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AlJarf R, Rodrigues CHM, Myung Y, Pires DEV, Ascher DB. piscesCSM: prediction of anticancer synergistic drug combinations. J Cheminform 2024; 16:81. [PMID: 39030592 PMCID: PMC11264925 DOI: 10.1186/s13321-024-00859-4] [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: 08/20/2023] [Accepted: 05/12/2024] [Indexed: 07/21/2024] Open
Abstract
While drug combination therapies are of great importance, particularly in cancer treatment, identifying novel synergistic drug combinations has been a challenging venture. Computational methods have emerged in this context as a promising tool for prioritizing drug combinations for further evaluation, though they have presented limited performance, utility, and interpretability. Here, we propose a novel predictive tool, piscesCSM, that leverages graph-based representations to model small molecule chemical structures to accurately predict drug combinations with favourable anticancer synergistic effects against one or multiple cancer cell lines. Leveraging these insights, we developed a general supervised machine learning model to guide the prediction of anticancer synergistic drug combinations in over 30 cell lines. It achieved an area under the receiver operating characteristic curve (AUROC) of up to 0.89 on independent non-redundant blind tests, outperforming state-of-the-art approaches on both large-scale oncology screening data and an independent test set generated by AstraZeneca (with more than a 16% improvement in predictive accuracy). Moreover, by exploring the interpretability of our approach, we found that simple physicochemical properties and graph-based signatures are predictive of chemotherapy synergism. To provide a simple and integrated platform to rapidly screen potential candidate pairs with favourable synergistic anticancer effects, we made piscesCSM freely available online at https://biosig.lab.uq.edu.au/piscescsm/ as a web server and API. We believe that our predictive tool will provide a valuable resource for optimizing and augmenting combinatorial screening libraries to identify effective and safe synergistic anticancer drug combinations. SCIENTIFIC CONTRIBUTION: This work proposes piscesCSM, a machine-learning-based framework that relies on well-established graph-based representations of small molecules to identify and provide better predictive accuracy of syngenetic drug combinations. Our model, piscesCSM, shows that combining physiochemical properties with graph-based signatures can outperform current architectures on classification prediction tasks. Furthermore, implementing our tool as a web server offers a user-friendly platform for researchers to screen for potential synergistic drug combinations with favorable anticancer effects against one or multiple cancer cell lines.
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Affiliation(s)
- Raghad AlJarf
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Carlos H M Rodrigues
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia
| | - Yoochan Myung
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- School of Computing and Information Systems, University of Melbourne, Melbourne, VIC, Australia
| | - David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC, Australia.
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia.
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia.
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2
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Malta CP, Barcelos RCS, Fernandes PS, Martins MO, Sagrillo MR, Bier CAS, Morgental RD. In silico toxicity and immunological interactions of components of calcium silicate-based and epoxy resin-based endodontic sealers. Clin Oral Investig 2024; 28:148. [PMID: 38353803 DOI: 10.1007/s00784-024-05548-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 02/07/2024] [Indexed: 02/16/2024]
Abstract
OBJECTIVES The present study aimed to determine in silico toxicity predictions of test compounds from hydraulic calcium silicate-based sealers (HCSBS) and AH Plus and computationally simulate the interaction between these substances and mediators of periapical inflammation via molecular docking. MATERIALS AND METHODS All chemical information of the test compounds was obtained from the PubChem site. Predictions for bioavailability and toxicity analyses were determined by the Molinspiration Cheminformatics, pkCSM, ProTox-II and OSIRIS Property Explorer platforms. Molecular docking was performed using the Autodock4 AMDock v.1.5.2 program to analyse interactions between proteins (IL-1β, IL-6, IL-8, IL-10 and TNF-α) and ligands (calcium silicate hydrate, zirconium oxide, bisphenol-A epoxy resin, dibenzylamine, iron oxide and calcium tungstate) to establish the affinity and bonding mode between systems. RESULTS Bisphenol-A epoxy resin had the lowest maximum dose tolerated in humans and was the test compound with the largest number of toxicological properties (hepatotoxicity, carcinogenicity and irritant). All systems had favourable molecular docking. However, the ligands bisphenol-A epoxy resin and dibenzylamine had the greatest affinity with the cytokines tested. CONCLUSION In silico predictions and molecular docking pointed the higher toxicity and greater interaction with mediators of periapical inflammation of the main test compounds from AH Plus compared to those from HCSBS. CLINICAL RELEVANCE This is the first in silico study involving endodontic materials and may serve as the basis for further research that can generate more data, producing knowledge on the interference of each chemical compound in the composition of different root canal sealers.
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Affiliation(s)
- Cristiana Pereira Malta
- Graduate Program in Dental Sciences, Universidade Federal de Santa Maria - UFSM, Av. Roraima 1000, Bairro Camobi, Prédio 26F (Odontologia), Santa Maria, RS, 97105-900, Brazil.
| | - Raquel Cristine Silva Barcelos
- Graduate Program in Dental Sciences, Universidade Federal de Santa Maria - UFSM, Av. Roraima 1000, Bairro Camobi, Prédio 26F (Odontologia), Santa Maria, RS, 97105-900, Brazil
| | - Pâmella Schramm Fernandes
- Graduate Program in Nanosciences, Universidade Franciscana - UFN, Rua dos Andradas 1614, Bairro Centro, Santa Maria, RS, 97010-030, Brazil
| | - Mirkos Ortiz Martins
- Graduate Program in Nanosciences, Universidade Franciscana - UFN, Rua dos Andradas 1614, Bairro Centro, Santa Maria, RS, 97010-030, Brazil
| | - Michele Rorato Sagrillo
- Graduate Program in Nanosciences, Universidade Franciscana - UFN, Rua dos Andradas 1614, Bairro Centro, Santa Maria, RS, 97010-030, Brazil
| | - Carlos Alexandre Souza Bier
- Graduate Program in Dental Sciences, Universidade Federal de Santa Maria - UFSM, Av. Roraima 1000, Bairro Camobi, Prédio 26F (Odontologia), Santa Maria, RS, 97105-900, Brazil
| | - Renata Dornelles Morgental
- Graduate Program in Dental Sciences, Universidade Federal de Santa Maria - UFSM, Av. Roraima 1000, Bairro Camobi, Prédio 26F (Odontologia), Santa Maria, RS, 97105-900, Brazil
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3
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Jonniya NA, Poddar S, Mahapatra S, Kar P. Computer-aided Affinity Enhancement of a Cross-reactive Antibody against Dengue Virus Envelope Domain III. Cell Biochem Biophys 2023; 81:737-755. [PMID: 37735329 DOI: 10.1007/s12013-023-01175-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2023] [Indexed: 09/23/2023]
Abstract
The dengue virus (DENV), composed of four distinct but serologically related Flaviviruses, causes the most important emerging viral disease, with nearly 400 million infections yearly. Currently, there are no approved therapies. Although DENV infection induces lifelong immunity against the same serotype, the antibodies raised contribute to severe disease in heterotypic infections. Therefore, understanding the mechanism of DENV neutralization by antibodies is crucial in the design of vaccines against all serotypes. This study reports a comparative structural and energetic analysis of the monoclonal antibody (mAb) 4E11 in complex with its target domain III of the envelope protein for all four DENV serotypes. We use extensive replica molecular dynamics simulations in conjunction with the binding free energy calculations. Further single point and double mutations were designed through computational site-directed mutagenesis and observed that the re-engineered antibody exhibits high affinity to binding and broadly neutralizing activity against serotypes. Our results showed improved binding affinity by the gain of enthalpy, which could be attributed to the stabilization of salt-bridge and hydrogen bond interactions at the antigen-antibody interface. The findings provide valuable results in understanding the structural dynamics and energetic contributions that will be helpful to the design of high-affinity antibodies against dengue infections.
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Affiliation(s)
- Nisha Amarnath Jonniya
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Khandwa Road, Indore, 453552, Madhya Pradesh, India
- Department of Pharmacology, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Sayan Poddar
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Khandwa Road, Indore, 453552, Madhya Pradesh, India
| | - Subhasmita Mahapatra
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Khandwa Road, Indore, 453552, Madhya Pradesh, India
| | - Parimal Kar
- Department of Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Khandwa Road, Indore, 453552, Madhya Pradesh, India.
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4
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Gheeraert A, Lesieur C, Batista VS, Vuillon L, Rivalta I. Connected Component Analysis of Dynamical Perturbation Contact Networks. J Phys Chem B 2023; 127:7571-7580. [PMID: 37641933 PMCID: PMC10493978 DOI: 10.1021/acs.jpcb.3c04592] [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: 07/07/2023] [Revised: 08/02/2023] [Indexed: 08/31/2023]
Abstract
Describing protein dynamical networks through amino acid contacts is a powerful way to analyze complex biomolecular systems. However, due to the size of the systems, identifying the relevant features of protein-weighted graphs can be a difficult task. To address this issue, we present the connected component analysis (CCA) approach that allows for fast, robust, and unbiased analysis of dynamical perturbation contact networks (DPCNs). We first illustrate the CCA method as applied to a prototypical allosteric enzyme, the imidazoleglycerol phosphate synthase (IGPS) enzyme from Thermotoga maritima bacteria. This approach was shown to outperform the clustering methods applied to DPCNs, which could not capture the propagation of the allosteric signal within the protein graph. On the other hand, CCA reduced the DPCN size, providing connected components that nicely describe the allosteric propagation of the signal from the effector to the active sites of the protein. By applying the CCA to the IGPS enzyme in different conditions, i.e., at high temperature and from another organism (yeast IGPS), and to a different enzyme, i.e., a protein kinase, we demonstrated how CCA of DPCNs is an effective and transferable tool that facilitates the analysis of protein-weighted networks.
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Affiliation(s)
- Aria Gheeraert
- Laboratoire
de Mathématiques (LAMA), Université
Savoie Mont Blanc, CNRS, 73376 Le Bourget du Lac, France
- Dipartimento
di Chimica Industriale “Toso Montanari”, Alma Mater
Studiorum, Università di Bologna, Viale del Risorgimento 4, 40136 Bologna, Italy
| | - Claire Lesieur
- Univ.
