| 1. |
- Andersson, Claes R., et al.
(author)
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Quantitative Chemogenomics : Machine-Learning Models of Protein-Ligand Interaction
- 2011
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In: Current Topics in Medicinal Chemistry. - : Bentham Science Publishers Ltd.. - 1568-0266 .- 1873-4294. ; 11:15, s. 1978-1993
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Research review (peer-reviewed)abstract
- Chemogenomics is an emerging interdisciplinary field that lies in the interface of biology, chemistry, and informatics. Most of the currently used drugs are small molecules that interact with proteins. Understanding protein-ligand interaction is therefore central to drug discovery and design. In the subfield of chemogenomics known as proteochemometrics, protein-ligand-interaction models are induced from data matrices that consist of both protein and ligand information along with some experimentally measured variable. The two general aims of this quantitative multi-structure-property-relationship modeling (QMSPR) approach are to exploit sparse/incomplete information sources and to obtain more general models covering larger parts of the protein-ligand space, than traditional approaches that focuses mainly on specific targets or ligands. The data matrices, usually obtained from multiple sparse/incomplete sources, typically contain series of proteins and ligands together with quantitative information about their interactions. A useful model should ideally be easy to interpret and generalize well to new unseen protein-ligand combinations. Resolving this requires sophisticated machine-learning methods for model induction, combined with adequate validation. This review is intended to provide a guide to methods and data sources suitable for this kind of protein-ligand-interaction modeling. An overview of the modeling process is presented including data collection, protein and ligand descriptor computation, data preprocessing, machine-learning-model induction and validation. Concerns and issues specific for each step in this kind of data-driven modeling will be discussed.
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| 3. |
- Lagerberg, Tove B, et al.
(author)
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Parent rating of intelligibility: A discussion of the construct validity of the Intelligibility in Context Scale (ICS) and normative data of the Swedish version of the ICS.
- 2021
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In: International journal of language & communication disorders. - : Wiley. - 1460-6984 .- 1368-2822. ; 56:4, s. 873-886
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Journal article (peer-reviewed)abstract
- Intelligibility can be defined as the speakers' ability to convey a message to the listener and it is considered the key functional measure of speech. The Intelligibility in Context Scale (ICS) is a parent rating scale used to assess intelligibility in children.To describe normative and validation data on the ICS in Swedish and to investigate how these are related to age, gender and multilingualism.Two studies were included. Study 1 included ICS forms from 319 Swedish-speaking children (3:2-9:2 years:months). Study 2 included video recordings and ICS forms from 14 children with speech sound disorder (SSD) and two with typical speech. The video recordings were transcribed in the validation process, resulting in intelligibility reference scores to which ICS scores were correlated.Study 1: The mean value of the ICS for the 319 children was 4.73. There were no differences in ICS score related to age or gender. The children in the multilingual group were significantly older than the monolingual group and had significantly lower ICS scores than the group of monolinguals. Study 2: There was a moderate correlation between the ICS score and the transcription-based intelligibility score, with the two children with typical speech excluded; however, this correlation was not significant.We contribute mean scores and percentiles on the ICS for Swedish-speaking children. The finding that the ICS does not provide valid measures of intelligibility for the included children with SSD suggests that the instrument measures a different construct.What is already known on the subject The ICS has been translated to numerous languages and validated against articulation measures in several previous studies. The validity of the Swedish version has been investigated against intelligibility based on transcription of single words. What this paper adds to existing knowledge The study provides normative values of the Swedish version of the ICS for children aged 3-9 years. This is the first study to use a gold standard measure of intelligibility in continuous speech to validate the ICS. The results show a somewhat dubious validity regarding ICS for the group of children with SSD included in the study. What are the potential or actual clinical implications of this work? The ICS's suitability as a measure of intelligibility is questionable; however, it might be of use for speech and language pathologists to give an overview of the parents' view of their child's ability to communicate, in order to make a decision on possible further assessment and intervention. The normative values of the Swedish version of the ICS could be of use in this decision process.
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| 5. |
- Strömbergsson, Helena, et al.
