Team:IIT Roorkee/Poster

Pyomancer: Novel Antibacterial Protein Complexes for MDR Infections
Presented by: Team IIT Roorkee

Team: Sanjeevani Marcha1, Muskaan Bhambri1, Harkirat Singh Arora1, Kushagra Rustagi1, Pradum Kumar1, Yash Aggrawal1, Nitish Verma1, Siddharth FItwe1, Tishee Natani1, Kartikey Kansal1, Kanishk Sugotra1, Lakshya Jain1, Mihir Sachdeva1, Dr Siva Ram Uppalapati2, Somok Bhowmik2, Professor Naveen K. Navani2, Professor Ranjana Pathania3

1iGEM Student team member

2iGEM Advisor

3iGEM Primary PI

Our project is aimed at creating novel antibacterial protein complexes called Seekercins, designed to specifically bind and kill species of multidrug-resistant bacteria. This would be achieved by the fusion of antibacterial proteins called pyocins and bacteriophage tail fibers. Furthermore, it aims to create a system for the easy production of such antimicrobials for the use of the iGEM community, consisting of a collection of parts and software tool. ML algorithms were used to uncover relationships between resistance genes so as to resensitize them to antibiotics. Validation will be done experimentally, targeting the pathogen Acinetobacter baumannii, and production will be done in E. coli.

Hospital-acquired infections are a leading cause of death worldwide with 1.4 million active infections every year. In India, the infection rate is alarming and is estimated at about 35-40% of all hospital admissions. With around 49 million cases of COVID-19 so far, the coronavirus pandemic is superimposed on the growing pandemic of drug resistant bacteria. Ventilator-associated pneumonia (VAPs) are one of the most severe comorbidities with ICU mortality rate of 45.6% to 60.9%, reaching 84.3% when caused by XDR A. baumannii.

As a solution, we use pyocins, which are high molecular weight bactericidal protein complexes produced by P. aeruginosa. Pyocins attach to receptors on the bacterial cell wall with tail fibers and penetrate it with the hollow tube, leading to dissipation of membrane potential and subsequent cell death. They have a high killing efficiency but a very narrow host spectrum, and are similar in structure to Myovirus bacteriophages.

Inspiration & Solution


Interviews with doctors and ICU specialists made us realise the necessity for alternative treatments for drug resistant infections in the face of rapid AMR development.

Our key inspiration was TAU Israel’s 2019 iGEM Project, Pyo-Pyo. They illustrated the similarity of pyocins and phages which led us to improve upon their strategy and software in our project. Heightened sensitivity to hygiene during the pandemic allowed us to disseminate WHO’s Global Action Plan against AMR. Nationwide lockdown due to the COVID-19 pandemic inspired us to explore protein modelling, development of software and machine learning tools.


Our solution is the development of ‘Seekercins’, novel antibacterial protein complexes by engineering of R-type pyocins to target specific drug resistant bacteria. R-type pyocins have a restricted antibacterial spectrum and here we exploit their similarity to phages to effectively retarget them. We made fusion tail fibres which mediate the binding to the receptor from genes of pyocin and phage tail fibers. The gene cluster will be cloned into an E. coli expression vector under an inducible promoter. We plan to purify the Seekercins and perform antimicrobial assays against MDR A. baumannii strains and biochemical characterization.

Principles of seekercin design-
  1. All native proteins of the pyocin tail structure including tube, spike, sheath and baseplate must be conserved for proper functioning
  2. The initial 164 N-terminal amino acid residues of the wild-type pyocin tail fiber must be conserved for proper functioning
  3. C-terminal of the fusion tail fiber (originating from the phage-tail) must bind specifically to a surface-accessible receptor(sugar or protein) on the cell wall of the target bacterium

After literature review of existing Acinetobacter baumannii phages, phage AP22 was selected as a suitable candidate for its large lytic spectrum.

Rational design of fusion tail fiber-

Analysis of key domains in AP22 tail fiber (Fig 4) revealed a conserved lectin-fold similar to that of R2-pyocin tail fiber. C-terminal sequence alignment revealed that the last 137 amino acids of AP22 constituting the head and shaft of AP22 tail fiber aligned with the last 134 amino acids of R2 pyocin tail fiber. We replaced the lectin-fold head region of R-pyocin tail fiber with a similar lectin-fold binding domain from A. baumannii specific bacteriophage AP22 tail fiber which naturally targets these bacteria. Models of the fusion tail fiber were verified for integrity.

