human-practices.html

{% extends "layout.html" %} {% block title %}Human Practices{% endblock %} {%
block lead %}{% endblock %} {% block page_content %}

<style>
  body {
    background-image: linear-gradient(
      to bottom,
      #f9dbbd 0%,
      #ffa5ab 20%,
      #da627d 40%,
      #a53860 60%,
      #450920 100%
    );
  }

  #float1 {
    background-image: url("https://static.igem.wiki/teams/5346/doodle/chemistry-teacher.png");
  }

  #float2 {
    background-image: url("https://static.igem.wiki/teams/5346/doodle/inspiration.png");
  }
</style>

<div class="col-md-3 sidebar"></div>
<div class="col-md-9 content">
  <h1>Human Practices</h1>
  <p>
    Our iGEM project,
    <b
      >IMPROViSeD (Integrated Modelling of Protein Complexes using
      Distance-Restraints and Energy-Assisted Modelling)</b
    >, has been shaped through an iterative process of ideation, expert
    feedback, and practical applications. This section outlines our engagement
    with stakeholders, detailing how their insights shaped the direction of our
    project. Our work not only aligns with scientific goals but also addresses
    broader societal challenges, particularly in therapeutic development.
  </p>
  <hr />
  <figure>
    <img
      src="https://static.igem.wiki/teams/5346/hp/oneplustwo.png"
      alt="Figure not downloaded"
    />
    <figcaption>
      Announcement of a Software and AI iGEM team at IISc.| Initial team
      meetings : Brainstorming
    </figcaption>
  </figure>

  <p>
    <figure>
      <iframe
        src="https://static.igem.wiki/teams/5346/hp/ideation-hp.pdf"
        class="presentation"
      ></iframe>
      <figcaption>
        Talk on computational biology by team members in collaboration with
        Naturalist(UG Biology club) followed by team recruitment discussions.
      </figcaption>
    </figure>
  </p>

  <h2>Early Ideation and Exploratory Concepts</h2>

  <p>
    In the initial stages, we explored multiple ideas, collaborating with
    experts and stakeholders from various scientific fields. These brainstorming
    sessions were crucial for understanding the scope and limitations of
    different approaches. Although we didn’t pursue these ideas for iGEM, they
    helped us refine our thinking and ultimately led us to the development of
    <b>IMPROViSeD</b>.
  </p>

  <h3>Prediction of Metal Binding Proteins</h3>
  <p>
    This idea stemmed from our discussions with
    <a href="https://biochem.iisc.ac.in/nagasuma-chandra.php"
      ><b> Prof. Nagasuma Chandra</b></a
    >
    from the Department of Biochemistry at IISc, who specializes in
    computational biology and the study of genome-wide perturbations in
    diseases. Our goal was to leverage computational models to predict peptide
    sequences that could bind metals, with potential applications in
    bioremediation or metal-ion detection.
  </p>
  <p>
    Following <b>Prof. Chandra’s</b> suggestion, we engaged with
    <a href="https://www.msruas.ac.in/faculty-staff/deepesh-nagarajan">
      <b> Dr. Deepesh Nagarajan </b>
    </a>
    from M.S. Ramaiah University of Applied Sciences. Dr. Nagarajan encouraged
    us to develop short peptides that could quench specific metals of interest.
    We focused on understanding metal-binding regions within proteins and
    explored the possibility of engineering these peptides for environmental or
    diagnostic applications.
  </p>
  <p>
    However, after further analysis, we realized that this approach lacked
    novelty. Several similar projects had been developed in the past, and the
    practical impact seemed limited. Additionally, while computationally
    interesting, this idea did not fully align with iGEM’s emphasis on
    innovation and societal relevance. Therefore, we decided to move forward in
    a more promising direction.
  </p>

