1
|
Qader SW, Ozdemir M, Benjamin I, Chima CM, Suvitha A, Rani JC, Gber TE, Kothandan G. Toxicity, Pharmacokinetic Profile, and Compound-Protein Interaction Study of Polygonum minus Huds Extract. Appl Biochem Biotechnol 2024; 196:2425-2450. [PMID: 37129743 DOI: 10.1007/s12010-023-04499-6] [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] [Accepted: 04/11/2023] [Indexed: 05/03/2023]
Abstract
Several phytochemicals with potential for bioactivity can be found in Polygonum minus (PM). The goal of this investigation was to establish the minimally toxic dose of PM for pharmaceutical use. To explain the stability and reactivity of the compounds under study, the lowest unoccupied molecular orbital (LUMO), the highest occupied molecular orbital (HOMO), and the natural bond orbital were all combined. Additionally, the cytotoxicity of the aqueous and ethanolic extract of PM on the (Hs888Lu) cell line was determined using the MTS Assay Kit (cell proliferation) (colorimetric). The hematological, hepatic, and renal functions were examined during the acute toxicity test on Sprague Dawley rats. SwissADME and ADMET were used to investigate the absorption, distribution, metabolism, excretion, and toxicity (ADMET) of the chemicals isolated from PM, including gallic acid, quercetin, rutin, and coumaric acid (PMCs). Molecular docking was used to examine the inhibitory effect against human H+/K+ ATPase, cyclooxygenase-2, and acetylcholinesterase. The outcomes indicated that neither the aqueous nor the ethanolic extract of PM is harmful. The development of plant-based medicine was made possible by the phenolic chemicals, primarily quercetin and rutin, which exhibit a considerable binding affinity to human H+/K+ ATPase, cyclooxygenase-2, and acetylcholinesterase.
Collapse
Affiliation(s)
- Suhailah Wasman Qader
- Department of Medical Laboratory Science, Knowledge University, 44002, Erbil, Kurdistan Region, Iraq.
| | - Mehmet Ozdemir
- Department of Dentistry, Faculty of Dentistry, Tishk International University, 44002, Erbil, Kurdistan Region, Iraq
| | - Innocent Benjamin
- Computational and Bio-Simulation Research Group, University of Calabar, Calabar, Nigeria.
| | - Chioma M Chima
- Computational and Bio-Simulation Research Group, University of Calabar, Calabar, Nigeria
| | - A Suvitha
- Department of Physics, CMR Institute of Technology, Bengaluru, 560037, Karnataka, India
| | - Jaquline Chinna Rani
- Department of Plant Biology and Biotechnology, Loyola College, Chennai, Tamil Nadu, India
| | - Terkumbur E Gber
- Computational and Bio-Simulation Research Group, University of Calabar, Calabar, Nigeria
| | - Gugan Kothandan
- Biopolymer Modeling and Protein Chemistry Laboratory, CAS in Crystallography and Biophysics, University of Madras, Chennai, Tamil Nadu, India
| |
Collapse
|
2
|
Vivek P, Rekha M, Suvitha A, Kowsalya M, Steephen A. Diamond morphology CuO nanomaterial’s elastic properties, ADMET, optical, structural studies, electrical conductivity and antibacterial activities analysis. INORG NANO-MET CHEM 2022. [DOI: 10.1080/24701556.2022.2046610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- P. Vivek
- Department of Physics, Sri Sankara Arts & Science College (Autonomous), Kanchipuram, India
| | - M. Rekha
- School of Electrical Engineering, Department of Energy and Power Electronics, Vellore Institute of Technology, Vellore, India
- Department of Instrumentation and Control Engineering (Autonomous), Sri Manakula Vinayagar Engineering College, Puducherry, India
| | - A. Suvitha
- Department of Physics, CMR Institute of technology, Bengaluru, India
| | - M. Kowsalya
- School of Electrical Engineering, Department of Energy and Power Electronics, Vellore Institute of Technology, Vellore, India
| | - Ananth Steephen
- Department of Physics, KPR Institute of Engineering and Technology (Autonomous), Coimbatore, India
| |
Collapse
|
3
|
Ojha PK, Kumar V, Roy J, Roy K. Recent advances in quantitative structure-activity relationship models of antimalarial drugs. Expert Opin Drug Discov 2021; 16:659-695. [PMID: 33356651 DOI: 10.1080/17460441.2021.1866535] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Due to emerging resistance to the first-line artemisinin-based antimalarials and lack of efficient vaccines and limited chemotherapeutic alternatives, there is an urgent need to develop new antimalarial compounds. In this regard, quantitative structure-activity relationship (QSAR) modeling can provide essential information about required physicochemical properties and structural parameters of antimalarial drug candidates. AREAS COVERED The authors provide an overview of recent advances of QSAR models covering different classes of antimalarial compounds as well as molecular docking studies of compounds acting on different antimalarial targets reported in the last 5 years (2015-2019) to explore the mode of interactions between the molecules and the receptors. We have tried to cover most of the QSAR models of antimalarials (along with results from some other related computational methods) reported during 2015-2019. EXPERT OPINION Many QSAR reports for antimalarial compounds are based on small number of data points. This review infers that most of the present work deals with analog-based QSAR approach with a limited applicability domain (a very few cases with wide domain) whereas novel target-based computational approach is reported in very few cases, which leads to huge voids of computational work based on novel antimalarial targets.
Collapse
Affiliation(s)
- Probir Kumar Ojha
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Vinay Kumar
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Joyita Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| |
Collapse
|
4
|
Shiri F, Bakhshayesh S, Ghasemi JB. Computer-aided molecular design of (E)-N-Aryl-2-ethene-sulfonamide analogues as microtubule targeted agents in prostate cancer. ARAB J CHEM 2019. [DOI: 10.1016/j.arabjc.2014.11.063] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
|
5
|
Wu J, Mai G, Deng B, Younseo J, Du D, Chen F, Ma Q. Quantitative Structure-activity Relationship of Acetylcholinesterase Inhibitors based on mRMR Combined with Support Vector Regression. LETT ORG CHEM 2019. [DOI: 10.2174/1570178615666181008125341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
In this work, support vector regression (SVR), an effective machine learning method, proposed by Vapnik was applied to establish QSAR model for a series of AchEI. Fourteen descriptors were selected for constructing the SVR mode by using mRMR-Forward feature selection method. The parameters (ε, C) were adjusted by leave-one-out cross validation (LOOCV) method which was used to judge the predictive power of different models. After optimization, one optimal SVR-QSAR model was attained, and the mean relative errors (MRE) of LOOCV by using SVR is 1.72%. As a result, LogP negatively affected the activity, Refractivity and Water Accessible Surface Area positively affected the activity.
Collapse
Affiliation(s)
- Jiaxiang Wu
- Shanghai Key Laboratory of Bio-Crops, College of Life Science, Shanghai University, Shanghai, China
| | - Guozhao Mai
- Department of Rehabilitation Medicine, The People's Hospital of Heshan, Guangdong, China
| | - Bowen Deng
- Shanghai Key Laboratory of Bio-Crops, College of Life Science, Shanghai University, Shanghai, China
| | - Jeong Younseo
- Center for Bioinformatics and Computational Biology, Pai Chai University, Daejeon, South Korea
| | - Dongsu Du
- Shanghai Key Laboratory of Bio-Crops, College of Life Science, Shanghai University, Shanghai, China
| | - Fuxue Chen
- Shanghai Key Laboratory of Bio-Crops, College of Life Science, Shanghai University, Shanghai, China
| | - Qiaorong Ma
- Department of Clinical Laboratory, Minzu Hospital of Guangxi Zhuang Autonomous Region, Affiliated Minzu Hospital of Guangxi Medical University, Nanning, Guangxi, China
| |
Collapse
|
6
|
Wang MY, Liang JW, Olounfeh KM, Sun Q, Zhao N, Meng FH. A Comprehensive In Silico Method to Study the QSTR of the Aconitine Alkaloids for Designing Novel Drugs. Molecules 2018; 23:E2385. [PMID: 30231506 PMCID: PMC6225272 DOI: 10.3390/molecules23092385] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Revised: 09/11/2018] [Accepted: 09/12/2018] [Indexed: 12/22/2022] Open
Abstract
A combined in silico method was developed to predict potential protein targets that are involved in cardiotoxicity induced by aconitine alkaloids and to study the quantitative structure⁻toxicity relationship (QSTR) of these compounds. For the prediction research, a Protein-Protein Interaction (PPI) network was built from the extraction of useful information about protein interactions connected with aconitine cardiotoxicity, based on nearly a decade of literature and the STRING database. The software Cytoscape and the PharmMapper server were utilized to screen for essential proteins in the constructed network. The Calcium-Calmodulin-Dependent Protein Kinase II alpha (CAMK2A) and gamma (CAMK2G) were identified as potential targets. To obtain a deeper insight on the relationship between the toxicity and the structure of aconitine alkaloids, the present study utilized QSAR models built in Sybyl software that possess internal robustness and external high predictions. The molecular dynamics simulation carried out here have demonstrated that aconitine alkaloids possess binding stability for the receptor CAMK2G. In conclusion, this comprehensive method will serve as a tool for following a structural modification of the aconitine alkaloids and lead to a better insight into the cardiotoxicity induced by the compounds that have similar structures to its derivatives.