The gene responsible for the tail fiber from the wild-type pyocin gene cluster, PA0620 has been replaced by the fusion tail fiber. The gene cluster and fusion tail fiber was synthesized by and received from IDT.

As a chassis we chose to produce them in Escherichia coli, strain BL21(DE3).As an expression vector, we chose pET-28 a from Novagen which contains a T7 promoter and lac operator, allowing IPTG-induced protein expression.

We modeled our protein using all the three bioinformatics-based modeling techniques. 11 models obtained through these methods were compared and the best one was chosen.

  • Used when we have a structurally unknown protein and a structurally similar known protein
  • Performed using Swiss-Model web server
  • Got 1 predicted model

  • Uses protein folds of similar proteins found in different databases to predict the structure of the unknown protein
  • Performed using I-Tasser web server
  • Got 5 predicted models

  • Used when structural information is not available for a similar protein
  • Performed using Robetta Baker Lab web server
  • Got 5 predicted models
We have introduced two new basic Parts to the iGEM registry as a contribution for us future iGEM teams.

Part: BBa_K3564001 :Gene cluster encoding for R-type pyocin with the tail fiber gene deleted

Part: BBa_K3564002 :Fusion tail fiber of R pyocin tail fiber and Acinetobacter phage AP22 tail fiber


Part:BBa_K3564102 : Composite of BBa_K3564001 and BBa_K3564002 that expresses a Seekercin, i.e. a pyocin targeted to specifically Bind and kill Acinetobacter baumannii

Characterization for the composite parts would be updated once the experiments planned are completed post-pandemic.
Integrated Human Practices

Dr. Nand Kishore Joshi

  • The team received insightful facts on the situation of AMR infections inside ICUs during COVID-19.
  • We got to know that doctors use IL-6 antagonists and immunosuppressants for COVID Regulation.
  • But they observed that the patient became more susceptible to deadly bacterial infections, which are mostly XDR/MDR inside the ICU, which increased the need for a solution.

Ms. Khushboo Gupta

  • Our team was intrigued and inspired by the patient's story throughout her battle with MDR Tuberculosis.
  • We got to know how mental trauma takes a toll on physical health.
  • We learned that meditation, yoga, a balanced diet, and positive vibes help in speedy recovery.

Dr. Prajwal & Dr. Sebastian Dümcke

  • The team had interactive discussion sessions with Clemedi (a Life Sciences Start-up) associates.
  • We learned about protein modeling and got inputs in designing the protein as well.
  • Our software team got useful insights on applications of Machine Learning in the project.
Team iGEM IIT Roorkee understands the importance of working in the team and for the team. We truly realize the impact of collaborative work projects on any social, technical, or cultural issue. With the same inspiration, we collaborated with competing iGEM teams to help spread their as well as our cause to aware, acknowledge, and educate the population worldwide. We reached out to huge masses and successfully increased the awareness quotient of people to an impressive 35 - 40% extent.
Sustainable Development Goals

Establishing peace and prosperity requires efforts from not one but every human being on the planet. With this achieved, we can eradicate all other issues prevalent globally, like poverty, poor health, hunger, inequality, poor usage of resources, deteriorating climatic conditions, injustice, and so on. To create an impact and eradicate the above-said problems, the United Nations (UN) came up with 17 focused SDGs to be achieved by 2030. So playing our part, we have contributed to three of such goals (as mentioned below) through our project Pyomancer.

Target 3.2
  • Providing a revolutionized treatment for HAIs
  • Running Awareness program on Social Media

Target 4.5
  • Educating children, young adults, and the general public about AMR, Synthetic Biology, and Technology

Target 17.16 & 17.17
  • Collaborations with fellow iGEM teams and college groups
  • Initiated and participated in iGEM x SDGs Projects
Science Communication & Public Engagement

Spreading awareness among children and young adults through interactive webinar sessions and initiating an open dialogue about the multidisciplinary fields.

