1
|
Shin HK. Topological Distance-Based Electron Interaction Tensor to Apply a Convolutional Neural Network on Drug-like Compounds. ACS OMEGA 2021; 6:35757-35768. [PMID: 34984306 PMCID: PMC8717557 DOI: 10.1021/acsomega.1c05693] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/08/2021] [Indexed: 05/15/2023]
Abstract
Deep learning (DL) models in quantitative structure-activity relationship fed the molecular structure directly to the network without using human-designed descriptors by representing molecule as a graph or string (e.g., SMILES code). However, these two representations were oversimplification of real molecules to reflect chemical properties of molecular structures. Given that the choice of molecular representation determines the architecture of the DL model to apply, a novel way of molecular representation can open a way to apply diverse DL networks developed and used in other fields. A topological distance-based electron interaction (TDEi) tensor has been developed in this study inspired by the quantum mechanical model of the molecule, which defines a molecule with electrons and protons. In the TDEi tensor, the atomic orbital (AO) of each atom is represented by an electron configuration (EC) vector, which is a bit string based on the presence and absence of electrons in each AO according to spin indicated by positive and negative signs. Interactions between EC vectors were calculated based on the topological distance between atoms in a molecule. As a molecular structure was translated into 3D array, CNN models (modified VGGNet) were applied using a TDEi tensor to predict four physicochemical properties of drug-like compound datasets: MP (275,131), Lipop (4193), Esol (1127), and Freesolv (639). Models achieved good prediction accuracy. PCA showed that a stronger correlation was observed between the extracted features and the target endpoint as features were extracted from the deeper layer.
Collapse
Affiliation(s)
- Hyun Kil Shin
- Department
of Predictive Toxicology, Korea Institute
of Toxicology, Daejeon 34114, Republic of Korea
- Human
and Environmental Toxicology, University
of Science and Technology, Daejeon 34113, Republic of Korea
| |
Collapse
|
2
|
Bo W, Chen L, Qin D, Geng S, Li J, Mei H, Li B, Liang G. Application of quantitative structure-activity relationship to food-derived peptides: Methods, situations, challenges and prospects. Trends Food Sci Technol 2021. [DOI: 10.1016/j.tifs.2021.05.031] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
3
|
Mahmoodi-Reihani M, Abbasitabar F, Zare-Shahabadi V. In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling. ACS OMEGA 2020; 5:5951-5958. [PMID: 32226875 PMCID: PMC7097998 DOI: 10.1021/acsomega.9b04302] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 02/27/2020] [Indexed: 05/15/2023]
Abstract
Predicting the bioactivity of peptides is an important challenge in drug development and peptide research. In this study, numerical descriptive vectors (NDVs) for peptide sequences were calculated based on the physicochemical properties of amino acids (AAs) and principal component analysis (PCA). The resulted NDV had the same length as the peptide sequence, so that each entry of NDV corresponded to one AA in the sequence. They were then applied to quantitative structure-activity relationship (QSAR) analysis of angiotensin-converting enzyme (ACE) inhibitor dipeptides, bitter-tasting dipeptides, and nonameric binding peptides of the human leukocyte antigens (HLA-A*0201). Multiple linear regression was used to construct the QSAR models. For each peptide set, a proper subset of physicochemical properties was chosen by the ant colony optimization algorithm. The leave-one-out cross-validation (q loo 2) values were 0.855, 0.936, and 0.642 and the root-mean-square errors (RMSEs) were 0.450, 0.149, and 0.461. Our results revealed that the new numerical descriptive vector can afford extensive characterization of peptide sequence so that it can be easily employed in peptide QSAR studies. Moreover, the proposed numerical descriptive vectors were able to determine hot spot residues in the peptides under study.
Collapse
Affiliation(s)
| | - Fatemeh Abbasitabar
- Department
of Chemistry, Marvdasht Branch, Islamic
Azad University, Marvdasht, Iran
| | - Vahid Zare-Shahabadi
- Department
of Chemistry, Mahshahr Branch, Islamic Azad
University, Mahshahr Iran
| |
Collapse
|
4
|
Ge C, Zhang W, He R, Cai H. Systematic Identification and Comparative Analysis of Human Cartilage-Derived Self-peptides Presented Differently by Ankylosing Spondylitis (AS)-Associated HLA-B*27:05 and Non-AS-associated HLA-B*27:09. Int J Pept Res Ther 2020. [DOI: 10.1007/s10989-019-09857-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
5
|
Quantitative sequence-activity modeling of ACE peptide originated from milk using ACC-QTMS amino acid indices. Amino Acids 2019; 51:1209-1220. [PMID: 31321559 DOI: 10.1007/s00726-019-02761-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 07/05/2019] [Indexed: 01/06/2023]
Abstract
Up to now, numerous peptides/hydrolysates derived from casein and whey protein have shown angiotensin-I-converting enzyme (ACE) inhibitory. In this research, quantum topological molecular similarity (QTMS) indices of amino acids were utilized in quantitative sequence-activity modeling (QSAM) to predict the activity of a set of milk-driven peptides with ACE inhibition. Since the derived peptides have not the same number of residues, we overcame this issue by auto cross covariance (ACC) methodology. Then, some QSAMs were built to predict the pIC50 value of ACE peptides derived from Bovine Casein and Whey. The model established an acceptable relationship between the selected variables and the pIC50 of the peptides. To estimate the performance of the developed models, casein and whey proteins from human, goat, bovine and sheep were virtually broken by trypsin and chymotrypsin enzymes and the ACE activity of the resultant virtual peptides were predicted and some new ACE peptides were proposed.