Collapse
Affiliation(s)
- Ming-Yang Wang
- School of Pharmacy, China Medical University, Shenyang 110122, Liaoning, China.
| | - Jing-Wei Liang
- School of Pharmacy, China Medical University, Shenyang 110122, Liaoning, China.
| | | | - Qi Sun
- School of Pharmacy, China Medical University, Shenyang 110122, Liaoning, China.
| | - Nan Zhao
- School of Pharmacy, China Medical University, Shenyang 110122, Liaoning, China.
| | - Fan-Hao Meng
- School of Pharmacy, China Medical University, Shenyang 110122, Liaoning, China.
| |
Collapse
|
7
|
Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression. Oncotarget 2018; 8:49359-49369. [PMID: 28467816 PMCID: PMC5564774 DOI: 10.18632/oncotarget.17210] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Accepted: 03/30/2017] [Indexed: 01/24/2023] Open
Abstract
Toxicity evaluation is an extremely important process during drug development. It is usually initiated by experiments on animals, which is time-consuming and costly. To speed up such a process, a quantitative structure-activity relationship (QSAR) study was performed to develop a computational model for correlating the structures of 581 aromatic compounds with their aquatic toxicity to tetrahymena pyriformis. A set of 68 molecular descriptors derived solely from the structures of the aromatic compounds were calculated based on Gaussian 03, HyperChem 7.5, and TSAR V3.3. A comprehensive feature selection method, minimum Redundancy Maximum Relevance (mRMR)-genetic algorithm (GA)-support vector regression (SVR) method, was applied to select the best descriptor subset in QSAR analysis. The SVR method was employed to model the toxicity potency from a training set of 500 compounds. Five-fold cross-validation method was used to optimize the parameters of SVR model. The new SVR model was tested on an independent dataset of 81 compounds. Both high internal consistent and external predictive rates were obtained, indicating the SVR model is very promising to become an effective tool for fast detecting the toxicity.
Collapse
|
8
|
Patil RB, Barbosa EG, Sangshetti JN, Sawant SD, Zambre VP. 3D-QSAR with R: A new 3D-QSAR methodology applied to a set of DGAT1 inhibitors [corrected]. Comput Biol Chem 2018; 74:123-131. [PMID: 29602042 DOI: 10.1016/j.compbiolchem.2018.02.021] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 02/23/2018] [Accepted: 02/25/2018] [Indexed: 12/21/2022]
Abstract
The rapid advances in computational methods for the drug design have resulted in the accurate predictions of biological activities of ligands with or without the availability of enzyme structures. 3D-QSAR is one of the computational methods used for such purpose. Currently, freely available 3D-QSAR methods suffer the limitations like complex methodologies, difficulty in the analysis of results, applying the statistical methods and validations of models built. Present work describes simple and novel 3D-QSAR methodology, which uses bash scripts LQTA_R_LJ, LQTA_R_QQ and LQTA_R_HB using freely available R statistical program. These scripts then generate Leenard-Jones, Coulomb and Hydrogen bond descriptors. These descriptors provide the steric 3D property, electrostatic property and hydrogen bond formation capacity respectively. These scripts have been tested for the set of DGAT1 inhibitors and results showed that the 3D-QSAR models built have better predictive abilities in terms of R2 0.735, Q2loo 0.635 and R2ext 0.715. The 3D-QSAR model suggested that the substitutions of the alkyl group at the oxadiazolyl ring at the 6th position of the pyrrolo-pyridazine ring is undesirable, on the contrary, substituted phenyl ring at 7th position is responsible for the improved DGAT1 inhibitory activity. The analysis also suggested that 6th position could be substituted with the oxadiazolyl ring or analogous heterocyclic rings, where the 3rd position of such heterocyclic rings substituted with rigid hydrophobic substitute can improve DGAT1 activity.
Collapse
Affiliation(s)
- Rajesh B Patil
- Department of Pharmaceutical Chemistry, Sinhgad Technical Education Society's, Smt. Kashibai Navale College of Pharmacy, Pune-Saswad Road, Kondhwa (Bk.), Pune, 411048, Maharashtra, India.
| | - Euzebio G Barbosa
- Chemistry Institute, University of Campinas (UNICAMP), POB 6154, Campinas, SP, 13083-970, Brazil
| | - Jaiprakash N Sangshetti
- Department of Pharmaceutical Chemistry, Y. B. Chavan College of Pharmacy, Dr. Rafiq Zakaria Campus, Aurangabad, 431001, Maharashtra, India
| | - Sanjay D Sawant
- Department of Pharmaceutical Chemistry, Sinhgad Technical Education Society's, Smt. Kashibai Navale College of Pharmacy, Pune-Saswad Road, Kondhwa (Bk.), Pune, 411048, Maharashtra, India
| | - Vishal P Zambre
- Department of Pharmaceutical Chemistry, Sinhgad Technical Education Society's, Smt. Kashibai Navale College of Pharmacy, Pune-Saswad Road, Kondhwa (Bk.), Pune, 411048, Maharashtra, India
| |
Collapse
|
9
|
Du QS, Wang SQ, Xie NZ, Wang QY, Huang RB, Chou KC. 2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications. Oncotarget 2017; 8:70564-70578. [PMID: 29050302 PMCID: PMC5642577 DOI: 10.18632/oncotarget.19757] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Accepted: 06/30/2017] [Indexed: 01/25/2023] Open
Abstract
A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development.
Collapse
Affiliation(s)
- Qi-Shi Du
- State Key Laboratory of China for Biomass Energy Enzyme Technology, National Engineering Research Center of China for Non-Food Biorefinery, Guangxi Academy of Sciences, Nanning 530007, China
- Gordon Life Science Institute, Boston, MA 02478, USA
| | - Shu-Qing Wang
- School of Pharmacy, Tianjin Medical University, Tianjin 300070, China
| | - Neng-Zhong Xie
- State Key Laboratory of China for Biomass Energy Enzyme Technology, National Engineering Research Center of China for Non-Food Biorefinery, Guangxi Academy of Sciences, Nanning 530007, China
| | - Qing-Yan Wang
- State Key Laboratory of China for Biomass Energy Enzyme Technology, National Engineering Research Center of China for Non-Food Biorefinery, Guangxi Academy of Sciences, Nanning 530007, China
| | - Ri-Bo Huang
- State Key Laboratory of China for Biomass Energy Enzyme Technology, National Engineering Research Center of China for Non-Food Biorefinery, Guangxi Academy of Sciences, Nanning 530007, China
| | - Kuo-Chen Chou
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
- Gordon Life Science Institute, Boston, MA 02478, USA
| |
Collapse
|
10
|
Tong J, Li L, Bai M, Li K. A New Descriptor of Amino Acids-SVGER and its Applications in Peptide QSAR. Mol Inform 2016; 36. [PMID: 27739658 DOI: 10.1002/minf.201501023] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Accepted: 09/27/2016] [Indexed: 11/10/2022]
Abstract
In the study of peptide quantitative structure activity relationship (QSAR), a new descriptor of amino acids (SVGER) was calculated. It was applied in two peptides which are angiotensin converting enzyme inhibitors and bitter tasting threshold of di-peptide. QSAR models were built by stepwise multiple regression-multiple linear regression (SMR-MLR) and stepwise multiple regression-partial least square regression (SMR-PLS). In the SMR-MLR models for angiotensin converting enzyme inhibitors, the squared cross-validation correlation coefficient (QLOO2 ) was 0.907, squared correlation coefficient between predicted and observed activities (Rcum2 ) was 0.977 and external multiple correlation coefficient (Qext2 ) was 0.867. The corresponding data for the bitter tasting threshold of di-peptide were 0.802, 0.966, 0.719. While in the SMR-PLS model, QLOO2 , Rcum2 and Qext2 were 0.804, 0.915, 0.858 for angiotensin converting enzyme inhibitors and 0.782, 0.881, 0.747 for bitter tasting threshold of di-peptide. Our results showed that descriptor SVGER can afford good account of relationships between activity and structure of peptide drugs.
