Anchored design of protein-protein interfaces

AbFlex: designing antibody complementarity determining regions with flexible CDR definition

MOTIVATION

Antibodies are proteins that the immune system produces in response to foreign pathogens. Designing antibodies that specifically bind to antigens is a key step in developing antibody therapeutics. The complementarity determining regions (CDRs) of the antibody are mainly responsible for binding to the target antigen, and therefore must be designed to recognize the antigen.

RESULTS

We develop an antibody design model, AbFlex, that exhibits state-of-the-art performance in terms of structure prediction accuracy and amino acid recovery rate. Furthermore, >38% of newly designed antibody models are estimated to have better binding energies for their antigens than wild types. The effectiveness of the model is attributed to two different strategies that are developed to overcome the difficulty associated with the scarcity of antibody-antigen complex structure data. One strategy is to use an equivariant graph neural network model that is more data-efficient. More importantly, a new data augmentation strategy based on the flexible definition of CDRs significantly increases the performance of the CDR prediction model.

AVAILABILITY AND IMPLEMENTATION

The source code and implementation are available at https://github.com/wsjeon92/AbFlex.

Assembly Requirements for the Construction of Large-Scale Binary Protein Structures

The precise assembly of multiple biomacromolecules into well-defined structures and materials is of great importance for various biomedical and nanobiotechnological applications. In this study, we investigate the assembly requirements for two-component materials using charged protein nanocages as building blocks. To achieve this, we designed several variants of ferritin nanocages to determine the surface characteristics necessary for the formation of large-scale binary three-dimensional (3D) assemblies. These nanocage variants were employed in protein crystallization experiments and macromolecular crystallography analyses, complemented by computational methods. Through the screening of nanocage variant combinations at various ionic strengths, we identified three essential features for successful assembly: (1) the presence of a favored crystal contact region, (2) the presence of a charged patch not involved in crystal contacts, and (3) sufficient distinctiveness between the nanocages. Surprisingly, the absence of noncrystal contact mediating patches had a detrimental effect on the assemblies, highlighting their unexpected importance. Intriguingly, we observed the formation of not only binary structures but also both negatively and positively charged unitary structures under previously exclusively binary conditions. Overall, our findings will inform future design strategies by providing some design rules, showcasing the utility of supercharging symmetric building blocks in facilitating the assembly of biomacromolecules into large-scale binary 3D assemblies.

Pyruvate dehydrogenase operates as an intramolecular nitroxyl generator during macrophage metabolic reprogramming

M1 macrophages enter a glycolytic state when endogenous nitric oxide (NO) reprograms mitochondrial metabolism by limiting aconitase 2 and pyruvate dehydrogenase (PDH) activity. Here, we provide evidence that NO targets the PDH complex by using lipoate to generate nitroxyl (HNO). PDH E2-associated lipoate is modified in NO-rich macrophages while the PDH E3 enzyme, also known as dihydrolipoamide dehydrogenase (DLD), is irreversibly inhibited. Mechanistically, we show that lipoate facilitates NO-mediated production of HNO, which interacts with thiols forming irreversible modifications including sulfinamide. In addition, we reveal a macrophage signature of proteins with reduction-resistant modifications, including in DLD, and identify potential HNO targets. Consistently, DLD enzyme is modified in an HNO-dependent manner at Cys477 and Cys484, and molecular modeling and mutagenesis show these modifications impair the formation of DLD homodimers. In conclusion, our work demonstrates that HNO is produced physiologically. Moreover, the production of HNO is dependent on the lipoate-rich PDH complex facilitating irreversible modifications that are critical to NO-dependent metabolic rewiring.

Rational peptide design for inhibition of the KIX-MLL interaction

The kinase-inducible domain interacting (KIX) domain is an integral part of the general transcriptional coactivator CREB-binding protein, and has been associated with leukemia, cancer, and various viral diseases. Hence, the KIX domain has attracted considerable attention in drug discovery and development. Here, we rationally designed a KIX inhibitor using a peptide fragment corresponding to the transactivation domain (TAD) of the transcriptional activator, mixed-lineage leukemia protein (MLL). We performed theoretical saturation mutagenesis using the Rosetta software to search for mutants expected to bind KIX more tightly than the wild-type MLL TAD. Mutant peptides with higher helical propensities were selected for experimental characterization. We found that the T2857W mutant of the MLL TAD peptide had the highest binding affinity for KIX compared to the other 12 peptides designed in this study. Moreover, the peptide had a high inhibitory effect on the KIX-MLL interaction with a half-maximal inhibitory concentration close to the dissociation constant for this interaction. To our knowledge, this peptide has the highest affinity for KIX among all previously reported inhibitors that target the MLL site of KIX. Thus, our approach may be useful for rationally developing helical peptides that inhibit protein-protein interactions implicated in the progression of various diseases.

Crystal structure of LGR ligand α2/β5 from Caenorhabditis elegans with implications for the evolution of glycoprotein hormones

A family of leucine-rich-repeat-containing G-protein-coupled receptors (LGRs) mediate diverse physiological responses when complexed with their cognate ligands. LGRs are present in all metazoan animals. In humans, the LGR ligands include glycoprotein hormones (GPHs) chorionic gonadotropin (hCG), luteinizing hormone, follicle-stimulating hormone (hFSH), and thyroid-stimulating hormone (hTSH). These hormones are αβ heterodimers of cystine-knot protein chains. LGRs and their ligand chains have coevolved. Ancestral hormone homologs, present in both bilaterian animals and chordates, are identified as α2β5. We have used single-wavelength anomalous diffraction and molecular replacement to determine structures of the α2β5 hormone from Caenorhabditis elegans (Ceα2β5). Ceα2β5 is unglycosylated, as are many other α2β5 hormones. Both Hsα2β5, the human homolog of Ceα2β5, and hTSH activate the same receptor (hTSHR). Despite having little sequence similarity to vertebrate GPHs, apart from the cysteine patterns from core disulfide bridges, Ceα2β5 is generally similar in structure to these counterparts; however, its α2 and β5 subunits are more symmetric as compared with α and β of hCG and hFSH. This quasisymmetry suggests a hypothetical homodimeric antecedent of the α2β5 and αβ heterodimers. Known structures together with AlphaFold models from the sequences for other LGR ligands provide representatives for the molecular evolution of LGR ligands from early metazoans through the present-day GPHs. The experimental Ceα2β5 structure validates its AlphaFold model, and thus also that for Hsα2β5; and interfacial characteristics in a model for the Hsα2β5:hTSHR complex are similar to those found in an experimental hTSH:hTSHR structure.

Hallucinating structure-conditioned antibody libraries for target-specific binders

Antibodies are widely developed and used as therapeutics to treat cancer, infectious disease, and inflammation. During development, initial leads routinely undergo additional engineering to increase their target affinity. Experimental methods for affinity maturation are expensive, laborious, and time-consuming and rarely allow the efficient exploration of the relevant design space. Deep learning (DL) models are transforming the field of protein engineering and design. While several DL-based protein design methods have shown promise, the antibody design problem is distinct, and specialized models for antibody design are desirable. Inspired by hallucination frameworks that leverage accurate structure prediction DL models, we propose the FvHallucinator for designing antibody sequences, especially the CDR loops, conditioned on an antibody structure. Such a strategy generates targeted CDR libraries that retain the conformation of the binder and thereby the mode of binding to the epitope on the antigen. On a benchmark set of 60 antibodies, FvHallucinator generates sequences resembling natural CDRs and recapitulates perplexity of canonical CDR clusters. Furthermore, the FvHallucinator designs amino acid substitutions at the VH-VL interface that are enriched in human antibody repertoires and therapeutic antibodies. We propose a pipeline that screens FvHallucinator designs to obtain a library enriched in binders for an antigen of interest. We apply this pipeline to the CDR H3 of the Trastuzumab-HER2 complex to generate in silico designs predicted to improve upon the binding affinity and interfacial properties of the original antibody. Thus, the FvHallucinator pipeline enables generation of inexpensive, diverse, and targeted antibody libraries enriched in binders for antibody affinity maturation.

