Protein Design Methods Research Articles

Solubility-Aware Protein Binding Peptide Design Using AlphaFold.

New protein–protein interactions (PPIs) are identified, but PPIs have different physicochemical properties compared with conventional targets, making it difficult to use small molecules. Peptides offer a new modality to target PPIs, but designing appropriate peptide sequences by computation is challenging. Recently, AlphaFold and RoseTTAFold have made it possible to predict protein structures from amino acid sequences with ultra-high accuracy, enabling de novo protein design. We designed peptides likely to have PPI as the target protein using the “binder hallucination” protocol of AfDesign, a de novo protein design method using AlphaFold. However, the solubility of the peptides tended to be low. Therefore, we designed a solubility loss function using solubility indices for amino acids and developed a solubility-aware AfDesign binder hallucination protocol. The peptide solubility in sequences designed using the new protocol increased with the weight of the solubility loss function; moreover, they captured the characteristics of the solubility indices. Moreover, the new protocol sequences tended to have higher affinity than random or single residue substitution sequences when evaluated by docking binding affinity. Our approach shows that it is possible to design peptide sequences that can bind to the interface of PPI while controlling solubility.

The road to fully programmable protein catalysis.

The ability to design efficient enzymes from scratch would have a profound effect on chemistry, biotechnology and medicine. Rapid progress in protein engineering over the past decade makes us optimistic that this ambition is within reach. The development of artificial enzymes containing metal cofactors and noncanonical organocatalytic groups shows how protein structure can be optimized to harness the reactivity of nonproteinogenic elements. In parallel, computational methods have been used to design protein catalysts for diverse reactions on the basis of fundamental principles of transition state stabilization. Although the activities of designed catalysts have been quite low, extensive laboratory evolution has been used to generate efficient enzymes. Structural analysis of these systems has revealed the high degree of precision that will be needed to design catalysts with greater activity. To this end, emerging protein design methods, including deep learning, hold particular promise for improving model accuracy. Here we take stock of key developments in the field and highlight new opportunities for innovation that should allow us to transition beyond the current state of the art and enable the robust design of biocatalysts to address societal needs.

Combining chemistry and protein engineering for new-to-nature biocatalysis

Biocatalysis, the application of enzymes to solve synthetic problems of human import, has blossomed into a powerful technology for chemical innovation. In the past decade, a threefold partnership, where nature provides blueprints for enzymatic catalysis, chemists introduce innovative activity modes with abiological substrates, and protein engineers develop new tools and algorithms to tune and improve enzymatic function, has unveiled the frontier of new-to-nature enzyme catalysis. In this perspective, we highlight examples of interdisciplinary studies which have helped to expand the scope of biocatalysis, including concepts of enzymatic versatility explored through the lens of biomimicry, to achieve both activities and selectivities that are not currently possible with chemocatalysis. We indicate how modern tools, such as directed evolution, computational protein design and machine learning-based protein engineering methods, have already impacted and will continue to influence enzyme engineering for new abiological transformations. A sustained collaborative effort across disciplines is anticipated to spur further advances in biocatalysis in the coming years.

Accelerating therapeutic protein design.

Protein structures provide for defined microenvironments that can support complex pharmacological functions, otherwise unachievable by small molecules. The advent of therapeutic proteins has thus greatly broadened the range of manageable disorders. Leveraging the knowledge and recent advances in de novo protein design methods has the prospect of revolutionizing how protein drugs are discovered and developed. This review lays out the main challenges facing therapeutic proteins discovery and development, and how present and future advancements of protein design can accelerate the protein drug pipelines.

De novo protein design by an energy function based on series expansion in distance and orientation dependence.

Despite many successes, de novo protein design is not yet a solved problem as its success rate remains low. The low success rate is largely because we do not yet have an accurate energy function for describing the solvent-mediated interaction between amino acid residues in a protein chain. Previous studies showed that an energy function based on series expansions with its parameters optimized for side-chain and loop conformations can lead to one of the most accurate methods for side chain (OSCAR) and loop prediction (LEAP). Following the same strategy, we developed an energy function based on series expansions with the parameters optimized in four separate stages (recovering single-residue types without and with orientation dependence, selecting loop decoys and maintaining the composition of amino acids). We tested the energy function for de novo design by using Monte Carlo simulated annealing. The method for protein design (OSCAR-Design) is found to be as accurate as OSCAR and LEAP for side-chain and loop prediction, respectively. In de novo design, it can recover native residue types ranging from 38% to 43% depending on test sets, conserve hydrophobic/hydrophilic residues at ∼75%, and yield the overall similarity in amino acid compositions at more than 90%. These performance measures are all statistically significantly better than several protein design programs compared. Moreover, the largest hydrophobic patch areas in designed proteins are near or smaller than those in native proteins. Thus, an energy function based on series expansion can be made useful for protein design. The Linux executable version is freely available for academic users at http://zhouyq-lab.szbl.ac.cn/resources/.

