Three-Stage Prediction of Protein Beta-Sheets by Neural Networks, Alignments, and Graph Algorithms
Jianlin Cheng and Pierre Baldi
Protein beta-sheets play a fundamental role in protein structure,
function, evolution, and bio-engineering. Accurate prediction and
assembly of protein beta-sheets, however, remains challenging
because protein beta-sheets require formation of hydrogen bonds
between linearly distant residues. Previous approaches for
predicting beta-sheet topological features, such as beta-strand
alignments, in general have not exploited the global covariation
and constraints characteristic of beta-sheet architectures.
We propose a modular approach to the problem of predicting/assembling
protein beta-sheets in a chain by integrating both local and global
constraints in three steps. The first step uses recursive neural
networks to predict pairing probabilities for all pairs of inter-strand
beta-residues from profile, secondary structure, and solvent
accessibility information. The second step applies dynamic programming
techniques to these probabilities to derive binding pseudo-energies and
optimal alignments between all pairs of beta-strands. Finally,
the third step, uses graph matching algorithms to predict the beta-sheet
architecture of the protein by optimizing the global pseudo-energy
while enforcing strong global beta-strand pairing constraints. The
approach is evaluated using cross-validation methods on a large
non-homologous dataset and yields significant improvements over previous
Download BETApro 1.0 (Linux version). See readme.txt in the zip file or click here for installation instructions. This software depends on SSpro4.0 (secondary structure predictor). You can download SSpro here.
Download the paper at Bioinformatics website or a quick powerpoint overview
The full dataset (BetaSheet916) used in the paper.
BetaSheet916 is splitted randomly and evenly into ten folds to perform cross-validation.
Question or need help? Please send email to firstname.lastname@example.org, email@example.com, or firstname.lastname@example.org.