  <h3>Genome-wide Prediction of miRNA</h3>
  <p>
    Another concept involved predicting microRNAs (miRNAs) at a genome-wide
    level, a task inspired by discussions with stakeholders such as
    <a href="https://dbg.iisc.ac.in/people/arun-kumar/">
      <b>Sakshi and Mukund from Prof. Arun Kumar’s lab </b></a
    >
    and Hemant Chandru Naik
    <a href="https://srimontasd.wixsite.com/gayen-lab">
      <b>from Srimonta Gayen's Lab</b></a
    >, from Developmental Biology and Genetics, IISc, as well as input from
    <a href="https://cbr-iisc.ac.in/people/shweta_ramdas/"
      ><b>Prof. Swetha Ramdas</b></a
    >
    at Center for Brain Research, IISc. miRNAs regulate gene expression
    post-transcriptionally and play crucial roles in various diseases, including
    cancer, making them attractive candidates for therapeutic targeting.
  </p>
  <p>
    Our idea was to use computational models to predict miRNA sequences that
    could potentially target disease-related genes, providing a platform for
    personalized medicine or biomarker discovery. This would have required
    extensive computational analysis of genomic data, as well as experimental
    validation. However, during the brainstorming phase, we encountered several
    challenges, including the difficulty of validating miRNA targets without
    significant experimental infrastructure. Furthermore, we recognized that
    while miRNA prediction is an exciting area of research, our approach lacked
    the experimental novelty and broader integration required for a successful
    iGEM project.
  </p>

  <h3>Ion Channels</h3>
  <p>
    In our initial brainstorming, we also explored the role of ion channels,
    specifically focusing on the sodium-chloride co-transporter (NCC) located in
    the distal convoluted tubule (DCT) of kidneys. This protein is essential for
    sodium ion reabsorption, and mutations in the SLC12A3 gene lead to Gitelman
    syndrome, which results in renal loss of sodium and potassium, causing
    various health issues such as hypokalaemia and metabolic alkalosis.
  </p>
  <p>
    To address this, we aimed to design a synthetic ion channel simulation to
    better understand NCC’s electrostatic potential and how SLC12A3 mutations
    affect its function. For this project, we received significant guidance from
    <a href="https://cds.iisc.ac.in/faculty/dpal/"><b>Prof. Debnath Pal</b></a>
    from Center for Data Sciences, IISc and his student,
    <a href="https://www.djmaity.com/"><b>Dibyajyoti Maity</b></a
    >. Prof. Debnath helped us grasp the foundational concepts and provided
    practical insights through discussions and visualizations using PyMol. He
    guided us through the calculation of electrostatic potentials during protein
    conformational changes, teaching us to interpret results using Delphi
    software. Additionally, he assisted in preparing PDB files for molecular
    dynamics simulations with Gromacs, which were vital for our analysis.
  </p>
  <p>
    Dibyajyoti, who made the software
    <a href="https://github.com/djmaity/md-davis">MD-Davis</a>, which is used
    for calculating the electrostatic potential of the protein, energy values,
    profiles, etc. His experience and knowledge was very helpful for us to
    perform the necessary calculations for the project. Despite living in the
    USA, offered support through Google Meet sessions, teaching us how to use
    Delphi and MD-Davis effectively. His guidance allowed us to streamline our
    workflow and analyze the electrostatic potential of the NCC protein
    efficiently. This collaborative effort laid the groundwork for understanding
    the role of ion channels in our project.
  </p>
  <p>
    Professor Arvind Penmatsa is a professor in the Molecular Biophysics Unit
    (MBU) in IISc. We met Prof. Penmatsa in the initial stages of ideation. He
    briefed us about the general mechanisms of working of ion transporters and
    the energetics of the process. Our protein shows 2 conformations, one facing
    inwards (cytoplasm) and the other facing outwards (lumen). A conformational
    change happens as the ion binds to the outward facing conformation, making
    it inward facing and leading to the subsequent release of the ions inwards.
    He gave us advice on whether we should continue a simulational study on the
    NCC protein and its mutations or not, and asked us also to look into the
    functionomics of different mutations that give rise to the Gitelman syndrome
    so that we have a coherent idea on how to proceed.
  </p>

