Creating a Homeodomain with High Stability and DNA Binding Affinity by Sequence Averaging

DNA binding analysis of rare variants in homeodomains reveals homeodomain specificity-determining residues

Homeodomains (HDs) are the second largest class of DNA binding domains (DBDs) among eukaryotic sequence-specific transcription factors (TFs) and are the TF structural class with the largest number of disease-associated mutations in the Human Gene Mutation Database (HGMD). Despite numerous structural studies and large-scale analyses of HD DNA binding specificity, HD-DNA recognition is still not fully understood. Here, we analyze 92 human HD mutants, including disease-associated variants and variants of uncertain significance (VUS), for their effects on DNA binding activity. Many of the variants alter DNA binding affinity and/or specificity. Detailed biochemical analysis and structural modeling identifies 14 previously unknown specificity-determining positions, 5 of which do not contact DNA. The same missense substitution at analogous positions within different HDs often exhibits different effects on DNA binding activity. Variant effect prediction tools perform moderately well in distinguishing variants with altered DNA binding affinity, but poorly in identifying those with altered binding specificity. Our results highlight the need for biochemical assays of TF coding variants and prioritize dozens of variants for further investigations into their pathogenicity and the development of clinical diagnostics and precision therapies.

Derivation of a novel antimicrobial peptide from the Red Sea Brine Pools modified to enhance its anticancer activity against U2OS cells

Cancer associated drug resistance is a major cause for cancer aggravation, particularly as conventional therapies have presented limited efficiency, low specificity, resulting in long term deleterious side effects. Peptide based drugs have emerged as potential alternative cancer treatment tools due to their selectivity, ease of design and synthesis, safety profile, and low cost of manufacturing. In this study, we utilized the Red Sea metagenomics database, generated during AUC/KAUST Red Sea microbiome project, to derive a viable anticancer peptide (ACP). We generated a set of peptide hits from our library that shared similar composition to ACPs. A peptide with a homeodomain was selected, modified to improve its anticancer properties, verified to maintain high anticancer properties, and processed for further in-silico prediction of structure and function. The peptide's anticancer properties were then assessed in vitro on osteosarcoma U2OS cells, through cytotoxicity assay (MTT assay), scratch-wound healing assay, apoptosis/necrosis detection assay (Annexin/PI assay), RNA expression analysis of Caspase 3, KI67 and Survivin, and protein expression of PARP1. L929 mouse fibroblasts were also assessed for cytotoxicity treatment. In addition, the antimicrobial activity of the peptide was also examined on E coli and S. aureus, as sample representative species of the human bacterial microbiome, by examining viability, disk diffusion, morphological assessment, and hemolytic analysis. We observed a dose dependent cytotoxic response from peptide treatment of U2OS, with a higher tolerance in L929s. Wound closure was debilitated in cells exposed to the peptide, while annexin fluorescent imaging suggested peptide treatment caused apoptosis as a major mode of cell death. Caspase 3 gene expression was not altered, while KI67 and Survivin were both downregulated in peptide treated cells. Additionally, PARP-1 protein analysis showed a decrease in expression with peptide exposure. The peptide exhibited minimal antimicrobial activity on critical human microbiome species E. coli and S. aureus, with a low inhibition rate, maintenance of structural morphology and minimal hemolytic impact. These findings suggest our novel peptide displayed preliminary ACP properties against U2OS cells, through limited specificity, while triggering apoptosis as a primary mode of cell death and while having minimal impact on the microbiological species E. coli and S. aureus.

High-Accuracy Prediction of Stabilizing Surface Mutations to the Three-Helix Bundle, UBA(1), with EmCAST

The accurate modeling of energetic contributions to protein structure is a fundamental challenge in computational approaches to protein analysis and design. We describe a general computational method, EmCAST (empirical Cα stabilization), to score and optimize the sequence to the structure in proteins. The method relies on an empirical potential derived from the database of the Cα dihedral angle preferences for all possible four-residue sequences, using the data available in the Protein Data Bank. Our method produces stability predictions that naturally correlate one-to-one with the experimental results for solvent-exposed mutation sites. EmCAST predicted four mutations that increased the stability of a three-helix bundle, UBA(1), from 2.4 to 4.8 kcal/mol by optimizing residues in both helices and turns. For a set of eight variants, the predicted and experimental stabilizations correlate very well (R2 = 0.97) with a slope near 1 and with a 0.16 kcal/mol standard error for EmCAST predictions. Tests against literature data for the stability effects of surface-exposed mutations show that EmCAST outperforms the existing stability prediction methods. UBA(1) variants were crystallized to verify and analyze their structures at an atomic resolution. Thermodynamic and kinetic folding experiments were performed to determine the magnitude and mechanism of stabilization. Our method has the potential to enable the rapid, rational optimization of natural proteins, expand the analysis of the sequence/structure relationship, and supplement the existing protein design strategies.

