Exhibit 99.3

Investor Presentation Transcript

Chris Blessington:

Thank you for joining our call today. In this call we’ll be discussing information contained in our press release issued today and available at www.nautilus.bio.  Before we discuss what we believe is a very exciting announcement and a significant milestone for both Nautilus Biotechnology and Arya III, I will make some important disclaimers. Please note that today’s presentation is neither an offering of securities nor a solicitation of a proxy vote. The information discussed today is qualified in its entirety by the registration statement on Form S-4, containing a prospectus/proxy statement, that Arya III and Nautilus will file with the SEC in the future. The shareholders of Arya III are urged to read those filings carefully when they become available because they will contain important information about the proposed transaction. Additionally, during the presentation we will make certain forward-looking statements that reflect our current views related to our future financial performance, future events, and industry and market conditions, as well as forward-looking statements related to the business combination, including the timing, proceeds and benefits of the transaction, as well as statements about the potential attributes and benefits of Nautilus’ technology and timing of Nautilus’ milestones.

These forward-looking statements are subject to risks and uncertainties that could cause actual results to differ materially from such forward-looking statements.  We strongly encourage you to review the information that Arya III files with the SEC regarding specific risks and uncertainties - in particular, those that are described in the risk factors section of Arya III’s most recent filings.

And with that, I’ll turn the call over to Adam Stone.

Adam Stone:

Good morning everyone, and thanks for joining. When Arya III was formed, our objective was to identify a company with the potential to successfully address a significant, unmet need in biomedical science. In Nautilus, we are combining with a company we believe has the potential to transform the field of proteomics, unlocking access to the proteome and enabling fundamental advancements in biology, human health, and medicine. Over the course of our diligence, we've come to believe that the company’s innovative science, thoughtful commercialization strategy, and experienced leadership team make it an exceptional fit to meet our objectives.

We’re pleased to be on this journey with you and your team, Sujal…

Sujal Patel:

Thanks Adam, and hello everyone. I'm Sujal Patel, co-founder and CEO of Nautilus Biotechnology. We are really pleased to continue our work with the great team at Perceptive including Joe, Mike, Konstantin, and, of course, Adam. Our long-standing relationship with Perceptive, combined with a strong syndicate of other investors, make us confident that this transaction aligns well with our goals of unlocking what we believe to be a significant opportunity in biological science, and creating long-term shareholder value.

And with that, I’m excited to introduce everyone to Nautilus Biotechnology. Here with me today are Parag Mallick, our co-founder and chief scientist, and the man behind the idea that has become Nautilus, and Anna Mowry, our company CFO. I'm going to start by telling you a little bit about the background of how Nautilus got started, as well as telling you a bit about my background.

I spent the first 20 years of my career purely in the tech world and I'm a tech entrepreneur who started a company in January 2001 called Isilon, and I led that company as CEO through commercialization and an initial public offering in 2006, and ultimately a sale to EMC in 2010 for $2.6B. I was at that company with Anna Mowry, who’s our Nautilus CFO, and together, the two of us built a commercial engine from pre-revenue all the way through $100M a quarter and a positive 20% operating margin before we sold it to EMC, and then both of us stayed onboard to grow it through a $1B run rate, and beyond in Anna’s case.

My previous company, Isilon, was focused on building data storage solutions for the new world – unstructured data, and 20 years ago, the world was in transition from text-based information to video, images, graphics, and machine generated data. That's where I met Parag, my co-founder here at Nautilus. 15 years ago, I met Parag while he was running clinical proteomics, the study of proteins, at Cedars Sinai Medical Center. Parag become a large customer of mine and I built a close relationship with Parag that parlayed itself into Parag calling me about 10 years ago and telling me about the lab he was building at Stanford, sitting at the intersection of computing and biochemistry. My wife and I were so excited about Parag’s work that we’ve been philanthropically supporting the Stanford lab for the last decade continuously, and that built a really close relationship with Parag and I, and in 2016, Parag called me up one day and he said, “I have to start a company because you know I've struggled with proteomics my whole career and I've come up with a new method”, and that's the method that we're going to tell you about here, and that's the method that Nautilus Biotechnology is commercializing.

Please note the company’s disclaimer referencing our forward-looking statements and the fact that this communication and this presentation is for informational purposes only.

We believe that our company has the potential to revolutionize biomedicine. The proteome, the make-up of all the proteins in the human, is among the most dynamic and valuable sources of biological insight. Unlike the genome, which is pretty much static from the day you were born to the day you die, the proteome is dynamic; it's different in a cardiac cell than it is in a liver cell. The proteome is different in healthy cells and sick cells, and this has big implications across healthcare.