Collapse
Affiliation(s)
- Jianbo Tong
- Shaanxi University of Science & Technology, Xi'an, PR China
| | - Lingxiao Li
- Shaanxi University of Science & Technology, Xi'an, PR China
| | - Min Bai
- Shaanxi University of Science & Technology, Xi'an, PR China
| | - Kangnan Li
- Shaanxi University of Science & Technology, Xi'an, PR China
| |
Collapse
|
11
|
Thillainayagam M, Anbarasu A, Ramaiah S. Comparative molecular field analysis and molecular docking studies on novel aryl chalcone derivatives against an important drug target cysteine protease in Plasmodium falciparum. J Theor Biol 2016; 403:110-128. [DOI: 10.1016/j.jtbi.2016.05.019] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Revised: 05/03/2016] [Accepted: 05/10/2016] [Indexed: 01/08/2023]
|
12
|
Vázquez-Prieto S, Paniagua E, Ubeira FM, González-Díaz H. QSPR-Perturbation Models for the Prediction of B-Epitopes from Immune Epitope Database: A Potentially Valuable Route for Predicting “In Silico” New Optimal Peptide Sequences and/or Boundary Conditions for Vaccine Development. Int J Pept Res Ther 2016. [DOI: 10.1007/s10989-016-9524-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
13
|
Leal FD, da Silva Lima CH, de Alencastro RB, Castro HC, Rodrigues CR, Albuquerque MG. Hologram QSAR models of a series of 6-arylquinazolin-4-amine inhibitors of a new Alzheimer's disease target: dual specificity tyrosine-phosphorylation-regulated kinase-1A enzyme. Int J Mol Sci 2015; 16:5235-53. [PMID: 25756379 PMCID: PMC4394473 DOI: 10.3390/ijms16035235] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2014] [Revised: 02/05/2015] [Accepted: 02/10/2015] [Indexed: 12/29/2022] Open
Abstract
Dual specificity tyrosine-phosphorylation-regulated kinase-1A (DYRK1A) is an enzyme directly involved in Alzheimer's disease, since its increased expression leads to β-amyloidosis, Tau protein aggregation, and subsequent formation of neurofibrillary tangles. Hologram quantitative structure-activity relationship (HQSAR, 2D fragment-based) models were developed for a series of 6-arylquinazolin-4-amine inhibitors (36 training, 10 test) of DYRK1A. The best HQSAR model (q2 = 0.757; SEcv = 0.493; R2 = 0.937; SE = 0.251; R2pred = 0.659) presents high goodness-of-fit (R2 > 0.9), as well as high internal (q2 > 0.7) and external (R2pred > 0.5) predictive power. The fragments that increase and decrease the biological activity values were addressed using the colored atomic contribution maps provided by the method. The HQSAR contribution map of the best model is an important tool to understand the activity profiles of new derivatives and may provide information for further design of novel DYRK1A inhibitors.
Collapse
Affiliation(s)
- Felipe Dias Leal
- Instituto de Química, Laboratório de Modelagem Molecular (LabMMol), Universidade Federal do Rio de Janeiro (UFRJ), 21949-900 Rio de Janeiro, RJ, Brazil.
| | - Camilo Henrique da Silva Lima
- Instituto de Química, Laboratório de Modelagem Molecular (LabMMol), Universidade Federal do Rio de Janeiro (UFRJ), 21949-900 Rio de Janeiro, RJ, Brazil.
| | - Ricardo Bicca de Alencastro
- Instituto de Química, Laboratório de Modelagem Molecular (LabMMol), Universidade Federal do Rio de Janeiro (UFRJ), 21949-900 Rio de Janeiro, RJ, Brazil.
| | - Helena Carla Castro
- Instituto de Biologia, Laboratório de Antibióticos, Bioquímica, Ensino e Modelagem Molecular (LABiEMol), Universidade Federal Fluminense (UFF), 24210-130 Niterói, RJ, Brazil.
| | - Carlos Rangel Rodrigues
- Faculdade de Farmácia, Laboratório de Modelagem Molecular & 3D-QSAR (ModMolQSAR), Universidade Federal do Rio de Janeiro (UFRJ), 21941-590 Rio de Janeiro, RJ, Brazil.
| | - Magaly Girão Albuquerque
- Instituto de Química, Laboratório de Modelagem Molecular (LabMMol), Universidade Federal do Rio de Janeiro (UFRJ), 21949-900 Rio de Janeiro, RJ, Brazil.
| |
Collapse
|
14
|
Correction: Xie, H.; et al. 3D QSAR studies, pharmacophore modeling and virtual screening on a series of steroidal aromatase inhibitors. Int. J. Mol. Sci. 2014, 15, 20927-20947. Int J Mol Sci 2015; 16:5072-5. [PMID: 25751723 PMCID: PMC4394465 DOI: 10.3390/ijms16035072] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2015] [Revised: 02/16/2015] [Accepted: 02/18/2015] [Indexed: 11/17/2022] Open
|
15
|
3D hydrophobic moment vectors as a tool to characterize the surface polarity of amphiphilic peptides. Biophys J 2015; 106:2385-94. [PMID: 24896117 DOI: 10.1016/j.bpj.2014.04.020] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2013] [Revised: 04/02/2014] [Accepted: 04/07/2014] [Indexed: 11/22/2022] Open
Abstract
The interaction of membranes with peptides and proteins is largely determined by their amphiphilic character. Hydrophobic moments of helical segments are commonly derived from their two-dimensional helical wheel projections, and the same is true for β-sheets. However, to the best of our knowledge, there exists no method to describe structures in three dimensions or molecules with irregular shape. Here, we define the hydrophobic moment of a molecule as a vector in three dimensions by evaluating the surface distribution of all hydrophilic and lipophilic regions over any given shape. The electrostatic potential on the molecular surface is calculated based on the atomic point charges. The resulting hydrophobic moment vector is specific for the instantaneous conformation, and it takes into account all structural characteristics of the molecule, e.g., partial unfolding, bending, and side-chain torsion angles. Extended all-atom molecular dynamics simulations are then used to calculate the equilibrium hydrophobic moments for two antimicrobial peptides, gramicidin S and PGLa, under different conditions. We show that their effective hydrophobic moment vectors reflect the distribution of polar and nonpolar patches on the molecular surface and the calculated electrostatic surface potential. A comparison of simulations in solution and in lipid membranes shows how the peptides undergo internal conformational rearrangement upon binding to the bilayer surface. A good correlation with solid-state NMR data indicates that the hydrophobic moment vector can be used to predict the membrane binding geometry of peptides. This method is available as a web application on http://www.ibg.kit.edu/HM/.
Collapse
|
16
|
3D QSAR studies, pharmacophore modeling and virtual screening on a series of steroidal aromatase inhibitors. Int J Mol Sci 2014; 15:20927-47. [PMID: 25405729 PMCID: PMC4264204 DOI: 10.3390/ijms151120927] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2014] [Revised: 09/28/2014] [Accepted: 10/22/2014] [Indexed: 12/12/2022] Open
Abstract
Aromatase inhibitors are the most important targets in treatment of estrogen-dependent cancers. In order to search for potent steroidal aromatase inhibitors (SAIs) with lower side effects and overcome cellular resistance, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of SAIs to build 3D QSAR models. The reliable and predictive CoMFA and CoMSIA models were obtained with statistical results (CoMFA: q2 = 0.636, r2ncv = 0.988, r2pred = 0.658; CoMSIA: q2 = 0.843, r2ncv = 0.989, r2pred = 0.601). This 3D QSAR approach provides significant insights that can be used to develop novel and potent SAIs. In addition, Genetic algorithm with linear assignment of hypermolecular alignment of database (GALAHAD) was used to derive 3D pharmacophore models. The selected pharmacophore model contains two acceptor atoms and four hydrophobic centers, which was used as a 3D query for virtual screening against NCI2000 database. Six hit compounds were obtained and their biological activities were further predicted by the CoMFA and CoMSIA models, which are expected to design potent and novel SAIs.
