase, particularly within TMS2 and TMS8. Construction of a tertiary structure homology model for Wzx indicated that TMS2 and TMS8 line a central cationic lumen. This is the first report to describe a charged flippase lumen for mediating anionic O-unit translocation across the hydrophobic IM.
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Affiliation(s)
- Salim T Islam
- Department of Molecular and Cellular Biology Biophysics Interdepartmental Group, University of Guelph, Guelph, ON N1G 2W1, Canada
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Joo H, Chavan AG, Phan J, Day R, Tsai J. An amino acid packing code for α-helical structure and protein design. J Mol Biol 2012; 419:234-54. [PMID: 22426125 DOI: 10.1016/j.jmb.2012.03.004] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2011] [Revised: 02/22/2012] [Accepted: 03/07/2012] [Indexed: 11/19/2022]
Abstract
This work demonstrates that all packing in α-helices can be simplified to repetitive patterns of a single motif: the knob-socket. Using the precision of Voronoi Polyhedra/Delauney Tessellations to identify contacts, the knob-socket is a four-residue tetrahedral motif: a knob residue on one α-helix packs into the three-residue socket on another α-helix. The principle of the knob-socket model relates the packing between levels of protein structure: the intra-helical packing arrangements within secondary structure that permit inter-helix tertiary packing interactions. Within an α-helix, the three-residue sockets arrange residues into a uniform packing lattice. Inter-helix packing results from a definable pattern of interdigitated knob-socket motifs between two α-helices. Furthermore, the knob-socket model classifies three types of sockets: (1) free, favoring only intra-helical packing; (2) filled, favoring inter-helical interactions; and (3) non, disfavoring α-helical structure. The amino acid propensities in these three socket classes essentially represent an amino acid code for structure in α-helical packing. Using this code, we used a novel yet straightforward approach for the design of α-helical structure to validate the knob-socket model. Unique sequences for three peptides were created to produce a predicted amount of α-helical structure: mostly helical, some helical, and no helix. These three peptides were synthesized, and helical content was assessed using CD spectroscopy. The measured α-helicity of each peptide was consistent with the expected predictions. These results and analysis demonstrate that the knob-socket motif functions as the basic unit of packing and presents an intuitive tool to decipher the rules governing packing in protein structure.
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Affiliation(s)
- Hyun Joo
- Department of Chemistry, University of the Pacific, Stockton, CA 95211, USA
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Gideon DA, Kumari R, Lynn AM, Manoj KM. What is the Functional Role of N-terminal Transmembrane Helices in the Metabolism Mediated by Liver Microsomal Cytochrome P450 and its Reductase? Cell Biochem Biophys 2012; 63:35-45. [PMID: 22302675 DOI: 10.1007/s12013-012-9339-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We sought to clarify on the hitherto unresolved role of N-terminal transmembrane segments (TMS) of cytochrome P450 (CYP) and its' reductase (CPR) in protein interaction/catalysis. TMS analyses show little evolutionary conservation in CYPs. The conserved CPR's TMS poses limited scope for predictable/consistent hetero-recognition with the wide bevy of CYPs' TMS, as evident from preliminary analyses and TMhit server predictions for inter-helical binding. Further, experimentations with four different CPR preparations (preps) and two liver microsomal CYPs (2C9 and 2E1) shows that the hydroxylated product formation rate is not quantitatively correlated to the extent of integrity of the CPR N-terms. Incorporation of cytochrome b (5) in some reactions afforded similar rates while employing either fully intact or partially intact CPR. A survey of literature shows that liver microsomal CYPs function quite well even without the TMS or with significantly altered TMS. These observations negate the hypothesis that N-term TMS of CPR or CYP is obligatory for CYP-CPR interaction and catalysis. Also, in CYP2E1-mediated hydroxylation of para-nitrophenol, the extent of intactness or truncation did not significantly affect the CPR preps' catalytic role at very low or high substrate concentrations. To interpret these results, we draw support from recently published research on reduced nicotinamide adenide dinucleotide phosphate oxidase (Takac et al., J Biol Chem, 286:13304-13313, 2011) and from our pertinent earlier works. We infer that CPR' free TMS segment could alter the diffusible reactive oxygen species' dynamics in the microenvironment, thereby altering the reaction outcome. Based on the evidence, we conclude that TMS merely facilitates "interaction/catalysis" by anchoring the CYP and CPR in the lipid interface.
