CPred: a web server for predicting viable circular permutations in proteins

Protein engineering using circular permutation - structure, function, stability, and applications

Protein engineering is important for creating novel variants from natural proteins, enabling a wide range of applications. Approaches such as rational design and directed evolution are routinely used to make new protein variants. Computational tools like de novo design can introduce new protein folds. Expanding the amino acid repertoire to include unnatural amino acids with non-canonical side chains in vitro by native chemical ligation and in vivo via codon expansion methods broadens sequence and structural possibilities. Circular permutation (CP) is an invaluable approach to redesigning a protein by rearranging the amino acid sequence, where the connectivity of the secondary structural elements is altered without changing the overall structure of the protein. Artificial CP proteins (CPs) are employed in various applications such as biocatalysis, sensing of small molecules by fluorescence, genome editing, ligand-binding protein switches, and optogenetic engineering. Many studies have shown that CP can lead to either reduced or enhanced stability or catalytic efficiency. The effects of CP on a protein's energy landscape cannot be predicted a priori. Thus, it is important to understand how CP can affect the thermodynamic and kinetic stability of a protein. In this review, we discuss the discovery and advancement of techniques to create protein CP, and existing reviews on CP. We delve into the plethora of biological applications for designed CP proteins. We subsequently discuss the experimental and computational reports on the effects of CP on the thermodynamic and kinetic stabilities of proteins of various topologies. An understanding of the various aspects of CP will allow the reader to design robust CP proteins for their specific purposes.

Construction and characterization of a functional variant hFGF7 with enhanced properties by circular permutation

Human fibroblast growth factor 7 (hFGF7) is a member of the paracrine-acting FGF family and mediates various reactions such as wound healing, tissue homeostasis, and liver regeneration. These activities make it a plausible candidate for pharmaceutical applications as a drug. However, the low expression level and stability of the recombinant hFGF7 were known to be major hurdles for further applications. Here, the expression level and stability of hFGF7 were attempted to improve by changing the order of amino acids through circular permutation (CP), thereby expecting an alternative fate according to the N-end rule. CP-hFGF7 variants were constructed systematically by using putative amino acid residues in the loop region that avoided the disruption of the structural integrity especially in the functional motif. Among them, cp-hFGF7115-114 revealed a relatively higher expression level in the soluble fraction than the wild-type hFGF7 and was efficiently purified (7 mg L-1) to apparent homogeneity. The activity and stability of the purified variant cp-hFGF7115-114 were comparable or superior to that of the wild-type hFGF7, thereby strongly suggesting that CP could be an alternative tool for the functional expression of hFGF7 in Escherichia coli.

Engineering protein and DNA tools for creating DNA-dependent protein switches

Switchable proteins are capable of changing conformations from inactive (OFF) to active (ON) forms in response to inputs such as ligand binding, pH or temperature change, or light absorption. A particularly powerful class of protein switches, exemplified by the Cas nucleases of CRISPR systems, are activated by binding of specific DNA or RNA sequences. The mechanism by which oligonucleotide binding regulates biological activity is complex and highly specialized in the case of Cas enzymes, but recent advancements in protein and DNA engineering have made it possible to introduce this mode of control into other enzymes. This chapter highlights recent examples of protein switches that combine these two fields of engineering for the purpose of creating biosensors that detect pathogen and other genomic sequences. One protein engineering method-alternate frame folding-has the potential to convert many proteins into ligand-activated switches by inserting a binding protein (input domain) into an enzyme (output domain). The steps for doing so are illustrated using GCN4 as a DNA recognition domain and nanoluciferase as a luminescent reporter that changes color as a result of DNA binding. DNA engineering protocols are included for creating DNA tools (de novo designed hairpins and modified aptamers), that enable the biosensor to be activated by arbitrary DNA/RNA sequences and small molecules/proteins, respectively. These methodologies can be applied to other proteins to gain control of their functions by DNA binding.