Collapse
|
17
|
Lin TH, Tsai TL. Constructing a linear QSAR for some metabolizable drugs by human or pig flavin-containing monooxygenases using some molecular features selected by a genetic algorithm trained SVM. J Theor Biol 2014; 356:85-97. [DOI: 10.1016/j.jtbi.2014.04.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2013] [Revised: 04/01/2014] [Accepted: 04/16/2014] [Indexed: 10/25/2022]
|
18
|
Munteanu CR, Pedreira N, Dorado J, Pazos A, Pérez-Montoto LG, Ubeira FM, González-Díaz H. LECTINPred: web Server that Uses Complex Networks of Protein Structure for Prediction of Lectins with Potential Use as Cancer Biomarkers or in Parasite Vaccine Design. Mol Inform 2014; 33:276-85. [DOI: 10.1002/minf.201300027] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2013] [Accepted: 12/11/2014] [Indexed: 01/05/2023]
|
19
|
Mondal C, Halder AK, Adhikari N, Jha T. Cholesteryl ester transfer protein inhibitors in coronary heart disease: Validated comparative QSAR modeling of N, N-disubstituted trifluoro-3-amino-2-propanols. Comput Biol Med 2013; 43:1545-55. [PMID: 24034746 DOI: 10.1016/j.compbiomed.2013.07.034] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2013] [Revised: 07/25/2013] [Accepted: 07/28/2013] [Indexed: 11/24/2022]
Abstract
Cholesteryl ester transfer protein (CETP) converts high density lipoprotein cholesterol to low density lipoproteins. It is a promising target for treatment of coronary heart disease. Two dimensional quantitative structure activity relationship (2D-QSAR), hologram QSAR (HQSAR) studies and comparative molecular field analysis (CoMFA) as well as comparative molecular similarity analysis (CoMSIA) were performed on 104 CETP inhibitors. The statistical qualities of generated models were justified by internal and external validation, i.e., q(2) and R(2)pred respectively. The best 2D-QSAR model was obtained with q(2) and R(2)pred values of 0.794 and 0.796 respectively. The 2D-QSAR study suggests that unsaturation, branching and van der Waals volumes may play important roles. The HQSAR model showed q(2) and R(2)pred values of 0.628 and 0.550 respectively. Similarly, CoMFA model showed q(2) and R(2)pred values of 0.707 and 0.755 respectively whereas CoMSIA model was obtained with q(2) and R(2)pred values of 0.696 and 0.703 respectively. CoMFA and CoMSIA studies indicate that steric factors are important at substituted phenoxy and tetrafluoroethoxy groups whereas electropositive factors play important role at difluoromethyl group. The results of 3D-QSAR studies validate those of 2D-QSAR and HQSAR studies as well as the earlier observed SAR data. Current work may help to develop better CETP inhibitors.
Collapse
Affiliation(s)
- Chanchal Mondal
- Natural Science Laboratory, Division of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical Technology, P.O. Box-17020, Jadavpur University, Kolkata 700032, India
| | | | | | | |
Collapse
|
20
|
Cao DS, Xu QS, Hu QN, Liang YZ. ChemoPy: freely available python package for computational biology and chemoinformatics. ACTA ACUST UNITED AC 2013; 29:1092-4. [PMID: 23493324 DOI: 10.1093/bioinformatics/btt105] [Citation(s) in RCA: 123] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
MOTIVATION Molecular representation for small molecules has been routinely used in QSAR/SAR, virtual screening, database search, ranking, drug ADME/T prediction and other drug discovery processes. To facilitate extensive studies of drug molecules, we developed a freely available, open-source python package called chemoinformatics in python (ChemoPy) for calculating the commonly used structural and physicochemical features. It computes 16 drug feature groups composed of 19 descriptors that include 1135 descriptor values. In addition, it provides seven types of molecular fingerprint systems for drug molecules, including topological fingerprints, electro-topological state (E-state) fingerprints, MACCS keys, FP4 keys, atom pairs fingerprints, topological torsion fingerprints and Morgan/circular fingerprints. By applying a semi-empirical quantum chemistry program MOPAC, ChemoPy can also compute a large number of 3D molecular descriptors conveniently. AVAILABILITY The python package, ChemoPy, is freely available via http://code.google.com/p/pychem/downloads/list, and it runs on Linux and MS-Windows. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Dong-Sheng Cao
- Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha, P. R. China
| | | | | | | |
Collapse
|
21
|
Characterization of structure–antioxidant activity relationship of peptides in free radical systems using QSAR models: Key sequence positions and their amino acid properties. J Theor Biol 2013; 318:29-43. [DOI: 10.1016/j.jtbi.2012.10.029] [Citation(s) in RCA: 130] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2012] [Revised: 10/21/2012] [Accepted: 10/22/2012] [Indexed: 11/22/2022]
|
22
|
Pirhadi S, Ghasemi JB. Pharmacophore Identification, Molecular Docking, Virtual Screening, and In Silico ADME Studies of Non-Nucleoside Reverse Transcriptase Inhibitors. Mol Inform 2012; 31:856-66. [PMID: 27476739 DOI: 10.1002/minf.201200018] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2012] [Accepted: 11/19/2012] [Indexed: 01/26/2023]
Abstract
Non-nucleoside reverse transcriptase inhibitors (NNRTIs) have gained a definitive place due to their unique antiviral potency, high specificity and low toxicity in antiretroviral combination therapies used to treat HIV. In this study, chemical feature based pharmacophore models of different classes of NNRT inhibitors of HIV-1 have been developed. The best HypoRefine pharmacophore model, Hypo 1, which has the best correlation coefficient (0.95) and the lowest RMS (0.97), contains two hydrogen bond acceptors, one hydrophobic and one ring aromatic feature, as well as four excluded volumes. Hypo 1 was further validated by test set and Fischer validation method. The best pharmacophore model was then utilized as a 3D search query to perform a virtual screening to retrieve potential inhibitors. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five and docking studies by Libdock and Gold methods to refine the retrieved hits. Finally, 7 top ranked compounds based on Gold score fitness function were subjected to in silico ADME studies to investigate for compliance with the standard ranges.
Collapse
Affiliation(s)
- Somayeh Pirhadi
- Chemistry Department, Faculty of Sciences, K. N. Toosi University of Technology, Tehran, Iran fax: +98-21-22853650; tel: +98-21-22850266
| | - Jahan B Ghasemi
- Chemistry Department, Faculty of Sciences, K. N. Toosi University of Technology, Tehran, Iran fax: +98-21-22853650; tel: +98-21-22850266.
| |
Collapse
|
23
|
Fernandez YAD, Pasotti L, Pallavicini P, Hechavarria JMF. Exploiting micelle-driven coordination to evaluate the lipophilicity of molecules. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2012; 28:9930-9943. [PMID: 22655966 DOI: 10.1021/la3012316] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
We present a systematic study based on the calculation of complexation constants between a Zn-complex solubilized in Triton X-100 micellar solutions and a series of linear mono- and dicarboxylic acids, under physiological pH conditions, that allowed the evaluation of the lipophilicity of these molecules. This empirical lipophilicity parameter describes conveniently the partition of organic molecules between hydrophobic microdomains and water. The results can be used to predict the lipophilicity of molecules with similar structure and allows the distinction of intrinsic contributions of the carboxylates and of the methylene groups to the lipophilicity of the molecule.
