Garegin Papoian, the Monroe Martin Professor at the University of Maryland, is seen sitting at a desk with an open laptop in front of him. He has turned around in his seat to face the camera.
Garegin Papoian is the Monroe Martin Professor at the University of Maryland. Within his Papoian Lab, a theoretical physical chemistry group located at the university, his team is working toward developing fundamental molecular models of the whole cell, a concept still in its infancy
Courtesy of Garegin Papoian

Garegin Papoian’s quest to model an elusive class of proteins

With the support of an Amazon Research Award, Papoian’s team is deciphering the dynamics of intrinsically disordered proteins.

How do molecules come together and start to behave like a living system? This is the type of question that drives Garegin Papoian’s research. At the University of Maryland, where he is the Monroe Martin Professor, he has been focusing on computational modeling of biological molecules like proteins and DNA. Within his Papoian Lab, a theoretical physical chemistry group also located at the university, his team is also working toward developing fundamental molecular models of the whole cell, a concept still in its infancy.

Papoian’s path into science was determined early on. Growing up in Armenia, then a part of the Soviet Union, he went to a special school of physics and mathematics, where he was introduced to Science Olympiads. While in high school, he won the first place in the Republic of Armenia in separate Olympiads in chemistry, physics, mathematics and biology. “Science Olympiads were a big reason why I got drawn into science, in particular to chemistry and physics”, he says.

Because of his success in the competitions, he was invited to study at an advanced chemistry college in Moscow established specifically for Olympiad winners.

“I was 16,” he says, “but it was assumed that we already knew all university level chemistry. So, they would start immediately with a very high-level training.” The program included an internship in the United States, at the University of Kansas. From there he eventually enrolled as a graduate student at Cornell University, where he pursued his PhD in quantum chemistry, working under the Nobel Laureate, Roald Hoffmann.

During his postdoc, he turned to classical physics with a particular emphasis on biophysics. “I was interested in bringing concepts of physical chemistry to understand biological phenomena from the molecular perspective,” he says. “And my long-term career goal is to develop concepts both for proteins and cells.”

Predicting a protein’s shape

A protein is a large molecule essential to all living things. The sequence of amino acids that form a protein determines its three-dimensional structure. Each protein has a unique shape that dictates its function. Being able to predict what a protein structure looks like from its amino acid sequence has been a long-standing scientific challenge and one of the research interests of Papoian’s group, for which he received an AWS Machine Learning Research Award in 2018.

This animation shows the structure of a protein called linker histone H1
This animation shows the structure of a protein called linker histone H1, including its disordered tails, predicted by Papoian's team. "We discovered that interactions of those disordered tails with DNA help to structurally position H1 with respect to the nucleosome. In terms of the bigger picture, the H1-nucleosome interactions regulate epigenetic processes, determining for example which particular genes should be turned on or off,” says Papoian.

One of the applications of protein structure prediction is drug design. “When you design a drug, you need to know what the target looks like,” says Papoian. If you know that the target protein has a certain pocket, for example, you can develop a molecule that will fit nicely into that pocket. While identifying genes associated with diseases has become easier, the sequence of a gene doesn’t tell you what the protein expressed by it looks like, and experimental methods to determine the protein shape are lengthy and expensive.

IDPs ... are more like this crazy spaghetti. It's very hard to deal with them both experimentally and computationally.
Garegin Papoian

Even in the wake of DeepMind demonstrating that AlphaFold is capable of predicting protein structures with an unprecedented level of accuracy, challenges still remain.

It turns out that a large proportion of human proteins are not completely structured in neat three-dimensional shapes. These are called the intrinsically disordered proteins (IDPs). “They are much more dynamic and mostly never fall into a single structure,” says Papoian. “They are more like this crazy spaghetti. It's very hard to deal with them both experimentally and computationally because they are so elusive.” He notes that about a third of human proteins are like that, including many important disease-causing proteins.

Papoian’s AWS Machine Learning Research Award enabled his team to advance the development of a system that is better suited to simulating these proteins.

