Peptide Design in Contemporary DMTA Cycles: How DMTA Drives Translational Refinement

By Dr Jordan Fletcher, Scientific Fellow – Peptides

Where Design Begins

When people think about peptide design, they typically envision starting from a clean slate and employing a computationally driven process built on structural data, molecular modelling, and rational design from first principles. In practice, however, this is not usually where our work with a client begins. At CatSci, we are rarely involved in the kind of first-generation design that would satisfy the purest instincts of a computational designer, protein engineer, or molecular-docking expert.

That’s probably an unlikely thing to say for a company that puts the Design-Make-Test-Analyse (DMTA) workflow front and centre of its core offering. After all, DMTA starts with a D. In practice, however, for most CROs operating in the peptide, oligonucleotide, and hybrid modality space, the very first step in the design and discovery phase is almost always driven by the customer.

Typically, programmes come to us with first-generation sequences already in hand. Sometimes these are well-validated and genuine hits; other times – very early in the discovery phase – they are entirely unproven candidate sequences produced from the client’s own hypotheses, computational efforts, or screening programmes.

This is not a limitation, and it’s certainly not unique to us. The earliest, and most creative phases of discovery programmes typically happen very close to the underlying science, with innovation maturing internally before external partners come into frame. As such, many people engage with CROs once ideas have already taken shape, IP is secured, and there is something tangible to Make, Test, and Analyse. In this way, the very first round of the DMTA cycle a CRO encounters often looks more like -MTA.

Where CROs like CatSci add value is in supporting iterative and translational rounds of design, and in identifying how the lead compound can be systematically refined.

Peptide Design Heuristics

It is worth remembering that peptide design has not always looked the way it does today. Many early peptide drug candidates were derived more-or-less directly from endogenous molecules – a strategy that remains valid – with sequences truncated, tweaked, and modified to identify minimal pharmacophores, enhance receptor binding affinity, and produce favourable DMPK profiles.

Progress was empirical, driven by rules of thumb, and often painfully slow. Approaches such as systematic truncation, amino acid substitution, cyclisation, and stapling were used to stabilise secondary structure, enhance efficacy, and improve pharmacokinetic profiles. These heuristics were usually disarmingly simple and frequently applied primarily through trial and error, but they laid the foundations for the structure-based methodologies that would follow. The structural biology revolution would eventually make the process far more rational, with a vast structural database offering detailed insight into peptide/receptor binding events.

From Structure-Guided Intuition to Display Technologies

Although plenty of hits still emerge from less elegant programmes, publicly accessible structures of endogenous ligands and other molecules bound to a protein of interest remain common starting points today, particularly where the biology is well understood. Short binding motifs lifted from protein-protein interactions provide ideal cassettes of amino acids which can be repurposed directly as peptides or grafted onto known scaffolds.

While design here can utilise sophisticated methods, lower-complexity approaches still have their place and can be surprisingly effective in the right hands – a kind of “in silico guided, in biro design”, where visual inspection of binding sites and intuitive design can still yield meaningful results.

Display technologies have been a part of peptide discovery for decades too. Phage display, in particular, has been a mainstay of many discovery programmes since the early 90s. Modern platforms, however, operate on an entirely different scale, offering far greater chemical flexibility, with mRNA-based approaches extending this even further.

One recent example, which has garnered significant attention and widespread adoption, is the RaPID (Random nonstandard Peptides Integrated Discovery) system. Introduced by Goto and Suga in 2021, this technique combines mRNA display with a reprogrammed translation system that enables the ribosome to synthesise vast libraries of macrocyclic peptides containing non-canonical amino acids. Each peptide remains tethered to its encoding mRNA, allowing direct selection, iterative enrichment, and amplification of binders for a chosen target. As a result, vast libraries (in the order of ~10¹³ molecules) of cyclic peptides can be generated and screened rapidly and at low cost.

Computational Design and Modern Peptide Discovery

Advances in modern computational power, and the depth of collective expertise in the field, have changed the landscape in a different way. Most notably, DeepMind’s AlphaFold and David Baker’s Rosetta programmes have ensured that advanced modelling and design of peptides and proteins is accessible and more affordable.

