Next Generation Folding Architecture: De Novo Protein Design

Minel Cengiz^T., Elif Duymaz^E.

Proteins, one of the four groups of macromolecules necessary for the continuation of life, are initially linear polypeptide chains called primary structure and formed by the combination of amino acids in a certain order. Proteins perform a wide variety of functions within organisms, including copying DNA, responding to stimuli, providing structure and organisms to cells, and transporting molecules from one location to another; it also has the ability to naturally create spatial, that is, 3D (3 Dimension) structures^1,2. Proteins classified into two groups, structural and functional, exhibit secondary, tertiary or quaternary structure configurations depending on sequence length and amino acid content; however, they are key players that play a role in the realization of all physiological and metabolic processes in living organisms^3,4. The structure of a protein is of a complex nature. Understanding this complex formation, which consists of the unique polypeptide sequence and multiple domains as well as the participation of side chains, is important in revealing the relationship between structure and function^5-8. Physics-based (X-ray crystallography, nuclear magnetic resonance spectroscopy, etc.) or computational approaches (machine learning, deep learning, artificial intelligence) developed for protein structure prediction have made great strides in the field of structural biology. At the present time, high accuracy rates can be obtained in estimating the 3D structures of proteins from the amino acid sequence. Structure-based models and interdisciplinary applications of computational science are shown as the most important factor in revealing more and more functional properties of proteins ^9-11.

There are many natural proteins that have evolved over billions of years of evolutionary optimization. Despite the highly sophisticated developments in the structural predictions of proteins, due to both the burdens they carry in terms of time and cost, and the margin of error arising from the structural complexity of proteins, a great effort is made for alternative solutions in fields of science that mimic nature and natural processes such as synthetic biology. 

Protein design is a computational engineering application with a theoretical approach to 3D structure prediction, aiming to create structures at the same level of development with the present  information in the desired time without waiting for temporal and evolutionary changes^12-14. This approach, which started with the redesign of natural proteins, opened the doors to the world of de novo protein design, which is based on the reconstruction of a protein with a predetermined function on a synthetic scaffold, in order to perceive the relationship between structure and function in a wider perspective ^15-17. In this review, aims to evaluate the recent developments in the principles and application areas of de novo protein design.

1.   De novo Protein Design Principles

In the design of a protein whose amino acid sequence and structure are unknown, there is the possibility of 20ⁿ linear peptide sequence containing n number of residues of 20 amino acids found in nature. For this reason, research has advanced in the direction of designing proteins whose dynamics of folding, biochemistry and biophysics are known¹⁸. De novo design of proteins generally consists of two steps. First, a target structural model, called the scaffold or spine, needs to be established. Second, the determination of the amino acid sequence and side chains covering all residue positions in this structure is performed that is, an outline must be established in the first steps^19,20. The physical basis of the design aims to fold proteins in their lowest free energy states. Hydrophobic residues in the protein nucleus modulate protein folding in a solvent-free condition. The side chains in the nucleus need to be tightly packed to minimize the volumetric size occupied by the protein and to obtain the highest Van der Waals forces (Figure 1)^21-23. 

Figure 1. Energy dynamics in protein folding²¹.

In the light of the basic principles of de novo protein design, its historical development took place in 3 stages. Manual design based on a physical model, physicochemical-based computational design, and finally fragment and bioinformatics-based computational design²⁴.

1.1. Manual Protein Design

A ribonuclease with 34 amino acid residues and ribonuclease activity, designed and synthesized by Gutte and colleagues in 1979 based on X-ray crystallography data, is known as the origin of de novo proteins. The starting point of the study was based on amino acid sequence prediction from the 3D structure of Escherichia coli (E. coli) lac repressor published in 1973 and 1975²⁵. After this success, although studies on optimization of the resolution in manual protein design were carried out in the 1980s, no results beyond the draft for further stages could be obtained²⁴.

1.2. Physicochemical Based Computational Protein Design

In protein design, which is realized by characterizing its physical and chemical properties, it is aimed to determine the conformation of protein backbones with mathematical equations as a general principle.The design of three- and four-stranded bundles has passed into the history of synthetic biology as precursors to computational protein design (CPD) based on physicochemistry^26-28. In a series of studies initiated by Lombardi and his colleagues, the first de novo designs^29-31 of metallo proteins of helical coils were indicative of the important role of computational methods in addressing fundamental deficiencies in the folding problems of proteins.