Tackling disordered proteins

For the past few years, Papoian Lab has been working with a protein modeling framework called AWSEM-MD (pronounces “awesome”), which stands for associative memory, water-mediated, structure and energy model — molecular dynamics. It has been developed jointly with Peter Wolynes, Papoian’s former postdoctoral advisor who is currently at Rice University and with whom he continued to collaborate over the years.

Using the AWS Machine Learning Research Award, Papoian and his colleagues developed AWSEM-IDP, an AWSEM branch specifically designed to simulate intrinsically disordered proteins.

This system uses a database of protein fragment structures obtained experimentally, for example, through nuclear magnetic resonance (NMR) spectroscopy — a technique that determines the structure and dynamics of proteins. "These fragments serve as structural memories that guide the IDP to undergo structural transformations that are informed by the experiment,” Papoian explains. “This allows simulating more realistic IDP dynamics.”

The fragment database may also contain structures from atomistic simulations — a type of simulation where every atom of a protein is present. “The reason why we prefer not to do those in general is that they’re very expensive, so we cannot do very big simulations. But we can do atomistic simulations of short fragments to give us good fragment memories, again improving the accuracy of IDP’s structural exploration in AWSEM simulations,” he says.

An IDP will prefer multiple structures, not just one.

“That's the key difference from regular proteins: IDPs are multi-faceted in essence. But they still prefer certain structures over others. And the AWSEM-IDP model allows you to correctly describe those preferences,” Papoian explained. This model was described in a 2018 article published at the Journal of Physical Chemistry B.

In another work published earlier this year that was supported by the AWS Machine Learning Award, Papoian and his colleagues applied AWSEM-IDP to study a protein called linker histone H1, which plays an essential role in regulating many important biological processes. This protein has two intrinsically disordered regions, parts of its structure that are not well folded and resemble two tails. Because they are disordered, it’s much harder to understand what they do and how they interact.

Proteins like linker histone H1 regulate histone complexes, which act like a spool around which the DNA wraps to create structures called nucleosomes. “In this paper, we used AWSEM-IDP to model the nucleosome with linker histone H1, in particular with these disordered tails. And that allowed us to understand how the linker histone and the nucleosome come together and interact, and what's the role of these disordered tails,” says Papoian. Understanding proteins’ interactions with nucleosomes may give important insights on epigenetics, which is one of Papoian Lab’s interests.

Future challenges

Because making sense of IDPs is such a difficult process, Papoian says that AWSEM-IDP is an ongoing program with room for improvement. “What we have currently works better in some classes of proteins, and not so much in others. So next we’ll explore what are the challenges for what we currently have in ASWEM-IDP and try to come up with new advances to overcome them.”

In addition to IDPs, Papoian Lab will also continue to pursue the use of deep learning for structure prediction of well-folded proteins. Although there is some conceptual overlap with AlphaFold, Papoian believes that AWSEM-MD is a powerful tool and has advantages to other approaches when it comes to molecular dynamics.

Proteins are not frozen objects. Some of them are well structured, but many are not structured at all, and they are dynamic and move and shape-shift incessantly.
Garegin Papoian

“Proteins are not frozen objects,” he says. “Some of them are well structured, but many are not structured at all, and they are dynamic and move and shape-shift incessantly. So, to understand how these proteins function, you must model their dynamics and that’s what AWSEM-MD can do best.”

Papoian thinks one exciting area to be explored in coming decades will be combining machine learning and physics to work on protein structure prediction, protein dynamics, multiprotein complexes, and epigenetics.

“There are lots of things that still remain to be understood in our models. And I think that probably neither physics nor machine learning by themselves can tackle them. But a program that brings them together in a productive way can be very powerful,” he said.

Modeling an entire cell

Another ambitious project that Papoian and his colleagues are pursuing is to develop a computational model of an entire cell. “We still don’t have a blueprint of a cell the way we have a blueprint of a car or a Boeing airplane.”

To do that, his group develops their own software from scratch.