Discovering the Ultimate Limits of Protein Secondary Structure Prediction

Secondary structure prediction (SSP) of proteins is an important structural biology technique with many applications. There have been ~300 algorithms published in the past seven decades with fierce competition in accuracy. In the first 60 years, the accuracy of three-state SSP rose from ~56% to 81%; after that, it has long stayed at 81–86%. In the 1990s, the theoretical limit of three-state SSP accuracy had been estimated to be 88%. Thus, SSP is now generally considered not challenging or too challenging to improve. However, we found that the limit of three-state SSP might be underestimated. Besides, there is still much room for improving segment-based and eight-state SSPs, but the limits of these emerging topics have not been determined. This work performs large-scale sequence and structural analyses to estimate SSP accuracy limits and assess state-of-the-art SSP methods. The limit of three-state SSP is re-estimated to be ~92%, 4–5% higher than previously expected, indicating that SSP is still challenging. The estimated limit of eight-state SSP is 84–87%. Several proposals for improving future SSP algorithms are made based on our results. We hope that these findings will help move forward the development of SSP and all its applications.

CirPred, the first structure modeling and linker design system for circularly permuted proteins

Background

This work aims to help develop new protein engineering techniques based on a structural rearrangement phenomenon called circular permutation (CP), equivalent to connecting the native termini of a protein followed by creating new termini at another site. Although CP has been applied in many fields, its implementation is still costly because of inevitable trials and errors.

Results

Here we present CirPred, a structure modeling and termini linker design method for circularly permuted proteins. Compared with state-of-the-art protein structure modeling methods, CirPred is the only one fully capable of both circularly-permuted modeling and traditional co-linear modeling. CirPred performs well when the permutant shares low sequence identity with the native protein and even when the permutant adopts a different conformation from the native protein because of three-dimensional (3D) domain swapping. Linker redesign experiments demonstrated that the linker design algorithm of CirPred achieved subangstrom accuracy.

Conclusions

The CirPred system is capable of (1) predicting the structure of circular permutants, (2) designing termini linkers, (3) performing traditional co-linear protein structure modeling, and (4) identifying the CP-induced occurrence of 3D domain swapping. This method is supposed helpful for broadening the application of CP, and its web server is available at http://10.life.nctu.edu.tw/CirPred/ and http://lo.life.nctu.edu.tw/CirPred/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04403-1.

A secondary structure-based position-specific scoring matrix applied to the improvement in protein secondary structure prediction

Protein secondary structure prediction (SSP) has a variety of applications; however, there has been relatively limited improvement in accuracy for years. With a vision of moving forward all related fields, we aimed to make a fundamental advance in SSP. There have been many admirable efforts made to improve the machine learning algorithm for SSP. This work thus took a step back by manipulating the input features. A secondary structure element-based position-specific scoring matrix (SSE-PSSM) is proposed, based on which a new set of machine learning features can be established. The feasibility of this new PSSM was evaluated by rigid independent tests with training and testing datasets sharing <25% sequence identities. In all experiments, the proposed PSSM outperformed the traditional amino acid PSSM. This new PSSM can be easily combined with the amino acid PSSM, and the improvement in accuracy was remarkable. Preliminary tests made by combining the SSE-PSSM and well-known SSP methods showed 2.0% and 5.2% average improvements in three- and eight-state SSP accuracies, respectively. If this PSSM can be integrated into state-of-the-art SSP methods, the overall accuracy of SSP may break the current restriction and eventually bring benefit to all research and applications where secondary structure prediction plays a vital role during development. To facilitate the application and integration of the SSE-PSSM with modern SSP methods, we have established a web server and standalone programs for generating SSE-PSSM available at http://10.life.nctu.edu.tw/SSE-PSSM.

The influence of dataset homology and a rigorous evaluation strategy on protein secondary structure prediction

