The atomic structure of protein-protein recognition sites

Alternative target recognition elements for chimeric antigen receptor (CAR) T cells: beyond standard antibody fragments

BACKGROUND AIMS

Chimeric antigen receptor T (CAR-T) cells are a remarkably efficacious, highly promising and rapidly evolving strategy in the field of immuno-oncology. The precision of these targeted cellular therapies is driven by the specificity of the antigen recognition element (the "binder") encoded in the CAR. This binder redirects these immune effector cells precisely toward a defined antigen on the surface of cancer cells, leading to T-cell receptor-independent tumor lysis. Currently, for tumor targeting most CAR-T cells are designed using single-chain variable fragments (scFvs) derived from murine or human immunoglobulins. However, there are several emerging alternative binder modalities that are finding increasing utility for improved CAR function beyond scFvs.

METHODS

Here we review the most recent developments in the use of non-canonical protein binding domains in CAR design, including nanobodies, DARPins, natural ligands, and de novo-designed protein elements.

RESULTS

Overall, we describe how new protein binder formats, with their unique structural properties and mechanisms of action, may possess key advantages over traditional scFv CAR designs.

CONCLUSIONS

These alternative binder designs may contribute to enhanced CAR-T therapeutic options and, ultimately, improved outcomes for cancer patients.

Highlighting the Major Role of Cyclin C in Cyclin-Dependent Kinase 8 Activity through Molecular Dynamics Simulations

Dysregulation of cyclin-dependent kinase 8 (CDK8) activity has been associated with many diseases, including colorectal and breast cancer. As usual in the CDK family, the activity of CDK8 is controlled by a regulatory protein called cyclin C (CycC). But, while human CDK family members are generally activated in two steps, that is, the binding of the cyclin to CDK and the phosphorylation of a residue in the CDK activation loop, CDK8 does not require the phosphorylation step to be active. Another peculiarity of CDK8 is its ability to be associated with CycC while adopting an inactive form. These specificities raise the question of the role of CycC in the complex CDK8-CycC, which appears to be more complex than the other members of the CDK family. Through molecular dynamics (MD) simulations and binding free energy calculations, we investigated the effect of CycC on the structure and dynamics of CDK8. In a second step, we particularly focused our investigation on the structural and molecular basis of the protein-protein interaction between the two partners by finely analyzing the energetic contribution of residues and simulating the transition between the active and the inactive form. We found that CycC has a stabilizing effect on CDK8, and we identified specific interaction hotspots within its interaction surface compared to other human CDK/Cyc pairs. Targeting these specific interaction hotspots could be a promising approach in terms of specificity to effectively disrupt the interaction between CDK8. The simulation of the conformational transition from the inactive to the active form of CDK8 suggests that the residue Glu99 of CycC is involved in the orientation of three conserved arginines of CDK8. Thus, this residue may assume the role of the missing phosphorylation step in the activation mechanism of CDK8. In a more general view, these results point to the importance of keeping the CycC in computational studies when studying the human CDK8 protein in both the active and the inactive form.

Peptide-Based Inhibitors of the Induced Signaling Protein Interactions: Current State and Prospects

Formation of the transient protein complexes in response to activation of cellular receptors is a common mechanism by which cells respond to external stimuli. This article presents the concept of blocking interactions of signaling proteins by the peptide inhibitors, and describes the progress achieved to date in the development of signaling inhibitors that act by blocking the signal-dependent protein interactions.

A VHH single-domain platform enabling discovery and development of monospecific antibodies and modular neutralizing bispecifics against SARS-CoV-2 variants

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to evolve, escape coronavirus disease 2019 therapeutics and vaccines, and jeopardize public health. To combat SARS-CoV-2 antigenic escape, we developed a rapid, high-throughput pipeline to discover monospecific VHH antibodies and iteratively develop VHH-Fc-VHH bispecifics capable of neutralizing emerging SARS-CoV-2 variants. By panning VHH single-domain phage libraries against ancestral or beta spike proteins, we discovered high-affinity VHH antibodies with unique target epitopes. Combining two VHHs into a tetravalent bispecific construct conferred broad neutralization activity against multiple variants and was more resistant to antigenic escape than the monospecific antibody alone. Following the rise of the Omicron variant, a VHH in the original bispecific construct was replaced with another VHH discovered against the Omicron BA.1 receptor binding domain; the resulting bispecific exhibited neutralization against both BA.1 and BA.5 sublineage variants. A heavy chain-only tetravalent VHH-Fc-VHH bispecific platform derived from humanized synthetic libraries held a myriad of unique advantages: (i) synthetic preconstructed libraries minimized risk of liabilities and maximized discovery speed, (ii) VHH scaffolds allowed for a modular "plug-and-play" format that could be rapidly iterated upon as variants of concern arose, (iii) natural dimerization of single VHH-Fc-VHH polypeptides allowed for straightforward bispecific production and purification methods, and (iv) multivalent approaches enhanced avidity boosting effects and neutralization potency, and conferred more robust resistance to antigenic escape than monovalent approaches against specific variants. This iterative platform of rapid VHH discovery combined with modular bispecific design holds promise for long-term viral control efforts.

