High-resolution design of a protein loop

Decoding the Torsional Dynamics of Main-Chain Atoms Within CαNN Motif Facilitating Specific Anion Recognition

The structural plasticity of proteins at the molecular level is largely dictated by backbone torsion angles, which play a critical role in ligand recognition and binding. To establish the anion-induced cooperative arrangement of the main-chain (mc) torsion, herein, we analyzed a set of naturally occurring CαNN motifs as "static models" for their anion-binding competence through docking and molecular dynamics simulations and decoded its torsion angle influenced mc-driven anion recognition potential. By comparing a pool of 20 distinct sets of CαNN motif with identical sequences in their "anion bound/present, aP" and "anion free/absent, aA" versions, we could discern that there exists a positive correlation between the "difference of anion residence time (ΔRT)" and "difference among the main-chain torsion angle" of the aP and aA population. Notably, the anion interaction with CαNNs is locally energetically favorable even in a context-free non-proteinaceous environment and if the difference of the mc-torsion angles involving the Cα-1, N0, N1 residues for a population is higher between the aP and aA state, the difference among the ligand RT is also greater. At the atomistic level, the accommodation of anion is highly synergistic and cooperatively sways the interacting mc-atom torsions. By comparing the clustering of H-bonding patterns, the free energy of binding, and RT in both states, we provide evidence that to establish favorable thermodynamics and kinetics of ligand accommodation in these short structural motifs, proper reorientation of local-mc governed by torsions is a prerequisite. Our findings position the CαNN motif as a promising scaffold for peptidomimetic design and emphasize the critical role of loop region dynamics in protein structure-function relationships.

Collagen-targeted protein nanomicelles for the imaging of non-alcoholic steatohepatitis

In vivo molecular imaging tools hold immense potential to drive transformative breakthroughs by enabling researchers to visualize cellular and molecular interactions in real-time and/or at high resolution. These advancements will facilitate a deeper understanding of fundamental biological processes and their dysregulation in disease states. Here, we develop and characterize a self-assembling protein nanomicelle called collagen type I binding - thermoresponsive assembled protein (Col1-TRAP) that binds tightly to type I collagen in vitro with nanomolar affinity. For ex vivo visualization, Col1-TRAP is labeled with a near-infrared fluorescent dye (NIR-Col1-TRAP). Both Col1-TRAP and NIR-Col1-TRAP display approximately a 3.8-fold greater binding to type I collagen compared to TRAP when measured by surface plasmon resonance (SPR). We present a proof-of-concept study using NIR-Col1-TRAP to detect fibrotic type I collagen deposition ex vivo in the livers of mice with non-alcoholic steatohepatitis (NASH). We show that NIR-Col1-TRAP demonstrates significantly decreased plasma recirculation time as well as increased liver accumulation in the NASH mice compared to mice without disease over 4 hours. As a result, NIR-Col1-TRAP shows potential as an imaging probe for NASH with in vivo targeting performance after injection in mice. STATEMENT OF SIGNIFICANCE: Direct molecular imaging of fibrosis in NASH patients enables the diagnosis and monitoring of disease progression with greater specificity and resolution than do elastography-based methods or blood tests. In addition, protein-based imaging probes are more advantageous than alternatives due to their biodegradability and scalable biosynthesis. With the aid of computational modeling, we have designed a self-assembled protein micelle that binds to fibrillar and monomeric collagen in vitro. After the protein was labeled with near-infrared fluorescent dye, we injected the compound into mice fed on a NASH diet. NIR-Col1-TRAP clears from the serum faster in these mice compared to control mice, and accumulates significantly more in fibrotic livers.This work advances the development of targeted protein probes for in vivo fibrosis imaging.

In silico analysis and characterization of potential inhibitors of MmaA3, a methoxy mycolic acid synthase from Mycobacterium tuberculosis

The emergence of the multi-and extensively drug-resistant (MDR and XDR) strains of Mycobacterium tuberculosis (M.tb), necessitates paradigm-shifting therapeutic approaches. The impermeable waxy lipid layer, primarily composed of mycolic acids, is a key factor in conferring resistance to conventional drugs. This study introduces a novel strategy to combat drug resistance by targeting Methoxy mycolic acid synthase 3 (MmaA3), a critical enzyme in the mycolic acid biosynthesis pathway. MmaA3 is responsible for the O-methylation of hydroxymycolate precursors and emerges as a promising therapeutic target. Through homology-based modeling, we generated a three-dimensional structure of MmaA3, providing crucial insights into its structural characteristics. High throughput virtual screening was performed against the MmaA3 model, using diverse sources: knowledge-based, FDA-approved Drugbank, and Asinex-Elite libraries. Through rigorous computational analyses, including binding affinity assessments, molecular interactions analysis, and binding free energy calculations, potential inhibitors of MmaA3 have been identified. Subsequent validation studies evaluated the stability of top protein-ligand complexes, and free energy calculations using molecular dynamics simulations. The stability of complexes within the catalytic site was confirmed through RMSD and RMSF profile analyses. Furthermore, binding free energy calculations using the MM-GBSA approach revealed significant binding affinity of identified ligands for MmaA3 target protein, comparable to its substrate/cofactors. These findings underscore the potential of the proposed molecules as candidates for further experimental exploration, offering promising avenues for the development of effective inhibitors against M.tb. Overall, our research contributes to significantly advancing the formulation of progressive therapeutic strategies in combating drug-resistant tuberculosis.

Loop dynamics and the evolution of enzyme activity

In the early 2000s, Tawfik presented his 'New View' on enzyme evolution, highlighting the role of conformational plasticity in expanding the functional diversity of limited repertoires of sequences. This view is gaining increasing traction with increasing evidence of the importance of conformational dynamics in both natural and laboratory evolution of enzymes. The past years have seen several elegant examples of harnessing conformational (particularly loop) dynamics to successfully manipulate protein function. This Review revisits flexible loops as critical participants in regulating enzyme activity. We showcase several systems of particular interest: triosephosphate isomerase barrel proteins, protein tyrosine phosphatases and β-lactamases, while briefly discussing other systems in which loop dynamics are important for selectivity and turnover. We then discuss the implications for engineering, presenting examples of successful loop manipulation in either improving catalytic efficiency, or changing selectivity completely. Overall, it is becoming clearer that mimicking nature by manipulating the conformational dynamics of key protein loops is a powerful method of tailoring enzyme activity, without needing to target active-site residues.