Changes in antibody binding and functionality after humanizing a murine scFv anti-IFN-α2: From in silico studies to experimental analysis

The structural and dynamic changes introduced during antibody humanization continue to be a topic open to new contributions. For this reason, the study of structural and functional changes of a murine scFv (mu.scFv) anti-rhIFN-α2b after humanization was carried out. As it was shown by long molecular dynamics simulations and circular dichroism analysis, changes in primary sequence affected the tertiary structure of the humanized scFv (hz.scFv): the position of the variable domain of light chain (VL) respective to the variable domain of heavy chain (VH) in each scFv molecule was different. This change mainly impacted on conformation and dynamics of the complementarity-determining region 3 of VH (CDR-H3) which led to changes in the specificity and affinity of humanized scFv (hz.scFv). These observations agree with experimental results that showed a decrease in the antigen-binding strength of hz.scFv, and different capacities of these molecules to neutralize the in vitro rhIFN-α2b biological activity. Besides, experimental studies to characterize antigen-antibody binding showed that mu.scFv and hz.scFv bind to the same antigen area and recognize a conformational epitope, which is evidence of docking results. Finally, the differences between these molecules to neutralize the in vitro rhIFN-α2b biological activity were described as a consequence of the blockade of certain functionally relevant amino acids of the cytokine, after scFv binding. All these observations confirmed that humanization affected the affinity and specificity of hz.scFv and pointed out that two specific changes in the frameworks would be responsible.

Simulation of Energy-Resolved Mass Spectrometry Distributions from Surface-Induced Dissociation

Understanding the relationship between protein structure and experimental data is crucial for utilizing experiments to solve biochemical problems and optimizing the use of sparse experimental data for structural interpretation. Tandem mass spectrometry (MS/MS) can be used with a variety of methods to collect structural data for proteins. One example is surface-induced dissociation (SID), which is used to break apart protein complexes (via a surface collision) into intact subcomplexes and can be performed at multiple laboratory frame SID collision energies. These energy-resolved MS/MS experiments have shown that the profile of the breakages depends on the acceleration energy of the collision. It is possible to extract an appearance energy (AE) from energy-resolved mass spectrometry (ERMS) data, which shows the relative intensity of each type of subcomplex as a function of SID acceleration energy. We previously determined that these AE values for specific interfaces correlated with structural features related to interface strength. In this study, we further examined the structural relationships by developing a method to predict the full ERMS plot from the structure, rather than extracting a single value. First, we noted that for proteins with multiple interface types, we could reproduce the correct shapes of breakdown curves, further confirming previous structural hypotheses. Next, we demonstrated that interface size and energy density (measured using Rosetta) correlated with data derived from the ERMS plot (R2 = 0.71). Furthermore, based on this trend, we used native crystal structures to predict ERMS. The majority of predictions resulted in good agreement, and the average root-mean-square error was 0.20 for the 20 complexes in our data set. We also show that if additional information on cleavage as a function of collision energy could be obtained, the accuracy of predictions improved further. Finally, we demonstrated that ERMS prediction results were better for the native than for inaccurate models in 17/20 cases. An application to run this simulation has been developed in Rosetta, which is freely available for use.

Surface-induced Dissociation Mass Spectrometry as a Structural Biology Tool

Native mass spectrometry (nMS) is evolving into a workhorse for structural biology. The plethora of online and offline preparation, separation, and purification methods as well as numerous ionization techniques combined with powerful new hybrid ion mobility and mass spectrometry systems has illustrated the great potential of nMS for structural biology. Fundamental to the progression of nMS has been the development of novel activation methods for dissociating proteins and protein complexes to deduce primary, secondary, tertiary, and quaternary structure through the combined use of multiple MS/MS technologies. This review highlights the key features and advantages of surface collisions (surface-induced dissociation, SID) for probing the connectivity of subunits within protein and nucleoprotein complexes and, in particular, for solving protein structure in conjunction with complementary techniques such as cryo-EM and computational modeling. Several case studies highlight the significant role SID, and more generally nMS, will play in structural elucidation of biological assemblies in the future as the technology becomes more widely adopted. Cases are presented where SID agrees with solved crystal or cryoEM structures or provides connectivity maps that are otherwise inaccessible by "gold standard" structural biology techniques.

A TCR mimic monoclonal antibody reactive with the "public" phospho-neoantigen pIRS2/HLA-A*02:01 complex

Phosphopeptides derived from dysregulated protein phosphorylation in cancer cells can be processed and presented by MHC class I and class II molecules and, therefore, represent an untapped class of tumor-specific antigens that could be used as widely expressed “public” cancer neoantigens (NeoAgs). We generated a TCR mimic (TCRm) mAb, 6B1, specific for a phosphopeptide derived from insulin receptor substrate 2 (pIRS2) presented by HLA-A*02:01. The pIRS2 epitope’s presentation by HLA-A*02:01 was confirmed by mass spectrometry. The TCRm 6B1 specifically bound to pIRS2/HLA-A2 complex on tumor cell lines that expressed pIRS2 in the context of HLA-A*02:01. Bispecific mAbs engaging CD3 of T cells were able to kill tumor cell lines in a pIRS2- and HLA-A*02:01–restricted manner. Structure modeling shows a prerequisite for an arginine or lysine at the first position to bind mAb. Therefore, 6B1 could recognize phosphopeptides derived from various phosphorylated proteins with similar amino acid compositions. This raised the possibility that a TCRm specific for the pIRS2/HLA-A2 complex could target a range of phosphopeptides presented by HLA-A*02:01 in various tumor cells. This is the first TCRm mAb to our knowledge targeting a phosphopeptide/MHC class I complex; the potential of this class of agents for clinical applications warrants further investigation.

The Collagen Receptor Discoidin Domain Receptor 1b Enhances Integrin β1-Mediated Cell Migration by Interacting With Talin and Promoting Rac1 Activation

Integrins and discoidin domain receptors (DDRs) 1 and 2 promote cell adhesion and migration on both fibrillar and non fibrillar collagens. Collagen I contains DDR and integrin selective binding motifs; however, the relative contribution of these two receptors in regulating cell migration is unclear. DDR1 has five isoforms (DDR1a-e), with most cells expressing the DDR1a and DDR1b isoforms. We show that human embryonic kidney 293 cells expressing DDR1b migrate more than DDR1a expressing cells on DDR selective substrata as well as on collagen I in vitro. In addition, DDR1b expressing cells show increased lung colonization after tail vein injection in nude mice. DDR1a and DDR1b differ from each other by an extra 37 amino acids in the DDR1b cytoplasmic domain. Interestingly, these 37 amino acids contain an NPxY motif which is a central control module within the cytoplasmic domain of β integrins and acts by binding scaffold proteins, including talin. Using purified recombinant DDR1 cytoplasmic tail proteins, we show that DDR1b directly binds talin with higher affinity than DDR1a. In cells, DDR1b, but not DDR1a, colocalizes with talin and integrin β1 to focal adhesions and enhances integrin β1-mediated cell migration. Moreover, we show that DDR1b promotes cell migration by enhancing Rac1 activation. Mechanistically DDR1b interacts with the GTPase-activating protein (GAP) Breakpoint cluster region protein (BCR) thus reducing its GAP activity and enhancing Rac activation. Our study identifies DDR1b as a major driver of cell migration and talin and BCR as key players in the interplay between integrins and DDR1b in regulating cell migration.