Arginine (315) is required for the PLIN2-CGI-58 interface and plays a functional role in regulating nascent LDs formation in bovine adipocytes

Perilipin-2 (PLIN2) can anchor to lipid droplets (LDs) and play a crucial role in regulating nascent LDs formation. Bimolecular fluorescence complementation (BiFC) and flow cytometry were examined to verify the PLIN2-CGI-58 interaction efficiency in bovine adipocytes. GST-Pulldown assay was used to detect the key site arginine315 function in PLIN2-CGI-58 interaction. Experiments were also examined to research these mutations function of PLIN2 in LDs formation during adipocytes differentiation, LDs were measured after staining by BODIPY, lipogenesis-related genes were also detected. Results showed that Leucine (L371A, L311A) and glycine (G369A, G376A) mutations reduced interaction efficiencies. Serine (S367A) mutations enhanced the interaction efficiency. Arginine (R315A) mutations resulted in loss of fluorescence in the cytoplasm and disrupted the interaction with CGI-58, as verified by pulldown assay. R315W mutations resulted in a significant increase in the number of LDs compared with wild-type (WT) PLIN2 or the R315A mutations. Lipogenesis-related genes were either up- or downregulated when mutated PLIN2 interacted with CGI-58. Arginine315 in PLIN2 is required for the PLIN2-CGI-58 interface and could regulate nascent LD formation and lipogenesis. This study is the first to study amino acids on the PLIN2 interface during interaction with CGI-58 in bovine and highlight the role played by PLIN2 in the regulation of bovine adipocyte lipogenesis.

Proposing lead compounds for the development of SARS-CoV-2 receptor-binding inhibitors

The COVID-19 pandemic has had deleterious effects on the world and demands urgent measures to find therapeutic agents to combat the current and related future outbreaks. The entry of SARS-CoV-2 into the host's cell is facilitated by the interaction between the viral spike receptor-binding domain (sRBD) and the human angiotensin-converting enzyme 2 (hACE2). Although the interface of sRBD involved in the sRBD-hACE2 interaction has been projected as a primary vaccine and drug target, currently no small-molecule drugs have been approved for covid-19 treatment targeting sRBD. Herein structure-based virtual screening and molecular dynamics (MD) simulation strategies were applied to identify novel potential small-molecule binders of the SARS-CoV-2 sRBD from an sRBD-targeted compound library as leads for the development of anti-COVID-19 drugs. The library was initially screened against sRBD by using the GOLD docking program whereby 19 compounds were shortlisted based on docking scores after using a control compound to set the selection cutoff. The stability of each compound in MD simulations was used as a further standard to select four hits namely T4S1820, T4589, E634-1449, and K784-7078. Analyses of simulations data showed that the four compounds remained stably bound to sRBD for ≥ 80 ns with reasonable affinities and interacted with pharmacologically important amino acid residues. The compounds exhibited fair solubility, lipophilicity, and toxicity-propensity characteristics that could be improved through lead optimization regimes. The overall results suggest that the scaffolds of T4S1820, E634-1449, and K784-7078 could serve as seeds for developing potent small-molecule inhibitors of SARS-CoV-2 receptor binding and cell entry.Communicated by Ramaswamy H. Sarma.

The Multienzyme Complex Nature of Dehydroepiandrosterone Sulfate Biosynthesis

Dehydroepiandrosterone (DHEA), a precursor of steroid sex hormones, is synthesized by steroid 17-alpha-hydroxylase/17,20-lyase (CYP17A1) with the participation of microsomal cytochrome b5 (CYB5A) and cytochrome P450 reductase (CPR), followed by sulfation by two cytosolic sulfotransferases, SULT1E1 and SULT2A1, for storage and transport to tissues in which its synthesis is not available. The involvement of CYP17A1 and SULTs in these successive reactions led us to consider the possible interaction of SULTs with DHEA-producing CYP17A1 and its redox partners. Text mining analysis, protein-protein network analysis, and gene co-expression analysis were performed to determine the relationships between SULTs and microsomal CYP isoforms. For the first time, using surface plasmon resonance, we detected interactions between CYP17A1 and SULT2A1 or SULT1E1. SULTs also interacted with CYB5A and CPR. The interaction parameters of SULT2A1/CYP17A1 and SULT2A1/CYB5A complexes seemed to be modulated by 3'-phosphoadenosine-5'-phosphosulfate (PAPS). Affinity purification, combined with mass spectrometry (AP-MS), allowed us to identify a spectrum of SULT1E1 potential protein partners, including CYB5A. We showed that the enzymatic activity of SULTs increased in the presence of only CYP17A1 or CYP17A1 and CYB5A mixture. The structures of CYP17A1/SULT1E1 and CYB5A/SULT1E1 complexes were predicted. Our data provide novel fundamental information about the organization of microsomal CYP-dependent macromolecular complexes.