Singular value decomposition of protein sequences as a method to visualize sequence and residue space

Singular value decomposition (SVD) of multiple sequence alignments (MSAs) is an important and rigorous method to identify subgroups of sequences within the MSA, and to extract consensus and covariance sequence features that define the alignment and distinguish the subgroups. This information can be correlated to structure, function, stability, and taxonomy. However, the mathematics of SVD is unfamiliar to many in the field of protein science. Here, we attempt to present an intuitive yet comprehensive description of SVD analysis of MSAs. We begin by describing the underlying mathematics of SVD in a way that is both rigorous and accessible. Next, we use SVD to analyze sequences generated with a simplified model in which the extent of sequence conservation and covariance between different positions is controlled, to show how conservation and covariance produce features in the decomposed coordinate system. We then use SVD to analyze alignments of two protein families, the homeodomain and the Ras superfamilies. Both families show clear evidence of sequence clustering when projected into singular value space. We use k-means clustering to group MSA sequences into specific clusters, show how the residues that distinguish these clusters can be identified, and show how these clusters can be related to taxonomy and function. We end by providing a description a set of Python scripts that can be used for SVD analysis of MSAs, displaying results, and identifying and analyzing sequence clusters. These scripts are freely available on GitHub.

The stability and dynamics of computationally designed proteins

Protein stability, dynamics and function are intricately linked. Accordingly, protein designers leverage dynamics in their designs and gain insight to their successes and failures by analyzing their proteins' dynamics. Molecular dynamics (MD) simulations are a powerful computational tool for quantifying both local and global protein dynamics. This review highlights studies where MD simulations were applied to characterize the stability and dynamics of designed proteins and where dynamics were incorporated into computational protein design. First, we discuss the structural basis underlying the extreme stability and thermostability frequently observed in computationally designed proteins. Next, we discuss examples of designed proteins, where dynamics were not explicitly accounted for in the design process, whose coordinated motions or active site dynamics, as observed by MD simulation, enhanced or detracted from their function. Many protein functions depend on sizeable or subtle conformational changes, so we finally discuss the computational design of proteins to perform a specific function that requires consideration of motion by multi-state design.

Surface residues and non-additive interactions stabilize a consensus homeodomain protein

Despite the widely reported success of consensus design in producing highly stabilized proteins, little is known about the physical mechanisms underlying this stabilization. Here we explore the potential sources of stabilization by performing a systematic analysis of the 29 substitutions that we previously found to collectively stabilize a consensus homeodomain compared to an extant homeodomain. By separately introducing groups of consensus substitutions that alter or preserve charge state, occur at varying degrees of residue burial, and occur at positions of varying degrees of conservation, we determine the extent to which these three features contribute to the consensus stability enhancement. Surprisingly, we find that the largest total contribution to stability comes from consensus substitutions on the protein surface and that the largest per-substitution contributions come from substitutions that maintain charge state. This finding suggests that although consensus proteins are often enriched in charged residues, consensus stabilization does not result primarily from interactions involving charged residues. Although consensus substitutions at strongly conserved positions also contribute disproportionately to stabilization, significant stabilization is also contributed from substitutions at weakly conserved positions. Furthermore, we find that identical consensus substitutions show larger stabilizing effects when introduced into the consensus background than when introduced into an extant homeodomain, indicating that synergistic, stabilizing interactions among the consensus residues contribute to consensus stability enhancement of the homeodomain. By measuring DNA binding affinity for the same set of variants, we find that although consensus design of the homeodomain increases both affinity and folding stability, it does so using a largely non-overlapping set of substitutions.

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 (k_cat/K_m) 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.