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Affiliation(s)
- Daniel Andrew Gideon
- Heme & Flavo Proteins Laboratory, Center for Biomedical Research, VIT University, #204, Vellore, 632014, Tamil Nadu, India
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33
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Guo F, Li SC, Wang L. Protein-protein binding sites prediction by 3D structural similarities. J Chem Inf Model 2011; 51:3287-94. [PMID: 22077765 DOI: 10.1021/ci200206n] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Identifying the location of binding sites on proteins is of fundamental importance for a wide range of applications including molecular docking, de novo drug design, structure identification, and comparison of functional sites. In this paper, we develop an efficient approach for finding binding sites between proteins. Our approach consists of four steps: local sequence alignment, protein surface detection, 3D structure comparison, and candidate binding site selection. A comparison of our method with the LSA algorithm shows that the binding sites predicted by our method are somewhat closer to the actual binding sites in the protein-protein complexes. The software package is available at http://sites.google.com/site/guofeics/pro-bs for noncommercial use.
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Affiliation(s)
- Fei Guo
- School of Computer Science and Technology, Shandong University, Jinan 250101, Shandong, China
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34
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Predicting residue-residue contacts and helix-helix interactions in transmembrane proteins using an integrative feature-based random forest approach. PLoS One 2011; 6:e26767. [PMID: 22046350 PMCID: PMC3203928 DOI: 10.1371/journal.pone.0026767] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2011] [Accepted: 10/04/2011] [Indexed: 11/20/2022] Open
Abstract
Integral membrane proteins constitute 25-30% of genomes and play crucial roles in many biological processes. However, less than 1% of membrane protein structures are in the Protein Data Bank. In this context, it is important to develop reliable computational methods for predicting the structures of membrane proteins. Here, we present the first application of random forest (RF) for residue-residue contact prediction in transmembrane proteins, which we term as TMhhcp. Rigorous cross-validation tests indicate that the built RF models provide a more favorable prediction performance compared with two state-of-the-art methods, i.e., TMHcon and MEMPACK. Using a strict leave-one-protein-out jackknifing procedure, they were capable of reaching the top L/5 prediction accuracies of 49.5% and 48.8% for two different residue contact definitions, respectively. The predicted residue contacts were further employed to predict interacting helical pairs and achieved the Matthew's correlation coefficients of 0.430 and 0.424, according to two different residue contact definitions, respectively. To facilitate the academic community, the TMhhcp server has been made freely accessible at http://protein.cau.edu.cn/tmhhcp.
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Zhou W, Yan H. Prediction of DNA-binding protein based on statistical and geometric features and support vector machines. Proteome Sci 2011; 9 Suppl 1:S1. [PMID: 22166014 PMCID: PMC3289070 DOI: 10.1186/1477-5956-9-s1-s1] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Background Previous studies on protein-DNA interaction mostly focused on the bound structure of DNA-binding proteins but few paid enough attention to the unbound structures. As more new proteins are discovered, it is useful and imperative to develop algorithms for the functional prediction of unbound proteins. In our work, we apply an alpha shape model to represent the surface structure of the protein-DNA complex and extract useful statistical and geometric features, and use structural alignment and support vector machines for the prediction of unbound DNA-binding proteins. Results The performance of our method is evaluated by discriminating a set of 104 DNA-binding proteins from 401 non-DNA-binding proteins. In the same test, the proposed method outperforms the other method using conditional probability. The results achieved by our proposed method for; precision, 83.33%; accuracy, 86.53%; and MCC, 0.5368 demonstrate its good performance. Conclusions In this study we develop an effective method for the prediction of protein-DNA interactions based on statistical and geometric features and support vector machines. Our results show that interface surface features play an important role in protein-DNA interaction. Our technique is able to predict unbound DNA-binding protein and discriminatory DNA-binding proteins from proteins that bind with other molecules.
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Affiliation(s)
- Weiqiang Zhou
- Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong.
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36
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Wei Y, Floudas CA. Enhanced Inter-helical Residue Contact Prediction in Transmembrane Proteins. Chem Eng Sci 2011; 66:4356-4369. [PMID: 21892227 PMCID: PMC3164537 DOI: 10.1016/j.ces.2011.04.033] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
In this paper, based on a recent work by McAllister and Floudas who developed a mathematical optimization model to predict the contacts in transmembrane alpha-helical proteins from a limited protein data set [1], we have enhanced this method by 1) building a more comprehensive data set for transmembrane alpha-helical proteins and this enhanced data set is then used to construct the probability sets, MIN-1N and MIN-2N, for residue contact prediction, 2) enhancing the mathematical model via modifications of several important physical constraints and 3) applying a new blind contact prediction scheme on different protein sets proposed from analyzing the contact prediction on 65 proteins from Fuchs et al. [2]. The blind contact prediction scheme has been tested on two different membrane protein sets. Firstly it is applied to five carefully selected proteins from the training set. The contact prediction of these five proteins uses probability sets built by excluding the target protein from the training set, and an average accuracy of 56% was obtained. Secondly, it is applied to six independent membrane proteins with complicated topologies, and the prediction accuracies are 73% for 2ZY9A, 21% for 3KCUA, 46% for 2W1PA, 64% for 3CN5A, 77% for 3IXZA and 83% for 3K3FA. The average prediction accuracy for the six proteins is 60.7%. The proposed approach is also compared with a support vector machine method (TMhit [3]) and it is shown that it exhibits better prediction accuracy.