Designed Loop Extension Followed by Combinatorial Screening Confers High Specificity to a Broad Matrix MetalloproteinaseInhibitor

Matrix metalloproteinases (MMPs) are key drivers of various diseases, including cancer. Development of probes and drugs capable of selectively inhibiting the individual members of the large MMP family remains a persistent challenge. The inhibitory N-terminal domain of tissue inhibitor of metalloproteinases-2 (N-TIMP2), a natural broad MMP inhibitor, can provide a scaffold for protein engineering to create more selective MMP inhibitors. Here, we pursued a unique approach harnessing both computational design and combinatorial screening to confer high binding specificity toward a target MMP in preference to an anti-target MMP. We designed a loop extension of N-TIMP2 to allow new interactions with the non-conserved MMP surface and generated an efficient focused library for yeast surface display, which was then screened for high binding to the target MMP-14 and low binding to anti-target MMP-3. Deep sequencing analysis identified the most promising variants, which were expressed, purified, and tested for selectivity of inhibition. Our best N-TIMP2 variant exhibited 29 pM binding affinity to MMP-14 and 2.4 µM affinity to MMP-3, revealing 7500-fold greater specificity than WT N-TIMP2. High-confidence structural models were obtained by including NGS data in the AlphaFold multiple sequence alignment. The modeling together with experimental mutagenesis validated our design predictions, demonstrating that the loop extension packs tightly against non-conserved residues on MMP-14 and clashes with MMP-3. This study demonstrates how introduction of loop extensions in a manner guided by target protein conservation data and loop design can offer an attractive strategy to achieve specificity in design of protein ligands.

Multiple adaptations underly co-option of a CRISPR surveillance complex for RNA-guided DNA transposition

CRISPR-associated transposons (CASTs) are natural RNA-directed transposition systems. We demonstrate that transposon protein TniQ plays a central role in promoting R-loop formation by RNA-guided DNA-targeting modules. TniQ residues, proximal to CRISPR RNA (crRNA), are required for recognizing different crRNA categories, revealing an unappreciated role of TniQ to direct transposition into different classes of crRNA targets. To investigate adaptations allowing CAST elements to utilize attachment sites inaccessible to CRISPR-Cas surveillance complexes, we compared and contrasted PAM sequence requirements in both I-F3b CAST and I-F1 CRISPR-Cas systems. We identify specific amino acids that enable a wider range of PAM sequences to be accommodated in I-F3b CAST elements compared with I-F1 CRISPR-Cas, enabling CAST elements to access attachment sites as sequences drift and evade host surveillance. Together, this evidence points to the central role of TniQ in facilitating the acquisition of CRISPR effector complexes for RNA-guided DNA transposition.

Profiling Enzyme Activity of l-Asparaginase II by NMR-Based Methyl Fingerprinting at Natural Abundance

l-asparaginase II (MW 135 kDa) from E. coli is an FDA-approved protein drug used for the treatment of childhood leukemia. Despite its long history as a chemotherapeutic, the structural basis of enzyme action, in solution, remains widely contested. In this work, methyl-based 2D [¹H-¹³C]-heteronuclear single-quantum correlation (HSQC) NMR, at natural abundance, has been used to profile the enzymatic activity of the commercially available enzyme drug. The [¹H-¹³C]-HSQC NMR spectra of the protein reveal the role of a flexible loop segment in the activity of the enzyme, in solution. Addition of asparagine to the protein results in distinct conformational changes of the loop that could be signatures of intermediates formed in the catalytic reaction. To this end, an isothermal titration calorimetry (ITC)-based assay has been developed to measure the enzymatic reaction enthalpy, as a marker for its activity. Combining both ITC and NMR, it was shown that the disruption of the protein conformation can result in the loss of function. The scope, robustness, and validity of the loop fingerprints in relation to enzyme activity have been tested under different solution conditions. Overall, our results indicate that 2D NMR can be used reliably to gauge the structure-function of this enzyme, bypassing the need to label the protein. Such natural abundant NMR methods can be potentially extended to probe the structure-function aspects of high-molecular-weight protein therapeutics (glycosylated protein drugs, enzymes, therapeutic monoclonal antibodies, antibody-drug conjugates, and Fc-fusion proteins), where (a) flexible loops are required for their function and (b) isotope labeling may not be straightforward.

High-throughput sequencing analysis of nuclear-encoded mitochondrial genes reveals a genetic signature of human longevity

Mitochondrial dysfunction is a well-known contributor to aging and age-related diseases. The precise mechanisms through which mitochondria impact human lifespan, however, remain unclear. We hypothesize that humans with exceptional longevity harbor rare variants in nuclear-encoded mitochondrial genes (mitonuclear genes) that confer resistance against age-related mitochondrial dysfunction. Here we report an integrated functional genomics study to identify rare functional variants in ~ 660 mitonuclear candidate genes discovered by target capture sequencing analysis of 496 centenarians and 572 controls of Ashkenazi Jewish descent. We identify and prioritize longevity-associated variants, genes, and mitochondrial pathways that are enriched with rare variants. We provide functional gene variants such as those in MTOR (Y2396Lfs*29), CPS1 (T1406N), and MFN2 (G548*) as well as LRPPRC (S1378G) that is predicted to affect mitochondrial translation. Taken together, our results suggest a functional role for specific mitonuclear genes and pathways in human longevity.

LoopGrafter: a web tool for transplanting dynamical loops for protein engineering

The transplantation of loops between structurally related proteins is a compelling method to improve the activity, specificity and stability of enzymes. However, despite the interest of loop regions in protein engineering, the available methods of loop-based rational protein design are scarce. One particular difficulty related to loop engineering is the unique dynamism that enables them to exert allosteric control over the catalytic function of enzymes. Thus, when engaging in a transplantation effort, such dynamics in the context of protein structure need consideration. A second practical challenge is identifying successful excision points for the transplantation or grafting. Here, we present LoopGrafter (https://loschmidt.chemi.muni.cz/loopgrafter/), a web server that specifically guides in the loop grafting process between structurally related proteins. The server provides a step-by-step interactive procedure in which the user can successively identify loops in the two input proteins, calculate their geometries, assess their similarities and dynamics, and select a number of loops to be transplanted. All possible different chimeric proteins derived from any existing recombination point are calculated, and 3D models for each of them are constructed and energetically evaluated. The obtained results can be interactively visualized in a user-friendly graphical interface and downloaded for detailed structural analyses.

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.

