Displaying publications 1 - 20 of 538 in total

  1. Abd Aziz NA, Awang N, Chan KM, Kamaludin NF, Mohamad Anuar NN
    Molecules, 2023 Aug 03;28(15).
    PMID: 37570810 DOI: 10.3390/molecules28155841
    Organotin (IV) dithiocarbamate has recently received attention as a therapeutic agent among organotin (IV) compounds. The individual properties of the organotin (IV) and dithiocarbamate moieties in the hybrid complex form a synergy of action that stimulates increased biological activity. Organotin (IV) components have been shown to play a crucial role in cytotoxicity. The biological effects of organotin compounds are believed to be influenced by the number of Sn-C bonds and the number and nature of alkyl or aryl substituents within the organotin structure. Ligands target and react with molecules while preventing unwanted changes in the biomolecules. Organotin (IV) dithiocarbamate compounds have also been shown to have a broad range of cellular, biochemical, and molecular effects, with their toxicity largely determined by their structure. Continuing the investigation of the cytotoxicity of organotin (IV) dithiocarbamates, this mini-review delves into the appropriate method for synthesis and discusses the elemental and spectroscopic analyses and potential cytotoxic effects of these compounds from articles published since 2010.
    Matched MeSH terms: Structure-Activity Relationship
  2. Nugroho AE, Hashimoto A, Wong CP, Yokoe H, Tsubuki M, Kaneda T, et al.
    J Nat Med, 2019 Jun;73(3):682.
    PMID: 30945063 DOI: 10.1007/s11418-019-01301-y
    The article Ceramicines M-P from Chisocheton ceramicus: isolation and structure-activity relationship study.
    Matched MeSH terms: Structure-Activity Relationship
  3. Frimayanti N, Yam ML, Lee HB, Othman R, Zain SM, Rahman NA
    Int J Mol Sci, 2011;12(12):8626-44.
    PMID: 22272096 DOI: 10.3390/ijms12128626
    Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR) method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT) activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA) method. Based on the method, r(2) value, r(2) (CV) value and r(2) prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC(50) values ranging from 0.39 μM to 7.04 μM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r(2) prediction for external test set) of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  4. Algamal ZY, Lee MH, Al-Fakih AM, Aziz M
    SAR QSAR Environ Res, 2016 Sep;27(9):703-19.
    PMID: 27628959 DOI: 10.1080/1062936X.2016.1228696
    In high-dimensional quantitative structure-activity relationship (QSAR) modelling, penalization methods have been a popular choice to simultaneously address molecular descriptor selection and QSAR model estimation. In this study, a penalized linear regression model with L1/2-norm is proposed. Furthermore, the local linear approximation algorithm is utilized to avoid the non-convexity of the proposed method. The potential applicability of the proposed method is tested on several benchmark data sets. Compared with other commonly used penalized methods, the proposed method can not only obtain the best predictive ability, but also provide an easily interpretable QSAR model. In addition, it is noteworthy that the results obtained in terms of applicability domain and Y-randomization test provide an efficient and a robust QSAR model. It is evident from the results that the proposed method may possibly be a promising penalized method in the field of computational chemistry research, especially when the number of molecular descriptors exceeds the number of compounds.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  5. Al-Fakih AM, Algamal ZY, Lee MH, Aziz M
    SAR QSAR Environ Res, 2017 Aug;28(8):691-703.
    PMID: 28976224 DOI: 10.1080/1062936X.2017.1375010
    A robust screening approach and a sparse quantitative structure-retention relationship (QSRR) model for predicting retention indices (RIs) of 169 constituents of essential oils is proposed. The proposed approach is represented in two steps. First, dimension reduction was performed using the proposed modified robust sure independence screening (MR-SIS) method. Second, prediction of RIs was made using the proposed robust sparse QSRR with smoothly clipped absolute deviation (SCAD) penalty (RSQSRR). The RSQSRR model was internally and externally validated based on [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], Y-randomization test, [Formula: see text], [Formula: see text], and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of the RSQSRR for training dataset outperform the other two used modelling methods. The RSQSRR shows the highest [Formula: see text], [Formula: see text], and [Formula: see text], and the lowest [Formula: see text]. For the test dataset, the RSQSRR shows a high external validation value ([Formula: see text]), and a low value of [Formula: see text] compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed RSQSRR is an efficient approach for modelling high dimensional QSRRs and the method is useful for the estimation of RIs of essential oils that have not been experimentally tested.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  6. Alharthi AM, Kadir DH, Al-Fakih AM, Algamal ZY, Al-Thanoon NA, Qasim MK
    SAR QSAR Environ Res, 2023;34(10):831-846.