The secondary structure prediction (SSP) of proteins has long been an essential structural biology technique with various applications. Despite its vital role in many research and industrial fields, in recent years, as the accuracy of state-of-the-art secondary structure predictors approaches the theoretical upper limit, SSP has been considered no longer challenging or too challenging to make advances. With the belief that the substantial improvement of SSP will move forward many fields depending on it, we conducted this study, which focused on three issues that have not been noticed or thoroughly examined yet but may have affected the reliability of the evaluation of previous SSP algorithms. These issues are all about the sequence homology between or within the developmental and evaluation datasets. We thus designed many different homology layouts of datasets to train and evaluate SSP prediction models. Multiple repeats were performed in each experiment by random sampling. The conclusions obtained with small experimental datasets were verified with large-scale datasets using state-of-the-art SSP algorithms. Very different from the long-established assumption, we discover that the sequence homology between query datasets for training, testing, and independent tests exerts little influence on SSP accuracy. Besides, the sequence homology redundancy between or within most datasets would make the accuracy of an SSP algorithm overestimated, while the redundancy within the reference dataset for extracting predictive features would make the accuracy underestimated. Since the overestimating effects are more significant than the underestimating effect, the accuracy of some SSP methods might have been overestimated. Based on the discoveries, we propose a rigorous procedure for developing SSP algorithms and making reliable evaluations, hoping to bring substantial improvements to future SSP methods and benefit all research and application fields relying on accurate prediction of protein secondary structures.

Resources and Methods for Engineering "Designer" Glycan-Binding Proteins

This review provides information on available methods for engineering glycan-binding proteins (GBP). Glycans are involved in a variety of physiological functions and are found in all domains of life and viruses. Due to their wide range of functions, GBPs have been developed with diagnostic, therapeutic, and biotechnological applications. The development of GBPs has traditionally been hindered by a lack of available glycan targets and sensitive and selective protein scaffolds; however, recent advances in glycobiology have largely overcome these challenges. Here we provide information on how to approach the design of novel "designer" GBPs, starting from the protein scaffold to the mutagenesis methods, selection, and characterization of the GBPs.

A simple strategy to enhance the speed of protein secondary structure prediction without sacrificing accuracy

The secondary structure prediction of proteins is a classic topic of computational structural biology with a variety of applications. During the past decade, the accuracy of prediction achieved by state-of-the-art algorithms has been >80%; meanwhile, the time cost of prediction increased rapidly because of the exponential growth of fundamental protein sequence data. Based on literature studies and preliminary observations on the relationships between the size/homology of the fundamental protein dataset and the speed/accuracy of predictions, we raised two hypotheses that might be helpful to determine the main influence factors of the efficiency of secondary structure prediction. Experimental results of size and homology reductions of the fundamental protein dataset supported those hypotheses. They revealed that shrinking the size of the dataset could substantially cut down the time cost of prediction with a slight decrease of accuracy, which could be increased on the contrary by homology reduction of the dataset. Moreover, the Shannon information entropy could be applied to explain how accuracy was influenced by the size and homology of the dataset. Based on these findings, we proposed that a proper combination of size and homology reductions of the protein dataset could speed up the secondary structure prediction while preserving the high accuracy of state-of-the-art algorithms. Testing the proposed strategy with the fundamental protein dataset of the year 2018 provided by the Universal Protein Resource, the speed of prediction was enhanced over 20 folds while all accuracy measures remained equivalently high. These findings are supposed helpful for improving the efficiency of researches and applications depending on the secondary structure prediction of proteins. To make future implementations of the proposed strategy easy, we have established a database of size and homology reduced protein datasets at http://10.life.nctu.edu.tw/UniRefNR.

How to Untie a Protein Knot

The origin of protein backbone threading through a topological knot remains elusive. To understand the evolutionary origin of protein knots, in this issue of StructureKo et al. (2019) used circular permutation to untie a knotted protein. They showed that a domain-swapped dimer releases the knot and the associated high-energy state for substrate binding.

Tandem domain swapping: determinants of multidomain protein misfolding

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Domain swapping refers to the exchange of structural elements between protein domains.

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Experiments show that tandem homologous domains are prone to domain swapping.

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Recent studies establish a framework to understand the formation of tandem domain swaps.

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Prediction of tandem domain swaps is possible but hindered by the amount of available data.

Tandem homologous domains in proteins are susceptible to misfolding through the formation of domain swaps, non-native conformations involving the exchange of equivalent structural elements between adjacent domains. Cutting-edge biophysical experiments have recently allowed the observation of tandem domain swapping events at the single molecule level. In addition, computer simulations have shed light into the molecular mechanisms of domain swap formation and serve as the basis for methods to systematically predict them. At present, the number of studies on tandem domain swaps is still small and limited to a few domain folds, but they offer important insights into the folding and evolution of multidomain proteins with applications in the field of protein design.