Computationally grafting an IgE epitope onto a scaffold: Implications for a pan anti-allergy vaccine design

Allergy is becoming an intensifying disease among the world population, particularly in the developed world. Once allergy develops, sufferers are permanently trapped in a hyper-immune response that makes them sensitive to innocuous substances. The immune pathway concerned with developing allergy is the Th2 immune pathway where the IgE antibody binds to its Fc ∊ RI receptor on Mast and Basophil cells. This paper discusses a protocol that could disrupt the binding between the antibody and its receptor for a potential permanent treatment. Ten proteins were computationally designed to display a human IgE motif very close in proximity to the IgE antibody's Fc ∊ RI receptor's binding site in an effort for these proteins to be used as a vaccine against our own IgE antibody. The motif of interest was the FG loop motif and it was excised and grafted onto a Staphylococcus aureus protein (PDB ID 1YN3), then the motif + scaffold structure had its sequence re-designed around the motif to find an amino acid sequence that would fold to the designed structure correctly. These ten computationally designed proteins showed successful folding when simulated using Rosetta's AbinitioRelax folding simulation and the IgE epitope was clearly displayed in its native three-dimensional structure in all of them. These designed proteins have the potential to be used as a pan anti-allergy vaccine. This work employedin silicobased methods for designing the proteins and did not include any experimental verifications.

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.

Computational design and experimental optimization of protein binders with prospects for biomedical applications

Protein-based binders have become increasingly more attractive candidates for drug and imaging agent development. Such binders could be evolved from a number of different scaffolds, including antibodies, natural protein effectors and unrelated small protein domains of different geometries. While both computational and experimental approaches could be utilized for protein binder engineering, in this review we focus on various computational approaches for protein binder design and demonstrate how experimental selection could be applied to subsequently optimize computationally-designed molecules. Recent studies report a number of designed protein binders with pM affinities and high specificities for their targets. These binders usually characterized with high stability, solubility, and low production cost. Such attractive molecules are bound to become more common in various biotechnological and biomedical applications in the near future.

Potential Pathogenicity Determinants Identified from Structural Proteomics of SARS-CoV and SARS-CoV-2

Despite SARS-CoV and SARS-CoV-2 being equipped with highly similar protein arsenals, the corresponding zoonoses have spread among humans at extremely different rates. The specific characteristics of these viruses that led to such distinct outcomes remain unclear. Here, we apply proteome-wide comparative structural analysis aiming to identify the unique molecular elements in the SARS-CoV-2 proteome that may explain the differing consequences. By combining protein modeling and molecular dynamics simulations, we suggest nonconservative substitutions in functional regions of the spike glycoprotein (S), nsp1, and nsp3 that are contributing to differences in virulence. Particularly, we explain why the substitutions at the receptor-binding domain of S affect the structure–dynamics behavior in complexes with putative host receptors. Conservation of functional protein regions within the two taxa is also noteworthy. We suggest that the highly conserved main protease, nsp5, of SARS-CoV and SARS-CoV-2 is part of their mechanism of circumventing the host interferon antiviral response. Overall, most substitutions occur on the protein surfaces and may be modulating their antigenic properties and interactions with other macromolecules. Our results imply that the striking difference in the pervasiveness of SARS-CoV-2 and SARS-CoV among humans seems to significantly derive from molecular features that modulate the efficiency of viral particles in entering the host cells and blocking the host immune response.

Potentially adaptive SARS-CoV-2 mutations discovered with novel spatiotemporal and explainable AI models

BACKGROUND

A mechanistic understanding of the spread of SARS-CoV-2 and diligent tracking of ongoing mutagenesis are of key importance to plan robust strategies for confining its transmission. Large numbers of available sequences and their dates of transmission provide an unprecedented opportunity to analyze evolutionary adaptation in novel ways. Addition of high-resolution structural information can reveal the functional basis of these processes at the molecular level. Integrated systems biology-directed analyses of these data layers afford valuable insights to build a global understanding of the COVID-19 pandemic.

RESULTS

Here we identify globally distributed haplotypes from 15,789 SARS-CoV-2 genomes and model their success based on their duration, dispersal, and frequency in the host population. Our models identify mutations that are likely compensatory adaptive changes that allowed for rapid expansion of the virus. Functional predictions from structural analyses indicate that, contrary to previous reports, the Asp614Gly mutation in the spike glycoprotein (S) likely reduced transmission and the subsequent Pro323Leu mutation in the RNA-dependent RNA polymerase led to the precipitous spread of the virus. Our model also suggests that two mutations in the nsp13 helicase allowed for the adaptation of the virus to the Pacific Northwest of the USA. Finally, our explainable artificial intelligence algorithm identified a mutational hotspot in the sequence of S that also displays a signature of positive selection and may have implications for tissue or cell-specific expression of the virus.

CONCLUSIONS

These results provide valuable insights for the development of drugs and surveillance strategies to combat the current and future pandemics.

Protein loops with multiple meta-stable conformations: A challenge for sampling and scoring methods

Flexible regions in proteins, such as loops, cannot be represented by a single conformation. Instead, conformational ensembles are needed to provide a more global picture. In this context, identifying statistically meaningful conformations within an ensemble generated by loop sampling techniques remains an open problem. The difficulty is primarily related to the lack of structural data about these flexible regions. With the majority of structural data coming from x-ray crystallography and ignoring plasticity, the conception and evaluation of loop scoring methods is challenging. In this work, we compare the performance of various scoring methods on a set of eight protein loops that are known to be flexible. The ability of each method to identify and select all of the known conformations is assessed, and the underlying energy landscapes are produced and projected to visualize the qualitative differences obtained when using the methods. Statistical potentials are found to provide considerable reliability despite their being designed to tradeoff accuracy for lower computational cost. On a large pool of loop models, they are capable of filtering out statistically improbable states while retaining those that resemble known (and thus likely) conformations. However, computationally expensive methods are still required for more precise assessment and structural refinement. The results also highlight the importance of employing several scaffolds for the protein, due to the high influence of small structural rearrangements in the rest of the protein over the modeled energy landscape for the loop.

