Multi-level learning: improving the prediction of protein, domain and residue interactions by allowing information flow between levels

Abstract

Background: Proteins interact through specific binding interfaces that contain many residues in domains. Protein interactions thus occur on three different levels of a concept hierarchy: whole-proteins, domains, and residues. Each level offers a distinct and complementary set of features for computationally predicting interactions, including functional genomic features of whole proteins, evolutionary features of domain families and physical-chemical features of individual residues. The predictions at each level could benefit from using the features at all three levels. However, it is not trivial as the features are provided at different granularity.

Results: To link up the predictions at the three levels, we propose a multi-level machine-learning framework that allows for explicit information flow between the levels. We demonstrate, using representative yeast interaction networks, that our algorithm is able to utilize complementary feature sets to make more accurate predictions at the three levels than when the three problems are approached independently. To facilitate application of our multi-level learning framework, we discuss three key aspects of multi-level learning and the corresponding design choices that we have made in the implementation of a concrete learning algorithm. 1) Architecture of information flow: we show the greater flexibility of bidirectional flow over independent levels and unidirectional flow; 2) Coupling mechanism of the different levels: We show how this can be accomplished via augmenting the training sets at each level, and discuss the prevention of error propagation between different levels by means of soft coupling; 3) Sparseness of data: We show that the multi-level framework compounds data sparsity issues, and discuss how this can be dealt with by building local models in information-rich parts of the data. Our proof-of-concept learning algorithm demonstrates the advantage of combining levels, and opens up opportunities for further research.

Availability: The software and a readme file can be downloaded at http://networks.gersteinlab.org/mll. The programs are written in Java, and can be run on any platform with Java 1.4 or higher and Apache Ant 1.7.0 or higher installed. The software can be used without a license.

Figure 1

Schematic illustration of multi-level learning concepts. (a) The three levels of interactions. Top: the PDB structure 1piw of the homo-dime r yeast. NADP-dependent alcohol dehydrogenase 6. Middle: each chain contains two conserved Pfam domain instances, PF00107 (inner) and PF08240 (outer). The interaction interface is at PF00107. Bottom: two pairs of residues predicted by iPfam to interact: 283 (yellow) with 287 (cyan), and 285 (purple) with 285. (b) The three information flow architectures. i: independent levels, ii: unidirectional flow (illustrated by download flow), iii: bidirectional flow. (c) Coupling mechanisms for passing information from one level to another. 1: passing training information to expand the training set of the next level, 2: passing predictions as an additional feature of the next level, 3: passing predictions to expand the training set of the next level.

Figure 2

Receiver operator characteristic (ROC) curves of protein interaction predictions with different frameworks and training levels.

Figure 3

Receiver operator characteristic (ROC) curves of domain interaction predictions with different frameworks and training levels.

Figure 4

Receiver operator characteristic (ROC) curves of residue interaction predictions with different frameworks and training levels.