Circularly permuted variants of two CG-specific prokaryotic DNA methyltransferases

The highly similar prokaryotic DNA (cytosine-5) methyltransferases (C5-MTases) M.MpeI and M.SssI share the specificity of eukaryotic C5-MTases (5'-CG), and can be useful research tools in the study of eukaryotic DNA methylation and epigenetic regulation. In an effort to improve the stability and solubility of complementing fragments of the two MTases, genes encoding circularly permuted (CP) variants of M.MpeI and M.SssI were created, and cloned in a plasmid vector downstream of an arabinose-inducible promoter. MTase activity of the CP variants was tested by digestion of the plasmids with methylation-sensitive restriction enzymes. Eleven of the fourteen M.MpeI permutants and six of the seven M.SssI permutants had detectable MTase activity as indicated by the full or partial protection of the plasmid carrying the cpMTase gene. Permutants cp62M.MpeI and cp58M.SssI, in which the new N-termini are located between conserved motifs II and III, had by far the highest activity. The activity of cp62M.MpeI was comparable to the activity of wild-type M.MpeI. Based on the location of the split sites, the permutants possessing MTase activity can be classified in ten types. Although most permutation sites were designed to fall outside of conserved motifs, and the MTase activity of the permutants measured in cell extracts was in most cases substantially lower than that of the wild-type enzyme, the high proportion of circular permutation topologies compatible with MTase activity is remarkable, and is a new evidence for the structural plasticity of C5-MTases. A computer search of the REBASE database identified putative C5-MTases with CP arrangement. Interestingly, all natural circularly permuted C5-MTases appear to represent only one of the ten types of permutation topology created in this work.

Computational Prediction of New Intein Split Sites

Split inteins have emerged as a powerful tool in protein engineering. We describe a reliable in silico method to predict viable split sites for the design of new split inteins. A computational circular permutation (CP) prediction method facilitates the search for internal permissive sites to create artificial circular permutants. In this procedure, the original amino- and carboxyl-termini are connected and new termini are created. The identified new terminal sites are promising candidates for the generation of new split sites with the backbone opening being tolerated by the structural scaffold. Here we show how to integrate the online usage of the CP predictor, CPred, in the search of new split intein sites.

Ancestral Protein Reconstruction and Circular Permutation for Improving the Stability and Dynamic Range of FRET Sensors

Small molecule biosensors based on Förster resonance energy transfer (FRET) enable small molecule signaling to be monitored with high spatial and temporal resolution in complex cellular environments. FRET sensors can be constructed by fusing a pair of fluorescent proteins to a suitable recognition domain, such as a member of the solute-binding protein (SBP) superfamily. However, naturally occurring SBPs may be unsuitable for incorporation into FRET sensors due to their low thermostability, which may preclude imaging under physiological conditions, or because the positions of their N- and C-termini may be suboptimal for fusion of fluorescent proteins, which may limit the dynamic range of the resulting sensors. Here, we show how these problems can be overcome using ancestral protein reconstruction and circular permutation. Ancestral protein reconstruction, used as a protein engineering strategy, leverages phylogenetic information to improve the thermostability of proteins, while circular permutation enables the termini of an SBP to be repositioned to maximize the dynamic range of the resulting FRET sensor. We also provide a protocol for cloning the engineered SBPs into FRET sensor constructs using Golden Gate assembly and discuss considerations for in situ characterization of the FRET sensors.

