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Huang GJ, Parry TK, McLaughlin WA. Assessment of the Performances of the Protein Modeling Techniques Participating in CASP15 Using a Structure-Based Functional Site Prediction Approach: ResiRole. Bioengineering (Basel) 2023; 10:1377. [PMID: 38135968 PMCID: PMC10740689 DOI: 10.3390/bioengineering10121377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/27/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023] Open
Abstract
BACKGROUND Model quality assessments via computational methods which entail comparisons of the modeled structures to the experimentally determined structures are essential in the field of protein structure prediction. The assessments provide means to benchmark the accuracies of the modeling techniques and to aid with their development. We previously described the ResiRole method to gauge model quality principally based on the preservation of the structural characteristics described in SeqFEATURE functional site prediction models. METHODS We apply ResiRole to benchmark modeling group performances in the Critical Assessment of Structure Prediction experiment, round 15. To gauge model quality, a normalized Predicted Functional site Similarity Score (PFSS) was calculated as the average of one minus the absolute values of the differences of the functional site prediction probabilities, as found for the experimental structures versus those found at the corresponding sites in the structure models. RESULTS The average PFSS per modeling group (gPFSS) correlates with standard quality metrics, and can effectively be used to rank the accuracies of the groups. For the free modeling (FM) category, correlation coefficients of the Local Distance Difference Test (LDDT) and Global Distance Test-Total Score (GDT-TS) metrics with gPFSS were 0.98239 and 0.87691, respectively. An example finding for a specific group is that the gPFSS for EMBER3D was higher than expected based on the predictive relationship between gPFSS and LDDT. We infer the result is due to the use of constraints imprinted by function that are a part of the EMBER3D methodology. Also, we find functional site predictions that may guide further functional characterizations of the respective proteins. CONCLUSION The gPFSS metric provides an effective means to assess and rank the performances of the structure prediction techniques according to their abilities to accurately recount the structural features at predicted functional sites.
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Affiliation(s)
| | | | - William A. McLaughlin
- Department of Medical Education, Geisinger Commonwealth School of Medicine, 525 Pine Street, Scranton, PA 18509, USA (T.K.P.)
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2
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Ravnik V, Jukič M, Bren U. Identifying Metal Binding Sites in Proteins Using Homologous Structures, the MADE Approach. J Chem Inf Model 2023; 63:5204-5219. [PMID: 37557084 PMCID: PMC10466382 DOI: 10.1021/acs.jcim.3c00558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Indexed: 08/11/2023]
Abstract
In order to identify the locations of metal ions in the binding sites of proteins, we have developed a method named the MADE (MAcromolecular DEnsity and Structure Analysis) approach. The MADE approach represents an evolution of our previous toolset, the ProBiS H2O (MD) methodology, for the identification of conserved water molecules. Our method uses experimental structures of proteins homologous to a query, which are subsequently superimposed upon it. Areas with a particular species present in a similar location among many homologous protein structures are identified using a clustering algorithm. Dense clusters likely represent positions containing species important to the query protein structure or function. We analyze well-characterized apo protein structures and show that the MADE approach can identify clusters corresponding to the expected positions of metal ions in their binding sites. The greatest advantage of our method lies in its generality. It can in principle be applied to any species found in protein records; it is not only limited to metal ions. We additionally demonstrate that the MADE approach can be successfully applied to predict the location of cofactors in computer-modeled structures, e.g., via AlphaFold. We also conduct a careful protein superposition method comparison and find our methodology robust and the results largely independent of the selected protein superposition algorithm. We postulate that with increasing structural data availability, additional applications of the MADE approach will be possible such as non-protein systems, water network identification, protein binding site elaboration, and analysis of binding events, all in a dynamic manner. We have implemented the MADE approach as a plugin for the PyMOL molecular visualization tool. The MADE plugin is available free of charge at https://gitlab.com/Jukic/made_software.