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Affiliation(s)
- Y. Wei
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544-5263, U.S.A
| | - C. A. Floudas
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544-5263, U.S.A
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37
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Jha AN, Vishveshwara S, Banavar JR. Amino acid interaction preferences in helical membrane proteins. Protein Eng Des Sel 2011; 24:579-88. [PMID: 21666247 DOI: 10.1093/protein/gzr022] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Membrane proteins are involved in a number of important biological functions. Yet, they are poorly understood from the structure and folding point of view. The external environment being drastically different from that of globular proteins, the intra-protein interactions in membrane proteins are also expected to be different. Hence, statistical potentials representing the features of inter-residue interactions based exclusively on the structures of membrane proteins are much needed. Currently, a reasonable number of structures are available, making it possible to undertake such an analysis on membrane proteins. In this study we have examined the inter-residue interaction propensities of amino acids in the membrane spanning regions of the alpha-helical membrane (HM) proteins. Recently we have shown that valuable information can be obtained on globular proteins by the evaluation of the pair-wise interactions of amino acids by classifying them into different structural environments, based on factors such as the secondary structure or the number of contacts that a residue can make. Here we have explored the possible ways of classifying the intra-protein environment of HM proteins and have developed scoring functions based on different classification schemes. On evaluation of different schemes, we find that the scheme which classifies amino acids to different intra-contact environment is the most promising one. Based on this classification scheme, we also redefine the hydrophobicity scale of amino acids in HM proteins.
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Affiliation(s)
- Anupam Nath Jha
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012, India
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38
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Affiliation(s)
- Matthew Thoendel
- Department of Microbiology Roy J. and Lucille A. Carver College of Medicine University of Iowa, Iowa City, IA 52242
| | - Jeffrey S. Kavanaugh
- Department of Microbiology Roy J. and Lucille A. Carver College of Medicine University of Iowa, Iowa City, IA 52242
| | - Caralyn E. Flack
- Department of Microbiology Roy J. and Lucille A. Carver College of Medicine University of Iowa, Iowa City, IA 52242
| | - Alexander R. Horswill
- Department of Microbiology Roy J. and Lucille A. Carver College of Medicine University of Iowa, Iowa City, IA 52242
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Sodt AJ, Head-Gordon T. Driving forces for transmembrane alpha-helix oligomerization. Biophys J 2010; 99:227-37. [PMID: 20655851 DOI: 10.1016/j.bpj.2010.03.071] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2009] [Revised: 03/24/2010] [Accepted: 03/29/2010] [Indexed: 11/25/2022] Open
Abstract
We present what we believe to be a novel statistical contact potential based on solved structures of transmembrane (TM) alpha-helical bundles, and we use this contact potential to investigate the amino acid likelihood of stabilizing helix-helix interfaces. To increase statistical significance, we have reduced the full contact energy matrix to a four-flavor alphabet of amino acids, automatically determined by our methodology, in which we find that polarity is a more dominant factor of group identity than is size, with charged or polar groups most often occupying the same face, whereas polar/apolar residue pairs tend to occupy opposite faces. We found that the most polar residues strongly influence interhelical contact formation, although they occur rarely in TM helical bundles. Two-body contact energies in the reduced letter code are capable of determining native structure from a large decoy set for a majority of test TM proteins, at the same time illustrating that certain higher-order sequence correlations are necessary for more accurate structure predictions.
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Affiliation(s)
- Alex J Sodt
- Department of Bioengineering, University of California, Berkeley, California, USA.