    PMID: 37885432 DOI: 10.1080/1062936X.2023.2261855
    The horse herd optimization algorithm (HOA), one of the more contemporary metaheuristic algorithms, has demonstrated superior performance in a number of challenging optimization tasks. In the present work, the descriptor selection issue is resolved by classifying different essential oil retention indices using the binary form, BHOA. Based on internal and external prediction criteria, Z-shape transfer functions (ZTF) were tested to verify their efficiency in improving BHOA performance in QSPR modelling for predicting retention indices of essential oils. The evaluation criteria involved the mean-squared error of the training and testing datasets (MSE), and leave-one-out internal and external validation (Q2). The degree of convergence of the proposed Z-shaped transfer functions was compared. In addition, K-fold cross validation with k = 5 was applied. The results show that ZTF, especially ZTF1, greatly improves the performance of the original BHOA. Comparatively speaking, ZTF, especially ZTF1, exhibits the fastest convergence behaviour of the binary algorithms. It chooses the fewest descriptors and requires the fewest iterations to achieve excellent prediction performance.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  7. Borhani TN, Saniedanesh M, Bagheri M, Lim JS
    Water Res, 2016 07 01;98:344-53.
    PMID: 27124124 DOI: 10.1016/j.watres.2016.04.038
    In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  8. Nur Idayu Alimon, Nor Haniza Sarmin, Ahmad Erfanian
    MATEMATIKA, 2019;35(1):51-57.
    Topological indices are numerical values that can be analysed to predict the chemical properties of the molecular structure and the topological indices are computed for a graph related to groups. Meanwhile, the conjugacy class graph of is defined as a graph with a vertex set represented by the non-central conjugacy classes of . Two distinct vertices are connected if they have a common prime divisor. The main objective of this article is to find various topological indices including the Wiener index, the first Zagreb index and the second Zagreb index for the conjugacy class graph of dihedral groups of order where the dihedral group is the group of symmetries of regular polygon, which includes rotations and reflections. Many topological indices have been determined for simple and connected graphs in general but not graphs related to groups. In this article, the Wiener index and Zagreb index of conjugacy class graph of dihedral groups are generalized.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  9. Wongrattanakamon P, Lee VS, Nimmanpipug P, Jiranusornkul S
    Data Brief, 2016 Dec;9:35-42.
    PMID: 27626051 DOI: 10.1016/j.dib.2016.08.004
    The data is obtained from exploring the modulatory activities of bioflavonoids on P-glycoprotein function by ligand-based approaches. Multivariate Linear-QSAR models for predicting the induced/inhibitory activities of the flavonoids were created. Molecular descriptors were initially used as independent variables and a dependent variable was expressed as pFAR. The variables were then used in MLR analysis by stepwise regression calculation to build the linear QSAR data. The entire dataset consisted of 23 bioflavonoids was used as a training set. Regarding the obtained MLR QSAR model, R of 0.963, R (2)=0.927, [Formula: see text], SEE=0.197, F=33.849 and q (2)=0.927 were achieved. The true predictabilities of QSAR model were justified by evaluation with the external dataset (Table 4). The pFARs of representative flavonoids were predicted by MLR QSAR modelling. The data showed that internal and external validations may generate the same conclusion.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  10. Ng CH, Rullah K, Abas F, Lam KW, Ismail IS, Jamaludin F, et al.
    Molecules, 2018 Sep 30;23(10).
    PMID: 30274341 DOI: 10.3390/molecules23102509
    A new series of 2,4,6-trihydroxy-3-geranyl-acetophenone (tHGA) analogues were synthesized and evaluated for their lipoxygenase (LOX) inhibitory activity. Prenylated analogues 4a⁻g (half maximal inhibitory concentration (IC50) values ranging from 35 μ M to 95 μ M) did not exhibit better inhibitory activity than tHGA (3a) (IC50 value: 23.6 μ M) due to the reduction in hydrophobic interaction when the alkyl chain length was reduced. One geranylated analogue, 3d, with an IC50 value of 15.3 μ M, exhibited better LOX inhibitory activity when compared to tHGA (3a), which was in agreement with our previous findings. Kinetics study showed that the most active analogue (3e) and tHGA (3a) acted as competitive inhibitors. The combination of in silico approaches of molecular docking and molecular dynamic simulation revealed that the lipophilic nature of these analogues further enhanced the LOX inhibitory activity. Based on absorption, distribution, metabolism, excretion, and toxicity (ADMET) and toxicity prediction by komputer assisted technology (TOPKAT) analyses, all geranylated analogues (3a⁻g) showed no hepatotoxicity effect and were biodegradable, which indicated that they could be potentially safe drugs for treating inflammation.