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Affiliation(s)
- Vid Ravnik
- Faculty
of Chemistry and Chemical Engineering, University
of Maribor, Smetanova
ulica 17, Maribor SI-2000, Slovenia
| | - Marko Jukič
- Faculty
of Chemistry and Chemical Engineering, University
of Maribor, Smetanova
ulica 17, Maribor SI-2000, Slovenia
- The
Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, Koper SI-6000, Slovenia
- Institute
for Environmental Protection and Sensors, Beloruska ulica 7, Maribor SI-2000, Slovenia
| | - Urban Bren
- Faculty
of Chemistry and Chemical Engineering, University
of Maribor, Smetanova
ulica 17, Maribor SI-2000, Slovenia
- The
Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, Koper SI-6000, Slovenia
- Institute
for Environmental Protection and Sensors, Beloruska ulica 7, Maribor SI-2000, Slovenia
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3
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Preeti P, Nath SK, Arambam N, Sharma T, Choudhury PR, Choudhury A, Khanna V, Strych U, Hotez PJ, Bottazzi ME, Rawal K. Vaxi-DL: An Artificial Intelligence-Enabled Platform for Vaccine Development. Methods Mol Biol 2023; 2673:305-316. [PMID: 37258923 DOI: 10.1007/978-1-0716-3239-0_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Vaccine development is a complex and long process. It involves several steps, including computational studies, experimental analyses, animal model system studies, and clinical trials. This process can be accelerated by using in silico antigen screening to identify potential vaccine candidates. In this chapter, we describe a deep learning-based technique which utilizes 18 biological and 9154 physicochemical properties of proteins for finding potential vaccine candidates. Using this technique, a new web-based system, named Vaxi-DL, was developed which helped in finding new vaccine candidates from bacteria, protozoa, viruses, and fungi. Vaxi-DL is available at: https://vac.kamalrawal.in/vaxidl/ .
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Affiliation(s)
- P Preeti
- Centre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India
| | - Swarsat Kaushik Nath
- Centre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India
| | - Nevidita Arambam
- Centre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India
| | - Trapti Sharma
- Centre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India
| | - Priyanka Ray Choudhury
- Centre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India
| | - Alakto Choudhury
- Centre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India
| | - Vrinda Khanna
- Centre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India
| | - Ulrich Strych
- Department of Pediatrics, Division of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital Center for Vaccine Development, Houston, TX, USA
| | - Peter J Hotez
- Department of Pediatrics, Division of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital Center for Vaccine Development, Houston, TX, USA
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
- Department of Biology, Baylor University, Waco, TX, USA
| | - Maria Elena Bottazzi
- Department of Pediatrics, Division of Tropical Medicine, Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital Center for Vaccine Development, Houston, TX, USA
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, USA
- Department of Biology, Baylor University, Waco, TX, USA
| | - Kamal Rawal
- Centre for Computational Biology and Bioinformatics, AIB, Amity University, Noida, Uttar Pradesh, India.