Engineered pH-Sensitive Protein G/IgG Interaction

While natural protein-protein interactions have evolved to be induced by complex stimuli, rational design of interactions that can be switched-on-demand still remain challenging in the protein design world. Here, we demonstrate that a computationally redesigned natural interface for improved binding affinity could further be mutated to adopt a pH switchable interaction. The redesigned interface of Protein G/human IgG Fc domain (referred to as PrG/hIgG), when incorporated with histidine and glutamic acid on PrG (PrG-EHHE), showed a switch in binding affinity by 50-fold when the pH was altered from mild acidic to mild basic. The wild-type (WT) interface showed a negligible switch. The overall binding affinity under mild acidic pH for PrG-EHHE outperformed the wild-type PrG (PrG-WT) interaction. The new reagent PrG-EHHE can be revolutionary in IgG purification, since the standard method of using an extreme acidic pH for elution can be circumvented.

Recent Advances in the Scaffold Engineering of Protein Binders

In recent years, extensive attention has been given to the generation of new classes of ligand- specific binding proteins to supplement monoclonal antibodies. A combination of protein engineering and display technologies has been used to manipulate non-human antibodies for humanization and stabilization purposes or even the generation of new binding proteins. Engineered protein scaffolds can now be directed against therapeutic targets to treat cancer and immunological disorders. Although very few of these scaffolds have successfully passed clinical trials, their remarkable properties such as robust folding, high solubility, and small size motivate their employment as a tool for biology and applied science studies. Here, we have focused on the generation of new non-Ig binding proteins and single domain antibody manipulation, with a glimpse of their applications.

Tetracycline as an inhibitor to the SARS-CoV-2

The coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains an extant threat against public health on a global scale. Cell infection begins when the spike protein of SARS-CoV-2 binds with the human cell receptor, angiotensin-converting enzyme 2 (ACE2). Here, we address the role of tetracycline as an inhibitor for the receptor-binding domain (RBD) of the spike protein. Targeted molecular investigation show that tetracycline binds more favorably to the RBD (-9.40 kcal/mol) compared to doxycycline (-8.08 kcal/mol), chloroquine (-6.31 kcal/mol), or gentamicin (-4.83 kcal/mol) while inhibiting attachment to ACE2 to a greater degree (binding efficiency of 2.98 kcal/(mol nm² ) for tetracycline-RBD, 5.16 kcal/(mol nm² ) for doxycycline-RBD, 5.59 kcal/(mol nm² ) for chloroquine-RBD, and 7.02 kcal/(mol nm² ) for gentamicin-RBD. Stronger inhibition by tetracycline is verified with nonequilibrium PMF calculations, for which the tetracycline-RBD complex exhibits the lowest free energy profile along the dissociation pathway from ACE2. Tetracycline binds to tyrosine and glycine residues on the viral contact interface that are known to modulate molecular recognition and bonding affinity. These RBD residues also engage in significant hydrogen bonding with the human receptor ACE2. The ability to preclude cell infection complements the anti-inflammatory and cytokine suppressing capability of tetracycline; this may reduce the duration of ICU stays and mechanical ventilation induced by the coronavirus SARS-CoV-2.

Protein structure analysis of the interactions between SARS-CoV-2 spike protein and the human ACE2 receptor: from conformational changes to novel neutralizing antibodies

The recent severe acute respiratory syndrome, known as Coronavirus Disease 2019 (COVID-19) has spread so much rapidly and severely to induce World Health Organization (WHO) to declare a state of emergency over the new coronavirus SARS-CoV-2 pandemic. While several countries have chosen the almost complete lock-down for slowing down SARS-CoV-2 spread, the scientific community is called to respond to the devastating outbreak by identifying new tools for diagnosis and treatment of the dangerous COVID-19. With this aim, we performed an in silico comparative modeling analysis, which allows gaining new insights into the main conformational changes occurring in the SARS-CoV-2 spike protein, at the level of the receptor-binding domain (RBD), along interactions with human cells angiotensin-converting enzyme 2 (ACE2) receptor, that favor human cell invasion. Furthermore, our analysis provides (1) an ideal pipeline to identify already characterized antibodies that might target SARS-CoV-2 spike RBD, aiming to prevent interactions with the human ACE2, and (2) instructions for building new possible neutralizing antibodies, according to chemical/physical space restraints and complementary determining regions (CDR) mutagenesis of the identified existing antibodies. The proposed antibodies show in silico high affinity for SARS-CoV-2 spike RBD and can be used as reference antibodies also for building new high-affinity antibodies against present and future coronaviruses able to invade human cells through interactions of their spike proteins with the human ACE2. More in general, our analysis provides indications for the set-up of the right biological molecular context for investigating spike RBD-ACE2 interactions for the development of new vaccines, diagnostic kits, and other treatments based on the targeting of SARS-CoV-2 spike protein.

Reprogramming the Specificity of a Protein Interface by Computational and Data-Driven Design

The formation of specific protein complexes in a cell is a non-trivial problem given the co-existence of thousands of different polypeptide chains. A particularly difficult case are two glutamine amidotransferase complexes (anthranilate synthase [AS] and aminodeoxychorismate synthase [ADCS]), which are composed of homologous pairs of synthase and glutaminase subunits. We have attempted to identify discriminating interface residues of the glutaminase subunit TrpG from AS, which are responsible for its specific interaction with the synthase subunit TrpEx and prevent binding to the closely related synthase subunit PabB from ADCS. For this purpose, TrpG-specific interface residues were grafted into the glutaminase subunit PabA from ADCS by two different approaches, namely a computational and a data-driven one. Both approaches resulted in PabA variants that bound TrpEx with higher affinity than PabB. Hence, we have accomplished a reprogramming of protein-protein interaction specificity that provides insights into the evolutionary adaptation of protein interfaces.

Assessing multiple score functions in Rosetta for drug discovery

Rosetta is a computational software suite containing algorithms for a wide variety of macromolecular structure prediction and design tasks including small molecule protocols commonly used in drug discovery or enzyme design. Here, we benchmark RosettaLigand score functions and protocols in comparison to results of other software recently published in the Comparative Assessment of Score Functions (CASF-2016). The CASF-2016 benchmark covers a wide variety of tests including scoring and ranking multiple compounds against a target, ligand docking of a small molecule to a target, and virtual screening to extract binders from a compound library. Direct comparison to the score functions provided by CASF-2016 results shows that the original RosettaLigand score function ranks among the top software for scoring, ranking, docking and screening tests. Most notably, the RosettaLigand score function ranked 2/34 among other report score functions in CASF-2016. We additionally perform a ligand docking test with full sampling to mimic typical use cases. Despite improved performance of newer score functions in canonical protein structure prediction and design, we demonstrate here that more recent Rosetta score functions have reduced performance across all small molecule benchmarks. The tests described here have also been uploaded to the Rosetta scientific benchmarking server and will be run weekly to track performance as the code is continually being developed.

