RANKPROP: a web server for protein remote homology detection.

UNLABELLED: We present a large-scale implementation of the Rankprop protein homology ranking algorithm in the form of an openly accessible web server. We use the NRDB40 PSI-BLAST all-versus-all protein similarity network of 1.1 million proteins to construct the graph for the Rankprop algorithm, whereas previously, results were only reported for a database of 108 000 proteins. We also describe two algorithmic improvements to the original algorithm, including propagation from multiple homologs of the query and better normalization of ranking scores, that lead to higher accuracy and to scores with a probabilistic interpretation. AVAILABILITY: The Rankprop web server and source code are available at http://rankprop.gs.washington.edu

1 INTRODUCTION

Rankprop (Weston et al., 2004) is a network-based inference algorithm for identifying subtle protein sequence similarities, corresponding to remote homology relationships or to structural similarities. The algorithm operates on a protein similarity network, a graph in which each node is a protein and each weighted edge connecting two proteins indicates their similarity. Such a network can be built using existing tools, such as PSI-BLAST (Altschul et al., 1997).

The key idea of the Rankprop algorithm is to extract global information from a protein similarity network by propagating outward from a user-specified query protein. Effectively, the algorithm sums over all possible paths from the query to each target protein. Thus, after propagation, the resulting activation score for each node includes global information about that protein's relationship to the query. Ranking proteins by these scores is analogous to performing a database search using a tool such as PSI-BLAST, except that the ranking induced by Rankprop reflects the global topology of the protein similarity network.

In (Weston et al., 2004), PSI-BLAST is used to measure sequence similarity, and the unnormalized weight for the edge from node i to node j is W=exp(−S(i)/σ), where S(i) is the PSI-BLAST E-value assigned to protein i given query j, and the parameter σ is a positive constant. Edges are only included in the network for E-values smaller than a fixed threshold. We obtain a stochastic connectivity matrix M for the protein similarity network by row-normalizing edge weights W to obtain transition probabilities: M = W / ∑ W.

Given such a network and a query sequence q, the Rankprop algorithm is simple to describe. First, all nodes are assigned initial activation scores that reflect each target protein's similarity to q. Like the edge weights, these scores are computed from PSI-BLAST E-values using the same equation. At each iteration of the algorithm, the activation score at a given node is replaced by the weighted sum of the scores from all of its incoming edges. The update rule includes a diffusion constant α that controls the rate of diffusion through the network. Formally, we define the initial activation scores as P0= exp(−S(i)/σ). Viewing P as the column vector of activation levels at iteration t, the algorithm is given by P= αMP+P0 if P≠q and P = 1 otherwise, where α ∈(0,1). One can show that this iterative procedure converges to a fixed point, which in practice happens in a small number of iterations. The output of the Rankprop algorithm is a ranking of the nodes in the network according to their final activation values. Proteins that receive a high activation score are linked to the query via many strongly weighted paths and vice versa. A multidomain query protein will produce strong matches to any target protein that contains one or more of the query domains. A single domain query A may connect through a multidomain protein AB to infer a false relationship with B. However, previous work (Weston et al., 2004) has found that as long as the query sequence is connected to many other proteins, then the true homologs will be mutually reinforcing and receive a higher rank.

In this work, we extend the original Rankprop algorithm in two ways: (1) improving accuracy by propagating simultaneously from proteins that are very closely related to the query, and (2) improving the interpretability of the scores produced by Rankprop by empirically mapping them to probabilities. The mapped score can be interpreted as the probability that the target protein is a member of the same SCOP superfamily as the query. We also announce the availability of a free web server that allows individual queries against a protein similarity network derived from the NRDB40, comprising 1.1 million targets.

2 METHODS

The Rankprop server uses the PSI-BLAST all-versus-all similarity matrix for NRDB40 provided by the PairsDB website (Heger et al., 2008). NRDB40 is a subset of the non-redundant sequence database, filtered so that no pairs exhibit >40% sequence identity.

We generalize the Rankprop algorithm to accept a set Q of query proteins, rather than a single query protein. To use this extra information we perform propagation as usual, but we constrain the activation scores for all the query points such that they are highly ranked. In particular, we choose the set Q to be all the proteins that have a match with the initial query q with a PSI-BLAST E-value<0.001. We then constrain our algorithm to have P = 1−S(j), ∀ j∈Q. This modification is useful because, instead of propagating from a single query source node in the graph, we can propagate from several source nodes that all belong to the same family or superfamily that we are searching for.

The original Rankprop algorithm outputs scalar values that are not directly interpretable. In the new version of the algorithm, we map each Rankprop score to an estimate of the probability that the corresponding query and target proteins belong to the same structural superfamily. We employ the SCOP database (Murzin et al., 1995) to compute a histogram of empirical frequencies of the activation levels P for each protein i. More specifically, we choose bin centers v and compute the following quantities: n, the number of times P falls into bin v, and s, the number of times that the latter occurs and i is in the same superfamily as the query. We are interested in the value s / n, which can be interpreted as the probability for each activation value bin of the target being in the same superfamily as the query. We choose the bin centers v= (0,0.01,0.02,…, 0.2 , 0.3 ,…, 1), and we enforce monotonicity in the final output by setting p/n = p/n if p/n