Partial Consensus Design and Enhancement of Protein Function by Secondary-Structure-Guided Consensus Mutations.

Consensus design (CD) is a representative sequence-based protein design method that enables the design of highly functional proteins by analyzing vast amounts of protein sequence data. This study proposes a partial consensus design (PCD) of a protein as a derivative approach of CD. The method replaces the target protein sequence with a consensus sequence in a secondary-structure-dependent manner (i.e., regionally dependent and divided into α-helix, β-sheet, and loop regions). In this study, we generated several artificial partial consensus l-threonine 3-dehydrogenases (PcTDHs) by PCD using the TDH from Cupriavidus necator (CnTDH) as a target protein. Structural and functional analysis of PcTDHs suggested that thermostability would be independently improved when consensus mutations are introduced into the loop region of TDHs. On the other hand, enzyme kinetic parameters (kcat/Km) and average productivity would be synergistically enhanced by changing the combination of the mutations-replacement of one region of CnTDH with a consensus sequence provided only negative effects, but the negative effects were nullified when the two regions were replaced simultaneously. Taken together, we propose the hypothesis that there are protein regions that encode individual protein properties, such as thermostability and activity, and that the introduction of consensus mutations into these regions could additively or synergistically modify their functions.

Lattice protein design using Bayesian learning.

Protein design is the inverse approach of the three-dimensional (3D) structure prediction for elucidating the relationship between the 3D structures and amino acid sequences. In general, the computation of the protein design involves a double loop: A loop for amino acid sequence changes and a loop for an exhaustive conformational search for each amino acid sequence. Herein, we propose a novel statistical mechanical design method using Bayesian learning, which can design lattice proteins without the exhaustive conformational search. We consider a thermodynamic hypothesis of the evolution of proteins and apply it to the prior distribution of amino acid sequences. Furthermore, we take the water effect into account in view of the grand canonical picture. As a result, on applying the 2D lattice hydrophobic-polar (HP) model, our design method successfully finds an amino acid sequence for which the target conformation has a unique ground state. However, the performance was not as good for the 3D lattice HP models compared to the 2D models. The performance of the 3D model improves on using a 20-letter lattice proteins. Furthermore, we find a strong linearity between the chemical potential of water and the number of surface residues, thereby revealing the relationship between protein structure and the effect of water molecules. The advantage of our method is that it greatly reduces computation time, because it does not require long calculations for the partition function corresponding to an exhaustive conformational search. As our method uses a general form of Bayesian learning and statistical mechanics and is not limited to lattice proteins, the results presented here elucidate some heuristics used successfully in previous protein design methods.

Guaranteed Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods

Proteins are the main active molecules of life. Although natural proteins play many roles, as enzymes or antibodies for example, there is a need to go beyond the repertoire of natural proteins to produce engineered proteins that precisely meet application requirements, in terms of function, stability, activity or other protein capacities. Computational Protein Design aims at designing new proteins from first principles, using full-atom molecular models. However, the size and complexity of proteins require approximations to make them amenable to energetic optimization queries. These approximations make the design process less reliable, and a provable optimal solution may fail. In practice, expensive libraries of solutions are therefore generated and tested. In this paper, we explore the idea of generating libraries of provably diverse low-energy solutions by extending cost function network algorithms with dedicated automaton-based diversity constraints on a large set of realistic full protein redesign problems. We observe that it is possible to generate provably diverse libraries in reasonable time and that the produced libraries do enhance the Native Sequence Recovery, a traditional measure of design methods reliability.

The Role of Rigid Residues in Modulating TEM-1 β-Lactamase Function and Thermostability.