Collapse
|
24
|
A segmented principal component analysis—regression approach to QSAR study of peptides. J Theor Biol 2012; 305:37-44. [DOI: 10.1016/j.jtbi.2012.03.028] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2011] [Revised: 03/08/2012] [Accepted: 03/26/2012] [Indexed: 12/22/2022]
|
25
|
Du QS, Gao J, Wei YT, Du LQ, Wang SQ, Huang RB. Structure-Based and Multiple Potential Three-Dimensional Quantitative Structure–Activity Relationship (SB-MP-3D-QSAR) for Inhibitor Design. J Chem Inf Model 2012; 52:996-1004. [DOI: 10.1021/ci300066y] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Qi-Shi Du
- State Key
Laboratory of Non-food Biomass Energy and Enzyme Technology, National
Engineering Research Center for Non-food Biorefinery, Guangxi Academy of Sciences, Nanning, Guangxi 530007,
China
- Gordon Life Science Institute, San Diego, California, United States
| | - Jing Gao
- Department of Anesthesiology, The Second Hospital of Tianjin Medical University,
Tianjin 300211, China
| | - Yu-Tuo Wei
- State
Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources,
Life Science and Biotechnology College, Guangxi University, Nanning, Guangxi, 530004, China
| | - Li-Qin Du
- State
Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources,
Life Science and Biotechnology College, Guangxi University, Nanning, Guangxi, 530004, China
| | - Shu-Qing Wang
- Tianjin Key Laboratory on Technologies Enabling Development of Clinical
Therapeutics and Diagnostics (Theranostics), School of Pharmacy, Tianjin Medical University, Tianjin 300070, China
| | - Ri-Bo Huang
- State Key
Laboratory of Non-food Biomass Energy and Enzyme Technology, National
Engineering Research Center for Non-food Biorefinery, Guangxi Academy of Sciences, Nanning, Guangxi 530007,
China
- State
Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources,
Life Science and Biotechnology College, Guangxi University, Nanning, Guangxi, 530004, China
| |
Collapse
|
26
|
Roy K, Mitra I, Kar S, Ojha PK, Das RN, Kabir H. Comparative Studies on Some Metrics for External Validation of QSPR Models. J Chem Inf Model 2012; 52:396-408. [DOI: 10.1021/ci200520g] [Citation(s) in RCA: 350] [Impact Index Per Article: 29.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Affiliation(s)
- Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Division
of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical
Technology, Jadavpur University, Kolkata 700 032, India
| | - Indrani Mitra
- Drug Theoretics and Cheminformatics Laboratory, Division
of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical
Technology, Jadavpur University, Kolkata 700 032, India
| | - Supratik Kar
- Drug Theoretics and Cheminformatics Laboratory, Division
of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical
Technology, Jadavpur University, Kolkata 700 032, India
| | - Probir Kumar Ojha
- Drug Theoretics and Cheminformatics Laboratory, Division
of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical
Technology, Jadavpur University, Kolkata 700 032, India
| | - Rudra Narayan Das
- Drug Theoretics and Cheminformatics Laboratory, Division
of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical
Technology, Jadavpur University, Kolkata 700 032, India
| | - Humayun Kabir
- Drug Theoretics and Cheminformatics Laboratory, Division
of Medicinal and Pharmaceutical Chemistry, Department of Pharmaceutical
Technology, Jadavpur University, Kolkata 700 032, India
| |
Collapse
|
27
|
Arooj M, Thangapandian S, John S, Hwang S, Park JK, Lee KW. 3D QSAR pharmacophore modeling, in silico screening, and density functional theory (DFT) approaches for identification of human chymase inhibitors. Int J Mol Sci 2011; 12:9236-64. [PMID: 22272131 PMCID: PMC3257128 DOI: 10.3390/ijms12129236] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2011] [Revised: 11/18/2011] [Accepted: 11/23/2011] [Indexed: 11/18/2022] Open
Abstract
Human chymase is a very important target for the treatment of cardiovascular diseases. Using a series of theoretical methods like pharmacophore modeling, database screening, molecular docking and Density Functional Theory (DFT) calculations, an investigation for identification of novel chymase inhibitors, and to specify the key factors crucial for the binding and interaction between chymase and inhibitors is performed. A highly correlating (r = 0.942) pharmacophore model (Hypo1) with two hydrogen bond acceptors, and three hydrophobic aromatic features is generated. After successfully validating "Hypo1", it is further applied in database screening. Hit compounds are subjected to various drug-like filtrations and molecular docking studies. Finally, three structurally diverse compounds with high GOLD fitness scores and interactions with key active site amino acids are identified as potent chymase hits. Moreover, DFT study is performed which confirms very clear trends between electronic properties and inhibitory activity (IC(50)) data thus successfully validating "Hypo1" by DFT method. Therefore, this research exertion can be helpful in the development of new potent hits for chymase. In addition, the combinational use of docking, orbital energies and molecular electrostatic potential analysis is also demonstrated as a good endeavor to gain an insight into the interaction between chymase and inhibitors.
Collapse
Affiliation(s)
- Mahreen Arooj
- Division of Applied Life Science (BK21 Program), Systems and Synthetic Agrobiotech Center (SSAC), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science(RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Gazwa-dong, Jinju 660-701, Korea; E-Mails: (M.A.); (S.T.); (S.J.); (S.H.)
| | - Sundarapandian Thangapandian
- Division of Applied Life Science (BK21 Program), Systems and Synthetic Agrobiotech Center (SSAC), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science(RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Gazwa-dong, Jinju 660-701, Korea; E-Mails: (M.A.); (S.T.); (S.J.); (S.H.)
| | - Shalini John
- Division of Applied Life Science (BK21 Program), Systems and Synthetic Agrobiotech Center (SSAC), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science(RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Gazwa-dong, Jinju 660-701, Korea; E-Mails: (M.A.); (S.T.); (S.J.); (S.H.)
| | - Swan Hwang
- Division of Applied Life Science (BK21 Program), Systems and Synthetic Agrobiotech Center (SSAC), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science(RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Gazwa-dong, Jinju 660-701, Korea; E-Mails: (M.A.); (S.T.); (S.J.); (S.H.)
| | - Jong Keun Park
- Department of Chemistry Education, Research Institute of Natural Science (RINS), Educational Research Institute, Gyeongsang National University, Jinju 660-701, Korea; E-Mail:
| | - Keun Woo Lee
- Division of Applied Life Science (BK21 Program), Systems and Synthetic Agrobiotech Center (SSAC), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Research Institute of Natural Science(RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Gazwa-dong, Jinju 660-701, Korea; E-Mails: (M.A.); (S.T.); (S.J.); (S.H.)
| |
Collapse
|
28
|
Satpathy R, Ghosh S. In-silico Comparative Study and Quantitative Structure-activity Relationship Analysis of Some Structural and Physiochemical Descriptors of Elvitegravir Analogs. J Young Pharm 2011; 3:246-9. [PMID: 21897667 PMCID: PMC3159281 DOI: 10.4103/0975-1483.83776] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Elvitegravir is a new-generation drug which acts as an integrase inhibitor of the HIV virus. The potential inhibition has been tested from the clinical trial data. Here the work basically deals with the quantitative structure-activity relationship (QSAR) analysis by considering some of the physiochemical descriptors like molecular weight, logP, molar volume, and structural descriptors like Winers index, and molecular topological index of the drug analogs. The descriptors were calculated from the E-Dragon server and the multiple linear regression equation models were built by using Minitab tools. The different combinations of structural and physiochemical descriptors were considered for model derivation. The best three models were chosen by observing high R-Sq value, high F-value and low residual errors. The P values (regression) for the three models indicates the significance of the considered descriptors.The overall results obtained with these model suggest that for this perticular drug the activity is dependent on physiochemical descriptors.
Collapse
Affiliation(s)
- R Satpathy
- Department of Biotechnology, MIRC LAB, MITS Engineering College, Rayagada, Odisha, India
| | | |
Collapse
|
29
|
Huang RB, Du QS, Wang CH, Liao SM, Chou KC. A fast and accurate method for predicting pKa of residues in proteins. Protein Eng Des Sel 2010; 23:35-42. [PMID: 19926592 DOI: 10.1093/protein/gzp067] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Predicting the pH-activities of residues in proteins is an important problem in enzyme engineering and protein design. A novel predictor called 'Pred-pK(a)' was developed based on the physicochemical properties of amino acids and protein 3D structure. The Pred-pK(a) approach considers the influence of all other residues of the protein to predict the pK(a) value of an ionizable residue. An empirical equation was formulated, in which the pK(a) value was a distance-dependent function of physicochemical parameters of 20 amino acid types, describing their electrostatic and van der Waals interaction, as well as the effects of hydrogen bonds and solvation. Two sets of coefficients, {a(alpha)} and {b(l)}, were used in the predictor: {a(alpha)} is the weight factors of 20 amino acid types and {b(l)} is the weight factors of physicochemical properties of amino acids. An iterative double least square procedure was proposed to solve the two sets of weight factors alternately and iteratively in a training set. The two coefficient sets {a(alpha)} and {b(l)} thus obtained were used to predict the pK(a) values of residues in a protein. The average predictive error is +/-0.6 pH in less than a minute in common personal computer.
Collapse
Affiliation(s)
- Ri-Bo Huang
- Guangxi Academy of Sciences, 98 Daling Road, Nanning, Guangxi 530004, People's Republic of China
| | | | | | | | | |
Collapse
|
30
|
Pontiki E, Hadjipavlou-Litina D. Histone deacetylase inhibitors (HDACIs). Structure--activity relationships: history and new QSAR perspectives. Med Res Rev 2010; 32:1-165. [PMID: 20162725 DOI: 10.1002/med.20200] [Citation(s) in RCA: 63] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Histone deacetylase (HDAC) inhibition is a recent, clinically validated therapeutic strategy for cancer treatment. HDAC inhibitors (HDACIs) block angiogenesis, arrest cell growth, and lead to differentiation and apoptosis in tumor cells. In this article, a survey of published quantitative structure-activity relationships (QSARs) studies are presented and discussed in the hope of identifying the structural determinants for anticancer activity. Secondly a two-dimensional QSAR study was carried out on biological results derived from various types of HDACIs and from different assays using the C-QSAR program of Biobyte. The QSAR analysis presented here is an attempt to organize the knowledge on the HDACIs with the purpose of designing new chemical entities with enhanced inhibitory potencies and to study the mechanism of action of the compounds. This study revealed that lipophilicity is one of the most important determinants of activity. Additionally, steric factors such as the overall molar refractivity (CMR), molar volume (MgVol), the substituent's molar refractivity (MR) (linear or parabola), or the sterimol parameters B(1) and L are important. Electronic parameters indicated as σ(p), are found to be present only in one case.