90% of our FDA-approved drugs target a protein. Most of our molecular diagnostics target proteins and in order to develop these drugs and diagnostics, over $25B a year is spent on technology that profiles proteins. And even with that level of investment, our ability to measure proteins today is awful. The coverage, meaning the number of proteins that you can detect in a given sample like blood, is very low. Ease of use is not where it should be, and certain platforms have challenging performance, dynamic range and high cost. A recent article highlighted that therapeutic development over the last decade has been in a steep productivity decline. Today, of the drugs that reach the milestone of FDA approval, only two in 10 will ever return the R&D dollars that went into creating them, and that's because at this point, biopharma has picked off many of the easy drugs that are available to treat common ailments, and what's left is a difficult task that proteomics can help us with. We believe that humanity needs, and frankly deserves, a dramatic acceleration in drug development, and we think a scientific leap is needed to usher in this new world of precision and personalized medicine. To deliver, we believe we need to radically reinvent proteomics, and we believe that this is one of the last, untapped opportunities in biological science today.

Nautilus’ vision is to build a complete end-to-end platform comprised of instrumentation, reagents and software which takes a sample in and returns unique biological data and insight out. To accomplish this, we've designed a single molecule analysis platform of extreme sensitivity, scale and ease of use. Leveraging the unique architecture, advanced machine learning and algorithms, we believe our platform has the potential to identify substantially all proteins in a sample from almost any organism, of course human being the most important. In short, this is not an incremental or evolutionary instrument, but a complete reimagining, and we believe that this unlocks the $25B market opportunity in proteomics, as well as allows us to expand long term into many adjacent markets. Only a few months out of our stealth mode, in 2020, we quickly captured the attention of many of our future large biopharma customers, and at the end of 2020 we signed our first formal partnership with Genentech in a high impact technology application area. Lastly, we're a founder-led organization with a highly experienced leadership team that we're going to tell you about, but the most important thing is that our leadership team possesses a unique hybrid of tech and biotech experience which we believe is an important source of unique, long-term differentiation.

When Parag brought the idea behind Nautilus to me in 2016, I first looked at it through the eyes of an investor. At the time, I was investing in private companies, having made about 80 investments, and I asked myself some questions: Why is this idea unique? Why did Parag come up with this idea and not someone else? Why is it timely? I think the answers to these questions are really quite interesting. The core idea behind Nautilus is completely counterintuitive and it requires thinking at the intersection of 3 distinct disciplines that don't often come together: the life sciences, the computer and data sciences, and the physical sciences and engineering. Having sat at the intersection of these disciplines for Parag’s whole career and having academic degrees in both biochemistry and computer science, Parag was uniquely suited to be able to discover our core innovation. It is also timely; the compute power and bandwidth required in the cloud to run our core algorithm really wasn't feasible 5 to 7 years ago. Also, we rely on the maturity in the machine learning world to power our adaptive, decoding algorithm. Nautilus is pursuing the deep, hard science with an entrepreneurial mindset to go and bring this innovation to the world.

Over the last few years, in addition to building a strong management team, we’ve assembled a team of individuals with unique experience across many different disciplines, protein biochemists working with nanofabrication engineers, working side by side software and machine learning engineers and single molecule biophysicists and optical engineers, all working together to bring this innovation to the world. I’m going to introduce Parag Mallick now and let him walk through a little bit about the current proteomics landscape and our design criteria for this product and how it works. Parag.

Parag Mallick

Well, Sujal already gave you a bit of my background but I'll just briefly introduce myself. I'm Parag Mallick, Nautilus’ scientific co-founder. My training has been at the intersection of computer science, biochemistry and engineering, always with a focus on human health. And my faculty positions, first in Los Angeles and then at Stanford, my lab has been really active in proteomics, in nanotechnology and next generation AI through a variety of NIH and DARPA initiatives and in general what we've tried to do is use a variety of both computational and experimental approaches to bring together diverse measurement modalities with the best analytical tools to accelerate biomarker discovery and precision medicine.

Nautilus grew out of my personal experiences and frustrations in large scale multiomics and systems medicine. In the studies, it was clear how simple and straight forward the genomics pieces have become, and just how infuriating and difficult the proteomics pieces were. I don't want you to stare too closely at the slide; it gets overwhelming pretty quickly, but what is outlined here are the pieces of the canonical mass spectrometry-based proteomics workflow. Broadly speaking, starting with sample collection and extraction and fractionation, moving through digestion, where we take all the proteins and chop them up into fragments and then a series of further sample preparation steps before ultimately putting them into a mass spectrometer and doing data analysis.

Over the past 30 years, there's been a tremendous amount of effort required to analyze a sample, and myself and others have explored a variety of approaches to improve individual pieces of this workflow. But after turning the problem over and over again, I just couldn't ever get to a solution that would bring proteomics into parity with genomics.