Sparks of function by de novo protein design

Information in proteins flows from sequence to structure to function, with each step causally driven by the preceding one. Protein design is founded on inverting this process: specify a desired function, design a structure executing this function, and find a sequence that folds into this structure. This 'central dogma' underlies nearly all de novo protein-design efforts. Our ability to accomplish these tasks depends on our understanding of protein folding and function and our ability to capture this understanding in computational methods. In recent years, deep learning-derived approaches for efficient and accurate structure modeling and enrichment of successful designs have enabled progression beyond the design of protein structures and towards the design of functional proteins. We examine these advances in the broader context of classical de novo protein design and consider implications for future challenges to come, including fundamental capabilities such as sequence and structure co-design and conformational control considering flexibility, and functional objectives such as antibody and enzyme design.

Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein-Protein Binding Affinity

Protein-protein interactions (PPIs) play a central role in nearly all cellular processes. The strength of the binding in a PPI is characterized by the binding affinity (BA) and is a key factor in controlling protein-protein complex formation and defining the structure-function relationship. Despite advancements in understanding protein-protein binding, much remains unknown about the interfacial region and its association with BA. New models are needed to predict BA with improved accuracy for therapeutic design. Here, we use machine learning approaches to examine how well different types of interfacial contacts can be used to predict experimentally determined BA and to reveal the impact of the specific amino acids at the binding interface on BA. We create a series of multivariate linear regression models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids at the protein-protein interface. We found that the numbers of specific amino acids at the protein-protein interface were correlated with BA. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different data sets, indicating the importance of the identities of interfacial amino acids in underlying BA. When trained on a diverse set of complexes from two benchmark data sets, the best performing BA model was generated with an explicit linear equation involving six amino acids. Tyrosine, in particular, was identified as the key amino acid in controlling BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine and serine were identified as the next two most important amino acids in predicting BA. The results from this study further our understanding of PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry.

Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot Spots

With machine learning now transforming the sciences, successful prediction of biological structure or activity is mainly limited by the extent and quality of data available for training, the astute choice of features for prediction, and thorough assessment of the robustness of prediction on a variety of new cases. In this chapter, we address these issues while developing and sharing protocols to build a robust dataset and rigorously compare several predictive classifiers using the open-source Python machine learning library, scikit-learn. We show how to evaluate whether enough data has been used for training and whether the classifier has been overfit to training data. The most telling experiment is 500-fold repartitioning of the training and test sets, followed by prediction, which gives a good indication of whether a classifier performs consistently well on different datasets. An intuitive method is used to quantify which features are most important for correct prediction.The resulting well-trained classifier, hotspotter, can robustly predict the small subset of amino acid residues on the surface of a protein that are energetically most important for binding a protein partner: the interaction hot spots. Hotspotter has been trained and tested here on a curated dataset assembled from 1046 non-redundant alanine scanning mutation sites with experimentally measured change in binding free energy values from 97 different protein complexes; this dataset is available to download. The accessible surface area of the wild-type residue at a given site and its degree of evolutionary conservation proved the most important features to identify hot spots. A variant classifier was trained and validated for proteins where only the amino acid sequence is available, augmented by secondary structure assignment. This version of hotspotter requiring fewer features is almost as robust as the structure-based classifier. Application to the ACE2 (angiotensin converting enzyme 2) receptor, which mediates COVID-19 virus entry into human cells, identified the critical hot spot triad of ACE2 residues at the center of the small interface with the CoV-2 spike protein. Hotspotter results can be used to guide the strategic design of protein interfaces and ligands and also to identify likely interfacial residues for protein:protein docking.

