_Algorithms_**2009**, 2, 764-789; doi:10.3390/a2020764  
_Article_  
**SDPhound, a Mutual Information-Based Method to Investigate Specificity-Determining Positions**  
**[PERSON] \({}^{1,\dagger,\ddagger}\), [PERSON] \({}^{1,2,\dagger,\star}\), [PERSON] \({}^{1}\), [PERSON] \({}^{1}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) NEST Scuola Normale Superiore and CNR-INFM, Piazza dei Cavalieri 7, I-56126 Pisa, Italy  
\({}^{2}\) Drug Discovery and Development, Italian Institute of Technology, via Morego 30, Genova I-161631, Italy  
\({}^{\dagger}\) These authors contributed equally to the work. \({}^{\ddagger}\) Current address: Dipartimento di Fisica, Universita di Roma \"La Sapienza\", I-00158 Rome, Italy. E-mails: [PERSON]SS]; [EMAIL_ADDRESS]; [EMAIL_ADDRESS]; [EMAIL_ADDRESS]  
\({}^{\star}\) Author to whom correspondence should be addressed; E-mail: [EMAIL_ADDRESS]  
_Received: 22 December 2008; in revised form: 12 March 2009 / Accepted: 5 May 2009 / Published: 26 May 2009_  
**Abstract:** Considerable importance in molecular biophysics is attached to influencing by mutagenesis the specific properties of a protein family. The working hypothesis is that mutating residues at few selected positions can affect specificity. Statistical analysis of homologue sequences can identify putative specificity determining positions (SDPs) and help to shed some light on the peculiarities underlying their functional role. In this work, we present an approach to identify such positions inspired by state of the art mutual information-based SDP prediction methods. The algorithm based on this approach provides a systematic procedure to point at the relevant physical characteristics of putative SPDs and can investigate the effects of correlated mutations. The method is tested on two standard benchmarks in the field and further validated in the context of a biologically interesting problem: the multimerization of the Intrinsically Fluorescent Proteins (IFP). **Keywords:** specificity determining positions; intrinsically fluorescent proteins; mutual information. Introduction  
Considerable efforts in current biophysical research are devoted to identifying viable procedures to engineer mutations in a protein so as to manipulate its properties in desired directions. Most available methods rely on the assumption that the functional, or structural, specificities among homologs depend on relatively few crucial residues that are conserved among proteins sharing the same feature. The problem thus becomes recognizing such residues. The combinatorial number of possible mutations even in relatively small proteins often makes purely experimental approaches, such as random mutagenesis, not affordable and it compels to create reliable and efficient computational tools to assist experiments by predicting which residues are more likely to affect the desired property. Several _in silico_ approaches for predicting sites with some functional importance in proteins are available [1]. Some need the sequence and some need the knowledge of both sequence and structure. Of particular relevance in this context is a family of methods based on information theory, that build upon the Maximum Likelihood Estimator scheme [2]. These techniques exploit the growing amount of protein sequence data available for a wide variety of organisms. The general strategy consists in performing a statistical analysis of a multiple sequence alignment of proteins of a certain family and in relying on the assumption that since mutations at SDPs change the function of the protein, they are generally conserved between proteins with the same function, but tend to be distinct for proteins with different functions [3]. The correlation between the presence of a residue at a given position in the alignment and the inclusion of the protein in a class, identified by a specific function or quaternary structure, is evaluated through the joint probability \(P_{p}(\alpha,i)\) for the event \"amino acid \(\alpha\) occurs at position \(p\) and the protein belongs to the \(i^{th}\) class\".