Understanding the real on-ground situation of the hospital environment through surveying medical personnel and gauging the existing knowledge of AMR & basic Sciences of young adults and the general public to level-up the know-how of the issue.

Quiz Competition for the Special Children
Curating learning material for the specially-abled children to learn about the possible post-antibiotic era due to the Antimicrobial Resistance in an engaging way of Quizzing questions.

Machine Learning Workshop
Educating budding researchers about the increasing technological use in medicine through a riveting workshop on “Combating AMR using Machine Learning”.

Social Media
Influencing youth through trending online platforms like Instagram, & facebook; and promoting scientific fraternity and social inclusivity.

Policy Review Report
Getting acquainted with the policies and government schemes to analyse the contribution of governing bodies towards the healthcare industry and the impact that requires undivided attention.
Machine Learning

Detection of Antibiotic Resistant Genes (DARG)

Our machine learning approach called Detection of Antibiotic Resistant Genes abbreviated as DARG involves eight simple steps- beginning with data collection of genomes of different strains of A. baumannii along with their response to different antibiotics, followed by annotating the genomes to develop pan-genome. A particular strain is represented by a binary vector wherein 1 signifies the presence of particular allele and 0 signifying absence of particular allele.
The collected data is passed into an Machine learning algorithm called Support Vector Machines followed by interpretation of SVM. The SVM algorithm is used to compute the weights given to different alleles for predicting response of strains to particular antibiotics.
We perform correlation analysis among top alleles, followed by analysing the impact of mutations on the resistance of different strains.
Machine learning algorithm helps in uncovering genes conferring resistance to antibiotics. Different important genes which are detected are shown in red along with the mechanism they are involved in.



We have developed a web application TailScout which generates secondary structure of the Engineered pyocin by taking the name of the target bacterium from the user as Input. We have used the JPred REST API and Clustal Omega REST API on the Django REST framework for the processes.

Classifying broadly it is a three step process:
1. Phage Tail Fiber Detection using the bacteria name and phage tail library.
2. MSA of phage tail fiber sequences for identifying the conserved regions using Clustal Omega.
3. Secondary structure prediction of fusion protein using JPred.
Backend Workflow:

We suggest a surfactant-based formulation containing a mixture of fusion proteins to be used for endotracheal and endobronchial administration for the treatment of Ventilator-related pneumonia (VAP) is associated with increased stay and mortality in the intensive care unit ( ICU) with an estimated 30 % rise in underlying disease mortality.

Current strategy is the only way to deal with acquired infections is to monitor the spread of infections by administering antibiotics. Once the disorder is diagnosed, subsequent antibiotics are administered to the sites of infections, i.e. alveoli and trachea lining, and we have findings for the infection microbial profiles.

We plan to use a surfactant to improve the delivery of the medication and achieve a more homogeneous distribution of the drug by using the rapid installation of the bolus in conjunction with suitable alveolar recruitment techniques. It will require the administration of a carefully formulated formulation according to each infection's microbial diagnosis. By having a unique seekercin for various organisms, we aim to keep our therapy specific and targeted, thereby preventing any collateral damage and reducing the spectrum of production of resistance.

Surfactant consists of 80-85 % phospholipids, 5-10 % neutral lipids, and 8-10 % protein, with 5-6 % of the four specific proteins of surfactant. Broadly, the formulation has the following structure:

Mucolytics: Carbocisteine, Erdosteine. Bronchodilators:Beta-2 agonists, Anticholinergics, Theophylline
Seekercin A di-Palmitoylphosphatidylcholine (DPPC)
Seekercin B Phosphatidylglycerol
Seekercin C Phosphatidylinositol

By instilling a particular microvolume of liquid into the upper airways and moving the plug by programmed air ventilation into the desired region of the lung, the aqueous liquid containing soluble drugs can be delivered into targeted branches of the lung airway and deposited onto the lung epithelium .

We researched and decided on delivery of pyocins through nebulizers however, in India there is a disproportionately large problem with VAPs in the Newborn Intensive Care Units.
(NICU) (60k+ deaths per year).

On seeking an expert opinion from a Doctor (NICU/VAP specialist), he pointed out that neonates, especially preterm, have underdeveloped lungs that are too delicate for effective nebulizer based treatment.

