If you listened to our episode focused on science communication featuring proteomics leaders Ben Orsburn Ph.D. and Ben Neely Ph.D., then you've already heard about their excellent podcast, The Proteomics Show. On The Proteomics Show, they interview researchers in the proteomics community to learn about their motivations, their backstories, and their work. Today, we're sharing an episode of The Proteomics Show where the Bens interview Translating Proteomics host Parag Mallick. Check it out to learn about Parag's journey to proteomics, his efforts advocating for open data sharing, and his work as a professional magician.

After listening, be sure to check out more episodes of The Proteomics Show on their feed where you'll find over 70 interviews with many, many interesting people in the proteomics community. Find their feed here:

https://us-hupo.org/Podcasts

On this episode of Translating Proteomics, hosts Parag Mallick and Andreas Huhmer discuss their recent experience at the 2025 US Human Proteome Organization conference or US HUPO. Their conversation covers:

·      The rising use of multiple proteomics platforms

·      Advances in multiomics

And

·      What they hope to see at the next US HUPO

Chapters:

00:00 – Introduction

01:47 – Things Parag and Andreas were most excited about going into US HUPO

06:19 - Trends Parag observed at US HUPO

·      A bifurcation between studies that focused on measuring many proteins and those that focused on a specific biological process

·      People increasingly using multiomics to wholistically understanding biology as opposed to a means of comparing the different omes

·      A shift to researchers using multiple proteomics platforms

11:44 – The rising prominence of proteoforms at US HUPO

15:50 – The future of proteomics as informed by US HUPO

18:06 – What Parag and Andreas hope to see at the next US HUPO

19:57 - Outro

On this episode of Translating Proteomics, host Parag Mallick speaks with special guests doctors Ben Neely and Ben Orsburn, leaders in the proteomics community and hosts of “The Proteomics Show” podcast. Their insightful conversation focuses on the role of communication in proteomics and covers:

·      Impactful things they’ve learned while hosting The Proteomics Show

·      How their communication efforts have changed their research

·      Barriers to proteomics becoming more popular in the life sciences and in the broader public

00:00 – 03:04 – Intro

03:04 – 07:23 – Why the Bens created "The Proteomics Show"

07:23 – 10:42 – Ways the proteomics show has impacted the Bens’ research

10:42 – 16:44 – Every scientist is interesting!

16:44 – 20:36 – Ways the Bens' communication efforts have changed their research

20:36 – 25:08 – Trends in proteomics

25:08 – 35:34 – Barriers to communication between the proteomics community and others in the life science

34:34-48:34 - Barriers to communication between the proteomics community and the broader public

48:34 - End - Outro

• The Proteomics Show - Ben Neely and Ben Orsburn host this podcast sponsored by the Human Proteomics Organization. On it, they interview proteomics researchers in a “fireside chat” format. Their conversations cover not just the latest research, but also their guests’ motivations, hobbies, and histories. Definitely check it out!
• News in Proteomics Research - Ben Orsburn's blog on the latest developments in proteomics.
• Proteomics, the next truly massive investment opportunity - Forbes article on the potential of proteomics.
• The Magic School Bus Rides Again - Recently rebooted version of The Magic School Bus. Will we see an episode featuring proteomics?

On this episode of Translating Proteomics, hosts Parag Mallick and Andreas Huhmer discuss the many ways proteomics can impact our interactions with food. Some of the topics they touch upon in this wide-ranging conversation include:

·      Proteomics and food quality

·      Proteomics and food safety

·      Developing new kinds of food with proteomics

00:00 – 01:23 – Introduction

01:23 – 03:27 – Proteomics and alcohol fermentation

03:27 – 05:24 – Food properties and their relationship with molecular composition

05:24 – 07: 42 – How can we use proteoforms to improve food quality?

07:42 – 11:49 – Proteomics to aid plant and animal breeding

11:49 – 14:35 – Proteomics, Food Safety, and Food Security

14:35 – 17:05 – Proteomics and food authenticity

17:05 – 20:36 – Proteomics and terroir

20:36 – 22:48 – Proteomics, the microbiome, and health

22:48 – 24:29 – A fun party trick

24:29 – 30:24 – Creating new foods and flavors

30:24 – 34:33 – Designing food for space

34:33 – End – Outro

The post-translational modification landscape of commercial beers (Kerr et al. 2021)

·      Paper looking at the ways post-translational modification differ between different beers and how protein content relates to the properties of foam

Heat shock and structural proteins associated with meat tenderness in Nellore beef cattle, a Bos indicus breed (Carvalho et al. 2024)