An enumerative algorithm for de novo design of proteins with diverse pocket structures

To create new enzymes and biosensors from scratch, precise control over the structure of small-molecule binding sites is of paramount importance, but systematically designing arbitrary protein pocket shapes and sizes remains an outstanding challenge. Using the NTF2-like structural superfamily as a model system, we developed an enumerative algorithm for creating a virtually unlimited number of de novo proteins supporting diverse pocket structures. The enumerative algorithm was tested and refined through feedback from two rounds of large-scale experimental testing, involving in total the assembly of synthetic genes encoding 7,896 designs and assessment of their stability on yeast cell surface, detailed biophysical characterization of 64 designs, and crystal structures of 5 designs. The refined algorithm generates proteins that remain folded at high temperatures and exhibit more pocket diversity than naturally occurring NTF2-like proteins. We expect this approach to transform the design of small-molecule sensors and enzymes by enabling the creation of binding and active site geometries much more optimal for specific design challenges than is accessible by repurposing the limited number of naturally occurring NTF2-like proteins.

De novo protein design enables the precise induction of RSV-neutralizing antibodies

De novo protein design has been successful in expanding the natural protein repertoire. However, most de novo proteins lack biological function, presenting a major methodological challenge. In vaccinology, the induction of precise antibody responses remains a cornerstone for next-generation vaccines. Here, we present a protein design algorithm called TopoBuilder, with which we engineered epitope-focused immunogens displaying complex structural motifs. In both mice and nonhuman primates, cocktails of three de novo-designed immunogens induced robust neutralizing responses against the respiratory syncytial virus. Furthermore, the immunogens refocused preexisting antibody responses toward defined neutralization epitopes. Overall, our design approach opens the possibility of targeting specific epitopes for the development of vaccines and therapeutic antibodies and, more generally, will be applicable to the design of de novo proteins displaying complex functional motifs.

The crystal structure of the heme d1 biosynthesis-associated small c-type cytochrome NirC reveals mixed oligomeric states in crystallo

Monoheme c-type cytochromes are important electron transporters in all domains of life. They possess a common fold hallmarked by three α-helices that surround a covalently attached heme. An intriguing feature of many monoheme c-type cytochromes is their capacity to form oligomers by exchanging at least one of their α-helices, which is often referred to as 3D domain swapping. Here, the crystal structure of NirC, a c-type cytochrome co-encoded with other proteins involved in nitrite reduction by the opportunistic pathogen Pseudomonas aeruginosa, has been determined. The crystals diffracted anisotropically to a maximum resolution of 2.12 Å (spherical resolution of 2.83 Å) and initial phases were obtained by Fe-SAD phasing, revealing the presence of 11 NirC chains in the asymmetric unit. Surprisingly, these protomers arrange into one monomer and two different types of 3D domain-swapped dimers, one of which shows pronounced asymmetry. While the simultaneous observation of monomers and dimers probably reflects the interplay between the high protein concentration required for crystallization and the structural plasticity of monoheme c-type cytochromes, the identification of conserved structural motifs in the monomer together with a comparison with similar proteins may offer new leads to unravel the unknown function of NirC.

The characterization of pc-polylines representing protein backbones

The backbone of a protein is typically represented as either a C _α -polyline, a three-dimensional (3D) polyline that passes through the C _α atoms, or a tuple of ϕ,ψ pairs while its fold is usually assigned using the 3D topological arrangement of the secondary structure elements (SSEs). It is tricky to obtain the SSE composition for a protein from the C _α -polyline representation while its 3D SSE arrangement is not apparent in the two-dimensional (2D) ϕ,ψ representation. In this article, we first represent the backbone of a protein as a pc-polyline that passes through the centers of its peptide planes. We then analyze the pc-polylines for six different sets of proteins with high quality crystal structures. The results show that SSE composition becomes recognizable in pc-polyline presentation and consequently the geometrical property of the pc-polyline of a protein could be used to assign its secondary structure. Furthermore, our analysis finds that for each of the six sets the total length of a pc-polyline increases linearly with the number of the peptide planes. Interestingly a comparison of the six regression lines shows that they have almost identical slopes but different intercepts. Most interestingly there exist decent linear correlations between the intercepts of the six lines and either the average helix contents or the average sheet contents and between the intercepts and the average backbone hydrogen bonding energetics. Finally, we discuss the implications of the identified correlations for structure classification and protein folding, and the potential applications of pc-polyline representation to structure prediction and protein design.

Cryo-EM Structure of Chikungunya Virus in Complex with the Mxra8 Receptor

Mxra8 is a receptor for multiple arthritogenic alphaviruses that cause debilitating acute and chronic musculoskeletal disease in humans. Herein, we present a 2.2 Å resolution X-ray crystal structure of Mxra8 and 4 to 5 Å resolution cryo-electron microscopy reconstructions of Mxra8 bound to chikungunya (CHIKV) virus-like particles and infectious virus. The Mxra8 ectodomain contains two strand-swapped Ig-like domains oriented in a unique disulfide-linked head-to-head arrangement. Mxra8 binds by wedging into a cleft created by two adjacent CHIKV E2-E1 heterodimers in one trimeric spike and engaging a neighboring spike. Two binding modes are observed with the fully mature VLP, with one Mxra8 binding with unique contacts. Only the high-affinity binding mode was observed in the complex with infectious CHIKV, as viral maturation and E3 occupancy appear to influence receptor binding-site usage. Our studies provide insight into how Mxra8 binds CHIKV and creates a path for developing alphavirus entry inhibitors.

Computational design of structured loops for new protein functions

The ability to engineer the precise geometries, fine-tuned energetics and subtle dynamics that are characteristic of functional proteins is a major unsolved challenge in the field of computational protein design. In natural proteins, functional sites exhibiting these properties often feature structured loops. However, unlike the elements of secondary structures that comprise idealized protein folds, structured loops have been difficult to design computationally. Addressing this shortcoming in a general way is a necessary first step towards the routine design of protein function. In this perspective, we will describe the progress that has been made on this problem and discuss how recent advances in the field of loop structure prediction can be harnessed and applied to the inverse problem of computational loop design.

Ultrahigh specificity in a network of computationally designed protein-interaction pairs

Protein networks in all organisms comprise homologous interacting pairs. In these networks, some proteins are specific, interacting with one or a few binding partners, whereas others are multispecific and bind a range of targets. We describe an algorithm that starts from an interacting pair and designs dozens of new pairs with diverse backbone conformations at the binding site as well as new binding orientations and sequences. Applied to a high-affinity bacterial pair, the algorithm results in 18 new ones, with cognate affinities from pico- to micromolar. Three pairs exhibit 3-5 orders of magnitude switch in specificity relative to the wild type, whereas others are multispecific, collectively forming a protein-interaction network. Crystallographic analysis confirms design accuracy, including in new backbones and polar interactions. Preorganized polar interaction networks are responsible for high specificity, thus defining design principles that can be applied to program synthetic cellular interaction networks of desired affinity and specificity.