A big drawback of nebulizer therapy is its poor efficiency of delivery, much of the dose is lost in the ETT (mist deposition on the walls, medicine does not get misty well, etc.). Drug loss is no longer a concern when administering the bolus dose, since the substance itself is injected into the lungs, taking the drug with it.

As observed in in-vivo studies, our rapid and efficient delivery of bolus-injected surfactant solutions containing surfactant-associated proteins such as SP-A to the alveoli shows a strong precedent for delivery of Seekercin is also more likely to ensure the delivery of high doses (10 ^ 12 pyocins per ml) needed for successful treatment. The dose will easily enter the alveoli, and it is predicted that changes will be observed within seconds to minutes.

Future Direction
  • We would complete the planned experiments in time for next year’s iGEM competition as Phase 2 of our project
  • As Seekercins are protein-based therapeutics, an attempt to reduce their immunogenicity by eliminating relevant epitopes could be made
  • It is clear from the literature that the number of sequenced phages for pathogenic bacteria are still relatively small. This calls for the need for more extensive isolation, characterizing and sequencing of phages. 
  • Following positive results in cell line based assays, in-vivo studies in murine or Caenorhabditis models could be done
  • Comparison of efficacy of Seekercin formulations with various additives in cell line and in-vivo lung infection studies
  • Studies on efficacy and dosage requirements in multi-Seekercin cocktails for broader strain spectrum.
  • Increasing the numbers of Multiple Drug-Resistant Bacteria in our phage library.
  • Incorporating protein modelling of the pyocin bacteriophage fusion protein in features offered by TailScout
  • Result Analysis of Secondary and Tertiary structure of the fusion protein can also be incorporated into TailScout.
  • Improving the result and job status retrieval process of TailScout.

Wet Lab

Initial experiments were performed in March but had to be aborted due to the pandemic and closing of our institute. We have laid out contingency plans for multiple scenarios based on the expected results of our wet lab experiments.

Dry Lab

Root Mean Square Deviation
Calculated and compared the RMSD of each fusion-protein model with:
  • The R2-NTF tail fibre protein
  • The AP-22 bacteriophage tail fibre protein

Ramachandran Plot
  • Gives information about the energetically allowed and disallowed regions in the protein
  • Prepared and compared plots for 6 models

Model/Data RMSD with lower part (4mtm) RMSD with upper part (6cu2) Residues in disallowed regions Residues in favoured regions
Ab-initio Model 1  0.435 2.648 0% 89.2%
Ab-initio Model 2  0.419 6.264 0% 87.8%
Ab-initio Model 3  0.375 2.335 0% 86.1%
Ab-initio Model 4  0.475 4.024 0.2% 86%
Ab-initio Model 5  0.427 4.631 0 89.4%
Swiss Model 4.666 1.527 0.3% 88.1%

Best Model → Ab-initio Model 1
  • After choosing the best model, we performed energy minimization for it to ensure the most stable state
  • We used YASARA Energy Minimization Toolkit for this

References and Acknowledgements

  10. Scholl, D., Cooley, M., Williams, S.R., Gebhart, D., Martin, D., Bates, A. and Mandrell, R., 2009. An engineered R-type pyocin is a highly specific and sensitive bactericidal agent for the food-borne pathogen Escherichia coli O157: H7. Antimicrobial agents and chemotherapy, 53(7), pp.3074-3080.
  11. Scholl, D. and Martin, D.W., 2008. Antibacterial efficacy of R-type pyocins towards Pseudomonas aeruginosa in a murine peritonitis model. Antimicrobial agents and chemotherapy, 52(5), pp.1647-1652.
  12. Williams, S.R., Gebhart, D., Martin, D.W. and Scholl, D., 2008. Retargeting R-type pyocins to generate novel bactericidal protein complexes. Applied and environmental microbiology, 74(12), pp.3868-3876.
  13. Scholl, D., Gebhart, D., Williams, S.R., Bates, A. and Mandrell, R., 2012. Genome sequence of E. coli O104: H4 leads to rapid development of a targeted antimicrobial agent against this emerging pathogen. PLoS One, 7(3), p.e33637.


Darshak Bhatt, Mentor

Vivek Junghare

Student Technical Council, IIT Roorkee

Director IIT Roorkee

Dean SRIC IIT Roorkee