AUTHOR’S NAME STUDENT NUMBER
NAME OF FIELD SUPERVISOR NAME OF SUPERVISOSR AT I3L
INDONESIA INTERNATIONAL INSTITUTE FOR LIFE SCIENCES (i3L)
reinhardtii’s Ability To Produce
Biohydrogen With Prokaryotic FHL Gene
NICHOLAS 19010202
Mario Donald Bani M.Biotech (Field Supervisor)
Ikhsan Tria Pramanda M.Sc (i3L Supervisor)
INDONESIA INTERNATIONAL INSTITUTE FOR LIFE SCIENCES (i3L)
RESEARCH REPORT
IN-SILICO ENHANCEMENT OF CHLAMYDOMONAS REINHARDTII’S ABILITY TO PRODUCE BIOHYDROGEN
WITH PROKARYOTIC FHL GENE
By Nicholas 19010202
Submitted to
i3L – Indonesia International Institute for Life Sciences School of Life Sciences
in partial fulfilment of the enrichment program for the Bachelor of Science in
Biotechnology
Research Project Supervisor: Ihsan Tria Pramanda, S.Si., M.Sc.
Research Project Field Supervisor: Mario Donald Bani, S.P., M.Biotech.
Jakarta, Indonesia 2023
Nicholas Ihsan Tria Pramanda, S.Si., M.Sc. Mario Donald Bani, S.P., M.Biotech.
www.i3l.ac.id
Certificate of Approval
Student : Nicholas
Cohort : 2019
Title of final thesis project : Peningkatan kemampuan Chlamydomonas reinhardtii dalam menghasilkan biohidrogen memakai gen prokariotik FHL Enhancing Chlamydomonas reinhardtii ability to produce biohydrogen with prokaryotic FHL gene
We hereby declare that this final thesis project is from student’s own work. The final project/thesis has been read and presented to i3L’s Examination Committee. The final project/thesis has been found to be satisfactory and accepted as part of the requirements needed to obtain an i3L bachelor’s degree.
Names and signature of examination committee members present:
1 Thesis Supervisor : Ihsan T.P. S.Si., M.Sc. Approved
2 Lead Assessor : Mario D.B. , S.P., M.Biotech(Adv) Approved
3 Assessor 2 : Dr. Riahna B.K. , S.Si., M.Sc. Approved
Acknowledged by, Head of Study Program, Ihsan Tria Pramanda, S.Si., M.Sc.
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Nicholas
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STATEMENT OF ORIGINALITY
submitted to
Indonesia International Institute for Life Sciences (i3L)
I, Nicholas, do herewith declare that the material contained in my thesis manuscript entitled:
“IN-SILICO ENHANCEMENT OF CHLAMYDOMONAS REINHARDTII’S ABILITY TO PRODUCE BIOHYDROGEN WITH PROKARYOTIC FHL GENE” is original work performed by me under the guidance and advice of my Thesis Advisor, Mario Donald Bani, S.P., M.Biotech., as Field Supervisor, and Ihsan Tria Pramanda, S.Si., M.Sc., as I3L Supervisor. I have read and do understand the definition and information on use of source and citation style published by i3L. By signing this statement I unequivocally assert that the aforementioned thesis manuscript conforms to published information.
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Name of student: Nicholas Student ID: 19010202
Study Program: Biotechnology
Signature: Date: 23-12-2022
ABSTRACT
Overusing coal for Indonesian power plants has increased air pollutants yearly. Sustainable renewable energy is needed; however, most green energies are geographically restricted and expensive. Hydrogen gas has a high specific energy; thus, it can release more energy. Also, hydrogen combustion produces water vapor, making it a clean energy source. Nonetheless, mechanical hydrogen synthesis either results in CO2 release or is expensive. Biohydrogen could fix this issue as hydrogen synthesis is done through microbial metabolism. Depending on the microorganism selected, it can reduce the growth price, and yield may be increased. But, the most efficient biohydrogen production process yields less than mechanical methods. Formate hydrogenlyase can synthesize hydrogen with formate; thus, it is stipulated that hydrogen synthesis yield increases when FHL is expressed in a formate-producing organism such as Chlamydomonas reinhardtii. However, some subunits of FHL are unsuitable for expression in the organism. This in-silico study investigated the possibility of FHL expression and designing a hybrid FHL complex for C. reinhardtii through FHL subunit selection, protein parameter determination, identification of signaling peptides, molecular binding and dynamics, and formation of hybrid FHL complex. The resulting FHL complex has bad stability through the RMSD test; however, good binding affinity to the substrate and suitable parameters for expression in C. reinhardtii was detected. Therefore, the protein may be expressed in C. reinhardtii; however, modifications are required.
ACKNOWLEDGEMENTS
This research was able to be finished thanks to the support and assistance of many talented and wise individuals involved throughout the project completion.
First and foremost, I would like to thank God for the wisdom and patience given to me during many moments of this research so I may finish this research as intended while still keeping a healthy mental and physical state.
I would like to express my gratitude to my parents and family. Thank you for their support and presence so that I may focus on the research without worrying much about my daily needs and stress level.
I would like to thank Sir Ihsan Tria Pramanda, S.Si., M.Sc. as my research project supervisor for his help, especially in providing clarity on how the logic should flow. Thank you for the inputs and discussions which have covered many shortcomings and improved the depth of the study.
Many thanks to Mario Donald Bani, S.P., M.Biotech., my research project field supervisor, for providing insight especially during the initial period where the direction of the project was still unclear. I would also like to express my gratitude for the many suggestions which increased the depths of the study.
Finally, thank you to my colleagues, Sun Joshua and William Husada for their tremendous aid in finishing this project as many methodologies and major considerations were from their brilliant minds. Some processes whether in computing or editing were also personally done by them thus this project may be finished on time.