Collapse
Affiliation(s)
- Eleni Pontiki
- Department of Pharmaceutical Chemistry, School of Pharmacy, Aristotelian University of Thessaloniki, Thessaloniki 54124, Greece.
| | | |
Collapse
|
31
|
Xi L, Du J, Li S, Li J, Liu H, Yao X. A combined molecular modeling study on gelatinases and their potent inhibitors. J Comput Chem 2010; 31:24-42. [DOI: 10.1002/jcc.21279] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
|
32
|
A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer. J Theor Biol 2009; 261:449-58. [DOI: 10.1016/j.jtbi.2009.07.031] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2009] [Revised: 07/20/2009] [Accepted: 07/25/2009] [Indexed: 11/23/2022]
|
33
|
Shine Y, Kikuchi T. Estimation of relative binding free energy based on a free energy variational principle for quantitative structure activity relationship analyses. Chem Phys 2009. [DOI: 10.1016/j.chemphys.2009.09.023] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
34
|
Viña D, Uriarte E, Orallo F, González-Díaz H. Alignment-free prediction of a drug-target complex network based on parameters of drug connectivity and protein sequence of receptors. Mol Pharm 2009; 6:825-35. [PMID: 19281186 DOI: 10.1021/mp800102c] [Citation(s) in RCA: 60] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
There are many drugs described with very different affinity to a large number of receptors. In this work, we selected drug-receptor pairs (DRPs) of affinity/nonaffinity drugs to similar/dissimilar receptors and we represented them as a large network, which may be used to identify drugs that can act on a receptor. Computational chemistry prediction of the biological activity based on quantitative structure-activity relationships (QSAR) substantially increases the potentialities of this kind of networks avoiding time- and resource-consuming experiments. Unfortunately, most QSAR models are unspecific or predict activity against only one receptor. To solve this problem, we developed here a multitarget QSAR (mt-QSAR) classification model. Overall model classification accuracy was 72.25% (1390/1924 compounds) in training, 72.28% (459/635) in cross-validation. Outputs of this mt-QSAR model were used as inputs to construct a network. The observed network has 1735 nodes (DRPs), 1754 edges or pairs of DRPs with similar drug-target affinity (sPDRPs), and low coverage density d = 0.12%. The predicted network has 1735 DRPs, 1857 sPDRPs, and also low coverage density d = 0.12%. After an edge-to-edge comparison (chi-square = 9420.3; p < 0.005), we have demonstrated that the predicted network is significantly similar to the one observed and both have a distribution closer to exponential than to normal.
Collapse
Affiliation(s)
- Dolores Viña
- Department of Organic Chemistry, University of Santiago de Compostela, 15782, Spain
| | | | | | | |
Collapse
|
35
|
González-Díaz H, Dea-Ayuela MA, Pérez-Montoto LG, Prado-Prado FJ, Agüero-Chapín G, Bolas-Fernández F, Vazquez-Padrón RI, Ubeira FM. QSAR for RNases and theoretic-experimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein. Mol Divers 2009; 14:349-69. [PMID: 19578942 PMCID: PMC7088557 DOI: 10.1007/s11030-009-9178-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2009] [Accepted: 06/13/2009] [Indexed: 11/29/2022]
Abstract
The toxicity and low success of current treatments for Leishmaniosis determines the search of new peptide drugs and/or molecular targets in Leishmania pathogen species (L. infantum and L. major). For example, Ribonucleases (RNases) are enzymes relevant to several biologic processes; then, theoretical and experimental study of the molecular diversity of Peptide Mass Fingerprints (PMFs) of RNases is useful for drug design. This study introduces a methodology that combines QSAR models, 2D-Electrophoresis (2D-E), MALDI-TOF Mass Spectroscopy (MS), BLAST alignment, and Molecular Dynamics (MD) to explore PMFs of RNases. We illustrate this approach by investigating for the first time the PMFs of a new protein of L. infantum. Here we report and compare new versus old predictive models for RNases based on Topological Indices (TIs) of Markov Pseudo-Folding Lattices. These group of indices called Pseudo-folding Lattice 2D-TIs include: Spectral moments pi ( k )(x,y), Mean Electrostatic potentials xi ( k )(x,y), and Entropy measures theta ( k )(x,y). The accuracy of the models (training/cross-validation) was as follows: xi ( k )(x,y)-model (96.0%/91.7%)>pi ( k )(x,y)-model (84.7/83.3) > theta ( k )(x,y)-model (66.0/66.7). We also carried out a 2D-E analysis of biological samples of L. infantum promastigotes focusing on a 2D-E gel spot of one unknown protein with M<20, 100 and pI <7. MASCOT search identified 20 proteins with Mowse score >30, but not one >52 (threshold value), the higher value of 42 was for a probable DNA-directed RNA polymerase. However, we determined experimentally the sequence of more than 140 peptides. We used QSAR models to predict RNase scores for these peptides and BLAST alignment to confirm some results. We also calculated 3D-folding TIs based on MD experiments and compared 2D versus 3D-TIs on molecular phylogenetic analysis of the molecular diversity of these peptides. This combined strategy may be of interest in drug development or target identification.
Collapse
Affiliation(s)
- Humberto González-Díaz
- Department of Microbiology and Parasitology, and Department of Organic Chemistry, Faculty of Pharmacy, USC, 15782, Santiago de Compostela, Spain.
| | | | | | | | | | | | | | | |
Collapse
|
36
|
Zhang G, Li H, Fang B. Discriminating acidic and alkaline enzymes using a random forest model with secondary structure amino acid composition. Process Biochem 2009. [DOI: 10.1016/j.procbio.2009.02.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
37
|
Li G, Haney KM, Kellogg GE, Zhang Y. Comparative docking study of anibamine as the first natural product CCR5 antagonist in CCR5 homology models. J Chem Inf Model 2009; 49:120-32. [PMID: 19166361 DOI: 10.1021/ci800356a] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Anibamine, a novel pyridine quaternary alkaloid recently isolated from Aniba sp., has been found to effectively bind to the chemokine receptor CCR5 with an IC(50) at 1 microM in competition with (125)I-gp120, an HIV viral envelope protein binding to CCR5 with high affinity. Since CCR5, a G-protein-coupled receptor, is an essential coreceptor for the human immunodeficiency virus type I (HIV-1) entry to host cells, a CCR5 antagonist that inhibits the cellular entry of HIV-1 provides a new therapy choice for the treatment of HIV. Anibamine provides a novel structural skeleton that is remarkably different from all lead compounds previously identified as CCR5 antagonists. Here, we report comparative docking studies of anibamine with several other known CCR5 antagonists in two CCR5 homology models built based on the crystal structures of bovine rhodopsin and human beta(2)-adrenergic receptor. The binding pocket of anibamine has some common features shared with other high affinity CCR5 antagonists, suggesting that they may bind in similar binding sites and/or modes. At the same time, several unique binding features of anibamine were identified, and it will likely prove beneficial in future molecular design of novel CCR5 antagonists based on the anibamine scaffold.
Collapse
Affiliation(s)
- Guo Li
- Department of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University, Richmond, Virginia 23298-0540, USA
| | | | | | | |
Collapse
|
38
|
Predicting DNA- and RNA-binding proteins from sequences with kernel methods. J Theor Biol 2009; 258:289-93. [PMID: 19490865 DOI: 10.1016/j.jtbi.2009.01.024] [Citation(s) in RCA: 57] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2008] [Revised: 12/08/2008] [Accepted: 01/19/2009] [Indexed: 11/20/2022]
Abstract
In this paper, support vector machines (SVMs) are applied to predict the nucleic-acid-binding proteins. We constructed two classifiers to differentiate DNA/RNA-binding proteins from non-nucleic-acid-binding proteins by using a conjoint triad feature which extract information directly from amino acids sequence of protein. Both self-consistency and jackknife tests show promising results on the protein datasets in which the sequences identity is less than 25%. In the self-consistency test, the predictive accuracy is 90.37% for DNA-binding proteins and 89.70% for RNA-binding proteins. In the jackknife test, the predictive accuracies are 78.93% and 76.75%, respectively. Comparison results show that our method is very competitive by outperforming other previously published sequence-based prediction methods.
