We provide a model to integrate the visualization of biclusters extracted from gene expresion data and the underlying PPI networks. Such an integration conveys the biologically relevant interconnection between these two structures inferred from biological experiments. We model the reliabilities of the structures using directed graphs with vertex and edge weights. The resulting graphs are drawn using appropriate weighted modifications of the algorithms necessary for the layered drawings of directed graphs. We provide applications of the proposed visualization model on the S. cerevisiae dataset.
{"title":"An integrated model for visualizing biclusters from gene expression data and PPI networks","authors":"Ahmet Emre Aladağ, C. Erten, Melih Sözdinler","doi":"10.1145/1722024.1722052","DOIUrl":"https://doi.org/10.1145/1722024.1722052","url":null,"abstract":"We provide a model to integrate the visualization of biclusters extracted from gene expresion data and the underlying PPI networks. Such an integration conveys the biologically relevant interconnection between these two structures inferred from biological experiments. We model the reliabilities of the structures using directed graphs with vertex and edge weights. The resulting graphs are drawn using appropriate weighted modifications of the algorithms necessary for the layered drawings of directed graphs. We provide applications of the proposed visualization model on the S. cerevisiae dataset.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64108327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The analysis of progenitor cell proliferation in image sequences helps in understanding the formation of organ, identifying reason for decease and cell based therapies. We introduce a technique using morphological techniques for cell segmentation and extended h-maxima transformation for finding position of the cell in the frame. The over segmentation problem of watershed algorithms is reduced by morphologic erosion, allowing for more accurate quantification, even in low contrast images. The number of cells and the average cell size could be determined in the image. Application of this method to a difficult dataset allowed us to identify 96% of the cells in the image.
{"title":"Quantification and segmentation of progenitor cells in time-lapse microscopy","authors":"R. Suresh, N. Jayalakshmi","doi":"10.1145/1722024.1722071","DOIUrl":"https://doi.org/10.1145/1722024.1722071","url":null,"abstract":"The analysis of progenitor cell proliferation in image sequences helps in understanding the formation of organ, identifying reason for decease and cell based therapies. We introduce a technique using morphological techniques for cell segmentation and extended h-maxima transformation for finding position of the cell in the frame. The over segmentation problem of watershed algorithms is reduced by morphologic erosion, allowing for more accurate quantification, even in low contrast images. The number of cells and the average cell size could be determined in the image. Application of this method to a difficult dataset allowed us to identify 96% of the cells in the image.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64108482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
N. Reena, A. Chandrasekar, A. Riju, P. Nima, S. Eapen, M. Anandaraj
Expressed Sequence Tags (ESTs) are a rich source of information for gene discovery. In this paper, we describe the annotation of ESTs of Phytophthora capsici whose complete genome is not yet available. P. capsici is an Oomycete plant pathogen capable of infecting wide range of plants including cucumber, squash, melons, pumpkin, pepper, tomato and eggplant. In India it causes severe economic losses in black pepper, chillies and cocoa. Towards the understanding of gene function in P. capsici, we undertook the functional annotation of ESTs available at NCBI. A total of 56,457 ESTs were downloaded from NCBI and assembled into 5966 contigs. Functional categorization of these ESTs was performed using database similarity search. By functional analysis, we estimated that 84.73% of transcripts encode significant proteins. The most prominent sequences corresponds to members of metabolic pathways, avirulence-associated protein, beta-tubulin, calcium/calmodulin dependent protein kinase 3, catalase, endo-1, 4-betaglucanase, cyst germination specific acidic repeat protein precursor, elicitin-like protein, glucanase inhibitor protein, heat shock protein, Kazal-like serine protease inhibitor, mitogen-activated protein kinase, ribosomal protein, serine/threonine kinase, syntaxin and ubiquitin. This EST-gene discovery information can be used to design sequence specific markers for P. capsici identification.