Multi-perspectives and challenges in identifying B-cell epitopes

The identification of B-cell epitopes (BCEs) in antigens is a crucial step in developing recombinant vaccines or immunotherapies for various diseases. Over the past four decades, numerous in silico methods have been developed for predicting BCEs. However, existing reviews have only covered specific aspects, such as the progress in predicting conformational or linear BCEs. Therefore, in this paper, we have undertaken a systematic approach to provide a comprehensive review covering all aspects associated with the identification of BCEs. First, we have covered the experimental techniques developed over the years for identifying linear and conformational epitopes, including the limitations and challenges associated with these techniques. Second, we have briefly described the historical perspectives and resources that maintain experimentally validated information on BCEs. Third, we have extensively reviewed the computational methods developed for predicting conformational BCEs from the structure of the antigen, as well as the methods for predicting conformational epitopes from the sequence. Fourth, we have systematically reviewed the in silico methods developed in the last four decades for predicting linear or continuous BCEs. Finally, we have discussed the overall challenge of identifying continuous or conformational BCEs. In this review, we only listed major computational resources; a complete list with the URL is available from the BCinfo website (https://webs.iiitd.edu.in/raghava/bcinfo/).

Overcoming phenotypic switching: targeting protein-protein interactions in cancer

Alternative protein-protein interactions (PPIs) arising from mutations or post-translational modifications (PTMs), termed phenotypic switching (PS), are critical for the transmission of alternative pathogenic signals and are particularly significant in cancer. In recent years, PPIs have emerged as promising targets for rational drug design, primarily because their high specificity facilitates targeting of disease-related signaling pathways. However, obstacles exist at the molecular level that arise from the properties of the interaction interfaces and the propensity of small molecule drugs to interact with more than one cleft surface. The difficulty in identifying small molecules that act as activators or inhibitors to counteract the biological effects of mutations raises issues that have not been encountered before. For example, small molecules can bind tightly but may not act as drugs or bind to multiple sites (interaction promiscuity). Another reason is the absence of significant clefts on protein surfaces; if a pocket is present, it may be too small, or its geometry may prevent binding. PS, which arises from oncogenic (alternative) signaling, causes drug resistance and forms the basis for the systemic robustness of tumors. In this review, the properties of PPI interfaces relevant to the design and development of targeting drugs are examined. In addition, the interactions between three tyrosine kinase inhibitors (TKIs) employed as drugs are discussed. Finally, potential novel targets of one of these drugs were identified in silico.

Comparative analysis of permanent and transient domain-domain interactions in multi-domain proteins

Protein domains are structural, functional, and evolutionary units. These domains bring out the diversity of functionality by means of interactions with other co-existing domains and provide stability. Hence, it is important to study intra-protein inter-domain interactions from the perspective of types of interactions. Domains within a chain could interact over short timeframes or permanently, rather like protein-protein interactions (PPIs). However, no systematic study has been carried out between two classes, namely permanent and transient domain-domain interactions. In this work, we studied 263 two-domain proteins, belonging to either of these classes and their interfaces on the basis of several factors, such as interface area and details of interactions (number, strength, and types of interactions). We also characterized them based on residue conservation at the interface, correlation of residue motions across domains, its involvement in repeat formation, and their involvement in particular molecular processes. Finally, we could analyze the interactions arising from domains in two-domain monomeric proteins, and we observed significant differences between these two classes of domain interactions and a few similarities. This study will help to obtain a better understanding of structure-function and folding principles of multi-domain proteins.

Novel exported fusion enzymes with chorismate mutase and cyclohexadienyl dehydratase activity: Shikimate pathway enzymes teamed up in no man's land

Chorismate mutase (CM) and cyclohexadienyl dehydratase (CDT) catalyze two subsequent reactions in the intracellular biosynthesis of l-phenylalanine (Phe). Here, we report the discovery of novel and extremely rare bifunctional fusion enzymes, consisting of fused CM and CDT domains, which are exported from the cytoplasm. Such enzymes were found in only nine bacterial species belonging to non-pathogenic γ- or β-Proteobacteria. In γ-proteobacterial fusion enzymes, the CM domain is N-terminal to the CDT domain, whereas the order is inverted in β-Proteobacteria. The CM domains share 15% to 20% sequence identity with the AroQγ class CM holotype of Mycobacterium tuberculosis (∗MtCM), and the CDT domains 40% to 60% identity with the exported monofunctional enzyme of Pseudomonas aeruginosa (PheC). In vitro kinetics revealed a Km <7 μM, much lower than for ∗MtCM, whereas kinetic parameters are similar for CDT domains and PheC. There is no feedback inhibition of CM or CDT by the pathway's end product Phe, and no catalytic benefit of the domain fusion compared with engineered single-domain constructs. The fusion enzymes of Aequoribacter fuscus, Janthinobacterium sp. HH01, and Duganella sacchari were crystallized and their structures refined to 1.6, 1.7, and 2.4 Å resolution, respectively. Neither the crystal structures nor the size-exclusion chromatography show evidence for substrate channeling or higher oligomeric structure that could account for the cooperation of CM and CDT active sites. The genetic neighborhood with genes encoding transporter and substrate binding proteins suggests that these exported bifunctional fusion enzymes may participate in signaling systems rather than in the biosynthesis of Phe.