TABLE OF CONTENTS
Cover... 0
Certificate of Approval... 1
Copyright Notice... 2
Statement of Originality... 3
Abstract... 4
Acknowledgement... 5
Table of Contents... 6
List of Figures, Tables, and Illustrations... 8
List of Abbreviations... 10
Chapter 1: Introduction... 11
1.1. Research Background... 11
1.2. Research Aim... 12
1.3. Research Scope... 12
1.4. Hyphothesis... 12
Chapter 2: Literature Review... 13
2.1. Hydrogen as Potential Energy Vector and Its Production... 13
2.2. Biohydrogen Production... 13
2.3. Formate Hydrogenlyase... 14
2.4. Chlamydomons reinhardtii... 14
Chapter 3: Materials and Methods... 16
3.1. Materials... 16
3.1.1. Uniprot... 16
3.1.2. Protein Database... 16
3.1.3. AlphaFold... 16
3.1.4. Protparam... 16
3.1.5. PyMOL... 16
3.1.6. TargetP... 16
3.1.7. Clustal Omega... 16
3.1.8. Autodock Vina... 17
3.1.9. Command Prompt... 17
3.1.10. Discovery Studio... 17
3.1.11. Ubuntu... 17
3.1.12. Gromacs... 17
3.1.13. Notepad... 17
3.1.14. QtGrace... 17
3.1.15. ClusPro... 17
3.2. Methods... 17
3.2.1. Sample Collection... 17
3.2.2. Protein Characterization & Selection... 18
3.2.3. Signalling Protein Identification and Removal... 18
3.2.4. Molecular Binding of FdhF & HycE... 18
3.2.5. MD of FHL Subunits... 18
3.2.6. Molecular Binding and MD of FHL Subunits... 19
Chapter 4: Results and Discussion... 20
4.1. Sample Collection and Characterization Result... 20
4.2. Chloroplast and Thylakoid Lumen Transfer Peptide Identification Result... 22
4.3. Subunit MD EM and Equilibration Result... 26
4.4. Subunit MD Result... 36
4.5. FdhF & HycE Substrate Binding and FHL Complex Formation Result... 40
4.6. FHL Complex MD EM and Equilibration Result... 42
4.7. FHL Complex MD Result... 45
Chapter 5: Conclusion... 47
References... 48
LIST OF FIGURES, TABLES, AND ILLUSTRATIONS
Figures:
Figure 1. Biohydrogen Production Process... 13
Figure 2. Formate Hydrogenlyase... 14
Figure 3. Chlamydomonas reinhardtii………15
Figure 4. FdhF Alignment Result... 24
Figure 5. HycE Alignment Result... 25
Figure 6. HycB Alignment Result... 25
Figure 7. HycF Alignment Result... 26
Figure 8. HycG Alignment Result... 26
Figure 9. Subunits EM Result... 27
Figure 10. FdhF NVT Equilibration Result... 28
Figure 11. HycE NVT Equilibration Result... 28
Figure 12. HycB NVT Equilibration Result... 29
Figure 13. HycF NVT Equilibration Result... 29
Figure 14. HycG NVT Equilibration Result... 30
Figure 15. FdhF Pressure Equilibration Result... 31
Figure 16. HycE Pressure Equilibration Result... 31
Figure 17. HycB Pressure Equilibration Result... 32
Figure 18. HycF Pressure Equilibration Result... 32
Figure 19. HycG Pressure Equilibration Result... 33
Figure 20. FdhF Density Equilibration Result... 33
Figure 21. HycE Density Equilibration Result... 34
Figure 22. HycB Density Equilibration Result... 34
Figure 23. HycF Density Equilibration Result... 35
Figure 24. HycG Density Equilibration Result... 35
Figure 25. Subunits Total Radius of Gyration... 36
Figure 26. Subunits RMSD Result... 37
Figure 27. FdhF RMSF Result... 38
Figure 28. HycE RMSF Result... 38
Figure 29. HycB RMSF Result... 39
Figure 30. HycF RMSF Result... 39
Figure 31. HycG RMSF Result... 40
Figure 32. FdhF to Formate Binding Residue... 41
Figure 33. Final FHL Hybrid Complex... 42
Figure 34. FHL Complex EM Result... 43
Figure 35. FHL Complex NVT Equilibration Result... 43
Figure 36. FHL Complex Pressure Equilibration Result... 44
Figure 37. FHL Complex Density Equilibration Result... 44
Figure 38. FHL Complex Total Radius of Gyration Result... 45
Figure 39. FHL Complex RMSD Result... 46
Tables:
Table 1. Hydrogen Production Methods and Efficiency Comparison... 14
Table 2. Binding Parameters... 18
Table 3. FdhF Sample... 20
Table 4. HycE sample... 20
Table 5. HycB Sample... 21
Table 6. HycF Sample... 21
Table 7. HycG Sample... 22
Table 8. Selected FHL Subunits... 22
Table 9. Clustal Omega Inputs... 23
Table 10. TargetP Protein Subcellular Location Result... 26
Table 11. Subunits Final RMSD... 37
Table 12. FdhF & HycE Binding Affinity Comparison in Individual and Complex Setting……….. 40
Table 13. FHL Hybrid Complex Parameters... 41
LIST OF ABBREVIATIONS
AQI Air Quality Index FHL Formate Hydrogenlyase MD Molecular Dynamics SMR Steam Methane Reforming pI Isoelectric Point
II Instability Index AI Aliphatic Index
GRAVY Grand Average Hyropathicity RMSD Root Mean Square Deviation RMSF Root Mean Square Fluctuation EM Energy Minimization
I. INTRODUCTION
1.1. Research Background
For decades, Indonesia has been increasingly reliant on fossil fuels for Indonesia's national energy production ("International - U.S. Energy Information Administration (EIA)", 2022). For comparison, 67 million tons of coal were used in coal power plants by 2000. The number has increased to 616 million tons by 2019 (Esterman, 2022). This continually growing dependency on fossil fuels increases Indonesia's air quality index (AQI), which indicates increased average air pollutants per set of time (Sulaeman et al., 2020). This increases the morbidity of respiratory diseases in several locations with higher AQI ("Indonesia Air Quality Index (AQI) and Air Pollution information | IQAir", 2022). A transitional effort to renewable energy sources has been conducted, ex. Geothermal energy, however, differs in kilowatt-hour as most green methods of energy production are influenced by topography. Many other renewable energy sources also suffer from the topology, such as air turbines and solar panels. Therefore we need an energy source that is both clean and sustainable.
Hydrogen gas combustion and fuel cells have been known to produce more energy than gasoline due to the higher energy content in hydrogen (ICAE 2013, 2013). Additionally, these processes produced water vapor as waste products, making it a clean energy source. However, the production of hydrogen gasses is often done through processes requiring high energy input and producing high amounts of CO2. One example of these methods is steam methane reforming (SMR);
the process requires CH4to react to water vapor to produce CO and three hydrogen gas molecules.