Collapse
|
6
|
Ahmadi R, Hemmateenejad B, Safavi A, Shojaeifard Z, Mohabbati M, Firuzi O. Assessment of cytotoxicity of choline chloride-based natural deep eutectic solvents against human HEK-293 cells: A QSAR analysis. CHEMOSPHERE 2018; 209:831-838. [PMID: 30114731 DOI: 10.1016/j.chemosphere.2018.06.103] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 06/13/2018] [Accepted: 06/14/2018] [Indexed: 06/08/2023]
Abstract
Deep eutectic solvents (DESs) are a new generation of solvents. To consider them as green solvents, investigation of their toxicity is essential. In this work, the cytotoxicity of a number of natural deep eutectic solvents (NADESs) against HEK-293 human embryonic kidney cells was evaluated by MTT assay. The NADESs were prepared with choline chloride (ChCl) as hydrogen-bond acceptor (HBA) and different sugar alcohols as hydrogen-bond donor (HBD) constituents. They showed IC50 values in the range of 3.52-75.46 mM. These results were used to evaluate the effect of structural parameters on the cytotoxicity of the studied NADESs by using quantitative structure activity relationship (QSAR) analysis. A three-parameter linear model was obtained between - log(IC50) as a dependent variable and structural descriptors as independent variables. Rotatable bond number (RBN), mean atomic van der Waals volume (Mv) and the interaction of second power carbon numbers with the molar ratio of HBA to HBD in each NADES (C2 Ratio), were three major parameters. The statistical model covered about 76.4% and 69.8% variance of data in training and leave-one-out cross-validation, respectively. This work, as the first study on the QSAR analysis of DESs, can provide a good perspective for designing greener novel DESs.
Collapse
Affiliation(s)
- Raheleh Ahmadi
- Department of Chemistry, College of Sciences, Shiraz University, Shiraz, 7194684795, Iran
| | - Bahram Hemmateenejad
- Department of Chemistry, College of Sciences, Shiraz University, Shiraz, 7194684795, Iran; Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Afsaneh Safavi
- Department of Chemistry, College of Sciences, Shiraz University, Shiraz, 7194684795, Iran.
| | - Zahra Shojaeifard
- Department of Chemistry, College of Sciences, Shiraz University, Shiraz, 7194684795, Iran
| | - Maryam Mohabbati
- Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Omidreza Firuzi
- Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| |
Collapse
|
7
|
Pourbasheer E, Ahmadpour S, Zare-Dorabei R, Nekoei M. Quantitative structure activity relationship study of p38α MAP kinase inhibitors. ARAB J CHEM 2017. [DOI: 10.1016/j.arabjc.2013.05.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
|
8
|
Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. An Introduction to the Basic Concepts in QSAR-Aided Drug Design. Oncology 2017. [DOI: 10.4018/978-1-5225-0549-5.ch002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.
