Computational Modeling Immunology Lets Researchers Use Software To Run Binding Studies


can computational modeling open new avenues for scientists
Myoglobin bound to a heme group. Quantitative immunology will allow researchers to calculate binding schema like this one without running in vitro experiments. Source: Wikipedia user Thomas Splettstoesser.

Graduate students and laboratory technicians everywhere can breathe a sigh of relief: the age of the redundant binding study is slowly coming to a close, thanks to information technology. To replace the age of binding studies, the age of quantitative immunology rises from the ashes, using sophisticated simulation software and public data sets to compute the details of molecular interactions at each epitope.

According to a pair of new reviews published in the Current Opinion in Immunology Journal and Systems Medicine, quantitative immunology has matured enough to enable advanced design procedures which can catapult immunotherapy development into the big data age.1 2  Moving forward, immunologists and cellular biologists can solve many of their epitope binding questions with a database query and a quick glance at their biological modeling platform rather than a series of experiments.

There are implications for more effective therapies, too. Researchers could pick and choose which high-affinity tool to use to implement their therapeutic concept, potentially doing so with better effect than the therapy’s endogenous equivalents, if any. But there’s even more to the advent of quantitative immunology than better efficacy, tighter workflows, saved time, and saved costs. Quantitative immunology has the potential to fuel its own growth and eventually enable minutely customized therapeutics with the proliferation of powerful modeling software to bridge the gap between raw information and therapeutic design.

The Not-So-Obvious Benefits of Quantization

As mentioned in the review published in the Current Opinion in Immunology, quantitative immunology databases can be paired with simulation software to arbitrarily determine whether a known epitope will bind with ligands that aren’t accounted for in the database. This means that the scope of epitope binding databases is even larger than it might appear at first glance. Rather than perform costly and time consuming binding studies, researchers will soon be able to check their known or unknown epitope’s binding characteristics and compute whether another ligand will bind, and if so, how it compares to other ligands or epitopes which may be competing for binding.

Everything from commercially produced antibodies to one lab’s customized biologic will be streamlined in both development and use. If the ligand in question isn’t under intellectual property, researchers can then add its binding characteristics to the database, nudging the network effect even farther.

As noted by a third review in Nature Immunology, there are uniquely combinatorial benefits to quantized epitope databases when they’re paired with antibody libraries.3 By combining the information in epitope databases and antibody libraries, immunologists could easily learn critical information like:

  • Whether a new biologic has off-target binding or successfully binds to multiple diverse targets
  • Whether a new biologic could block the binding of endogenous factors, handicapping treatment or triggering side effects
  • Whether an epitope is damaged or clogged by binding with a new biologic
  • Whether an artificial epitope with multiple purposes can retain its original functionality
  • If canonical ligand binding exerts a previously unrecognized mechanical force to trigger a signal cascade
  • If cofactors to ligand binding compete with each other as a form of proportional rate limiting
  • If immunotherapeutic antigen presentation results in well-formed antigens or malformed peptide chunks4
  • If a cellular therapy will be identified as a threat by the host immune system

It’s no secret why researchers are investing heavily in software to help pre-empt basic biochemical questions. Imagine performing a complex of binding studies for each of these bullet points. Each binding study would require designing the assay from scratch before starting. A negative or undesirable result at any step along the way would require going back to the drawing board, negating all prior effort. It’d take months of a team’s time, and likely millions of dollars.

Calculating Immunology and Proteomics

The rise of quantitative immunology is mirrored by a similar apex of quantitative proteomics, meaning that researchers now have granular computational access to a huge number of inter cellular processes, as mentioned in the new reviews and elsewhere.5 6 The maturity of these quantitative systems biology databases and methods has a final large benefit: algorithmic or artificial intelligence now has a shot at contributing to the body of research.

For now, enterprising researchers have been limited to using machine learning methods on their immunological data sets, which will will continue to be instrumental.7 Moving forward, even more sophisticated software will be integrated into the everyday immunology workflow. If the immunologists of the future want to keep up with the rising technological bar of immunology, they’ll need molecular modeling software of their own to handle areas that aren’t covered by the current body of knowledge.  

BIOVIA Biologics is the molecular modeling and simulation platform that immunologists will use to make the most out of the quantitative immunology revolution. With Biologics, you’ll be able to start eschewing costly and time consuming binding studies and replace them with in silico simulations instead, saving everyone a big headache. Contact us today to find out how you can use Biologics to get involved with the quantitative future of immunology and contribute to the bleeding edge of research.

  1.  “Immunology by numbers: quantitation of antigen presentation completes the quantitative milieu of systems immunology!” June 2016,
  2. “Third-Kind Encounters in Biomedicine: Immunology Meets Mathematics and Informatics to Become Quantitative and Predictive.” May 2016,
  3. “Combinatorial antibody libraries: new advances, new immunological insights.” July 2016,
  4. “Generation and selection of immunized Fab phage display library against human B cell lymphoma.” July 2007,
  5.  “Quantitative proteomics to predict functional consequences of ubiquitylation events during T cell stimulation.” May 2016,
  6. “Immune atlas sheds light on anticancer responses.” May 2017,
  7.  “Machine Learning Reveals a Non-Canonical Mode of Peptide Binding to MHC Class II Molecules.” May 2017,