The subsequent CO and water vapor reaction produce CO2and a hydrogen gas molecule (Mokheimer et al., 2014). Thus the process still results in CO2 production, which acts as a pollutant. Another method is through electrolysis, where water molecules are split into oxygen and hydrogen with an electric current from an electrolyzer. Beswick et al. note that for every 1 kg of hydrogen produced, 9 kg of water is required. On a larger scale, the amount of water required for worldwide hydrogen energy applicable sectors would be 20.5 gigatons per year. This will impact the price of water usage for other sectors, such as agriculture (2021). Furthermore, the electrolyzer requires high monetary investment and maintenance fees, making it less viable in the long term. Therefore an alternative method of hydrogen production is required.
Most research on hydrogen production utilizing microbes, also called biohydrogen, is done in microalgae such as Chlamydomonas reinhardtii due to their easy growth requirement, understanding of their genetic code, and high hydrogen output (Fakhimi & Tavakoli, 2019). Recently, it has been found that formate is one of the byproducts of hydrogen production in C. reinhardtii's dark fermentation activity and is unutilized by the organism (Löwe et al., 2018). In E.coli, formate is oxidized by formate hydrogenlyase (FHL) into CO2 and proton to increase cytosolic pH due to the abundance of formic acid (Vivijs et al., 2015). The oxidation energy is then utilized in synthesizing hydrogen from protons. Thus it is hypothesized that if FHL is expressed in C. reinhardtii, formate within the organism may be utilized, and hydrogen synthesis is increased.
Additionally, the produced CO2from the process may be uptaken by the organism during the Calvin cycle to produce glucose, thus reducing the need for the metabolic substrate. The presence of
pyruvate formate-lyase in the organism also allows the conversion of pyruvate and cofactor A into acetyl-CoA and formate (Hemschemeier et al., 2008). Therefore the process does not result in the reduce of acetyl-CoA for the citric acid cycle. However, due to the absence of FHL expression research, the viability of the process still needs to be determined. Therefore an in-silico research on the subject is proposed.
The general methodology of the research is as follows. The FHL subunits were collected from protein databases such as Alphafold and Uniprot. Protein characteristics and parameters were assessed using Protparam, including pI, instability index, grand average hydropathicity, aliphatic index, and the amount of cysteine residue. The selections were made according to C. reinhardtii's cytosolic condition. Due to the absence of formate in the location, identification and removal of signaling peptides were made to prevent protein translocation to the chloroplast. Molecular dynamics (MD) were done to the selected subunits to assess additional protein stability. Molecular binding was done to the sites responsible for catalysis (FdhF & HycE) with Autodock Vina. The FHL complex was formed through ClusPro, and molecular binding and docking for the complex were conducted. Further MD process was conducted on the hybrid complex to assess protein stability.
1.2. Research Aim
This in-silico research aims to design an FHL complex suitable for expression in C. reinhardtii.
Discussion on the considerations needed for FHL expression in the organism was also given to provide additional information for future research on or similar to this topic. In addition, the predicted active sites of the protein will be given for future protein modification studies. Finally, this research fills the gap where research on the hybrid FHL complex is currently absent.
1.3. Research Scope
This research does not include in-vitro methods needed to measure designed FHL protein in C. reinhardtii. In addition, this research does not address the plasmid design needed to transport FHL subunit genes. Finally, protein-substrate molecular dynamics is also not conducted in this research due to the inability to access the appropriate forcefield for the protein-substrate MD process.
Therefore the in-silico research can only be used to give ideas on what organism to extract FHL subunits from, what modifications may be needed, and several considerations on the process.
1.4. Hypothesis
● The binding affinity of individual FdhF and HycE to their substrates are favorable
● Absence of signalling peptide in the selected subunits
● Protein stability test through molecular dynamics resulted in good value (less than 4A) for both individual subunits and the full complex
● Binding affinity of the active sites within formed FHL complex are favorable
II. LITERATURE REVIEW
2.1. Hydrogen as Potential Energy Vector and Its Production
Hydrogen has the highest specific energy compared to conventional energy vectors such as fossil fuels. For comparison, hydrogen has an energy content of 120 MJ/kg, while gasoline has only 44 MJ/kg ("Hydrogen Storage", n.d.). Hydrogen can also generate clean electricity by using fuel cells, leaving O2 as a byproduct. However, concerns such as operational cost and sustainability of several methods were still present. Two main hydrogen production methods include Steam Methane Reforming (SMR) and electrolysis (Yue et al., 2021).
With SMR, methane heating using steam paired with a catalyst is conducted to produce hydrogen and CO2. The method has an efficiency of around 74%; however, it results in the release of CO2 and CO (Velazquez Abad & Dodds, 2017). Therefore concern about this method is mostly environmental as the hydrogen produced through this method is not environmentally friendly nor renewable due to the use of methane in hydrogen synthesis. On the other hand, electrolysis utilizes electricity to split water molecules into hydrogen and oxygen in an electrolyzer. However, the method is cost-heavy, of the required fresh water for water splitting and the electrolyzer itself (Katebah et al., 2022). Therefore an alternative means of hydrogen production is required.
2.2. Biohydrogen Production
Biohydrogen is hydrogen produced by the metabolic pathway of organisms. This method has been seen as a potential hydrogen production method due to the lesser amount of pollution it creates. Current biohydrogen production research involves using microalgae, such as from the genus Chlamydomonas, through photolysis or fermentation. Microalgae is selected due to its lower cost of growth which requires sunlight and water. In addition, most autotroph already contains a form of hydrogenase within the chloroplast; therefore, the organism is already capable of hydrogen synthesis. However, microalgae are inhibited by oxygen which builds up in photosynthetic conditions, thus lowering their productivity. Moreover, the hydrogen production efficiency of the most efficient production method (dark fermentation) is less/equal to SMR or electrolysis method (Table 1).
Therefore, most research to increase hydrogen production efficiency focuses on editing enzymatic genes for hydrogen production or reducing oxygen inhibition (Khetkorn et al., 2017).
Figure 1. Biohydrogen Production Process (Chandrasekhar et al., 2015)
Table 1. Hydrogen Production Methods and Efficiency Comparison (Shiva Kumar & Himabindu, 2019) No Hydrogen Production Methods Efficiency (%)
1 Steam Methane Reforming 74-85
2 Electrolysis 60-80
3 Biophotolysis 10-11
4 Dark Fermentation 60-80
5 Photo Fermentation 0.1
2.3. Formate Hydrogenlyase
FHL is an enzymatic complex compromised of 7 subunits. The enzyme works as follows; FdhF (formate dehydrogenase h) catalyzes formate oxidation, resulting in CO2 and proton. The resulting electron from the reaction travels from the Mo ion in FdhF into three subsequent electron transporter proteins, HycB, HycF, and HycG, using iron-sulfate clusters in the three proteins. The electron is then used in HycE (Ni-Fe hydrogenase) activity, where 2 protons are catalyzed into hydrogen gas. The other two additional subunits, hycD and hycC, act as an anchor to the cell membrane (Steinhilper et al., 2022).