{"title":"Gene identification in Phytophthora capsici through expressed sequence tags","authors":"N. Reena, A. Chandrasekar, A. Riju, P. Nima, S. Eapen, M. Anandaraj","doi":"10.1145/1722024.1722043","DOIUrl":"https://doi.org/10.1145/1722024.1722043","url":null,"abstract":"Expressed Sequence Tags (ESTs) are a rich source of information for gene discovery. In this paper, we describe the annotation of ESTs of Phytophthora capsici whose complete genome is not yet available. P. capsici is an Oomycete plant pathogen capable of infecting wide range of plants including cucumber, squash, melons, pumpkin, pepper, tomato and eggplant. In India it causes severe economic losses in black pepper, chillies and cocoa. Towards the understanding of gene function in P. capsici, we undertook the functional annotation of ESTs available at NCBI. A total of 56,457 ESTs were downloaded from NCBI and assembled into 5966 contigs. Functional categorization of these ESTs was performed using database similarity search. By functional analysis, we estimated that 84.73% of transcripts encode significant proteins. The most prominent sequences corresponds to members of metabolic pathways, avirulence-associated protein, beta-tubulin, calcium/calmodulin dependent protein kinase 3, catalase, endo-1, 4-betaglucanase, cyst germination specific acidic repeat protein precursor, elicitin-like protein, glucanase inhibitor protein, heat shock protein, Kazal-like serine protease inhibitor, mitogen-activated protein kinase, ribosomal protein, serine/threonine kinase, syntaxin and ubiquitin. This EST-gene discovery information can be used to design sequence specific markers for P. capsici identification.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64107885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Reinaldo, Md. Anishur Rahman, Carlos F. Alves, A. Malucelli, Rui Camacho
Organ transplantation is a highly complex decision process that requires expert decisions. The major problem in a transplantation procedure is the possibility of the receiver's immune system attack and destroy the transplanted tissue. It is therefore of capital importance to find a donor with the highest possible compatibility with the receiver, and thus reduce rejection. Finding a good donor is not a straight-forward task because a complex network of relations exists between the immunological and the clinical variables that influence the receiver's acceptance of the transplanted organ. Currently the process of analysis of these variables involves a careful study by the clinical transplant team. The number and complexity of causal dependencies among variables make the manual process very slow. In this paper we assess the usefulness of Machine Learning algorithms as a tool to improve and speed up the decisions of a transplant team. We achieve that objective by analysing past real cases and constructing models as set of rules. Such models are accurate and understandable by experts.
{"title":"Machine learning support for kidney transplantation decision making","authors":"F. Reinaldo, Md. Anishur Rahman, Carlos F. Alves, A. Malucelli, Rui Camacho","doi":"10.1145/1722024.1722079","DOIUrl":"https://doi.org/10.1145/1722024.1722079","url":null,"abstract":"Organ transplantation is a highly complex decision process that requires expert decisions. The major problem in a transplantation procedure is the possibility of the receiver's immune system attack and destroy the transplanted tissue. It is therefore of capital importance to find a donor with the highest possible compatibility with the receiver, and thus reduce rejection. Finding a good donor is not a straight-forward task because a complex network of relations exists between the immunological and the clinical variables that influence the receiver's acceptance of the transplanted organ. Currently the process of analysis of these variables involves a careful study by the clinical transplant team. The number and complexity of causal dependencies among variables make the manual process very slow. In this paper we assess the usefulness of Machine Learning algorithms as a tool to improve and speed up the decisions of a transplant team. We achieve that objective by analysing past real cases and constructing models as set of rules. Such models are accurate and understandable by experts.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722079","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64108190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
RNA polymerase II (Pol II) promoter is a key region that regulates differential transcription of protein coding genes. The identification of the RNA polymerase II (Pol II) promoter is one of the most challenging problems in genome annotation. Though many promoter prediction methods and tools have been developed, they have not yet extracted informative features from large-scale DNA sequences to improve predictive accuracy. A prediction method ProPolyII, which involves mining informative nucleotide property composition (NPC) features, is proposed to design a support vector machine-based classifier. An existing data set HumP (1872 human promoters and 1870 non-promoters) is used to evaluate ProPolyII for promoter prediction. ProPolyII yields 70 informative NPC features with training and test accuracies of 99.1% and 95.1%, respectively. The 70 NPC features consist of 46 4-mer motifs, 3 nucleotide properties and 21 global descriptors. The accuracies are better than those of Prom-Machine (94.9% and 91.1%) and M1 (97.4% and 93.6%) which uses top 128 4-mer motifs and 36 global descriptors, respectively. The high predictive performance indicates that ProPolyII can be beneficial in the identification of promoters comparative to other methods.