Exploring binding positions and backbone conformations of peptide ligands of proteins with a backbone-centred statistical energy function

When designing peptide ligands based on the structure of a protein receptor, it can be very useful to narrow down the possible binding positions and bound conformations of the ligand without the need to choose its amino acid sequence in advance. Here, we construct and benchmark a tool for this purpose based on a recently reported statistical energy model named SCUBA (Sidechain-Unknown Backbone Arrangement) for designing protein backbones without considering specific amino acid sequences. With this tool, backbone fragments of different local conformation types are generated and optimized with SCUBA-driven stochastic simulations and simulated annealing, and then ranked and clustered to obtain representative backbone fragment poses of strong SCUBA interaction energies with the receptor. We computationally benchmarked the tool on 111 known protein-peptide complex structures. When the bound ligands are in the strand conformation, the method is able to generate backbone fragments of both low SCUBA energies and low root mean square deviations from experimental structures of peptide ligands. When the bound ligands are helices or coils, low-energy backbone fragments with binding poses similar to experimental structures have been generated for approximately 50% of benchmark cases. We have examined a number of predicted ligand-receptor complexes by atomistic molecular dynamics simulations, in which the peptide ligands have been found to stay at the predicted binding sites and to maintain their local conformations. These results suggest that promising backbone structures of peptides bound to protein receptors can be designed by identifying outstanding minima on the SCUBA-modeled backbone energy landscape.

A perspective on the sharing of docking data

Computational approaches are nowadays largely applied in drug discovery projects. Among these, molecular docking is the most used for hit identification against a drug target protein. However, many scientists in the field shed light on the lack of availability and reproducibility of the data obtained from such studies to the whole community. Consequently, sustaining and developing the efforts toward a large and fully transparent sharing of those data could be beneficial for all researchers in drug discovery. The purpose of this article is first to propose guidelines and recommendations on the appropriate way to conduct virtual screening experiments and second to depict the current state of sharing molecular docking data. In conclusion, we have explored and proposed several prospects to enhance data sharing from docking experiment that could be developed in the foreseeable future.

Roles of inter- and intramolecular tryptophan interactions in membrane-active proteins revealed by racemic protein crystallography

Tryptophan is frequently found on the surface of membrane-associated proteins that interact with the lipid membrane. However, because of their multifaceted interactions, it is difficult to pinpoint the structure-activity relationship of each tryptophan residue. Here, we describe the use of racemic protein crystallography to probe dedicated tryptophan interactions of a model tryptophan-rich bacteriocin aureocin A53 (AucA) by inclusion and/or exclusion of potential ligands. In the presence of tetrahedral anions that are isosteric to the head group of phospholipids, distinct tryptophan H-bond networks were revealed. H-bond donation by W40 was critical for antibacterial activity, as its substitution by 1-methyltryptophan resulted in substantial loss of activity against bacterial clinical isolates. Meanwhile, exclusion of tetrahedral ions revealed that W3 partakes in formation of a dimeric interface, thus suggesting that AucA is dimeric in solution and dissociated to interact with the phosphate head group in the presence of the lipid membrane. Based on these findings, we could predict the tryptophan residue responsible for activity as well as the oligomeric state of a distant homologue lacticin Q (48%).

What Makes a Good Protein-Protein Interaction Stabilizer: Analysis and Application of the Dual-Binding Mechanism

Protein-protein interactions (PPIs) are essential for biological processes including immune reactions and diseases. Inhibition of PPIs by drug-like compounds is a common basis for therapeutic approaches. In many cases the flat interface of PP complexes prevents discovery of specific compound binding to cavities on one partner and PPI inhibition. However, frequently new pockets are formed at the PP interface that allow accommodation of stabilizers which is often as desirable as inhibition but a much less explored alternative strategy. Herein, we employ molecular dynamics simulations and pocket detection to investigate 18 known stabilizers and associated PP complexes. For most cases, we find that a dual-binding mechanism, a similar stabilizer interaction strength with each protein partner, is an important prerequisite for effective stabilization. A few stabilizers follow an allosteric mechanism by stabilizing the protein bound structure and/or increase the PPI indirectly. On 226 protein-protein complexes, we find in >75% of the cases interface cavities suitable for binding of drug-like compounds. We propose a computational compound identification workflow that exploits new PP interface cavities and optimizes the dual-binding mechanism and apply it to 5 PP complexes. Our study demonstrates a great potential for in silico PPI stabilizers discovery with a wide range of therapeutic applications.

