Exploring therapeutic targets with the HMM-SA structural alphabet: Methods, tools, and application to HIV-2 protease.

The rapid expansion of available three-dimensional protein structures-derived from both experimental techniques and bioinformatic predictions-offers unprecedented opportunities for drug discovery, particularly for targets that have historically been difficult to characterize. However, the effective analysis of these increasingly complex and voluminous structural datasets remains a major challenge. Efficient representations of protein conformations are essential to facilitate large-scale comparison, structural classification, and functional interpretation in therapeutic contexts. The concept of structural alphabets, introduced by Pr S. Hazout in 1999, provides a robust and scalable framework to represent local protein backbone conformations using a limited set of recurring structural motifs. This representation enables a one-dimensional encoding of three-dimensional protein structures that retains essential geometric features beyond secondary structure, while allowing systematic and interpretable analyses. In this review, we focus on HMM-SA, a structural alphabet constructed using a hidden Markov model. HMM-SA defines 27 structural motifs, including 18 regions specifically dedicated to loops, and captures the statistical dependencies between them. We present a detailed overview of the HMM-SA framework, and of the computational tools derived from this structural alphabet, developed to explore protein function, conformational variability, and the structural determinants of molecular recognition. The utility of HMM-SA is illustrated through a case study on HIV-2 protease (PR2), a critical enzyme in antiretroviral drug development. By analyzing PR2 structural asymmetry, ligand-induced conformational changes, and mutation-driven alterations, we highlight the ability of HMM-SA-based methods to identify key structural features involved in ligand specificity and resistance mechanisms, thereby advancing therapeutic target analysis.