Translating Proteomics

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‘Translating Proteomics’ explores the science of proteomics and its growing impact on biological research, biomarker discovery, drug development, food and energy security, and a range of other timely topics. The goal of these conversations is to expose you to important issues in proteomics, deepen your love of science, and prompt you to question assumptions about what may be possible.

Your hosts are Drs. Parag Mallick and Andreas Huhmer of Nautilus Biotechnology. Parag is an Associate Professor at Stanford University whose lab performs systems biology studies that drive precision medicine approaches for cancer diagnosis and treatment. Andreas is a veteran scientist whose industry work has supported thousands of proteomics researchers by helping to bring the latest mass spec technologies into their labs.
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Most recent episode

Chapters:

00:00 – Introduction
01:12 – Decrypting the molecular basis of cellular drug phenotypes by dose-resolved expression proteomics
19:11 – Natural proteome diversity links aneuploidy tolerance to protein turnover
33:14 – Multi-pass, single-molecule nanopore reading of long protein strands
51:13 – Outro

On this special, year-end episode of Translating Proteomics, hosts Parag Mallick and Andreas Huhmer discuss three of their favorite proteomics publications from 2024. They’ll cover one paper in each of the following topic areas:

  • Proteomics in pre-clinical research
  • Proteomics in basic research
  • Technology development in proteomics

All episodes of the Translating Proteomics podcast

The idea to measure the proteome to get a clear understanding of healthy and diseased tissues at the molecular level has been around for many years but has not come to fruition in a broadly accessible and applicable way. In this episode we discuss:

  • Why now is the time to make this goal a reality
  • Why past efforts to broadly leverage proteomics did not work out
  • What we’ve learned from the past
  • What’s changed in proteomics and science in general that makes a proteomics breakthrough possible

Learn more about proteomics

Sure, proteomics may revolutionize precision medicine and biomarker discovery, but did you know it can help make better cheese? Listen to this episode of  “Translating Proteomics” featuring Nautilus Co-Founder and Chief Scientist, Parag Mallick, and Nautilus Senior Director of Scientific Affairs and Alliance Management, Andreas Huhmer to learn the many ways we can put the proteome to work as the proteomics revolution begins to bear fruit.

Learn more about applications of proteomics

It’s no surprise that biological systems change dramatically over space and time, but we often ignore these dynamics when comparing biological samples. In the latest episode of Translating Proteomics, Parag and Andreas discuss why it’s essential to take space and time into account and envision ways we can design experiments that explicitly incorporate spacial and temporal considerations.

Chapters:
00:00 – Biological systems as dynamic, adaptive systems
04:45 – How current experimental designs rarely take space and time into account
11:54 – The tools necessary to sufficiently measure biology in space and time

Some key takeaways from the conversation:

  • Different biological processes occur at very different time scales
  • Complex, multiomic interactions can only be understood over time and space
  • We need to properly collect, annotate, and share omics-level data in order to understand the rules that govern complex biology

Protein biomarkers are proteins measured as indicators of biological processes. People often hope biomarkers will take the form of elevated or decreased amounts of single proteins, but few single protein measurements provide specific and sensitive indications of biological processes. In this episode of Translating Proteomics, Parag and Andreas discuss why it is difficult to find new biomarkers and describe how new techniques can enable the development of multi protein, multi-time point, and even multiomic biomarkers that have more potential than any single protein measurement.

Learn more about biomarkers.

Chapters:
00:00 – What are biomarkers and why are they hard to find?
06:40 – What makes a good biomarker?
13:35 – How we can move beyond single-protein/single measurement biomarkers

Some key points of discussion:

  • Biomarkers are difficult to find because of the methods we use to find them and because there is a ton of variability in natural biological systems
  • Most proteins are biomarkers
  • We need more proteome-scale data over space and time to find new biomarkers

From high school biology on up, we’re taught the central dogma of biology – that biological information flows from DNA to RNA to proteins. This representation of the central dogma is, however, very much a simplification of its original formulation by Francis Crick and over-applying it can lead us down spurious paths and faulty conclusions. In this episode of Translating Proteomics, Parag and Andreas dive into the real meaning of the central dogma and discuss how modern biology research, including proteomics, shows we must drastically alter the ways we use and interpret the central dogma.

Chapters:
00:00 – What the central dogma actually says
08:06 – Why it’s important to develop models of biology that account for regulation
11:58 – How new tools will help us make better models of biology

Some key points of discussion:

  • The central dogma is a description of where proteins come from
  • Regulation is not encapsulated in the central dogma
  • We need new models of biology and perhaps even a general theory of biology

AI might be the biggest buzz word of the decade, but the buzz is warranted in terms of its practical potential in biological research. In this episode of Translating Proteomics, Parag and Andreas discuss some of the early wins for AI in biology, practical ways AI can be applied to biology research in the near term, challenges in that application, and how proteomics researchers in particular can use AI to advance their work.

Chapters:
00:00 – Why now is the time to apply AI to biomedicine
05:28 – Difficulties and potential solutions when applying AI to biology
14:20 – How will AI impact the study of proteins
19:34 – Risks of AI in biomedicine

Some key points of discussion:

  • AI has tremendous potential in biomedicine
  • AI can help us recognize patterns in biological data, but we need more data to maximize usefulness
  • We can better leverage AI in biomedicine if biological data and data sharing are standardized

Proteins are far more than just the output of genes. They can be modified in myriad ways to produce millions of proteoforms with altered dynamics, localization, and function. For a comprehensive understanding of biology that will propel drug development and biomarker discovery forward, we need to be able to measure proteoforms routinely. In this episode, Parag and Andreas discuss the incredible value that will come from studying proteoforms and describe what it will take to make proteoform measurement a routine part of biology research.