Lyon, CNRS, INSA Lyon, Université Claude Bernard Lyon 1, Ecole
Centrale de Lyon, Ampère UMR5005, Villeurbanne 69622, France
- Institut
Rhônalpin des Systèmes Complexes, IXXI-ENS-Lyon, Lyon 69007, France
| | - Victor S. Batista
- Department
of Chemistry, Yale University, New Haven, Connecticut 06520, United States
| | - Laurent Vuillon
- Laboratoire
de Mathématiques (LAMA), Université
Savoie Mont Blanc, CNRS, 73376 Le Bourget du Lac, France
- Institut
Rhônalpin des Systèmes Complexes, IXXI-ENS-Lyon, Lyon 69007, France
| | - Ivan Rivalta
- Dipartimento
di Chimica Industriale “Toso Montanari”, Alma Mater
Studiorum, Università di Bologna, Viale del Risorgimento 4, 40136 Bologna, Italy
- ENS
de Lyon,
CNRS, Laboratoire de Chimie UMR 5182, 69364 Lyon, France
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5
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Franke L, Peter C. Visualizing the Residue Interaction Landscape of Proteins by Temporal Network Embedding. J Chem Theory Comput 2023; 19:2985-2995. [PMID: 37122117 DOI: 10.1021/acs.jctc.2c01228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Characterizing the structural dynamics of proteins with heterogeneous conformational landscapes is crucial to understanding complex biomolecular processes. To this end, dimensionality reduction algorithms are used to produce low-dimensional embeddings of the high-dimensional conformational phase space. However, identifying a compact and informative set of input features for the embedding remains an ongoing challenge. Here, we propose to harness the power of Residue Interaction Networks (RINs) and their centrality measures, established tools to provide a graph theoretical view on molecular structure. Specifically, we combine the closeness centrality, which captures global features of the protein conformation at residue-wise resolution, with EncoderMap, a hybrid neural-network autoencoder/multidimensional-scaling like dimensionality reduction algorithm. We find that the resulting low-dimensional embedding is a meaningful visualization of the residue interaction landscape that resolves structural details of the protein behavior while retaining global interpretability. This feature-based graph embedding of temporal protein graphs makes it possible to apply the general descriptive power of RIN formalisms to the analysis of protein simulations of complex processes such as protein folding and multidomain interactions requiring no protein-specific input. We demonstrate this on simulations of the fast folding protein Trp-Cage and the multidomain signaling protein FAT10. Due to its generality and modularity, the presented approach can easily be transferred to other protein systems.
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Affiliation(s)
- Leon Franke
- Department of Chemistry, University of Konstanz, Universitätsstraße 10, Konstanz 78457, Germany
- Konstanz Research School Chemical Biology, University of Konstanz, Universitätsstraße 10, Konstanz 78457, Germany
| | - Christine Peter
- Department of Chemistry, University of Konstanz, Universitätsstraße 10, Konstanz 78457, Germany
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Rezende PM, Xavier JS, Ascher DB, Fernandes GR, Pires DEV. Evaluating hierarchical machine learning approaches to classify biological databases. Brief Bioinform 2022; 23:6611916. [PMID: 35724625 PMCID: PMC9310517 DOI: 10.1093/bib/bbac216] [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: 12/21/2021] [Revised: 04/29/2022] [Accepted: 05/09/2022] [Indexed: 12/04/2022] Open
Abstract
The rate of biological data generation has increased dramatically in recent years, which has driven the importance of databases as a resource to guide innovation and the generation of biological insights. Given the complexity and scale of these databases, automatic data classification is often required. Biological data sets are often hierarchical in nature, with varying degrees of complexity, imposing different challenges to train, test and validate accurate and generalizable classification models. While some approaches to classify hierarchical data have been proposed, no guidelines regarding their utility, applicability and limitations have been explored or implemented. These include ‘Local’ approaches considering the hierarchy, building models per level or node, and ‘Global’ hierarchical classification, using a flat classification approach. To fill this gap, here we have systematically contrasted the performance of ‘Local per Level’ and ‘Local per Node’ approaches with a ‘Global’ approach applied to two different hierarchical datasets: BioLip and CATH. The results show how different components of hierarchical data sets, such as variation coefficient and prediction by depth, can guide the choice of appropriate classification schemes. Finally, we provide guidelines to support this process when embarking on a hierarchical classification task, which will help optimize computational resources and predictive performance.
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Affiliation(s)
- Pâmela M Rezende
- Universidade Federal de Minas Gerais.,Instituto René Rachou, Fundação Oswaldo Cruz.,Stilingue Inteligência Artificial
| | - Joicymara S Xavier
- Universidade Federal de Minas Gerais.,Instituto René Rachou, Fundação Oswaldo Cruz.,Institute of Agricultural Sciences, Universidade Federal dos Vales do Jequitinhonha e Mucuri
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland.,Systems and Computational Biology, Bio 21 Institute, University of Melbourne.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute
| | | | - Douglas E V Pires
- Systems and Computational Biology, Bio 21 Institute, University of Melbourne.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute.,School of Computing and Information Systems, University of Melbourne
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7
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Ferreira Schopf P, Peglow Pinz M, Pereira da Motta K, Klein VP, Kolinski Machado A, Rhoden CRB, Wilhelm EA, Luchese C, Zanella I, Sagrillo M. SAFETY PROFILE AND PREVENTION OF COGNITIVE DEFICIT IN ALZHEIMER’S DISEASE MODEL OF GRAPHENE FAMILY NANOMATERIALS, TUCUMA OIL (Astrocaryum vulgare) AND ITS SYNERGISMS. INTERNATIONAL JOURNAL FOR INNOVATION EDUCATION AND RESEARCH 2022; 10:267-303. [DOI: 10.31686/ijier.vol10.iss3.3694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Alzheimer's disease is a worldwide health issue, and there are currently no treatments that can stop this disease. Oxidized graphene derivatives have gained prominence in use in biological systems due to their excellent physical-chemical characteristics, biocompatibility and ability to overcome the blood-brain barrier. Other substances highlighted are those of natural origin from the Amazon biome, such as tucuma, a fruit whose oil has been widely studied in therapeutic applications. Thus, the aim of this study was to investigate the action of graphene oxide, reduced graphene oxide and tucuma oil, isolated and combined, as an alternative for treatment of Alzheimer's disease through studies in silico, in vitro, in vivo and ex vivo. Computational simulation via docking was used to verify the affinity of the substances with the proteins β-amyloid and acetylcholinesterase, in which the reduced graphene oxide was the one that showed the most favorable interaction. The results of the ab initio simulation showed that the synergism between the nanostructures and the oil occurs through physical adsorption. The experimental results revealed that the substances and their combinations were nontoxic, both at the cellular and systemic level. In general, all treatments had positive results against induced memory deficit, but reduced graphene oxide was the most prominent, as it was able to protect against memory damage in all behavioral tests performed, with anticholinesterase activity and antioxidant effect. In conclusion, the reduced graphene oxide is, among the treatments studied, the one with great therapeutic potential to be investigated in the treatment of this disease.
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8
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Qin T, Zhu Z, Wang XS, Xia J, Wu S. Computational representations of protein-ligand interfaces for structure-based virtual screening. Expert Opin Drug Discov 2021; 16:1175-1192. [PMID: 34011222 DOI: 10.1080/17460441.2021.1929921] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Introduction: Structure-based virtual screening (SBVS) is an essential strategy for hit identification. SBVS primarily uses molecular docking, which exploits the protein-ligand binding mode and associated affinity score for compound ranking. Previous studies have shown that computational representation of protein-ligand interfaces and the later establishment of machine learning models are efficacious in improving the accuracy of SBVS.Areas covered: The authors review the computational methods for representing protein-ligand interfaces, which include the traditional ones that use deliberately designed fingerprints and descriptors and the more recent methods that automatically extract features with deep learning. The effects of these methods on the performance of machine learning models are briefly discussed. Additionally, case studies that applied various computational representations to machine learning are cited with remarks.Expert opinion: It has become a trend to extract binding features automatically by deep learning, which uses a completely end-to-end representation. However, there is still plenty of scope for improvement . The interpretability of deep-learning models, the organization of data management, the quantity and quality of available data, and the optimization of hyperparameters could impact the accuracy of feature extraction. In addition, other important structural factors such as water molecules and protein flexibility should be considered.
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Affiliation(s)
- Tong Qin
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zihao Zhu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiang Simon Wang
- Artificial Intelligence and Drug Discovery Core Laboratory for District of Columbia Center for AIDS Research (DC CFAR), Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, U.S.A
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of New Drug Research and Development, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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9
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Sobieraj M, Setny P. Entropy-based distance cutoff for protein internal contact networks. Proteins 2021; 89:1333-1339. [PMID: 34053102 DOI: 10.1002/prot.26154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 05/17/2021] [Accepted: 05/19/2021] [Indexed: 11/05/2022]
Abstract
Protein structure networks (PSNs) have long been used to provide a coarse yet meaningful representation of protein structure, dynamics, and internal communication pathways. An important question is what criteria should be applied to construct the network so that to include relevant interresidue contacts while avoiding unnecessary connections. To address this issue, we systematically considered varying residue distance cutoff length and the probability threshold for contact formation to construct PSNs based on atomistic molecular dynamics in order to assess the amount of mutual information within the resulting representations. We found that the minimum in mutual information is universally achieved at the cutoff length of 5 Å, irrespective of the applied contact formation probability threshold in all considered, distinct proteins. Assuming that the optimal PSNs should be characterized by the least amount of redundancy, which corresponds to the minimum in mutual information, this finding suggests an objective criterion for cutoff distance and supports the existing preference towards its customary selection around 5 Å length, typically based to date on heuristic criteria.
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Affiliation(s)
- Marcin Sobieraj
- Center of New Technologies, University of Warsaw, Warsaw, Poland.,Department of Physics, University of Warsaw, Warsaw, Poland
| | - Piotr Setny
- Center of New Technologies, University of Warsaw, Warsaw, Poland
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10
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Portelli S, Barr L, de Sá AG, Pires DE, Ascher DB. Distinguishing between PTEN clinical phenotypes through mutation analysis. Comput Struct Biotechnol J 2021; 19:3097-3109. [PMID: 34141133 PMCID: PMC8180946 DOI: 10.1016/j.csbj.2021.05.028] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 04/29/2021] [Accepted: 05/19/2021] [Indexed: 12/28/2022] Open
Abstract
Phosphate and tensin homolog on chromosome ten (PTEN) germline mutations are associated with an overarching condition known as PTEN hamartoma tumor syndrome. Clinical phenotypes associated with this syndrome range from macrocephaly and autism spectrum disorder to Cowden syndrome, which manifests as multiple noncancerous tumor-like growths (hamartomas), and an increased predisposition to certain cancers. It is unclear, however, the basis by which mutations might lead to these very diverse phenotypic outcomes. Here we show that, by considering the molecular consequences of mutations in PTEN on protein structure and function, we can accurately distinguish PTEN mutations exhibiting different phenotypes. Changes in phosphatase activity, protein stability, and intramolecular interactions appeared to be major drivers of clinical phenotype, with cancer-associated variants leading to the most drastic changes, while ASD and non-pathogenic variants associated with more mild and neutral changes, respectively. Importantly, we show via saturation mutagenesis that more than half of variants of unknown significance could be associated with disease phenotypes, while over half of Cowden syndrome mutations likely lead to cancer. These insights can assist in exploring potentially important clinical outcomes delineated by PTEN variation.
