The core of our project hinges on how the expressed foreign Thioesterase interacts with the native Fatty Acid Synthase (FAS) complex of yeast. The thioesterases of J.curcas are part of Fatty Acid Synthesis Type I (FAS I) pathway while the FAS complex from Y.lipolytica is part of a Fatty Acid Synthesis Type II (FAS II) pathway. There are two types of FAS's:

Fig: Table highlighting the features of the 2 FAS pathways

The essential difference between these two systems implies that the interactions brought in by our genetic interventions are "unusual" from an evolutionary perspective. Hence its reasonable to assume that there is scope for further improvement in the efficiency of this interaction via the principles of protein engineering. But any attempt in doing this is hindered by the lack of a structural model for these interactions.

No crystal structure has been determined for the integrated complex and hence we decided to insilico model this integration with the objective of using the predicted model for further engineering interventions. As our first attempt we structurally characterized the interaction between J. curcas thioesrerase B (JcFatB) and the FAS complex from Y. lipolytica.

Protein – Protein Docking

The first step in the modelling of this integration was to generate structural predictions for the combined complex comprising both JcFatB from J.curcas and the Fatty Acid Synthase(FAS) complex from Y.lipolytica.

Predicting this structure from the FASTA sequence using AlphaFold 2.0 turned out to be not feasible, because of the huge size of the complex (24,186 residues). Hence, we decided to use protein - protein docking as the strategy to predict the integrated complex structure. Owing to its capability to handle large input files, we decided to use PatchDock server for this purpose.[1]


The PatchDock algorithm draws inspiration from object recognition and image segmentation methods commonly used in the field of Computer Vision. When provided with two molecules, it divides their surfaces into patches based on their shape, mirroring the idea of visually distinctive puzzle piece patterns. After identifying these patches, the algorithm can align them using shape complimentarity techniques.[2][3]

The Docking Algorithm

The algorithm can be divided into three major steps:

For our purpose of modelling the integration, a PatchDock protein – protein docking was performed with the following inputs:

Now these proteins (JcFatB from J.curcas and FAS complex from Y.lipolytica) were not structurally characterized in literature. For JcFatB we ran an AlphaFold 2.0 prediction and used the prediction with highest confidence score as the input structure.As the second input, the structure of FAS complex from S.cerevisiae (available in Protein Data Bank, PDB ID: 6QL6) was used since the actual structure was too large to be predicted using AlphaFold 2.0 and a 99% query cover was found between the sequences from these yeast species.

Fig: JcFatB(left) and FAS complex(right) structures

The server generated 3000 docked structures as output, which were filtered in the next step to obtain the most probable structure.

Validation of the Docking Structures

Fig:Progression of the validation process

Once we obtained 3000 output structures as the possible configurations in which JcFatB could interact with the FAS complex, the next task at hand was to validate one as the most probable model of interaction. A combination of docking score cutoffs and biologically valid rationale was used for this purpose. The series of filters used in this process are as follows:

Identification of Interacting Residues

The most probable model of integration was analysed using PyMol and interacting residues were identified.

Fig: The most probable structure of integration, with JcFATB colored yellow and the interacting residues highlighted in red

With the information of these interacting residues (and the active residues) we propose to engineer the JcFatB via random mutations with objective of improving its binding affinity towards the FAS complex without affecting its catalytic function.

In summary, this research sheds light on the intriguing interaction between the thioesterase (JcFatB) from J.curcas and the Fatty Acid Synthase (FAS) complex from Y.lipolyitca . By using computational modeling techniques, we tackled the challenge of understanding their interaction, despite lacking a physical structure. Our validated model provides a practical framework for exploring and optimizing this unique interaction, with potential applications in further improving the efficiency of our biomanufacturing process.