The enzyme is important as during fermentative conditions; bacterias undergo mixed-acid fermentation. Formate, as one of the byproducts, might reach a critical level which causes a drop in pH. Therefore the FHL is responsible for taking up formate and catalyzing it into hydrogen and CO2, preventing the further decrease in cytosolic pH (McDowall et al., 2014).
Figure 2. Formate Hydrogenlyase (Sokol et al., 2019)
2.4.
Chlamydomons reinhardtiiC. reinhardtii (Figure 3) is a single-cell green alga found in soil and freshwater. The organism is about 10 mm in diameter and has two flagella to move. Much research on cell and molecular biology has been done on the organism due to the low growth cost and high understanding of the organism’s genome, making genetic engineering simpler. In addition, in hydrogen research, FeFe hydrogenase present in C. reinhardtii’s chloroplast allows for high hydrogen production; thus, C.
reinhardtii becomes one of the potential organisms for hydrogen production research (Hemschemeier et al., 2007).
Figure 3. Chlamydomonas reinhardtii (Dongo & Penna, 2022)
III. MATERIALS & METHODS
3.1. Materials 3.1.1. Uniprot
Uniprot is a database containing numerous genes and proteins from different organisms and thus is widely used for gathering samples for in-silico studies involving sequences. Uniprot is mainly used for gathering the sequence of the subunits used in this research. (https://www.uniprot.org/)
3.1.2. Protein Database
The protein database contains the structure of many proteins listed in Uniprot; therefore, it is useful in protein studies requiring 3D images of the protein, such as structural studies. This database is mainly used for gathering reviewed protein structures in .pdb format, which is useful as a point of reference. (https://www.rcsb.org/)
3.1.3. AlphaFold
AlphaFold is a database and tool containing numerous protein structures not found in Protein Database. This is because some of the protein structures from the database are predicted by the artificial intelligence of the database itself. Therefore some areas of the protein structure may have low confidence, shown in the database. AlphaFold is used in this research to obtain high-confidence protein structures not found in Protein Database. (https://alphafold.ebi.ac.uk/)
3.1.4. Protparam
Protparam is an online tool that computes the physical and chemical traits of a given protein listed in Swiss-prot or TrEMBL. The tool also works with accession codes found in Uniprot and AlphaFold. When the accession code can not be accessed/not found in Uniprot, the online tool also uses protein sequences. Protparam is used in obtaining isoelectric point (pI), amount of cysteine residue, Instability Index (II), Aliphatic Index (AI), and Grand Average Hydropathicity (GRAVY) of sampled FHL subunits. (https://web.expasy.org/protparam/)
3.1.5. PyMOL
PyMOL is an open-source software used for 3D molecular visualization of macromolecules.
The utilities of the software can be further enhanced with the addition of plugins. PyMOL uses .pdb extension files as the data input and is used in this research for active site determination in substrate-bound-subunits if Discovery Studio result is unavailable. PyMOL is also later used to visualize the final constructed FHL. (https://pymol.org/2/)
3.1.6. TargetP
TargetP ver. 2.0 is a server that predicts the presence of signal peptides. The server was primarily used in detecting chloroplast transit peptides as the designed protein is meant to be expressed in algae. (https://services.healthtech.dtu.dk/service.php?TargetP-2.0)
3.1.7. Clustal Omega
Clustal Omega is a web tool allowing for multiple sequence alignment of 3 or more protein/DNA/RNA sequences utilizing seeded guide trees and HMM profile-profile technique. The
web tool is used to align all samples to determine the presence of signaling protein to prevent FHL translocation to the chloroplast. (https://www.ebi.ac.uk/Tools/msa/clustalo/)
3.1.8. Autodock Vina
Autodock Vina is a visual screening software designed for drug computation. However, the software can also be used for non-drug-related molecular docking. Therefore Autodock Vina is used for molecular binding purposes between substrate and subunit and between subunits.
(https://vina.scripps.edu/)
3.1.9. Command Prompt
Command Prompt (cmd.exe) is a default command-line interpreter present in windows. The program was primarily used to run vina scripts for molecular binding and separate several binding results into smaller sub-files.
3.1.10. Discovery Studio
Discovery Studio is a visualization software for viewing, sharing, and analyzing proteins. The software checks the polar and non-polar molecular binding results from Autodock Vina.
(https://discover.3ds.com/discovery-studio-visualizer-download)
3.1.11. Ubuntu
Ubuntu is an open-source operating system on Linux based on Debian. The program is used in operating GROMACS for the MD process. (https://ubuntu.com/)
3.1.12. GROMACS
GROMACS is a Linux-based free, open-source software used for MD, in this case for RMSD and RMSF. Gromacs is built-in with several potential force fields; however, additional force fields may be downloaded through the appropriate website. (https://www.gromacs.org/)
3.1.13. Notepad
Notepad was primarily used in editing the parameters for MD processes. The program was meant to alter the protein sequence when a signaling peptide is present.
3.1.14. Qtgrace
Qtgrace is a version of the grace tools for windows. The program was primarily used in opening .xvg files due to MD processes. (https://sourceforge.net/projects/qtgrace/)
3.1.15. ClusPro
ClusPro is a free, open-source website that allows for the protein docking of submitted proteins. The website utilizes cluster size scores for predicting and displaying docking results.
Formation of the hybrid FHL was done through this website. (https://cluspro.bu.edu/login.php)
3.2. Methods
3.2.1. Sample Collection
10+ samples from different organisms were obtained from Protein Database and AlphaFold for each FHL subunit (FdhF, HycE, HycB, HycF, and HycG). In addition, the accession code, source organism, and protein sequence were noted.
3.2.2. Protein Characterization & Selection
Accession code (or protein sequence if accession code is unusable) was input into Protparam and computed. Information such as pI, amount of cysteine residue, II, AI, and GRAVY was noted. The subunits are then selected based on the previous information. The subunits with pI close to the cytosolic pH of C. reinhardtii, low cysteine residue, II lower than 40, high AI, and negative GRAVY were selected. When none of the screened subunits fulfilled one/more of the criteria, the order of importance was as follows; pI, II, GRAVY, cysteine amount, and AI.