Protein structure analysis of the interactions between SARS-CoV-2 spike protein and the human ACE2 receptor: from conformational changes to novel neutralizing antibodies

The recent severe acute respiratory syndrome, known as Coronavirus Disease 2019 (COVID-19) has spread so much rapidly and severely to induce World Health Organization (WHO) to declare a state of emergency over the new coronavirus SARS-CoV-2 pandemic. While several countries have chosen the almost complete lock-down for slowing down SARS-CoV-2 spread, the scientific community is called to respond to the devastating outbreak by identifying new tools for diagnosis and treatment of the dangerous COVID-19. With this aim, we performed an in silico comparative modeling analysis, which allows gaining new insights into the main conformational changes occurring in the SARS-CoV-2 spike protein, at the level of the receptor-binding domain (RBD), along interactions with human cells angiotensin-converting enzyme 2 (ACE2) receptor, that favor human cell invasion. Furthermore, our analysis provides (1) an ideal pipeline to identify already characterized antibodies that might target SARS-CoV-2 spike RBD, aiming to prevent interactions with the human ACE2, and (2) instructions for building new possible neutralizing antibodies, according to chemical/physical space restraints and complementary determining regions (CDR) mutagenesis of the identified existing antibodies. The proposed antibodies show in silico high affinity for SARS-CoV-2 spike RBD and can be used as reference antibodies also for building new high-affinity antibodies against present and future coronaviruses able to invade human cells through interactions of their spike proteins with the human ACE2. More in general, our analysis provides indications for the set-up of the right biological molecular context for investigating spike RBD–ACE2 interactions for the development of new vaccines, diagnostic kits, and other treatments based on the targeting of SARS-CoV-2 spike protein.

Development and Differentiation in Monobodies Based on the Fibronectin Type 3 Domain

As a non-antibody scaffold, monobodies based on the fibronectin type III (FN3) domain overcome antibody size and complexity while maintaining analogous binding loops. However, antibodies and their derivatives remain the gold standard for the design of new therapeutics. In response, clinical-stage therapeutic proteins based on the FN3 domain are beginning to use native fibronectin function as a point of differentiation. The small and simple structure of monomeric monobodies confers increased tissue distribution and reduced half-life, whilst the absence of disulphide bonds improves stability in cytosolic environments. Where multi-specificity is challenging with an antibody format that is prone to mis-pairing between chains, multiple FN3 domains in the fibronectin assembly already interact with a large number of molecules. As such, multiple monobodies engineered for interaction with therapeutic targets are being combined in a similar beads-on-a-string assembly which improves both efficacy and pharmacokinetics. Furthermore, full length fibronectin is able to fold into multiple conformations as part of its natural function and a greater understanding of how mechanical forces allow for the transition between states will lead to advanced applications that truly differentiate the FN3 domain as a therapeutic scaffold.

Structure-Guided Design of a Peptide Lock for Modular Peptide Binders

Peptides play an important role in intermolecular interactions and are frequent analytes in diagnostic assays, also as unstructured, linear epitopes in whole proteins. Yet, due to the many different sequence possibilities even for short peptides, classical selection of binding proteins from a library, one at a time, is not scalable to proteomes. However, moving away from selection to a rational assembly of preselected modules binding to predefined linear epitopes would split the problem into smaller parts. These modules could then be reassembled in any desired order to bind to, in principle, arbitrary sequences, thereby circumventing any new rounds of selection. Designed Armadillo repeat proteins (dArmRPs) are modular, and they do bind elongated peptides in a modular way. Their consensus sequence carries pockets that prefer arginine and lysine. In our quest to select pockets for all amino acid side chains, we had discovered that repetitive sequences can lead to register shifts and peptide flipping during selections from libraries, hindering the selection of new binding specificities. To solve this problem, we now created an orthogonal binding specificity by a combination of grafting from β-catenin, computational design and mutual optimization of the pocket and the bound peptide. We have confirmed the design and the desired interactions by X-ray structure determination. Furthermore, we could confirm the absence of sliding in solution by a single-molecule Förster resonance energy transfer. The new pocket could be moved from the N-terminus of the protein to the middle, retaining its properties, further underlining the modularity of the system.

Discovery of novel helix binding sites at protein-protein interfaces

Protein-protein interactions (PPIs) play a key role in numerous biological processes. Many efforts have been undertaken to develop PPI modulators for therapeutic applications; however, to date, most of the peptide binders designed to target PPIs are derived from native binding helices or using the native helix binding site, which has limited the applications of protein-protein interface binding peptide design. Here, we developed a general computational algorithm, HPer (Helix Positioner), that locates single-helix binding sites at protein-protein interfaces based on the structure of protein targets. HPer performed well on known single-helix-mediated PPIs and recaptured the key interactions and hot-spot residues of native helical binders. We also screened non-helical-mediated PPIs in the PDBbind database and identified 17 PPIs that were suitable for helical peptide binding, and the helical binding sites in these PPIs were also predicted for designing novel peptide ligands. The L2 domain of EGFR, which was the top ranked, was selected as an example to show the protocol and results of designing novel helical peptide ligands on the searched binding site. The binding stability of the designed sequences were further investigated using molecular dynamics simulations.

Effect of diversity in gp41 membrane proximal external region of primary HIV-1 Indian subtype C sequences on interaction with broadly neutralizing antibodies 4E10 and 10E8

Human Immunodeficiency Virus-1 Clade C (HIV-1C) dominates the AIDS epidemic in India, afflicting 2.1 million individuals within the country and more than 15 million people worldwide. Membrane proximal external region (MPER) is an attractive target for broadly neutralizing antibody (bNAb) based therapies. However, information on MPER sequence diversity from India is meagre due to limited sampling of primary viral sequences. In the present study, we examined the variation in MPER of HIV-1C from 24 individuals in Mumbai, India by high throughput sequencing of uncultured viral sequences. Deep sequencing of MPER (662-683; HXB2 envelope amino acid numbering) allowed quantification of intra-individual variation up to 65% at positions 662, 665, 668, 674 and 677 within this region. These variable positions included contact sites targeted by bNAbs 2F5, Z13e1, 4E10 as well as 10E8. Both major and minor epitope variants i.e. 'haplotypes' were generated for each sample dataset. A total of 23, 34 and 25 unique epitope haplotypes could be identified for bNAbs 2F5, Z13e1 and 4E10/10E8 respectively. Further analysis of 4E10 and 10E8 epitopes from our dataset and meta-analysis of previously reported HIV-1 sequences from India revealed 26 epitopes (7 India-specific), heretofore untested for neutralization sensitivity. Peptide-Ab docking predicted 13 of these to be non-binding to 10E8. ELISA, Surface Plasmon Resonance and peptide inhibition of HIV-1 neutralization assays were then performed which validated predicted weak/non-binding interactions for peptides corresponding to six of these epitopes. These results highlight the under-representation of 10E8 non-binding HIV-1C MPER sequences from India. Our study thus underscores the need for increased surveillance of primary circulating envelope sequences for development of efficacious bNAb-based interventions in India.