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40
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Ahmad S, Singh YH, Paudel Y, Mori T, Sugita Y, Mizuguchi K. Integrated prediction of one-dimensional structural features and their relationships with conformational flexibility in helical membrane proteins. BMC Bioinformatics 2010; 11:533. [PMID: 20977780 PMCID: PMC3247134 DOI: 10.1186/1471-2105-11-533] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2010] [Accepted: 10/27/2010] [Indexed: 01/04/2023] Open
Abstract
Background Many structural properties such as solvent accessibility, dihedral angles and helix-helix contacts can be assigned to each residue in a membrane protein. Independent studies exist on the analysis and sequence-based prediction of some of these so-called one-dimensional features. However, there is little explanation of why certain residues are predicted in a wrong structural class or with large errors in the absolute values of these features. On the other hand, membrane proteins undergo conformational changes to allow transport as well as ligand binding. These conformational changes often occur via residues that are inherently flexible and hence, predicting fluctuations in residue positions is of great significance. Results We performed a statistical analysis of common patterns among selected one-dimensional equilibrium structural features (ESFs) and developed a method for simultaneously predicting all of these features using an integrated system. Our results show that the prediction performance can be improved if multiple structural features are trained in an integrated model, compared to the current practice of developing individual models. In particular, the performance of the solvent accessibility and bend-angle prediction improved in this way. The well-performing bend-angle prediction can be used to predict helical positions with severe kinks at a modest success rate. Further, we showed that single-chain conformational dynamics, measured by B-factors derived from normal mode analysis, could be predicted from observed and predicted ESFs with good accuracy. A web server was developed (http://tardis.nibio.go.jp/netasa/htmone/) for predicting the one-dimensional ESFs from sequence information and analyzing the differences between the predicted and observed values of the ESFs. Conclusions The prediction performance of the integrated model is significantly better than that of the models performing the task separately for each feature for the solvent accessibility and bend-angle predictions. The predictability of the features also plays a role in determining flexible positions. Although the dynamics studied here concerns local atomic fluctuations, a similar analysis in terms of global structural features will be helpful in predicting large-scale conformational changes, for which work is in progress.
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Affiliation(s)
- Shandar Ahmad
- National Institute of Biomedical Innovation, 7-6-8 Saito-asagi, Ibaraki, Osaka 567 0085, Japan.
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Hubert P, Sawma P, Duneau JP, Khao J, Hénin J, Bagnard D, Sturgis J. Single-spanning transmembrane domains in cell growth and cell-cell interactions: More than meets the eye? Cell Adh Migr 2010; 4:313-24. [PMID: 20543559 PMCID: PMC2900628 DOI: 10.4161/cam.4.2.12430] [Citation(s) in RCA: 67] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2010] [Accepted: 05/20/2010] [Indexed: 01/28/2023] Open
Abstract
As a whole, integral membrane proteins represent about one third of sequenced genomes, and more than 50% of currently available drugs target membrane proteins, often cell surface receptors. Some membrane protein classes, with a defined number of transmembrane (TM) helices, are receiving much attention because of their great functional and pharmacological importance, such as G protein-coupled receptors possessing 7 TM segments. Although they represent roughly half of all membrane proteins, bitopic proteins (with only 1 TM helix) have so far been less well characterized. Though they include many essential families of receptors, such as adhesion molecules and receptor tyrosine kinases, many of which are excellent targets for biopharmaceuticals (peptides, antibodies, et al.). A growing body of evidence suggests a major role for interactions between TM domains of these receptors in signaling, through homo and heteromeric associations, conformational changes, assembly of signaling platforms, etc. Significantly, mutations within single domains are frequent in human disease, such as cancer or developmental disorders. This review attempts to give an overview of current knowledge about these interactions, from structural data to therapeutic perspectives, focusing on bitopic proteins involved in cell signaling.
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Affiliation(s)
- Pierre Hubert
- LISM UPR 9027, CNRS-Aix-Marseille University, Marseille, France.
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42
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Nugent T, Jones DT. Predicting transmembrane helix packing arrangements using residue contacts and a force-directed algorithm. PLoS Comput Biol 2010; 6:e1000714. [PMID: 20333233 PMCID: PMC2841610 DOI: 10.1371/journal.pcbi.1000714] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2009] [Accepted: 02/10/2010] [Indexed: 11/24/2022] Open
Abstract
Alpha-helical transmembrane proteins constitute roughly 30% of a typical genome and are involved in a wide variety of important biological processes including cell signalling, transport of membrane-impermeable molecules and cell recognition. Despite significant efforts to predict transmembrane protein topology, comparatively little attention has been directed toward developing a method to pack the helices together. Here, we present a novel approach to predict lipid exposure, residue contacts, helix-helix interactions and finally the optimal helical packing arrangement of transmembrane proteins. Using molecular dynamics data, we have trained and cross-validated a support vector machine (SVM) classifier to predict per residue lipid exposure with 69% accuracy. This information is combined with additional features to train a second SVM to predict residue contacts which are then used to determine helix-helix interaction with up to 65% accuracy under stringent cross-validation on a non-redundant test set. Our method is also able to discriminate native from decoy helical packing arrangements with up to 70% accuracy. Finally, we employ a force-directed algorithm to construct the optimal helical packing arrangement which demonstrates success for proteins containing up to 13 transmembrane helices. This software is freely available as source code from http://bioinf.cs.ucl.ac.uk/memsat/mempack/.
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Affiliation(s)
- Timothy Nugent
- Bioinformatics Group, Department of Computer Science, University College London, London, United Kingdom
| | - David T. Jones
- Bioinformatics Group, Department of Computer Science, University College London, London, United Kingdom
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