If we go to the next slide, slide 7. Despite all the effort that has occurred over the past decades, when you pile up those optimizations and ask: what can this mass spec proteomics workflow observe? It's limited. In the plot here, using mass spec, the average lab can only identify about 8% of the proteins present in a blood sample, shown here in red. From a cell or from a tissue sample, it's a bit better at around 30%, but just because you can identify a protein doesn't mean you can quantify it very well. And still, most of that blue proteome ocean isn't accessible. We don't believe anyone has ever measured the entire proteome, and that's only part of the story. There's this whole other dimension of data known as proteoforms that are essentially invisible to existing shotgun proteomics approaches. Proteoforms are alternative versions of the same protein that have different decorations on them that are critical to how the protein functions, defining their activation, how they move throughout the cell, and more. I’m personally really excited by the fact that there is so much of the dark proteome and these millions of invisible proteoforms available, as tapping into them could have tremendous potential as novel drug targets or biomarkers to really help catalyze the future of personalized medicine.

After years of trying to incrementally fix the mass spec proteomics workflow, it became clear that we had to do something radically different. Turning the problem in my head over and over again, I started with a blank sheet of paper, wrote down what scientists and researchers like me want, and then tried to design something from the ground up to achieve those goals. There were a very clear set of design criteria. Number one was we wanted the whole proteome, we wanted to be able to measure everything, not just 8% of it. It had to be easy to use so that virtually any lab can benefit from using it. Anyone who wants a proteome, gets a proteome, rather than restricting it to a small collection of power user, analytical chemists. Of course, the platform has to be ultra-sensitive. Unlike NGS technologies, where you can leverage natural processes to amplify DNA and RNA, you can't amplify proteins. You have one molecule in a sample? You need to be able to measure one molecule in a sample. If you want to measure the complete proteome of a single cell? You need a method that has the sensitivity to pull that off. Of course, the process has to be reproducible and robust. To have any value, the results you get today must be the same as the results you get tomorrow. It also has to be complete. One of the largest challenges we have with mass spec is that when you run the same sample twice, you might observe different proteins in one run versus another. Critically, it has to be fast. You want to be able to churn through tens of thousands of samples. And importantly, it has to be integrated. You want to go from sample to insight. You want the biologist to be able to put a sample in and get an answer out.

With these objectives as the design criteria, we set out to create a transformational platform which we show here on slide 9. The platform itself has key technical innovations across sample preparation, instrumentation, and machine learning, designed to achieve the system specifications I outlined above.

The first innovation was the recognition that we would need a single molecule protein array. This is really hard and upside down of how people typically think of the problem where they immobilized antibodies or aptamers in bulk and tried to capture proteins in solution. In this instance we're going to flip things upside down and put the single protein molecules on a hyperdense array. This allows you to go beyond bulk measurements of proteins, which is how everything is done today and is necessary to achieve the sensitivity and scale we want. Remember, we can't copy proteins like we can DNA and RNA. There's only one molecule: that's what we have to work with. That array has to be hyperdense so we can be really efficient with our measurement and not waste measurement time measuring empty space.

And that leads to our second major breakthrough, which was the instrument itself. This innovation was in the creation of a stable ultrafast, ultrasensitive, multi-cycle imaging process, which we can use to repetitively probe each individual protein molecule on our array over and over and over again with different binding reagents. Each cycle allows us to get more and more information about each individual molecule.

And the third significant innovation is the integration of a sophisticated machine learning framework within the measurement. Most platforms, if they do use ML, they tack it on at the end when they're comparing their cases in their controls or their responders and their non-responders. We recognized that by bringing ML up in the process, into the measurement itself, it fundamentally changes the landscape of what's possible and potentially unlocks your ability to measure substantially the entire proteome. In the next few slides, I'm going to walk through the pieces of the process in more detail. The IP is quite complex but remember that the ultimate product our customers will see will be a fully integrated sample-in, answer-out approach.

Moving to Slide 10, our sample prep process was designed to support the generation of a single molecule protein array. In prior work, people had attempted to build such things by fabricating substrates with really small landing pads, so that only 1 protein could dock at a given coordinate because the 2 proteins wouldn't be able to fit. But that approach isn't cost effective or robust at scale, so the team came up with a really clever approach. Instead of making the landing pad small, what we're going to do is we're going to make the proteins big. This allows us to fabricate a chip with much larger landing pads, which is easier to make, cheaper and more robust. We make the proteins big by conjugating them to a very special scaffold that we've designed to have exactly 1 protein attachment site. That scaffold is about 300 nanometers in dimension and holds exactly 1 protein. Accordingly, the chip is fabricated with landing pads that are 300 nanometers and hold exactly 1 scaffold. You put together both of those and you have a self-assembling, high density, single molecule array. You can see from the experimental results on the right, after years of development and optimization, we've achieved a method for conjugation that's both rapid and high efficiency. This is critical for giving biologists easy, quick and robust sample handling; a huge contrast to existing approaches.