  <h1>Experimental Design and Approach</h1>
  <hr />
  <h2>Pivot to IMPROViSeD: A Focus on Protein Complexes</h2>
  <p>
    With the realization that our early ideas weren’t feasible within the
    constraints of the iGEM competition, we shifted our focus. Guided by our PI,
    <b>Prof. Debnath Pal</b>, and our instructor, <b>Niladri Ranjan Das</b>, we
    began developing a project centered around the
    <b>structural modelling of protein complexes</b>. This pivot was heavily
    influenced by ongoing discussions with
    <a href="https://www.ncbs.res.in/faculty/shruthi"
      ><b>Dr. Shruthi Viswanath</b></a
    >, who has extensive experience working with integrated modelling platforms.
  </p>
  <p>
    As we moved forward, our weekly meetings began to focus on how to integrate
    <b>DREAM (Distance Restraints and Energy-Assisted Modelling)</b> into an
    <b>Integrated Modelling Platform (IMP)</b> for protein complexes. This led
    us to the development of <b>IMPROViSeD</b>, a platform designed to model
    protein structures using experimental distance restraints derived from
    techniques such as <b>cross-linking mass spectrometry (XLMS)</b>.
  </p>

  <figure>
    <img
      src="https://static.igem.wiki/teams/5346/hp/threeplusfour.png"
      alt="Figure not downloaded"
    />
    <figcaption>
      Hybrid/online meetings during summer to finalise the project idea
    </figcaption>
  </figure>
  <hr />
  <p>
    Around this time, we also sought input from
    <a href="https://www.msctr.org/2016/06/08/dr-manjula-das/"
      ><b>Dr. Manjula Das</b></a
    >, an expert in antibody-based therapeutics. She highlighted the therapeutic
    potential of understanding protein-protein interactions, especially in the
    context of cancer metastasis. This interaction led us to focus on the
    <b>LCN2-MMP9 complex</b>, a pair of proteins known to play a significant
    role in the spread of cancer. With Dr. Das’ input, we decided to demonstrate
    the power of our platform by applying it to the structural modelling of this
    complex. This decision provided a clear use case that aligned with both
    societal needs and the goals of iGEM.
  </p>

  <h1>Identification of Stake Holders</h1>
  <p>
    Identifying and engaging with key stakeholders is essential for ensuring the
    project’s relevance and broader impact.
  </p>
  <figure>
    <img
      src="https://static.igem.wiki/teams/5346/aiim/stakeholders-mindmap.png"
      alt="stakeholders-mindmap"
    />
  </figure>
  <p></p>
  <h2>AIIM-Interaction with iGEM ambassadors, iGEM judges and other iGEMers</h2>
  <figure>
    <img
      src="https://static.igem.wiki/teams/5346/aiim/aiim-poster-presentation.jpg"
      alt="AIIM Poster Presentation"
    />
    <figcaption>AIIM Poster Presentation</figcaption>
  </figure>
  <figure>
    <img
      src="https://static.igem.wiki/teams/5346/aiim/poster-1.png"
      alt="AIIM Poster"
    />
    <figcaption>AIIM Poster of IISc-Software Team</figcaption>
  </figure>

