Proteins are now being rationally designed to revolutionize fields from medicine to food. Cutting-edge computational methods are enabling the creation of tailor-made proteins with enhanced stability, novel functions, and the potential to address major challenges facing humanity.

Proteins are often referred to as “the building blocks of life”. This relies on the fundamental role proteins play in the structure, function, and regulation of cells and tissues in all living organisms. Virtually every aspect of life, from growth and development to metabolism and immunity, relies on proteins. Dr. Naama Kopelman 2024 From enzymes catalyzing chemical reactions to antibodies defending against pathogens, proteins are ubiquitous in nature, serving as the workhorses of cellular machinery. The universal presence of proteins underscores their fundamental importance, making protein design a cornerstone of scientific exploration and technological innovation.


During protein biosynthesis, i.e. the creation of a new protein, the DNA sequence of a gene is transcribed into messenger RNA (mRNA), and the mRNA is decoded by ribosomes to synthesize a specific protein. Proteins serve as the primary source of amino acids, which are the building blocks required for protein synthesis within the body.

The proteins we eat are broken down into amino acids during digestion and absorbed into the bloodstream. They are then transported to various tissues and organs and utilized by the cells. In fact, insufficient consumption of proteins can lead to several physiological consequences such as muscle loss, weakened immune function, and impaired hormone production.

As early as 1987, researchers sought to create novel proteins, setting the grounds for a new era of designing new proteins with practical applications and uses. Such newly designed proteins are generally based and inspired by proteins found in the wild, as the natural abundance of proteins, both sequence- wise and structure-wise, is staggering.

A string of different Amino Acids forming a protein

In the 90s, the field of protein design began to take shape with the development of computational methods for predicting protein stability and designing novel protein structures. A landmark achievement was the design of a α- helical protein (i.e a protein containing a helical 3D structural element) by DeGrado and colleagues in 1991, demonstrating the feasibility of rational protein design, an approach used to design

proteins with desired properties by making specific, targeted modifications. Unlike directed evolution, which relies on random mutagenesis and selection, rational protein design involves a systematic, hypothesis- driven approach guided by principles of molecular biology, biochemistry, and biophysics.


Computational Protein Design (CPD) has become a powerful tool for designing new proteins with specific functions. CPD

incorporates bioinformatic and computational biophysics tools to gather data that is fed into various computational methods that allow for structure prediction, thermodynamic analysis, dynamics simulations of protein movement (molecular dynamics) and more.

Computational methods have been developed to predict protein structures, design new proteins, and optimize proteins for stability and function. Herein, stability may include short- and long-time stability of heat, cold, acid, or the presence of fat, proteins or other materials in the proteins’ microenvironment.

These tools have been used to design proteins with applications in medicine, energy, materials science, food science and more. The stability of designer proteins is of utmost importance,. High temperatures can lead proteins to the lose of the protein’s functional folded 3D structure.


The future of CPD is bright. As computational methods continue to improve, computers become more robust and artificial intelligence methods become more advanced and widespread in the field of protein design, it will become possible to design proteins with even more complex functions and interactions.

In fact, already today, structure prediction, i.e. the prediction of a protein folded structure based on the protein sequence, is already a largely solved computational problem. CPD, also called ‘the inverse folding problem’ is a far more complex challenge as given a requested structure and characterizations of it, one needs to search sequence and structure space to find the unique protein sequence that will fold into the requested protein.

One area of research that will surely benefit is the design of proteins that can be used as drugs. Already today, protein drugs are the drugs with the largest sales. Designed proteins certainly have a place in the battle against climate change as proteins and enzymes may be designed to efficiently remove carbon dioxide from the atmosphere.


Edible proteins are required to be tasty as well as super stable, as food is often processed in manners which are extremely challenging to stability, such as heating, acidic environment, long shelf-life and more. In recent years, the food industry has embraced alternative proteins to meet the growing demand for sustainable, healthy, and ethical nutritious food options for the

meat, milk, egg, plant, dietary supplement, and enzyme industry. In this approach, specific microorganisms are taught to produce desired compounds or proteins with high precision and efficiency.

However, proteins found in the wild are often not sufficiently stable or cannot be produced cost- efficiently for the harsh environment of the mass food market. Therefore, there is a need to learn from proteins that reside in harsh environments, termed ‘extremophiles’ e.g. the Dead Sea, hot springs, deep ocean vents, acidic swamps and alike. Designing an extremophile using CPD will allow to enjoy the amazing functionalities of proteins in a manner fit for the mass food and beverage market as to taste, stability, hypoallergenicity and cost.


Precision fermentation can facilitate the production of a wide range of products, from alternative proteins to therapeutic proteins and enzymes. Combining rational computational protein design with precision fermentation, the startup company Amai Proteins designs and manufactures sweet proteins as well as other proteins for the food industry. The company is

using an agile-integrative computational pipeline incorporating machine learning along with a wide array of sequence and structural approaches. Amai Proteins is launching its first product this year: a designer sweet protein which is on average 3,000 times sweeter than sugar, with a clean taste and excellent stability, making it highly suitable for the the mass food market. The protein will be marketed under the brand name ‘sweelin®’.

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