RNA聚合酶II (RNA polymerase II, Pol II)启动子是调控蛋白质编码基因差异转录的关键区域。RNA聚合酶II (Pol II)启动子的鉴定是基因组注释中最具挑战性的问题之一。虽然已经开发了许多启动子预测方法和工具，但它们尚未从大规模DNA序列中提取信息特征以提高预测准确性。提出了一种挖掘信息核苷酸属性组成(NPC)特征的预测方法ProPolyII，用于设计基于支持向量机的分类器。使用现有的数据集驼峰(1872个人类启动子和1870个非启动子)来评估ProPolyII的启动子预测。ProPolyII产生70个信息丰富的NPC特征，训练和测试准确率分别为99.1%和95.1%。这70个NPC特征包括46个4-mer基序，3个核苷酸特性和21个全局描述符。该方法的准确率分别高于promm - machine(94.9%和91.1%)和M1(97.4%和93.6%)，前者分别使用了前128个4-mer motif和36个全局描述符。与其他方法相比，ProPolyII具有较高的预测性能，可用于启动子的识别。
{"title":"Human Pol II promoter prediction by using nucleotide property composition features","authors":"Wen-Lin Huang, C. Tung, Shinn-Ying Ho","doi":"10.1145/1722024.1722050","DOIUrl":"https://doi.org/10.1145/1722024.1722050","url":null,"abstract":"RNA polymerase II (Pol II) promoter is a key region that regulates differential transcription of protein coding genes. The identification of the RNA polymerase II (Pol II) promoter is one of the most challenging problems in genome annotation. Though many promoter prediction methods and tools have been developed, they have not yet extracted informative features from large-scale DNA sequences to improve predictive accuracy. A prediction method ProPolyII, which involves mining informative nucleotide property composition (NPC) features, is proposed to design a support vector machine-based classifier. An existing data set HumP (1872 human promoters and 1870 non-promoters) is used to evaluate ProPolyII for promoter prediction. ProPolyII yields 70 informative NPC features with training and test accuracies of 99.1% and 95.1%, respectively. The 70 NPC features consist of 46 4-mer motifs, 3 nucleotide properties and 21 global descriptors. The accuracies are better than those of Prom-Machine (94.9% and 91.1%) and M1 (97.4% and 93.6%) which uses top 128 4-mer motifs and 36 global descriptors, respectively. The high predictive performance indicates that ProPolyII can be beneficial in the identification of promoters comparative to other methods.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64108262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Genomic Islands are parts of a genome that has evidence of horizontal origins. The present work is a continuation of our earlier work that identified 25 regions downstream of the small RNAs as hotspots of genomic island integration by analyzing three strains of E. coli and one strain of Shigella spp genomes. Till that work, genomic islands have been identified only at tRNA/tmRNA genes in the enterobacterial genomes. Current work reports 11 distinct small RNAs as potent integration sites for genomic islands in 12 Salmonella spp strains. The tRNAcc 1.0 software package has been used to identify genomic islands associated with small RNAs csrC, rprA, ryeB, sraD, sroB, ssrS, rydB, micF, rnpB rtT, and spf. The coordinates of 36 such small RNA associated genomic islands are presented. Also, the nature of genomic sequences encoded within the identified genomic islands were analyzed and validated using virulence factors database, GenBank annotation features, atypical sequence compositions and the genomic block rearrangements.