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4
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Toth JM, DePietro PJ, Haas J, McLaughlin WA. ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques. Bioinformatics 2021; 37:351-359. [PMID: 32780798 PMCID: PMC8058773 DOI: 10.1093/bioinformatics/btaa712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 07/31/2020] [Accepted: 08/05/2020] [Indexed: 11/25/2022] Open
Abstract
Motivation Methods to assess the quality of protein structure models are needed for user applications. To aid with the selection of structure models and further inform the development of structure prediction techniques, we describe the ResiRole method for the assessment of the quality of structure models. Results Structure prediction techniques are ranked according to the results of round-robin, head-to-head comparisons using difference scores. Each difference score was defined as the absolute value of the cumulative probability for a functional site prediction made with the FEATURE program for the reference structure minus that for the structure model. Overall, the difference scores correlate well with other model quality metrics; and based on benchmarking studies with NaïveBLAST, they are found to detect additional local structural similarities between the structure models and reference structures. Availabilityand implementation Automated analyses of models addressed in CAMEO are available via the ResiRole server, URL http://protein.som.geisinger.edu/ResiRole/. Interactive analyses with user-provided models and reference structures are also enabled. Code is available at github.com/wamclaughlin/ResiRole. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Joshua M Toth
- Department of Medical Education, Geisinger Commonwealth School of Medicine, Scranton, PA 18510, USA
| | - Paul J DePietro
- Department of Medical Education, Geisinger Commonwealth School of Medicine, Scranton, PA 18510, USA
| | - Juergen Haas
- Biozentrum, University of Basel and SIB Swiss Institute of Bioinformatics, CH-4056 Basel, Switzerland
| | - William A McLaughlin
- Department of Medical Education, Geisinger Commonwealth School of Medicine, Scranton, PA 18510, USA
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5
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Castanheira DD, Santana EP, Godoy-Santos F, Diniz RHS, Faria-Oliveira F, Pereira RR, Trópia MJM, Castro IM, Brandão RL. Lpx1p links glucose-induced calcium signaling and plasma membrane H+-ATPase activation in Saccharomyces cerevisiae cells. FEMS Yeast Res 2018; 18:4643176. [PMID: 29177424 DOI: 10.1093/femsyr/fox088] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 11/17/2017] [Indexed: 11/12/2022] Open
Abstract
In yeast, as in other eukaryotes, calcium plays an essential role in signaling transduction to regulate different processes. Many pieces of evidence suggest that glucose-induced activation of plasma membrane H+-ATPase, essential for yeast physiology, is related to calcium signaling. Until now, no protein that could be regulated by calcium in this context has been identified. Lpx1p, a serine-protease that is also involved in the glucose-induced activation of the plasma membrane H+-ATPase, could be a candidate to respond to intracellular calcium signaling involved in this process. In this work, by using different approaches, we obtained many pieces of evidence suggesting that the requirement of calcium signaling for activation of the plasma membrane H+-ATPase is due to its requirement for activation of Lpx1p. According to the current model, activation of Lpx1p would cause hydrolysis of an acetylated tubulin that maintains the plasma membrane H+-ATPase in an inactive state. Therefore, after its activation, Lpx1p would hydrolyze the acetylated tubulin making the plasma membrane H+-ATPase accessible for phosphorylation by at least one protein kinase.
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Affiliation(s)
- Diogo Dias Castanheira
- Laboratório de Biologia Celular e Molecular, Núcleo de Pesquisas em Ciências Biológicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus do Morro do Cruzeiro, Ouro Preto, MG 35.400-000, Brazil
| | - Eduardo Perovano Santana
- Laboratório de Biologia Celular e Molecular, Núcleo de Pesquisas em Ciências Biológicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus do Morro do Cruzeiro, Ouro Preto, MG 35.400-000, Brazil
| | - Fernanda Godoy-Santos
- Laboratório de Biologia Celular e Molecular, Núcleo de Pesquisas em Ciências Biológicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus do Morro do Cruzeiro, Ouro Preto, MG 35.400-000, Brazil
| | - Raphael Hermano Santos Diniz
- Laboratório de Biologia Celular e Molecular, Núcleo de Pesquisas em Ciências Biológicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus do Morro do Cruzeiro, Ouro Preto, MG 35.400-000, Brazil
