Redundancy-weighting for better inference of protein structural features.
BACKGROUND
Structural knowledge, extracted from the Protein Data Bank (PDB), underlies numerous potential functions and prediction methods. The PDB, however, is highly biased: many proteins have more than one entry, while entire protein families are represented by a single structure, or even not at all. The standard solution to this problem is to limit the studies to non-redundant subsets of the PDB. While alleviating biases, this solution hides the many-to-many relations between sequences and structures. That is, non-redundant datasets conceal the diversity of sequences that share the same fold and the existence of multiple conformations for the same protein. A particularly disturbing aspect of non-redundant subsets is that they hardly benefit from the rapid pace of protein structure determination, as most newly solved structures fall within existing families.


RESULTS
In this study we explore the concept of redundancy-weighted datasets, originally suggested by Miyazawa and Jernigan. Redundancy-weighted datasets include all available structures and associate them (or features thereof) with weights that are inversely proportional to the number of their homologs. Here, we provide the first systematic comparison of redundancy-weighted datasets with non-redundant ones. We test three weighting schemes and show that the distributions of structural features that they produce are smoother (having higher entropy) compared with the distributions inferred from non-redundant datasets. We further show that these smoothed distributions are both more robust and more correct than their non-redundant counterparts. We suggest that the better distributions, inferred using redundancy-weighting, may improve the accuracy of knowledge-based potentials and increase the power of protein structure prediction methods. Consequently, they may enhance model-driven molecular biology.
Machine Learning for Healthcare and Life-Sciences, Analytics Department, IBM Research Laboratory, Haifa, 3490002, Department of Software Engineering, Shamoon College of Engineering, Beer-Sheva 84100, Israel, Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA, Department of Computer Science, University of Haifa, Mount Carmel, Haifa, 3498838 and Departments of Life Sciences and Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel.
Chen
C
Yanover
Machine Learning for Healthcare and Life-Sciences, Analytics Department, IBM Research Laboratory, Haifa, 3490002, Department of Software Engineering, Shamoon College of Engineering, Beer-Sheva 84100, Israel, Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA, Department of Computer Science, University of Haifa, Mount Carmel, Haifa, 3498838 and Departments of Life Sciences and Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel.
Natalia
N
Vanetik
Machine Learning for Healthcare and Life-Sciences, Analytics Department, IBM Research Laboratory, Haifa, 3490002, Department of Software Engineering, Shamoon College of Engineering, Beer-Sheva 84100, Israel, Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA, Department of Computer Science, University of Haifa, Mount Carmel, Haifa, 3498838 and Departments of Life Sciences and Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel.
Michael
M
Levitt
Machine Learning for Healthcare and Life-Sciences, Analytics Department, IBM Research Laboratory, Haifa, 3490002, Department of Software Engineering, Shamoon College of Engineering, Beer-Sheva 84100, Israel, Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA, Department of Computer Science, University of Haifa, Mount Carmel, Haifa, 3498838 and Departments of Life Sciences and Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel.
Rachel
R
Kolodny
Machine Learning for Healthcare and Life-Sciences, Analytics Department, IBM Research Laboratory, Haifa, 3490002, Department of Software Engineering, Shamoon College of Engineering, Beer-Sheva 84100, Israel, Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA, Department of Computer Science, University of Haifa, Mount Carmel, Haifa, 3498838 and Departments of Life Sciences and Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel.
Chen
C
Keasar
D000596:Amino Acids / Q000737:chemistry; D057225:Data Mining; D030562:Databases, Protein; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry
D000596:Amino Acids; D011506:Proteins