    Matched MeSH terms: Structure-Activity Relationship
  11. Shy TW, Gaurav A
    Cent Nerv Syst Agents Med Chem, 2021;21(3):195-204.
    PMID: 34970959 DOI: 10.2174/1871524922666211231115638
    AIM: The aim of the present study was to apply pharmacophore based virtual screening to a natural product database to identify potential PDE1B inhibitor lead compounds for neurodegenerative and neuropsychiatric disorders.

    BACKGROUND: Neurodegenerative and neuropsychiatric disorders are a major health burden globally. The existing therapies do not provide optimal relief and are associated with substantial adverse effects. This has resulted in a huge unmet medical need for newer and more effective therapies for these disorders. Phosphodiesterase (PDEs) enzymes have been identified as potential targets of drugs for neurodegenerative and neuropsychiatric disorders, and one of the subtypes, i.e., PDE1B, accounts for more than 90 % of total brain PDE activity associated with learning and memory process, making it an interesting drug target for the treatment of neurodegenerative disorders.

    OBJECTIVES: The present study has been conducted to identify potential PDE1B inhibitor lead compounds from the natural product database.

    METHODS: Ligand-based pharmacophore models were generated and validated; they were then employed for virtual screening of Universal Natural Products Database (UNPD) followed by docking with PDE1B to identify the best hit compound.

    RESULTS: Virtual screening led to the identification of 85 compounds which were then docked into the active site of PDE1B. Out of the 85 compounds, six showed a higher affinity for PDE1B than the standard PDE1B inhibitors. The top scoring compound was identified as Cedreprenone.

    CONCLUSION: Virtual screening of UNPD using Ligand based pharmacophore led to the identification of Cedreprenone, a potential new natural PDE1B inhibitor lead compound.

    Matched MeSH terms: Quantitative Structure-Activity Relationship
  12. Nugroho AE, Hashimoto A, Wong CP, Yokoe H, Tsubuki M, Kaneda T, et al.
    J Nat Med, 2018 Jan;72(1):64-72.
    PMID: 28822030 DOI: 10.1007/s11418-017-1109-2
    Ceramicines are a series of limonoids which were isolated from the bark of Malaysian Chisocheton ceramicus (Meliaceae) and show various biological activities. Ceramicine B, in particular, has been reported to show a strong lipid droplet accumulation (LDA) inhibitory activity on a mouse pre-adipocyte cell line (MC3T3-G2/PA6). With the purpose of discovering compounds with stronger activity than ceramicine B, we further investigated the constituents of C. ceramicus. As a result, from the bark of C. ceramicus four new ceramicines (ceramicines M-P, 1-4) were isolated, and their structures were determined on the basis of NMR and mass spectroscopic analyses in combination with NMR chemical shift calculations. LDA inhibitory activity of 1-4 was evaluated. Compounds 1-3 showed LDA inhibitory activity, and 3 showed better selectivity than ceramicine B while showing activity at the same order of magnitude as ceramicine B. Since 3, which possess a carbonyl group at C-7, showed better selectivity than 5, which possess a 7α-OH group, while showing activity at the same order of magnitude as 5, we also investigated the effect of the substituent at C-7 by synthesizing several derivatives and evaluating their LDA inhibitory activity. Accordingly, we confirmed the importance of the presence of a 7α-OH group to the LDA inhibitory activity.
    Matched MeSH terms: Structure-Activity Relationship
  13. Abdo A, Salim N, Ahmed A
    J Biomol Screen, 2011 Oct;16(9):1081-8.
    PMID: 21862688 DOI: 10.1177/1087057111416658
    Recently, the use of the Bayesian network as an alternative to existing tools for similarity-based virtual screening has received noticeable attention from researchers in the chemoinformatics field. The main aim of the Bayesian network model is to improve the retrieval effectiveness of similarity-based virtual screening. To this end, different models of the Bayesian network have been developed. In our previous works, the retrieval performance of the Bayesian network was observed to improve significantly when multiple reference structures or fragment weightings were used. In this article, the authors enhance the Bayesian inference network (BIN) using the relevance feedback information. In this approach, a few high-ranking structures of unknown activity were filtered from the outputs of BIN, based on a single active reference structure, to form a set of active reference structures. This set of active reference structures was used in two distinct techniques for carrying out such BIN searching: reweighting the fragments in the reference structures and group fusion techniques. Simulated virtual screening experiments with three MDL Drug Data Report data sets showed that the proposed techniques provide simple ways of enhancing the cost-effectiveness of ligand-based virtual screening searches, especially for higher diversity data sets.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  14. Masand VH, Mahajan DT, Alafeefy AM, Bukhari SN, Elsayed NN
    Eur J Pharm Sci, 2015 Sep 18;77:230-7.