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Affiliation(s)
- Stephanie Portelli
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Lucy Barr
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
| | - Alex G.C. de Sá
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia
| | - Douglas E.V. Pires
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - David B. Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry, University of Melbourne, Melbourne, Victoria, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia
- Department of Biochemistry, University of Cambridge, 80 Tennis Ct Rd, Cambridge CB2 1GA, United States
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11
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de Oliveira PV, Goulart L, Dos Santos CL, Rossato J, Fagan SB, Zanella I, Cordeiro MNDS, Ruso JM, González-Durruthy M. Computational Modeling of Environmental Co-exposure on Oil-Derived Hydrocarbon Overload by Using Substrate-Specific Transport Protein (TodX) with Graphene Nanostructures. Curr Top Med Chem 2020; 20:2308-2325. [PMID: 32819247 DOI: 10.2174/1568026620666200820145412] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 07/21/2020] [Accepted: 07/21/2020] [Indexed: 12/07/2022]
Abstract
BACKGROUND Bioremediation is a biotechnology field that uses living organisms to remove contaminants from soil and water; therefore, they could be used to treat oil spills from the environment. METHODS Herein, we present a new mechanistic approach combining Molecular Docking Simulation and Density Functional Theory to modeling the bioremediation-based nanointeractions of a heterogeneous mixture of oil-derived hydrocarbons by using pristine and oxidized graphene nanostructures and the substrate-specific transport protein (TodX) from Pseudomonas putida. RESULTS The theoretical evidences pointing that the binding interactions are mainly based on noncovalent bonds characteristic of physical adsorption mechanism mimicking the "Trojan-horse effect". CONCLUSION These results open new horizons to improve bioremediation strategies in over-saturation conditions against oil-spills and expanding the use of nanotechnologies in the context of environmental modeling health and safety.
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Affiliation(s)
| | - Luiza Goulart
- Nanoscience Department, Universidade Franciscana, 97010-032 Rio Grande do Sul-RS, Brazil
| | | | - Jussane Rossato
- Nanoscience Department, Universidade Franciscana, 97010-032 Rio Grande do Sul-RS, Brazil
| | - Solange Binotto Fagan
- Nanoscience Department, Universidade Franciscana, 97010-032 Rio Grande do Sul-RS, Brazil
| | - Ivana Zanella
- Nanoscience Department, Universidade Franciscana, 97010-032 Rio Grande do Sul-RS, Brazil
| | - M Natália D S Cordeiro
- LAQV-REQUINTE of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169- 007 Porto, Portugal
| | - Juan M Ruso
- Soft Matter and Molecular Biophysics Group, Department of Applied Physics, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Michael González-Durruthy
- LAQV-REQUINTE of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169- 007 Porto, Portugal,Soft Matter and Molecular Biophysics Group, Department of Applied Physics, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
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12
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Newaz K, Ghalehnovi M, Rahnama A, Antsaklis PJ, Milenković T. Network-based protein structural classification. ROYAL SOCIETY OPEN SCIENCE 2020; 7:191461. [PMID: 32742675 PMCID: PMC7353965 DOI: 10.1098/rsos.191461] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 05/05/2020] [Indexed: 06/11/2023]
Abstract
Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification (PSC) can help, by allowing for determining structural classes of currently unclassified proteins based on their features, and then relying on the fact that proteins with similar structures have similar functions. Existing PSC approaches rely on sequence-based or direct three-dimensional (3D) structure-based protein features. By contrast, we first model 3D structures of proteins as protein structure networks (PSNs). Then, we use network-based features for PSC. We propose the use of graphlets, state-of-the-art features in many research areas of network science, in the task of PSC. Moreover, because graphlets can deal only with unweighted PSNs, and because accounting for edge weights when constructing PSNs could improve PSC accuracy, we also propose a deep learning framework that automatically learns network features from weighted PSNs. When evaluated on a large set of approximately 9400 CATH and approximately 12 800 SCOP protein domains (spanning 36 PSN sets), the best of our proposed approaches are superior to existing PSC approaches in terms of accuracy, with comparable running times. Our data and code are available at https://doi.org/10.5281/zenodo.3787922.
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Affiliation(s)
- Khalique Newaz
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
- Center for Network and Data Science, University of Notre Dame, Notre Dame, IN 46556, USA
- Eck institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Mahboobeh Ghalehnovi
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Arash Rahnama
- Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Panos J. Antsaklis
- Department of Electrical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Tijana Milenković
- Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
- Center for Network and Data Science, University of Notre Dame, Notre Dame, IN 46556, USA
- Eck institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA
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13
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Small Conformational Changes Underlie Evolution of Resistance to NNRTI in HIV Reverse Transcriptase. Biophys J 2020; 118:2489-2501. [PMID: 32348721 DOI: 10.1016/j.bpj.2020.04.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 02/12/2020] [Accepted: 04/06/2020] [Indexed: 11/23/2022] Open
Abstract
Despite achieving considerable success in reducing the number of fatalities due to acquired immunodeficiency syndrome, emergence of resistance against the reverse transcriptase (RT) inhibitor drugs remains one of the biggest challenges of the human immunodeficiency virus antiretroviral therapy (ART). Non-nucleoside reverse transcriptase inhibitors (NNRTIs) form a large class of drugs and a crucial component of ART. In NNRTIs, even a single resistance mutation is known to make the drugs completely ineffective. Additionally, several inhibitor-bound RTs with single resistance mutations do not exhibit any significant variations in their three-dimensional structures compared with the inhibitor-bound RT but completely nullify their inhibitory functions. This makes understanding the structural mechanism of these resistance mutations crucial for drug development. Here, we study several single resistance mutations in the allosteric inhibitor (nevirapine)-bound RT to analyze the mechanism of small structural changes leading to these large functional effects. In this study, we have shown that in absence of significant conformational variations in the inhibitor-bound wild-type RT and RT with single resistance mutations, the protein contact network analysis of their static structures, along with molecular dynamics simulations, can be a useful approach to understand the functional effect of small local conformational variations. The simple network analysis exposes the localized contact changes that lead to global rearrangement in the communication pattern within RT. Furthermore, these conformational changes have implications on the overall dynamics of RT. Using various measures, we show that a single resistance mutation can change the network structure and dynamics of RT to behave more like unbound RT, even in the presence of the inhibitor. This combined coarse-grained contact network and molecular dynamics approach promises to be a useful tool to analyze structure-function studies of proteins that show large functional changes with negligible variations in their overall conformation.
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14
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Queiroz FC, Vargas AMP, Oliveira MGA, Comarela GV, Silveira SA. ppiGReMLIN: a graph mining based detection of conserved structural arrangements in protein-protein interfaces. BMC Bioinformatics 2020; 21:143. [PMID: 32293241 PMCID: PMC7158050 DOI: 10.1186/s12859-020-3474-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 03/27/2020] [Indexed: 12/15/2022] Open
Abstract
Background Protein-protein interactions (PPIs) are fundamental in many biological processes and understanding these interactions is key for a myriad of applications including drug development, peptide design and identification of drug targets. The biological data deluge demands efficient and scalable methods to characterize and understand protein-protein interfaces. In this paper, we present ppiGReMLIN, a graph based strategy to infer interaction patterns in a set of protein-protein complexes. Our method combines an unsupervised learning strategy with frequent subgraph mining in order to detect conserved structural arrangements (patterns) based on the physicochemical properties of atoms on protein interfaces. To assess the ability of ppiGReMLIN to point out relevant conserved substructures on protein-protein interfaces, we compared our results to experimentally determined patterns that are key for protein-protein interactions in 2 datasets of complexes, Serine-protease and BCL-2. Results ppiGReMLIN was able to detect, in an automatic fashion, conserved structural arrangements that represent highly conserved interactions at the specificity binding pocket of trypsin and trypsin-like proteins from Serine-protease dataset. Also, for the BCL-2 dataset, our method pointed out conserved arrangements that include critical residue interactions within the conserved motif LXXXXD, pivotal to the binding specificity of BH3 domains of pro-apoptotic BCL-2 proteins towards apoptotic suppressors. Quantitatively, ppiGReMLIN was able to find all of the most relevant residues described in literature for our datasets, showing precision of at least 69% up to 100% and recall of 100%. Conclusions ppiGReMLIN was able to find highly conserved structures on the interfaces of protein-protein complexes, with minimum support value of 60%, in datasets of similar proteins. We showed that the patterns automatically detected on protein interfaces by our method are in agreement with interaction patterns described in the literature.
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Affiliation(s)
- Felippe C Queiroz
- Department of Computer Science, Universidade Federal de Viçosa, Av Peter Henry Rolfs, Viçosa, MG, Brazil.
| | - Adriana M P Vargas
- Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Av Peter Henry Rolfs, Viçosa, MG, Brazil
| | - Maria G A Oliveira
- Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Av Peter Henry Rolfs, Viçosa, MG, Brazil.,Instituto de Biotecnologia aplicada a Agropecuaria, BIOAGRO-UFV, Av Peter Henry Rolfs, Viçosa MG, Brazil
| | - Giovanni V Comarela
- Department of Computer Science, Universidade Federal do Espírito Santo, Av Fernando Ferrari, Vitória, ES, Brazil
| | - Sabrina A Silveira
- Department of Computer Science, Universidade Federal de Viçosa, Av Peter Henry Rolfs, Viçosa, MG, Brazil.,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK
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15
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Ribeiro VS, Santana CA, Fassio AV, Cerqueira FR, da Silveira CH, Romanelli JPR, Patarroyo-Vargas A, Oliveira MGA, Gonçalves-Almeida V, Izidoro SC, de Melo-Minardi RC, Silveira SDA. visGReMLIN: graph mining-based detection and visualization of conserved motifs at 3D protein-ligand interface at the atomic level. BMC Bioinformatics 2020; 21:80. [PMID: 32164574 PMCID: PMC7068867 DOI: 10.1186/s12859-020-3347-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background Interactions between proteins and non-proteic small molecule ligands play important roles in the biological processes of living systems. Thus, the development of computational methods to support our understanding of the ligand-receptor recognition process is of fundamental importance since these methods are a major step towards ligand prediction, target identification, lead discovery, and more. This article presents visGReMLIN, a web server that couples a graph mining-based strategy to detect motifs at the protein-ligand interface with an interactive platform to visually explore and interpret these motifs in the context of protein-ligand interfaces. Results To illustrate the potential of visGReMLIN, we conducted two cases in which our strategy was compared with previous experimentally and computationally determined results. visGReMLIN allowed us to detect patterns previously documented in the literature in a totally visual manner. In addition, we found some motifs that we believe are relevant to protein-ligand interactions in the analyzed datasets. Conclusions We aimed to build a visual analytics-oriented web server to detect and visualize common motifs at the protein-ligand interface. visGReMLIN motifs can support users in gaining insights on the key atoms/residues responsible for protein-ligand interactions in a dataset of complexes.
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Affiliation(s)
- Vagner S Ribeiro
- Department of Computer Science, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil
| | - Charles A Santana
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Alexandre V Fassio
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Fabio R Cerqueira
- Department of Production Engineering, Universidade Federal Fluminense, Petrópolis, 25650-050, Brazil
| | - Carlos H da Silveira
- Department of Computer Engineering, Advanced Campus at Itabira, Universidade Federal de Itajubá, Itabira, 35903-087, Brazil
| | - João P R Romanelli
- Department of Computer Engineering, Advanced Campus at Itabira, Universidade Federal de Itajubá, Itabira, 35903-087, Brazil
| | - Adriana Patarroyo-Vargas
- Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil
| | - Maria G A Oliveira
- Department of Biochemistry and Molecular Biology, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil.,Instituto de Biotecnologia aplicada à Agropecuária (BIOAGRO), Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil
| | - Valdete Gonçalves-Almeida
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Sandro C Izidoro
- Department of Computer Engineering, Advanced Campus at Itabira, Universidade Federal de Itajubá, Itabira, 35903-087, Brazil
| | - Raquel C de Melo-Minardi
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Sabrina de A Silveira
- Department of Computer Science, Universidade Federal de Viçosa, Viçosa, 36570-900, Brazil. .,European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, CB10 1SD, UK.