Collapse
Affiliation(s)
| | | | - Siavoush Dastmalchi
- Biotechnology Research Center, Tabriz University of Medical Sciences, Iran & School of Pharmacy, Tabriz University of Medical Sciences, Iran
| |
Collapse
|
9
|
Comprehensive comparison of twenty structural characterization scales applied as QSAM of antimicrobial dodecapeptides derived from Bac2A against P. aeruginosa. J Mol Graph Model 2017; 71:88-95. [DOI: 10.1016/j.jmgm.2016.11.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2015] [Revised: 11/02/2016] [Accepted: 11/06/2016] [Indexed: 02/04/2023]
|
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
|
|
12
|
Nekoeinia M, Yousefinejad S, Abdollahi-Dezaki A. Prediction of ETN Polarity Scale of Ionic Liquids Using a QSPR Approach. Ind Eng Chem Res 2015. [DOI: 10.1021/acs.iecr.5b02982] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Mohsen Nekoeinia
- Department
of Chemistry, Payame Noor University, P.O. BOX 19395-3697, Tehran, Iran
| | | | | |
Collapse
|
13
|
Structure–electrochemistry relationship in non-aqueous solutions: Predicting the reduction potential of anthraquinones derivatives in some organic solvents. J Mol Liq 2015. [DOI: 10.1016/j.molliq.2015.08.055] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
14
|
Variable selection in multivariate calibration based on clustering of variable concept. Anal Chim Acta 2015; 902:70-81. [PMID: 26703255 DOI: 10.1016/j.aca.2015.11.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2015] [Revised: 10/31/2015] [Accepted: 11/04/2015] [Indexed: 11/22/2022]
Abstract
Recently we have proposed a new variable selection algorithm, based on clustering of variable concept (CLoVA) in classification problem. With the same idea, this new concept has been applied to a regression problem and then the obtained results have been compared with conventional variable selection strategies for PLS. The basic idea behind the clustering of variable is that, the instrument channels are clustered into different clusters via clustering algorithms. Then, the spectral data of each cluster are subjected to PLS regression. Different real data sets (Cargill corn, Biscuit dough, ACE QSAR, Soy, and Tablet) have been used to evaluate the influence of the clustering of variables on the prediction performances of PLS. Almost in the all cases, the statistical parameter especially in prediction error shows the superiority of CLoVA-PLS respect to other variable selection strategies. Finally the synergy clustering of variable (sCLoVA-PLS), which is used the combination of cluster, has been proposed as an efficient and modification of CLoVA algorithm. The obtained statistical parameter indicates that variable clustering can split useful part from redundant ones, and then based on informative cluster; stable model can be reached.
Collapse
|
15
|
Yousefinejad S, Honarasa F, Saeed N. Quantitative structure-retardation factor relationship of protein amino acids in different solvent mixtures for normal-phase thin-layer chromatography. J Sep Sci 2015; 38:1771-6. [DOI: 10.1002/jssc.201401427] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Revised: 02/12/2015] [Accepted: 02/13/2015] [Indexed: 11/10/2022]
Affiliation(s)
- Saeed Yousefinejad
- Department of Chemistry; Shiraz University; Shiraz Iran
- Department of Chemistry; Farhangian University; Tehran Iran
| | - Fatemeh Honarasa
- Department of Chemistry, Shiraz Branch; Islamic Azad University; Shiraz Iran
| | - Negar Saeed
- Department of Chemistry, Shiraz Branch; Islamic Azad University; Shiraz Iran
| |
Collapse
|
16
|
Hamzeh-Mivehroud M, Sokouti B, Dastmalchi S. An Introduction to the Basic Concepts in QSAR-Aided Drug Design. QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS IN DRUG DESIGN, PREDICTIVE TOXICOLOGY, AND RISK ASSESSMENT 2015. [DOI: 10.4018/978-1-4666-8136-1.ch001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.
Collapse
Affiliation(s)
- Maryam Hamzeh-Mivehroud
- Biotechnology Research Center & School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Babak Sokouti
- Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Siavoush Dastmalchi
- Biotechnology Research Center & School of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
| |
Collapse
|
17
|
Quantitative sequence–activity modeling of antimicrobial hexapeptides using a segmented principal component strategy: an approach to describe and predict activities of peptide drugs containing l/d and unnatural residues. Amino Acids 2014; 47:125-34. [DOI: 10.1007/s00726-014-1850-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2014] [Accepted: 10/03/2014] [Indexed: 12/20/2022]
|
18
|
Dai Z, Wang L, Chen Y, Wang H, Bai L, Yuan Z. A pipeline for improved QSAR analysis of peptides: physiochemical property parameter selection via BMSF, near-neighbor sample selection via semivariogram, and weighted SVR regression and prediction. Amino Acids 2014; 46:1105-19. [PMID: 24468929 DOI: 10.1007/s00726-014-1667-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2013] [Accepted: 01/03/2014] [Indexed: 12/21/2022]
Abstract
In this paper, we present a pipeline to perform improved QSAR analysis of peptides. The modeling involves a double selection procedure that first performs feature selection and then conducts sample selection before the final regression analysis. Five hundred and thirty-one physicochemical property parameters of amino acids were used as descriptors to characterize the structure of peptides. These high-dimensional descriptors then go through a feature selection process given by the binary matrix shuffling filter (BMSF) to obtain a set of important low-dimensional features. Each descriptor that passes the BMSF filtering also receives a weight defined through its contribution to reduce the estimation error. These selected features served as the predictors for subsequent sample selection and modeling. Based on the weighted Euclidean distances between samples, a common range was determined with high-dimensional semivariogram and then used as a threshold to select the near-neighbor samples from the training set. For each sample to be predicted, the QSAR model was established using SVR with the weighted, selected features based on the exclusive set of near-neighbor training samples. Prediction was conducted for each test sample accordingly. The performances of this pipeline are tested with the QSAR analysis of angiotensin-converting enzyme inhibitors and HLA-A*0201 data sets. Improved prediction accuracy was obtained in both applications. This pipeline can optimize the QSAR modeling from both the feature selection and sample selection perspectives. This leads to improved accuracy over single selection methods. We expect this pipeline to have extensive application prospect in the field of regression prediction.