Data-driven computational protein design

Computational protein design can generate proteins not found in nature that adopt desired structures and perform novel functions. Although proteins could, in theory, be designed with ab initio methods, practical success has come from using large amounts of data that describe the sequences, structures, and functions of existing proteins and their variants. We present recent creative uses of multiple-sequence alignments, protein structures, and high-throughput functional assays in computational protein design. Approaches range from enhancing structure-based design with experimental data to building regression models to training deep neural nets that generate novel sequences. Looking ahead, deep learning will be increasingly important for maximizing the value of data for protein design.

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 (t_1/2) 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.

The use of consensus sequence information to engineer stability and activity in proteins

The goal of protein design is to create proteins that are stable, soluble, and active. Here we focus on one approach to protein design in which sequence information is used to create a "consensus" sequence. Such consensus sequences comprise the most common residue at each position in a multiple sequence alignment (MSA). After describing some general ideas that relate MSA and consensus sequences and presenting a statistical thermodynamic framework that relates consensus and non-consensus sequences to stability, we detail the process of designing a consensus sequence and survey reports of consensus design and characterization from the literature. Many of these consensus proteins retain native biological activities including ligand binding and enzyme activity. Remarkably, in most cases the consensus protein shows significantly higher stability than extant versions of the protein, as measured by thermal or chemical denaturation, consistent with the statistical thermodynamic model. To understand this stability increase, we compare various features of consensus sequences with the extant MSA sequences from which they were derived. Consensus sequences show enrichment in charged residues (most notably glutamate and lysine) and depletion of uncharged polar residues (glutamine, serine, and asparagine). Surprisingly, a survey of stability changes resulting from point substitutions show little correlation with residue frequencies at the corresponding positions within the MSA, suggesting that the high stability of consensus proteins may result from interactions among residue pairs or higher-order clusters. Whatever the source, the large number of reported successes demonstrates that consensus design is a viable route to generating active and in many cases highly stabilized proteins.

Phylogenetic spread of sequence data affects fitness of consensus enzymes: Insights from triosephosphate isomerase

The concept of consensus in multiple sequence alignments (MSAs) has been used to design and engineer proteins previously with some success. However, consensus design implicitly assumes that all amino acid positions function independently, whereas in reality, the amino acids in a protein interact with each other and work cooperatively to produce the optimum structure required for its function. Correlation analysis is a tool that can capture the effect of such interactions. In a previously published study, we made consensus variants of the triosephosphate isomerase (TIM) protein using MSAs that included sequences form both prokaryotic and eukaryotic organisms. These variants were not completely native-like and were also surprisingly different from each other in terms of oligomeric state, structural dynamics, and activity. Extensive correlation analysis of the TIM database has revealed some clues about factors leading to the unusual behavior of the previously constructed consensus proteins. Among other things, we have found that the more ill-behaved consensus mutant had more broken correlations than the better-behaved consensus variant. Moreover, we report three correlation and phylogeny-based consensus variants of TIM. These variants were more native-like than the previous consensus mutants and considerably more stable than a wild-type TIM from a mesophilic organism. This study highlights the importance of choosing the appropriate diversity of MSA for consensus analysis and provides information that can be used to engineer stable enzymes.

Consensus sequence design as a general strategy to create hyperstable, biologically active proteins

Consensus sequence design offers a promising strategy for designing proteins of high stability while retaining biological activity since it draws upon an evolutionary history in which residues important for both stability and function are likely to be conserved. Although there have been several reports of successful consensus design of individual targets, it is unclear from these anecdotal studies how often this approach succeeds and how often it fails. Here, we attempt to assess generality by designing consensus sequences for a set of six protein families with a range of chain lengths, structures, and activities. We characterize the resulting consensus proteins for stability, structure, and biological activities in an unbiased way. We find that all six consensus proteins adopt cooperatively folded structures in solution. Strikingly, four of six of these consensus proteins show increased thermodynamic stability over naturally occurring homologs. Each consensus protein tested for function maintained at least partial biological activity. Although peptide binding affinity by a consensus-designed SH3 is rather low, K _m values for consensus enzymes are similar to values from extant homologs. Although consensus enzymes are slower than extant homologs at low temperature, they are faster than some thermophilic enzymes at high temperature. An analysis of sequence properties shows consensus proteins to be enriched in charged residues, and rarified in uncharged polar residues. Sequence differences between consensus and extant homologs are predominantly located at weakly conserved surface residues, highlighting the importance of these residues in the success of the consensus strategy.

