Protein modeling with hybrid Hidden Markov Model/neural network architectures.
Hidden Markov Models (HMMs) are useful in a number of tasks in computational molecular biology, and in particular to model and align protein families. We argue that HMMs are somewhat optimal within a certain modeling hierarchy. Single first order HMMs, however, have two potential limitations: a large number of unstructured parameters, and a built-in inability to deal with long-range dependencies. Hybrid HMM/Neural Network (NN) architectures attempt to overcome these limitations. In hybrid HMM/NN, the HMM parameters are computed by a NN. This provides a reparametrization that allows for flexible control of model complexity, and incorporation of constraints. The approach is tested on the immunoglobulin family. A hybrid model is trained, and a multiple alignment derived, with less than a fourth of the number of parameters used with previous single HMMs. To capture dependencies, however, one must resort to a larger hybrid model class, where the data is modeled by multiple HMMs. The parameters of the HMMs, and their modulation as a function of input or context, is again calculated by a NN.
Baldi
P
P
Division of Biology and JPL California Institute of Technology, Pasadena 91125, USA.
Chauvin
Y
Y
D000595:Amino Acid Sequence; D000818:Animals; D003198:Computer Simulation; D007136:Immunoglobulins / Q000737:chemistry*; D008390:Markov Chains; D008433:Mathematics; D008962:Models, Theoretical; D008969:Molecular Sequence Data; D016571:Neural Networks, Computer; D011506:Proteins / Q000737:chemistry* / Q000145:classification
D007136:Immunoglobulins; D011506:Proteins