1.3. Trailer and Bioinformatics Based Computational Protein Design

Each of the conformations in packaging the side chains in the protein is called a rotamer. Determining the most appropriate rotamers involves the identification of side chain and chemical group positions associated with the protein main chain by leveraging structural information in the Protein Data Bank (PDB). Then, with the dead-end elimination algorithm, belief propagation, integer linear programming, and simulated annealing, various variations of Markov Chain Monte Carlo (MCMC) methods and optimization of rheumater and amino acid residues are carried out³².

For the optimization of energy functions according to the side chains of the protein backbone to be designed, physics-based methods such as RosettaDesign, EvoEF2, PROTEUS or statistics-based methods such as TERM and ABACUS can be used³³. RosettaDesign has also been distinguished by its side chain conformations and the possibility of screening suitable amino acid sequences^34-36. The most important reason for this is that the first fragment-based computational method successfully designed the TOP7 artificial 93-residue protein³⁷. Current developments have made it possible to create and use PDB-derived spine fragment libraries in determining backbone-dependent rotators of de novo proteins^38-40.

Figure 2. Example of computational protein design generated using the Rotamer library⁴⁰.

Developed to create more flexible backbones in fragment-based CPD, RosettaRemodel⁴¹ has provided the researchers with a wide range of engineering capabilities, allowing them to still successfully implement this method today^42-44. However, when protein design is considered a folding puzzle, a significant limitation of fragment-based approaches is that it can restrict the ability to explore a wide variety of protein folds due to the relatively unrepresentative content of part libraries.Another disadvantage is that their use does not fully improve the understanding of the structure relationship from the array^45,46. Protein design is important beyond understanding protein dynamics and for the development of industrial and new therapeutics. So far with CPD, vaccines, new protein mechanisms, antibodies, membrane proteins, new folds, engineering products such as ligand binding proteins have been successfully demonstrated^47,48. Recent advances in synthetic biology in de novo protein design have increased interest in protein structure prediction algorithms, which help to understand the 3D structure and folding architectures of proteins, and thus bioinformatic tools⁴⁹.

AlphaFold⁵⁰ and RoseTTAFold⁵¹ have recently produced effective solutions regarding the nature of protein folding, which have been attracting attention with accurate structure predictions. AlphaFold, developed by DeepMind, is designed as a deep learning algorithm that uses multiple sequence alignments (MSAs) and distograms as parameters. In the 13^th Critical Assessment of Structure Prediction (CASP13) competition, free modeling (FM) generated a lot of excitement by predicting 25 of the 43 proteins with an accuracy of 6.6Â (angstrom); however, both the time cost and the resolution problems encountered led to breakthroughs in the development of the algorithm^52-54. In CASP14, AlphaFold2, which identifies protein folding patterns, was developed using an attention mechanism and a transformer-based neural network architecture, and predicted the median backbone of proteins with higher atomic accuracy compared to experimentally determined structures. Then another team designed a three-channel neural network architecture based on deep learning algorithms of Rosetta and AlphaFold2, which enables sequential transformation and integration of data in the array, the distance between pairs of amino acids, and coordinate planes, and named RoseTTAFold^51,58. The common feature of these two methods, which use folding patterns for structural prediction in the protein structure-function relationship, is that they use an MSA-based approach. The problem with substituting missing data in amino acid sequences has resulted in the development of ESMFold as a result of using a language learning approach with evolutionary scale Modeling (ESM) for 3D structure prediction of proteins. Making structure predictions using just one amino acid sequence instead of MSA has provided concrete evidence of the contribution of artificial intelligence (AI) in the protein world ^59,60. An example of the effects of 3D structure prediction algorithms of proteins on de novo protein design is the development of the AlphaDesign design framework. This method, which can design de novo monomer proteins with ab initio structure prediction based on fragment assembly, has carried out data verification with molecular dynamic simulations and Rosetta approaches⁴⁹. The ADesign method was then developed, which provides large-scale comparison and validation for use in the design of amino acid sequences from AlphaFold databases and AlphaDesign-based protein structures. In this method, the confidence-sensitive protein decoder (CPD) and the simplified graphical converter encoder (SGT) are introduced as new features in the sequence determination of protein angles. When the performance of the training set with geometric vector sensors (GVPs) compared to GraphTrans and StructGNN (graphical neural network-based models) was evaluated, the result was ADesign ≈ StructGNN > GraphTrans > GVP (Figure 3)⁶¹.

