Predictive Computational Models of Substrate Binding by a Nucleoside Transporter

• Published in: The Journal of biological chemistry 2009-12, Vol.284 (49), p.34028-34035
• Main Authors: Collar, Catharine J., Al-Salabi, Mohammed I., Stewart, Mhairi L., Barrett, Michael P., Wilson, W.David, de Koning, Harry P.
• Format: Article
• Language: English
• Subjects: Animals; Chemistry, Pharmaceutical - methods; Computational Biology - methods; Computers; Drug Design; Female; Kinetics; Melarsoprol - pharmacology; Membrane Transport, Structure, Function, and Biogenesis; Nucleoside Transport Proteins - chemistry; Pentamidine - pharmacology; Rats; Rats, Wistar; Software; Trypanocidal Agents - chemistry; Trypanocidal Agents - pharmacology; Trypanosoma brucei; Trypanosoma brucei brucei - metabolism
• ISSN: 0021-9258; 1083-351X; 1083-351X
• DOI: 10.1074/jbc.M109.049726

Transporters play a vital role in both the resistance mechanisms of existing drugs and effective targeting of their replacements. Melarsoprol and diamidine compounds similar to pentamidine and furamidine are primarily taken up by trypanosomes of the genus Trypanosoma brucei through the P2 aminopurine transporter. In standardized competition experiments with [3H]adenosine, P2 transporter inhibition constants (Ki) have been determined for a diverse dataset of adenosine analogs, diamidines, Food and Drug Administration-approved compounds and analogs thereof, and custom-designed trypanocidal compounds. Computational biology has been employed to investigate compound structure diversity in relation to P2 transporter interaction. These explorations have led to models for inhibition predictions of known and novel compounds to obtain information about the molecular basis for P2 transporter inhibition. A common pharmacophore for P2 transporter inhibition has been identified along with other key structural characteristics. Our model provides insight into P2 transporter interactions with known compounds and contributes to strategies for the design of novel antiparasitic compounds. This approach offers a quantitative and predictive tool for molecular recognition by specific transporters without the need for structural or even primary sequence information of the transport protein.