  <h3>Judges After the Mock Presentation</h3>
  <p>
    The feedback we received for our iGEM project was both constructive and
    encouraging. Judges commended the <b>clarity</b> of our presentation and saw
    significant <b>potential</b> in our platform for the
    <b>structural computational biology</b> field. They recommended expanding
    our <b>outreach efforts</b>, and example use cases to our wiki. There were
    calls to present more results to demonstrate project progress, consider the
    microenvironment's effects on distance predictions, and include other data
    types beyond distances. Additionally, the importance of developing a
    <b>user-friendly interface</b> for non-computational biologists was
    highlighted, as well as providing a demo video for easier understanding by a
    broader audience. Expanding <b>industrial human practices</b> was also
    encouraged to ensure the platform’s applicability to real-world use.
  </p>
  <p>
    We took the feedback seriously and expanded our outreach efforts, engaging
    with additional industrial experts as key <b>stakeholders</b>. We added
    example use cases to our
    <a href="https://2024.igem.wiki/iisc-software/proof-of-concept"
      >Proof of Concept page </a
    >
    to illustrate the platform's capabilities. Our platform can now model using
    <b
      >limited crosslinks, incorporating both experimental and synthetic
      results</b
    >, and we've accounted for various molecular orientations. Additionally,
    we've ensured thorough documentation to make the platform
    <b>user-friendly</b> and accessible to a <b>broader</b> audience.
  </p>

  <h3>Ambassadors and other iGEMers During the Poster Presentation</h3>
  <p>
    This year’s iGEM Ambassadors,
    <b>Ms. Srimathi Lakshminarayan, Ms. Hana Lukman, and Mr. Sara Thomas</b>,
    encouraged us to broaden our stakeholder engagement and strengthen our
    outreach efforts, particularly for the Education special prize. They also
    recommended, we specify the cyber safety aspects to consider while making
    our project. Following their advice, we expanded our initiatives,
    <b>reaching students and communities</b> across the country to
    <b>raise awareness</b> about synthetic biology. We made a concerted effort
    to consult with all relevant stakeholders and
    <b>incorporate their feedback</b> into our project. We also incorporated a
    <b>cyber safety</b> section in our
    <a href="https://2024.igem.wiki/iisc-software/safety">safety page </a> to
    ensure that our platform is secure.
  </p>
  <p>
    During our poster presentation, we showcased our work to other iGEM teams
    and ambassadors. We received valuable constructive feedback, among which one
    of the suggestions was to create a
    <b>narrative</b> with engaging characters to make our project
    <b>more accessible</b> to a wider audience. We took this advice seriously,
    and our
    <a href="https://2024.igem.wiki/iisc-software/index.html">Home</a> page now
    reflects this approach.
  </p>

  <hr>
  <h2>Interactions with PhD students</h2>
  <p>
    As part of our Human Practices outreach, we engaged with PhD students
    specializing in structural biology to gain insights that were pivotal to our
    project. <b>Roohani Basavaraj</b> from <b>Prof. Utpal Tatu’s Lab</b> in the
    Biochemistry Department provided crucial guidance on the in-gel digestion
    protocol and advised us on proper protein handling during our XLMS
    (Cross-Linking Mass Spectrometry) workflow. We incorporated all of her
    advice into our wet lab protocol. Additionally, we consulted
    <b>Muskaan</b> from <b>Dr. Shruthi Viswanath’s Lab</b> at the National
    Centre for Biological Sciences, who offered valuable assistance in
    navigating cross-link databases, helping us refine our approach to data
    analysis. These interactions significantly enriched our understanding of key
    experimental processes.
  </p>
  <hr>
  <h2>Refining Our Approach: Meetings with Experts</h2>
  <p>
    As we refined our project, we engaged with various experts who helped us
    address technical challenges and optimize our methodology.
  </p>