Figure 3. Schematic representation of the ADesign workflow⁶¹.

2. De novo Protein Design and Therapies

The de novo protein design has generally led to the production of designer-proteins that do not share homology with natural amino acid sequences¹⁸. So far, the de novo design of proteins with catalysis, binding, and membrane transition functions has been successfully performed^62-64. Thanks to the algorithms used in the design and the pulses in the field of biophysics, it has been possible to create protein structures that have been developed with unique or theoretically superior biological and chemical properties or have gained new functions^20,65-67. With the contributions of computational sciences, de novo protein design has been used in many applications, especially in the biomedical field⁶⁸. The de novo design process of a drug is shown in Figure 4⁶⁹. The de novo design of molecules aims to create the best model of a molecule’s chemical profile. This method, also called productive chemistry, works to achieve standardization by creating optimized pharmacokinetic properties, especially in drug discoveries⁷⁰. Today, a different point has been reached in medicine thanks to therapeutics developed with biomacromolecules such as peptides, proteins, or amino acids, including the transport of small molecules^71-72. The biological and physicochemical properties of de novo designed proteins are shaped according to the method applied by the researcher; however, chemical reactions that are not yet understood in nature aim to adapt to organisms and obtain more stable proteins. This approach covers not only biomolecules and artificial components, but also biomolecules engineering and sheds light on the future⁷³. Proteins are structurally highly dynamic molecules. This situation is seen as a significant challenge in the absence of templates that will allow the exact nature of proteins to be determined. As a solution to this issue, a study by Sesterhenn and colleagues aimed to determine the topological motifs of proteins in order to develop vaccines with de novo protein design.With the TopoBuilder application they developed, an effective protein design is aimed by combining adaptive protein topologies with differential functional motifs⁷⁴. In vaccine studies, Correira et al. used the Rosetta method to produce topology-based de novo immunogens to create stable functional motifs that trigger the production of neutralising antibodies against the respiratory syncytial virus (RSV)⁷⁵. Against SARS-CoV-2 infection, Cao and colleagues designed small, stable proteins that prevent the pathogen from binding spike protein to the host cell. Synthesized de novo proteins bind with high affinity to the pathogen-interacting region in mammalian Vero E6 cells, preventing SARS-CoV-2 from entering the cell⁷⁶.

Chevalier and colleagues have created a large pool of de novo protein designs that act as inhibitors for influenza a H1 hemagglutinin and botulinum neurotoxin B. Researchers have shown that a large number of design proteins can be tested thanks to advanced experimental techniques and advances in bioinformatics tools. There is considerable research on the design of de novo proteins in cancer treatment⁷⁷. The supply/demand imbalance, especially in immunotherapy agents, has led to a drive towards meeting these therapeutic agents with their synthetic equivalents. For example, interleukin-2 (IL-2), which has limited clinical use in cancer treatment due to its toxic side effects, has been designed with a de vono protein called neoleukin-2/15 (Neo-2/15), which is free of the side effect that mimics IL-15, which is found in the same signaling pathway. In the mouse model developed with colorectal carcinoma and melanoma, Neo-2/15 was found to activate immune cells, slow tumor growth, and do not show side effects of IL-2 in a dose-independent manner⁷⁸. There are also experimental studies on the design of various de novo proteins, such as map kinase inhibitors ⁷⁹, histone methyltransferase⁸⁰, SHP2⁸¹ and ROCK⁸² inhibitors in cancer treatment. In addition, studies are being carried out to improve the biocompatibility of antimicrobial peptides (MPPs) used against antibiotic resistance⁸³. With the logic gates called co-LOCKR, it has been shown that the tissue-specific effects of CAR-T treatment can be increased ^84,85. Proteins are specific structures in terms of their curvature and conformation structure. Therefore, they are faced with difficulties in resolving their own characteristics. At this point, the de novo technique, developed for both existing and newly discovered proteins, aims to produce protein designs by mimicking naturally occurring proteins. This system, which will be used in many areas, is developing further in every period and sheds light on the future.

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