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16
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Marques MB, González-Durruthy M, da Silva Nornberg BF, Oliveira BR, Almeida DV, de Souza Votto AP, Marins LF. New Mechanistic Insight on the PIM-1 Kinase Inhibitor AZD1208 Using Multidrug Resistant Human Erythroleukemia Cell Lines and Molecular Docking Simulations. Curr Top Med Chem 2019; 19:914-926. [DOI: 10.2174/1568026619666190509121606] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 03/25/2019] [Accepted: 03/25/2019] [Indexed: 12/13/2022]
Abstract
Background:PIM-1 is a kinase which has been related to the oncogenic processes like cell survival, proliferation, and multidrug resistance (MDR). This kinase is known for its ability to phosphorylate the main extrusion pump (ABCB1) related to the MDR phenotype.Objective:In the present work, we tested a new mechanistic insight on the AZD1208 (PIM-1 specific inhibitor) under interaction with chemotherapy agents such as Daunorubicin (DNR) and Vincristine (VCR).Materials and Methods:In order to verify a potential cytotoxic effect based on pharmacological synergism, two MDR cell lines were used: Lucena (resistant to VCR) and FEPS (resistant to DNR), both derived from the K562 non-MDR cell line, by MTT analyses. The activity of Pgp was ascertained by measuring accumulation and the directional flux of Rh123. Furthermore, we performed a molecular docking simulation to delve into the molecular mechanism of PIM-1 alone, and combined with chemotherapeutic agents (VCR and DNR).Results:Our in vitro results have shown that AZD1208 alone decreases cell viability of MDR cells. However, co-exposure of AZD1208 and DNR or VCR reverses this effect. When we analyzed the ABCB1 activity AZD1208 alone was not able to affect the pump extrusion. Differently, co-exposure of AZD1208 and DNR or VCR impaired ABCB1 activity, which could be explained by compensatory expression of abcb1 or other extrusion pumps not analyzed here. Docking analysis showed that AZD1208 is capable of performing hydrophobic interactions with PIM-1 ATP- binding-site residues with stronger interaction-based negative free energy (FEB, kcal/mol) than the ATP itself, mimicking an ATP-competitive inhibitory pattern of interaction. On the same way, VCR and DNR may theoretically interact at the same biophysical environment of AZD1208 and also compete with ATP by the PIM-1 active site. These evidences suggest that AZD1208 may induce pharmacodynamic interaction with VCR and DNR, weakening its cytotoxic potential in the ATP-binding site from PIM-1 observed in the in vitro experiments.Conclusion:Finally, the current results could have a pre-clinical relevance potential in the rational polypharmacology strategies to prevent multiple-drugs resistance in human leukemia cancer therapy.
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Affiliation(s)
- Maiara Bernardes Marques
- Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Rio Grande - FURG, Rio Grande, RS, Brazil
| | | | - Bruna Félix da Silva Nornberg
- Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Rio Grande - FURG, Rio Grande, RS, Brazil
| | - Bruno Rodrigues Oliveira
- Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Rio Grande - FURG, Rio Grande, RS, Brazil
| | - Daniela Volcan Almeida
- Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Rio Grande - FURG, Rio Grande, RS, Brazil
| | - Ana Paula de Souza Votto
- Laboratory of Cell Culture, Institute of Biological Sciences, Federal University of Rio Grande - FURG, Rio Grande, RS, Brazil
| | - Luis Fernando Marins
- Laboratory of Molecular Biology, Institute of Biological Sciences, Federal University of Rio Grande - FURG, Rio Grande, RS, Brazil
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17
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Yao XQ, Momin M, Hamelberg D. Establishing a Framework of Using Residue–Residue Interactions in Protein Difference Network Analysis. J Chem Inf Model 2019; 59:3222-3228. [DOI: 10.1021/acs.jcim.9b00320] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Xin-Qiu Yao
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30302-3965, United States
| | - Mohamed Momin
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30302-3965, United States
| | - Donald Hamelberg
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30302-3965, United States
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18
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González-Durruthy M, Monserrat JM, Viera de Oliveira P, Fagan SB, Werhli AV, Machado K, Melo A, González-Díaz H, Concu R, D. S. Cordeiro MN. Computational MitoTarget Scanning Based on Topological Vacancies of Single-Walled Carbon Nanotubes with the Human Mitochondrial Voltage-Dependent Anion Channel (hVDAC1). Chem Res Toxicol 2019; 32:566-577. [DOI: 10.1021/acs.chemrestox.8b00266] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Michael González-Durruthy
- Department of Chemistry and Biochemistry, Faculty of Science, University of Porto, 4169-007 Porto, Portugal
| | - José M. Monserrat
- Institute of Biological Sciences (ICB), Universidade Federal do Rio Grande (FURG), 96270-900 Rio Grande, RS, Brazil
- ICB-FURG Post-Graduate Program in Physiological Sciences, 96270-900 Rio Grande, RS, Brazil
| | | | - Solange Binotto Fagan
- Post-Graduate Program in Nanoscience, Franciscana University (UFN), 97010-032 Santa Maria, RS, Brazil
| | - Adriano V. Werhli
- Center of Computational Sciences (C3), Federal University of Rio Grande (FURG), Cx. P. 474, CEP, 96200-970 Rio Grande, RS, Brazil
| | - Karina Machado
- Center of Computational Sciences (C3), Federal University of Rio Grande (FURG), Cx. P. 474, CEP, 96200-970 Rio Grande, RS, Brazil
| | - André Melo
- Department of Chemistry and Biochemistry, Faculty of Science, University of Porto, 4169-007 Porto, Portugal
| | - Humberto González-Díaz
- Department of Organic Chemistry II, University of the Basque Country (UPV/EHU), 48940 Leioa, Biscay, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
| | - Riccardo Concu
- Department of Chemistry and Biochemistry, Faculty of Science, University of Porto, 4169-007 Porto, Portugal
| | - M. Natália D. S. Cordeiro
- Department of Chemistry and Biochemistry, Faculty of Science, University of Porto, 4169-007 Porto, Portugal
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19
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Pražnikar J, Tomić M, Turk D. Validation and quality assessment of macromolecular structures using complex network analysis. Sci Rep 2019; 9:1678. [PMID: 30737447 PMCID: PMC6368557 DOI: 10.1038/s41598-019-38658-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Accepted: 01/07/2019] [Indexed: 02/06/2023] Open
Abstract
Validation of three-dimensional structures is at the core of structural determination methods. The local validation criteria, such as deviations from ideal bond length and bonding angles, Ramachandran plot outliers and clashing contacts, are a standard part of structure analysis before structure deposition, whereas the global and regional packing may not yet have been addressed. In the last two decades, three-dimensional models of macromolecules such as proteins have been successfully described by a network of nodes and edges. Amino acid residues as nodes and close contact between the residues as edges have been used to explore basic network properties, to study protein folding and stability and to predict catalytic sites. Using complex network analysis, we introduced common network parameters to distinguish between correct and incorrect three-dimensional protein structures. The analysis showed that correct structures have a higher average node degree, higher graph energy, and lower shortest path length than their incorrect counterparts. Thus, correct protein models are more densely intra-connected, and in turn, the transfer of information between nodes/amino acids is more efficient. Moreover, protein graph spectra were used to investigate model bias in protein structure.
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Affiliation(s)
- Jure Pražnikar
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, Koper, Slovenia.
- Department of Biochemistry, Molecular and Structural Biology, Institute Jožef Stefan, Jamova 39, Ljubljana, Slovenia.
| | - Miloš Tomić
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, Koper, Slovenia
| | - Dušan Turk
- Department of Biochemistry, Molecular and Structural Biology, Institute Jožef Stefan, Jamova 39, Ljubljana, Slovenia
- Center of excellence for Integrated Approaches in Chemistry and Biology of Proteins, Jamova 39, Ljubljana, Slovenia
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20
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Tonel MZ, González-Durruthy M, Zanella I, Fagan SB. Interactions of graphene derivatives with glutamate-neurotransmitter: A parallel first principles - Docking investigation. J Mol Graph Model 2019; 88:121-127. [PMID: 30703687 DOI: 10.1016/j.jmgm.2019.01.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 01/11/2019] [Accepted: 01/14/2019] [Indexed: 01/14/2023]
Abstract
Glutamate plays an important role in excitatory neurotransmission, learning, and memory processes, and under pathological conditions it is directly associated with several chronic neurological disorders, such as depression, epilepsy, schizophrenia, and Parkinson's. Therefore, the detection and quantification of Glutamate is important for the rapid diagnosis of these diseases. Using first principles and molecular docking simulations we have evaluated the energetic, structural, and binding properties of graphene derivatives, such as pristine graphene (pristine-Gr) and oxidized graphene with carboxylic (Gr-COOH), carbonyl (Gr-COH), hydroxyl (Gr-OH), and epoxy (-O-) groups interacting with the glutamate neurotransmitter. The calculated binding affinity free energies from the docking complexes (glutamate-graphene family) suggest higher oxidized graphene-based glutamate molecular recognition than the pristine-Gr, with the following order of oxidized graphene derivatives according to ab initio results: (Gr-O∼Gr-COOH ∼ Gr-COH > Gr-OH)>pristine-Gr. Herein, the ab initio binding energies found for the glutamate-graphene family complexes are in the range of 0.24-0.80 eV. The configurations studied showed a biophysical adsorption regime without significant changes in the physico-chemical properties of the adsorbed glutamate neurotransmitter, in accordance with the general acceptance criteria of the detection systems.
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Affiliation(s)
- Mariana Zancan Tonel
- Instituto de Física, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil; Programa de Pós-graduação em Nanociências, Universidade Franciscana, Santa Maria, RS, Brazil.
| | | | - Ivana Zanella
- Programa de Pós-graduação em Nanociências, Universidade Franciscana, Santa Maria, RS, Brazil.
| | - Solange Binotto Fagan
- Programa de Pós-graduação em Nanociências, Universidade Franciscana, Santa Maria, RS, Brazil.
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21
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Basith S, Lee Y, Choi S. Understanding G Protein-Coupled Receptor Allostery via Molecular Dynamics Simulations: Implications for Drug Discovery. Methods Mol Biol 2018; 1762:455-472. [PMID: 29594786 DOI: 10.1007/978-1-4939-7756-7_23] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Unraveling the mystery of protein allostery has been one of the greatest challenges in both structural and computational biology. However, recent advances in computational methods, particularly molecular dynamics (MD) simulations, have led to its utility as a powerful and popular tool for the study of protein allostery. By capturing the motions of a protein's constituent atoms, simulations can enable the discovery of allosteric hot spots and the determination of the mechanistic basis for allostery. These structural and dynamic studies can provide a foundation for a wide range of applications, including rational drug design and protein engineering. In our laboratory, the use of MD simulations and network analysis assisted in the elucidation of the allosteric hotspots and intracellular signal transduction of G protein-coupled receptors (GPCRs), primarily on one of the adenosine receptor subtypes, A2A adenosine receptor (A2AAR). In this chapter, we describe a method for calculating the map of allosteric signal flow in different GPCR conformational states and illustrate how these concepts have been utilized in understanding the mechanism of GPCR allostery. These structural studies will provide valuable insights into the allosteric and orthosteric modulations that would be of great help to design novel drugs targeting GPCRs in pathological states.