(author)
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A chemogenomics view on protein-ligand spaces
- 2009
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In: BMC Bioinformatics. - 1471-2105. ; 10:Suppl.6, s. S13-
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Journal article (peer-reviewed)abstract
- BACKGROUND: Chemogenomics is an emerging inter-disciplinary approach to drug discovery that combines traditional ligand-based approaches with biological information on drug targets and lies at the interface of chemistry, biology and informatics. The ultimate goal in chemogenomics is to understand molecular recognition between all possible ligands and all possible drug targets. Protein and ligand space have previously been studied as separate entities, but chemogenomics studies deal with large datasets that cover parts of the joint protein-ligand space. Since drug discovery has traditionally focused on ligand optimization, the chemical space has been studied extensively. The protein space has been studied to some extent, typically for the purpose of classification of proteins into functional and structural classes. Since chemogenomics deals not only with ligands but also with the macromolecules the ligands interact with, it is of interest to find means to explore, compare and visualize protein-ligand subspaces. RESULTS: Two chemogenomics protein-ligand interaction datasets were prepared for this study. The first dataset covers the known structural protein-ligand space, and includes all non-redundant protein-ligand interactions found in the worldwide Protein Data Bank (PDB). The second dataset contains all approved drugs and drug targets stored in the DrugBank database, and represents the approved drug-drug target space. To capture biological and physicochemical features of the chemogenomics datasets, sequence-based descriptors were computed for the proteins, and 0, 1 and 2 dimensional descriptors for the ligands. Principal component analysis (PCA) was used to analyze the multidimensional data and to create global models of protein-ligand space. The nearest neighbour method, computed using the principal components, was used to obtain a measure of overlap between the datasets. CONCLUSION: In this study, we present an approach to visualize protein-ligand spaces from a chemogenomics perspective, where both ligand and protein features are taken into account. The method can be applied to any protein-ligand interaction dataset. Here, the approach is applied to analyze the structural protein-ligand space and the protein-ligand space of all approved drugs and their targets. We show that this approach can be used to visualize and compare chemogenomics datasets, and possibly to identify cross-interaction complexes in protein-ligand space.
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| 6. |
- Strömbergsson, Helena, 1975-
(author)
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Chemogenomics: Models of Protein-Ligand Interaction Space
- 2009
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Doctoral thesis (other academic/artistic)abstract
- The large majority of the currently used drugs are small molecules that interact with proteins. Understanding protein-ligand recognition is thus central to drug discovery and design. Improved experimental techniques have resulted in an immense growth of drug target information. This has stimulated the development of chemogenomics and proteochemometrics (PCM) that take target information as well as ligand information into account to study the genomic effect of potential drugs. This thesis is concerned with modeling protein-ligand recognition, and the aim is to develop models that generalize to the entire protein-ligand space. To this end, protein-ligand interaction data has been extracted and manually curated from public databases, protein and ligand descriptors have been computed, and predictive models have been induced with machine-learning methods. An introduction to chemogenomics, machine learning, and PCM modeling is given in the thesis summary, which is followed by five research papers. Paper I shows that it is possible to induce interpretable models with a non-linear rule-based method, and paper II demonstrates that local descriptors of protein structure may be used to induce PCM models that cover proteins differing in sequence and fold. In paper III, such local descriptors are used to induce a PCM model on a large dataset that includes all major enzyme classes. This demonstrates that the local descriptors may be used to induce generalized models that span the entire known structural enzyme-ligand space. Paper IV describes a step towards proteome-wide PCM models, and shows that it is possible to predict high- and low-affinity complexes using a set of protein and ligand descriptors that do not require knowledge of 3D structure. Finally, paper V presents a method to visualize and compare protein-ligand chemogenomic subspaces, which may be used to predict unwanted cross-interactions of drugs with other proteins in the proteome.
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| 7. |
- Strömbergsson, Helena, et al.
(author)
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Generalized modeling of enzyme-ligand interactions using proteochemometrics and local protein substructures
- 2006
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In: Proteins. - : Wiley. - 0887-3585 .- 1097-0134. ; 65:3, s. 568-579
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Journal article (peer-reviewed)abstract
- Modeling and understanding protein-ligand interactions is one of the most important goals in computational drug discovery. To this end, proteochemometrics uses structural and chemical descriptors from several proteins and several ligands to induce interaction-models. Here, we present a new and generalized approach in which proteins varying greatly in terms of sequence and structure are represented by a library of local substructures. Using linear regression and rule-based learning, we combine such local substructures with chemical descriptors from the ligands to model binding affinity for a training set of hydrolase and lyase enzymes. We evaluate the predictive performance of these models using cross validation and sets of unseen ligand with unknown three-dimensional structure. The models are shown to generalize by outperforming models using descriptors from only proteins or only ligands, or models using global structure similarities rather than local similarities. Thus, we demonstrate that this approach is capable of describing dependencies between local structural properties and ligands in otherwise dissimilar protein structures. These dependencies are often, but not always, associated with local substructures that are in contact with the ligands. Finally, we show that strongly bound enzyme-ligand complexes require the presence of particular local substructures, while weakly bound complexes may be described by the absence of certain properties. The results demonstrate that the alignment-independent approach using local substructures is capable of describing protein-ligand interaction for largely different proteins and hence opens up for proteochemometrics-analysis of the interaction-space of entire proteomes. Current approaches are limited to families of closely related proteins. families of closely related proteins.
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| 8. |
- Strömbergsson, Helena, 1975-, et al.