·      Paper looking at the relationship between proteins and meat tenderness

Comprehensive proteome analysis of bread dicphering the allergenic potential of bread wheat, spelt and rye (Zimmermann et al. 2021)

·      Research measuring the levels of allergens in different kinds of bread

Differential proteomic analysis by SWATH-MS unravels the most dominant mechanisms underlying yeast adaptation to non-optimal temperatures under anaerobic conditions

·      Study using proteomics to reveal how yeast adapt to growth at different temperatures

On this episode of Translating Proteomics, co-hosts Parag Mallick and Andreas Huhmer of Nautilus Biotechnology discuss the reproducibility crisis in biology and specifically focus on how we can enhance reproducibility in computational proteomics. Key topics they cover include:

•               What the reproducibility crisis is

•               Factors that make it difficult to replicate multiomics research

•               Steps we can take to make biology research more reproducible

Chapters 

00:00 – 01:20 – Introduction

01:20– 03:10 – What is reproducibility in research and why is it important?

03:10 – 05:42 – Recent work from the Mallick Lab focused on computational proteomics reproducibility

05:42 – 09:32 – Ways to help improve reproducibility in computational proteomics – More detailed documentation, moving beyond papers as our main form of documentation, and ensuring computational workflows are available,

09:32 – 11:30 – Why Parag got interested reproducibility – Attempts to build AI layers on top of current workflows

11:30 – 14:00 – The need to create repositories of analytical workflows codified in a structured way that AI can learn from

14:00 – 15:24 – A role for dedicated data curators

15:24 – 18:31 – Moving beyond the idea of study endpoints and recognizing data as part of a larger whole

18:31 – 21:32 – How does AI fit into the continuous analysis and incorporation of new datasets

21:32 – 23:36 – The role of AI in helping researchers design experiments

23:36 – 27:25 – Three things we can do today to increase the reproducibility of computational proteomics experiments:

·      Be clear about the stated hypothesis

·      Document analyses through workflow engines and containerized workflows

·      Advocate for support for funding for reproducibility and reproducibility tools

27:25 – End – Outro

Resources

Parag’s Gilbert S. Omenn Computational Proteomics Award Lecture

o   In this lecture, Parag describes his vision for a more reproducible future in proteomics

Nature Special on “Challenges in irreproducible research”

o   A list of articles and perspective pieces discussing the “reproducibility crisis” in research

Why Most Published Research Findings Are False (Ioannidis 2005)

o   Article outlining many of the issues that make it difficult to reproduce research findings

Reproducibility Project: Cancer Biology

o   eLife initiative investigating reproducibility in preclinical cancer research

Center for Open Science Preregistration Initiative

o   Resources for preregistering a hypothesis as part of a study

National Institute of Standards and Technology (NIST)

o   US government agency that aims to be “the world’s leader in creating critical measurement solutions and promoting equitable standards.”

MSstats

o   Open source software for mass spec data analysis from Bioconductor

National Institute of General Medical Sciences

o   US government agency focused on “basic research that increases understanding of biological processes and lays the foundation for advances in disease diagnosis, treatment, and prevention.”

Chan Zuckerberg Initiative – Essential Open Source Software for Science

o   CZI program supporting “software maintenance, growth, development, and community engagement for critical open source tools.”

On this episode of Translating Proteomics, co-hosts Parag Mallick and Andreas Huhmer of Nautilus Biotechnology discuss how clinical researchers can leverage proteomics for drug development. Some of the themes covered in this episode include:

·      Proteomics and pre-clinical models

·      How proteomics can drive patient selection

·      Choosing the right end points in clinical trials

Chapters

00:00 – 01:06 – Introduction

01:06 – 06:51 – Proteomics in pre-clinical studies

06:51 – 11:40 – The importance of choosing the right model for preclinical work

11:40 – 17:10 – How proteomics is used in Phase I/II clinical trials

17:10 – 19:29 – Proteomics tools in patient selection

19:29 – 24:33 – Useful information that we get from proteomics that we can’t get from genomics or transcriptomics

24:33 – 28:14 – Proteomics in Phase III clinical trials and picking the best indications of drug efficacy

28:14 – 29:19 - Understanding why clinical trials fail

29:19 – End - Outro

Resources

• The National Cancer Institute’s webpage covering how clinical trials work
• Preprint on the miBrain model - An example of a new in vitro brain model
• Geary et al., 2021 - Discovery and Evaluation of Protein Biomarkers as a Signature of Wellness in Late-Stage Cancer Patients in Early Phase Clinical Trials
• Wasko et al., 2024 - Tumour-selective activity of RAS-GTP inhibition in pancreatic cancer
• The NCI-MATCH trail: Lessons for precision oncology – Report on a large-scale trial using genomic biomarkers to match cancer patients to treatments
• Tumour-selective activity of RAS-GTP inhibition in pancreatic cancer – Study using proteomics to understand mechanisms of resistance to a cancer drug

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

Synopses of each of the papers can be found below and you can find many more insights in the podcast.

