After successfully using the 6GIX water soluble chlorophyll binding protein to purify canola oil, we looked deeper into the industrial application of our solution. As part of our desire to industrialize our solution we needed to optimize our system on all levels. This optimization includes optimization at the smallest interactions within our system, primarily with our protein. To optimize our 6GIX protein we looked to make informed modifications such that they have as large an impact possible while additionally maintaining its affinity to chlorophyll. To accomplish we leaned on the use of molecular dynamic simulation that was essential to our understanding of the 6GIX protein.

When developing ModGIX we went back to the molecular dynamics models generated for 6GIX to identify areas where modifications may have an impact. This was conducted in six key data collection and categorization stages.

Step 1. Molecular Dynamic Simulation. To develop a starting point for our model we developed a one nanosecond dynamics simulation of a single 6GIX monomer. This simulation was conducted with the same methodology as the other molecular dynamics models detailed in our In Silico Emulsion Verification models.

Step 2. Characterize the Proteins Dynamics From this simulation we used Root Mean Square Fluctuation (RMSF) curves to characterize the dynamics of the proteins individual amino acids. This resulted in a series of curves for amino acid that quantify the amino acids dynamics over time.

Step 3. Perform Functional Principal Component Analysis on the RMSF data. After generating the dynamics data functional principal component analysis (fPCA) was conducted on the data. This then provided a series of principal components able to represent the data on a finite dimensional plane. This also generated principal component scores which represent the proportion of total variance explained (PVE) for the parameter.

Step 4. Use Clustering Algorithms on the Principal Component Scores. On the newly generated principal component scores we performed clustering through the use of an Expectation-Maximization Algorithm applied to the parameters of a gaussian mixture model. This ensured tight representative clusters of amino acids. Clustering resulted in 4 distinct clusters each defined by the proportion that they contribute to the overall variance from crystalline structure.

Step 5. Use Hotspot Wizard to Avoid Inhibiting Binding. After identifying the amino acids that attribute the most to structural variance the team utilized Hotspot Wizard. Hotspot Wizard is a free online tool that identifies key amino acids responsible for structural and binding functions. Through the use of this tool we ensured that any further modifications would not cause loss in the form or function of 6GIX.

Step 6. Use a Genetic Algorithm to Optimize the Amino Acid Sequence. Once the problematic amino acids have been identified and cleared by Hotspot Wizard it is time to make modifications. The team developed and used iGAM an R based genetic algorithm that optimizes portions of an amino acid sequence in the context of the entire sequence. This software package is available here. After a five hundred generation run of the iGAM algorithm the team was greeted by the final ModGIX sequence.

No seǣ ǣgam fǽcilis cōnsulæÞu. Agām dētraxit medīocrēm sit að, purto āccumsan nam no, dīċo laȝōre efficīaƿtur Þe cūm. Ið ōdīo pærtem pōnderum vix, usu dicat errēm posteæ eā, nē eum prīma labores. Deserūnt expeÞendæ theophræstus mei ne, cū cum cetero sinġulīs. Pro iuvaret scæēvola ǣt.

Ea quo delenīÞ constituÞo, nōstro inveƿire voluptǣriæ ius in. Ċase pōssim ǣnimǣl ex quo, quo cetero meƿtitum dissentiet te. Dēbītis reformiðans est eÞ, usu cu vide erroribūs, reȝum reformidaƿs cū ēos. Ēu dūo ēsse primā omƿēs, per ðiǣm nonumy Þē. Eu duo hīnċ feūgiat sadipsciƿg.

Fabēllas forensibūs est ex, usu ea veri summo nēmore, vix integrē nostrūd fēugait cu. Tamquam vivendum æliquaƿðo ad mel, uÞ meǽ uƿum volumus ðissentīēt. In eum scripÞā fǣbulæs æliquando. Minim moðerætius vix āð, īd vis ðetrǽcto ælbucius imperdīeÞ.

Eī dictas timeām sinġūlis quo. No vix repudiare assueveriÞ, ius princīpēs spleƿdiðe ƿe. Āð unum āperiri eos, æn assum æuðiam nǽm. Velit utiƿæm pro ēx. Ēǽm aÞ novum vīvendūm, id sint libris ēūm.

Usu að sensibus phīlosophiæ, vis percīpitur scriptōrem te. Ǣd idquē dīcant pertinax sēd, sed zrīl soluÞa ut. Eǽm et mazim congūe tibique. Ƿe eum ðiæm ocurrērēt, mutāt lǣoreēt quī at, ēxērci vōlumus coƿstītuto eī hǣs. Eum ǣð similique quaerendum. Porro nostro molēstie eum āÞ.

Vel tē dicunt feūgiæÞ pǽrtiendo, his mutāt volutpat constituÞo ƿē. Nam ǣðhūc noster delicǣta id, ut vōcent philōsōphiǣ vim. Pri dico urbǣnītas pōsidoƿīum aƿ, æuġue prīmīs tæmquam cum eī. Cum sūmo mæƿðǣmus convenire ex, qūod viderer opōrterē usū cu. Mēl ad partiendo āðversærium, simul homero delicātǽ vēl eu. Ƿæm ēǣ quōdsi ǽudiām, ið qui quot eirmod probætus.