Structure-informed thinking is now the norm. Docking, molecular dynamics, and machine-learning approaches are routinely exploited to drive innovation and ensure that each round of the DMTA cycle is effective, informed, and rapid.

Iterative Refinement

The cumulative effect of the myriad advances is that there are now many ways to arrive at the design for a first peptide hit, and these techniques are more accessible than ever. While peptide design is often treated as synonymous with computational design methods, this view is too narrow. In reality, any route that produces a testable molecule can be considered part of a design process.

Whether the design for an initial hit arises from a structure-informed tweak, display-based selection, or advanced computational methods matters less than its biological activity, and the fact that the DMTA cycle has something tangible to work with. Early refinement typically focuses on pragmatic considerations such as potency-stability trade-offs, receptor selectivity, obvious protease vulnerabilities, and even downstream manufacturability.

This is usually the point at which a CRO like CatSci becomes most valuable. With a candidate molecule in hand, the focus for the R&D programme steps from invention to refinement, in the form of iterative DMTA cycles. Each cycle starts with questions around Design, shaped by what was learned in the previous DMTA cycle(s). Importantly, DMTA is not a linear process, and it is not a one-off. Rather, it is a cycle that is repeated numerous times, ensuring that design does not end once the lead peptide is in identified, but reappears continuously as data accumulates and the body of knowledge supporting the programme grows.

Design Cannot be in Isolation

At this stage, the design becomes more grounded and pragmatic. With the lead molecule in hand, the central question becomes: how do we design next-generation peptides that will actually deliver real benefit to patients, and that satisfy regulatory requirements so that we can get them to the clinic?

This is where our contribution to design ultimately sits. We work closely with customers to refine designs, synthesise peptides, screen them across a range of assays, and interrogate the resulting data. We then sit down together and work with them to plan the next generation of peptides for their programme.

Sometimes these changes are modest – e.g. a conservative substitution here, a residue removed there, or a small adjustment to charge or hydrophobicity. Other times they are more deliberate, with non-canonical residues and modifications to slow proteolytic degradation, or terminal modification to alter clearance. In every case, each change is a design decision grounded in empirical reasoning.

The critical factor throughout this process, though, is how effectively the peptide programme advances through each and every round of the DMTA cycle, and how quickly each round can be completed. For DMTA to be truly effective, each of the teams responsible for the Design, Make, Test, and Analysis steps need to be strong and aligned with all others in the programme. For example, in many instances designs may be more sophisticated and synthetically challenging – cyclic peptides, highly constrained scaffolds, conjugates, exotic chemistries, etc. – there is almost no limit to the kind of molecules that can be designed. Here, a talented and ambitious integrated team of chemists in the programme allows greater scope for far more ambitious designs to be considered. Similarly, each round of design is only as effective as the data on which it is built. As such, a competent team of biologists able to produce robust and reproducible data is vital.

So, whilst the design step is where the breakthrough molecule may be first conceptualised, in practice, peptide design is so reliant on the other aspects of the DMTA cycle that it becomes effectively inseparable from them.

Design Through Iteration and Refinement

Seen through this lens, the apparent contradiction at the start of this article can be better understood in the context it was intended: we don’t generally design first-generation hits from first principles or do so in the most theoretical of senses. However, we are deeply involved in the design that follows across multiple turns of the DMTA cycle.

Nowadays, when there are more ways than ever to generate a viable first hit, it is often this quieter, pragmatic, data-driven refinement in peptide design – repeated through each DMTA cycle – that provides the greatest impact on the road to regulatory approval and the clinic.

At CatSci, we support peptide programmes through fully integrated Design–Make–Test–Analyse workflows, combining deep expertise in peptide synthesis, modification, and conjugation with robust biological testing and data-driven analysis. By working across disciplines within a single, coordinated team, we help ensure that each round of design refinement delivers clearer insight and more confident progression toward development and the clinic.

If you’d like to explore how integrated DMTA could support your peptide programme, I’d be very happy to discuss your project.