In this work from Professor Bernhard Kuster’s Lab at the Technical University of Munich, researchers assess protein abundance changes that result from treating Jurkat acute T cell leukemia cells with 144 drugs over five drug doses. The researchers use their proteomic data to generate millions of dose response curves for the thousands of proteins measured and discover that the drugs impact many more proteins and pathways than those identified as drug targets. In addition, they checked how 7 of the drug treatments impacted the transcriptome and found there was often discordance between impacts at the mRNA level and the protein level. This works highlights the many ways drugs can impact biological systems and suggests that similar studies will help researchers understand the effects of drug treatments and may even aid in the development of more effective or more specific therapies.

Find the publication here.

As we discussed on a previous episode of Translating Proteomics, genome alterations often fail to faithfully propagate to the proteome. In this work, researchers from the labs of Professor Judith Berman at Tel Aviv University and Professor Markus Ralser at the Charité - Universitätsmedizin Berlin, investigate the means through which yeast strains adapt to chromosome gains or losses (aneuploidy). They assess the concordance between changes in mRNA and protein expression in aneuploid yeast that were either found in nature or generated in the lab. The researchers observed dosage compensation, a tendency to return to expression levels associated with normal chromosome numbers, for both mRNAs and proteins expressed on aneuploid chromosomes. However, dosage compensation was much stronger at the protein level than the mRNA level and even stronger at the protein level in naturally aneuploid strains compared to lab-generated strains. This work suggests that multiomics efforts are necessary to determine the effects of genomic alterations. In addition, the authors find that protein degradation, as observed through increased ubiquitination, increased turnover of proteins encoded in aneuploid chromosomes, and the up regulation of the proteasome complex, is a key means of dosage compensation. Finally, because the naturally aneuploid strains achieved a higher level of dosage compensation than the lab-generated strains, the authors suggest there has been selection for natural aneuploid strains that down-regulate proteins causing detrimental effects.

Find the publication here.

Single-molecule protein sequencing is a long sought after goal of the proteomics field. Nanopore-based sequencers thread single protein molecules through tiny pores imbedded in membranes. Changes in the electrical properties of the pores due to protein threading can theoretically be measured to sequence the threaded proteins. To date, the complex signals coming from full protein molecules cannot be effectively interpreted to determine complete protein sequences. In this work from Professor Jeff Nivala’s lab at the University of Washington, researchers bring the field one step closer to full protein sequencing by showing they can use nanopore-based devices to effectively conduct long reads and identify single amino acid substitutions in repetitive protein sequences with otherwise highly similar readouts. In addition, they develop a method that makes it possible to conduct repeated reads of the same protein molecule and thereby improve read accuracy. They leverage their methodology to show they can design protein barcodes, identify PTMs on otherwise identical protein molecules, and identify traces indicative of particular proteins. While they do not sequence full proteins, these researchers suggest their techniques can be used for proteoform identification and the creation of millions to billions of individually distinguishable barcodes.

Find the publication here.

While not discussed in this podcast, Parag and Andreas also loved the papers listed below. Do you have any favorites that aren’t featured here? Please share them in the comments or email them to us at [email protected]. We can’t wait to hear from you.

Additional favorites from Parag and Andreas:

·      Exploring structural diversity across the protein universe with the Encyclopedia of Domains

·      Atlas of the plasma proteome in health and disease in 53,026 adults

·      Integrating multiplexed imaging and multiscale modeling identifies tumor phenotype conversion as a critical component of therapeutic T cell efficacy

·      Brain-wide alterations revealed by spatial transcriptomics and proteomics in COVID-19 infection

Parag Mallick discusses the role of AI and machine learning in biotech with special guests Vijay Pande from Andreessen Horowitz and Matt McIlwain from Madrona Venture Group. Their fascinating conversation covers:

• 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

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

Resources

• Learn more about Matt McIlwain
• Learn more about Vijay Pande
• Folding at Home
• Learn about various types of machine learning on IBM's website
• Learn about autoencoders on IBM's website
• Learn more about transformers on NVIDIA's blog
• Translating Proteomics Episode 6 - The Future of AI in Biomedicine

Do you have a question you'd like answered on a future episode of Translating Proteomics? Send it to [email protected]!