Predicting Protein Complex Structure from Surface-Induced Dissociation Mass Spectrometry Data

Recently, mass spectrometry (MS) has become a viable method for elucidation of protein structure. Surface-induced dissociation (SID), colliding multiply charged protein complexes or other ions with a surface, has been paired with native MS to provide useful structural information such as connectivity and topology for many different protein complexes. We recently showed that SID gives information not only on connectivity and topology but also on relative interface strengths. However, SID has not yet been coupled with computational structure prediction methods that could use the sparse information from SID to improve the prediction of quaternary structures, i.e., how protein subunits interact with each other to form complexes. Protein-protein docking, a computational method to predict the quaternary structure of protein complexes, can be used in combination with subunit structures from X-ray crystallography and NMR in situations where it is difficult to obtain an experimental structure of an entire complex. While de novo structure prediction can be successful, many studies have shown that inclusion of experimental data can greatly increase prediction accuracy. In this study, we show that the appearance energy (AE, defined as 10% fragmentation) extracted from SID can be used in combination with Rosetta to successfully evaluate protein-protein docking poses. We developed an improved model to predict measured SID AEs and incorporated this model into a scoring function that combines the RosettaDock scoring function with a novel SID scoring term, which quantifies agreement between experiments and structures generated from RosettaDock. As a proof of principle, we tested the effectiveness of these restraints on 57 systems using ideal SID AE data (AE determined from crystal structures using the predictive model). When theoretical AEs were used, the RMSD of the selected structure improved or stayed the same in 95% of cases. When experimental SID data were incorporated on a different set of systems, the method predicted near-native structures (less than 2 Å root-mean-square deviation, RMSD, from native) for 6/9 tested cases, while unrestrained RosettaDock (without SID data) only predicted 3/9 such cases. Score versus RMSD funnel profiles were also improved when SID data were included. Additionally, we developed a confidence measure to evaluate predicted model quality in the absence of a crystal structure.

Relative interfacial cleavage energetics of protein complexes revealed by surface collisions

To fulfill their biological functions, proteins must interact with their specific binding partners and often function as large assemblies composed of multiple proteins or proteins plus other biomolecules. Structural characterization of these complexes, including identification of all binding partners, their relative binding affinities, and complex topology, is integral for understanding function. Understanding how proteins assemble and how subunits in a complex interact is a cornerstone of structural biology. Here we report a native mass spectrometry (MS)-based method to characterize subunit interactions in globular protein complexes. We demonstrate that dissociation of protein complexes by surface collisions, at the lower end of the typical surface-induced dissociation (SID) collision energy range, consistently cleaves the weakest protein:protein interfaces, producing products that are reflective of the known structure. We present here combined results for multiple complexes as a training set, two validation cases, and four computational models. We show that SID appearance energies can be predicted from structures via a computationally derived expression containing three terms (number of residues in a given interface, unsatisfied hydrogen bonds, and a rigidity factor).

Tertiary Structural Motif Sequence Statistics Enable Facile Prediction and Design of Peptides that Bind Anti-apoptotic Bfl-1 and Mcl-1

Understanding the relationship between protein sequence and structure well enough to design new proteins with desired functions is a longstanding goal in protein science. Here, we show that recurring tertiary structural motifs (TERMs) in the PDB provide rich information for protein-peptide interaction prediction and design. TERM statistics can be used to predict peptide binding energies for Bcl-2 family proteins as accurately as widely used structure-based tools. Furthermore, design using TERM energies (dTERMen) rapidly and reliably generates high-affinity peptide binders of anti-apoptotic proteins Bfl-1 and Mcl-1 with just 15%-38% sequence identity to any known native Bcl-2 family protein ligand. High-resolution structures of four designed peptides bound to their targets provide opportunities to analyze the strengths and limitations of the computational design method. Our results support dTERMen as a powerful approach that can complement existing tools for protein engineering.

In Silico Engineering of Synthetic Binding Proteins from Random Amino Acid Sequences

Synthetic proteins with high affinity and selectivity for a protein target can be used as research tools, biomarkers, and pharmacological agents, but few methods exist to design such proteins de novo. To this end, the In-Silico Protein Synthesizer (InSiPS) was developed to design synthetic binding proteins (SBPs) that bind pre-determined targets while minimizing off-target interactions. InSiPS is a genetic algorithm that refines a pool of random sequences over hundreds of generations of mutation and selection to produce SBPs with pre-specified binding characteristics. As a proof of concept, we design SBPs against three yeast proteins and demonstrate binding and functional inhibition of two of three targets in vivo. Peptide SPOT arrays confirm binding sites, and a permutation array demonstrates target specificity. Our foundational approach will support the field of de novo design of small binding polypeptide motifs and has robust applicability while offering potential advantages over the limited number of techniques currently available.

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InSiPS engineers synthetic binding proteins (SBPs) using primary protein sequence

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SBPs are designed to a bind a target protein and avoid “off-target” interactions

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Binding and functional inhibition of two of three target proteins in yeast is demonstrated

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Our new approach offers advantages over alternative tools that rely on 3D models

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.

RosettaAntibodyDesign (RAbD): A general framework for computational antibody design

A structural-bioinformatics-based computational methodology and framework have been developed for the design of antibodies to targets of interest. RosettaAntibodyDesign (RAbD) samples the diverse sequence, structure, and binding space of an antibody to an antigen in highly customizable protocols for the design of antibodies in a broad range of applications. The program samples antibody sequences and structures by grafting structures from a widely accepted set of the canonical clusters of CDRs (North et al., J. Mol. Biol., 406:228-256, 2011). It then performs sequence design according to amino acid sequence profiles of each cluster, and samples CDR backbones using a flexible-backbone design protocol incorporating cluster-based CDR constraints. Starting from an existing experimental or computationally modeled antigen-antibody structure, RAbD can be used to redesign a single CDR or multiple CDRs with loops of different length, conformation, and sequence. We rigorously benchmarked RAbD on a set of 60 diverse antibody-antigen complexes, using two design strategies-optimizing total Rosetta energy and optimizing interface energy alone. We utilized two novel metrics for measuring success in computational protein design. The design risk ratio (DRR) is equal to the frequency of recovery of native CDR lengths and clusters divided by the frequency of sampling of those features during the Monte Carlo design procedure. Ratios greater than 1.0 indicate that the design process is picking out the native more frequently than expected from their sampled rate. We achieved DRRs for the non-H3 CDRs of between 2.4 and 4.0. The antigen risk ratio (ARR) is the ratio of frequencies of the native amino acid types, CDR lengths, and clusters in the output decoys for simulations performed in the presence and absence of the antigen. For CDRs, we achieved cluster ARRs as high as 2.5 for L1 and 1.5 for H2. For sequence design simulations without CDR grafting, the overall recovery for the native amino acid types for residues that contact the antigen in the native structures was 72% in simulations performed in the presence of the antigen and 48% in simulations performed without the antigen, for an ARR of 1.5. For the non-contacting residues, the ARR was 1.08. This shows that the sequence profiles are able to maintain the amino acid types of these conserved, buried sites, while recovery of the exposed, contacting residues requires the presence of the antigen-antibody interface. We tested RAbD experimentally on both a lambda and kappa antibody-antigen complex, successfully improving their affinities 10 to 50 fold by replacing individual CDRs of the native antibody with new CDR lengths and clusters.

