• Microbiology and Biotechnology Letters
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• v.47 no.2
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• pp.278-288
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• 2019

In the present investigation, we attempted to design a protocol to develop a hybrid protein with better bioremediation capacity. Using in silico approaches, a Hybrid Open Reading Frame (Hybrid ORF) is developed targeting the genes of microorganisms known for degradation of organophosphates. Out of 21 genes identified through BLAST search, 8 structurally similar genes (opdA, opd, opaA, pte RO, pdeA, parC, mpd and phnE) involved in biodegradation were screened. Gene conservational analysis categorizes these organophosphates degrading 8 genes into 4 super families i.e., Metallo-dependent hydrolases, Lactamase B, MPP and TM_PBP2 superfamily. Hybrid protein structure was modeled using multi-template homology modeling (3S07_A; 99%, 1P9E_A; 98%, 2ZO9_B; 33%, 2DXL_A; 33%) by $Schr{\ddot{o}}dinger$ software suit version 10.4.018. Structural verification of protein models was done using Ramachandran plot, it was showing 96.0% residue in the favored region, which was verified using RAMPAGE. The phosphotriesterase protein was showing the highest structural similarity with hybrid protein having raw score 984. The 5 binding sites of hybrid protein were identified through binding site prediction. The docking study shows that hybrid protein potentially interacts with 10 different organophosphates. The study results indicate that the hybrid protein designed has the capability of degrading a wide range of organophosphate compounds.

• Journal of Integrative Natural Science
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• v.9 no.2
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• pp.136-143
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• 2016

KiSS1-derived peptide receptor, a GPCR protein, binds with the hormone kiss peptin. They are important in the neuroendocrine regulation of reproduction and in the secretion of gonadotrophin-releasing hormone. Thus, analysing the structural features of the receptor becomes important. However, the three dimensional structure of the protein is unavailable. Hence in this study, we have performed the homology modelling of KiSS1-derived peptide receptor with 5 different templates. 30 models were constructed using two platforms - Easymodeller and ITasser. The optimal models were chosen based on the model validation. Two models were selected after validation. The developed models could provide useful for analysing the structural features of KiSS1-derived peptide receptor and their pathophysiological role in various disorders related to them.

• Journal of Integrative Natural Science
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• v.9 no.4
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• pp.234-240
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• 2016

Bradykinin receptor B2, a GPCR protein, binds with the inflammatory mediator hormone bradkynin. It plays an important role in cross-talk between the renin-angiotensin system (RAS) and the kinin-kallikrein system (KKS). Also, it is involved in many processes including vasodilation, edema, smooth muscle spasm and pain fiber stimulation. Hence, studuying the structural features of the receptor becomes important. But the unavailability of the three dimensional structure of the protein makes the analysis difficult. Hence we have performed the homology modelling of Bradykinin receptor B2 with 5 different templates. 25 different homology models were constructed. Two best models were selected based on the model validation. The developed models could be helpful in analysing the structural features of Bradykinin receptor B2 and in pathophysiology of various disorders related to them.

• Journal of Integrative Natural Science
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• v.10 no.1
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• pp.7-13
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• 2017

Neuromedin U receptor 1 is a GPCR protein which binds with the neuropeptide, neuromedin. It is involved in the regulation of feeding and energy homeostasis and related with immune mediated inflammatory diseases like asthma. It plays an important role in maintaining the biological clock and in the regulation of smooth muscle contraction in the gastrointestinal and genitourinary tract. Analysing the structural features of the receptor is crucial in studying the pathophysiology of the diseases related to the receptor important. As the three dimensional structure of the protein is not available, in this study, we have performed the homology modelling of the receptor using 5 different templates. The models were subjected to model validation and two models were selected as optimal. These models could be helpful in analysing the structural features of neuromedin U receptor 1 and their role in disorders related to them.