    PMID: 26066412 DOI: 10.1016/j.ejps.2015.06.001
    Multiple separate quantitative structure-activity relationships (QSARs) models were built for the antiproliferative activity of substituted Phenyl 4-(2-Oxoimidazolidin-1-yl)-benzenesulfonates (PIB-SOs). A variety of descriptors were considered for PIB-SOs through QSAR model building. Genetic algorithm (GA), available in QSARINS, was employed to select optimum number and set of descriptors to build the multi-linear regression equations for a dataset of PIB-SOs. The best three parametric models were subjected to thorough internal and external validation along with Y-randomization using QSARINS, according to the OECD principles for QSAR model validation. The models were found to be statistically robust with high external predictivity. The best three parametric model, based on steric, 3D- and finger print descriptors, was found to have R(2)=0.91, R(2)ex=0.89, and CCCex=0.94. The CoMFA model, which is based on a combination of steric and electrostatic effects and graphically inferred using contour plots, gave F=229.34, R(2)CV=0.71 and R(2)=0.94. Steric repulsion, frequency of occurrence of carbon and nitrogen at topological distance of seven, and internal electronic environment of the molecule were found to have correlation with the anti-tumor activity of PIB-SOs.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  15. Alharthi AM, Lee MH, Algamal ZY, Al-Fakih AM
    SAR QSAR Environ Res, 2020 Aug;31(8):571-583.
    PMID: 32628042 DOI: 10.1080/1062936X.2020.1782467
    One of the most challenging issues when facing a Quantitative structure-activity relationship (QSAR) classification model is to deal with the descriptor selection. Penalized methods have been adapted and have gained popularity as a key for simultaneously performing descriptor selection and QSAR classification model estimation. However, penalized methods have drawbacks such as having biases and inconsistencies that make they lack the oracle properties. This paper proposes an adaptive penalized logistic regression (APLR) to overcome these drawbacks. This is done by employing a ratio (BWR) of the descriptors between-groups sum of squares (BSS) to the within-groups sum of squares (WSS) for each descriptor as a weight inside the L1-norm. The proposed method was applied to one dataset that consists of a diverse series of antimicrobial agents with their respective bioactivities against Candida albicans. By experimental study, it has been shown that the proposed method (APLR) was more efficient in the selection of descriptors and classification accuracy than the other competitive methods that could be used in developing QSAR classification models. Another dataset was also successfully experienced. Therefore, it can be concluded that the APLR method had significant impact on QSAR analysis and studies.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  16. Al-Fakih AM, Algamal ZY, Lee MH, Aziz M
    SAR QSAR Environ Res, 2018 May;29(5):339-353.
    PMID: 29493376 DOI: 10.1080/1062936X.2018.1439531
    A penalized quantitative structure-property relationship (QSPR) model with adaptive bridge penalty for predicting the melting points of 92 energetic carbocyclic nitroaromatic compounds is proposed. To ensure the consistency of the descriptor selection of the proposed penalized adaptive bridge (PBridge), we proposed a ridge estimator ([Formula: see text]) as an initial weight in the adaptive bridge penalty. The Bayesian information criterion was applied to ensure the accurate selection of the tuning parameter ([Formula: see text]). The PBridge based model was internally and externally validated based on [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], the Y-randomization test, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of PBridge for the training dataset outperforms the other methods used. PBridge shows the highest [Formula: see text] of 0.959, [Formula: see text] of 0.953, [Formula: see text] of 0.949 and [Formula: see text] of 0.959, and the lowest [Formula: see text] and [Formula: see text]. For the test dataset, PBridge shows a higher [Formula: see text] of 0.945 and [Formula: see text] of 0.948, and a lower [Formula: see text] and [Formula: see text], indicating its better prediction performance. The results clearly reveal that the proposed PBridge is useful for constructing reliable and robust QSPRs for predicting melting points prior to synthesizing new organic compounds.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  17. Mathew B, Ravichandran V, Raghuraman S, Rangarajan TM, Abdelgawad MA, Ahmad I, et al.