The relationship between protein motions (i.e., dynamics) and enzymatic function has begun to be explored in β-lactamases as a way to advance our understanding of these proteins. In a recent study, we analyzed the dynamic profiles of TEM-1 (a ubiquitous class A β-lactamase) and several ancestrally reconstructed homologues. A chief finding of this work was that rigid residues that were allosterically coupled to the active site appeared to have profound effects on enzyme function, even when separated from the active site by many angstroms. In the present work, our aim was to further explore the implications of protein dynamics on β-lactamase function by altering the dynamic profile of TEM-1 using computational protein design methods. The Rosetta software suite was used to mutate amino acids surrounding either rigid residues that are highly coupled to the active site or to flexible residues with no apparent communication with the active site. Experimental characterization of ten designed proteins indicated that alteration of residues surrounding rigid, highly coupled residues, substantially affected both enzymatic activity and stability; in contrast, native-like activities and stabilities were maintained when flexible, uncoupled residues, were targeted. Our results provide additional insight into the structure-function relationship present in the TEM family of β-lactamases. Furthermore, the integration of computational protein design methods with analyses of protein dynamics represents a general approach that could be used to extend our understanding of the relationship between dynamics and function in other enzyme classes.

Breakthroughs in computational design methods open up new frontiers for de novo protein engineering.

Proteins catalyze the majority of chemical reactions in organisms, and harnessing this power has long been the focus of the protein engineering field. Computational protein design aims to create new proteins and functions in silico, and in doing so, accelerate the process, reduce costs and enable more sophisticated engineering goals to be accomplished. Challenges that very recently seemed impossible are now within reach thanks to several landmark advances in computational protein design methods. Here, we summarize these new methods, with a particular emphasis on de novo protein design advancements occurring within the past 5years.

A general Fc engineering platform for the next generation of antibody therapeutics.

Rationale: Fc engineering has become the focus of antibody drug development. The current mutagenesis and in silico protein design methods are confined by the limited throughput and high cost, while the high-throughput phage display and yeast display technologies are not suitable for screening glycosylated Fc variants. Here we developed a mammalian cell display-based Fc engineering platform.Methods: By using mammalian cell display and next generation sequencing, we screened millions of Fc variants for optimized affinity and specificity for FcγRIIIa or FcγRIIb. The identified Fc variants with improved binding to FcγRIIIa were substituted into trastuzumab and rituximab and the effector function of antibodies were examined in the PBMC-based assay. On the other hand, the identified Fc variants with selectively enhanced FcγRIIb binding were applied to CD40 agonist antibody and the activities of the antibodies were measured on different cell assays. The immunostimulatory activity of CD40 antibodies was also evaluated by OVA-specific CD8+ T cell response model in FcγR/CD40-humanized mice.Results: Using this approach, we screened millions of Fc variant and successfully identified several novel Fc variants with enhanced FcγRIIIa or FcγRIIb binding. These identified Fc variants displayed a dramatic increase in antibody-dependent cellular cytotoxicity in PBMC-based assay. Novel variants with selectively enhanced FcγRIIb binding were also identified. CD40 agonist antibodies substituted with these Fc variants displayed activity more potent than the parental antibody in the in vitro and in vivo models.Conclusions: This approach increased the throughput of Fc variant screening from thousands to millions magnitude, enabled screening variants containing multiple mutations and could be integrated with glycoengineering technology, represents an ideal platform for Fc engineering. The initial efforts demonstrated the capability of the platform and the novel Fc variants could be substituted into nearly any antibody for the next generation of antibody therapeutics.

Physics-Based Computational Protein Design: An Update.

We describe methods for physics-based protein design and some recent applications from our work. We present the physical interpretation of a MC simulation in sequence space and show that sequences and conformations form a well-defined statistical ensemble, explored with Monte Carlo and Boltzmann sampling. The folded state energy combines molecular mechanics for solutes with continuum electrostatics for solvent. We usually assume one or a few fixed protein backbone structures and discrete side chain rotamers. Methods based on molecular dynamics, which introduce additional backbone and side chain flexibility, are under development. The redesign of a PDZ domain and an aminoacyl-tRNA synthetase enzyme were successful. We describe a versatile, adaptive, Wang-Landau MC method that can be used to design for substrate affinity, catalytic rate, catalytic efficiency, or the specificity of these properties. The methods are transferable to all biomolecules, can be systematically improved, and give physical insights.

New computational protein design methods for de novo small molecule binding sites.