The molecular basis of ultrahigh specificity in protein-protein interactions remains obscure. The authors present a computational method to design atomically accurate new pairs exhibiting >100,000-fold specificity switches, generating a large and complex interaction network.

Rosetta FunFolDes - A general framework for the computational design of functional proteins

The robust computational design of functional proteins has the potential to deeply impact translational research and broaden our understanding of the determinants of protein function and stability. The low success rates of computational design protocols and the extensive in vitro optimization often required, highlight the challenge of designing proteins that perform essential biochemical functions, such as binding or catalysis. One of the most simplistic approaches for the design of function is to adopt functional motifs in naturally occurring proteins and transplant them to computationally designed proteins. The structural complexity of the functional motif largely determines how readily one can find host protein structures that are "designable", meaning that are likely to present the functional motif in the desired conformation. One promising route to enhance the "designability" of protein structures is to allow backbone flexibility. Here, we present a computational approach that couples conformational folding with sequence design to embed functional motifs into heterologous proteins-Rosetta Functional Folding and Design (FunFolDes). We performed extensive computational benchmarks, where we observed that the enforcement of functional requirements resulted in designs distant from the global energetic minimum of the protein. An observation consistent with several experimental studies that have revealed function-stability tradeoffs. To test the design capabilities of FunFolDes we transplanted two viral epitopes into distant structural templates including one de novo "functionless" fold, which represent two typical challenges where the designability problem arises. The designed proteins were experimentally characterized showing high binding affinities to monoclonal antibodies, making them valuable candidates for vaccine design endeavors. Overall, we present an accessible strategy to repurpose old protein folds for new functions. This may lead to important improvements on the computational design of proteins, with structurally complex functional sites, that can perform elaborate biochemical functions related to binding and catalysis.

Engineering Improved Antiphosphotyrosine Antibodies Based on an Immunoconvergent Binding Motif

Phosphotyrosine (pY) is one of the most highly studied posttranslational modifications that is responsible for tightly regulating many signaling pathways in eukaryotes. Pan-specific pY antibodies have emerged as powerful tools for understanding the role of these modifications. Nevertheless, structures have not been reported for pan-specific pY antibodies, greatly impeding the further development of tools for integrating this ubiquitous posttranslational modification using structure-guided designs. Here, we present the first crystal structures of two widely utilized pan-specific pY antibodies, PY20 and 4G10. The two antibodies, although developed independently from animal immunizations, have surprisingly similar modes of recognition of the phosphate group, implicating a generic binding structure among pan-specific pY antibodies. Sequence alignments revealed that many pY binding residues are predominant in the mouse V germline genes, which consequently led to the convergent antibodies. On the basis of the convergent structure, we designed a phage display library by lengthening the CDR-L3 loop with the aid of computational modeling. Panning with this library resulted in a series of 4G10 variants with 4 to 11-fold improvements in pY binding affinities. The crystal structure of one improved variant showed remarkable superposition to the computational model, where the lengthened CDR-L3 loop creates an additional hydrogen bond indirectly bound to the phosphate group via a water molecule. The engineered variants exhibited superior performance in Western blot and immunofluorescence.

Engineering a Protein Binder Specific for p38α with Interface Expansion

Protein binding specificities can be manipulated by redesigning contacts that already exist at an interface or by expanding the interface to allow interactions with residues adjacent to the original binding site. Previously, we developed a strategy, called AnchorDesign, for expanding interfaces around linear binding epitopes. The epitope is embedded in a loop of a scaffold protein, in our case a monobody, and then surrounding residues on the monobody are optimized for binding using directed evolution or computational design. Using this strategy, we have increased binding affinities by >100-fold, but we have not tested whether it can be used to control protein binding specificities. Here, we test whether AnchorDesign can be used to engineer a monobody that binds specifically to the mitogen-activated protein kinase (MAPK) p38α but not to the related MAPKs ERK2 and JNK. To anchor the binding interaction, we used a small (D) docking motif from the mitogen-activated protein kinase kinase (MAP2K) MKK6 that interacts with similar affinity with p38α and ERK2. Our hypothesis was that by embedding the motif in a larger protein that we could expand the interface and create contacts with residues that are not conserved between p38α and ERK2. Molecular modeling was used to inform insertion of the D motif into the monobody, and a combination of phage and yeast display were used to optimize the interface. Binding experiments demonstrate that the engineered monobody binds to the target surface on p38α and does not exhibit detectable binding to ERK2 or JNK.

A computational method for the design of nested proteins by loop-directed domain insertion

The computational design of novel nested proteins-in which the primary structure of one protein domain (insert) is flanked by the primary structure segments of another (parent)-would enable the generation of multifunctional proteins. Here we present a new algorithm, called Loop-Directed Domain Insertion (LooDo), implemented within the Rosetta software suite, for the purpose of designing nested protein domain combinations connected by flexible linker regions. Conformational space for the insert domain is sampled using large libraries of linker fragments for linker-to-parent domain superimposition followed by insert-to-linker superimposition. The relative positioning of the two domains (treated as rigid bodies) is sampled efficiently by a grid-based, mutual placement compatibility search. The conformations of the loop residues, and the identities of loop as well as interface residues, are simultaneously optimized using a generalized kinematic loop closure algorithm and Rosetta EnzymeDesign, respectively, to minimize interface energy. The algorithm was found to consistently sample near-native conformations and interface sequences for a benchmark set of structurally similar but functionally divergent domain-inserted enzymes from the α/β hydrolase superfamily, and discriminates well between native and nonnative conformations and sequences, although loop conformations tended to deviate from the native conformations. Furthermore, in cross-domain placement tests, native insert-parent domain combinations were ranked as the best-scoring structures compared to nonnative domain combinations. This algorithm should be broadly applicable to the design of multi-domain protein complexes with any combination of inserted or tandem domain connections.

