The figure of merit in the analysis is provided by the average mutual information:  
\[I_{p}=\sum_{i=1}^{N}\sum_{\alpha=1}^{20}P_{p}(\alpha,i)\mathrm{ln}\frac{P_{p}( \alpha,i)}{P_{p}(\alpha)P(i)} \tag{1}\]  
The sum over \(i\) spans the number of specificity classes and \(\alpha\) covers the amino acid set. \(P_{p}(\alpha)\) is the probability to find residue \(\alpha\) at position \(p\) and \(P(i)\) the probability that a sequence belongs to the \(i^{th}\) class. The definition above establishes a measure of the specificity content of a position in the alignment: larger values of \(I_{p}\) are expected to indicate more relevant positions in identifying a given subfamily. Unfortunately, actual values of the probabilities in eq. (1) are not known. They can only be estimated and the main difference among mutual information-based methods, lies in the choice of the most appropriate estimator for the joint probability. In this work, we describe a protocol, called SDPhound, to identify SDPs and analyze the physical characteristics that may be responsible for their role. The core of the procedure is a prescription for constructing an estimator of \(P_{p}(\alpha,i)\) that is rather general and rigorous in its probabilistic interpretation. Once this estimator is available, the specificity content of the different positions can be ranked via the mutual information. Although the formal structure of the estimator is fixed, variations on the specific functions used in it make it possible to implement a set of steps that, appropriately combined, allow to first screen and then characterize putative SDPs. The steps will be described in detail in sections 2. and 3., a preliminary summary is as follows. The estimator of \(P_{p}(\alpha,i)\) is based on the frequency of appearance of a residue at a given position in a given alignment. We combine this basic ingredient with different smoothing functions that can dress the estimated probability to account for non relevant statistical fluctuations. A reasonable stability of positions ranking with respect to the choice of the smoothing functions is a good indicator of the robustness of the predictions with respect to, for example, bias due to the finite size of the aligned set. The structure of the estimator can also be exploited, together with the concept of substitution classes, to investigate the physical and/or chemical factors responsible for specificity both by an _a priori_ classification according to some predefined properties and by an automatically generated partitioning that preserves the mutual information content. The procedure can also be generalized to describe possible pairwise correlation effects among SDPs. The performance of SDPhound is assessed in three applications, as illustrated in sections 4. and 5. We begin by validating the approach by comparing its performance to that of a popular alternative scheme due to [PERSON] _et al._[2] in the applications they chose as benchmarks [2]. In particular, we investigate positions responsible for specific features of the Major Intrinsic Protein (MIP) and the bacterial transcription factor LacI families. In both cases, after verifying that SDPhound performs as well as the more established method in ranking putative SDPs, we shall refine the analysis by examining the physical characteristics of the positions. This second step is, to the best of our knowledge, an original feature of SDPhound. We shall further apply the method to identify a set of mutations able to affect the multimeric state of the Intrinsically Fluorescent Protein (IFP) family. This is a problem of considerable interest since IFP are extremely important in molecular biology and biotechnology where they are used in a variety of _in vivo_ visualization techniques [4; 5; 6]. The first discovered member of the family was the Green Fluorescent Protein (GFP) from the jellyfish _A_euorea Victoria (avGFP) [7].