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Biomedical subjects

Paul D Dobson

Publications and source records attributed to Paul D Dobson.

5 recordsLinked to original sources
PubMed2005

Predicting enzyme class from protein structure without alignments.

Methods for predicting protein function from structure are becoming more important as the rate at which structures are solved increases more rapidly than experimental knowledge. As a result, protein structures now frequently lack functional annotations. The majority of methods for predicting protein function are reliant upon identifying a similar protein and transferring its annotations to the query protein. This method fails when a similar protein cannot be identified, or when any similar proteins identified also lack reliable annotations. Here, we describe a method that can assign function from structure without the use of algorithms reliant upon alignments. Using simple attributes that can be calculated from any crystal structure, such as secondary structure content, amino acid propensities, surface properties and ligands, we describe each enzyme in a non-redundant set. The set is split according to Enzyme Classification (EC) number. We combine the predictions of one-class versus one-class support vector machine models to make overall assignments of EC number to an accuracy of 35% with the top-ranked prediction, rising to 60% accuracy with the top two ranks. In doing so we demonstrate the utility of simple structural attributes in protein function prediction and shed light on the link between structure and function. We apply our methods to predict the function of every currently unclassified protein in the Protein Data Bank.

Algorithms↗
PubMed2004

Prediction of protein function in the absence of significant sequence similarity.

Tremendous progress in DNA sequencing has yielded the genomes of a host of important organisms. The utilisation of these resources requires understanding of the function of each gene. Standard methods of functional assignment involve sequence alignment to a gene of known function; however such methods often fail to find any significant matches. Here we discuss a number of recent alternative methods that may be of use when sequence alignment fails. Function can be defined in a number of ways including E.C. number and MIPS and KEGG functional classes. Phylogenetic profiles show the pattern of presence or absence of a protein between genomes. Protein-protein interactions can be identified by searching for interacting pairs of proteins that are fused to a single protein chain in another organism. The gene neighbour method uses the observation that if the genes that encode two proteins are close on a chromosome, the proteins tend to be functionally related. More general methods use sequence properties such as amino acid composition, mean hydrophobicity, predicted secondary structure and post-translational modification sites. Data mining methods devise rules in the form of IF... THEN statements that make predictions of function using sequence based attributes, predicted secondary structure and sequence similarity. Finally, structural features can be used, after modelling the structure of a protein from its sequence or solving its structure. Protein fold class can be strongly indicative of function, while other structural features, such as secondary structure content, cleft size and 3D structural motifs are also useful.

Computer Simulation↗
PubMed2003

Distinguishing enzyme structures from non-enzymes without alignments.

The ability to predict protein function from structure is becoming increasingly important as the number of structures resolved is growing more rapidly than our capacity to study function. Current methods for predicting protein function are mostly reliant on identifying a similar protein of known function. For proteins that are highly dissimilar or are only similar to proteins also lacking functional annotations, these methods fail. Here, we show that protein function can be predicted as enzymatic or not without resorting to alignments. We describe 1178 high-resolution proteins in a structurally non-redundant subset of the Protein Data Bank using simple features such as secondary-structure content, amino acid propensities, surface properties and ligands. The subset is split into two functional groupings, enzymes and non-enzymes. We use the support vector machine-learning algorithm to develop models that are capable of assigning the protein class. Validation of the method shows that the function can be predicted to an accuracy of 77% using 52 features to describe each protein. An adaptive search of possible subsets of features produces a simplified model based on 36 features that predicts at an accuracy of 80%. We compare the method to sequence-based methods that also avoid calculating alignments and predict a recently released set of unrelated proteins. The most useful features for distinguishing enzymes from non-enzymes are secondary-structure content, amino acid frequencies, number of disulphide bonds and size of the largest cleft. This method is applicable to any structure as it does not require the identification of sequence or structural similarity to a protein of known function.

Algorithms↗
PubMed2003

Length preferences and periodicity in beta-strands. Antiparallel edge beta-sheets are more likely to finish in non-hydrogen bonded rings.

We analysed the length distributions of different types of beta-strand in a high resolution, non-homologous set of 500 protein structures, finding differences in their mean lengths. Antiparallel edge strands in strand-turn-strand motifs show a preference for an even number of residues. This propensity is enhanced if the length is corrected for beta-bulges, which insert an extra residue into the strand. Residues in antiparallel edge beta-strands alternate between being in hydrogen bonded and non-hydrogen bonded rings. Antiparallel edges with an even number of residues are more likely to have their final beta residue in a non-hydrogen bonded ring. This suggests that non-hydrogen bonded rings are intrinsically more stable than hydrogen bonded rings, perhaps because its side chain packing is closer. Therefore, we suggest that a simple way to increase beta-hairpin stability, or the stability of an antiparallel edge strand, is to have a non-hydrogen bonded ring at the end of the strand.

Computational Biology↗