3.2.3. Signalling Protein Identification and Removal
The selected FHL subunit was computed in TargetP to predict the probability of signaling peptides within a range of protein residues. An additional identification was made through multiple sequence alignment of the selected subunit with the crystalized form of the subunit taken from Uniprot. If signaling peptides were detected, removal of the signaling peptide portion was done manually with the notepad.
3.2.4. Molecular Binding of FdhF & HycE
Formate and hydronium as substrates were downloaded in .pdb format. Selected FdhF and formate were inputted into Autodock Vina, where FdhF is allocated as a macromolecule and formate as a ligand. A blind docking method was utilized; thus, the area containing the full subunit was selected according to table 2. The result was downloaded, and the binding affinity was noted. The result was separated into smaller subfiles through vina split script and opened in Discovery studio, and the binding type was investigated. If the binding type was unavailable in Discovery studio, the binding type was investigated through Pymol instead of following the instructions from Molecular Memory (https://www.youtube.com/watch?v=mBlMI82JRfI). The results were noted as well. The process was repeated for HycE with hydronium as substrate.
Table 2. Binding Parameters
Job
Center Size
X Y Z X Y Z
FdhF to formate 0.022 -0.397 1.828 62 62 72
HycE to hydronium 3.325 2.014 0.734 74 80 72
3.2.5. MD of FHL Subunits
MD process in this research was done through adaptation to the gromacs tutorial by Justin A.
Lemkul, Ph.D. (http://www.mdtutorials.com/gmx/), according to the “Lysozyme in water” tutorial.
The .pdb molecular binding result from Autodock Vina was run using the pdb2gmx command to create the topology for the molecule in Gromacs. The type of forcefield, water solvent, and ion parameters was selected next. If the selected forcefield did not recognize the substrate, manual assembly of the macromolecule and ligand was done through the notepad. The box type for the system was selected next, and the solvent was added. Ions were next added to the box to neutralize the system, preventing the accumulation of electrostatic energy and inaccurate result. Energy minimization (EM) was conducted next to prevent steric clashes. Temperature and pressure
equilibration is next to prevent system collapse due to unoptimized solvent-solute. MD simulation was finally conducted, which was used to compute RMSD, the radius of gyration, and RMSF
3.2.6. Molecular Binding and MD of FHL Subunits
The incorporation of FHL subunits was done through ClusPro, where one of the subunits acts as a ligand and the other as a macromolecule. The process was done step by step from FdhF to HycB, then from the formed complex to HycF, continuously until the full hybrid FHL complex was formed.
The result model selection from ClusPro was made according to the subunits' model score and binding position. Therefore the structure of the final hybrid FHL complex would imitate normal FHL structure. Additional protein MD process would be conducted for the hybrid protein to assess protein stability similar to 3.2.5 further.
IV. RESULTS AND DISCUSSION
4.1. Sample Collection and Characterization Result
Ten randomly selected FdhF, HycE, HycB, HycF, and HycG samples were taken from different prokaryotic organisms and ran through protparam (see Table 3-7). The cytosolic pH of C. reinhardtii is around 7.4 (Braun & Hegemann, 1999). Therefore, the subunits are selected with a pI difference of at most one from the average pH of C. reinhardtii. The pH level can affect the conformation of each subunit; therefore, pI needs to be considered (Di Russo et al., 2012). In this case, pI, much lower than the cytosolic pH of C. reinhardtii, can cause a conformational change in each subunit. Low amounts of cysteine residue (cys) were preferred to prevent aggregation unless the amount of cysteine is constant throughout most selected samples, such as the HycB samples. Subunits with II less than 40 were the ideal selection; however, when most samples had II of >40, samples with II close to 40 were selected. Samples with higher AI were preferred to account for thermal stability. Finally, the lowest negative GRAVY value was selected. The final chosen subunits are listed in table 8.
