Darren Flower - Academia.edu (original) (raw)
Papers by Darren Flower
Bioinformatics for Vaccinology
2014 International Conference on Information Visualization Theory and Applications (IVAPP), 2014
Projection of a high-dimensional dataset onto a two-dimensional space is a useful tool to visuali... more Projection of a high-dimensional dataset onto a two-dimensional space is a useful tool to visualise structures and relationships in the dataset. However, a single two-dimensional visualisation may not display all the intrinsic structure. Therefore, hierarchical/multi-level visualisation methods have been used to extract more detailed understanding of the data. Here we propose a multi-level Gaussian process latent variable model (MLGPLVM). MLGPLVM works by segmenting data (with e.g. K-means, Gaussian mixture model or interactive clustering) in the visualisation space and then fitting a visualisation model to each subset. To measure the quality of multi-level visualisation (with respect to parent and child models), metrics such as trustworthiness, continuity, mean relative rank errors, visualisation distance distortion and the negative log-likelihood per point are used. We evaluate the MLGPLVM approach on the ‘Oil Flow’ dataset and a dataset of protein electrostatic potentials for the...
Bioinformatics for Vaccinology
BMC bioinformatics, Jan 23, 2006
The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellu... more The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. SVRMHC is an accurate and easy-to-use prediction...
Vaccine is a substance that trains the host immune system to protect itself against a pathogen. V... more Vaccine is a substance that trains the host immune system to protect itself against a pathogen. Vaccine antigen databases are compilations of different types of resources directly or indirectly leading to the development of vaccines. In order to train, a variety of antigens are used to activate different arms of immune system such as B- and T-cell epitopes, and innate immune components. This entry describes databases of various components of the immune system used to design a vaccine.
Current Computer Aided-Drug Design, 2014
Trends in biotechnology, 2008
Genome sequences from many organisms, including humans, have been completed, and high-throughput ... more Genome sequences from many organisms, including humans, have been completed, and high-throughput analyses have produced burgeoning volumes of 'omics' data. Bioinformatics is crucial for the management and analysis of such data and is increasingly used to accelerate progress in a wide variety of large-scale and object-specific functional analyses. Refined algorithms enable biotechnologists to follow 'computer-aided strategies' based on experiments driven by high-confidence predictions. In order to address compound problems, current efforts in immuno-informatics and reverse vaccinology are aimed at developing and tuning integrative approaches and user-friendly, automated bioinformatics environments. This will herald a move to 'computer-aided biotechnology': smart projects in which time-consuming and expensive large-scale experimental approaches are progressively replaced by prediction-driven investigations.
In Silico Immunology
UKDarren. f lower® j enner .ac.uk Summary. Human leukocyte antigen (HLA) recognizes antigenic fra... more UKDarren. f lower® j enner .ac.uk Summary. Human leukocyte antigen (HLA) recognizes antigenic fragments and presents them to T cells. HLA is polymorphic. There are over 2000 different HLA alleles at present and the number is constantly increasing. However, antigen binding studies are limited to a small proportion of these alleles; the binding specificities of most alleles are unknown. Several research groups have attempted to partition different HLA alleles into groups. In this chapter previous classifications are reviewed and we present two chemometric approaches to classifying class I HLA alleles. The program GRID is used to calculate interaction energy between protein molecules and defined chemical probes. These interaction energy values are imported into another program GOLPE and used for principal component analysis (PCA) calculation, which groups HLA alleles into supertypes. Amino acids that are involved in the classification are displayed in the loading plots of the PCA model. Another method, hierarchical clustering based on comparative molecular similarity indices (CoMSIA) is also applied to classify HLA alleles and the results are compared with those of the PCA models.
Molecular Immunology, 2006
Cleavage by the proteasome is responsible for generating the C terminus of T-cell epitopes. Model... more Cleavage by the proteasome is responsible for generating the C terminus of T-cell epitopes. Modeling the process of proteasome cleavage as part of a multi-step algorithm for T-cell epitope prediction will reduce the number of non-binders and increase the overall accuracy of the predictive algorithm. Quantitative matrix-based models for prediction of the proteasome cleavage sites in a protein were developed using a training set of 489 naturally processed T-cell epitopes (nonamer peptides) associated with HLA-A and HLA-B molecules. The models were validated using an external test set of 227 T-cell epitopes. The performance of the models was good, identifying 76% of the C-termini correctly. The best model of proteasome cleavage was incorporated as the first step in a three-step algorithm for T-cell epitope prediction, where subsequent steps predicted TAP affinity and MHC binding using previously derived models.