| | - Fábio Faria-Oliveira
- Laboratório de Biologia Celular e Molecular, Núcleo de Pesquisas em Ciências Biológicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus do Morro do Cruzeiro, Ouro Preto, MG 35.400-000, Brazil
| | - Renata Rebeca Pereira
- Laboratório de Biologia Celular e Molecular, Núcleo de Pesquisas em Ciências Biológicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus do Morro do Cruzeiro, Ouro Preto, MG 35.400-000, Brazil
| | - Maria José Magalhães Trópia
- Laboratório de Biologia Celular e Molecular, Núcleo de Pesquisas em Ciências Biológicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus do Morro do Cruzeiro, Ouro Preto, MG 35.400-000, Brazil
| | - Ieso Miranda Castro
- Laboratório de Biologia Celular e Molecular, Núcleo de Pesquisas em Ciências Biológicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus do Morro do Cruzeiro, Ouro Preto, MG 35.400-000, Brazil
| | - Rogelio Lopes Brandão
- Laboratório de Biologia Celular e Molecular, Núcleo de Pesquisas em Ciências Biológicas, Escola de Farmácia, Universidade Federal de Ouro Preto, Campus do Morro do Cruzeiro, Ouro Preto, MG 35.400-000, Brazil
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6
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Computational approaches for de novo design and redesign of metal-binding sites on proteins. Biosci Rep 2017; 37:BSR20160179. [PMID: 28167677 PMCID: PMC5482196 DOI: 10.1042/bsr20160179] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Revised: 02/06/2017] [Accepted: 02/06/2017] [Indexed: 12/25/2022] Open
Abstract
Metal ions play pivotal roles in protein structure, function and stability. The functional and structural diversity of proteins in nature expanded with the incorporation of metal ions or clusters in proteins. Approximately one-third of these proteins in the databases contain metal ions. Many biological and chemical processes in nature involve metal ion-binding proteins, aka metalloproteins. Many cellular reactions that underpin life require metalloproteins. Most of the remarkable, complex chemical transformations are catalysed by metalloenzymes. Realization of the importance of metal-binding sites in a variety of cellular events led to the advancement of various computational methods for their prediction and characterization. Furthermore, as structural and functional knowledgebase about metalloproteins is expanding with advances in computational and experimental fields, the focus of the research is now shifting towards de novo design and redesign of metalloproteins to extend nature’s own diversity beyond its limits. In this review, we will focus on the computational toolbox for prediction of metal ion-binding sites, de novo metalloprotein design and redesign. We will also give examples of tailor-made artificial metalloproteins designed with the computational toolbox.
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7
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CRHunter: integrating multifaceted information to predict catalytic residues in enzymes. Sci Rep 2016; 6:34044. [PMID: 27665935 PMCID: PMC5036049 DOI: 10.1038/srep34044] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Accepted: 09/07/2016] [Indexed: 11/08/2022] Open
Abstract
A variety of algorithms have been developed for catalytic residue prediction based on either feature- or template-based methodology. However, no studies have systematically compared these two strategies and further considered whether their combination could improve the prediction performance. Herein, we developed an integrative algorithm named CRHunter by simultaneously using the complementarity between feature- and template-based methodologies and that between structural and sequence information. Several novel structural features were generated by the Delaunay triangulation and Laplacian transformation of enzyme structures. Combining these features with traditional descriptors, we invented two support vector machine feature predictors based on both structural and sequence information. Furthermore, we established two template predictors using structure and profile alignments. Evaluated on datasets with different levels of homology, our feature predictors achieve relatively stable performance, whereas our template predictors yield poor results when the homological relationships become weak. Nevertheless, the hybrid algorithm CRHunter consistently achieves optimal performance among all our predictors. We also illustrate that our methodology can be applied to the predicted structures of enzymes. Compared with state-of-the-art methods, CRHunter yields comparable or better performance on various datasets. Finally, the application of this algorithm to structural genomics targets sheds light on solved protein structures with unknown functions.