Integration of molecular dynamics simulation and hotspot residues grafting for de novo scFv design against Salmonella Typhi TolC protein

With the development of de novo binders for protein targets from non-related scaffolds, many possibilities for therapeutics and diagnostics have been created. In this study, we described the use of de novo design approach to create single-chain fragment variable (scFv) for Salmonella enterica subspecies enterica serovar Typhi TolC protein. Typhoid fever is a global health concern in developing and underdeveloped countries. Rapid typhoid diagnostics will improve disease management and therapy. In this work, molecular dynamics simulation was first performed on a homology model of TolC protein in POPE membrane bilayer to obtain the central structure that was subsequently used as the target for scFv design. Potential hotspot residues capable of anchoring the binders to the target were identified by docking "disembodied" amino acid residues against TolC surface. Next, scFv scaffolds were selected from Protein Data Bank to harbor the computed hotspot residues. The hotspot residues were then incorporated into the scFv scaffold complementarity determining regions. The designs recapitulated binding energy, shape complementarity, and interface surface area of natural protein-antibody interfaces. This approach has yielded 5 designs with high binding affinity against TolC that may be beneficial for the future development of antigen-based detection agents for typhoid diagnostics.

The Interaction between the Third Type III Domain from Fibronectin and Anastellin Involves β-Strand Exchange

Anastellin is a small recombinant fragment derived from the extracellular matrix protein fibronectin; it comprises the first type III (FN3) domain without the two N-terminal β-strands. It inhibits angiogenesis, tumor growth, and metastasis in mouse models and requires endogenous fibronectin for its in vivo anti-angiogenic activity. It binds to fibronectin in vitro and converts the soluble protein to insoluble fibrils that structurally and functionally resemble fibronectin fibrils deposited in the extracellular matrix by cells. Anastellin binds to several FN3 domains in fibronectin, but how it interacts with these domains and why the interactions lead to aggregation of fibronectin are not well understood. In this work, we investigated the interaction between anastellin and the third FN3 domain (3FN3) from fibronectin. We show that anastellin binds with high affinity to a peptide comprising the two N-terminal β-strands from 3FN3, and we present here the structure of the resulting complex. The peptide and anastellin form a composite FN3 domain, with the two N-terminal β-strands from 3FN3 bound in place of the two β-strands that are missing in anastellin. We also demonstrate using disulfide cross-linking that a similar interaction involving the two N-terminal β-strands of 3FN3 occurs when intact 3FN3 binds to anastellin. 3FN3 adopts a compact globular fold in solution, and to interact with anastellin in a manner consistent with our data, it has to open up and expose a β-strand edge that is not accessible in the context of the folded domain.

Engineering protein stability with atomic precision in a monomeric miniprotein

Miniproteins simplify the protein-folding problem, allowing the dissection of forces that stabilize protein structures. Here we describe PPα-Tyr, a designed peptide comprising an α-helix buttressed by a polyproline II helix. PPα-Tyr is water soluble and monomeric, and it unfolds cooperatively with a midpoint unfolding temperature (T_M) of 39 °C. NMR structures of PPα-Tyr reveal proline residues docked between tyrosine side chains, as designed. The stability of PPα is sensitive to modifications in the aromatic residues: replacing tyrosine with phenylalanine, i.e., changing three solvent-exposed hydroxyl groups to protons, reduces the T_M to 20 °C. We attribute this result to the loss of CH-π interactions between the aromatic and proline rings, which we probe by substituting the aromatic residues with nonproteinogenic side chains. In analyses of natural protein structures, we find a preference for proline-tyrosine interactions over other proline-containing pairs, and observe abundant CH-π interactions in biologically important complexes between proline-rich ligands and SH3 and similar domains.

Engineering carboxypeptidase G2 circular permutations for the design of an autoinhibited enzyme

Carboxypeptidase G2 (CPG2) is an Food and Drug Administration (FDA)-approved enzyme drug used to treat methotrexate (MTX) toxicity in cancer patients receiving MTX treatment. It has also been used in directed enzyme-prodrug chemotherapy, but this strategy has been hampered by off-site activation of the prodrug by the circulating enzyme. The development of a tumor protease activatable CPG2, which could be achieved using a circular permutation of CPG2 fused to an inactivating 'prodomain', would aid in these applications. We report the development of a protease accessibility-based screen to identify candidate sites for circular permutation in proximity of the CPG2 active site. The resulting six circular permutants showed similar expression, structure, thermal stability, and, in four cases, activity levels compared to the wild-type enzyme. We rationalize these results based on structural models of the permutants obtained using the Rosetta software. We developed a cell growth-based selection system, and demonstrated that when fused to periplasm-directing signal peptides, one of our circular permutants confers MTX resistance in Escherichia coli with equal efficiency as the wild-type enzyme. As the permutants have similar properties to wild-type CPG2, these enzymes are promising starting points for the development of autoinhibited, protease-activatable zymogen forms of CPG2 for use in therapeutic contexts.

Achievements and Challenges in Computational Protein Design

Computational protein design (CPD), a yet evolving field, includes computer-aided engineering for partial or full de novo designs of proteins of interest. Designs are defined by a requested structure, function, or working environment. This chapter describes the birth and maturation of the field by presenting 101 CPD examples in a chronological order emphasizing achievements and pending challenges. Integrating these aspects presents the plethora of CPD approaches with the hope of providing a "CPD 101". These reflect on the broader structural bioinformatics and computational biophysics field and include: (1) integration of knowledge-based and energy-based methods, (2) hierarchical designated approach towards local, regional, and global motifs and the integration of high- and low-resolution design schemes that fit each such region, (3) systematic differential approaches towards different protein regions, (4) identification of key hot-spot residues and the relative effect of remote regions, (5) assessment of shape-complementarity, electrostatics and solvation effects, (6) integration of thermal plasticity and functional dynamics, (7) negative design, (8) systematic integration of experimental approaches, (9) objective cross-assessment of methods, and (10) successful ranking of potential designs. Future challenges also include dissemination of CPD software to the general use of life-sciences researchers and the emphasis of success within an in vivo milieu. CPD increases our understanding of protein structure and function and the relationships between the two along with the application of such know-how for the benefit of mankind. Applied aspects range from biological drugs, via healthier and tastier food products to nanotechnology and environmentally friendly enzymes replacing toxic chemicals utilized in the industry.

Computational protein design with backbone plasticity

The computational algorithms used in the design of artificial proteins have become increasingly sophisticated in recent years, producing a series of remarkable successes. The most dramatic of these is the de novo design of artificial enzymes. The majority of these designs have reused naturally occurring protein structures as ‘scaffolds’ onto which novel functionality can be grafted without having to redesign the backbone structure. The incorporation of backbone flexibility into protein design is a much more computationally challenging problem due to the greatly increased search space, but promises to remove the limitations of reusing natural protein scaffolds. In this review, we outline the principles of computational protein design methods and discuss recent efforts to consider backbone plasticity in the design process.

