Conservation helps to identify biologically relevant crystal contacts.
Some crystal contacts are biologically relevant, most are not. We assess the utility of combining measures of size and conservation to discriminate between biological and non-biological contacts. Conservation and size information is calculated for crystal contacts in 53 families of homodimers and 65 families of monomers. Biological contacts are shown to be usually conserved and typically the largest contact in the crystal. A range of neural networks accepting different combinations and encodings of this information is used to answer the following questions: (1) is a given crystal contact biological, and (2) given all crystal contacts in a homodimer, which is the biological one? Predictions for (1) are performed on both homodimer and monomer datasets. The best performing neural network combined size and conservation inputs. For the homodimers, it correctly classified 48 out of 53 biological contacts and 364 out of 366 non-biological contacts, giving a combined accuracy of 98.3 %. A more robust performance statistic, the phi-coefficient, which accounts for imbalances in the dataset, gave a value of 0.92. Taking all 535 non-biological contacts from the 65 monomers, this predictor made erroneous classifications only 4.3 % of the time. Predictions for (2) were performed on homodimers only. The best performing network achieved a prediction accuracy of 98.1 % using size information alone. We conclude that in answering question (1) size and conservation combined discriminate biological from non-biological contacts better than either measure alone. For answering question (2), we conclude that in our dataset size is so powerful a discriminant that conservation adds little predictive benefit.
Valdar
W S
WS
Biomolecular Structure and Modelling Unit, Biochemistry and Molecular Biology Department, University College London, UK.
Thornton
J M
JM
D000818:Animals; D001665:Binding Sites; D002417:Cattle; D019295:Computational Biology / Q000379:methods; D017124:Conserved Sequence; D003460:Crystallization; D030562:Databases, Protein; D019281:Dimerization; D004798:Enzymes / Q000737:chemistry / Q000378:metabolism; D008024:Ligands; D008956:Models, Chemical; D008958:Models, Molecular; D016571:Neural Networks, Computer; D011336:Probability; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D051381:Rats; D015203:Reproducibility of Results; D012107:Research Design; D012680:Sensitivity and Specificity; D016415:Sequence Alignment; D013499:Surface Properties
D004798:Enzymes; D008024:Ligands; D011506:Proteins