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8
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Han L, Zhang YJ, Song J, Liu MS, Zhang Z. Identification of catalytic residues using a novel feature that integrates the microenvironment and geometrical location properties of residues. PLoS One 2012; 7:e41370. [PMID: 22829945 PMCID: PMC3400608 DOI: 10.1371/journal.pone.0041370] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2012] [Accepted: 06/20/2012] [Indexed: 11/18/2022] Open
Abstract
Enzymes play a fundamental role in almost all biological processes and identification of catalytic residues is a crucial step for deciphering the biological functions and understanding the underlying catalytic mechanisms. In this work, we developed a novel structural feature called MEDscore to identify catalytic residues, which integrated the microenvironment (ME) and geometrical properties of amino acid residues. Firstly, we converted a residue's ME into a series of spatially neighboring residue pairs, whose likelihood of being located in a catalytic ME was deduced from a benchmark enzyme dataset. We then calculated an ME-based score, termed as MEscore, by summing up the likelihood of all residue pairs. Secondly, we defined a parameter called Dscore to measure the relative distance of a residue to the center of the protein, provided that catalytic residues are typically located in the center of the protein structure. Finally, we defined the MEDscore feature based on an effective nonlinear integration of MEscore and Dscore. When evaluated on a well-prepared benchmark dataset using five-fold cross-validation tests, MEDscore achieved a robust performance in identifying catalytic residues with an AUC1.0 of 0.889. At a ≤ 10% false positive rate control, MEDscore correctly identified approximately 70% of the catalytic residues. Remarkably, MEDscore achieved a competitive performance compared with the residue conservation score (e.g. CONscore), the most informative singular feature predominantly employed to identify catalytic residues. To the best of our knowledge, MEDscore is the first singular structural feature exhibiting such an advantage. More importantly, we found that MEDscore is complementary with CONscore and a significantly improved performance can be achieved by combining CONscore with MEDscore in a linear manner. As an implementation of this work, MEDscore has been made freely accessible at http://protein.cau.edu.cn/mepi/.
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Affiliation(s)
- Lei Han
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, People's Republic of China
| | - Yong-Jun Zhang
- State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, People's Republic of China
| | - Jiangning Song
- National Engineering Laboratory for Industrial Enzymes and Key Laboratory of Systems Microbial Biotechnology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, People's Republic of China
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, Monash University, Melbourne, Victoria, Australia
| | - Ming S. Liu
- CSIRO - Mathematics, Informatics and Statistics, Clayton, Victoria, Australia
- * E-mail: (MSL); (ZZ)
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, People's Republic of China
- * E-mail: (MSL); (ZZ)
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9
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Gipson B, Hsu D, Kavraki LE, Latombe JC. Computational models of protein kinematics and dynamics: beyond simulation. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2012; 5:273-91. [PMID: 22524225 PMCID: PMC4866812 DOI: 10.1146/annurev-anchem-062011-143024] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Physics-based simulation represents a powerful method for investigating the time-varying behavior of dynamic protein systems at high spatial and temporal resolution. Such simulations, however, can be prohibitively difficult or lengthy for large proteins or when probing the lower-resolution, long-timescale behaviors of proteins generally. Importantly, not all questions about a protein system require full space and time resolution to produce an informative answer. For instance, by avoiding the simulation of uncorrelated, high-frequency atomic movements, a larger, domain-level picture of protein dynamics can be revealed. The purpose of this review is to highlight the growing body of complementary work that goes beyond simulation. In particular, this review focuses on methods that address kinematics and dynamics, as well as those that address larger organizational questions and can quickly yield useful information about the long-timescale behavior of a protein.
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Affiliation(s)
- Bryant Gipson
- Computer Science Department, Rice University, Houston, Texas 77005, USA.