Journal of Chemical Information and Computer Sciences, 2003
JenPep is a relational database containing a compendium of thermodynamic binding data for the int... more JenPep is a relational database containing a compendium of thermodynamic binding data for the interaction of peptides with a range of important immunological molecules: the major histocompatibility complex, TAP transporter, and T cell receptor. The database also includes annotated lists of B cell and T cell epitopes. Version 2.0 of the database is implemented in a bespoke postgreSQL database system and is fully searchable online via a perl/HTML interface (URL: http://www.jenner.ac.uk/JenPep).
Journal of Biomedicine and Biotechnology, 2010
Vaccines are the greatest single instrument of prophylaxis against infectious diseases, with imme... more Vaccines are the greatest single instrument of prophylaxis against infectious diseases, with immeasurable benefits to human wellbeing. The accurate and reliable prediction of peptide-MHC binding is fundamental to the robust identification of T-cell epitopes and thus the successful design of peptide- and protein-based vaccines. The prediction of MHC class II peptide binding has hitherto proved recalcitrant and refractory. Here we illustrate the utility of existing computational tools for in silico prediction of peptides binding to class II MHCs. Most of the methods, tested in the present study, detect more than the half of the true binders in the top 5% of all possible nonamers generated from one protein. This number increases in the top 10% and 15% and then does not change significantly. For the top 15% the identified binders approach 86%. In terms of lab work this means 85% less expenditure on materials, labour and time. We show that while existing caveats are well founded, nonethe...
Current Proteomics, 2008
... Pro-tein groups are therefore clustered by their similarity to each *Address correspondence t... more ... Pro-tein groups are therefore clustered by their similarity to each *Address correspondence to this author at the Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK; Tel: 0207 631 6842; E-mail: Matthew.Davies@iop.kcl.ac.uk other. ...
Immunology and Immunogenetics Insights, 2013
The immune system is perhaps the largest yet most diffuse and distributed somatic system in verte... more The immune system is perhaps the largest yet most diffuse and distributed somatic system in vertebrates. It plays vital roles in fighting infection and in the homeostatic control of chronic disease. As such, the immune system in both pathological and healthy states is a prime target for therapeutic interventions by drugs–-both small-molecule and biologic. Comprising both the innate and adaptive immune systems, human immunity is awash with potential unexploited molecular targets. Key examples include the pattern recognition receptors of the innate immune system and the major histocompatibility complex of the adaptive immune system. Moreover, the immune system is also the source of many current and, hopefully, future drugs, of which the prime example is the monoclonal antibody, the most exciting and profitable type of present-day drug moiety. This brief review explores the identity and synergies of the hierarchy of drug targets represented by the human immune system, with particular e...
Bioinformatics for Vaccinology
2014 International Conference on Information Visualization Theory and Applications (IVAPP), 2014
Projection of a high-dimensional dataset onto a two-dimensional space is a useful tool to visuali... more Projection of a high-dimensional dataset onto a two-dimensional space is a useful tool to visualise structures and relationships in the dataset. However, a single two-dimensional visualisation may not display all the intrinsic structure. Therefore, hierarchical/multi-level visualisation methods have been used to extract more detailed understanding of the data. Here we propose a multi-level Gaussian process latent variable model (MLGPLVM). MLGPLVM works by segmenting data (with e.g. K-means, Gaussian mixture model or interactive clustering) in the visualisation space and then fitting a visualisation model to each subset. To measure the quality of multi-level visualisation (with respect to parent and child models), metrics such as trustworthiness, continuity, mean relative rank errors, visualisation distance distortion and the negative log-likelihood per point are used. We evaluate the MLGPLVM approach on the ‘Oil Flow’ dataset and a dataset of protein electrostatic potentials for the...
Bioinformatics for Vaccinology
BMC bioinformatics, Jan 23, 2006
The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellu... more The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. SVRMHC is an accurate and easy-to-use prediction...
Vaccine is a substance that trains the host immune system to protect itself against a pathogen. V... more Vaccine is a substance that trains the host immune system to protect itself against a pathogen. Vaccine antigen databases are compilations of different types of resources directly or indirectly leading to the development of vaccines. In order to train, a variety of antigens are used to activate different arms of immune system such as B- and T-cell epitopes, and innate immune components. This entry describes databases of various components of the immune system used to design a vaccine.
Current Computer Aided-Drug Design, 2014
Trends in biotechnology, 2008
Genome sequences from many organisms, including humans, have been completed, and high-throughput ... more Genome sequences from many organisms, including humans, have been completed, and high-throughput analyses have produced burgeoning volumes of 'omics' data. Bioinformatics is crucial for the management and analysis of such data and is increasingly used to accelerate progress in a wide variety of large-scale and object-specific functional analyses. Refined algorithms enable biotechnologists to follow 'computer-aided strategies' based on experiments driven by high-confidence predictions. In order to address compound problems, current efforts in immuno-informatics and reverse vaccinology are aimed at developing and tuning integrative approaches and user-friendly, automated bioinformatics environments. This will herald a move to 'computer-aided biotechnology': smart projects in which time-consuming and expensive large-scale experimental approaches are progressively replaced by prediction-driven investigations.