Binding affinity prediction of nanobody-protein complexes by scoring of molecular dynamics trajectories

Nanobodies offer a viable alternative to antibodies for engineering high affinity binders. Their small size has an additional advantage: it allows exploiting computational protocols for optimizing their biophysical features, such as the binding affinity. The efficient prediction of this quantity is still considered a daunting task especially for modelled complexes. We show how molecular dynamics can successfully assist in the binding affinity prediction of modelled nanobody-protein complexes. The approximate initial configurations obtained by in silico design must undergo large rearrangements before achieving a stable conformation, in which the binding affinity can be meaningfully estimated. The scoring functions developed for the affinity evaluation of crystal structures will provide accurate estimates for modelled binding complexes if the scores are averaged over long finite temperature molecular dynamics simulations.

A Recurrent Mutation in Anaplastic Lymphoma Kinase with Distinct Neoepitope Conformations

The identification of recurrent human leukocyte antigen (HLA) neoepitopes driving T cell responses against tumors poses a significant bottleneck in the development of approaches for precision cancer therapeutics. Here, we employ a bioinformatics method, Prediction of T Cell Epitopes for Cancer Therapy, to analyze sequencing data from neuroblastoma patients and identify a recurrent anaplastic lymphoma kinase mutation (ALK R1275Q) that leads to two high affinity neoepitopes when expressed in complex with common HLA alleles. Analysis of the X-ray structures of the two peptides bound to HLA-B*15:01 reveals drastically different conformations with measurable changes in the stability of the protein complexes, while the self-epitope is excluded from binding due to steric hindrance in the MHC groove. To evaluate the range of HLA alleles that could display the ALK neoepitopes, we used structure-based Rosetta comparative modeling calculations, which accurately predict several additional high affinity interactions and compare our results with commonly used prediction tools. Subsequent determination of the X-ray structure of an HLA-A*01:01 bound neoepitope validates atomic features seen in our Rosetta models with respect to key residues relevant for MHC stability and T cell receptor recognition. Finally, MHC tetramer staining of peripheral blood mononuclear cells from HLA-matched donors shows that the two neoepitopes are recognized by CD8+ T cells. This work provides a rational approach toward high-throughput identification and further optimization of putative neoantigen/HLA targets with desired recognition features for cancer immunotherapy.

Combination of type II fatty acid biosynthesis enzymes and thiolases supports a functional β-oxidation reversal

An engineered reversal of the β-oxidation cycle (r-BOX) and the fatty acid biosynthesis (FAB) pathway are promising biological platforms for advanced fuel and chemical production in part due to their iterative nature supporting the synthesis of various chain length products. While diverging in their carbon-carbon elongation reaction mechanism, iterative operation of each pathway relies on common chemical conversions (reduction, dehydration, and reduction) differing only in the attached moiety (acyl carrier protein (ACP) in FAB vs Coenzyme A in r-BOX). Given this similarity, we sought to determine whether FAB enzymes can be used in the context of r-BOX as a means of expanding available r-BOX components with a ubiquitous set of well characterized enzymes. Using enzymes from the type II FAB pathway (FabG, FabZ, and FabI) in conjunction with a thiolase catalyzing a non-decarboxylative condensation, we demonstrate that FAB enzymes support a functional r-BOX. Pathway operation with FAB enzymes was improved through computationally directed protein design to develop FabZ variants with amino acid substitutions designed to disrupt hydrogen bonding at the FabZ-ACP interface and introduce steric and electrostatic repulsion between the FabZ and ACP. FabZ with R126W and R121E substitutions resulted in improved carboxylic acid and alcohol production from one- and multiple-turn r-BOX compared to the wild-type enzyme. Furthermore, the ability for FAB enzymes to operate on functionalized intermediates was exploited to produce branched chain carboxylic acids through an r-BOX with functionalized priming. These results not only provide an expanded set of enzymes within the modular r-BOX pathway, but can also potentially expand the scope of products targeted through this pathway by operating with CoA intermediates containing various functional groups.

d-Peptides as inhibitors of PR3-membrane interactions

Proteinase 3 (PR3) is a neutrophil serine protease present in cytoplasmic granules but also expressed at the neutrophil surface where it mediates proinflammatory effects. Studies of the underlying molecular mechanisms have been hampered by the lack of inhibitors of the PR3 membrane anchorage. Indeed while there exist inhibitors of the catalytic activity of PR3, its membrane interfacial binding site (IBS) is distinct from its catalytic site. The IBS has been characterized both by mutagenesis experiments and molecular modeling. Through docking and molecular dynamics simulations we have designed d-peptides targeting the PR3 IBS. We used surface plasmon resonance to evaluate their effect on the binding of PR3 to phospholipid bilayers. Next, we verified their ability of binding to PR3 via fluorescence spectroscopy and isothermal titration calorimetry. The designed peptides did not affect the catalytic activity of PR3. A few peptides bound to PR3 hydrophobic pockets and inhibited PR3 binding to lipids. While the (KFF)₃K d-peptide inconveniently showed a significant affinity for the lipids, another d-peptide (SAKEAFFKLLAS) did not and it inhibited the PR3-membrane binding site with IC₅₀ of about 40μM. Our work puts forward d-peptides as promising inhibitors of peripheral protein-membrane interactions, which remain high-hanging fruits in drug design.

Computational design of a specific heavy chain/κ light chain interface for expressing fully IgG bispecific antibodies

The use of bispecific antibodies (BsAbs) to treat human diseases is on the rise. Increasingly complex and powerful therapeutic mechanisms made possible by BsAbs are spurring innovation of novel BsAb formats and methods for their production. The long-lived in vivo pharmacokinetics, optimal biophysical properties and potential effector functions of natural IgG monoclonal (and monospecific) antibodies has resulted in a push to generate fully IgG BsAb formats with the same quaternary structure as monoclonal IgGs. The production of fully IgG BsAbs is challenging because of the highly heterogeneous pairing of heavy chains (HCs) and light chains (LCs) when produced in mammalian cells with two IgG HCs and two LCs. A solution to the HC heterodimerization aspect of IgG BsAb production was first discovered two decades ago; however, addressing the LC mispairing issue has remained intractable until recently. Here, we use computational and rational engineering to develop novel designs to the HC/LC pairing issue, and particularly for κ LCs. Crystal structures of these designs highlight the interactions that provide HC/LC specificity. We produce and characterize multiple fully IgG BsAbs using these novel designs. We demonstrate the importance of specificity engineering in both the variable and constant domains to achieve robust HC/LC specificity within all the BsAbs. These solutions facilitate the production of fully IgG BsAbs for clinical use.

Rosetta:MSF: a modular framework for multi-state computational protein design

Computational protein design (CPD) is a powerful technique to engineer existing proteins or to design novel ones that display desired properties. Rosetta is a software suite including algorithms for computational modeling and analysis of protein structures and offers many elaborate protocols created to solve highly specific tasks of protein engineering. Most of Rosetta’s protocols optimize sequences based on a single conformation (i. e. design state). However, challenging CPD objectives like multi-specificity design or the concurrent consideration of positive and negative design goals demand the simultaneous assessment of multiple states. This is why we have developed the multi-state framework MSF that facilitates the implementation of Rosetta’s single-state protocols in a multi-state environment and made available two frequently used protocols. Utilizing MSF, we demonstrated for one of these protocols that multi-state design yields a 15% higher performance than single-state design on a ligand-binding benchmark consisting of structural conformations. With this protocol, we designed de novo nine retro-aldolases on a conformational ensemble deduced from a (βα)₈-barrel protein. All variants displayed measurable catalytic activity, testifying to a high success rate for this concept of multi-state enzyme design.