De novo design of protein interactions with learned surface fingerprints

Physical interactions between proteins are essential for most biological processes governing life1. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications2-9. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein-protein interactions10. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.

Targeting Protein-Protein Interfaces with Peptides: The Contribution of Chemical Combinatorial Peptide Library Approaches

Protein-protein interfaces play fundamental roles in the molecular mechanisms underlying pathophysiological pathways and are important targets for the design of compounds of therapeutic interest. However, the identification of binding sites on protein surfaces and the development of modulators of protein-protein interactions still represent a major challenge due to their highly dynamic and extensive interfacial areas. Over the years, multiple strategies including structural, computational, and combinatorial approaches have been developed to characterize PPI and to date, several successful examples of small molecules, antibodies, peptides, and aptamers able to modulate these interfaces have been determined. Notably, peptides are a particularly useful tool for inhibiting PPIs due to their exquisite potency, specificity, and selectivity. Here, after an overview of PPIs and of the commonly used approaches to identify and characterize them, we describe and evaluate the impact of chemical peptide libraries in medicinal chemistry with a special focus on the results achieved through recent applications of this methodology. Finally, we also discuss the role that this methodology can have in the framework of the opportunities, and challenges that the application of new predictive approaches based on artificial intelligence is generating in structural biology.

Computational and cellular exploration of the protein-protein interaction between Vibrio fischeri STAS domain protein SypA and serine kinase SypE

Anti-sigma factor antagonists SpoIIAA and RsbV from Bacillus subtilis are the archetypes for single-domain STAS proteins in bacteria. The structures and mechanisms of these proteins along with their cognate anti-sigma factors have been well studied. SpoIIAA and RsbV utilize a partner-switching mechanism to regulate gene expression through protein-protein interactions to control the activity of their downstream anti-sigma factor partners. The Vibrio fischeri STAS domain protein SypA is also proposed to employ a partner-switching mechanism with its partner SypE, a serine kinase/phosphatase that controls SypA's phosphorylation state. However, this regulation appears opposite to the canonical pathway, with SypA being the more downstream component rather than SypE. Here we explore the commonalities and differences between SypA and the canonical single-domain STAS proteins SpoIIAA and RsbV. We use a combination of AlphaFold 2 structure predictions and computational modeling to investigate the SypA-SypE binding interface. We then test a subset of our predictions in V.fischeri by generating and expressing SypA variants. Our findings suggest that, while SypA shares many sequence and structural traits with anti-sigma factor antagonist STAS domain proteins, there are significant differences that may account for SypA's distinct regulatory output.

Selected Approaches to Disrupting Protein-Protein Interactions within the MAPK/RAS Pathway

Within the MAPK/RAS pathway, there exists a plethora of protein-protein interactions (PPIs). For many years, scientists have focused efforts on drugging KRAS and its effectors in hopes to provide much needed therapies for patients with KRAS-mutant driven cancers. In this review, we focus on recent strategies to inhibit RAS-signaling via disrupting PPIs associated with SOS1, RAF, PDEδ, Grb2, and RAS.

Structural Analyses of a Dominant Cryptosporidium parvum Epitope Presented by H-2Kb Offer New Options To Combat Cryptosporidiosis