Protein binding to small molecules is fundamental to many biological processes, yet it remains challenging to predictively design this functionality de novo. Current state-of-the-art computational design methods typically rely on existing small molecule binding sites or protein scaffolds with existing shape complementarity for a target ligand. Here we introduce new methods that utilize pools of discrete contacts between protein side chains and defined small molecule ligand substructures (ligand fragments) observed in the Protein Data Bank. We use the Rosetta Molecular Modeling Suite to recombine protein side chains in these contact pools to generate hundreds of thousands of energetically favorable binding sites for a target ligand. These composite binding sites are built into existing scaffold proteins matching the intended binding site geometry with high accuracy. In addition, we apply pools of side chain rotamers interacting with the target ligand to augment Rosetta’s conventional design machinery and improve key metrics known to be predictive of design success. We demonstrate that our method reliably builds diverse binding sites into different scaffold proteins for a variety of target molecules. Our generalizable de novo ligand binding site design method provides a foundation for versatile design of protein to interface previously unattainable molecules for applications in medical diagnostics and synthetic biology.

Protein Sequence Selection Method That Enables Full Consensus Design of Artificial l-Threonine 3-Dehydrogenases with Unique Enzymatic Properties.

Exponentially increasing protein sequence data enables artificial enzyme design using sequence-based protein design methods, including full-consensus protein design (FCD). The success of artificial enzyme design is strongly dependent on the nature of the sequences used. Hence, sequences must be selected from databases and curated libraries prepared to enable a successful design by FCD. In this study, we proposed a selection approach regarding several key residues as sequence motifs. We used l-threonine 3-dehydrogenase (TDH) as a model to test the validity of this approach. In the classification, four residues (143, 174, 188, and 214) were used as key residues. We classified thousands of TDH homologous sequences into five groups containing hundreds of sequences. Utilizing sequences in the libraries, we designed five artificial TDHs by FCD. Among the five, we successfully expressed four in soluble form. Biochemical analysis of artificial TDHs indicated that their enzymatic properties vary; half of the maximum measured enzyme activity (t1/2) and activation energies were distributed from 53 to 65 °C and from 38 to 125 kJ/mol, respectively. The artificial TDHs had unique kinetic parameters, distinct from one another. Structural analysis indicates that consensus mutations are mainly introduced in the secondary or outer shell. The functional diversity of the artificial TDHs is due to the accumulation of mutations that affect their physicochemical properties. Taken together, our findings indicate that our proposed approach can help generate artificial enzymes with unique enzymatic properties.

Dynamics, a Powerful Component of Current and Future in Silico Approaches for Protein Design and Engineering.

Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development.

Barreling Forward: Advancements in De Novo Protein Design

Natural evolution and protein redesign have only explored a tiny fraction of theoretically possible protein structures. However, de novo protein design will prove powerful in engineering novel proteins with specific functions by comprehensively sampling the protein space using computational methods guided by protein folding principles. De novo protein design involves working backwards from a desired protein with special structure and function to derive an amino acid sequence capable of folding into such a conformation—a challenging approach due to the vast number of protein conformations to evaluate. Here, we present a review of the latest advancements in de novo protein design methods and applications in conjunction with a physical model of a de novo fluorescence‐activating β‐barrel protein. Approaches to de novo protein design include: (1) simulating the folding of a fixed amino acid sequence to determine each residue’s most stable conformation, (2) determining the amino acid sequence that folds into a desired protein conformation, and (3) performing comprehensive rapid evaluations of each possible conformation. This novel protein is then synthesized and validated through in vitro or in vivo assays. In one recent breakthrough by Baker et. al., a de novo approach was used to design a β‐barrel protein comprised of a cylindrical pleated β‐sheet which binds the fluorogenic compound DFHBI. This study is the first instance of a de novo protein designed to bind to a small molecule of interest, with applications in visualizing cell movement, gene expression, DNA replication, protein translation, and tumor progression. In designing this fluorescence activating β‐barrel protein, structural irregularities were introduced to stabilize the structure and enlarge the β‐barrel cavity. Subsequently, the optimal sequences for β‐barrels with reasonable binding site and affinity for DFHBI were designed by computationally adjusting individual residue conformations. Finally, this optimized protein was synthesized, which had greater affinity with DFHBI and increased fluorescence in in vitro and in vivo experiments.Support or Funding InformationThis is a SMART Team project supported through the contributions of Dr. Jinan Wang of the University of Kansas Center for Computational Biology, the Milwaukee School of Engineering, and the Olathe Medical Professions 21st Century Academy.