Synthetic beta-solenoid proteins with the fragment-free computational design of a beta-hairpin extension

The ability to design and construct structures with atomic level precision is one of the key goals of nanotechnology. Proteins offer an attractive target for atomic design because they can be synthesized chemically or biologically and can self-assemble. However, the generalized protein folding and design problem is unsolved. One approach to simplifying the problem is to use a repetitive protein as a scaffold. Repeat proteins are intrinsically modular, and their folding and structures are better understood than large globular domains. Here, we have developed a class of synthetic repeat proteins based on the pentapeptide repeat family of beta-solenoid proteins. We have constructed length variants of the basic scaffold and computationally designed de novo loops projecting from the scaffold core. The experimentally solved 3.56-Å resolution crystal structure of one designed loop matches closely the designed hairpin structure, showing the computational design of a backbone extension onto a synthetic protein core without the use of backbone fragments from known structures. Two other loop designs were not clearly resolved in the crystal structures, and one loop appeared to be in an incorrect conformation. We have also shown that the repeat unit can accommodate whole-domain insertions by inserting a domain into one of the designed loops.

Design of Heteronuclear Metalloenzymes

Heteronuclear metalloenzymes catalyze some of the most fundamentally interesting and practically useful reactions in nature. However, the presence of two or more metal ions in close proximity in these enzymes makes them more difficult to prepare and study than homonuclear metalloenzymes. To meet these challenges, heteronuclear metal centers have been designed into small and stable proteins with rigid scaffolds to understand how these heteronuclear centers are constructed and the mechanism of their function. This chapter describes methods for designing heterobinuclear metal centers in a protein scaffold by giving specific examples of a few heme-nonheme bimetallic centers engineered in myoglobin and cytochrome c peroxidase. We provide step-by-step procedures on how to choose the protein scaffold, design a heterobinuclear metal center in the protein scaffold computationally, incorporate metal ions into the protein, and characterize the resulting metalloproteins, both structurally and functionally. Finally, we discuss how an initial design can be further improved by rationally tuning its secondary coordination sphere, electron/proton transfer rates, and the substrate affinity.

The effect of chirality and steric hindrance on intrinsic backbone conformational propensities: tools for protein design

The conformational propensities of amino acids are an amalgamation of sequence effects, environmental effects and underlying intrinsic behavior. Many have attempted to investigate neighboring residue effects to aid in our understanding of protein folding and improve structure prediction efforts, especially with respect to difficult to characterize states, such as disordered or unfolded states. Host-guest peptide series are a useful tool in examining the propensities of the amino acids free from the surrounding protein structure. Here, we compare the distributions of the backbone dihedral angles (φ/ψ) of the 20 proteogenic amino acids in two different sequence contexts using the AAXAA and GGXGG host-guest pentapeptide series. We further examine their intrinsic behaviors across three environmental contexts: water at 298 K, water at 498 K, and 8 M urea at 298 K. The GGXGG systems provide the intrinsic amino acid propensities devoid of any conformational context. The alanine residues in the AAXAA series enforce backbone chirality, thereby providing a model of the intrinsic behavior of amino acids in a protein chain. Our results show modest differences in φ/ψ distributions due to the steric constraints of the Ala side chains, the magnitudes of which are dependent on the denaturing conditions. One of the strongest factors modulating φ/ψ distributions was the protonation of titratable side chains, and the largest differences observed were in the amino acid propensities for the rarely sampled αL region.

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.

Shortening a loop can increase protein native state entropy

Protein loops are essential structural elements that influence not only function but also protein stability and folding rates. It was recently reported that shortening a loop in the AcP protein may increase its native state conformational entropy. This effect on the entropy of the folded state can be much larger than the lower entropic penalty of ordering a shorter loop upon folding, and can therefore result in a more pronounced stabilization than predicted by polymer model for loop closure entropy. In this study, which aims at generalizing the effect of loop length shortening on native state dynamics, we use all-atom molecular dynamics simulations to study how gradual shortening a very long or solvent-exposed loop region in four different proteins can affect their stability. For two proteins, AcP and Ubc7, we show an increase in native state entropy in addition to the known effect of the loop length on the unfolded state entropy. However, for two permutants of SH3 domain, shortening a loop results only with the expected change in the entropy of the unfolded state, which nicely reproduces the observed experimental stabilization. Here, we show that an increase in the native state entropy following loop shortening is not unique to the AcP protein, yet nor is it a general rule that applies to all proteins following the truncation of any loop. This modification of the loop length on the folded state and on the unfolded state may result with a greater effect on protein stability.

Computational design and experimental verification of a symmetric protein homodimer

Homodimers are the most common type of protein assembly in nature and have distinct features compared with heterodimers and higher order oligomers. Understanding homodimer interactions at the atomic level is critical both for elucidating their biological mechanisms of action and for accurate modeling of complexes of unknown structure. Computation-based design of novel protein-protein interfaces can serve as a bottom-up method to further our understanding of protein interactions. Previous studies have demonstrated that the de novo design of homodimers can be achieved to atomic-level accuracy by β-strand assembly or through metal-mediated interactions. Here, we report the design and experimental characterization of a α-helix-mediated homodimer with C2 symmetry based on a monomeric Drosophila engrailed homeodomain scaffold. A solution NMR structure shows that the homodimer exhibits parallel helical packing similar to the design model. Because the mutations leading to dimer formation resulted in poor thermostability of the system, design success was facilitated by the introduction of independent thermostabilizing mutations into the scaffold. This two-step design approach, function and stabilization, is likely to be generally applicable, especially if the desired scaffold is of low thermostability.

Design of symmetric TIM barrel proteins from first principles

Background

Computational protein design is a rapidly maturing field within structural biology, with the goal of designing proteins with custom structures and functions. Such proteins could find widespread medical and industrial applications. Here, we have adapted algorithms from the Rosetta software suite to design much larger proteins, based on ideal geometric and topological criteria. Furthermore, we have developed techniques to incorporate symmetry into designed structures. For our first design attempt, we targeted the (α/β)₈ TIM barrel scaffold. We gained novel insights into TIM barrel folding mechanisms from studying natural TIM barrel structures, and from analyzing previous TIM barrel design attempts.