Protein engineering, i. e. the targeted modification or design of proteins has tremendous potential for medical and industrial applications. One generally applicable strategy for protein engineering is rational protein design: based on detailed knowledge of structure and function, computer programs like Rosetta propose the sequence of a protein possessing the desired properties. So far, most computer protocols have used rigid structures for design, which is a simplification because a protein’s structure is more accurately specified by a conformational ensemble. We have now implemented a framework for computational protein design that allows certain design protocols of Rosetta to make use of multiple design states like structural ensembles. An in silico assessment simulating ligand-binding design showed that this new approach generates more reliably native-like sequences than a single-state approach. As a proof-of-concept, we introduced de novo retro-aldolase activity into a scaffold protein and characterized nine variants experimentally, all of which were catalytically active.

Modeling and Design of Peptidomimetics to Modulate Protein-Protein Interactions

We describe a modular approach to identify and inhibit protein-protein interactions (PPIs) that are mediated by protein secondary and tertiary structures with rationally designed peptidomimetics. Our analysis begins with entries of high-resolution complexes in the Protein Data Bank and utilizes conformational sampling, scoring, and design capabilities of advanced biomolecular modeling software to develop peptidomimetics.

The Intersection of Structural and Chemical Biology - An Essential Synergy

The continual improvement in our ability to generate high resolution structural models of biological molecules has stimulated and supported innovative chemical biology projects that target increasingly challenging ligand interaction sites. In this review we outline some of the recent developments in chemical biology and rational ligand design and show selected examples that illustrate the synergy between these research areas.

Differential protein structural disturbances and suppression of assembly partners produced by nonsense GABRG2 epilepsy mutations: implications for disease phenotypic heterogeneity

Mutations in GABA_A receptor subunit genes are frequently associated with epilepsy, and nonsense mutations in GABRG2 are associated with several epilepsy syndromes including childhood absence epilepsy, generalized tonic clonic seizures and the epileptic encephalopathy, Dravet syndrome. The molecular basis for the phenotypic heterogeneity of mutations is unclear. Here we focused on three nonsense mutations in GABRG2 (GABRG2(R136*), GABRG2(Q390*) and GABRG2(W429*)) associated with epilepsies of different severities. Structural modeling and structure-based analysis indicated that the surface of the wild-type γ2 subunit was naturally hydrophobic, which is suitable to be buried in the cell membrane. Different mutant γ2 subunits had different stabilities and different interactions with their wild-type subunit binding partners because they adopted different conformations and had different surface hydrophobicities and different tendency to dimerize. We utilized flow cytometry and biochemical approaches in combination with lifted whole cell patch-clamp recordings. We demonstrated that the truncated subunits had no to minimal surface expression and unchanged or reduced surface expression of wild-type partnering subunits. The amplitudes of GABA-evoked currents from the mutant α1β2γ2(R136*), α1β2γ2(Q390*) and α1β2γ2(W429*) receptors were reduced compared to the currents from α1β2γ2 receptors but with differentially reduced levels. This thus suggests differential protein structure disturbances are correlated with disease severity.

Archaeal flagellin combines a bacterial type IV pilin domain with an Ig-like domain

The bacterial flagellar apparatus, which involves ∼40 different proteins, has been a model system for understanding motility and chemotaxis. The bacterial flagellar filament, largely composed of a single protein, flagellin, has been a model for understanding protein assembly. This system has no homology to the eukaryotic flagellum, in which the filament alone, composed of a microtubule-based axoneme, contains more than 400 different proteins. The archaeal flagellar system is simpler still, in some cases having ∼13 different proteins with a single flagellar filament protein. The archaeal flagellar system has no homology to the bacterial one and must have arisen by convergent evolution. However, it has been understood that the N-terminal domain of the archaeal flagellin is a homolog of the N-terminal domain of bacterial type IV pilin, showing once again how proteins can be repurposed in evolution for different functions. Using cryo-EM, we have been able to generate a nearly complete atomic model for a flagellar-like filament of the archaeon Ignicoccus hospitalis from a reconstruction at ∼4-Å resolution. We can now show that the archaeal flagellar filament contains a β-sandwich, previously seen in the FlaF protein that forms the anchor for the archaeal flagellar filament. In contrast to the bacterial flagellar filament, where the outer globular domains make no contact with each other and are not necessary for either assembly or motility, the archaeal flagellin outer domains make extensive contacts with each other that largely determine the interesting mechanical properties of these filaments, allowing these filaments to flex.

Designing peptide sequences in flexible chain conformations to bind RNA: a search algorithm combining Monte Carlo, self-consistent mean field and concerted rotation techniques

A search algorithm combining Monte Carlo, self-consistent mean field, and concerted rotation techniques was developed to discover peptide sequences that are reasonable HIV drug candidates due to their exceptional binding to human tRNAUUU(Lys3), the primer of HIV replication. The search algorithm allows for iteration between sequence mutations and conformation changes during sequence evolution. Searches conducted for different classes of peptides identified several potential peptide candidates. Analysis of the energy revealed that the asparagine and cysteine at residues 11 and 12 play important roles in "recognizing" tRNA(Lys3) via van der Waals interactions, contributing to binding specificity. Arginines preferentially attract the phosphate linkage via charge-charge interaction, contributing to binding affinity. Evaluation of the RNA/peptide complex's structure revealed that adding conformation changes to the search algorithm yields peptides with better binding affinity and specificity to tRNA(Lys3) than a previous mutation-only algorithm.

Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles

The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies.

Few proteins carry out their tasks in isolation. Instead, proteins combine with each other in complicated ways that can be affected by either the natural genetic variation that occurs among people or by disease causing mutations such as those that occur in cancer or in genetic disorders. To understand how these mutations affect our health, it is necessary to understand how mutations can affect the strength of the interactions that bind proteins together. This is a difficult task to do in a laboratory on a large scale and scientists are increasingly turning to computational methods to predict these effects in advance. We show that by looking at the multiple alignments of similar protein-protein complex structures at the interface regions, new constraints based on the evolution of the three dimensional structures of proteins can be made to predict which mutations are compatible with two proteins interacting and which are not.

Engineering a genetically encoded competitive inhibitor of the KEAP1-NRF2 interaction via structure-based design and phage display

In its basal state, KEAP1 binds the transcription factor NRF2 (Kd = 5 nM) and promotes its degradation by ubiquitylation. Changes in the redox environment lead to modification of key cysteines within KEAP1, resulting in NRF2 protein accumulation and the transcription of genes important for restoring the cellular redox state. Using phage display and a computational loop grafting protocol, we engineered a monobody (R1) that is a potent competitive inhibitor of the KEAP1-NRF2 interaction. R1 bound to KEAP1 with a Kd of 300 pM and in human cells freed NRF2 from KEAP1 resulting in activation of the NRF2 promoter. Unlike cysteine-reactive small molecules that lack protein specificity, R1 is a genetically encoded, reversible inhibitor designed specifically for KEAP1. R1 should prove useful for studying the role of the KEAP1-NRF2 interaction in several disease states. The structure-based phage display strategy employed here is a general approach for engineering high-affinity binders that compete with naturally occurring interactions.