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10
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Liu T, Altman RB. Using multiple microenvironments to find similar ligand-binding sites: application to kinase inhibitor binding. PLoS Comput Biol 2011; 7:e1002326. [PMID: 22219723 PMCID: PMC3248393 DOI: 10.1371/journal.pcbi.1002326] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2011] [Accepted: 11/10/2011] [Indexed: 11/20/2022] Open
Abstract
The recognition of cryptic small-molecular binding sites in protein structures is important for understanding off-target side effects and for recognizing potential new indications for existing drugs. Current methods focus on the geometry and detailed chemical interactions within putative binding pockets, but may not recognize distant similarities where dynamics or modified interactions allow one ligand to bind apparently divergent binding pockets. In this paper, we introduce an algorithm that seeks similar microenvironments within two binding sites, and assesses overall binding site similarity by the presence of multiple shared microenvironments. The method has relatively weak geometric requirements (to allow for conformational change or dynamics in both the ligand and the pocket) and uses multiple biophysical and biochemical measures to characterize the microenvironments (to allow for diverse modes of ligand binding). We term the algorithm PocketFEATURE, since it focuses on pockets using the FEATURE system for characterizing microenvironments. We validate PocketFEATURE first by showing that it can better discriminate sites that bind similar ligands from those that do not, and by showing that we can recognize FAD-binding sites on a proteome scale with Area Under the Curve (AUC) of 92%. We then apply PocketFEATURE to evolutionarily distant kinases, for which the method recognizes several proven distant relationships, and predicts unexpected shared ligand binding. Using experimental data from ChEMBL and Ambit, we show that at high significance level, 40 kinase pairs are predicted to share ligands. Some of these pairs offer new opportunities for inhibiting two proteins in a single pathway. Small molecule drugs may interact with many proteins. Some of these interactions may cause unexpected effects, including side effects or potentially useful therapeutic effects. One way to predict these effects is to analyze the three-dimensional structure of target proteins, and identify new binding sites for small molecule drugs. Several methods have been proposed for predicting new binding sites, relying on geometric and functional complementarity of the sites and the small molecules. In this paper, we report on a new method for identifying novel protein-drug interactions by analyzing the similarity between binding sites in proteins. The method has relatively weak geometric requirements and allows for conformational change or dynamics in both the ligand and protein. Our results show that geometric flexibility is useful for effectively comparing sites. We have applied the method to evolutionarily distant kinases, and find unexpected shared inhibitor binding. Our results may be valuable for drug repurposing in order to find novel uses for existing kinase inhibitors.
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Affiliation(s)
- Tianyun Liu
- Department of Genetics, Stanford University, Stanford, California, United States of America
| | - Russ B. Altman
- Department of Genetics, Stanford University, Stanford, California, United States of America
- Department of Bioengineering, Stanford University, Stanford, California, United States of America
- * E-mail:
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11
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Tang GW, Altman RB. Remote thioredoxin recognition using evolutionary conservation and structural dynamics. Structure 2011; 19:461-70. [PMID: 21481770 DOI: 10.1016/j.str.2011.02.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2010] [Revised: 02/06/2011] [Accepted: 02/16/2011] [Indexed: 12/25/2022]
Abstract
The thioredoxin family of oxidoreductases plays an important role in redox signaling and control of protein function. Not only are thioredoxins linked to a variety of disorders, but their stable structure has also seen application in protein engineering. Both sequence-based and structure-based tools exist for thioredoxin identification, but remote homolog detection remains a challenge. We developed a thioredoxin predictor using the approach of integrating sequence with structural information. We combined a sequence-based Hidden Markov Model (HMM) with a molecular dynamics enhanced structure-based recognition method (dynamic FEATURE, DF). This hybrid method (HMMDF) has high precision and recall (0.90 and 0.95, respectively) compared with HMM (0.92 and 0.87, respectively) and DF (0.82 and 0.97, respectively). Dynamic FEATURE is sensitive but struggles to resolve closely related protein families, while HMM identifies these evolutionary differences by compromising sensitivity. Our method applied to structural genomics targets makes a strong prediction of a novel thioredoxin.