• Journal of Integrative Natural Science
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• v.10 no.3
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• pp.119-124
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• 2017

Glucagon-like peptide 2 receptor, a GPCR, binds with the glucagon-like peptide, GLP-2 and regulates various metabolic functions in the gastrointestinal tract. It plays an important role in the nutrient homeostasis related to nutrient assimilation by regulating mucosal epithelium. GLP-2 receptor affects the cellular response to external injury, by controlling the intestinal crypt cell proliferation. As they are therapeutically attractive towards diseases related with the gastrointestinal tract, it becomes essential to analyse their structural features to study the pathophysiology of the diseases. As the three dimensional structure of the protein is not available, in this study, we have performed the homology modelling of the receptor based on single- and multiple template modeling. The models were subjected to model validation and a reliable model based on the validation statistics was identified. The predicted model could be useful in studying the structural features of GLP-2 receptor and their role in various diseases related to them.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.6
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• pp.59-72
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• 2012

Since a protein displays its specific functions when disorder region of protein sequence transits to order region with provoking a biological reaction, the separation of disorder region and order region from the sequence data is urgently necessary for predicting three dimensional structure and characteristics of the protein. To classify the disorder and order region efficiently, this paper proposes a classification/prediction method using sequence data while acquiring a non-biased result on a specific characteristics of protein and improving the classification speed. The emerging patterns based EPs-TFP methods utilizes only the essential emerging pattern in which the redundant emerging patterns are removed. This classification method finds the sequence patterns of disorder region, such sequence patterns are frequently shown in disorder region but relatively not frequently in the order region. We expand P-tree and T-tree conceptualized TFP method into a classification/prediction method in order to improve the performance of the proposed algorithm. We used Disprot 4.9 and CASP 7 data to evaluate EPs-TFP technique, the results of order/disorder classification show sensitivity 73.6, specificity 69.51 and accuracy 74.2.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.49-59
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• 2022

In this paper, we designed a new enzyme function prediction model PSCREM based on a study that compared and evaluated CNN and LSTM/GRU models, which are the most widely used deep learning models in the field of predicting functions and structures using protein sequences in 2020, under the same conditions. Sequence evolution information was used to preserve detailed patterns which would miss in CNN convolution, and the relationship information between amino acids with functional significance was extracted through overlapping RNNs. It was referenced to feature map production. The RNN family of algorithms used in small CNN-RNN models are LSTM algorithms and GRU algorithms, which are usually stacked two to three times over 100 units, but in this paper, small RNNs consisting of 10 and 20 units are overlapped. The model used the PSSM profile, which is transformed from protein sequence data. The experiment proved 86.4% the performance for the problem of predicting the main classes of enzyme number, and it was confirmed that the performance was 84.4% accurate up to the sub-sub classes of enzyme number. Thus, PSCREM better identifies unique patterns related to protein function through overlapped RNN, and Overlapped RNN is proposed as a novel methodology for protein function and structure prediction extraction.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.730-736
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• 2009

It is more important to analysis of huge gemonics data in Bioinformatics. Here we present a novel datamining approach to predict structure and function using protein's primnary structure only. We propose not also to develope n-Block substring search algorithm in reducing enormous search space effectively in relation to feature selection, but to formulate weighted linear algorithm in a prediction of structure and function of a protein using primary structure. And we show efficient in protein domain characterization and classification by calculation weight value in determining domain association in each selected substring, and also reveal that more efficient results are acquired through claculated model score result in an inference about degree of association with each CDS(coding sequence) in domain.

• Journal of Microbiology and Biotechnology
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• v.17 no.8
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• pp.1236-1241
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• 2007

Five fusion proteins between Z domains derived from Staphylococcal Protein A and Green Fluorescent Protein or Human Proinsulin were produced on the periplasm of Escherichia coli. The effects of the molecular weight and amino acid composition of the translocated peptide, culture medium composition, and growth phase of the bacterial culture were analyzed regarding the expression and periplasmic secretion of the recombinant proteins. It was found that secretion was not affected by the size of the translocated peptide (17-42 kDa) and that the highest periplasmic production values were obtained on the exponential phase of growth. Moreover, the highest periplasmic values were obtained in minimal medium, showing the relevance of the culture medium composition on secretion. In silico prediction analysis suggested that with respect to the five proteins used in this study, those that are prone to form ${\alpha}$-helix structures are more translocated to the periplasm.

• Journal of the Korean Magnetic Resonance Society
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• v.27 no.3
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• pp.17-22
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• 2023

Bloom syndrome protein (BLM) is a pivotal RecQ helicase necessary for genetic stability through DNA repair processes. Our investigation focuses on the N-terminal region of BLM, which has been considered as an intrinsically disordered region (IDR). This IDR plays a critical role in DNA metabolism by interacting with other proteins. In this study, we performed triple resonance experiments of BLM_220-300 and presented the backbone chemical shifts. The secondary structure prediction based on chemical shifts of the backbone atoms shows the region is disordered. Our data could help further interaction studies between BLM_220-300 and its binding partners using NMR.