Students, Researchers Push Boundaries of Computational Biochemistry

Photo of students learning computational biochemistry

The complexity of life makes it difficult to study. In biochemistry, there are often just too many processes and reactions taking place in a cell for humans to wrap their heads around. What helps biochemists make sense of it all?

Cue computational biology and biochemistry. Computation has been used in biology and biochemistry since the dawn of computers and is used today by many researchers in the University of Wisconsin–Madison Department of Biochemistry.

“The main reason people use computers in biochemistry is because biological systems are composed of thousands of interacting components and predicting the behaviors of these complex systems becomes way too difficult to grasp within your brain,” says assistant professor Philip Romero, who uses computation in his lab in the Department of Biochemistry. “Computers are much better at handling large amounts of information.”

Computational biochemistry can be defined as the use of computational methods and simulations to predict and understand various biological processes. Scientists are able to use computers to make sense of large amounts of data and use that data to make predictions.

Still Classic Biochemistry: Sequence Begets Structure Begets Function

One of the most important applications of computation in biochemistry is the prediction of the structure and function of a protein or other macromolecule — and using that understanding to possibly design new ones.

“We investigate how proteins in the cell membrane come together to form complexes,” associate professor of biochemistry Alessandro Senes explains. “We use molecular modeling tools to study something that is often very hard to do with conventional experimental structural methods.”

Photo of biochemistry graduate student leading a lab meeting
Integrated Program in Biochemistry (IPiB) graduate student Samantha Anderson leads a lab meeting for the Senes Lab by walking through computer code and how it can be used to make computational models. IPiB is the joint graduate program of the Department of Biochemistry and Department of Biomolecular Chemistry. Photo by Robin Davies. 

The basic premise of much of this work — more accurately called computational structural biology — taking place in the department is simple. The Senes Lab, for example, uses structural prediction to obtain an “educated guess” of what a certain membrane protein may look like. Often, they integrate the modeling using experimental information — such as the result of mutation or data suggested by analyzing the evolutionary variation of the protein — which makes the prediction more reliable. 

In the Senes Lab, researchers use those models to predict how a protein works and how a mutation would affect the structure and thus its function. Designing mutations and seeing their effects is a classic biochemical approach to discerning the mechanism of proteins.

“The computation in our lab informs the experiments,” explains Samson Condon, an Integrated Program in Biochemistry (IPiB) graduate student in the Senes Lab. “The results of the experiments tell us which parts we predicted correctly and which parts we didn’t. With that feedback, we can go back and perform another round and, this way, keep refining our computation.”

IPiB is the joint graduate program of the Department of Biochemistry and Department of Biomolecular Chemistry. The prediction process can also go a step further and predict a protein’s function based on the sequence and structure in a similar way.

Photo of computing equipment and student at the microscope in the Romero Lab in biochemistry
Top: Image of computing equipment in UW–Madison's Center for
High Throughput Computing. 
Bottom: Hridindu Roychowdhury, an IPiB student in the Romero Lab
works on a microscope. 
Photos by Robin Davies. 

“Protein prediction and design allow you to directly test function,” Senes says. “Function tends to depend on structure. If you can predict structure and predict the consequences of changing structure or stability, you can use this information to predict, infer or confirm biological function. So, a combination of modeling and experiments is a good way to test our understanding of how proteins work.”

More specifically, the Senes Lab studies the bacterial divisome, a large complex of membrane proteins which helps bacterial cells divide — but that has an unknown structure. They are trying to gain an understanding of how the complex’s mechanism functions and how its proteins interact with each other. Information like this could come in handy for other researchers looking for a way to prevent bacteria from dividing, and potentially lead to the discovery of new antibiotics.

Another project in the lab focuses on the biophysics regulating how membrane proteins interact with each other. Computation is helping them understand the general physical principles that govern the stability of these membrane proteins and how they recognize each other. With this information, they hope to be able to design new proteins with precise control of their structure and function.

“The use of computational techniques is very powerful and saves a lot of time,” says Samantha Anderson, another IPiB student in the Senes Lab. “In addition to structural modeling, there are also other bioinformatics and biostatistics techniques that can be applied. With them, I am able to cull a lot of data and compare thousands upon millions of sequences when doing so manually would be nearly impossible.”

Condon and Anderson both add that before coming to graduate school they didn’t know how to code, but were able to learn on their own from reading books on coding and taking some computer science classes on campus. They say IPiB gave them the flexibility to pick up these new skills.

“One of my favorite parts of learning how to program was looking at the physical code and then treating it like a puzzle and figuring out where things went wrong to make it work,” Anderson says. “It’s fun because it’s a logic puzzle. A lot of people think computation and coding are very abstract but they are actually tangible things you can see and work with concretely, which makes them really rewarding.”

Computation Is for Biochemistry What Pandora Is for Music

Many often hear of algorithms when talking about social media platforms like Facebook or music streaming services like Pandora. These function by learning from the user what they want to see or listen to and tailoring what is presented. A lot of computational work in biochemistry functions in the same way.

Microfluidics in the Romero Lab
Mark Politz, a postdoc in the Romero Lab, constructs a microfluidic
plate to carry out high-throughput experiments. Photo by Robin 

Davies.

“It’s actually really similar,” Romero says. “You have to teach the machine what is good and bad so you can make a prediction or design. We look at a sequence and know there are some parts that work poorly for what we want — we give those a thumbs down — and others that maybe produce something we are interested in — those we might give a thumbs up. The computer can then slowly learn what makes a good sequence good and a bad sequence bad. The examples allow the computer to extrapolate what makes a really good sequence and deliver that to us.”

Like the Senes Lab, the Romero Lab’s broad interest is in trying to understand the relationships between protein sequence, structure, and function and how they can learn about these relationships from large data sets. They then apply those principles to design new proteins with optimized properties. The thumbs down and thumbs up allow the computer to get a correlation between what the researcher is looking for and the sequence that represents that. The computer then learns these rules that can help make predictions.

“Our lab is very applied, with almost everyone working on engineering proteins that probably will have some use, whether that be in bioenergy, chemical production, or human health,” Romero says. “We try to develop new protein engineering methods while maintaining a focus on important applications.”

The researchers utilize computer clusters on campus, which are groups of computers that break up computation jobs to perform them in a fraction of the time. The Senes Lab has its own cluster, as does the Department of Biochemistry. All university researchers also have access to the Center for High Throughput Computing. Romero says UW–Madison’s computational resources are some of the best and largest he’s seen at similar institutions. 

Along with Senes and Romero, many other labs use computational methods in their research. Assistant professors Vatsan Raman and Ophelia Venturelli work in this area, as does professor Julie Mitchell who is currently serving as deputy director of the biosciences division at Oak Ridge National Laboratory in Tennessee. Their expansion of these techniques is creating new opportunities, including coursework on quantitative approaches, for researchers and students interested in this area of biochemistry.

“Computer power is getting faster and faster and cheaper and cheaper,” Romero says. “This technology is only getting better and getting your foot in the door and investing in these tools and skills can be very valuable. They will play an increasingly important role in biological research. All of us, and the department as a whole, have a mission to stay ahead of this rapidly evolving technology.”


Read more about Senes and Romero and computational biochemistry in the UW–Madison Department of Biochemistry:

Romero Targets Wide-ranging Applications with Data-driven Protein Engineering

The Romero Lab page

The Senes Lab page

The Integrated Program in Biochemistry (IPiB) page

Photo By: 
Robin Davies