  <a href="https://www.uu.nl/staff/AMJJBonvin">
    <h3>Dr. Alexandre Bonvin</h3>
  </a>
  <p>
    A key figure in the development of the <b>HADDOCK</b> platform for modelling
    biomolecular complexes, Dr. Bonvin provided invaluable feedback on improving
    the accuracy of our structural models. His recommendations included:
  </p>
  <ol>
    <li>
      <b>Inclusion of Surface Atoms:</b>
      <p>
        Dr. Bonvin highlighted the potential for clashes when only using C-alpha
        atoms for localization and suggested including surface atoms with lower
        bounds between them. While we initially had concerns about the large
        number of lower bounds this might introduce, we adapted his advice by
        first localizing C-alpha atoms and then refining the model by focusing
        on surface atoms from the protein interface.
      </p>
    </li>
    <li>
      <b>Handling Protein Flexibility:</b>
      <p>
        He also recommended breaking down flexible regions of the protein and
        performing multi-registration, a suggestion that we incorporated into
        our approach.
      </p>
    </li>
    <li>
      <b>Crosslink Ambiguities:</b>
      <p>
        Assigning experimental crosslink data to specific residue pairs can be
        challenging due to ambiguities. Dr. Bonvin reassured us that as long as
        the C-alpha distances were comparable, the proposed method would be
        tolerant to these ambiguities.
      </p>
    </li>
    <li>
      <b>Iterative Pruning:</b>
      <p>
        Finally, he advised iterating multiple times to generate different
        localization solutions and then pruning based on crosslink violations.
        This iterative process became a core aspect of our modelling workflow.
      </p>
    </li>
  </ol>

  <figure>
    <img
      src="https://static.igem.wiki/teams/5346/hp/bonvin-interaction.png"
      alt="interaction with Dr. Bonvin"
    />
    <figcaption>Our interaction with Dr. Alexandre Bonvin</figcaption>
  </figure>

  <a
    href="https://imsb.ethz.ch/research/beltrao.html#:~:text=Prof.%20Pedro%20Beltrao.%20chevron_right%20Contact"
  >
    <h3>Dr. Pedro Beltrao</h3></a
  >
  <p>
    The professor provided critical feedback on improving our integrative
    protein modelling platform. Their recommendations were:
  </p>
  <ol>
    <li>
      <b>Time Efficiency of Guided Docking:</b>
      <p>
        The professor questioned whether our platform could outperform others in
        terms of speed. We explained that our approach uses automated guided
        docking, where localization takes less than a second, and registration
        completes in 3-4 seconds. This allows each iteration to finish in under
        10 seconds, significantly faster than existing platforms.
      </p>
    </li>
    <li>
      <b>Handling Crosslink Ambiguities:</b>
      <p>
        They inquired about the accuracy of our algorithm in distinguishing
        between crosslinks, such as residue A to B versus A to C. We confirmed
        that our platform has been rigorously tested to resolve such ambiguities
        and has consistently delivered successful results.
      </p>
    </li>
    <li>
      <b>Comparison with Other Platforms:</b>
      <p>
        The professor emphasized the need to demonstrate how our platform
        compares with others in terms of processing time. They noted that by
        speeding up the initial conformation formulation using crosslink data,
        our platform could perform more detailed energy calculations earlier in
        the process.
      </p>
    </li>
    <li>
      <b>Avoiding Energy Minimization Artifacts:</b>
      <p>
        We discussed how our team’s computational expertise allows us to
        minimize the chances of introducing artifacts. By using experimental
        evidence as a starting point rather than random structures, we reduce
        the risk of inaccuracies in energy minimization.
      </p>
    </li>
    <li>
      <b>Escaping Local Minimums:</b>
      <p>
        The professor highlighted the importance of avoiding local minimum
        traps, particularly in cases involving NMR data. They agreed that our
        method could help resolve this issue by improving the process of
        escaping incorrect structural interfaces.
      </p>
    </li>
  </ol>

  <figure>
    <img
      src="https://static.igem.wiki/teams/5346/hp/pedro-interaction.png"
      alt="interaction with Dr. Beltrao"
    />
    <figcaption>Our interaction with Dr. Pedro Beltrao</figcaption>
  </figure>