Garegin Papoian: How do cells move? Chemistry meets mechanics

“We basically do the science, the physics, and biophysics of what is needed to model our cells. We derive the needed algorithms from scratch based on the laws of physics and chemistry and then we program that into a computer and run simulations on a supercomputer,” he explained. This has to be done at a single molecule resolution, he adds, meaning that they have to track every single molecule within a cell.

To achieve that, the Papoian Lab developed a model called MEDYAN.

“We can already model some number of proteins, the membrane, we can model rich chemistry. We have developed some of the fundamental chemistry and physics components of what needs to be done,” he says. The next step is to scale it. “We usually do simulations with several types of proteins. So instead of several, you will need maybe hundreds or thousands of different types of proteins, so it just brings more complexity.”

When that happens, it will be a huge revolution in biomedicine, he says. “Then lots of things that people laboriously spend years doing in the laboratory could just run on AWS servers. And you could do your experiments and search for treatments computationally, which would be much cheaper and faster.”

Research areas

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Re-imagining the realms of what’s possible in advertising. Amazon is re-imagining advertising. Amazon Ads operates at the intersection of eCommerce and advertising and offering a rich array of advertising solutions and audience insights so businesses and brands can create relevant campaigns that produce measurable results. At Amazon Ads, you can build models that impact millions every day. And we’re passionate about solving real-world problems while using cutting-edge machine learning and artificial intelligence to do this. For example, our applied science teams leverage a variety of advanced machine learning and cloud computing techniques to power Amazon's advertising offerings. This includes building algorithms and cloud services using clustering, deep neural networks, and other ML approaches to make ads more relevant while respecting privacy. They develop machine learning models to predict ad outcomes and select the optimal ad for each shopper, context, and advertiser objective, leveraging techniques like multi-task learning, bandit/reinforcement learning, counterfactual estimation, and low-latency extreme ML. The teams also utilize Spark, EMR, and Elasticsearch to extract insights from big data and deliver recommendations to advertisers at scale, continuously improving through offline analysis and impact evaluation. Additionally, they apply generative AI models for dynamic creative optimization and video experimentation and automation. Underpinning these efforts are unique technical challenges, such as operating at unprecedented scale (hundreds of thousands of requests per second with 40ms latency) while respecting privacy and customer trust guarantees, and solving a wide variety of complex computational advertising problems related to traffic quality, viewability, brand safety, and more. Help us take innovation in advertising to the next level. Our teams are based in our fast-growing tech hubs in London and Edinburgh. Learn more about Amazon Ads, employee stories and available opportunities here: https://www.amazon.jobs/content/en/teams/advertising/applied-science-machine-learning-research?ref_=a20m_us_car_lp_asml Key job responsibilities * Design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both analysis and business judgment. * Collaborate with software engineering teams to integrate successful experiments into large-scale, highly complex Amazon production systems. * Promote the culture of experimentation and applied science at Amazon. * Demonstrate ability to meet deadlines while managing multiple projects. * Excel communication and presentation skills working with multiple peer groups and different levels of management * Influence and continuously improve a sustainable team culture that exemplifies Amazon’s leadership principles * Develop a deep and wide understanding of large ad tech solutions to which you will contribute, and how they interact with components owned by other teams. * Anticipate obstacles and look around corners, effectively prioritising work, solving trade-offs and influencing the development of advertising products beyond the scope of your immediate team. We are open to hiring candidates to work out of one of the following locations: Edinburgh, MLN, GBR | London, GBR
US, VA, Arlington
Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center at AWS is a new strategic team that helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, data scientists, engineers, and solution architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for Data Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. A key focus of this role is GenAI model customization using techniques such as fine-tuning and continued pre-training to help customers build differentiating solutions with their unique data. Key job responsibilities As a Data Scientist, you will: Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms to address real-world challenges Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder Provide customer and market feedback to Product and Engineering teams to help define product direction About the team Sales, Marketing and Global Services (SMGS) AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest-growing small- and mid-market accounts to enterprise-level customers, including the public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. The Professional Services team is part of Global Services. About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | Denver, CO, USA | Herndon, VA, USA | New York, NY, USA | Santa Clara, CA, USA | Seattle, WA, USA | Washington Dc, DC, USA