Table 3. FdhF Sample (Computed by Protparam) No Accession
number Organism name PI cys II AI GRAVY
1 P07658 Escherichia coli (strain K12) 5.92 15 26.44 78.91 -0.326
2 G8CQS9 Treponema primitia ZAS-1 6.01 23 37.2 74.91 -0.289
3 W9BPI2 Klebsiella pneumoniae 6.27 19 25.45 80.28 -0.273
4 A0A0D1EIQ0 Jannaschia aquimarina 5.36 11 33.14 69.4 -0.439
5 A0A2R2IBC2 Bacillus cereus 5.33 27 35.22 82.02 -0.321
6 UPI001928EAD7 Citrobacter freundii 6.07 21 28.72 78.31 -0.319
7 A0A641A7F6 Staphylococcus aureus 4.99 26 29.42 73.41 -0.524
8 A0A842EJT9 Listeria booriae 5.26 32 36.97 75.82 -0.324
9 A0A7H0FQS9 Enterococcus faecalis 5.77 33 38.09 77.59 -0.273
10 A0A655EIR1 Mycobacterium tuberculosis 7.81 3 35.77 81.5 -0.295
Table 4. HycE sample (Computed by Protparam) No Accession
number Organism name PI cys II AI GRAVY
1 A0A655IEK4 Mycobacterium tuberculosis 5.72 1 29.65 102.54 0.083
2 A0A7X1LP57 Klebsiella pneumoniae 6.34 8 31.52 83.6 -0.343
3 A0A221C8Q9 Staphylococcus hyicus 7.14 8 38.08 84.12 -0.524
4 A0A511Z1V7 Actinotalea fermentans 6.15 3 31.67 98.4 -0.001
5 A0A7D7B005 Citrobacter freundii 5.93 9 31.15 84.59 -0.312
6 A0A5M3XKD3 Acrocarpospora pleiomorpha 6.23 2 31.46 98.62 0.011
7 A0A0C6EKT1 Dehalococcoides sp. UCH007 5.96 4 37.42 99.58 -0.063
8 A0A7I7JSQ1 Mycobacterium novum 5.94 3 26.44 100.02 -0.039
9 A0A6F8XRV6 Phytohabitans flavus 6.54 1 30 100.62 0.012
10 A0A8A5HLC1 Escherichia coli O89m:H9 6.15 8 29.76 84.41 -0.354
Table 5. HycB Sample (Computed by Protparam) No Accession
number Organism name PI cys II AI GRAVY
1 A0A221C8U8 Staphylococcus hyicus 9.99 0 13.64 162.22 1.3
2 G8CQU5 Treponema primitia ZAS-1 5.94 16 49.89 84.14 0.076
3 A5JYF9 Klebsiella aerogenes
(Enterobacter aerogenes) 6.41 16 59.42 84.19 0.006
4 A0A7D6TBE2 Citrobacter freundii 7.45 16 58.79 78.92 -0.067
5 A0A7L5V7K9 Escherichia coli 6.29 16 58.05 81.28 -0.059
6 A0A7T8LKX2 Salmonella bongori 7.48 16 59.73 78.42 -0.091
7 A0A7W3FB70 Citrobacter freundii 7.45 16 59.21 78.92 -0.056
8 R4Y641 Klebsiella pneumoniae subsp.
rhinoscleromatis SB3432 6.17 16 57.81 81.27 0.055
9 A0A7X1BPC9 Citrobacter cronae 7.45 16 57.85 78.92 -0.062
10 G8LPP5 Enterobacter ludwigii 8.34 16 63.17 85.99 -0.027
Table 6. HycF Sample (Computed by Protparam) No Accession
number Organism name PI cys II AI GRAVY
1 A0A221C8S9 Staphylococcus hyicus 6.34 5 35.05 99.56 -0.29
2 A0A6L9DBS1 Escherichia coli 7.9 14 53.47 73.78 -0.314
3 A0A7D6YAZ3 Citrobacter freundii 7.9 14 46.34 73.22 -0.347
4 A0A3V9NQZ0 Salmonella gallinarum 7.44 14 44.49 73.22 -0.329
5 A0A702PKL9 Salmonella houtenae 7.91 14 45.62 74.28 -0.347
6 A0A5P2MLC0 Citrobacter werkmanii 7.9 14 45.09 72.67 -0.368
7 A0A549VC67 Citrobacter youngae 8.18 14 44.02 72.72 -0.338
8 A0A3V7INF2 Salmonella rubislaw 7.91 14 42.35 75.39 -0.304
9 A0A614ADP0 Salmonella enteritidis 7.44 14 44.85 72.17 -0.343
10 A0A6X7SPB0 Salmonella enteritidis 6.86 14 45.56 72.67 -0.359
Table 7. HycG Sample (Computed by Protparam) No Accession
number Organism name PI cys II AI GRAVY
1 A0A181CCD5 Komagataeibacter rhaeticus 6.05 4 43.99 98.94 0.386
2 A0A5A9CCQ2 Citrobacter portucalensis 6.17 7 39.57 90.31 -0.129
3 A0A764ITH3 Salmonella enterica (Salmonella
choleraesuis) 6.08 7 40.66 91.1 -0.142
4 A0A377ELF7 Escherichia coli 6.09 7 39.7 90.67 -0.169
5 A0A2I5HD62 Salmonella diarizonae 6.31 7 39.22 92.63 -0.009
6 A0A1Y6GIF9 Raoultella ornithinolytica
(Klebsiella ornithinolytica) 8.14 7 41.36 91.84 -0.132
7 A0A1C1F2K8 Klebsiella quasipneumoniae 6.95 7 39.6 94.12 -0.103
8 A0A718WJH1 Salmonella typhimurium (strain
SL1344) 6.08 8 41.03 93.76 -0.064
9 A0A655AB47 Mycobacterium tuberculosis 6.71 3 33.08 97.55 0.411
10 A0A377IWW8 Haemophilus pittmaniae 6.88 9 35.58 79.11 -0.101
Table 8. Selected FHL Subunits Subunit Accession
number Organism name PI cys II AI GRAVY
FdhF A0A655EIR1 Mycobacterium tuberculosis 7.81 3 35.77 81.5 -0.295
HycE A0A8A5HLC1 Escherichia coli O89m:H9 6.15 8 29.76 84.41 -0.354
HycB A0A7X1BPC9 Citrobacter cronae 7.45 16 57.85 78.92 -0.062
HycF A0A614ADP0 Salmonella enteritidis 7.44 14 44.85 72.17 -0.343
HycG A0A655AB47 Mycobacterium tuberculosis 6.71 3 33.08 97.55 0.411
As can be seen from the table, the chosen HycB and HycF have cysteine residues above 10.
Further study was needed to know the exact cause of this particular trait in bacterial HycB and HycF.
However, it is predicted that this trait was an evolutionary characteristic that occurred throughout the evolution of prokaryotes due to their nature (Macek et al., 2019). Prokaryotes do not have any compartments like the Golgi apparatus or endoplasmic reticulum that play a role in protein transport and post-translational modification. Therefore, they have a relatively limited protein modification ability and sometimes need to rely on the natural binding activity of their protein in order for them to create a proper protein complex (Macek et al., 2019).
4.2. Chloroplast and Thylakoid Lumen Transfer Peptide Identification Result
Clustal Omega sequence alignment of the selected subunits, to predict the presence of chloroplast and thylakoid lumen transfer peptide, was done to the crystal form of the protein from the same species except for HycB due to the absence of specific information. The protein does not have any chloroplast and thylakoid lumen transfer peptide in most protein crystal structures due to
its removal before the X-ray crystallography process (Tsokos, 2016). The FHL subunit alignment pairs are shown in table 9. Results of the alignment process are shown in figure 4-8; the selected subunits were shown as “edited”, while the paired subunits were shown as “full”.
A resemblance of sequence removal of the N-terminus was observed in FdhF and HycG. This showed the removal of chloroplast and thylakoid lumen transfer peptide prior to the X-ray crystallography. The absence of gaps in the other three subunit alignments was also observed. The chloroplast and thylakoid lumen transfer peptide has been removed even in the paired subunits. The hypothesis was tested by inputting the sequences in TargetP. Results are shown in table 10.
Chloroplast and thylakoid lumen transfer peptide likelihood of <0.01 was obtained, which showed a low presence of chloroplast and thylakoid lumen transfer peptide, suggesting the initial absence or removal of chloroplast and thylakoid lumen transfer peptide.