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Affiliation(s)
- Grace W Tang
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
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12
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Spitzer R, Cleves AE, Jain AN. Surface-based protein binding pocket similarity. Proteins 2011; 79:2746-63. [PMID: 21769944 DOI: 10.1002/prot.23103] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2011] [Revised: 05/06/2011] [Accepted: 05/25/2011] [Indexed: 11/08/2022]
Abstract
Protein similarity comparisons may be made on a local or global basis and may consider sequence information or differing levels of structural information. We present a local three-dimensional method that compares protein binding site surfaces in full atomic detail. The approach is based on the morphological similarity method which has been widely applied for global comparison of small molecules. We apply the method to all-by-all comparisons two sets of human protein kinases, a very diverse set of ATP-bound proteins from multiple species, and three heterogeneous benchmark protein binding site data sets. Cases of disagreement between sequence-based similarity and binding site similarity yield informative examples. Where sequence similarity is very low, high pocket similarity can reliably identify important binding motifs. Where sequence similarity is very high, significant differences in pocket similarity are related to ligand binding specificity and similarity. Local protein binding pocket similarity provides qualitatively complementary information to other approaches, and it can yield quantitative information in support of functional annotation.
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Affiliation(s)
- Russell Spitzer
- Department of Bioengineering and Therapeutic Sciences, Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California 94158-9001, USA
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13
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Xin F, Myers S, Li YF, Cooper DN, Mooney SD, Radivojac P. Structure-based kernels for the prediction of catalytic residues and their involvement in human inherited disease. BMC Bioinformatics 2010. [DOI: 10.1186/1471-2105-11-s10-o4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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14
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Doppelt-Azeroual O, Delfaud F, Moriaud F, de Brevern AG. Fast and automated functional classification with MED-SuMo: an application on purine-binding proteins. Protein Sci 2010; 19:847-67. [PMID: 20162627 DOI: 10.1002/pro.364] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Ligand-protein interactions are essential for biological processes, and precise characterization of protein binding sites is crucial to understand protein functions. MED-SuMo is a powerful technology to localize similar local regions on protein surfaces. Its heuristic is based on a 3D representation of macromolecules using specific surface chemical features associating chemical characteristics with geometrical properties. MED-SMA is an automated and fast method to classify binding sites. It is based on MED-SuMo technology, which builds a similarity graph, and it uses the Markov Clustering algorithm. Purine binding sites are well studied as drug targets. Here, purine binding sites of the Protein DataBank (PDB) are classified. Proteins potentially inhibited or activated through the same mechanism are gathered. Results are analyzed according to PROSITE annotations and to carefully refined functional annotations extracted from the PDB. As expected, binding sites associated with related mechanisms are gathered, for example, the Small GTPases. Nevertheless, protein kinases from different Kinome families are also found together, for example, Aurora-A and CDK2 proteins which are inhibited by the same drugs. Representative examples of different clusters are presented. The effectiveness of the MED-SMA approach is demonstrated as it gathers binding sites of proteins with similar structure-activity relationships. Moreover, an efficient new protocol associates structures absent of cocrystallized ligands to the purine clusters enabling those structures to be associated with a specific binding mechanism. Applications of this classification by binding mode similarity include target-based drug design and prediction of cross-reactivity and therefore potential toxic side effects.
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Affiliation(s)
- Olivia Doppelt-Azeroual
- INSERM UMR-S 665, Dynamique des Structures et Interactions des Macromolécules Biologiques (DSIMB), Université Paris Diderot-Paris 7, Institut National de la Transfusion Sanguine (INTS), 6, rue Alexandre Cabanel, 75739 Paris cedex 15, France.