The nano fabricated biochip that we have designed has a few very important features to it. The first is that it is massive; each chip is about the size of an index card and holds 10 billion protein molecules. What you're looking at here on the left is a tiny, tiny, tiny portion of our array. Our actual array has about 10,000 of these squares on it. If you zoom in far enough you can see the individual landing pads holding single protein molecules. You might ask why measuring 10 billion individual molecules is so important, and the answer comes back to our design criteria of sensitivity. In talking to our pharma partners, their high content screening is done in microtitre plates, where it's common to have somewhere between 100 and 1,000 cells per well. And they want to be able to capture down to 1 molecule in those 1,000 cells. In order to measure that, with that sensitivity and dynamic range, you need to measure about 10 billion intact protein molecules. A similar dynamic range problem exists with blood profiling. The experimental results of a titration experiment demonstrate sensitivity is approaching the atomolar range, an amazing dynamic range, allowing us to really ask the critical biological questions we want.

Moving to the next slide, slide 12. Once we have the proteins down on the array, we load the flow cells into the measurement instrument to characterize each individual molecule. Typically, one might attempt to identify each protein using highly targeted and specific antibodies, but that would be slow and depends on having great affinity reagents for every protein in the proteome, which is likely interactable. So, we do something different. People often complain that their antibodies aren't specific enough; they cross-react. We're going to take advantage of that concept and take it to extremes, intentionally generating a special class of proprietary probes that are highly, highly cross-reactive. Such probes that recognize very short peptide epitopes, we call these multi-affinity probes. To your typical analytical chemist, this kind of reagent might be considered crazy talk, but it's a key part of our approach. We introduce each probe into the flow cell, rinse out the unbound fraction, image, and then wipe them all away and do this again, over, and over, and over again. Each of these probes, binds to a multiplicity of proteins. While each probe on its own is not specific to any particular protein, each probing gives us a little nugget of information. The counterintuitive, but important observation is that with the right machine learning framework, we believe it's possible to take all those tiny little nuggets of information and decode substantially the entire proteome. The instrument itself is designed to be fast, allowing us to perform hundreds of cycles within about a day. In addition, we intend to offer 6-sample multiplexing initially, with more multiplexing coming in the future to further enable high throughput application. This is also very important because multiplexing is a great way to drive the cost per sample down, while maintaining a dynamic range that is far greater than other approaches.

Once we’ve iteratively imaged the flow cells, we take that gargantuan amount of protein characterization digital information, about 20TB, and process it down to a simple binary binding matrix that simply says that a given probe bound or didn't bind to each coordinate. So, for example, here we look at coordinate to 2, 1, this would say that we had no binding from probe 1, but we did have binding from probes 2 and 3 and maybe ultimately 83 and 95 and on and on. We take this data and push it up into the cloud for decoding. This cloud ecosystem also allows us to provide a great experience for customers, allowing us to get to that sample-in, insight-out paradigm that is so critical.

In the cloud, the machine learning now takes in all of that information and converts it to protein identities, asking the question, ‘given this pattern of finding, what protein is compatible’? For instance, if we look at coordinate 2, 1 again, we might take that pattern of 2, 3, 85, 92 and then say okay, given this set of probe landings, which protein could this be? Now, it's important to recognize that while it's tempting to say that there's a signature so that every time I see cMET, I'm going to see probes 2, 3, 85 and 92 landing, but that's simply not the case. There's so much stochasticity in the system, as a single molecule platform, that we really need to ask the question, is this pattern of probe bindings compatible? And that's part of the machine learning architecture that we've built over the past 4 years, asking those kinds of questions in a sophisticated manner. Once we've identified virtually every protein on the array at their individual coordinates, we accomplish our quantitation by simply counting. As a single molecule platform, it is designed to be definitionally among the most sensitive you can get. It is also designed to be definitionally among the most accurate you can get. It also provides an absolute quantitation read out. A critical and important aspect of the platform is that the machine learning is on the inside and then it learns from every experiment. So, there may be a spot that in some experiment today, the machine learning says, “well, I'm not really totally sure what this is. I've only got about 80% confidence.” But after many, many, more years of experiments and getting smarter and learning the details of how these probes bind, we might be able to go back and say, “You know what? Actually, we figured it out, we now know that this protein is BRAF.” That stickiness, that iterative learning, the fact that the platform gets smarter over time is a really important attribute of our platform.

What we've been designing over the past 4 years is a technology that could do both the discovery science and the clinical science required to take that discovery into health care practice. And to really understand how stable this process was to make sure it was highly robust, we had to test the durability of proteins on our array and our ability to detect them over time. And as you can see on the left, over many, many, many cycles, the protein loss is very, is incredibly minimal: less than 1%.  And on the right, the proteins after probing, and re-probing, and re-probing, they continue to be re-probable over, and over, and over again.