Table 9. Clustal Omega Inputs (Generated with Google Sheets)
No Subunit
Clustal Omega Inputs
Selected Subunit (Edited) Paired Subunits (Full)
Accession code Organism name Accession code Organism name
1 FdhF A0A655EIR1 Mycobacterium
tuberculosis ALB20103 Mycobacterium
tuberculosis
2 HycE A0A8A5HLC1 Escherichia coli
O89m:H9 QTF32022 Escherichia coli
O89m:H9
3 HycB A0A7X1BPC9 Citrobacter cronae WP_201745973 Citrobacter
4 HycF A0A614ADP0 Salmonella enteritidis USZ58017 Salmonella
enteritidis
5 HycG A0A655AB47 Mycobacterium
tuberculosis OMH57947 Mycobacterium
tuberculosis
Figure 4. FdhF Alignment Result (Computed with Clustal Omega)
Figure 5. HycE Alignment Result (Computed with Clustal Omega)
Figure 6. HycB Alignment Result (Computed with Clustal Omega)
Figure 7. HycF Alignment Result (Computed with Clustal Omega)
Figure 8. HycG Alignment Result (Computed with Clustal Omega) Table 10. TargetP Protein Subcellular Location Result (Computed with TargetP)
No Subunit Accession Code
Likelihood
Other Signaling Peptide
Transfer Peptides
Mitochondria Chloroplast Thylakoid Lumen
1 FdhF A0A655EIR1 0.9982 0.0012 0.0006 0 0
2 HycE A0A8A5HLC1 0.9999 0.0001 0 0 0
3 HycB A0A7X1BPC9 0.8477 0.1521 0.0002 0.0001 0.0004
4 HycF A0A614ADP0 0.9866 0.0039 0.0095 0.0004 0.0005
5 HycG A0A655AB47 0.9878 0.0105 0.0017 0.0075 0.0005
4.3. Subunit MD EM and Equilibration Result
The successful energy minimization (EM) process for subunit MD can be seen in figure 9, where steady convergence of potential energy is observed on all five subunits. Therefore further processes will not result in steric clashes and inappropriate geometry, which will affect the result of further processes.
Figure 9. Subunits EM Result (Generated with QtGrace)
Optimization of solvent to the solute was done with the equilibration process, which includes temperature (NVT) equilibration and pressure (NPT) equilibration. The absence of the equilibration process would result in system collapse. Therefore, NVT equilibration was conducted first under constant particles, volume, and temperature. The result of NVT equilibration can be observed in Figures 10-14. In addition, it can be observed that the system's temperature for all subunits generally fluctuates around 300K according to the parameters set. Therefore, the NVT equilibration process may be shortened as all processes reach the 300K point before the full 100 ns duration.
Figure 10. FdhF NVT Equilibration Result (Generated with QtGrace)
Figure 11. HycE NVT Equilibration Result (Generated with QtGrace)
Figure 12. HycB NVT Equilibration Result (Generated with QtGrace)
Figure 13. HycF NVT Equilibration Result (Generated with QtGrace)
Figure 14. HycG NVT Equilibration Result (Generated with QtGrace)
Next, the NPT equilibration was conducted under a constant number of particles, pressure, and temperature. The equilibration result was investigated through pressure and density simulation, as shown in figures 15-19 and 20-24, respectively. The resulting graphs fluctuate widely over the set reference values (1 bar for pressure and 1000 kg/m3 for density) for all five subunits. Due to the graph generally approaching the reference values, it can be concluded that the NPT equilibration process was successful; however longer simulation duration may be needed for a more accurate conclusion.
Figure 15. FdhF Pressure Equilibration Result (Generated with QtGrace)
Figure 16. HycE Pressure Equilibration Result (Generated with QtGrace)
Figure 17. HycB Pressure Equilibration Result (Generated with QtGrace)
Figure 18. HycF Pressure Equilibration Result (Generated with QtGrace)
Figure 19. HycG Pressure Equilibration Result (Generated with QtGrace)
Figure 20. FdhF Density Equilibration Result (Generated with QtGrace)
Figure 21. HycE Density Equilibration Result (Generated with QtGrace)
Figure 22. HycB Density Equilibration Result (Generated with QtGrace)
Figure 23. HycF Density Equilibration Result (Generated with QtGrace)
Figure 24. HycG Density Equilibration Result (Generated with QtGrace)
4.4. Subunit MD Result
Three main results were obtained from the MD process; the radius of gyration (Rg), root mean square deviation (RMSD), and root mean square fluctuation (RMSF). The radius of gyration is a measure of protein compactness, where stable protein folding would result in the relatively constant value of Rg. RMSD is the average distance between, in this case, the protein backbone, which indicates good protein stability with a lower RMSD value. Lastly, RMSF indicates the displacement of atoms within a timeframe, which could indicate possible positions for binding. RMSF may also explain the result of RMSD due to the correlation between both concepts.
From figure 25, the Rg value of the five subunits is variably stable throughout the 1000 ps duration, thus indicating protein folding stability. However, the overall Rg value of FdhF declines in the 1000 ps duration, thus indicating an increase in protein compactness over time. Moreover, further time addition to the simulation is needed to allow a proper assessment of the previous theory. It is believed this increase in compactness would result in a change in protein folding mechanism and rate, according to Galzitskaya et al. (2008).
Figure 25. Subunits Total Radius of Gyration (Generated with QtGrace)
Subunit RMSD results over a one ns period can be seen in figure 26, while the final RMSD value and verdict can be seen in table 11. The resulting verdict was given based on Erick Lindahl’s
lecture on Protein structure RMSD vs. sequence identity
(https://www.youtube.com/watch?v=0cnPzJs8Ctw), where the very good result was given if the RMSD value is 1A, good when RMSD value is 2-3A, bad when RMSD value is 4-5A, and very bad when RMSD value is >6A. As can be seen, only HycE and HycG resulted in a very good verdict, FdhF, and HycB resulted in a good verdict, and HycF resulted in a bad verdict. To explain the bad RMSD verdict,
RMSF graphs (Figure 27-31) will be discussed. It is thought that the high RMSD value of HycF corresponds to the high maximum fluctuation in the RMSF graph (Figure 30). Another way RMSF may impact RMSD value is through the number of fluctuations within the graph. This phenomenon is seen in figure 27, where a large number of moderate fluctuations resulted in an RMSD value of 3.6 for FdhF.
Figure 26. Subunits RMSD Result (Generated with QtGrace) Table 11. Subunits Final RMSD
Subunit ~ Value (A) Verdict
FdhF 3.6 Good
HycE 1.6 Very good
HycB 3.2 Good
HycF 4.5 Bad
HycG 1.8 Very good
Figure 27. FdhF RMSF Result (Generated with QtGrace)
Figure 28. HycE RMSF Result (Generated with QtGrace)
Figure 29. HycB RMSF Result (Generated with QtGrace)
Figure 30. HycF RMSF Result (Generated with QtGrace)
Figure 31. HycG RMSF Result (Generated with QtGrace)
4.5. FdhF & HycE Substrate Binding and FHL Complex Formation Result
Substrate binding affinity between FdhF and HycE, both from individual subunit and complex to formate and hydronium, respectively, were shown in table 11. Molecular docking with Autodock Vina showed that formate could bind with a binding affinity of -2.5 kcal/mol to FdhF. Discovery Studio shows the binding residues involved, including conventional hydrogen bonds at Ser77, Gly51, Asn81, Lys79, and Thr78, and Van der Waals interactions at His84 and Gly50 (Figure 32).