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Xin F, Myers S, Li YF, Cooper DN, Mooney SD, Radivojac P. Structure-based kernels for the prediction of catalytic residues and their involvement in human inherited disease. ACTA ACUST UNITED AC 2010; 26:1975-82. [PMID: 20551136 DOI: 10.1093/bioinformatics/btq319] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
MOTIVATION Enzyme catalysis is involved in numerous biological processes and the disruption of enzymatic activity has been implicated in human disease. Despite this, various aspects of catalytic reactions are not completely understood, such as the mechanics of reaction chemistry and the geometry of catalytic residues within active sites. As a result, the computational prediction of catalytic residues has the potential to identify novel catalytic pockets, aid in the design of more efficient enzymes and also predict the molecular basis of disease. RESULTS We propose a new kernel-based algorithm for the prediction of catalytic residues based on protein sequence, structure and evolutionary information. The method relies upon explicit modeling of similarity between residue-centered neighborhoods in protein structures. We present evidence that this algorithm evaluates favorably against established approaches, and also provides insights into the relative importance of the geometry, physicochemical properties and evolutionary conservation of catalytic residue activity. The new algorithm was used to identify known mutations associated with inherited disease whose molecular mechanism might be predicted to operate specifically though the loss or gain of catalytic residues. It should, therefore, provide a viable approach to identifying the molecular basis of disease in which the loss or gain of function is not caused solely by the disruption of protein stability. Our analysis suggests that both mechanisms are actively involved in human inherited disease. AVAILABILITY AND IMPLEMENTATION Source code for the structural kernel is available at www.informatics.indiana.edu/predrag/.
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Affiliation(s)
- Fuxiao Xin
- School of Informatics and Computing, Indiana University, Bloomington, IN 47408, USA
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Wu S, Liu T, Altman RB. Identification of recurring protein structure microenvironments and discovery of novel functional sites around CYS residues. BMC STRUCTURAL BIOLOGY 2010; 10:4. [PMID: 20122268 PMCID: PMC2833161 DOI: 10.1186/1472-6807-10-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2009] [Accepted: 02/02/2010] [Indexed: 11/29/2022]
Abstract
Background The emergence of structural genomics presents significant challenges in the annotation of biologically uncharacterized proteins. Unfortunately, our ability to analyze these proteins is restricted by the limited catalog of known molecular functions and their associated 3D motifs. Results In order to identify novel 3D motifs that may be associated with molecular functions, we employ an unsupervised, two-phase clustering approach that combines k-means and hierarchical clustering with knowledge-informed cluster selection and annotation methods. We applied the approach to approximately 20,000 cysteine-based protein microenvironments (3D regions 7.5 Å in radius) and identified 70 interesting clusters, some of which represent known motifs (e.g. metal binding and phosphatase activity), and some of which are novel, including several zinc binding sites. Detailed annotation results are available online for all 70 clusters at http://feature.stanford.edu/clustering/cys. Conclusions The use of microenvironments instead of backbone geometric criteria enables flexible exploration of protein function space, and detection of recurring motifs that are discontinuous in sequence and diverse in structure. Clustering microenvironments may thus help to functionally characterize novel proteins and better understand the protein structure-function relationship.
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Affiliation(s)
- Shirley Wu
- 23andMe, 1390 Shorebird Way, Mountain View, CA, USA
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Halperin I, Glazer DS, Wu S, Altman RB. The FEATURE framework for protein function annotation: modeling new functions, improving performance, and extending to novel applications. BMC Genomics 2008; 9 Suppl 2:S2. [PMID: 18831785 PMCID: PMC2559884 DOI: 10.1186/1471-2164-9-s2-s2] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Structural genomics efforts contribute new protein structures that often lack significant sequence and fold similarity to known proteins. Traditional sequence and structure-based methods may not be sufficient to annotate the molecular functions of these structures. Techniques that combine structural and functional modeling can be valuable for functional annotation. FEATURE is a flexible framework for modeling and recognition of functional sites in macromolecular structures. Here, we present an overview of the main components of the FEATURE framework, and describe the recent developments in its use. These include automating training sets selection to increase functional coverage, coupling FEATURE to structural diversity generating methods such as molecular dynamics simulations and loop modeling methods to improve performance, and using FEATURE in large-scale modeling and structure determination efforts.
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Affiliation(s)
- Inbal Halperin
- Department of Genetics, 318 Campus Drive, Clark Center S240, Stanford, CA 94305, USA.
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