(author)
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Interaction Model Based on Local Protein Substructures Generalizes to the Entire Structural Enzyme-Ligand Space
- 2008
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In: Journal of chemical information and modelling. - : American Chemical Society (ACS). - 1549-960X .- 1549-9596. ; 48:11, s. 2278-2288
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Journal article (peer-reviewed)abstract
- Chemogenomics is a new strategy in in silico drug discovery, where the ultimate goal is to understand molecular recognition for all molecules interacting with all proteins in the proteome. To study such cross interactions, methods that can generalize over proteins that vary greatly in sequence, structure, and function are needed. We present a general quantitative approach to protein−ligand binding affinity prediction that spans the entire structural enzyme-ligand space. The model was trained on a data set composed of all available enzymes cocrystallized with druglike ligands, taken from four publicly available interaction databases, for which a crystal structure is available. Each enzyme was characterized by a set of local descriptors of protein structure that describe the binding site of the cocrystallized ligand. The ligands in the training set were described by traditional QSAR descriptors. To evaluate the model, a comprehensive test set consisting of enzyme structures and ligands was manually curated. The test set contained enzyme-ligand complexes for which no crystal structures were available, and thus the binding modes were unknown. The test set enzymes were therefore characterized by matching their entire structures to the local descriptor library constructed from the training set. Both the training and the test set contained enzyme-ligand complexes from all major enzyme classes, and the enzymes spanned a large range of sequences and folds. The experimental binding affinities (pKi) ranged from 0.5 to 11.9 (0.7−11.0 in the test set). The induced model predicted the binding affinities of the external test set enzyme-ligand complexes with an r2 of 0.53 and an RMSEP of 1.5. This demonstrates that the use of local descriptors makes it possible to create rough predictive models that can generalize over a wide range of protein targets.
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| 9. |
- Strömbergsson, Helena, et al.
(author)
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Proteochemometrics modelling of receptor ligand interactions using rough sets
- 2004
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In: Proceedings of the German conference on Bioinformatics. - 3885793822 ; , s. 85-94
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Conference paper (peer-reviewed)abstract
- We report on a model for the interaction of chimeric melanocortin G-protein coupled receptors with peptide ligands using the rough set approach. Rough sets generate If-Then rule models using Boolean reasoning. Two separate datasets have been analyzed, for which the binding affinities have previously been measured experimentally. The receptors and ligands are described by vectors of strings. Different partitions of each dataset were evaluated in order to find an optimal partition into rough set decision classes. To obtain a measurement of the accuracy of the rough set classifier generated from each dataset, a 10-fold cross validation (CV) was performed. The Area Under Curve (AUC) was calculated for each iteration during CV. This resulted in an AUC mean of 0.94 (SD 0.12) and 0.93 (SD 0.16) for the first and second dataset respectively. The CV results show that the rough set models exhibit a high classification quality. The decision rules generated from the rough set model inductions are easy to interpret. We apply this information to develop models of the interaction between ligands and receptors.
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| 10. |
- Strömbergsson, Helena, et al.
(author)
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Rough set-based proteochemometrics modeling of G-protein-coupled receptor-ligand
- 2006
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In: Proteins: Structure, Function, and Bioinformatics. - 1097-0134. ; 63:1, s. 24-34
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Journal article (peer-reviewed)abstract
- G-Protein-coupled receptors (GPCRs) are among the most important drug targets. Because of a shortage of 3D crystal structures, most of the drug design for GPCRs has been ligand-based. We propose a novel, rough set-based proteochemometric approach to the study of receptor and ligand recognition. The approach is validated on three datasets containing GPCRs. In proteochemometrics, properties of receptors and ligands are used in conjunction and modeled to predict binding affinity. The rough set (RS) rule-based models presented herein consist of minimal decision rules that associate properties of receptors and ligands with high or low binding affinity. The information provided by the rules is then used to develop a mechanistic interpretation of interactions between the ligands and receptors included in the datasets. The first two datasets contained descriptors of melanocortin receptors and peptide ligands. The third set contained descriptors of adrenergic receptors and ligands. All the rule models induced from these datasets have a high predictive quality. An example of a decision rule is If R1_ligand(Ethyl) and TM helix 2 position 27(Methionine) then Binding(High). The easily interpretable rule sets are able to identify determinative receptor and ligand parts. For instance, all three models suggest that transmembrane helix 2 is determinative for high and low binding affinity. RS models show that it is possible to use rule-based models to predict ligand-binding affinities. The models may be used to gain a deeper biological understanding of the combinatorial nature of receptor-ligand interactions.
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| 11. |
- Strömbergsson, Helena, et al.
(author)
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Towards proteome-wide interaction models using the proteochemometrics approach
- 2010
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In: Molecular Informatics. - : Wiley. - 1868-1743 .- 1868-1751. ; 29:6-7, s. 499-508
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Journal article (peer-reviewed)abstract
- A proteochemometrics model was induced from all interaction data in the BindingDB database, comprizing in all 7078 protein-ligand complexes with representatives from all major drug target categories. Proteins were represented by alignment-independent sequence descriptors holding information on properties such as hydrophobicity, charge, and secondary structure. Ligands were represented by commonly used QSAR descriptors. The inhibition constant (pK(i)) values of protein-ligand complexes were discretized into "high" and "low" interaction activity. Different machine-learning techniques were used to induce models relating protein and ligand properties to the interaction activity. The best was decision trees, which gave an accuracy of 80% and an area under the ROC curve of 0.81. The tree pointed to the protein and ligand properties, which are relevant for the interaction. As the approach does neither require alignments nor knowledge of protein 3D structures virtually all available protein-ligand interaction data could be utilized, thus opening a way to completely general interaction models that may span entire proteomes.
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