On this slide we're going to go under the hood of the machine learning to understand: How does it figure out what protein is on a particular landing pad? And what you can see on the graph on the left, is that over time as a particular landing pad is getting more and more touches, from more and more landings from probes, initially the algorithm can't quite figure out which protein it is. But after about 12 or 15 touches, it starts to lock in on the right answer this GRK6 protein. And from that point onward it just gets increasingly confident, as all those little pieces of nuggets of information start to build up. To the point that GRK6 is the only possible answer, it's incredibly confident and every other protein is exceedingly improbable.

One key question you might ask is, how many cycles and probes are required to decode the whole human proteome? Based on our internal test the answer is surprisingly low. In about 200 to 300 cycles, you're able to decode the vast majority of the proteome. We believe this method and our analysis approach represent a quantum leap forward in studying the proteome. It's just so exciting.

Beyond broad scale profiling, our platform also unlocks a tremendously exciting opportunity to measure proteoforms. For those who aren't familiar, each protein exists in potentially hundreds or thousands of different forms with different patterns of modifications on them. So, you can see some of these modifications in the center panel here; methylation, acetylation, phosphorylation. These modifications govern the protein's function, its activity, its location and are compounded. So, on the right, you can see multiple forms of the same protein that are decorated in different ways. It's believed that there are potentially millions of different proteoforms in the world. And the pattern of these modifications is biologically critical in defining drug function, in defining potential biomarkers and more.

Historically, peptide-centric proteomics methods are unable to differentiate mixtures of proteoforms. What we can see on the left, is we have a mixture where we have one protein that has three modifications on it at different locations. And then two molecules that are unmodified at all. On the right, we have three different molecules each of which is modified in a different location. These are clearly distinct from a biological function perspective, their molecular heterogeneity is very distinct and yet, to existing peptide-centric platforms, one could not differentiate these 2 samples at all. They look identical, because you take the proteins, you break them up into little pieces and the context that helps you understand the difference between having one modification on each individual molecule versus three modifications on a single molecule is lost.

Moving to slide 20. In our platform, because we study the proteins intact our approach is designed to be able to preserve that context to truly measure the proteoforms. The way it works is we come in with an existing reagent, a commercially available reagent perhaps, that allows us to first identify a given protein on the surface. So, for instance EGFR, and, say, here on our array are where all the EGFRs are. We next come in with a position-specific reagent that allows us to interrogate. For instance, threonine at position 678, wipe it off and come in with a third reagent and then a fourth one and a fifth one. And you can see that very quickly, this allows us to elucidate the molecular heterogeneity of these diverse proteoforms. In five cycles, looking at 32 distinct proteoforms, which should be completely invisible to any other platform. On the right, you see a demonstration of this where we looked at samples that were dominated by either one proteoform or another or a mix, and being able to quantitatively resolve samples that would have looked identical to any other method, it is incredibly thrilling.

Moving to slide 21, one very important aspect of our technology is that it’s highly open and customizable with compatibility to a wide variety of binary agents. This lets us tune the system to be very good at asking different types of questions based on the profile of binding reagents we introduced. As an example, on the left we have an entire process developed in house for creating these multi-affinity probe reagents. These aptamers and mini-protein binders let us probe broadly to study substantially the entire proteome. On the right we've tested targeted reagents from partners and vendors with high affinity to specific proteins and proteoforms. On our platform these reagents provide a detailed view of the proteome form landscape and the molecular heterogeneity of individual proteins and pathways. Additionally, with a simple labeling kit our platform is designed to be able to access virtually any reagent in the library of biologicals that have been created by biopharma, or academia, or commercial antibody manufacturers today. This is an incredibly powerful aspect of our technology which makes it customizable for the community to ask the questions that they want to ask. Now I'm going to turn the conversation back over to Sujal.

Sujal Patel:

Thank you, Parag. We're going to turn our attention now to talking about the market opportunity, talking about our commercial strategy, and then I'll wrap up with the team and a little bit about our first partner collaboration. The global proteomics market is a very large, immediate opportunity for Nautilus. The market is sized at about $25B and it's growing at a 12% compound annual growth rate. This market includes the sales of mass spectrometry instruments and reagents, it includes assays, it includes, micro-arrays and it’s about 50% biopharma, 30% applied markets like agriculture, industrial science and environmental and 20% academic and research. While that market opportunity is an immediate focus area for us and really exciting, we think what's more exciting is that we believe that long term, there's a transformation of healthcare that is enabled by proteomics and there's huge high value applications in precision and personalized medicine, in drug discovery and in diagnostics that are unlocked by broad proteomic profiling. The clinical diagnostic opportunity alone by itself is interesting just in this first phase where we're partnering with biomarker discovery companies in building new diagnostics and better diagnostics, but in the long run we believe that our platform could be used in the clinical setting as a molecular diagnostic as well.