    J Biomol Struct Dyn, 2023 Nov;41(19):9256-9266.
    PMID: 36411738 DOI: 10.1080/07391102.2022.2146198
    Candidates generated from unsaturated ketone (chalcone) demonstrated as strong, reversible and specific monoamine oxidase-B (MAO-B) inhibitory activity. For the research on MAO-B inhibition, our team has synthesized and evaluated a panel of aldoxime-chalcone ethers (ACE) and hydroxylchalcones (HC). The MAO-B inhibitory activity of several candidates is in the micro- to nanomolar range in these series. The purpose of this research was to develop predictive QSAR models and look into the relation between MAO-B inhibition by aldoxime and hydroxyl-functionalized chalcones. It was shown that the molecular descriptors ETA Shape P, MDEO-12, ETA dBetaP, SpMax1 Bhi and ETA EtaP B are significant in the inhibitory action of the MAO-B target. Using the current 2D QSAR models, potential chalcone-based MAO-B inhibitors might be created. The lead molecules were further analyzed by the detailed molecular dynamics study to establish the stability of the ligand-enzyme complex.Communicated by Ramaswamy H. Sarma.
    Matched MeSH terms: Structure-Activity Relationship; Quantitative Structure-Activity Relationship
  18. Cheng Z, Hwang SS, Bhave M, Rahman T, Chee Wezen X
    J Chem Inf Model, 2023 Nov 13;63(21):6912-6924.
    PMID: 37883148 DOI: 10.1021/acs.jcim.3c01252
    Polo-like kinase 1 (PLK1) and p38γ mitogen-activated protein kinase (p38γ) play important roles in cancer pathogenesis by controlling cell cycle progression and are therefore attractive cancer targets. The design of multitarget inhibitors may offer synergistic inhibition of distinct targets and reduce the risk of drug-drug interactions to improve the balance between therapeutic efficacy and safety. We combined deep-learning-based quantitative structure-activity relationship (QSAR) modeling and hybrid-based consensus scoring to screen for inhibitors with potential activity against the targeted proteins. Using this combination strategy, we identified a potent PLK1 inhibitor (compound 4) that inhibited PLK1 activity and liver cancer cell growth in the nanomolar range. Next, we deployed both our QSAR models for PLK1 and p38γ on the Enamine compound library to identify dual-targeting inhibitors against PLK1 and p38γ. Likewise, the identified hits were subsequently subjected to hybrid-based consensus scoring. Using this method, we identified a promising compound (compound 14) that could inhibit both PLK1 and p38γ activities. At nanomolar concentrations, compound 14 inhibited the growth of human hepatocellular carcinoma and hepatoblastoma cells in vitro. This study demonstrates the combined screening strategy to identify novel potential inhibitors for existing targets.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  19. Yoon YK, Choon TS
    Arch Pharm (Weinheim), 2016 Jan;349(1):1-8.
    PMID: 26616218 DOI: 10.1002/ardp.201500337
    Benzimidazole derivatives have been shown to possess sirtuin-inhibitory activity. In the continuous search for potent sirtuin inhibitors, systematic changes on the terminal benzene ring were performed on previously identified benzimidazole-based sirtuin inhibitors, to further investigate their structure-activity relationships. It was demonstrated that the sirtuin activities of these novel compounds followed the trend where meta-substituted compounds possessed markedly weaker potency than ortho- and para-substituted compounds, with the exception of halogenated substituents. Molecular docking studies were carried out to rationalize these observations. Apart from this, the methods used to synthesize the interesting compounds are also discussed.
    Matched MeSH terms: Structure-Activity Relationship
  20. Mohamad SB, Ong AL, Ripen AM
    Bioinformation, 2008 Jun 18;2(9):369-72.
    PMID: 18795108
    Laccase belongs to the family of blue multi-copper oxidases and are capable of oxidizing a wide range of aromatic compounds. Laccases have industrial applications in paper pulping or bleaching and hydrocarbon bioremediation as a biocatalyst. We describe the design of a laccase with broader substrate spectrum in bioremediation. The application of evolutionary trace (ET) analysis of laccase at the ligand binding site for optimal design of the enzyme is described. In this attempt, class specific sites from ET analysis were mapped onto known crystal structure of laccase. The analysis revealed 162PHE as a critical residue in structure function relationship studies.
    Matched MeSH terms: Structure-Activity Relationship
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