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Affiliation(s)
- Shaherin Basith
- National Leading Research Laboratory (NLRL) of Molecular Modeling & Drug Design, College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, Republic of Korea
| | - Yoonji Lee
- National Leading Research Laboratory (NLRL) of Molecular Modeling & Drug Design, College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, Republic of Korea
| | - Sun Choi
- National Leading Research Laboratory (NLRL) of Molecular Modeling & Drug Design, College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, Republic of Korea.
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22
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González-Durruthy M, Werhli AV, Seus V, Machado KS, Pazos A, Munteanu CR, González-Díaz H, Monserrat JM. Decrypting Strong and Weak Single-Walled Carbon Nanotubes Interactions with Mitochondrial Voltage-Dependent Anion Channels Using Molecular Docking and Perturbation Theory. Sci Rep 2017; 7:13271. [PMID: 29038520 PMCID: PMC5643473 DOI: 10.1038/s41598-017-13691-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Accepted: 09/25/2017] [Indexed: 01/30/2023] Open
Abstract
The current molecular docking study provided the Free Energy of Binding (FEB) for the interaction (nanotoxicity) between VDAC mitochondrial channels of three species (VDAC1-Mus musculus, VDAC1-Homo sapiens, VDAC2-Danio rerio) with SWCNT-H, SWCNT-OH, SWCNT-COOH carbon nanotubes. The general results showed that the FEB values were statistically more negative (p < 0.05) in the following order: (SWCNT-VDAC2-Danio rerio) > (SWCNT-VDAC1-Mus musculus) > (SWCNT-VDAC1-Homo sapiens) > (ATP-VDAC). More negative FEB values for SWCNT-COOH and OH were found in VDAC2-Danio rerio when compared with VDAC1-Mus musculus and VDAC1-Homo sapiens (p < 0.05). In addition, a significant correlation (0.66 > r2 > 0.97) was observed between n-Hamada index and VDAC nanotoxicity (or FEB) for the zigzag topologies of SWCNT-COOH and SWCNT-OH. Predictive Nanoparticles-Quantitative-Structure Binding-Relationship models (nano-QSBR) for strong and weak SWCNT-VDAC docking interactions were performed using Perturbation Theory, regression and classification models. Thus, 405 SWCNT-VDAC interactions were predicted using a nano-PT-QSBR classifications model with high accuracy, specificity, and sensitivity (73–98%) in training and validation series, and a maximum AUROC value of 0.978. In addition, the best regression model was obtained with Random Forest (R2 of 0.833, RMSE of 0.0844), suggesting an excellent potential to predict SWCNT-VDAC channel nanotoxicity. All study data are available at https://doi.org/10.6084/m9.figshare.4802320.v2.
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Affiliation(s)
- Michael González-Durruthy
- Institute of Biological Sciences (ICB)- Federal University of Rio Grande - FURG, Postgraduate Program in Physiological Sciences, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil.
| | - Adriano V Werhli
- Center of Computational Sciences (C3)- Federal University of Rio Grande - FURG, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil
| | - Vinicius Seus
- Center of Computational Sciences (C3)- Federal University of Rio Grande - FURG, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil
| | - Karina S Machado
- Center of Computational Sciences (C3)- Federal University of Rio Grande - FURG, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil
| | - Alejandro Pazos
- Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), A Coruña, 15006, Spain.,RNASA-IMEDIR, Computer Science Faculty, University of A Coruña, Campus de Elviña s/n, 15071, A Coruña, Spain
| | - Cristian R Munteanu
- RNASA-IMEDIR, Computer Science Faculty, University of A Coruña, Campus de Elviña s/n, 15071, A Coruña, Spain
| | - Humberto González-Díaz
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Spain.,IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain
| | - José M Monserrat
- Institute of Biological Sciences (ICB)- Federal University of Rio Grande - FURG, Postgraduate Program in Physiological Sciences, Cx. P. 474, CEP 96200-970, Rio Grande, RS, Brazil
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23
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Salamanca Viloria J, Allega MF, Lambrughi M, Papaleo E. An optimal distance cutoff for contact-based Protein Structure Networks using side-chain centers of mass. Sci Rep 2017; 7:2838. [PMID: 28588190 PMCID: PMC5460117 DOI: 10.1038/s41598-017-01498-6] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Accepted: 03/28/2017] [Indexed: 02/05/2023] Open
Abstract
Proteins are highly dynamic entities attaining a myriad of different conformations. Protein side chains change their states during dynamics, causing clashes that are propagated at distal sites. A convenient formalism to analyze protein dynamics is based on network theory using Protein Structure Networks (PSNs). Despite their broad applicability, few efforts have been devoted to benchmarking PSN methods and to provide the community with best practices. In many applications, it is convenient to use the centers of mass of the side chains as nodes. It becomes thus critical to evaluate the minimal distance cutoff between the centers of mass which will provide stable network properties. Moreover, when the PSN is derived from a structural ensemble collected by molecular dynamics (MD), the impact of the MD force field has to be evaluated. We selected a dataset of proteins with different fold and size and assessed the two fundamental properties of the PSN, i.e. hubs and connected components. We identified an optimal cutoff of 5 Å that is robust to changes in the force field and the proteins. Our study builds solid foundations for the harmonization and standardization of the PSN approach.
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Affiliation(s)
- Juan Salamanca Viloria
- Computational Biology Laboratory, Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark
| | - Maria Francesca Allega
- Computational Biology Laboratory, Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark
| | - Matteo Lambrughi
- Computational Biology Laboratory, Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark
| | - Elena Papaleo
- Computational Biology Laboratory, Danish Cancer Society Research Center, Strandboulevarden 49, 2100, Copenhagen, Denmark.
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24
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Pires DEV, Ascher DB. CSM-lig: a web server for assessing and comparing protein-small molecule affinities. Nucleic Acids Res 2016; 44:W557-61. [PMID: 27151202 PMCID: PMC4987933 DOI: 10.1093/nar/gkw390] [Citation(s) in RCA: 84] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2016] [Accepted: 04/28/2016] [Indexed: 12/21/2022] Open
Abstract
Determining the affinity of a ligand for a given protein is a crucial component of drug development and understanding their biological effects. Predicting binding affinities is a challenging and difficult task, and despite being regarded as poorly predictive, scoring functions play an important role in the analysis of molecular docking results. Here, we present CSM-Lig (http://structure.bioc.cam.ac.uk/csm_lig), a web server tailored to predict the binding affinity of a protein-small molecule complex, encompassing both protein and small-molecule complementarity in terms of shape and chemistry via graph-based structural signatures. CSM-Lig was trained and evaluated on different releases of the PDBbind databases, achieving a correlation of up to 0.86 on 10-fold cross validation and 0.80 in blind tests, performing as well as or better than other widely used methods. The web server allows users to rapidly and automatically predict binding affinities of collections of structures and assess the interactions made. We believe CSM-lig would be an invaluable tool for helping assess docking poses, the effects of multiple mutations, including insertions, deletions and alternative splicing events, in protein-small molecule affinity, unraveling important aspects that drive protein–compound recognition.
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Affiliation(s)
- Douglas E V Pires
- Centro de Pesquisas René Rachou, Fundação Oswaldo Cruz, Belo Horizonte, 30190-002, Brazil
| | - David B Ascher
- Centro de Pesquisas René Rachou, Fundação Oswaldo Cruz, Belo Horizonte, 30190-002, Brazil Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK Department of Biochemistry, University of Melbourne, Victoria 3010, Australia
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25
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Niknam N, Khakzad H, Arab SS, Naderi-Manesh H. PDB2Graph: A toolbox for identifying critical amino acids map in proteins based on graph theory. Comput Biol Med 2016; 72:151-9. [PMID: 27043857 DOI: 10.1016/j.compbiomed.2016.03.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2015] [Revised: 02/15/2016] [Accepted: 03/17/2016] [Indexed: 12/31/2022]
Abstract
The integrative and cooperative nature of protein structure involves the assessment of topological and global features of constituent parts. Network concept takes complete advantage of both of these properties in the analysis concomitantly. High compatibility to structural concepts or physicochemical properties in addition to exploiting a remarkable simplification in the system has made network an ideal tool to explore biological systems. There are numerous examples in which different protein structural and functional characteristics have been clarified by the network approach. Here, we present an interactive and user-friendly Matlab-based toolbox, PDB2Graph, devoted to protein structure network construction, visualization, and analysis. Moreover, PDB2Graph is an appropriate tool for identifying critical nodes involved in protein structural robustness and function based on centrality indices. It maps critical amino acids in protein networks and can greatly aid structural biologists in selecting proper amino acid candidates for manipulating protein structures in a more reasonable and rational manner. To introduce the capability and efficiency of PDB2Graph in detail, the structural modification of Calmodulin through allosteric binding of Ca(2+) is considered. In addition, a mutational analysis for three well-identified model proteins including Phage T4 lysozyme, Barnase and Ribonuclease HI, was performed to inspect the influence of mutating important central residues on protein activity.
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Affiliation(s)
- Niloofar Niknam
- Department of Biophysics, School of Biological Sciences, Tarbiat Modares University, Tehran, Iran.
| | - Hamed Khakzad
- Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran.
| | - Seyed Shahriar Arab
- Department of Biophysics, School of Biological Sciences, Tarbiat Modares University, Tehran, Iran.
| | - Hossein Naderi-Manesh
- Department of Biophysics, School of Biological Sciences, Tarbiat Modares University, Tehran, Iran.
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26
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González-Durruthy M, Werhli AV, Cornetet L, Machado KS, González-Díaz H, Wasiliesky W, Ruas CP, Gelesky MA, Monserrat JM. Predicting the binding properties of single walled carbon nanotubes (SWCNT) with an ADP/ATP mitochondrial carrier using molecular docking, chemoinformatics, and nano-QSBR perturbation theory. RSC Adv 2016. [DOI: 10.1039/c6ra08883j] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Interactions between single walled carbon nanotubes (SWCNT) family with mitochondrial ADP/ATP carrier (ANT-1) were evaluated using constitutional and electronic nanodescriptors defined by (n, m)-Hamada indexes (armchair, zig-zag and chiral).