Let's talk about some of the potential markets and applications for this technology. When Parag and I got this company off the ground, I was really fortunate. My co-founder Parag is a KOL of the proteomic space and he already knew the five or six key application areas where our technology would have the potential to be transformative. But very early on in our company's life, we had over 100 conversations with biopharma companies, with academic and research organizations, and the feedback was a bit surprising. What we learned was that there is a much wider range of potential applications for our future customers and they were keenly interested in what we were doing. Some customers wanted to do longitudinal monitoring of proteome dynamics for therapeutic response monitoring, some of our customers were interested in profiling exosomes. Some were interested in the proteoform composition and landscape. Some were interested in drug repurpose and rescue and a big, broad swath of our customers were focused on translational research. These translation research customers are focused on building drugs, they spend in aggregate over $200B a year and some key themes have emerged. Biomarker and drug target discovery is a major area of pain because of existing proteomics solutions. Mechanism impaction studies, trying to understand how a target compound interacts with different cell lines and trying to pick between 5,6,7 different compounds which one might be right, is another area where proteomics can have a substantial benefit. And last, in toxicity profiling and prediction there's a real need for full proteomic profiling to figure out where cross reactivity from particular compounds exist so that it could be either mitigated or understood better, making the drug development process more effective and less time consuming.

On this slide you see a number of significant milestones that Nautilus has up to comprehensive proteomic profiling, and then through comprehensive proteomic profiling. At the end of 2020 and through 2021 we have been and will continue to work with pharma partners, leveraging the single molecule hyperdense array that we've demonstrated, and being able to run multiple reagents through that array in multiple cycles each time putting data together in a way that returns unique biological insight to our customers. In 2022 and up through the middle of 2023 we will move through a number of milestones related to broad proteomic profiling. We'll move through the milestone of being able to get through 2,500 proteins per run, 10,000 proteins per run, and by run I mean in a single sample identified. And then by mid-2023, comprehensive proteomic profiling. In these first 4 phases and milestones we're really focused on a model where the customer can send us the sample, we can analyze it in our facility on our prototype equipment with our own staff and return data for the customer in the cloud. At the end of 2023, we anticipate launching an instrument and from that point forward the predominant business model we expect is selling instruments, consumables and software. This is the point where the significant revenue ramp is really expected to begin for our company. Customers will be installing this instrument in their labs. They’ll be buying consumables from us. They’ll be leveraging it for all of their life sciences experiments in drug discovery, translational research, diagnostic and other applications. And we believe from this point forward our revenue ramp has the potential to look pretty similar to that of other disruptive life sciences instrumentation companies, many of which you're familiar with. For example, the Illumina NGS ramp post the acquisition of Solexa. And of course, from late 2023 on, this is really a business model where the customers are largely generating data from instruments that are in their own facilities.

The milestones that I just described to you map to a 3-phase commercial strategy, and we're in phase 1 right now. In phase 1, we're focused on partnerships and collaborations that are getting key KOLs in the proteomics industry familiar with our platform; we're starting to validate the reproducibility of it, and we're working with pharmaceutical companies to get endorsement of our platform's capabilities prior to the stages 2 and 3 that we’ve outlined here. The primary goal of these engagements is to jointly publish data and to start showing what our platform is capable of doing. In phase 1 we also see a unique opportunity in that some of these partnerships are really close to a development type of opportunity and may yield high potential discovery and IP licensing opportunities as well. In phase 2 of our commercial strategy we launch what’s called our early access program. While we do that we scale up our in house data production capability to be able to support a wider range of customers who are looking to get familiar with our platform’s performance, they’re getting familiar with how to manage the data coming out of our platform and the tools that are required to analyze it, and in this phase our goal is really not just to scale up but to also start building the early pipelines for instrument sales, and in actuality starting preorders of the instrument is a key goal for us in this early access program. As the early access program progresses and we're getting closer and closer to our platform launch, many of these engagements will really look like paid proof of concept studies where a customer is paying us to analyze some samples, they're getting value from the data, and that leads to a signed pre order for the instrument.

In phase 3 of this commercial strategy we largely are moving to that instrument, consumables and software model. In North America, we’ll largely go to market with a direct sales force, and in international geographies we’ll use distribution channels, a very common model in life sciences, Dx and tools, and a model that is very similar to the enterprise sales motion that my CFO Anna and I worked on in our last company from $0 of revenue through $100M a quarter as a public company. Once we're in this stage, we have already thought through a multi-year roadmap of product innovation and really view this as the significant inflection point in our top line.