Cryptosporidium parvum has gained much attention as a major cause of diarrhea in the world, particularly in those with compromised immune systems. The data currently available on how the immune system recognizes C. parvum are growing rapidly, but we lack data on the interactions among host major histocompatibility complex (MHC) diversity and parasitic T-cell epitopes. To identify antigenic epitopes in a murine model, we performed systematic profiling of H-2K^b-restricted peptides by screening the dominant Cryptosporidium antigens. The results revealed that the glycoprotein-derived epitope Gp40/15-SVF9 induced an immunodominant response in C. parvum-recovered C57BL/6 mice, and injection of the cytotoxic-T-lymphocyte (CTL) peptide with the adjuvant activated peptide-specific CD8⁺ T cells. Notably, the SVF9 epitope was highly conserved across Cryptosporidium hominis, C. parvum, and many other Cryptosporidium species. SVF9 also formed stable peptide-MHC class I (MHC I) complexes with HLA-A*0201, suggesting cross-reactivity between H-2K^b and human MHC I specificities. Crystal structure analyses revealed that the interactions of peptide-MHC surface residues of H-2K^b and HLA-A*0201 are highly conserved. The hydrogen bonds of H-2K^b-SVF9 are similar to those of a dominant epitope presented by HLA-A*0201, which can be recognized by a public human T-cell receptor (TCR). Notably, we found double conformations in position 4 (P4), 5 (P5) of the SVF9 peptide, which showed high flexibility, and multiple peptide conformations generated more molecular surfaces that can potentially be recognized by TCRs. Our findings demonstrate that an immunodominant C. parvum epitope and its homologs from different Cryptosporidium species and subtypes can benefit vaccine development to combat cryptosporidiosis. IMPORTANCE Adaptive immune responses and T lymphocytes have been implicated as important mechanisms of parasite-induced protection. However, the role of CD8⁺ T lymphocytes in the resolution of C. parvum infection is largely unresolved. Our results revealed that the glycoprotein-derived epitope Gp40/15-SVF9 induced an immunodominant CD8⁺ T-cell response in C57BL/6 mice. Crystal structure analyses revealed that the interactions of the H-2K^b-SVF9 peptide are similar to those of a dominant epitope presented by HLA-A*0201, which can be recognized by human TCRs. In addition, we found double conformations of the SVF9 peptide, which showed high flexibility and multiple peptide conformations that can potentially be recognized by TCRs.

Residues E53, L55, H59, and G70 of the cellular receptor protein Tva mediate cell binding and entry of the novel subgroup K avian leukosis virus

Subgroup K avian leukosis virus (ALV-K) is a novel subgroup of ALV isolated from Chinese native chickens. As for a retrovirus, the interaction between its envelope protein and cellular receptor is a crucial step in ALV-K infection. Tva, a protein previously determined to be associated with vitamin B₁₂/cobalamin uptake, has been identified as the receptor of ALV-K. However, the molecular mechanism underlying the interaction between Tva and the envelope protein of ALV-K remains unclear. In this study, we identified the C-terminal loop of the LDL-A module of Tva as the minimal functional domain that directly interacts with gp85, the surface component of the ALV-K envelope protein. Further point-mutation analysis revealed that E53, L55, H59, and G70, which are exposed on the surface of Tva and are spatially adjacent, are key residues for the binding of Tva and gp85 and facilitate the entry of ALV-K. Homology modeling analysis indicated that the substitution of these four residues did not significantly impact the Tva structure but impaired the interaction between Tva and gp85 of ALV-K. Importantly, the gene-edited DF-1 cell line with precisely substituted E53, L55, H59, and G70 was completely resistant to ALV-K infection and did not affect vitamin B₁₂/cobalamin uptake. Collectively, these findings not only contribute to a better understanding of the mechanism of ALV-K entry into host cells but also provide an ideal gene-editing target for antiviral study.

Analytical Method for Experimental Validation of Computer-Designed Antibody

In the computational design of antibodies, the interaction analysis between target antigen and antibody is an essential process to obtain feedback for validation and optimization of the design. Kinetic and thermodynamic parameters as well as binding affinity (K_D) allow for a more detailed evaluation and understanding of the molecular recognition. In this chapter, we summarize the conventional experimental methods which can calculate K_D value (ELISA, FP), analyze a binding activity to actual cells (FCM), and evaluate the kinetic and thermodynamic parameters (ITC, SPR, BLI), including high-throughput analysis and a recently developed experimental technique.

Explicit versus implicit consideration of binding partners in protein-protein complex to elucidate intrinsic dynamics

The binding of many proteins to their protein partners is tightly regulated via control of their relative intrinsic dynamics during the binding process, a phenomenon which can in turn be modulated. Therefore, investigating the intrinsic dynamics of proteins is necessary to understand function in a comprehensive way. By intrinsic dynamics herein, we principally refer to the vibrational signature of a protein molecule popularly obtained from normal modes or essential modes. For normal modes, one often considers that the molecule under investigation is a collection of springs in a solvent-free or implicit-solvent medium. In the context of a protein-binding partner, the analysis of vibration of the target protein is often complicated due to molecular interaction within the complex. Generally, it is assumed that the isolated bound conformation of the target protein captures the implicit effect of the binding partner on the intrinsic dynamics, therefore suggesting that any influence of the partner molecule is also already integrated. Such an assumption allows large-scale studies of the conservation of protein flexibility. However, in cases where a partner protein directly influences the vibration of the target via critical contacts at the protein-protein interface, the above assumption falls short of providing a detailed view. In this review article, we discuss the implications of considering the dynamics of a protein in a protein-protein complex, as modelled implicitly and explicitly with methods dependent on elastic network models. We further propose how such an explicit consideration can be applied to understand critical protein-protein contacts that can be targeted in future studies.

