Computational Methods for Protein Structure Prediction and Its Application in Drug Design

Toomula, Nishant

doi:10.4172/jpb.1000203

Cited by 7 publications

(5 citation statements)

References 75 publications

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“…For Physio-chemical characterization, theoretical Isoelectric Point (pI), molecular weight, total number of positive and negative residues, extinction coefficient, instability index, aliphatic index and grand average of hydropathy (GRAVY) were computed using the Expasy Protparm server (Pramanik et al, 2017;Akilesh et al, 2012). Primary structure analysis is important in understanding the biochemical and cellular function of protein (Toomula et al, 2011). C. Secondary structure prediction SOPMA (Self Optimized Prediction Method with Alignment) was used for the 2˚ structure prediction.…”

Section: B Primary Structure Predictionmentioning

confidence: 99%

“…Neural network method (PHD) is the basis of this tool. In this method, the algorithm first searches the database for the homologous proteins, then filters the close homologues and finally submit the sequence, alignment data for structure prediction (Toomula et al, 2011). Secondary structure prediction is important for identifying the conformational changes in the target protein (Roy et al, 2015).…”

Section: B Primary Structure Predictionmentioning

confidence: 99%

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Structure Prediction of 30s Ribosomal S3, RNA Polymerase II Proteins of Phoenix Pusilla using Bioinformatics Tools

Bharathi¹,

Anuradha²

2020

JSR

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Proteins have different functions like defense action, structural function, transport action, intracellular signalling etc. As proteins are important in many ways its composition analysis, structure and function determination is required. Protein structure prediction is necessary for drug designing, protein engineering and prediction of binding sites. Phoenix pusilla proteins primary structure prediction was done with Expasy protoparam, secondary structure with SOSUI, tertiary structure with swiss model and transmembrane region was identified with TMHMM v 2.0. Secondary structure prediction of 30SRibosomal protein S3 and RNA polymerase II, showed that α-helix, random coil, β-turn and extended strand predominates. Transmembrane region prediction showed that both the proteins were soluble form. As RNA polymerase II of Phoenix pusilla has only 27aminoacid residues, 3D structure has to be predicted with other logarithmic tool and so 30Sribosomal protein 3D structure was predicted with Swiss model server.

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Section: B Primary Structure Predictionmentioning

confidence: 99%

Section: B Primary Structure Predictionmentioning

confidence: 99%

Structure Prediction of 30s Ribosomal S3, RNA Polymerase II Proteins of Phoenix Pusilla using Bioinformatics Tools

Bharathi¹,

Anuradha²

2020

JSR

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“…Analisis struktur primer atau urutan asam amino memberikan wawasan mengenai struktur protein, sehingga dapat memberikan kemudahan dalam memahami fungsi biokimia maupun fungsi seluler [16]. Hasil analisis sifat fisikokimia diantaranya berupa komposisi asam amino penyusun protein ROR-1 dapat dijadikan sebagai acuan dalam menentukan sifat fisiko-kimia protein ROR-1, serta nilai dari beberapa parameter fisikokimia protein ROR-1.…”

Section: Analisis Sifat Fisikokimiaunclassified

Pemodelan Homologi Protein Receptor Orphan Receptor-1 (ROR-1) Sebagai Target Terapi Chronic Lymphocytic Leukemia (CLL

2019

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Receptor Orphan Reseptor-1 (ROR-1) is a trans-membrane protein consists of 937 amino acid residues located on 1p31.3 chomosome. ROR-1 has an important role in leukemogenesis of CLL cells. Co-expressions of ROR-1 and TCL enhance leukemogenesis and caused AKT’s phosphorylation, cell’s proliferations, and resistances to apoptosis. This study had done physicochemical characteristics analysis, secondary structure and tertiary structure prediction of ROR-1 Protein using few types of bioinformation tools. The physicochemical characteristics analysis was done by using Expasy Protparam server and the prediction of secondary structure was using Sopma and Psipred. The prediction of ROR-1’s tertiary structure was done by using Modeller 9.19 software and resulting five ROR-1’s tertiary structures predicted model. The accuration of the results then evaluated using Ramachandran’s plot analysis and it was showed Model 3 is the best models wich percentage is 82%.Keywords: ROR-1, Choronic Lymphocytic Leukemia (CLL), homology modeling, Modeller, Ramachandran plot

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“…Second, as happens almost systematically in homology model construction, the multiple sequence alignments used for selecting templates cannot be 100% ascertained because of gaps and insertions, especially with superfamily members characterized by a low sequence identity (< 40%), even around the active site, and even when multiple templates are used and class-specific information (position-specific sequence conservation) is implemented in the sequence alignment [ 76 , 96 , 97 ]. Third, despite elaborate algorithms using conformational propensities derived from X-ray structures, it is difficult to accurately assert which residue side chain conformations are physically relevant for each particular ligand-receptor association and the positions of the residue side chain lining the active site of a homology constructed model remains questionable [ 79 , 98 – 100 ]. Fourth, the possible effect due to the presence of mediating water molecules and/or counter ions inside the active site is difficult to predict, except for the few conserved (tightly bound/structural) examples that are inferred from X-ray structures [ 101 ].…”

Section: Different Types Of Ligand-receptor Complexes and Different Cmentioning

confidence: 99%

Current Progress in Structure-Based Rational Drug Design Marks a New Mindset in Drug Discovery

Lounnas

Ritschel

Kelder³

et al. 2013

Computational and Structural Biotechnology Journal

190

123

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The past decade has witnessed a paradigm shift in preclinical drug discovery with structure-based drug design (SBDD) making a comeback while high-throughput screening (HTS) methods have continued to generate disappointing results. There is a deficit of information between identified hits and the many criteria that must be fulfilled in parallel to convert them into preclinical candidates that have a real chance to become a drug. This gap can be bridged by investigating the interactions between the ligands and their receptors. Accurate calculations of the free energy of binding are still elusive; however progresses were made with respect to how one may deal with the versatile role of water. A corpus of knowledge combining X-ray structures, bioinformatics and molecular modeling techniques now allows drug designers to routinely produce receptor homology models of increasing quality. These models serve as a basis to establish and validate efficient rationales used to tailor and/or screen virtual libraries with enhanced chances of obtaining hits. Many case reports of successful SBDD show how synergy can be gained from the combined use of several techniques. The role of SBDD with respect to two different classes of widely investigated pharmaceutical targets: (a) protein kinases (PK) and (b) G-protein coupled receptors (GPCR) is discussed. Throughout these examples prototypical situations covering the current possibilities and limitations of SBDD are presented.

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Computational Methods for Protein Structure Prediction and Its Application in Drug Design

Cited by 7 publications

References 75 publications

Structure Prediction of 30s Ribosomal S3, RNA Polymerase II Proteins of Phoenix Pusilla using Bioinformatics Tools

Structure Prediction of 30s Ribosomal S3, RNA Polymerase II Proteins of Phoenix Pusilla using Bioinformatics Tools

Pemodelan Homologi Protein Receptor Orphan Receptor-1 (ROR-1) Sebagai Target Terapi Chronic Lymphocytic Leukemia (CLL

Current Progress in Structure-Based Rational Drug Design Marks a New Mindset in Drug Discovery

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