Subsequently, hydronium or proton can bind to HycE with a binding affinity of -1.5 kcal/mol;
however, due to the inability of Discovery studio to process the binding of small molecules, ex.
Protons and hydronium binding sites were predicted through Pymol Instead. Hydrogens of hydronium were found to form polar contacts with Leu33, Trp87, and Thr89. The negative binding affinity results indicate that the ligand can favorably bind to the enzyme or receptor without any external energy output (Pantsar & Poso, 2018). However, in the hybrid complex form, shown in figure 33, the same binding affinity was obtained for FdhF to formate; however, a change in binding affinity to -1.1 kcal/mol was observed in the binding of HycE to hydronium. This binding affinity change might be caused by conformational change within the protein due to the docking process.
Table 12. FdhF & HycE Binding Affinity Comparison in Individual and Complex Setting
Subunit
Binding Affinity (kcal/mol) Individual
Subunit
Within Complex
FdhF -2.5 -2.5
HycE -1.7 -1.1
Figure 32. FdhF to Formate Binding Residue (Generated with Discovery Studio)
As seen in table 12, the parameters for the hybrid FHL were almost compatible with the cytosolic environment of C. reinhardtii. The pI of the protein complex was enough to prevent protein denature and losing enzymatic activity. The relatively high AI also indicated thermal stability. The II also implies that the resulting hybrid protein was stable enough under laboratory conditions in the test tube (Enany, 2014). The GRAVY score also indicates that the protein was hydrophilic and, therefore, can be dissolved in water and cytosols. However, the cysteine residues were high. This was most likely due to the combination of cysteine residues from the five subunits, especially due to the high cysteine residues in HycB and HycF.
Table 13. FHL Hybrid Complex Parameters (Computed with Protparam)
pI cys II AI GRAVY
6.54 40 37.48 79.51 -0.30
Figure 33. Final FHL Hybrid Complex (Generated with Pymol) (red = FdhF, green = HycB, blue = HycF, yellow = HycG, magenta = HycE)
The molecular docking method done in this research was blind docking or molecular docking without prior knowledge of the active site. This was because the resulting FHL design was a novel hybrid FHL complex composed of subunits from multiple bacterial species. The conformational changes need to be studied further to predict the location and changes in the active site of both FdhF and HycE. Moreover, molecular docking was done without the presence of metal cofactors. The FdhF needs Mo4+ ion, while HycE is a NiFe hydrogenase that utilizes Nickel and Iron in its active site (Iobbi-Nivol & Leimkühler, 2013; Shafaat et al., 2013).
4.6. FHL Complex MD EM and Equilibration Result
The EM and equilibration process for the FHL complex was performed under the same conditions as the subunits; therefore, the results will be compared to the previous results. The EM result showed steady convergence of potential energy (Figure 34); thus, high-energy molecular structure removal has been conducted. This will result in structures in the global energy minimum state, as Roy et al. (2015) discussed, which are energetically favorable for the simulation. NVT and NPT equilibration had also been conducted in figures 35 to 37, with the results being expected as the fluctuation point was relatively around the reference point (300K for NVT/temperature, 1 bar for pressure, and 1000 kg/m3 for density). Therefore the process was deemed successful for further MD process.
Figure 34. FHL Complex EM Result (Generated with QtGrace)
Figure 35. FHL Complex NVT Equilibration Result (Generated with QtGrace)
Figure 36. FHL Complex Pressure Equilibration Result (Generated with QtGrace)
Figure 37. FHL Complex Density Equilibration Result (Generated with QtGrace)
4.7. FHL Complex MD Result
A constant Rg value around 5.7 nm was observed in figure 38, which indicated the folding stability of the formed FHL complex (Lobanov et al., 2008). In addition, a final RMSD value of 4.5A (0.45 nm) was detected (Figure 39), which is a bad result for RMSD. The high movement within the protein backbone is believed to be caused by improper subunit-to-subunit bonding done through ClusPro. The model result selection from ClusPro was made according to the model score and subunit binding position, where the latter is focused more. Therefore, it is possible that less stability resulted from selecting the model with a lower score. In addition, the model lacked the metal ions in the complex, which may reduce the complex stability. Finally, the possibility of the improper file editing process is present, which may result in the complex not being processed properly by gromacs, thus leading to altered results. Another way to reduce the RMSD value may be through optimization of the MD process, as the current MD parameters follow the previous tutorial without many changes to the parameter script.
Figure 38. FHL Complex Total Radius of Gyration Result (Generated with QtGrace)
Figure 39. FHL Complex RMSD Result (Generated with QtGrace)
Overall, it is believed that the hybrid FHL complex is not ready for expression studies as the complex has not passed the MD stability test, which may indicate low protein-protein binding stability or improper protein folding (Castro-Alvarez et al., 2017). However, the complex has also shown its favorable binding affinity to the substrates, and the parameters of the complex are also favorable for expression in C. reinhardtii. Therefore, alterations to the MD parameters would be necessary to reduce the final RMSD value. In addition, modifications to the complex are also required to increase the binding affinity further and, possibly, increase the protein stability.
V. CONCLUSION
This research showed considerations and possible issues in the in-silico formation of hybrid FHL complex for expression in C. reinhardtii. The FHL subunits selection was made through parameters focused on C. reinhardtii's expression. The five subunits selected were found to be absent from signaling peptides which allowed further processing without sequence modification.
Favorable binding affinity was observed in the two catalytic subunits, FdhF and HycE. Protein stability test through RMSD and Rg showed stable protein folding; however, HycF exhibited bad RMSD results (4.5A). This was thought to be caused by the high protein movement within its residue. The formed FHL complex from 5 organisms exhibited favorable binding affinity to formate and hydronium.
Additionally, the protein parameters of the complex were suitable for C. reinhardtii's expression.
However, the protein stability test from RMSD had a bad result (4.5A), where rapid protein movement within the backbone may indicate low protein-protein binding stability. Moreover, the absence of metal ions from the five subunits may result in lower binding affinity and protein stability compared to its native form. Due to the absence of a protein structure file containing the metal ions, this theory was not provable in this research. The absence of metal ions may also impact the resulting FHL complex structure as the metal ions should form an electron transport cascade from FdhF to HycE. Finally, modifications to the protein sequence may increase protein-substrate binding affinity and stability. Overall, this research has shown the possibility of the in-silico design of a hybrid FHL complex for expression in C. reinhardtii for further in-vivo studies.
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