A Dynamic Hydrophobic Core and Surface Salt Bridges Thermostabilize a Designed Three-Helix Bundle

Thermostable proteins are advantageous in industrial applications, as pharmaceuticals or biosensors, and as templates for directed evolution. As protein-design methodologies improve, bioengineers are able to design proteins to perform a desired function. Although many rationally designed proteins end up being thermostable, how to intentionally design de novo, thermostable proteins is less clear. UVF is a de novo-designed protein based on the backbone structure of the Engrailed homeodomain (EnHD) and is highly thermostable (T_m > 99°C vs. 52°C for EnHD). Although most proteins generally have polar amino acids on their surfaces and hydrophobic amino acids buried in their cores, protein engineers followed this rule exactly when designing UVF. To investigate the contributions of the fully hydrophobic core versus the fully polar surface to UVF’s thermostability, we built two hybrid, chimeric proteins combining the sets of buried and surface residues from UVF and EnHD. Here, we determined a structural, dynamic, and thermodynamic explanation for UVF’s thermostability by performing 4 μs of all-atom, explicit-solvent molecular dynamics simulations at 25 and 100°C, Tanford-Kirkwood solvent accessibility Monte Carlo electrostatic calculations, and a thermodynamic analysis of 40 temperature runs by the weighted-histogram analysis method of heavy-atom, structure-based models of UVF, EnHD, and both chimeric proteins. Our models showed that UVF was highly dynamic because of its fully hydrophobic core, leading to a smaller loss of entropy upon folding. The charged residues on its surface made favorable electrostatic interactions that contributed enthalpically to its thermostability. In the chimeric proteins, both the hydrophobic core and charged surface independently imparted thermostability.

Enhancing thermostability and removing hemin inhibition of Rhodopseudomonas palustris 5-aminolevulinic acid synthase by computer-aided rational design

OBJECTIVE

To enhance the thermostability and deregulate the hemin inhibition of 5-aminolevulinic acid (ALA) synthase from Rhodopseudomonas palustris (RP-ALAS) by a computer-aided rational design strategy.

RESULTS

Eighteen RP-ALAS single variants were rationally designed and screened by measuring their residual activities upon heating. Among them, H29R and H15K exhibited a 2.3 °C and 6.0 °C higher melting temperature than wild-type, respectively. A 6.7-fold and 10.3-fold increase in specific activity after 1 h incubation at 37 °C was obtained for H29R (2.0 U/mg) and H15K (3.1 U/mg) compared to wild-type (0.3 U/mg). Additionally, higher residual activities in the presence of hemin were obtained for H29R and H15K (e.g., 64% and 76% at 10 μM hemin vs. 27% for wild-type). The ALA titer was increased by 6% and 22% in fermentation using Corynebacterium glutamicum ATCC 13032 expressing H29R and H15K, respectively.

CONCLUSION

H29R and H15K showed high thermostability, reduced hemin inhibition and slightly high activity, indicating that these two variants are good candidates for bioproduction of ALA.

TGIF1 homeodomain interacts with Smad MH1 domain and represses TGF-β signaling

TGIF1 is a multifunctional protein that represses TGF-β-activated transcription by interacting with Smad2-Smad4 complexes. We found that the complex structure of TGIF1-HD bound to the TGACA motif revealed a combined binding mode that involves the HD core and the major groove, on the one hand, and the amino-terminal (N-term) arm and the minor groove of the DNA, on the other. We also show that TGIF1-HD interacts with the MH1 domain of Smad proteins, thereby indicating that TGIF1-HD is also a protein-binding domain. Moreover, the formation of the HD-MH1 complex partially hinders the DNA-binding site of the complex, preventing the efficient interaction of TGIF1-HD with DNA. We propose that the binding of the TGIF1 C-term to the Smad2-MH2 domain brings both the HD and MH1 domain into close proximity. This local proximity facilitates the interaction of these DNA-binding domains, thus strengthening the formation of the protein complex versus DNA binding. Once the protein complex has been formed, the TGIF1-Smad system would be released from promoters/enhancers, thereby illustrating one of the mechanisms used by TGIF1 to exert its function as an active repressor of Smad-induced TGF-β signaling.

