Protein fold recognition is the problem of determining whether a given protein sequence folds into a previously observed structure. An uncertainty complication is that it is not always true that the structure has been previously observed. Markov logic networks (MLNs) are a powerful representation that combines first-order logic and probability by attaching weights to first-order formulas and using these as templates for features of Markov networks. In this chapter, we describe a simple temporal extension of MLNs that is able to deal with sequences of logical atoms. We also propose iterated robust tabu search (IRoTS) for maximum a posteriori (MAP) inference and Markov Chain-IRoTS (MC-IRoTS) for conditional inference in the new framework. We show how MC-IRoTS can also be used for discriminative weight learning. We describe how sequences of protein secondary structure can be modeled through the proposed language and show through some preliminary experiments the promise of our approach for the problem of protein fold recognition from these sequences.
Protein Fold recognition using Markov logic networks
ESPOSITO, FLORIANA
2011-01-01
Abstract
Protein fold recognition is the problem of determining whether a given protein sequence folds into a previously observed structure. An uncertainty complication is that it is not always true that the structure has been previously observed. Markov logic networks (MLNs) are a powerful representation that combines first-order logic and probability by attaching weights to first-order formulas and using these as templates for features of Markov networks. In this chapter, we describe a simple temporal extension of MLNs that is able to deal with sequences of logical atoms. We also propose iterated robust tabu search (IRoTS) for maximum a posteriori (MAP) inference and Markov Chain-IRoTS (MC-IRoTS) for conditional inference in the new framework. We show how MC-IRoTS can also be used for discriminative weight learning. We describe how sequences of protein secondary structure can be modeled through the proposed language and show through some preliminary experiments the promise of our approach for the problem of protein fold recognition from these sequences.
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