In Silico Immunology
UKDarren. f lower® j enner .ac.uk Summary. Human leukocyte antigen (HLA) recognizes antigenic fra... more UKDarren. f lower® j enner .ac.uk Summary. Human leukocyte antigen (HLA) recognizes antigenic fragments and presents them to T cells. HLA is polymorphic. There are over 2000 different HLA alleles at present and the number is constantly increasing. However, antigen binding studies are limited to a small proportion of these alleles; the binding specificities of most alleles are unknown. Several research groups have attempted to partition different HLA alleles into groups. In this chapter previous classifications are reviewed and we present two chemometric approaches to classifying class I HLA alleles. The program GRID is used to calculate interaction energy between protein molecules and defined chemical probes. These interaction energy values are imported into another program GOLPE and used for principal component analysis (PCA) calculation, which groups HLA alleles into supertypes. Amino acids that are involved in the classification are displayed in the loading plots of the PCA model. Another method, hierarchical clustering based on comparative molecular similarity indices (CoMSIA) is also applied to classify HLA alleles and the results are compared with those of the PCA models.
Molecular Immunology, 2006
Cleavage by the proteasome is responsible for generating the C terminus of T-cell epitopes. Model... more Cleavage by the proteasome is responsible for generating the C terminus of T-cell epitopes. Modeling the process of proteasome cleavage as part of a multi-step algorithm for T-cell epitope prediction will reduce the number of non-binders and increase the overall accuracy of the predictive algorithm. Quantitative matrix-based models for prediction of the proteasome cleavage sites in a protein were developed using a training set of 489 naturally processed T-cell epitopes (nonamer peptides) associated with HLA-A and HLA-B molecules. The models were validated using an external test set of 227 T-cell epitopes. The performance of the models was good, identifying 76% of the C-termini correctly. The best model of proteasome cleavage was incorporated as the first step in a three-step algorithm for T-cell epitope prediction, where subsequent steps predicted TAP affinity and MHC binding using previously derived models.
Journal of Chemical Information and Computer Sciences, 2003
JenPep is a relational database containing a compendium of thermodynamic binding data for the int... more JenPep is a relational database containing a compendium of thermodynamic binding data for the interaction of peptides with a range of important immunological molecules: the major histocompatibility complex, TAP transporter, and T cell receptor. The database also includes annotated lists of B cell and T cell epitopes. Version 2.0 of the database is implemented in a bespoke postgreSQL database system and is fully searchable online via a perl/HTML interface (URL: http://www.jenner.ac.uk/JenPep).
Journal of Biomedicine and Biotechnology, 2010
Vaccines are the greatest single instrument of prophylaxis against infectious diseases, with imme... more Vaccines are the greatest single instrument of prophylaxis against infectious diseases, with immeasurable benefits to human wellbeing. The accurate and reliable prediction of peptide-MHC binding is fundamental to the robust identification of T-cell epitopes and thus the successful design of peptide- and protein-based vaccines. The prediction of MHC class II peptide binding has hitherto proved recalcitrant and refractory. Here we illustrate the utility of existing computational tools for in silico prediction of peptides binding to class II MHCs. Most of the methods, tested in the present study, detect more than the half of the true binders in the top 5% of all possible nonamers generated from one protein. This number increases in the top 10% and 15% and then does not change significantly. For the top 15% the identified binders approach 86%. In terms of lab work this means 85% less expenditure on materials, labour and time. We show that while existing caveats are well founded, nonethe...
Current Proteomics, 2008
... Pro-tein groups are therefore clustered by their similarity to each *Address correspondence t... more ... Pro-tein groups are therefore clustered by their similarity to each *Address correspondence to this author at the Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK; Tel: 0207 631 6842; E-mail: Matthew.Davies@iop.kcl.ac.uk other. ...
Immunology and Immunogenetics Insights, 2013
The immune system is perhaps the largest yet most diffuse and distributed somatic system in verte... more The immune system is perhaps the largest yet most diffuse and distributed somatic system in vertebrates. It plays vital roles in fighting infection and in the homeostatic control of chronic disease. As such, the immune system in both pathological and healthy states is a prime target for therapeutic interventions by drugs–-both small-molecule and biologic. Comprising both the innate and adaptive immune systems, human immunity is awash with potential unexploited molecular targets. Key examples include the pattern recognition receptors of the innate immune system and the major histocompatibility complex of the adaptive immune system. Moreover, the immune system is also the source of many current and, hopefully, future drugs, of which the prime example is the monoclonal antibody, the most exciting and profitable type of present-day drug moiety. This brief review explores the identity and synergies of the hierarchy of drug targets represented by the human immune system, with particular e...