One of the other keys to our commercial strategy is this land and expand sales model that you see on the slide. And this model is true in all 3 phases of our commercial strategy. Our goal is, just like working with our first pharma partner, to establish an initial beachhead in an account. Focus on one research group, one therapeutic area, one application. And from there we want to show value to the customer, we want to demonstrate the reproducibility of our results, we want to grow into other research groups, other therapeutic areas, perhaps even other geographies within that one account. From there we expect that we’ll grow their workflows. Not just doing target discovery but maybe doing target discovery and mechanism of action. Maybe from there we're going to toxicity. We grow our capacity by having a world where we’re adding value to our customer, they want to run more samples through the system per experiment, and whereby more reagents and more capacity is being utilized and that leads to more product sales. And this is really a way to build a high lifetime value in a single account. And the thing that really is great about that is it leads to a very efficient sales model. On the right you see our target customers we've talked about bio pharma and academic and research quite a bit, but we also see significant opportunity to work with large scale multiomicscore laboratories and CROs, and we see diagnostic service partnerships as another key area, and these are the five that really round out the initial customers that we see over the course of the next 3 to 5 years.

So, what you see here on this slide is how the milestones that I laid out just a couple of minutes ago map to the 3 phases of the commercial strategy that I outlined. You see that early collaborations and partnerships is the first section here. We're focused on working with pharma and biotech as well as influential academic sites and our goal here is to create foundational publications to demonstrate utility and value to build confidence around our solution, and our goal here is really to validate the technology. Because we will be introducing new capabilities for many, many years to come, you see that this phase will probably continue indefinitely. Starting in the middle to late 2022 time period we’ll begin that early access period that I talked about. Here, in addition to working with biopharma and academic customers, we’ll also add advanced proteomic laboratories. And our goal here, as I mentioned earlier, is to seed the market for instrument pre-sales by expanding the applications we focus on, by showing value and utility, and really the goal here is to start to build that customer pipeline for our instrument launch, which comes at the end of 2023. And then, lastly, at the end of 2023, with this instrument launch, this is where we really set ourselves up to have a diversified customer base. This launch catalyzes our business growth because the instrument sale in total is about $1M and $1M sales average selling price, you're able to grow the topline very quickly with putting instruments out into the marketplace, and then of course there's the recurring revenues from consumables sales and software that comes after.

There are a number of planned strategic elements of our platform that are designed to create competitive advantage in the field. As we've talked about, we are building an end-to-end platform that focuses on sample-in and data- and insight-out. We believe that a platform that is end-to-end and delivers broad proteomic profiling is going to be incredibly disruptive to the market and unlock primary sources of biological information.

And we believe, based on our current development, that we'll have first-mover advantage in this very large and expanding market. The thing that gets you is it gets you an early base of customers, and gets you to the point where you're generating data for proteomic information. We view data as an asset. As we generate that data, we build algorithms and we use machine learning to learn off of that data. That delivers more value to our customers, that in turn makes our customers want to run more samples through the system, and you create this cycle where it's reinforcing and it creates value for the customer and it makes our products more sticky.

The commercial model that we're employing is a proven commercial model. Many life sciences research tools companies (Illumina included) use a model that's very similar to this, but the big advantage that we have is that in addition to that land-and-expand sales motion that I talked about, our competitor in the marketplace is a mass spectrometer. We don't directly compete with a mass spectrometer, but our price point is harmonized with where a mass spectrometer would typically be for a discovery proteomics application. And with that high ASP as a starting place, it makes for a very efficient sales model. Lastly, in conjunction with that instrument sale, there are a number of recurring revenue sources that create a diversified revenue model. With each instrument sale there's a recurring stream of consumable sales, there's a recurring stream of service and support, and software as a service. If you look at instruments, consumables, services support and software in conjunction with partnerships where we’re working closely with customers on joint development types of opportunities, what you have is a layer cake of revenue streams that creates diversity and more predictability in our business, which we think is very valuable.

In this slide, we describe the collaboration that we’ve signed with Genentech. In December 2020, we signed a research collaboration agreement with Genentech to use the Nautilus platform to analyze and map the proteoform landscape of a particular protein target of interest to Genentech. The primary goal of this collaboration is to reach a publication by late 2021. And really the way the Nautilus technology works here is very similar to what Parag described earlier in terms of the technology that we built for mapping proteoform landscapes. It's hypothesized that these proteform patterns, different modifications, and isoforms play a critical role in understanding molecular disease progression and understanding molecular disease pathways, and we’ll focus on working with Genentech and eventually with other customers on trying to figure out if these protein modifications could be a biomarker, or whether they're indicative of therapeutic response. It's really an exciting area and we think there's a lot of room to grow.

One of the things that we’ve thought a lot about at Nautilus is trying to build a competitive moatthat gives us a long-term competitive advantage. Some of the things that we have are really quite great for that. Our solution is very counterintuitive. It requires specialized experience and expertise to build the type of product that we're trying to bring to market. And we’ve spent many years building a diverse set of talent in our company that's capable of building all of these components and integrating them together. The third leg of that stool is really an IP portfolio. We were very fortunate in 2016 when Parag came up with this idea that the IP landscape for what Parag conceived of was relatively open. And the reason for that is it's really a very counterintuitive and different prospect than anything else that had come out before that. We've spent substantial time, energy and money over the course of the last 4+ years filing patents both in the US and internationally, and multiple patent families across each of the major areas of our process. We have 3 allowed US patents today and we have many more patents in various stages of prosecution.