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Affiliation(s)
- Michael González-Durruthy
- Instituto de Ciências Biológicas (ICB) – Universidade Federal do Rio Grande-FURG
- Rio Grande
- Brazil
- Programa de Pós-Graduação em Ciências Fisiológicas-Fisiologia Animal Comparada-ICB-FURG
- Brazil
| | - Adriano V. Werhli
- Centro de Ciências Computacionais (C3) – Universidade Federal do Rio Grande-FURG
- Brazil
- Programa de Pós-Graduação em Computação-C3-FURG
- Brazil
| | - Luisa Cornetet
- Centro de Ciências Computacionais (C3) – Universidade Federal do Rio Grande-FURG
- Brazil
- Programa de Pós-Graduação em Computação-C3-FURG
- Brazil
| | - Karina S. Machado
- Centro de Ciências Computacionais (C3) – Universidade Federal do Rio Grande-FURG
- Brazil
- Programa de Pós-Graduação em Computação-C3-FURG
- Brazil
| | - Humberto González-Díaz
- Department of Organic Chemistry II
- Faculty of Science and Technology
- University of the Basque Country UPV/EHU
- Leioa
- Spain
| | | | | | - Marcos A. Gelesky
- Programa de Pós-graduação em Química Tecnológica e Ambiental
- FURG
- Brazil
| | - José M. Monserrat
- Instituto de Ciências Biológicas (ICB) – Universidade Federal do Rio Grande-FURG
- Rio Grande
- Brazil
- Programa de Pós-Graduação em Ciências Fisiológicas-Fisiologia Animal Comparada-ICB-FURG
- Brazil
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27
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Isaac AE, Sinha S. Analysis of core-periphery organization in protein contact networks reveals groups of structurally and functionally critical residues. J Biosci 2015; 40:683-99. [PMID: 26564971 DOI: 10.1007/s12038-015-9554-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
The representation of proteins as networks of interacting amino acids, referred to as protein contact networks (PCN), and their subsequent analyses using graph theoretic tools, can provide novel insights into the key functional roles of specific groups of residues. We have characterized the networks corresponding to the native states of 66 proteins (belonging to different families) in terms of their core-periphery organization. The resulting hierarchical classification of the amino acid constituents of a protein arranges the residues into successive layers - having higher core order - with increasing connection density, ranging from a sparsely linked periphery to a densely intra-connected core (distinct from the earlier concept of protein core defined in terms of the three-dimensional geometry of the native state, which has least solvent accessibility). Our results show that residues in the inner cores are more conserved than those at the periphery. Underlining the functional importance of the network core, we see that the receptor sites for known ligand molecules of most proteins occur in the innermost core. Furthermore, the association of residues with structural pockets and cavities in binding or active sites increases with the core order. From mutation sensitivity analysis, we show that the probability of deleterious or intolerant mutations also increases with the core order. We also show that stabilization centre residues are in the innermost cores, suggesting that the network core is critically important in maintaining the structural stability of the protein. A publicly available Web resource for performing core-periphery analysis of any protein whose native state is known has been made available by us at http://www.imsc.res.in/ ~sitabhra/proteinKcore/index.html.
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Affiliation(s)
- Arnold Emerson Isaac
- Bioinformatics Division, School of Bio Sciences and Technology, VIT University, Vellore, India
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28
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Di Paola L, Platania CBM, Oliva G, Setola R, Pascucci F, Giuliani A. Characterization of Protein-Protein Interfaces through a Protein Contact Network Approach. Front Bioeng Biotechnol 2015; 3:170. [PMID: 26579512 PMCID: PMC4626657 DOI: 10.3389/fbioe.2015.00170] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2015] [Accepted: 10/12/2015] [Indexed: 11/13/2022] Open
Abstract
Anthrax toxin comprises three different proteins, jointly acting to exert toxic activity: a non-toxic protective agent (PA), toxic edema factor (EF), and lethal factor (LF). Binding of PA to anthrax receptors promotes oligomerization of PA, binding of EF and LF, and then endocytosis of the complex. Homomeric forms of PA, complexes of PA bound to LF and to the endogenous receptor capillary morphogenesis gene 2 (CMG2) were analyzed. In this work, we characterized protein–protein interfaces (PPIs) and identified key residues at PPIs of complexes, by means of a protein contact network (PCN) approach. Flexibility and global and local topological properties of each PCN were computed. The vulnerability of each PCN was calculated using different node removal strategies, with reference to specific PCN topological descriptors, such as participation coefficient, contact order, and degree. The participation coefficient P, the topological descriptor of the node’s ability to intervene in protein inter-module communication, was the key descriptor of PCN vulnerability of all structures. High P residues were localized both at PPIs and other regions of complexes, so that we argued an allosteric mechanism in protein–protein interactions. The identification of residues, with key role in the stability of PPIs, has a huge potential in the development of new drugs, which would be designed to target not only PPIs but also residues localized in allosteric regions of supramolecular complexes.
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Affiliation(s)
- Luisa Di Paola
- Facoltà Dipartimentale di Ingegneria, Università Campus Bio-Medico di Roma , Rome , Italy
| | | | - Gabriele Oliva
- Facoltà Dipartimentale di Ingegneria, Università Campus Bio-Medico di Roma , Rome , Italy
| | - Roberto Setola
- Facoltà Dipartimentale di Ingegneria, Università Campus Bio-Medico di Roma , Rome , Italy
| | - Federica Pascucci
- Dipartimento di Informatica e Automazione, Università degli studi Roma Tre , Rome , Italy
| | - Alessandro Giuliani
- Dipartimento di Ambiente e Connessa Prevenzione Primaria, Istituto Superiore di Sanità , Rome , Italy
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29
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Computational approaches to study the effects of small genomic variations. J Mol Model 2015; 21:251. [PMID: 26350246 DOI: 10.1007/s00894-015-2794-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Accepted: 08/23/2015] [Indexed: 10/23/2022]
Abstract
Advances in DNA sequencing technologies have led to an avalanche-like increase in the number of gene sequences deposited in public databases over the last decade as well as the detection of an enormous number of previously unseen nucleotide variants therein. Given the size and complex nature of the genome-wide sequence variation data, as well as the rate of data generation, experimental characterization of the disease association of each of these variations or their effects on protein structure/function would be costly, laborious, time-consuming, and essentially impossible. Thus, in silico methods to predict the functional effects of sequence variations are constantly being developed. In this review, we summarize the major computational approaches and tools that are aimed at the prediction of the functional effect of mutations, and describe the state-of-the-art databases that can be used to obtain information about mutation significance. We also discuss future directions in this highly competitive field.
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30
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Gonçalves WRS, Gonçalves-Almeida VM, Arruda AL, Meira W, da Silveira CH, Pires DEV, de Melo-Minardi RC. PDBest: a user-friendly platform for manipulating and enhancing protein structures. Bioinformatics 2015; 31:2894-6. [PMID: 25910698 DOI: 10.1093/bioinformatics/btv223] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2015] [Accepted: 04/19/2015] [Indexed: 11/14/2022] Open
Abstract
UNLABELLED PDBest (PDB Enhanced Structures Toolkit) is a user-friendly, freely available platform for acquiring, manipulating and normalizing protein structures in a high-throughput and seamless fashion. With an intuitive graphical interface it allows users with no programming background to download and manipulate their files. The platform also exports protocols, enabling users to easily share PDB searching and filtering criteria, enhancing analysis reproducibility. AVAILABILITY AND IMPLEMENTATION PDBest installation packages are freely available for several platforms at http://www.pdbest.dcc.ufmg.br CONTACT wellisson@dcc.ufmg.br, dpires@dcc.ufmg.br, raquelcm@dcc.ufmg.br SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Aleksander L Arruda
- Department of Computer Science, Universidade Federal de Minas Gerais, Brazil
| | - Wagner Meira
- Department of Computer Science, Universidade Federal de Minas Gerais, Brazil
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31
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Pires DEV, Blundell TL, Ascher DB. pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures. J Med Chem 2015; 58:4066-72. [PMID: 25860834 PMCID: PMC4434528 DOI: 10.1021/acs.jmedchem.5b00104] [Citation(s) in RCA: 1948] [Impact Index Per Article: 216.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
![]()
Drug development has a high attrition
rate, with poor pharmacokinetic
and safety properties a significant hurdle. Computational approaches
may help minimize these risks. We have developed a novel approach
(pkCSM) which uses graph-based signatures to develop predictive models
of central ADMET properties for drug development. pkCSM performs as
well or better than current methods. A freely accessible web server
(http://structure.bioc.cam.ac.uk/pkcsm), which retains
no information submitted to it, provides an integrated platform to
rapidly evaluate pharmacokinetic and toxicity properties.
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Affiliation(s)
- Douglas E V Pires
- †Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Sanger Building, Cambridge, Cambridgshire CB2 1GA, U.K.,‡Centro de Pesquisas René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil
| | - Tom L Blundell
- †Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Sanger Building, Cambridge, Cambridgshire CB2 1GA, U.K
| | - David B Ascher
- †Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Sanger Building, Cambridge, Cambridgshire CB2 1GA, U.K
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32
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Aumentado-Armstrong TT, Istrate B, Murgita RA. Algorithmic approaches to protein-protein interaction site prediction. Algorithms Mol Biol 2015; 10:7. [PMID: 25713596 PMCID: PMC4338852 DOI: 10.1186/s13015-015-0033-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2014] [Accepted: 01/07/2015] [Indexed: 12/19/2022] Open
Abstract
Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.
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33
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Ascher DB, Jubb HC, Pires DEV, Ochi T, Higueruelo A, Blundell TL. Protein-Protein Interactions: Structures and Druggability. MULTIFACETED ROLES OF CRYSTALLOGRAPHY IN MODERN DRUG DISCOVERY 2015. [DOI: 10.1007/978-94-017-9719-1_12] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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34
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Silveira SA, Fassio AV, Gonçalves-Almeida VM, de Lima EB, Barcelos YT, Aburjaile FF, Rodrigues LM, Meira W, de Melo-Minardi RC. VERMONT: Visualizing mutations and their effects on protein physicochemical and topological property conservation. BMC Proc 2014; 8:S4. [PMID: 25237391 PMCID: PMC4155615 DOI: 10.1186/1753-6561-8-s2-s4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
In this paper, we propose an interactive visualization called VERMONT which tackles the problem of visualizing mutations and infers their possible effects on the conservation of physicochemical and topological properties in protein families. More specifically, we visualize a set of structure-based sequence alignments and integrate several structural parameters that should aid biologists in gaining insight into possible consequences of mutations. VERMONT allowed us to identify patterns of position-specific properties as well as exceptions that may help predict whether specific mutations could damage protein function.
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Affiliation(s)
- Sabrina A Silveira
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Alexandre V Fassio
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil.,Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Valdete M Gonçalves-Almeida
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Elisa B de Lima
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil.,Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Yussif T Barcelos
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Flávia F Aburjaile
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Laerte M Rodrigues
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil.,Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Wagner Meira
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Raquel C de Melo-Minardi
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
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35
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Pires DEV, Ascher DB, Blundell TL. DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach. Nucleic Acids Res 2014; 42:W314-9. [PMID: 24829462 PMCID: PMC4086143 DOI: 10.1093/nar/gku411] [Citation(s) in RCA: 570] [Impact Index Per Article: 57.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Cancer genome and other sequencing initiatives are generating extensive data on non-synonymous single nucleotide polymorphisms (nsSNPs) in human and other genomes. In order to understand the impacts of nsSNPs on the structure and function of the proteome, as well as to guide protein engineering, accurate in silicomethodologies are required to study and predict their effects on protein stability. Despite the diversity of available computational methods in the literature, none has proven accurate and dependable on its own under all scenarios where mutation analysis is required. Here we present DUET, a web server for an integrated computational approach to study missense mutations in proteins. DUET consolidates two complementary approaches (mCSM and SDM) in a consensus prediction, obtained by combining the results of the separate methods in an optimized predictor using Support Vector Machines (SVM). We demonstrate that the proposed method improves overall accuracy of the predictions in comparison with either method individually and performs as well as or better than similar methods. The DUET web server is freely and openly available at http://structure.bioc.cam.ac.uk/duet.
