A sequence-profile-based HMM for predicting and discriminating β barrel membrane proteins

Abstract

We develop a HMM model, which can predict the topology of beta barrel membrane proteins using, as input, evolutionary information. The model is cyclic with 6 types of states: two for the beta strand transmembrane core, one for the beta strand cap on either side of the membrane, one for the inner loop, one for the outer loop and one for the globular domain state in the middle of each loop. The development of a specific input for HMM based on multiple sequence alignment is novel. The accuracy per residue of the model is 83% when a jack knife procedure is adopted. With a model optimisation method using a dynamic programming algorithm seven topological models out of the twelve proteins included in the testing set are also correctly predicted. When used as a discriminator, the model is rather selective. At a fixed probability value, it retains 84% of a non-redundant set comprising 145 sequences of well-annotated outer membrane proteins. Concomitantly, it correctly rejects 90% of a set of globular proteins including about 1200 chains with low sequence identity (<30%) and 90% of a set of all alpha membrane proteins, including 188 chains.

Frequent-subsequence-based prediction of outer membrane proteins

“…In fact, even for the predictive methods that are capable of identifying OMPs, precision remains poor [4,8,14,19,25,28]. Furthermore, the datasets used to train and evaluate these existing methods are often small and not manually curated.…”

“…Scientists have previously used neural network-based methods [4,8], hydrophobicity analysis [19], and combinations of methods, including homology analysis and amino acid abundance [25,28], to varying degrees of success. The most recent approach, reported by Martelli et al [14] is, to date, the most successful attempt at OMP classification. They used a hidden Markov model (HMM) to represent the prototypes of OMPs, as it is known that each amino acid residue of a β-barrel membrane protein can be categorized into one of three types: outer/extracellular loops, transmembrane β-strands and inner/periplasmic turns.…”

“…Currently, the state-of-the-art OMP prediction algorithm is the one developed by researchers in the biology field [14], using an HMM based on highly specialized biological domain knowledge. For a more complete comparison, we have supplied our dataset to the authors of [14] to obtain classification results of their method.…”

“…For a more complete comparison, we have supplied our dataset to the authors of [14] to obtain classification results of their method. Note that their method used OMPs with known 3D structures to build the prediction model and, therefore, did not use our training set.…”

“…Note that their method used OMPs with known 3D structures to build the prediction model and, therefore, did not use our training set. Instead, the model that they have built from the 12 OMPs with atomically resolved 3D structures as described in [14] was applied directly to classify our test set. The results they reported on our dataset were a precision of 64% and a recall of 71% (averages of the 5-fold cross validation).…”