Collapse
|
39
|
Du QS, Huang RB, Wei YT, Pang ZW, Du LQ, Chou KC. Fragment-based quantitative structure-activity relationship (FB-QSAR) for fragment-based drug design. J Comput Chem 2009; 30:295-304. [PMID: 18613071 DOI: 10.1002/jcc.21056] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
In cooperation with the fragment-based design a new drug design method, the so-called "fragment-based quantitative structure-activity relationship" (FB-QSAR) is proposed. The essence of the new method is that the molecular framework in a family of drug candidates are divided into several fragments according to their substitutes being investigated. The bioactivities of molecules are correlated with the physicochemical properties of the molecular fragments through two sets of coefficients in the linear free energy equations. One coefficient set is for the physicochemical properties and the other for the weight factors of the molecular fragments. Meanwhile, an iterative double least square (IDLS) technique is developed to solve the two sets of coefficients in a training data set alternately and iteratively. The IDLS technique is a feedback procedure with machine learning ability. The standard Two-dimensional quantitative structure-activity relationship (2D-QSAR) is a special case, in the FB-QSAR, when the whole molecule is treated as one entity. The FB-QSAR approach can remarkably enhance the predictive power and provide more structural insights into rational drug design. As an example, the FB-QSAR is applied to build a predictive model of neuraminidase inhibitors for drug development against H5N1 influenza virus.
Collapse
Affiliation(s)
- Qi-Shi Du
- College of Life Science and Technology, Guangxi University, Nanning, Guangxi, 530004, China.
| | | | | | | | | | | |
Collapse
|
40
|
Yan S, Wu G. Descriptively quantitative relationship between mutatedN-acetylgalactosamine-6-sulfatase and mucopolysaccharidosis IVA. Biopolymers 2009; 92:399-404. [DOI: 10.1002/bip.21205] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
41
|
Sagar S, Kaur M, Dawe A, Seshadri SV, Christoffels A, Schaefer U, Radovanovic A, Bajic VB. DDESC: Dragon database for exploration of sodium channels in human. BMC Genomics 2008; 9:622. [PMID: 19099596 PMCID: PMC2631582 DOI: 10.1186/1471-2164-9-622] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2008] [Accepted: 12/20/2008] [Indexed: 12/03/2022] Open
Abstract
Background Sodium channels are heteromultimeric, integral membrane proteins that belong to a superfamily of ion channels. The mutations in genes encoding for sodium channel proteins have been linked with several inherited genetic disorders such as febrile epilepsy, Brugada syndrome, ventricular fibrillation, long QT syndrome, or channelopathy associated insensitivity to pain. In spite of these significant effects that sodium channel proteins/genes could have on human health, there is no publicly available resource focused on sodium channels that would support exploration of the sodium channel related information. Results We report here Dragon Database for Exploration of Sodium Channels in Human (DDESC), which provides comprehensive information related to sodium channels regarding different entities, such as "genes and proteins", "metabolites and enzymes", "toxins", "chemicals with pharmacological effects", "disease concepts", "human anatomy", "pathways and pathway reactions" and their potential links. DDESC is compiled based on text- and data-mining. It allows users to explore potential associations between different entities related to sodium channels in human, as well as to automatically generate novel hypotheses. Conclusion DDESC is first publicly available resource where the information related to sodium channels in human can be explored at different levels. This database is freely accessible for academic and non-profit users via the worldwide web .
Collapse
Affiliation(s)
- Sunil Sagar
- South African National Bioinformatics Institute, University of the Western Cape, Private Bag- X17, Modderdam Road, Bellville, Cape Town 7535, South Africa.
| | | | | | | | | | | | | | | |
Collapse
|
42
|
Vilar S, González-Díaz H, Santana L, Uriarte E. QSAR model for alignment-free prediction of human breast cancer biomarkers based on electrostatic potentials of protein pseudofolding HP-lattice networks. J Comput Chem 2008; 29:2613-22. [PMID: 18478581 DOI: 10.1002/jcc.21016] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Network theory allows relationships to be established between numerical parameters that describe the molecular structure of genes and proteins and their biological properties. These models can be considered as quantitative structure-activity relationships (QSAR) for biopolymers. The work described here concerns the first QSAR model for 122 proteins that are associated with human breast cancer (HBC), as identified experimentally by Sjöblom et al. (Science 2006, 314, 268) from over 10,000 human proteins. In this study, the 122 proteins related to HBC (HBCp) and a control group of 200 proteins that are not related to HBC (non-HBCp) were forced to fold in an HP lattice network. From these networks a series of electrostatic potential parameters (xi(k)) was calculated to describe each protein numerically. The use of xi(k) as an entry point to linear discriminant analysis led to a QSAR model to discriminate between HBCp and non-HBCp, and this model could help to predict the involvement of a certain gene and/or protein in HBC. In addition, validation procedures were carried out on the model and these included an external prediction series and evaluation of an additional series of 1000 non-HBCp. In all cases good levels of classification were obtained with values above 80%. This study represents the first example of a QSAR model for the computational chemistry inspired search of potential HBC protein biomarkers.
Collapse
Affiliation(s)
- Santiago Vilar
- Unit of Bioinformatics and Connectivity Analysis, Institute of Industrial Pharmacy, and Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, Santiago de Compostela 15782, Spain
| | | | | | | |
Collapse
|
43
|
Prediction of subcellular location apoptosis proteins with ensemble classifier and feature selection. Amino Acids 2008; 38:975-83. [PMID: 19048186 DOI: 10.1007/s00726-008-0209-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2008] [Accepted: 11/03/2008] [Indexed: 10/21/2022]
Abstract
Apoptosis proteins have a central role in the development and the homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. The function of an apoptosis protein is closely related to its subcellular location. It is crucial to develop powerful tools to predict apoptosis protein locations for rapidly increasing gap between the number of known structural proteins and the number of known sequences in protein databank. In this study, amino acids pair compositions with different spaces are used to construct feature sets for representing sample of protein feature selection approach based on binary particle swarm optimization, which is applied to extract effective feature. Ensemble classifier is used as prediction engine, of which the basic classifier is the fuzzy K-nearest neighbor. Each basic classifier is trained with different feature sets. Two datasets often used in prior works are selected to validate the performance of proposed approach. The results obtained by jackknife test are quite encouraging, indicating that the proposed method might become a potentially useful tool for subcellular location of apoptosis protein, or at least can play a complimentary role to the existing methods in the relevant areas. The supplement information and software written in Matlab are available by contacting the corresponding author.
Collapse
|
44
|
Xiao X, Lin WZ. Application of protein grey incidence degree measure to predict protein quaternary structural types. Amino Acids 2008; 37:741-9. [DOI: 10.1007/s00726-008-0212-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2008] [Accepted: 11/10/2008] [Indexed: 10/21/2022]
|
45
|
Huang RB, Du QS, Wei YT, Pang ZW, Wei H, Chou KC. Physics and chemistry-driven artificial neural network for predicting bioactivity of peptides and proteins and their design. J Theor Biol 2008; 256:428-35. [PMID: 18835398 DOI: 10.1016/j.jtbi.2008.08.028] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2008] [Revised: 08/25/2008] [Accepted: 08/25/2008] [Indexed: 10/21/2022]
Abstract
Predicting the bioactivity of peptides and proteins is an important challenge in drug development and protein engineering. In this study we introduce a novel approach, the so-called "physics and chemistry-driven artificial neural network (Phys-Chem ANN)", to deal with such a problem. Unlike the existing ANN approaches, which were designed under the inspiration of biological neural system, the Phys-Chem ANN approach is based on the physical and chemical principles, as well as the structural features of proteins. In the Phys-Chem ANN model the "hidden layers" are no longer virtual "neurons", but real structural units of proteins and peptides. It is a hybridization approach, which combines the linear free energy concept of quantitative structure-activity relationship (QSAR) with the advanced mathematical technique of ANN. The Phys-Chem ANN approach has adopted an iterative and feedback procedure, incorporating both machine-learning and artificial intelligence capabilities. In addition to making more accurate predictions for the bioactivities of proteins and peptides than is possible with the traditional QSAR approach, the Phys-Chem ANN approach can also provide more insights about the relationship between bioactivities and the structures involved than the ANN approach does. As an example of the application of the Phys-Chem ANN approach, a predictive model for the conformational stability of human lysozyme is presented.