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Affiliation(s)
- Douglas E V Pires
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK
| | - David B Ascher
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK ACRF Rational Drug Discovery Centre and Biota Structural Biology Laboratory, St Vincents Institute of Medical Research, Fitzroy, VIC 3065, Australia
| | - Tom L Blundell
- Department of Biochemistry, University of Cambridge, Cambridge, CB2 1GA, UK
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36
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Markov mean properties for cell death-related protein classification. J Theor Biol 2014; 349:12-21. [DOI: 10.1016/j.jtbi.2014.01.033] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Revised: 01/21/2014] [Accepted: 01/24/2014] [Indexed: 11/18/2022]
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37
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Yan W, Zhou J, Sun M, Chen J, Hu G, Shen B. The construction of an amino acid network for understanding protein structure and function. Amino Acids 2014; 46:1419-39. [PMID: 24623120 DOI: 10.1007/s00726-014-1710-6] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2013] [Accepted: 02/21/2014] [Indexed: 01/08/2023]
Abstract
Amino acid networks (AANs) are undirected networks consisting of amino acid residues and their interactions in three-dimensional protein structures. The analysis of AANs provides novel insight into protein science, and several common amino acid network properties have revealed diverse classes of proteins. In this review, we first summarize methods for the construction and characterization of AANs. We then compare software tools for the construction and analysis of AANs. Finally, we review the application of AANs for understanding protein structure and function, including the identification of functional residues, the prediction of protein folding, analyzing protein stability and protein-protein interactions, and for understanding communication within and between proteins.
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Affiliation(s)
- Wenying Yan
- Center for Systems Biology, Soochow University, Suzhou, 215006, Jiangsu, China
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38
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Winck AT, Machado KS, de Souza ON, Ruiz DD. Context-based preprocessing of molecular docking data. BMC Genomics 2014; 14 Suppl 6:S6. [PMID: 24564276 PMCID: PMC3909228 DOI: 10.1186/1471-2164-14-s6-s6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Background Data preprocessing is a major step in data mining. In data preprocessing, several known techniques can be applied, or new ones developed, to improve data quality such that the mining results become more accurate and intelligible. Bioinformatics is one area with a high demand for generation of comprehensive models from large datasets. In this article, we propose a context-based data preprocessing approach to mine data from molecular docking simulation results. The test cases used a fully-flexible receptor (FFR) model of Mycobacterium tuberculosis InhA enzyme (FFR_InhA) and four different ligands. Results We generated an initial set of attributes as well as their respective instances. To improve this initial set, we applied two selection strategies. The first was based on our context-based approach while the second used the CFS (Correlation-based Feature Selection) machine learning algorithm. Additionally, we produced an extra dataset containing features selected by combining our context strategy and the CFS algorithm. To demonstrate the effectiveness of the proposed method, we evaluated its performance based on various predictive (RMSE, MAE, Correlation, and Nodes) and context (Precision, Recall and FScore) measures. Conclusions Statistical analysis of the results shows that the proposed context-based data preprocessing approach significantly improves predictive and context measures and outperforms the CFS algorithm. Context-based data preprocessing improves mining results by producing superior interpretable models, which makes it well-suited for practical applications in molecular docking simulations using FFR models.
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de Moraes FR, Neshich IAP, Mazoni I, Yano IH, Pereira JGC, Salim JA, Jardine JG, Neshich G. Improving predictions of protein-protein interfaces by combining amino acid-specific classifiers based on structural and physicochemical descriptors with their weighted neighbor averages. PLoS One 2014; 9:e87107. [PMID: 24489849 PMCID: PMC3904977 DOI: 10.1371/journal.pone.0087107] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2012] [Accepted: 12/22/2013] [Indexed: 11/18/2022] Open
Abstract
Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR). We formulated a linear discriminative analysis (LDA) classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/) are suitable for such a task. Receiver operating characteristic (ROC) analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication) or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study is now integrated into the BlueStar STING suite of programs. Consequently, the prediction of protein-protein interfaces for all proteins available in the PDB is possible through STING_interfaces module, accessible at the following website: (http://www.cbi.cnptia.embrapa.br/SMS/predictions/index.html).
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Affiliation(s)
- Fábio R. de Moraes
- Biology Institute, University of Campinas, Campinas, São Paulo, Brazil
- Brazilian Agricultural Research Corporation (EMBRAPA), National Center for Agricultural Informatics, Campinas, São Paulo, Brazil
| | - Izabella A. P. Neshich
- Biology Institute, University of Campinas, Campinas, São Paulo, Brazil
- Brazilian Agricultural Research Corporation (EMBRAPA), National Center for Agricultural Informatics, Campinas, São Paulo, Brazil
| | - Ivan Mazoni
- Biology Institute, University of Campinas, Campinas, São Paulo, Brazil
- Brazilian Agricultural Research Corporation (EMBRAPA), National Center for Agricultural Informatics, Campinas, São Paulo, Brazil
| | - Inácio H. Yano
- Brazilian Agricultural Research Corporation (EMBRAPA), National Center for Agricultural Informatics, Campinas, São Paulo, Brazil
| | - José G. C. Pereira
- Biology Institute, University of Campinas, Campinas, São Paulo, Brazil
- Brazilian Agricultural Research Corporation (EMBRAPA), National Center for Agricultural Informatics, Campinas, São Paulo, Brazil
| | - José A. Salim
- School of Electrical and Computer Engineering, University of Campinas, Campinas, São Paulo, Brazil
| | - José G. Jardine
- Brazilian Agricultural Research Corporation (EMBRAPA), National Center for Agricultural Informatics, Campinas, São Paulo, Brazil
| | - Goran Neshich
- Brazilian Agricultural Research Corporation (EMBRAPA), National Center for Agricultural Informatics, Campinas, São Paulo, Brazil
- * E-mail:
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Pires DEV, Ascher DB, Blundell TL. mCSM: predicting the effects of mutations in proteins using graph-based signatures. ACTA ACUST UNITED AC 2013; 30:335-42. [PMID: 24281696 PMCID: PMC3904523 DOI: 10.1093/bioinformatics/btt691] [Citation(s) in RCA: 648] [Impact Index Per Article: 58.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Motivation: Mutations play fundamental roles in evolution by introducing diversity into genomes. Missense mutations in structural genes may become either selectively advantageous or disadvantageous to the organism by affecting protein stability and/or interfering with interactions between partners. Thus, the ability to predict the impact of mutations on protein stability and interactions is of significant value, particularly in understanding the effects of Mendelian and somatic mutations on the progression of disease. Here, we propose a novel approach to the study of missense mutations, called mCSM, which relies on graph-based signatures. These encode distance patterns between atoms and are used to represent the protein residue environment and to train predictive models. To understand the roles of mutations in disease, we have evaluated their impacts not only on protein stability but also on protein–protein and protein–nucleic acid interactions. Results: We show that mCSM performs as well as or better than other methods that are used widely. The mCSM signatures were successfully used in different tasks demonstrating that the impact of a mutation can be correlated with the atomic-distance patterns surrounding an amino acid residue. We showed that mCSM can predict stability changes of a wide range of mutations occurring in the tumour suppressor protein p53, demonstrating the applicability of the proposed method in a challenging disease scenario. Availability and implementation: A web server is available at http://structure.bioc.cam.ac.uk/mcsm. Contact:dpires@dcc.ufmg.br; tom@cryst.bioc.cam.ac.uk Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Douglas E V Pires
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK and ACRF Rational Drug Discovery Centre and Biota Structural Biology Laboratory, St Vincents Institute of Medical Research, Fitzroy, VIC, 3065, Australia
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41
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Lee Y, Choi S, Hyeon C. Mapping the intramolecular signal transduction of G-protein coupled receptors. Proteins 2013; 82:727-43. [PMID: 24166702 DOI: 10.1002/prot.24451] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2013] [Revised: 09/13/2013] [Accepted: 10/10/2013] [Indexed: 11/07/2022]
Abstract
G-protein coupled receptors (GPCRs), a major gatekeeper of extracellular signals on plasma membrane, are unarguably one of the most important therapeutic targets. Given the recent discoveries of allosteric modulations, an allosteric wiring diagram of intramolecular signal transductions would be of great use to glean the mechanism of receptor regulation. Here, by evaluating betweenness centrality (CB ) of each residue, we calculate maps of information flow in GPCRs and identify key residues for signal transductions and their pathways. Compared with preexisting approaches, the allosteric hotspots that our CB -based analysis detects for A2 A adenosine receptor (A2 A AR) and bovine rhodopsin are better correlated with biochemical data. In particular, our analysis outperforms other methods in locating the rotameric microswitches, which are generally deemed critical for mediating orthosteric signaling in class A GPCRs. For A2 A AR, the inter-residue cross-correlation map, calculated using equilibrium structural ensemble from molecular dynamics simulations, reveals that strong signals of long-range transmembrane communications exist only in the agonist-bound state. A seemingly subtle variation in structure, found in different GPCR subtypes or imparted by agonist bindings or a point mutation at an allosteric site, can lead to a drastic difference in the map of signaling pathways and protein activity. The signaling map of GPCRs provides valuable insights into allosteric modulations as well as reliable identifications of orthosteric signaling pathways.
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Affiliation(s)
- Yoonji Lee
- National Leading Research Lab (NLRL) of Molecular Modeling and Drug Design College of Pharmacy Graduate School of Pharmaceutical Sciences and Global Top 5 Research Program, Ewha Womans University, Seoul, 120-750, Korea
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42
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Li X, Fleetwood AD, Bayas C, Bilwes AM, Ortega DR, Falke JJ, Zhulin IB, Crane BR. The 3.2 Å resolution structure of a receptor: CheA:CheW signaling complex defines overlapping binding sites and key residue interactions within bacterial chemosensory arrays. Biochemistry 2013; 52:3852-65. [PMID: 23668907 PMCID: PMC3694592 DOI: 10.1021/bi400383e] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Bacterial chemosensory arrays are composed of extended networks of chemoreceptors (also known as methyl-accepting chemotaxis proteins, MCPs), the histidine kinase CheA, and the adaptor protein CheW. Models of these arrays have been developed from cryoelectron microscopy, crystal structures of binary and ternary complexes, NMR spectroscopy, mutational, data and biochemical studies. A new 3.2 Å resolution crystal structure of a Thermotoga maritima MCP protein interaction region in complex with the CheA kinase-regulatory module (P4-P5) and adaptor protein CheW provides sufficient detail to define residue contacts at the interfaces formed among the three proteins. As in a previous 4.5 Å resolution structure, CheA-P5 and CheW interact through conserved hydrophobic surfaces at the ends of their β-barrels to form pseudo 6-fold symmetric rings in which the two proteins alternate around the circumference. The interface between P5 subdomain 1 and CheW subdomain 2 was anticipated from previous studies, whereas the related interface between CheW subdomain 1 and P5 subdomain 2 has only been observed in these ring assemblies. The receptor forms an unexpected structure in that the helical hairpin tip of each subunit has "unzipped" into a continuous α-helix; four such helices associate into a bundle, and the tetramers bridge adjacent P5-CheW rings in the lattice through interactions with both P5 and CheW. P5 and CheW each bind a receptor helix with a groove of conserved hydrophobic residues between subdomains 1 and 2. P5 binds the receptor helix N-terminal to the tip region (lower site), whereas CheW binds the same helix with inverted polarity near the bundle end (upper site). Sequence comparisons among different evolutionary classes of chemotaxis proteins show that the binding partners undergo correlated changes at key residue positions that involve the lower site. Such evolutionary analyses argue that both CheW and P5 bind to the receptor tip at overlapping positions. Computational genomics further reveal that two distinct CheW proteins in Thermotogae utilize the analogous recognition motifs to couple different receptor classes to the same CheA kinase. Important residues for function previously identified by mutagenesis, chemical modification and biophysical approaches also map to these same interfaces. Thus, although the native CheW-receptor interaction is not observed in the present crystal structure, the bioinformatics and previous data predict key features of this interface. The companion study of the P5-receptor interface in native arrays (accompanying paper Piasta et al. (2013) Biochemistry, DOI: 10.1021/bi400385c) shows that, despite the non-native receptor fold in the present crystal structure, the local helix-in-groove contacts of the crystallographic P5-receptor interaction are present in native arrays and are essential for receptor regulation of kinase activity.