So, with that, let me introduce Anna Mowry, the company CFO. She's going to walk you through leadership and some of our financial targets and then I look forward to wrapping up after that. Anna.

Anna Mowry:

Great, thank you, Sujal. As you just heard, Sujal and Parag bring together a unique and powerful combination of talent and experience. That deep interdisciplinary experience is now reflected in the leadership team that they've assembled together here. That’s important because as Nautilus evolves from its research roots into commercialization, we believe this is the team to do it. Chris, Mary, and myself, under Sujal’s leadership as CEO, worked closely together at Isilon, where we built a $1B revenue engine from the ground up. Nick, Subra, John and Matt Posard on our board were in leadership roles at Illumina at the time of the Solexa acquisition, and they drove the NGS ramp from there. We have a first-class set of investors who put together a seasoned board, who have experience leading public companies, as well as proven operational experience. Finally, on our scientific advisory board we have an amazing team of KOLs. Ruedi Aebersold is widely considered the father of modern proteomics. We have a Nobel laureate in Lee Hartwell, and he earned that prize for discovering the protein that drives cell division. And Joshua Labaer is the head of the Biodesign Institute at ASU. He’s also the former president of the Human Proteome Organization. As you can see, we’ve put together an extraordinary team in anticipation of the enormous opportunity that's in front of us, and we're well set up to execute.

Sujal talked about our commercial strengths just a few minutes ago. I want to show you how those strengths translate into our long-term financial targets.  Our long-term gross margin target of 70% is really a function of our high ASP, that's based off of the high cost of alternatives out in the market today. It's also a function of our multiple revenue streams. Once launched we believed initially, the majority of our revenue will come from those instruments. But very quickly the pull-through revenue coming from software, support, and consumables will drive a recurring revenue stream that's predictable and highly profitable. On the other hand, the cost to manufacture the equipment is expected to remain relatively modest, because we believe we’ll be able to use readily available parts from a well-established supply chain. On the opex side, we intend to continue investing in R&D. Right now, we're focused on development and preparing for commercialization, but over the longer term we intend to extend our lead, build on early successes, and expand into new categories and use cases. In SG&A we expect significant leverage from our high ASP, but also from the land-and-expand motion that will drive a high lifetime value for each account. Our highly profitable sales combined with productive spending are expected to lead to an operating margin target over the long term, in the range of 25 to 30%, but we believe there are so many adjacent opportunity that about 25%, we expect to reinvest in R&D and strategic opportunities in order to grow our top line even further. We've been very capital-efficient to date, but we believe now is the time to accelerate the pace of our investment, and Sujal will recap why we think this is a great time to get involved. Sujal.

Sujal Patel:

Thank you, Anna. As we talked for the last hour, we believe that a radical reinvention of proteomics is necessary. And we believe that Nautilus has a platform with the potential to revolutionize biomedicine. And in doing that, we could unlock significant new potential market opportunities, and we're building a platform that we believe is fine-tuned for what our customers need in drug development, diagnostics development, and a wide range of proteomics applications. Over the course of the last four years, we’ve built a proven team that is driven to win. And next, I’m going to talk to you about this transaction with Arya III, and why we believe it aligns us with investors on a path to long term shareholder value creation.

Let me take a minute and talk about the merger with Arya III from Nautilus’ perspective. This transaction allows Nautilus a more direct path to an IPO where the company will experience greater visibility and greater access to capital as a public company. It enables us to go public in one step, versus a traditional two or three step process, with venture-backed private rounds and a traditional IPO. And in addition to that, it enables us to establish a premier healthcare – focused investor base and allows us to continue our long-term partnership with Perceptive. The $385 million we expect to have through a combination of cash on hand at Nautilus and the merger with the SPAC and our PIPE is going to be used to fund long-term development of our proteomics platform. This capital is expected to provide the cash runway to achieve all the key catalyst and commercialization milestones we talked about earlier, and the capital enables us to accelerate the development of our proteomics platform and expand into new opportunities and more staff across our scientific, engineering and commercial teams.

This slide summarizes the terms of the transaction. The transaction with Arya III ascribes a $900M pre-money equity value to Nautilus. The implied pro forma enterprise value of the combined enterprise is approximately $905M. Of the cash coming in from the transaction, $150M of it is in trust with the Arya III SPAC, and $200M is being raised through a PIPE. Of the PIPE, $55 million is committed by Perceptive and an additional $38 million is committed from existing Nautilus shareholders, not including Perceptive. We’re very pleased to have a strong syndicate of investors supporting this PIPE, as well as the 45% plus participation from insiders.

With that, we'd like to conclude our presentation. Thank you so much for allowing us to take this opportunity to tell you more about Nautilus and our plans for the future.