Collapse
Affiliation(s)
- Ri-Bo Huang
- Guangxi Academy of Sciences, 98 Daling Road, Nanning, Guangxi 530004, China
| | | | | | | | | | | |
Collapse
|
46
|
Dea-Ayuela MA, Pérez-Castillo Y, Meneses-Marcel A, Ubeira FM, Bolas-Fernández F, Chou KC, González-Díaz H. HP-Lattice QSAR for dynein proteins: experimental proteomics (2D-electrophoresis, mass spectrometry) and theoretic study of a Leishmania infantum sequence. Bioorg Med Chem 2008; 16:7770-6. [PMID: 18662882 DOI: 10.1016/j.bmc.2008.07.023] [Citation(s) in RCA: 48] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2008] [Revised: 06/23/2008] [Accepted: 07/02/2008] [Indexed: 10/21/2022]
Abstract
The toxicity and inefficacy of actual organic drugs against Leishmaniosis justify research projects to find new molecular targets in Leishmania species including Leishmania infantum (L. infantum) and Leishmaniamajor (L. major), both important pathogens. In this sense, quantitative structure-activity relationship (QSAR) methods, which are very useful in Bioorganic and Medicinal Chemistry to discover small-sized drugs, may help to identify not only new drugs but also new drug targets, if we apply them to proteins. Dyneins are important proteins of these parasites governing fundamental processes such as cilia and flagella motion, nuclear migration, organization of the mitotic splinde, and chromosome separation during mitosis. However, despite the interest for them as potential drug targets, so far there has been no report whatsoever on dyneins with QSAR techniques. To the best of our knowledge, we report here the first QSAR for dynein proteins. We used as input the Spectral Moments of a Markov matrix associated to the HP-Lattice Network of the protein sequence. The data contain 411 protein sequences of different species selected by ClustalX to develop a QSAR that correctly discriminates on average between 92.75% and 92.51% of dyneins and other proteins in four different train and cross-validation datasets. We also report a combined experimental and theoretic study of a new dynein sequence in order to illustrate the utility of the model to search for potential drug targets with a practical example. First, we carried out a 2D-electrophoresis analysis of L. infantum biological samples. Next, we excised from 2D-E gels one spot of interest belonging to an unknown protein or protein fragment in the region M<20,200 and pI<4. We used MASCOT search engine to find proteins in the L. major data base with the highest similarity score to the MS of the protein isolated from L. infantum. We used the QSAR model to predict the new sequence as dynein with probability of 99.99% without relying upon alignment. In order to confirm the previous function annotation we predicted the sequences as dynein with BLAST and the omniBLAST tools (96% alignment similarity to dyneins of other species). Using this combined strategy, we have successfully identified L. infantum protein containing dynein heavy chain, and illustrated the potential use of the QSAR model as a complement to alignment tools.
Collapse
|
47
|
Munteanu CR, González-Díaz H, Magalhães AL. Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices. J Theor Biol 2008; 254:476-82. [PMID: 18606172 DOI: 10.1016/j.jtbi.2008.06.003] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2008] [Revised: 05/15/2008] [Accepted: 06/06/2008] [Indexed: 10/21/2022]
Abstract
The huge amount of new proteins that need a fast enzymatic activity characterization creates demands of protein QSAR theoretical models. The protein parameters that can be used for an enzyme/non-enzyme classification includes the simpler indices such as composition, sequence and connectivity, also called topological indices (TIs) and the computationally expensive 3D descriptors. A comparison of the 3D versus lower dimension indices has not been reported with respect to the power of discrimination of proteins according to enzyme action. A set of 966 proteins (enzymes and non-enzymes) whose structural characteristics are provided by PDB/DSSP files was analyzed with Python/Biopython scripts, STATISTICA and Weka. The list of indices includes, but it is not restricted to pure composition indices (residue fractions), DSSP secondary structure protein composition and 3D indices (surface and access). We also used mixed indices such as composition-sequence indices (Chou's pseudo-amino acid compositions or coupling numbers), 3D-composition (surface fractions) and DSSP secondary structure amino acid composition/propensities (obtained with our Prot-2S Web tool). In addition, we extend and test for the first time several classic TIs for the Randic's protein sequence Star graphs using our Sequence to Star Graph (S2SG) Python application. All the indices were processed with general discriminant analysis models (GDA), neural networks (NN) and machine learning (ML) methods and the results are presented versus complexity, average of Shannon's information entropy (Sh) and data/method type. This study compares for the first time all these classes of indices to assess the ratios between model accuracy and indices/model complexity in enzyme/non-enzyme discrimination. The use of different methods and complexity of data shows that one cannot establish a direct relation between the complexity and the accuracy of the model.
Collapse
Affiliation(s)
- Cristian Robert Munteanu
- REQUIMTE/Faculty of Science, Chemistry Department, University of Porto, Porto 4169-007, Portugal.
| | | | | |
Collapse
|
48
|
Prado-Prado FJ, González-Díaz H, de la Vega OM, Ubeira FM, Chou KC. Unified QSAR approach to antimicrobials. Part 3: first multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. Bioorg Med Chem 2008; 16:5871-80. [PMID: 18485714 DOI: 10.1016/j.bmc.2008.04.068] [Citation(s) in RCA: 104] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2008] [Revised: 04/22/2008] [Accepted: 04/25/2008] [Indexed: 10/22/2022]
Abstract
Several pathogen parasite species show different susceptibilities to different antiparasite drugs. Unfortunately, almost all structure-based methods are one-task or one-target Quantitative Structure-Activity Relationships (ot-QSAR) that predict the biological activity of drugs against only one parasite species. Consequently, multi-tasking learning to predict drugs activity against different species by a single model (mt-QSAR) is vitally important. In the two previous works of the present series we reported two single mt-QSAR models in order to predict the antimicrobial activity against different fungal (Bioorg. Med. Chem.2006, 14, 5973-5980) or bacterial species (Bioorg. Med. Chem.2007, 15, 897-902). These mt-QSARs offer a good opportunity (unpractical with ot-QSAR) to construct drug-drug similarity Complex Networks and to map the contribution of sub-structures to function for multiple species. These possibilities were unattended in our previous works. In the present work, we continue this series toward other important direction of chemotherapy (antiparasite drugs) with the development of an mt-QSAR for more than 500 drugs tested in the literature against different parasites. The data were processed by Linear Discriminant Analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 212 out of 244 (87.0%) cases in training series and 207 out of 243 compounds (85.4%) in external validation series. In order to illustrate the performance of the QSAR for the selection of active drugs we carried out an additional virtual screening of antiparasite compounds not used in training or predicting series; the model recognized 97 out of 114 (85.1%) of them. We also give the procedures to construct back-projection maps and to calculate sub-structures contribution to the biological activity. Finally, we used the outputs of the QSAR to construct, by the first time, a multi-species Complex Networks of antiparasite drugs. The network predicted has 380 nodes (compounds), 634 edges (pairs of compounds with similar activity). This network allows us to cluster different compounds and identify on average three known compounds similar to a new query compound according to their profile of biological activity. This is the first attempt to calculate probabilities of antiparasitic action of drugs against different parasites.
Collapse
|
49
|
Ivanciuc O, Braun W. Robust quantitative modeling of peptide binding affinities for MHC molecules using physical-chemical descriptors. Protein Pept Lett 2008; 14:903-16. [PMID: 18045233 DOI: 10.2174/092986607782110257] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Major histocompatibility complex (MHC) molecules bind short peptides resulting from intracellular processing of foreign and self proteins, and present them on the cell surface for recognition by T-cell receptors. We propose a new robust approach to quantitatively model the binding affinities of MHC molecules by quantitative structure-activity relationships (QSAR) that use the physical-chemical amino acid descriptors E1-E5. These QSAR models are robust, sequence-based, and can be used as a fast and reliable filter to predict the MHC binding affinity for large protein databases.
Collapse
Affiliation(s)
- Ovidiu Ivanciuc
- Sealy Center for Structural Biology and Molecular Biophysics, Department of Biochemistry and Molecular Biology, University of Texas Medical Branch, 301 University Boulevard, Galveston, Texas 77555-0857, USA
| | | |
Collapse
|
50
|
Fang Y, Feng Y, Li M. Optimal QSAR Analysis of the Carcinogenic Activity of Aromatic and Heteroaromatic Amines. ACTA ACUST UNITED AC 2007. [DOI: 10.1002/qsar.200710077] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|