On this episode of Translating Proteomics, host Andreas Huhmer discusses advances in Alzheimer’s research with special guest and Curie Bio Drug Maker in Residence, Sarah DeVos Ph.D. Their conversation focuses on:

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

*Small edit on Sarah's background - She did her graduate work at Washington University in St. Louis and a Postdoc at Massachusetts General Hospital*

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

Dominant Inherited Alzheimer’s Network (DIAN) trials research updates

o   In the DIAN trials, researchers work with families to study various clinical and basic science aspects of dominantly inherited Alzheimer’s disease.

Amyloid plaque reducing clinical trials:

o   Two Randomized Phase 3 Studies of Aducanumab in Early Alzheimer's Disease (Haeberlein et al. 2022)

o   Donanemab in Early Symptomatic Alzheimer Disease - The TRAILBLAZER-ALZ 2 Randomized Clinical Trial (Sims et al. 2023)

o   Lecanemab in Early Alzheimer’s Disease (Van Duck et al. 2022)

Blood Biomarkers to Detect Alzheimer Disease in Primary Care and Secondary Car (Palmqvist et al. 2024)

o   Clinical research into a new phospo-tau biomarker that can help physicians more effectively diagnose Alzheimer’s disease

Resurrecting the Mysteries of Big Tau (Fischer and Baas 2021)

o   Review covering a potentially neuro-protective form of tau called “Big tau”

Integrated Proteomics to Understand the Role of Neuritin (NRN1) as a Mediator of Cognitive Resilience to Alzheimer’s Disease (Hurst et al. 2023)

o   Paper linking the NRN1 protein to cognitive resilience in Alzheimer’s

o   Nautilus blog post about this paper

Do you have a question you'd like answered on a future episode of Translating Proteomics? Send it to [email protected]!

On this episode of Translating Proteomics, hosts Parag Mallick and Andreas Huhmer of Nautilus Biotechnology discuss the challenges and opportunities of plasma proteomics. Their conversation focuses on:

·      Why blood plasma may be a good source of protein biomarkers

·      Current methodologies and pitfalls in plasma proteomics

·      The path forward for plasma proteomics

For those who are new to this topic, plasma is the liquid portion of the blood distinct from fractions containing red and white blood cells. Given the relatively non-invasive ways physicians can collect patient plasma, and the blood’s intimate association with tissues throughout the body, plasma is potentially an excellent source of protein biomarkers. Yet, it is quite difficult to measure the levels of all plasma proteins because their concentrations span over 12 orders of magnitude. This episode features an in-depth discussion of the ways plasma proteomics efforts have and have not lived up to the promise of biomarker discovery and what we can do to advance plasma biomarker discovery efforts in the future.

00:00 – 01:01 – Intro

01:02 – 4:55 – What is the promise of plasma proteomics?

04:55  – 07:23 – Is the plasma proteome really the best source of biomarkers?

07:23 – 10:16 – How do proteins get into the blood and what are the implications for biomarker discovery?

10:16 – 13:59 – Is it clear that proteins are the best candidates for blood biomarkers?

13:59 – 19:57 – Advances in and the future of comprehensive plasma proteomics

19:57 – 22:31 – Pros and cons of fractionating the plasma proteome to discover biomarkers

22:31 – 28:14 – Progress in identifying multiomic plasma biomarkers and the path forward

28:14 – End – Outro

Nano-omics: nanotechnology-based multidimensional harvesting of the blood-circulating cancerome (Gardner et al. 2022)

o   Review from focused on the development multiomics liquid biopsies

Multicompartment modeling of protein shedding kinetics during vascularized tumor growth (Machiraju et al. 2020)

o   Work from Parag’s Lab investigating tumor protein shedding

Simulation of the Protein-Shedding Kinetics of a Fully Vascularized Tumor (Frieboes et al. 2015)

o   Tumor protein shedding work from Parag’s Lab

Mathematical model identifies blood biomarker-based early cancer detection strategies and limitations (Hori and Gambhir et al. 2011)

o   Study modeling how much protein could be shed and detected from different size tumors

The human plasma proteome: history, character, and diagnostic prospects (Anderson and Anderson 2002)

o   Review discussing the clinical importance of the plasma proteome and the wide range of protein abundance in the plasma proteome

Blood Biomarkers to Detect Alzheimer Disease in Primary Care and Secondary Care (Palmqvist et al. 2024)

o   An example of the potential power of blood plasma as a source of biomarkers

Plasma Proteome Project