DenseCPD: Improving the Accuracy of Neural-Network-Based Computational Protein Sequence Design with DenseNet.

Computational protein design remains a challenging task despite its remarkable success in the past few decades. With the rapid progress of deep-learning techniques and the accumulation of three-dimensional protein structures, the use of deep neural networks to learn the relationship between protein sequences and structures and then automatically design a protein sequence for a given protein backbone structure is becoming increasingly feasible. In this study, we developed a deep neural network named DenseCPD that considers the three-dimensional density distribution of protein backbone atoms and predicts the probability of 20 natural amino acids for each residue in a protein. The accuracy of DenseCPD was 53.24 ± 0.17% in a 5-fold cross-validation on the training set and 55.53% and 50.71% on two independent test sets, which is more than 10% higher than those of previous state-of-the-art methods. Two approaches for using DenseCPD predictions in computational protein design were analyzed. The approach using the cutoff of accumulative probability had a smaller sequence search space compared with the approach that simply uses the top-k predictions and therefore enabled higher sequence identity in redesigning three proteins with Rosetta. The network and the datasets are available on a web server at http://protein.org.cn/densecpd.html. The results of this study may benefit the further development of computational protein design methods.

Multimedia Jupyter Notebooks for Learning Structure Prediction and Design

PyRosetta is a Python library of the Rosetta computational methods for protein structure prediction and design. Because the field attracts students from many different majors, teaching a class with a wide variety of skills and experience levels is a challenge. Therefore, the goal of this project is to develop interactive multimedia material that promotes self-paced active learning. We have developed a hands-on education strategy with a set of nine modules to teach topics in the field, from protein structural analysis to protein folding and docking. These workshop modules are created in Jupyter Notebooks, a shareable web application that supports live code, visualizations, and text. When used in the Google Colaboratory environment, students can access and learn PyRosetta with no local machine setup necessary. By capitalizing on the advantages of this online digital format, we have embedded images, molecular visualization movies, and interactive coding examples. This multimedia approach may better reach students from different majors and experience levels as well as attract more researchers from smaller labs and related disciplines to leverage PyRosetta in their work.

Comparison of Rosetta flexible-backbone computational protein design methods on binding interactions.

Computational design of binding sites in proteins remains difficult, in part due to limitations in our current ability to sample backbone conformations that enable precise and accurate geometric positioning of side chains during sequence design. Here we present a benchmark framework for comparison between flexible-backbone design methods applied to binding interactions. We quantify the ability of different flexible backbone design methods in the widely used protein design software Rosetta to recapitulate observed protein sequence profiles assumed to represent functional protein/protein and protein/small molecule binding interactions. The CoupledMoves method, which combines backbone flexibility and sequence exploration into a single acceptance step during the sampling trajectory, better recapitulates observed sequence profiles than the BackrubEnsemble and FastDesign methods, which separate backbone flexibility and sequence design into separate acceptance steps during the sampling trajectory. Flexible-backbone design with the CoupledMoves method is a powerful strategy for reducing sequence space to generate targeted libraries for experimental screening and selection.

Approaching protein design with multisite λ dynamics: Accurate and scalable mutational folding free energies in T4 lysozyme.

The estimation of changes in free energy upon mutation is central to the problem of protein design. Modern protein design methods have had remarkable success over a wide range of design targets, but are reaching their limits in ligand binding and enzyme design due to insufficient accuracy in mutational free energies. Alchemical free energy calculations have the potential to supplement modern design methods through more accurate molecular dynamics based prediction of free energy changes, but suffer from high computational cost. Multisite λ dynamics (MSλD) is a particularly efficient and scalable free energy method with potential to explore combinatorially large sequence spaces inaccessible with other free energy methods. This work aims to quantify the accuracy of MSλD and demonstrate its scalability. We apply MSλD to the classic problem of calculating folding free energies in T4 lysozyme, a system with a wealth of experimental measurements. Single site mutants considering 32 mutations show remarkable agreement with experiment with a Pearson correlation of 0.914 and mean unsigned error of 1.19 kcal/mol. Multisite mutants in systems with up to five concurrent mutations spanning 240 different sequences show comparable agreement with experiment. These results demonstrate the promise of MSλD in exploring large sequence spaces for protein design.