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.

Prediction of stability changes upon mutation in an icosahedral capsid

Identifying the contributions to thermodynamic stability of capsids is of fundamental and practical importance. Here we use simulation to assess how mutations affect the stability of lumazine synthase from the hyperthermophile Aquifex aeolicus, a T = 1 icosahedral capsid; in the simulations the icosahedral symmetry of the capsid is preserved by simulating a single pentamer and imposing crystal symmetry, in effect simulating an infinite cubic lattice of icosahedral capsids. The stability is assessed by estimating the free energy of association using an empirical method previously proposed to identify biological units in crystal structures. We investigate the effect on capsid formation of seven mutations, for which it has been experimentally assessed whether they disrupt capsid formation or not. With one exception, our approach predicts the effect of the mutations on the capsid stability. The method allows the identification of interaction networks, which drive capsid assembly, and highlights the plasticity of the interfaces between subunits in the capsid. Proteins 2015; 83:1733–1741. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc

Archaeal actin from a hyperthermophile forms a single-stranded filament

The prokaryotic origins of the actin cytoskeleton have been firmly established, but it has become clear that the bacterial actins form a wide variety of different filaments, different both from each other and from eukaryotic F-actin. We have used electron cryomicroscopy (cryo-EM) to examine the filaments formed by the protein crenactin (a crenarchaeal actin) from Pyrobaculum calidifontis, an organism that grows optimally at 90 °C. Although this protein only has ∼ 20% sequence identity with eukaryotic actin, phylogenetic analyses have placed it much closer to eukaryotic actin than any of the bacterial homologs. It has been assumed that the crenactin filament is double-stranded, like F-actin, in part because it would be hard to imagine how a single-stranded filament would be stable at such high temperatures. We show that not only is the crenactin filament single-stranded, but that it is remarkably similar to each of the two strands in F-actin. A large insertion in the crenactin sequence would prevent the formation of an F-actin-like double-stranded filament. Further, analysis of two existing crystal structures reveals six different subunit-subunit interfaces that are filament-like, but each is different from the others in terms of significant rotations. This variability in the subunit-subunit interface, seen at atomic resolution in crystals, can explain the large variability in the crenactin filaments observed by cryo-EM and helps to explain the variability in twist that has been observed for eukaryotic actin filaments.

Hydrocarbon binding by proteins: structures of protein binding sites for ≥C10 linear alkanes or long-chain alkyl and alkenyl groups

In order to identify potential de novo enzyme templates for the cleavage of C–C single bonds in long-chain hydrocarbons, we analyzed protein structures that bind substrates containing alkyl and alkenyl functional groups. A survey of ligand-containing protein structures deposited in the Protein Data Bank resulted in 874 entries, consisting of 194 unique ligands that have ≥10 carbons in a linear chain. Fatty acids and phospholipids are the most abundant types of ligands. Hydrophobic amino acids forming α-helical structures frequently line the binding pockets. Occupation of these binding sites was evaluated by calculating both the buried surface area and volume employed by the ligands; these quantities are similar to those computed for drug–protein complexes. Surface complementarity is relatively low due to the nonspecific nature of the interaction between the long-chain hydrocarbons and the hydrophobic amino acids. The selected PDB structures were annotated on the basis of their SCOP and EC identification numbers, which will facilitate design template searches based on structural and functional homologies. Relatively low surface complementarity and ∼55% volume occupancy, also observed in synthetic-host, alkane-guest systems, suggest general principles for the recognition of long-chain linear hydrocarbons.

The length of the linker between the epidermal growth factor-like domains in factor VIIa is critical for a productive interaction with tissue factor

Formation of the factor VIIa (FVIIa)-tissue factor (TF) complex triggers the blood coagulation cascade. Using a structure-based rationale, we investigated how the length of the linker region between the two epidermal growth factor (EGF)-like domains in FVIIa influences TF binding and the allosteric activity enhancement, as well as the interplay between the γ-carboxyglutamic acid (Gla)-containing and protease domains. Removal of two residues from the native linker was compatible with normal cofactor binding and accompanying stimulation of the enzymatic activity, as was extension by two (Gly-Ser) residues. In sharp contrast, truncation by three or four residues abolished the TF-mediated stabilization of the active conformation of FVIIa and abrogated TF-induced activity enhancement. In addition, FVIIa variants with short linkers associated 80-fold slower with soluble TF (sTF) as compared with wild-type FVIIa, resulting in a corresponding increase in the equilibrium dissociation constant. Molecular modeling suggested that the shortest FVIIa variants would have to be forced into a tense and energetically unfavorable conformation in order to be able to interact productively with TF, explaining our experimental observations. We also found a correlation between linker length and the residual intrinsic enzymatic activity of Ca(2+)-free FVIIa; stepwise truncation resulting in gradually higher activity with des(83-86)-FVIIa reaching the level of Gla-domainless FVIIa. The linker appears to determine the average distance between the negatively charged Gla domain and a structural element in the protease domain, presumably of opposite charge, and proximity has a negative impact on apo-FVIIa activity.

Computational design of protein antigens that interact with the CDR H3 loop of HIV broadly neutralizing antibody 2F5

Rational design of proteins with novel binding specificities and increased affinity is one of the major goals of computational protein design. Epitope-scaffolds are a new class of antigens engineered by transplanting viral epitopes of predefined structure to protein scaffolds, or by building protein scaffolds around such epitopes. Epitope-scaffolds are of interest as vaccine components to attempt to elicit neutralizing antibodies targeting the specified epitope. In this study we developed a new computational protocol, MultiGraft Interface, that transplants epitopes but also designs additional scaffold features outside the epitope to enhance antibody-binding specificity and potentially influence the specificity of elicited antibodies. We employed MultiGraft Interface to engineer novel epitope-scaffolds that display the known epitope of human immunodeficiency virus 1 (HIV-1) neutralizing antibody 2F5 and that also interact with the functionally important CDR H3 antibody loop. MultiGraft Interface generated an epitope-scaffold that bound 2F5 with subnanomolar affinity (K(D) = 400 pM) and that interacted with the antibody CDR H3 loop through computationally designed contacts. Substantial structural modifications were necessary to engineer this antigen, with the 2F5 epitope replacing a helix in the native scaffold and with 15% of the native scaffold sequence being modified in the design stage. This epitope-scaffold represents a successful example of rational protein backbone engineering and protein-protein interface design and could prove useful in the field of HIV vaccine design. MultiGraft Interface can be generally applied to engineer novel binding partners with altered specificity and optimized affinity.

Generation of bispecific IgG antibodies by structure-based design of an orthogonal Fab interface

Robust generation of IgG bispecific antibodies has been a long-standing challenge. Existing methods require extensive engineering of each individual antibody, discovery of common light chains, or complex and laborious biochemical processing. Here we combine computational and rational design approaches with experimental structural validation to generate antibody heavy and light chains with orthogonal Fab interfaces. Parental monoclonal antibodies incorporating these interfaces, when simultaneously co-expressed, assemble into bispecific IgG with improved heavy chain-light chain pairing. Bispecific IgGs generated with this approach exhibit pharmacokinetic and other desirable properties of native IgG, but bind target antigens monovalently. As such, these bispecific reagents may be useful in many biotechnological applications.