{"title":"sRNA associated genomic islands in Salmonella spp.","authors":"J. Sridhar, K. Kavitha","doi":"10.1145/1722024.1722058","DOIUrl":"https://doi.org/10.1145/1722024.1722058","url":null,"abstract":"Genomic Islands are parts of a genome that has evidence of horizontal origins. The present work is a continuation of our earlier work that identified 25 regions downstream of the small RNAs as hotspots of genomic island integration by analyzing three strains of E. coli and one strain of Shigella spp genomes. Till that work, genomic islands have been identified only at tRNA/tmRNA genes in the enterobacterial genomes. Current work reports 11 distinct small RNAs as potent integration sites for genomic islands in 12 Salmonella spp strains. The tRNAcc 1.0 software package has been used to identify genomic islands associated with small RNAs csrC, rprA, ryeB, sraD, sroB, ssrS, rydB, micF, rnpB rtT, and spf. The coordinates of 36 such small RNA associated genomic islands are presented. Also, the nature of genomic sequences encoded within the identified genomic islands were analyzed and validated using virulence factors database, GenBank annotation features, atypical sequence compositions and the genomic block rearrangements.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64108519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Neuroserpin, a clade of serine proteinase inhibitors (serpins) is a selective inhibitor of tissue-type plasminogen activator (tPA) and usually has more than 220 residues. The crystal structure of native human neuroserpin has been reported by Sayaka et al., [17] at 2.1 Å resolution. The native fold of neuroserpin is composed of a five stranded β-sheet A and a mobile helical reactive center loop (RCL). The structure also contains an omega loop (Ω-loop), which contributes to the inhibition of tPA and a helix 'F' that plays an important role in folding, complex formation and polymerization. In this study, we identify new members of the neuroserpin family by comparative sequence analyses, and we analyze the conservation of the reactive center loop, the omega loop, the helix 'F' and other consensus residues, in the newly found relatives, which differ from the consensus sequences of other clades of serpins. By comparative structural analyses of neuroserpin with its structurally similar proteins, we reveal the structural patterns and the stabilizing interactions, that are unique among the members of neuroserpin family.
{"title":"Comparative sequence and structural analyses of neuroserpin: the serine protease inhibitor family","authors":"Kuchi Srikeerthana, P. De Causmaecker","doi":"10.1145/1722024.1722031","DOIUrl":"https://doi.org/10.1145/1722024.1722031","url":null,"abstract":"Neuroserpin, a clade of serine proteinase inhibitors (serpins) is a selective inhibitor of tissue-type plasminogen activator (tPA) and usually has more than 220 residues. The crystal structure of native human neuroserpin has been reported by Sayaka et al., [17] at 2.1 Å resolution. The native fold of neuroserpin is composed of a five stranded β-sheet A and a mobile helical reactive center loop (RCL). The structure also contains an omega loop (Ω-loop), which contributes to the inhibition of tPA and a helix 'F' that plays an important role in folding, complex formation and polymerization.\u0000 In this study, we identify new members of the neuroserpin family by comparative sequence analyses, and we analyze the conservation of the reactive center loop, the omega loop, the helix 'F' and other consensus residues, in the newly found relatives, which differ from the consensus sequences of other clades of serpins. By comparative structural analyses of neuroserpin with its structurally similar proteins, we reveal the structural patterns and the stabilizing interactions, that are unique among the members of neuroserpin family.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64108040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The study examines and implements a strategy to build soluble analogues of various hydrophobic transmembane proteins. The design is done using an information-theoretic approach which allows one to perturb certain properties of the protein while keeping the others constant. Knowledge-based force fields are used to obtain self and cross-interaction energies. A novel postprocessing technique is added to the theory to obtain a library of analogues with controlled but varying degrees of solubility. Not all mutations suggested by the information theoretic approach are kept. Only the significant mutations as determined by a threshold value are made. The technique also applies a sliding window protocol and implements point mutations at certain structurally and functionally 'non-conserved' locations. The library approach provides greater flexibility during actual experimental synthesis of the mutant. The overall structural and functional properties are preserved even in the altered context and this is validated by alignment and homology modelling.