Chapters:
00:00 – Introduction to proteoforms
09:38 – Evidence that proteoforms are important and how we can use proteoform data
19:28 – What technology advances do we need to understand proteoform biology

Some key points of discussion:

  • Proteins are dynamic constructs whose modifications consequentially impact their functions.
  • There are many proteoforms are they are currently challenging to measure.
  • People have begun to recognize the importance of proteoforms and we will have technologies to routinely measure them soon.

Despite incredible leaps in our understanding of molecular biology, the majority of drug development efforts still fail, and those that succeed often fail to return investment dollars. Proteomics has the potential to change that by providing high-resolution views of the biochemical drivers of biological function – proteins. In this episode of Translating Proteomics, Parag and Andreas discuss how proteomics can help researchers identify good drug targets, personalize drug development, and advance precision medicine.

Chapters:
00:00 – How do we define good drug targets and “druggable” in the age of proteomics
08:16 – Advancing personalized medicine through proteomics
10:58 – How proteomics technologies have changed drug development
15:13 – New abilities next-generation proteomics technologies give us in drug development

Some key points of discussion:

  • What makes a good drug target
  • How we can better personalize drug development
  • How proteomics has and will change drug development

In our Translating Proteomics episode titled “Harnessing Proteoforms to Understand Life’s Complexity”, Parag and Andreas discussed why proteoforms are important in a theoretical sense. In this episode, Parag sits down with Northwestern University Professor and proteoform pioneer, Neil Kelleher to dive deep into the biology of proteoforms.

Chapters:

00:00 – Introduction to proteoforms
03:36 – Why the term “proteoform” was coined
11:55 – Are proteins a myth?
13:39 – Evidence for the importance of proteoforms
22:45 – The Human Proteoform Atlas and Project
30:09 – How many proteoforms are there?
33:32 – Technological developments required for routine proteoform analysis
41:45 – Takeaways with Parag and Andreas

Some key points of discussion:

  • What are proteoforms?
  • Examples of the importance of proteoforms
  • The scale of and technological advances needed to meet the challenges of proteoform biology

Chapters:

00:00 – Introduction
2:09 – Factors that give rise to the complexities of protein function
9:00 – Are all the variants of proteins that can be made consequential? Why don’t we know?
12:28 – When the concept of protein structure and function changed for Parag and Kathryn
19:09 – The importance of understanding the complexities of protein function in order to drug proteins
20:38 – How much do proteins change localization and what does it mean?
28:38 – How much do proteins moonlight?
32:12 – How do we think about genetic associations given the complexities of protein function?
33:58 – How do we teach others about protein function when it is so complicated
38:34 – Staying motivated in the face of functional complexity
41:21 – Key takeaways with Parag and Andreas

Some key points of discussion:

  • How Kathryn and Parag came to realize protein function is more complex than one gene, one enzyme, one function
  • Factors that give rise to the dynamic complexity of protein function including proteoforms, protein localization, and moonlighting
  • Steps we can take to better understand and teach others about the complexities of protein function

Chapters:

00:00 – Introduction
01:54 – Why Sarah began studying Alzheimer’s
03:39 – Current tools and needs for future Alzheimer’s diagnostics
09:52 – Recent drug approvals in the Alzheimer’s space and their relationship to diagnostics
14:26 – Is it possible to develop biomarkers that detect Alzheimer’s at its earliest stages?
16:36 – What is limiting the development of new Alzheimer’s biomarkers?
17:51 – The DIAN trials and learnings from studying dominantly inherited Alzheimer’s
19:33 – The genetics of Alzheimer’s
22:19 – Novel approaches to identifying and understanding Alzheimer’s pathology
25:54 – Where can proteomics advance Alzheimer’s research?
31:25 – The role of proteomics in Alzheimer’s animal models
34:33 – Sarah’s hopes for the next 10 years of Alzheimer’s research
41:39 – Outro

Some key points of discussion:

    • The impact of molecular diagnostics on Alzheimer’s research
    • Recent Alzheimer’s drug approvals
    • The future of Alzheimer’s research

Chapters:

00:00 – Introduction
04:37 – How did Vijay and Matt get into AI and ML
07:33 – The importance of structured data, advances in compute, and algorithmic advances in driving the boom in machine learning
18:44 – The Intersection of AI and biology
21:57 – The evolution of biological models
31:55 – The Complexity of biological data
39:42 – Ways founders and biotech startups are using AI
43:25 – Favorite/Impactful applications of AI/ML
47:00 – AI for experimental design
50:13 – The future of AI in bio/health

Some key points of discussion:

  • Advances that have enabled biotech to make use of AI and machine learning
  • How founders are applying AI and machine learning in biotech
  • The future of AI and machine learning in biotech

Chapters:

00:00 – Introduction
01:12 – Decrypting the molecular basis of cellular drug phenotypes by dose-resolved expression proteomics
19:11 – Natural proteome diversity links aneuploidy tolerance to protein turnover
33:14 – Multi-pass, single-molecule nanopore reading of long protein strands
51:13 – Outro

On this special, year-end episode of Translating Proteomics, hosts Parag Mallick and Andreas Huhmer discuss three of their favorite proteomics publications from 2024. They’ll cover one paper in each of the following topic areas:

  • Proteomics in pre-clinical research
  • Proteomics in basic research
  • Technology development in proteomics

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