Collapse
Affiliation(s)
- Zhijun Dai
- Hunan Provincial Key Laboratory for Germplasm Innovation and Utilization of Crop, Hunan Agricultural University, Changsha, China,
| | | | | | | | | | | |
Collapse
|
19
|
Yousefinejad S, Hemmateenejad B. A chemometrics approach to predict the dispersibility of graphene in various liquid phases using theoretical descriptors and solvent empirical parameters. Colloids Surf A Physicochem Eng Asp 2014. [DOI: 10.1016/j.colsurfa.2013.03.020] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
|
20
|
Borkar MR, Pissurlenkar RRS, Coutinho EC. HomoSAR: Bridging comparative protein modeling with quantitative structural activity relationship to design new peptides. J Comput Chem 2013; 34:2635-46. [DOI: 10.1002/jcc.23436] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2013] [Revised: 08/17/2013] [Accepted: 08/21/2013] [Indexed: 12/19/2022]
Affiliation(s)
- Mahesh R. Borkar
- Department of Pharmaceutical Chemistry; Bombay College of Pharmacy; Kalina, Santacruz (East) Mumbai 400098 India
| | - Raghuvir R. S. Pissurlenkar
- Department of Pharmaceutical Chemistry; Bombay College of Pharmacy; Kalina, Santacruz (East) Mumbai 400098 India
| | - Evans C. Coutinho
- Department of Pharmaceutical Chemistry; Bombay College of Pharmacy; Kalina, Santacruz (East) Mumbai 400098 India
| |
Collapse
|
21
|
Saethang T, Hirose O, Kimkong I, Tran VA, Dang XT, Nguyen LAT, Le TKT, Kubo M, Yamada Y, Satou K. PAAQD: Predicting immunogenicity of MHC class I binding peptides using amino acid pairwise contact potentials and quantum topological molecular similarity descriptors. J Immunol Methods 2012; 387:293-302. [PMID: 23058674 DOI: 10.1016/j.jim.2012.09.016] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2012] [Accepted: 09/17/2012] [Indexed: 12/11/2022]
Abstract
Prediction of peptide immunogenicity is a promising approach for novel vaccine discovery. Conventionally, epitope prediction methods have been developed to accelerate the process of vaccine production by searching for candidate peptides from pathogenic proteins. However, recent studies revealed that peptides with high binding affinity to major histocompatibility complex molecules (MHCs) do not always result in high immunogenicity. Therefore, it is promising to predict the peptide immunogenicity rather than epitopes in order to discover new vaccines more effectively. To this end, we developed a novel T-cell reactivity predictor which we call PAAQD. Nonapeptides were encoded numerically, using combining information of amino acid pairwise contact potentials (AAPPs) and quantum topological molecular similarity (QTMS) descriptors. Encoded data were used in the construction of our classification model. Our numerical experiments suggested that the predictive performance of PAAQD is at least comparable with POPISK, one of the pioneering techniques for T-cell reactivity prediction. Also, our experiment suggested that the first and eighth positions of nonapeptides are the most important for immunogenicity and most of the anchor residues in epitope prediction were not important in T-cell reactivity prediction. The R implementation of PAAQD is available at http://pirun.ku.ac.th/~fsciiok/PAAQD.rar.
Collapse
Affiliation(s)
- Thammakorn Saethang
- Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan.
| | | | | | | | | | | | | | | | | | | |
Collapse
|
22
|
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]
|
23
|
Toropov AA, Toropova AP, Rasulev BF, Benfenati E, Gini G, Leszczynska D, Leszczynski J. Coral: QSPR modeling of rate constants of reactions between organic aromatic pollutants and hydroxyl radical. J Comput Chem 2012; 33:1902-6. [DOI: 10.1002/jcc.23022] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2012] [Revised: 04/12/2012] [Accepted: 04/16/2012] [Indexed: 12/18/2022]
|
24
|
Toropova AP, Toropov AA, Rasulev BF, Benfenati E, Gini G, Leszczynska D, Leszczynski J. QSAR models for ACE-inhibitor activity of tri-peptides based on representation of the molecular structure by graph of atomic orbitals and SMILES. Struct Chem 2012. [DOI: 10.1007/s11224-012-9996-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
25
|
New autocorrelation QTMS-based descriptors for use in QSAM of peptides. JOURNAL OF THE IRANIAN CHEMICAL SOCIETY 2012. [DOI: 10.1007/s13738-012-0070-y] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|