{"title":"Computational design of soluble variants of transmembrane proteins: an information theoretic approach","authors":"Jishnu Das","doi":"10.1145/1722024.1722033","DOIUrl":"https://doi.org/10.1145/1722024.1722033","url":null,"abstract":"The study examines and implements a strategy to build soluble analogues of various hydrophobic transmembane proteins. The design is done using an information-theoretic approach which allows one to perturb certain properties of the protein while keeping the others constant. Knowledge-based force fields are used to obtain self and cross-interaction energies. A novel postprocessing technique is added to the theory to obtain a library of analogues with controlled but varying degrees of solubility. Not all mutations suggested by the information theoretic approach are kept. Only the significant mutations as determined by a threshold value are made. The technique also applies a sliding window protocol and implements point mutations at certain structurally and functionally 'non-conserved' locations. The library approach provides greater flexibility during actual experimental synthesis of the mutant. The overall structural and functional properties are preserved even in the altered context and this is validated by alignment and homology modelling.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64108127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
MicroRNAs (miRNAs) play an important role in eukaryotic gene regulation. Although thousands of miRNAs have been identified in laboratories around the world, most of their targets still remain unknown. Different computational techniques exist to predict miRNA targets. In this article, we propose a new method for identifying human miRNA-mRNA interactions based on a genetic algorithm. Our cross-validation results indicate that the genetic algorithm-based miRNA target predictor outperforms the MiRanda package as evidenced by high true positive rates and moderate false positive rates.
{"title":"Identifying human miRNA targets with a genetic algorithm","authors":"Kalle Karhu, S. Khuri, Juho Mäkinen, J. Tarhio","doi":"10.1145/1722024.1722059","DOIUrl":"https://doi.org/10.1145/1722024.1722059","url":null,"abstract":"MicroRNAs (miRNAs) play an important role in eukaryotic gene regulation. Although thousands of miRNAs have been identified in laboratories around the world, most of their targets still remain unknown. Different computational techniques exist to predict miRNA targets. In this article, we propose a new method for identifying human miRNA-mRNA interactions based on a genetic algorithm. Our cross-validation results indicate that the genetic algorithm-based miRNA target predictor outperforms the MiRanda package as evidenced by high true positive rates and moderate false positive rates.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64108554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article describes a variable string length genetic algorithm for de novo ligand design. The input to the algorithm is the active site dimensions which guides the ligand construction. A library of forty one fragments is used to construct the ligands by evaluating the combinations of these fragments. Bond stretching, angle bending, torsional terms, van der Waals and electrostatic interaction energy with distance dependent dielectric constant contribute are used to evaluate the internal energy of the ligand and the interaction energy of the ligand receptor complex. Domain specific genetic operators are used to evolve the solutions to obtain better ligands. Experimental results for HIV-1 Protease and Thrombin are provided which underline the superiority of the proposed scheme over three existing approaches.
{"title":"Evolving fragments to lead molecules","authors":"Soumi Sengupta, S. Bandyopadhyay","doi":"10.1145/1722024.1722061","DOIUrl":"https://doi.org/10.1145/1722024.1722061","url":null,"abstract":"This article describes a variable string length genetic algorithm for de novo ligand design. The input to the algorithm is the active site dimensions which guides the ligand construction. A library of forty one fragments is used to construct the ligands by evaluating the combinations of these fragments. Bond stretching, angle bending, torsional terms, van der Waals and electrostatic interaction energy with distance dependent dielectric constant contribute are used to evaluate the internal energy of the ligand and the interaction energy of the ligand receptor complex. Domain specific genetic operators are used to evolve the solutions to obtain better ligands. Experimental results for HIV-1 Protease and Thrombin are provided which underline the superiority of the proposed scheme over three existing approaches.","PeriodicalId":39379,"journal":{"name":"In Silico Biology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2010-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/1722024.1722061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64108163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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