DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures

Wold, Svante; Jönsson, Jan‐Ingvar; Sjörström, M.; Sandberg, Monica; Rännar, Stefan

doi:10.1016/0003-2670(93)80437-p

Cited by 199 publications

(158 citation statements)

References 15 publications

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“…This transformation generates a N × 5 matrix. -Auto Cross Covariance Transformation: The ACC transformation [8,15] is a more sophisticated transformation, which captures the correlation of the physico-chemical descriptors along the sequence. First the physico-chemical properties are represented by means of the five z-scores of each amino-acid as described by [13].…”

Section: Methodsmentioning

confidence: 99%

“…In this previous work, the analysis of the alignmnent-free sequences entailed a transformation of the symbolic sequences into real-valued feature vectors on the basis of the physicochemical properties of their constituent amino acids. In this study, the same transformations are used, including the Auto-Cross Covariance (ACC) transformation [15] and a more simple one: the amino acid composition (AA). To these, we add the Mean Transformation [10].…”

Section: Introductionmentioning

confidence: 99%

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SVM-Based Classification of Class C GPCRs from Alignment-Free Physicochemical Transformations of Their Sequences

König

Cruz-Barbosa

Alquézar

et al. 2013

New Trends in Image Analysis and Processing – ICIAP 2013

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Abstract. G protein-coupled receptors (GPCRs) have a key function in regulating the function of cells due to their ability to transmit extracelullar signals. Given that the 3D structure and the functionality of most GPCRs is unknown, there is a need to construct robust classification models based on the analysis of their amino acid sequences for protein homology detection. In this paper, we describe the supervised classification of the different subtypes of class C GPCRs using support vector machines (SVMs). These models are built on different transformations of the amino acid sequences based on their physicochemical properties. Previous research using semi-supervised methods on the same data has shown the usefulness of such transformations. The obtained classification models show a robust performance, as their Matthews correlation coefficient is close to 0.91 and their prediction accuracy is close to 0.93.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SVM-Based Classification of Class C GPCRs from Alignment-Free Physicochemical Transformations of Their Sequences

König

Cruz-Barbosa

Alquézar

et al. 2013

New Trends in Image Analysis and Processing – ICIAP 2013

View full text Add to dashboard Cite

show abstract

“…Among these, some rely on transformations based on the amino acid physicochemical characteristics, such as the ACC transformation [22,41] and the mean transformation [26]. Some advantages of these representations are that: a) they can help to discover divergent receptor genes [20]; b) they do not require a homologous relationship among similar sequences to be assumed; and c) their transformed output can directly be used by standard pattern recognition and machine learning methods.…”

Section: Alignment-free Gpcr Representationsmentioning

confidence: 99%

“…For the more sophisticated ACC transformation [22,41], time series models are applied to the protein sequences in order to extract their sequential patterns and, consequently, the extracted information is sequence-order dependent. This representation was originally developed in [41] and then applied and modified in [22] and [27].…”

Section: Alignment-free Gpcr Representationsmentioning

confidence: 99%

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The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors

Cruz-Barbosa

Vellido

Giraldo

2014

Med Biol Eng Comput

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G protein-coupled receptors (GPCRs) are integral cell membrane proteins of relevance for pharmacology. The tertiary structure of the transmembrane domain, a gate to the study of protein functionality, is unknown for almost all members of class C GPCRs, which are the target of the current study. As a result, their investigation must often rely on alignments of their amino acid sequences. Sequence alignment entails the risk of missing relevant information. Various approaches have attempted to circumvent this risk through alignment-free transformations of the sequences on the basis of different amino acid physicochemical properties. In this paper, we use several of these alignment-free methods, as well as a basic amino acid composition representation, to transform the available sequences. Novel semi-supervised statistical machine learning methods are then used to discriminate the different class C GPCRs types from the transformed data. This approach is relevant due to the existence of orphan proteins to which type labels should be assigned in a process of deorphanization or reverse pharmacology. The reported experiments show that the proposed techniques provide accurate classification even in settings of extreme class-label scarcity and that fair accuracy can be achieved even with very simple transformation strategies that ignore the sequence ordering.

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Extended Summary: Design of active analogues of a peptide using D-optimal Design, QSAR models and a combinatorial search algorithm

et al. 1997

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A novel set of algorithms and computational tools has been developed within PROMETHEUSTM a comprehensive computational environment for molecular modelling, design and simulation. These have been developed for the design of peptide analogue sets from a single lead and have been applied to optimise the potency of a 15-residue peptide. The chosen lead peptide, CAMELO, was designed in the laboratory of Boman.1h5 It is a hybrid of cecropin A, a 37-residue antimicrobial peptide produced by silk moth larvae, and melittin, a 26-residue peptide extracted from bee venom, and combines to some extent the cytolytic activity of melittin while retaining the selectivity of cecropin A for prokaryotic cells. Our objective was to test analogues of CAMELO and to Ðnd alternative sequences with increased potency against a panel of bacterial strains.The physicochemical properties of the molecules were described by the residue-based parameters of Hellberg and co-workers (Z scales)6 and Norinder (ID Scales)7 * To whom correspondence should be addressed. and covariance functions derived from these (Table 1). A third group of descriptors, referred to as the "Design parametersÏ, represent particular molecular properties which have been suggested as relevant to structureÈ activity relationships for this class.A D-optimal design8 in the Principal Components sub-space of these descriptors produced a QSAR training set of 60 molecules well distributed in the possible design space for 15mer peptides.The antibacterial potencies of the peptides were assayed and shown to be well spread in activity space. Partial Least Squares (PLS) analysis was used to develop a variety of QSAR models. The best model identiÐed had a cross-validated R2 value \ 0.65. Descriptors based on covariances of the Z-scales appeared to provide the most predictive model of the training set data, and with this data set, they performed better than the Norinder scales. The covariance scales were calculated using similar methods to Wold et al.9Like them, we found that the covariance parameters gave an improved model of the data, which may be

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DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures

Cited by 199 publications

References 15 publications

SVM-Based Classification of Class C GPCRs from Alignment-Free Physicochemical Transformations of Their Sequences

SVM-Based Classification of Class C GPCRs from Alignment-Free Physicochemical Transformations of Their Sequences

The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors

Extended Summary: Design of active analogues of a peptide using D-optimal Design, QSAR models and a combinatorial search algorithm

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