Abstracts (first author)
The Jackprot Simulation: slot-machine model to teach the non-random nature of protein evolution
Protein evolution is not a random process. We use slot-machine probabilities and ion channels, in an inquiry-based learning scenario, to show biological directionality on molecular change. The slot-machine represents the cellular chemical apparatus, product itself of Darwinian evolution, required to generate, step by step, each of the nucleotides coding for an amino acid of a model protein. Teachers and students can access the Jackprot Simulation and run statistical analysis of protein evolution by cutting and pasting nucleotide sequences obtained from the WWW. The Jackprot generates statistics on nucleotide evolution under selection (observed vs. expected values) and at random (without selection). We will use the following example when explaining hands-on how to use the Jackprot: Because ion channels reside in the lipid bilayer of cell membranes, their residue location must be in balance with the membrane’s hydrophobic/philic nature; a selective ‘pore’ for ion passage is located within the hydrophobic region. We will contrast the random generation of DNA sequence for KcsA, a bacterial two-transmembrane-domain (2TM) potassium channel, from Streptomyces lividans, with an under-selection scenario, the ‘Jackprot,’ which predicts much faster evolution than chance. We will distribute guidelines on how to use the online interface The Jackprot Simulation (JAVA APPLET Version 1.0) to model a numerical interaction between mutation rate and natural selection during the scenario of polypeptide evolution. Winning the ‘Jackprot,’ or highest-fitness complete-peptide sequence, requires cumulative smaller ‘wins’ (rewarded by selection) at the first, second and third positions in each of the 161 KcsA codons (‘jackdons’ that led to ‘jackacids’ that led to the ‘Jackprot’). The ‘Jackprot,’ as didactic tool, helps students understand how mutation rate coupled with natural selection suffice to explain the evolution of specialized, complex proteins. Student learning data will be shared.