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Affiliation(s)
- Xiaoxiao Li
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853, United States
| | - Aaron D. Fleetwood
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 United States and Department of Microbiology, University of Tennessee, Knoxville TN 37996 United States
| | - Camille Bayas
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853, United States
| | - Alexandrine M. Bilwes
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853, United States
| | - Davi R. Ortega
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 United States and Department of Microbiology, University of Tennessee, Knoxville TN 37996 United States
| | | | - Igor B. Zhulin
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 United States and Department of Microbiology, University of Tennessee, Knoxville TN 37996 United States,To whom correspondence should be addressed , Tel (607) 254-8634 (B.R.C); (I.B.Z), Tel (865) 201-1860
| | - Brian R. Crane
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY 14853, United States,To whom correspondence should be addressed , Tel (607) 254-8634 (B.R.C); (I.B.Z), Tel (865) 201-1860
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The effect of edge definition of complex networks on protein structure identification. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013; 2013:365410. [PMID: 23533536 PMCID: PMC3600232 DOI: 10.1155/2013/365410] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2012] [Revised: 01/23/2013] [Accepted: 01/25/2013] [Indexed: 12/29/2022]
Abstract
The main objective of this study is to explore the contribution of complex network together with its different definitions of vertexes and edges to describe the structure of proteins. Protein folds into a specific conformation for its function depending on interactions between residues. Consequently, in many studies, a protein structure was treated as a complex system comprised of individual components residues, and edges were interactions between residues. What is the proper time for representing a protein structure as a network? To confirm the effect of different definitions of vertexes and edges in constructing the amino acid interaction networks, protein domains and the structural unit of proteins were described using this method. The identification performance of 2847 proteins with domain/domains proved that the structure of proteins was described well when RCα
was around 5.0–7.5 Å, and the optimal cutoff value for constructing the protein structure networks was 5.0 Å (Cα-Cα distances) while the ideal community division method was community structure detection based on edge betweenness in this study.
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Pires DEV, de Melo-Minardi RC, da Silveira CH, Campos FF, Meira W. aCSM: noise-free graph-based signatures to large-scale receptor-based ligand prediction. ACTA ACUST UNITED AC 2013; 29:855-61. [PMID: 23396119 DOI: 10.1093/bioinformatics/btt058] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
MOTIVATION Receptor-ligand interactions are a central phenomenon in most biological systems. They are characterized by molecular recognition, a complex process mainly driven by physicochemical and structural properties of both receptor and ligand. Understanding and predicting these interactions are major steps towards protein ligand prediction, target identification, lead discovery and drug design. RESULTS We propose a novel graph-based-binding pocket signature called aCSM, which proved to be efficient and effective in handling large-scale protein ligand prediction tasks. We compare our results with those described in the literature and demonstrate that our algorithm overcomes the competitor's techniques. Finally, we predict novel ligands for proteins from Trypanosoma cruzi, the parasite responsible for Chagas disease, and validate them in silico via a docking protocol, showing the applicability of the method in suggesting ligands for pockets in a real-world scenario. AVAILABILITY AND IMPLEMENTATION Datasets and the source code are available at http://www.dcc.ufmg.br/∼dpires/acsm. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Douglas E V Pires
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6627, Pampulha Belo Horizonte - MG, 31270-901, Brazil.
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45
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Di Paola L, De Ruvo M, Paci P, Santoni D, Giuliani A. Protein Contact Networks: An Emerging Paradigm in Chemistry. Chem Rev 2012. [DOI: 10.1021/cr3002356] [Citation(s) in RCA: 173] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- L. Di Paola
- Faculty of Engineering, Università CAMPUS BioMedico, Via A. del Portillo,
21, 00128 Roma, Italy
| | | | | | - D. Santoni
- BioMathLab, CNR-Institute of Systems Analysis and Computer Science (IASI), viale Manzoni 30, 00185
Roma, Italy
| | - A. Giuliani
- Environment
and Health Department, Istituto Superiore di Sanità, Viale Regina Elena
299, 00161, Roma, Italy
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46
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47
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Atilgan AR, Atilgan C. Local motifs in proteins combine to generate global functional moves. Brief Funct Genomics 2012; 11:479-88. [PMID: 22811517 DOI: 10.1093/bfgp/els027] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Literature on the topological properties of folded proteins that has emerged as a field in its own right in the past decade is reviewed. Physics-based construction of coarse-grained models of proteins from knowledge of all-atom coordinates of the average structure is discussed. Once network is thus obtained with the node and link information, local motifs provide plethora of information on protein function. The hierarchical structure of the proteins manifested in the interrelations of local motifs is emphasized. Motifs are also related to modularity of the structure, and they quantify shifts in the landscapes upon conformational changes induced by, e.g. ligand binding. Redundancy emerges as a balance between local and global network descriptors and is related to the collectivity of the protein motions. Introducing weight on links followed by sequential removal of least cohesive contacts allows interactions in proteins to be represented as the superposition of essential and redundant sets. Lack of the former makes the network non-functional, while the latter ensures robust functioning under a wide range of perturbation scenarios.
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Affiliation(s)
- Ali Rana Atilgan
- Faculty of Engineering and Natural Sciences, Sabanci University, 34956 Istanbul, Turkey
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48
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Noel JK, Whitford PC, Onuchic JN. The shadow map: a general contact definition for capturing the dynamics of biomolecular folding and function. J Phys Chem B 2012; 116:8692-702. [PMID: 22536820 DOI: 10.1021/jp300852d] [Citation(s) in RCA: 165] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Structure-based models (SBMs) are simplified models of the biomolecular dynamics that arise from funneled energy landscapes. We recently introduced an all-atom SBM that explicitly represents the atomic geometry of a biomolecule. While this initial study showed the robustness of the all-atom SBM Hamiltonian to changes in many of the energetic parameters, an important aspect, which has not been explored previously, is the definition of native interactions. In this study, we propose a general definition for generating atomically grained contact maps called "Shadow". The Shadow algorithm initially considers all atoms within a cutoff distance and then, controlled by a screening parameter, discards the occluded contacts. We show that this choice of contact map is not only well behaved for protein folding, since it produces consistently cooperative folding behavior in SBMs but also desirable for exploring the dynamics of macromolecular assemblies since, it distributes energy similarly between RNAs and proteins despite their disparate internal packing. All-atom structure-based models employing Shadow contact maps provide a general framework for exploring the geometrical features of biomolecules, especially the connections between folding and function.
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Affiliation(s)
- Jeffrey K Noel
- Center for Theoretical Biological Physics and Department of Physics, Rice University, Houston, Texas 77005, United States
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49
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Atilgan C, Okan OB, Atilgan AR. Network-based models as tools hinting at nonevident protein functionality. Annu Rev Biophys 2012; 41:205-25. [PMID: 22404685 DOI: 10.1146/annurev-biophys-050511-102305] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Network-based models of proteins are popular tools employed to determine dynamic features related to the folded structure. They encompass all topological and geometric computational approaches idealizing proteins as directly interacting nodes. Topology makes use of neighborhood information of residues, and geometry includes relative placement of neighbors. Coarse-grained approaches efficiently predict alternative conformations because of inherent collectivity in the protein structure. Such collectivity is moderated by topological characteristics that also tune neighborhood structure: That rich residues have richer neighbors secures robustness toward random loss of interactions/nodes due to environmental fluctuations/mutations. Geometry conveys the additional information of force balance to network models, establishing the local shape of the energy landscape. Here, residue and/or bond perturbations are critically evaluated to suggest new experiments, as network-based computational techniques prove useful in capturing domain movements and conformational shifts resulting from environmental alterations. Evolutionarily conserved residues are optimally connected, defining a subnetwork that may be utilized for further coarsening.
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Affiliation(s)
- Canan Atilgan
- Faculty of Engineering and Natural Sciences, Sabanci University, 34956 Istanbul, Turkey
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50
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Pires DEV, de Melo-Minardi RC, dos Santos MA, da Silveira CH, Santoro MM, Meira W. Cutoff Scanning Matrix (CSM): structural classification and function prediction by protein inter-residue distance patterns. BMC Genomics 2011; 12 Suppl 4:S12. [PMID: 22369665 PMCID: PMC3287581 DOI: 10.1186/1471-2164-12-s4-s12] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Background The unforgiving pace of growth of available biological data has increased the demand for efficient and scalable paradigms, models and methodologies for automatic annotation. In this paper, we present a novel structure-based protein function prediction and structural classification method: Cutoff Scanning Matrix (CSM). CSM generates feature vectors that represent distance patterns between protein residues. These feature vectors are then used as evidence for classification. Singular value decomposition is used as a preprocessing step to reduce dimensionality and noise. The aspect of protein function considered in the present work is enzyme activity. A series of experiments was performed on datasets based on Enzyme Commission (EC) numbers and mechanistically different enzyme superfamilies as well as other datasets derived from SCOP release 1.75. Results CSM was able to achieve a precision of up to 99% after SVD preprocessing for a database derived from manually curated protein superfamilies and up to 95% for a dataset of the 950 most-populated EC numbers. Moreover, we conducted experiments to verify our ability to assign SCOP class, superfamily, family and fold to protein domains. An experiment using the whole set of domains found in last SCOP version yielded high levels of precision and recall (up to 95%). Finally, we compared our structural classification results with those in the literature to place this work into context. Our method was capable of significantly improving the recall of a previous study while preserving a compatible precision level. Conclusions We showed that the patterns derived from CSMs could effectively be used to predict protein function and thus help with automatic function annotation. We also demonstrated that our method is effective in structural classification tasks. These facts reinforce the idea that the pattern of inter-residue distances is an important component of family structural signatures. Furthermore, singular value decomposition provided a consistent increase in precision and recall, which makes it an important preprocessing step when dealing with noisy data.
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Affiliation(s)
- Douglas E V Pires
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil.
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