Extreme stability in de novo-designed repeat arrays is determined by unusually stable short-range interactions

Designed helical repeats (DHRs) are modular helix-loop-helix-loop protein structures that are tandemly repeated to form a superhelical array. Structures combining tandem DHRs demonstrate a wide range of molecular geometries, many of which are not observed in nature. Understanding cooperativity of DHR proteins provides insight into the molecular origins of Rosetta-based protein design hyperstability and facilitates comparison of energy distributions in artificial and naturally occurring protein folds. Here, we use a nearest-neighbor Ising model to quantify the intrinsic and interfacial free energies of four different DHRs. We measure the folding free energies of constructs with varying numbers of internal and terminal capping repeats for four different DHR folds, using guanidine-HCl and glycerol as destabilizing and solubilizing cosolvents. One-dimensional Ising analysis of these series reveals that, although interrepeat coupling energies are within the range seen for naturally occurring repeat proteins, the individual repeats of DHR proteins are intrinsically stable. This favorable intrinsic stability, which has not been observed for naturally occurring repeat proteins, adds to stabilizing interfaces, resulting in extraordinarily high stability. Stable repeats also impart a downhill shape to the energy landscape for DHR folding. These intrinsic stability differences suggest that part of the success of Rosetta-based design results from capturing favorable local interactions.

Benchmark Analysis of Native and Artificial NAD+-Dependent Enzymes Generated by a Sequence-Based Design Method with or without Phylogenetic Data

The expansion of protein sequence databases has enabled us to design artificial proteins by sequence-based design methods, such as full-consensus design (FCD) and ancestral-sequence reconstruction (ASR). Artificial proteins with enhanced activity levels compared with native ones can potentially be generated by such methods, but successful design is rare because preparing a sequence library by curating the database and selecting a method is difficult. Utilizing a curated library prepared by reducing conservation energies, we successfully designed two artificial l-threonine 3-dehydrogenases (SDR-TDH) with higher activity levels than native SDR-TDH, FcTDH-N1, and AncTDH, using FCD and ASR, respectively. The artificial SDR-TDHs had excellent thermal stability and NAD⁺ recognition compared to native SDR-TDH from Cupriavidus necator (CnTDH); the melting temperatures of FcTDH-N1 and AncTDH were about 10 and 5 °C higher than that of CnTDH, respectively, and the dissociation constants toward NAD⁺ of FcTDH-N1 and AncTDH were 2- and 7-fold lower than that of CnTDH, respectively. Enzymatic efficiency of the artificial SDR-TDHs were comparable to that of CnTDH. Crystal structures of FcTDH-N1 and AncTDH were determined at 2.8 and 2.1 Å resolution, respectively. Structural and MD simulation analysis of the SDR-TDHs indicated that only the flexibility at specific regions was changed, suggesting that multiple mutations introduced in the artificial SDR-TDHs altered their flexibility and thereby affected their enzymatic properties. Benchmark analysis of the SDR-TDHs indicated that both FCD and ASR can generate highly functional proteins if a curated library is prepared appropriately.

Lysine Side-Chain Dynamics in the Binding Site of Homeodomain/DNA Complexes As Observed by NMR Relaxation Experiments and Molecular Dynamics Simulations

An important but poorly characterized contribution to the thermodynamics of protein-DNA interactions is the loss of entropy that occurs from restricting the conformational freedom of amino acid side chains. The effect of restricting the flexibility of several side chains at a protein-DNA interface may be comparable in many cases to the other factors that determine the binding thermodynamics and may, therefore, play a key role in dictating the binding affinity and/or specificity. Because the entropic contributions, including the presence and influence of side-chain dynamics, are especially difficult to estimate based on structural information, it is important to pursue experimental and theoretical studies that can provide direct information regarding these issues. We report on studies of a model system, the homeodomain/DNA complex, focusing on the Lys50 class of homeodomains where a key lysine residue in position 50 was shown previously to be critical for binding site specificity. NMR methodology was employed for determining the dynamics of lysine side-chain amino groups via 15N relaxation measurements in the Lys50-class homeodomains from the Drosophila protein Bicoid and the human protein Pitx2. In the case of Pitx2, complexes with both a consensus and a nonconsensus DNA binding site were examined. NMR-derived order parameters indicated moderate to substantial conformational freedom for the lysine NH3+ group in the complexes studied. To complement the experimental NMR measurements, molecular dynamics simulations were performed for the consensus complexes to gain further, detailed insights regarding the dynamics of the Lys50 side chain and other important residues in the protein-DNA interface.