The Structure of a Thermophilic Kinase Shapes Fitness upon Random Circular Permutation

Proteins can be engineered for synthetic biology through circular permutation, a sequence rearrangement in which native protein termini become linked and new termini are created elsewhere through backbone fission. However, it remains challenging to anticipate a protein's functional tolerance to circular permutation. Here, we describe new transposons for creating libraries of randomly circularly permuted proteins that minimize peptide additions at their termini, and we use transposase mutagenesis to study the tolerance of a thermophilic adenylate kinase (AK) to circular permutation. We find that libraries expressing permuted AKs with either short or long peptides amended to their N-terminus yield distinct sets of active variants and present evidence that this trend arises because permuted protein expression varies across libraries. Mapping all sites that tolerate backbone cleavage onto AK structure reveals that the largest contiguous regions of sequence that lack cleavage sites are proximal to the phosphotransfer site. A comparison of our results with a range of structure-derived parameters further showed that retention of function correlates to the strongest extent with the distance to the phosphotransfer site, amino acid variability in an AK family sequence alignment, and residue-level deviations in superimposed AK structures. Our work illustrates how permuted protein libraries can be created with minimal peptide additions using transposase mutagenesis, and it reveals a challenge of maintaining consistent expression across permuted variants in a library that minimizes peptide additions. Furthermore, these findings provide a basis for interpreting responses of thermophilic phosphotransferases to circular permutation by calibrating how different structure-derived parameters relate to retention of function in a cellular selection.

Structural Determinants of Misfolding in Multidomain Proteins

Recent single molecule experiments, using either atomic force microscopy (AFM) or Förster resonance energy transfer (FRET) have shown that multidomain proteins containing tandem repeats may form stable misfolded structures. Topology-based simulation models have been used successfully to generate models for these structures with domain-swapped features, fully consistent with the available data. However, it is also known that some multidomain protein folds exhibit no evidence for misfolding, even when adjacent domains have identical sequences. Here we pose the question: what factors influence the propensity of a given fold to undergo domain-swapped misfolding? Using a coarse-grained simulation model, we can reproduce the known propensities of multidomain proteins to form domain-swapped misfolds, where data is available. Contrary to what might be naively expected based on the previously described misfolding mechanism, we find that the extent of misfolding is not determined by the relative folding rates or barrier heights for forming the domains present in the initial intermediates leading to folded or misfolded structures. Instead, it appears that the propensity is more closely related to the relative stability of the domains present in folded and misfolded intermediates. We show that these findings can be rationalized if the folded and misfolded domains are part of the same folding funnel, with commitment to one structure or the other occurring only at a relatively late stage of folding. Nonetheless, the results are still fully consistent with the kinetic models previously proposed to explain misfolding, with a specific interpretation of the observed rate coefficients. Finally, we investigate the relation between interdomain linker length and misfolding, and propose a simple alchemical model to predict the propensity for domain-swapped misfolding of multidomain proteins.

Circular permutation of E. coli EPSP synthase: increased inhibitor resistance, improved catalytic activity, and an indicator for protein fragment complementation

We performed the first circular permutation analysis for E. coli 5-enolpyruvylshikimate-3-phosphate synthase, and identified one circular permutant with notably increased resistance to its specific inhibitor and several others with moderately improved catalytic activity. Valid circular permutation sites can be used as effective split sites of protein fragment complementation.

Circular permutation prediction reveals a viable backbone disconnection for split proteins: an approach in identifying a new functional split intein

Split-protein systems have emerged as a powerful tool for detecting biomolecular interactions and reporting biological reactions. However, reliable methods for identifying viable split sites are still unavailable. In this study, we demonstrated the feasibility that valid circular permutation (CP) sites in proteins have the potential to act as split sites and that CP prediction can be used to search for internal permissive sites for creating new split proteins. Using a protein ligase, intein, as a model, CP predictor facilitated the creation of circular permutants in which backbone opening imposes the least detrimental effects on intein folding. We screened a series of predicted intein CPs and identified stable and native-fold CPs. When the valid CP sites were introduced as split sites, there was a reduction in folding enthalpy caused by the new backbone opening; however, the coincident loss in entropy was sufficient to be compensated, yielding a favorable free energy for self-association. Since split intein is exploited in protein semi-synthesis, we tested the related protein trans-splicing (PTS) activities of the corresponding split inteins. Notably, a novel functional split intein composed of the N-terminal 36 residues combined with the remaining C-terminal fragment was identified. Its PTS activity was shown to be better than current reported two-piece intein with a short N-terminal segment. Thus, the incorporation of in silico CP prediction facilitated the design of split intein as well as circular permutants.