Proteomics is coming to an end

Proteomics is coming to an end ...

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Advances in next-generation sequencing have transformed genomics research, enabling scientists to sequence a human genome in a day for less than $1000. Until today, technological advancements are approaching the same deadline for proteomics, with the $1000 proteome in mind. Protectural advancements have highlighted the importance of proteomics in drug discovery and our understanding of diseases.

Anna MacDonald (AM): According to recent opinion articles, proteomics is now shining, despite progress in MS technology accelerating a speedy shift in capabilities. Why has this shift not been focused on proteomics up until now, and what has spurred this shift to transcend the genome?

Oliver Rinner (OR): While working on a genome annotation program myself, I still remember the joy of discovering and annotating new genes every day. While learning to read the code of life, and simultaneously possessing tools that develop with unprecedented velocity in scale and cost-efficacy are probably the reason why we almost forgot about protein science for a while.

However, we must learn that the relationship between genotype and phenotype is far from straightforward. Although knowing that a gene is transcribed does not show how the result protein is shaped or organized into a functional state that ultimately drives the phenotype. The proteotype, or the organization and functional relationships of all proteins in a specific tissue in a particular state, connects the gap between genotype and phenotype. Without protein-level data, this critical link is missed.

Proteomics is a large-scale synthetic proteotype that cannot be competed with genomics'' high throughput and low cost. Despite advancements in large-scale mass spectrometry proteomics, we are seeing the picture as a reality. The $1000 proteome, or 1 cent per data point, is now a reality, bringing an unprecedented depth and information content of quantitative proteomic data.

AM: There are several strategies available for large-scale proteomics. Why do you believe mass spectrometry is the most scalable?

MS is a physical technology that generates data by breaking proteins into smaller peptide fragments and then identifying and quantifying them according to their mass and charge. Alternative techniques, i.e., affinity-based methods rely on detecting specific proteins with a panel of antibodies or aptamers.

The fundamental difference in an analytical outcome between these approaches is that the mass spectrometer may be run in a true discovery mode and provide peptide- or even amino acid resolution, which affinity-based methods cannot. MS does not rely on affinity recognition, availability of antibodies or aptamers, and conformational modifications and aggregation of proteins do not interfere with the analysis. It can therefore be hypothesis-free because the technique allows to investigate the entire proteome and detect mutations (e.g., various proteo

The cost per run of mass spectrometry systems is steadily improving, with the same effect as the cost per base pair in next-generation sequencing.

AM: Are there any recent Biognosys research that you want to highlight?

OR: Since Biognosys'' debut, 500 publications mentioning Biognosys technology and tools increased, bringing up 2,000 publications.

Mechanistic Insights into a CDK9 Inhibitor Via Orthogonal Proteomics Methods was recently published on ACS Chemical Biology. The study demonstrates how orthogonal proteomics techniques can be applied to the selection of new compounds. Additional features of our TrueTarget product include a peptide-level resolution that is incorporated into the product''s target list, as well as the binding affinity estimation and binding site localization.

On our ultra-deep plasma profiling, the final sample of bioRxiv biomarker candidates, identified predominantly from the Low Abundant Area, and its use for oncology biomarker discovery is a highlight. The publication provides a novel automated calibration methodology for deep plasma profiling, and reveals how it affects a 180-sample cohort of human plasma from lung, breast, colorectal, pancreatic, and prostate cancer. The survey also reveals biological features of intra

AM: What other advancements in the field have influenced you?

OR: The year''s end was the publication of AlphaFold structure predictions that gives us access to static protein structures. Moreover, the publication of Prof. Paola Picotti''s scientific advisor, Dynamic 3D protoomes, has shown how a dynamic perspective on protein structure might change the way we think about signaling cascade. The study also explored the possibility of integrating structural and abundance-based proteomics to achieve deeper insights into biological processes.

The group of Jesper Olsen analyzed the spatiotemporal changes in the phosphoproteome and demonstrated that it is possible to see signaling events with an organelle resolution.

Immunotherapy-Chemo Combination Therapy Can Benefit Metastatic Pancreatic Cancer Patients. The abstract was published at ASCO 21. Our colleagues from the Parker Instititure for Cancer Immunotherapy (PICI) also highlighted the PRINCE trial data, which includes the possibility of a proteomics study. The study examined how to activate the immune system to eradicate pancreatic tumors using chemotherapy combinations and/or an experimental antibody that targets the CD40 protein and activates immune cells. Note that the two immunotherapies,

AM: When it comes to protein research, it is vital to acquire action shots to fully understand the foundation of biology. How does LiP-MS help you achieve this, and what benefits does it provide over other proteomics approaches?

OR: LiP-MS detects structural or surface accessibility concerns rather than static structures, like an event camera. Moreover, it does so with a resolution of a few amino acids, which allows the mechanist to interpret the changes.

AM: Biognosys wants to overlay LiP-MS data on AlphaFold structures predicted. What possibilities will this offer in drug discovery?

OR: Protein function''s structure is the most important factor. The unique possibility of using our mass spectrometry-based technique for structural proteomics will modify the way scientists look at the data. For example, if you open a classical biochemistry textbook, you will see that all fundamental mechanisms are explained with respect to protein structure.

In our latest pan-cancer study, we mapped each protein onto DeepMind''s AlphaFold2 protein structures and UniProt''s topological domains. This portal will continue to allow researchers to see the proteome in 3D and gain deep biological insights.

AM: What other approaches can be used for in conjunction with LiP-MS to provide proteome-wide structural and functional information?

Structure biomarkers might become very useful in the future. Many disease-relevant processes do not lead to protein expression modifications, but changes in protein interactions. Such changes may be caused by modifications, protein cleavage, or protein aggregation.

AM: What is the future ahead of structural proteomics? How do you know if you can do this.

OR: Although interpreting structural changes in a functional context is still difficult, we observe significant changes in binding pockets outside compound binding sites. These are less obvious factors and may be caused by changes in protein complexes or modifications. The method''s sensitivity is crucial in identifying proteins where modifications occur.

AM: What future progress are you envision in the field? How close are we to proteomics''s potential?

Because, unlike genome sequencing, there is no such endpoint in proteomics. After all, the functional proteome plays on several analytical dimensions. Hence, the probability that future advances will come from improved sample preparation, chromatography, and instrumentation. Deep learning techniques will therefore significantly help uncover functional insights.

Biognosys'' Co-founder and CEO Dr. Oliver Rinner

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