  <a
    href="https://ganjilab.org/#:~:text=We%20are%20a%20part%20of%20the%20Department%20of%20Biochemistry%20at"
  >
    <h3>Dr. Mahipal Ganji</h3>
  </a>
  <p>
    The professor provided valuable guidance in securing resources within our
    university for the UV crosslinking experiments we initially planned.
  </p>
  <ol>
    <li>
      <b>Selection of Crosslinkers:</b>
      <p>
        They recommended broadening our range of chemical crosslinkers to
        enhance accuracy, advising us to consider different crosslinkers based
        on the experimental context. By targeting specific amino acid residues
        or chemical groups, we could significantly improve the precision of our
        protein models.
      </p>
    </li>
    <li>
      <b>Crosslinkers Like DSG:</b>
      <p>
        The professor emphasized the potential of using DSG (Disuccinimidyl
        Glutarate), a crosslinker known for its effectiveness in capturing
        interactions between protein subunits. Incorporating DSG into our
        protocol would not only increase the accuracy of our integrative models
        but also strengthen their overall robustness. They encouraged us to
        experiment with a variety of crosslinkers to further refine the
        platform, potentially expanding its capabilities in future iterations.
      </p>
    </li>
  </ol>

  <hr />
  <h2>Industry and Medical Foundation Engagement</h2>
  <p>
    As part of our efforts to ensure that our project had real-world
    applications, we reached out to both industry professionals and medical
    experts for their input.
  </p>

  <h3>ThermoFisher Scientific</h3>
  <p>
    We had the opportunity to present our work to a panel of experts at
    <b>ThermoFisher Scientific</b>, a leading company in scientific
    instrumentation and research solutions. During this meeting, the panel
    provided constructive feedback on how our platform could be applied to
    large-scale datasets, emphasizing the potential for <b>IMPROViSeD</b> to be
    used in drug discovery and other protein interaction studies. The
    discussions encouraged us to think about scalability and real-world
    applications, further refining our approach to ensure that our project could
    have a tangible impact beyond the academic sphere.
  </p>

  <figure>
    <img
      src="https://static.igem.wiki/teams/5346/hp/five.png"
      alt="Figure not downloaded"
    />
    <figcaption>ThermoFisher Scientific visit</figcaption>
  </figure>

  <h3>Majumdar Shaw Medical Foundation</h3>
  <p>
    We also presented our work at the
    <b>Majumdar Shaw Medical Foundation</b>, a prominent institution in cancer
    research and treatment. Here, we received specific feedback on the potential
    applications of <b>IMPROViSeD</b> in modelling cancer-related protein
    complexes. The experts we interacted with underscored the importance of
    accurate structural data for the development of antibody-based therapies. By
    focusing on the <b>LCN2-MMP9 complex</b>, we positioned our project to
    directly contribute to cancer metastasis research, with potential long-term
    benefits in therapeutic development.
  </p>

  <figure>
    <img
      src="https://static.igem.wiki/teams/5346/hp/sixplusseven.png"
      alt="Figure not downloaded"
    />
    <figcaption>MSMF visit</figcaption>
  </figure>

  <hr />
  <h2>Conclusion: A Collaborative and Impactful Project</h2>
  <p>
    Our project, <b>IMPROViSeD</b>, has been shaped by continuous stakeholder
    engagement, expert feedback, and real-world considerations. Through our
    early explorations of ideas such as <b>metal-binding peptides</b> and
    <b>miRNA prediction</b>, we gained a deeper understanding of computational
    biology and its limitations. This process led us to focus on a project with
    clear societal relevance—the structural modelling of protein complexes
    involved in cancer metastasis.
  </p>
  <p>
    By learning from experts such as <b>Dr. Alexandre Bonvin</b> and
    <b>Dr. Pedro Beltrao</b>, we have built a platform that not only addresses
    fundamental questions in structural biology but also has the potential to
    improve therapeutic development for cancer treatment.
  </p>
  <p>
    Our interactions with industry professionals at
    <b>ThermoFisher Scientific</b> and medical experts at the
    <b>Majumdar Shaw Medical Foundation</b> have further broadened the scope of
    our project, ensuring that <b>IMPROViSeD</b> can be applied to real-world
    problems in drug discovery and cancer therapeutics. Through this
    collaborative and iterative process, we believe <b>IMPROViSeD</b> has the
    potential to make a lasting impact on the scientific and medical
    communities.
  </p>
</div>

{% endblock %}