It has been twenty years since the cloning of the first γ‐aminobutyric acid (GABA) transporter (GAT1; SLC6A1) in 1990. During this time, over fifty published studies have provided structure‐function information on investigator‐designed GAT1 mutants. To date, nearly 200 of 599 GAT1 residues have been replaced with different amino acids, and the resulting transporter functional properties have significantly advanced our understanding of this member of the neurotransmitter:sodium symporter family. Given this large body of mutagenesis data, there is merit in having a system which organizes this information and makes it publicly available through a searchable, internet‐based database portal. We first created a relational database which accommodates existing functional and pharmacological data reported on the GABA transporters. Using this database, we manually annotated published data on GAT1 mutants. Finally, we developed a web portal for accessing, searching, and managing the database. We have named this web portal GABA Transporter Mutagenesis Database (GATMD). GATMD is available on the internet at http://physiology.sci.csupomona.edu/GATMD. (Supported by NIH grant SC1GM086344)

Background: Efforts to predict functional sites from globular proteins is increasingly common; however, the most successful of these methods generally require structural insight. Unfortunately, despite several recent technological advances, structural coverage of membrane integral proteins continues to be sparse. ConSequently, sequence-based methods represent an important alternative to illuminate functional roles. In this report, we critically examine the ability of several computational methods to provide functional insight within two specific areas. First, can phylogenomic methods accurately describe the functional diversity across a membrane integral protein family? And second, can sequence-based strategies accurately predict key functional sites? Due to the presence of a recently solved structure and a vast amount of experimental mutagenesis data, the neurotransmitter/Na+ symporter (NSS) family is an ideal model system to assess the quality of our predictions.Results: The raw NSS sequence dataset contains 181 sequences, which have been aligned by various methods. The resultant phylogenetic trees always contain six major subfamilies are consistent with the functional diversity across the family. Moreover, in well-represented subfamilies, phylogenetic clustering recapitulates several nuanced functional distinctions. Functional sites are predicted using six different methods ( phylogenetic motifs, two methods that identify subfamily-specific positions, and three different conservation scores). A canonical set of 34 functional sites identified by Yamashita et al. within the recently solved LeuT(Aa) structure is used to assess the quality of the predictions, most of which are predicted by the bioinformatic methods. Remarkably, the importance of these sites is largely confirmed by experimental mutagenesis. Furthermore, the collective set of functional site predictions qualitatively clusters along the proposed transport pathway, further demonstrating their utility. Interestingly, the various prediction schemes provide results that are predominantly orthogonal to each other. However, when the methods do provide overlapping results, specificity is shown to increase dramatically ( e. g., sites predicted by any three methods have both accuracy and coverage greater than 50%).Conclusion: The results presented herein clearly establish the viability of sequence-based bioinformatic strategies to provide functional insight within the NSS family. As such, we expect similar bioinformatic investigations will streamline functional investigations within membrane integral families in the absence of structure.

The variability within calculated protein residue pKa values calculated using Poisson-Boltzmann continuum theory with respect to small conformational fluctuations is investigated. As a general rule, sites buried in the protein core have the largest pKa fluctuations but the least amount of conformational variability; conversely, sites on the protein surface generally have large conformational fluctuations but very small pKa fluctuations. These results occur because of the heterogeneous or uniform nature of the electrostatic microenvironments at the protein core or surface, respectively. Atypical surface sites with large pKa fluctuations occur at the interfaces between significant anionic and cationic potentials.