Methods

Computational protein design and analysis was performed using the Rosetta software suite and custom scripts. Genes encoding all designed proteins were synthesized and cloned on the pET20-b vector. Standard circular dichroism and gel chromatographic experiments were performed to determine protein biophysical characteristics. 1D NMR and 2D HSQC experiments were performed to determine protein structural characteristics.

Results

Extensive protein design simulations coupled with ab initio modeling yielded several all-atom models of ideal, 4-fold symmetric TIM barrels. Four such models were experimentally characterized. The best designed structure (Symmetrin-1) contained a polar, histidine-rich pore, forming an extensive hydrogen bonding network. Symmetrin-1 was easily expressed and readily soluble. It showed circular dichroism spectra characteristic of well-folded alpha/beta proteins. Temperature melting experiments revealed cooperative and reversible unfolding, with a T_m of 44 °C and a Gibbs free energy of unfolding (ΔG°) of 8.0 kJ/mol. Urea denaturing experiments confirmed these observations, revealing a C_m of 1.6 M and a ΔG° of 8.3 kJ/mol. Symmetrin-1 adopted a monomeric conformation, with an apparent molecular weight of 32.12 kDa, and displayed well resolved 1D-NMR spectra. However, the HSQC spectrum revealed somewhat molten characteristics.

Conclusions

Despite the detection of molten characteristics, the creation of a soluble, cooperatively folding protein represents an advancement over previous attempts at TIM barrel design. Strategies to further improve Symmetrin-1 are elaborated. Our techniques may be used to create other large, internally symmetric proteins.

Electronic supplementary material

The online version of this article (doi:10.1186/s12858-015-0047-4) contains supplementary material, which is available to authorized users.

AbDesign: An algorithm for combinatorial backbone design guided by natural conformations and sequences

Computational design of protein function has made substantial progress, generating new enzymes, binders, inhibitors, and nanomaterials not previously seen in nature. However, the ability to design new protein backbones for function--essential to exert control over all polypeptide degrees of freedom--remains a critical challenge. Most previous attempts to design new backbones computed the mainchain from scratch. Here, instead, we describe a combinatorial backbone and sequence optimization algorithm called AbDesign, which leverages the large number of sequences and experimentally determined molecular structures of antibodies to construct new antibody models, dock them against target surfaces and optimize their sequence and backbone conformation for high stability and binding affinity. We used the algorithm to produce antibody designs that target the same molecular surfaces as nine natural, high-affinity antibodies; in five cases interface sequence identity is above 30%, and in four of those the backbone conformation at the core of the antibody binding surface is within 1 Å root-mean square deviation from the natural antibodies. Designs recapitulate polar interaction networks observed in natural complexes, and amino acid sidechain rigidity at the designed binding surface, which is likely important for affinity and specificity, is high compared to previous design studies. In designed anti-lysozyme antibodies, complementarity-determining regions (CDRs) at the periphery of the interface, such as L1 and H2, show greater backbone conformation diversity than the CDRs at the core of the interface, and increase the binding surface area compared to the natural antibody, potentially enhancing affinity and specificity.

Design of Protein Multi-specificity Using an Independent Sequence Search Reduces the Barrier to Low Energy Sequences

Computational protein design has found great success in engineering proteins for thermodynamic stability, binding specificity, or enzymatic activity in a 'single state' design (SSD) paradigm. Multi-specificity design (MSD), on the other hand, involves considering the stability of multiple protein states simultaneously. We have developed a novel MSD algorithm, which we refer to as REstrained CONvergence in multi-specificity design (RECON). The algorithm allows each state to adopt its own sequence throughout the design process rather than enforcing a single sequence on all states. Convergence to a single sequence is encouraged through an incrementally increasing convergence restraint for corresponding positions. Compared to MSD algorithms that enforce (constrain) an identical sequence on all states the energy landscape is simplified, which accelerates the search drastically. As a result, RECON can readily be used in simulations with a flexible protein backbone. We have benchmarked RECON on two design tasks. First, we designed antibodies derived from a common germline gene against their diverse targets to assess recovery of the germline, polyspecific sequence. Second, we design "promiscuous", polyspecific proteins against all binding partners and measure recovery of the native sequence. We show that RECON is able to efficiently recover native-like, biologically relevant sequences in this diverse set of protein complexes.

Emerging Vaccine Technologies

Vaccination has proven to be an invaluable means of preventing infectious diseases by reducing both incidence of disease and mortality. However, vaccines have not been effectively developed for many diseases including HIV-1, hepatitis C virus (HCV), tuberculosis and malaria, among others. The emergence of new technologies with a growing understanding of host-pathogen interactions and immunity may lead to efficacious vaccines against pathogens, previously thought impossible.

Structural and dynamic changes associated with beneficial engineered single-amino-acid deletion mutations in enhanced green fluorescent protein

Single-amino-acid deletions are a common part of the natural evolutionary landscape but are rarely sampled during protein engineering owing to limited and prejudiced molecular understanding of mutations that shorten the protein backbone. Single-amino-acid deletion variants of enhanced green fluorescent protein (EGFP) have been identified by directed evolution with the beneficial effect of imparting increased cellular fluorescence. Biophysical characterization revealed that increased functional protein production and not changes to the fluorescence parameters was the mechanism that was likely to be responsible. The structure EGFP(D190Δ) containing a deletion within a loop revealed propagated changes only after the deleted residue. The structure of EGFP(A227Δ) revealed that a `flipping' mechanism was used to adjust for residue deletion at the end of a β-strand, with amino acids C-terminal to the deletion site repositioning to take the place of the deleted amino acid. In both variants new networks of short-range and long-range interactions are generated while maintaining the integrity of the hydrophobic core. Both deletion variants also displayed significant local and long-range changes in dynamics, as evident by changes in B factors compared with EGFP. Rather than being detrimental, deletion mutations can introduce beneficial structural effects through altering core protein properties, folding and dynamics, as well as function.

N-terminal β-strand swapping in a consensus-derived alternative scaffold driven by stabilizing hydrophobic interactions

The crystal structure of an N-terminal β-strand-swapped consensus-derived tenascin FN3 alternative scaffold has been determined. A comparison with the unswapped structure reveals that the side chain of residue F88 orients differently and packs more tightly with the hydrophobic core of the domain. Dimer formation also results in the burial of a hydrophobic patch on the surface of the domain. Thus, it appears that tighter packing of F88 in the hydrophobic core and burial of surface hydrophobicity provide the driving forces for the N-terminal β-strand swapping, leading to the formation of a stable compact dimer.