Antibody CDR amino acids underlying the functionality of antibody repertoires in recognizing diverse protein antigens

Antibodies recognize protein antigens with exquisite specificity in a complex aqueous environment, where interfacial waters are an integral part of the antibody–protein complex interfaces. In this work, we elucidate, with computational analyses, the principles governing the antibodies’ specificity and affinity towards their cognate protein antigens in the presence of explicit interfacial waters. Experimentally, in four model antibody–protein complexes, we compared the contributions of the interaction types in antibody–protein antigen complex interfaces with the antibody variants selected from phage-displayed synthetic antibody libraries. Evidently, the specific interactions involving a subset of aromatic CDR (complementarity determining region) residues largely form the predominant determinant underlying the specificity of the antibody–protein complexes in nature. The interfacial direct/water-mediated hydrogen bonds accompanying the CDR aromatic interactions are optimized locally but contribute little in determining the epitope location. The results provide insights into the phenomenon that natural antibodies with limited sequence and structural variations in an antibody repertoire can recognize seemingly unlimited protein antigens. Our work suggests guidelines in designing functional artificial antibody repertoires with practical applications in developing novel antibody-based therapeutics and diagnostics for treating and preventing human diseases.

Increased slow dynamics defines ligandability of BTB domains

Efficient determination of protein ligandability, or the propensity to bind small-molecules, would greatly facilitate drug development for novel targets. Ligandability is currently assessed using computational methods that typically consider the static structural properties of putative binding sites or by experimental fragment screening. Here, we evaluate ligandability of conserved BTB domains from the cancer-relevant proteins LRF, KAISO, and MIZ1. Using fragment screening, we discover that MIZ1 binds multiple ligands. However, no ligands are uncovered for the structurally related KAISO or LRF. To understand the principles governing ligand-binding by BTB domains, we perform comprehensive NMR-based dynamics studies and find that only the MIZ1 BTB domain exhibits backbone µs-ms time scale motions. Interestingly, residues with elevated dynamics correspond to the binding site of fragment hits and recently defined HUWE1 interaction site. Our data argue that examining protein dynamics using NMR can contribute to identification of cryptic binding sites, and may support prediction of the ligandability of novel challenging targets.

Exploiting V-Gene Bias for Rapid, High-Throughput Monoclonal Antibody Isolation from Horses

Horses and humans share a close relationship that includes both species' viromes. Many emerging infectious diseases can be transmitted between horses and humans and can exhibit mortality rates as high as 90% in both populations. Antibody biologics represents an emerging field of rapidly discoverable and potent antiviral therapeutics. These biologics can be used to provide passive immunity, as well as blueprints for the rational design of novel active vaccine antigens. Here, we exploit the limited diversity of immunoglobulin variable genes used by horses to develop a rapid, high-throughput monoclonal antibody discovery pipeline. The antibodies isolated from two horses in this study were developed with near exclusivity from a few highly related germline genes within a single IgHV and IgλV gene family and could be recovered for cloning with just three primer pairs. This variable gene pairing was compatible with both horse and human immunoglobulin G isotypes, confirming the suitability of an equine antibody discovery pipeline for developing novel therapeutics to meet the One Health approach to infectious diseases.

Residues L55 and W69 of Tva Mediate Entry of Subgroup A Avian Leukosis Virus

The receptor of the subgroup A avian leukosis virus (ALV-A) in chicken is Tva, which is the homologous protein of human CD320 (huCD320), contains a low-density lipoprotein (LDL-A) module and is involved in the uptake of transcobalamin bound vitamin B₁₂/cobalamin (Cbl). To map the functional determinants of Tva responsible for ALV-A receptor activity, a series of chimeric receptors were created by swapping the LDL-A module fragments between huCD320 and Tva. These chimeric receptors were then used for virus entry and binding assays to map the minimal ALV-A functional domain of Tva. The results showed that Tva residues 49 to 71 constituted the minimal functional domain that directly interacted with the ALV-A gp85 protein to mediate ALV-A entry. Single-residue substitution analysis revealed that L55 and W69, which were spatially adjacent on the surface of the Tva structure, were key residues that mediate ALV-A entry. Structural alignment results indicated that L55 and W69 substitutions did not affect the Tva protein structure but abolished the interaction force between Tva and gp85. Furthermore, substituting the corresponding residues of huCD320 with L55 and W69 of Tva converted huCD320 into a functional receptor of ALV-A. Importantly, soluble huCD320 harboring Tva L55 and W69 blocked ALV-A entry. Finally, we constructed a Tva gene-edited cell line with L55R and W69L substitutions that could fully resist ALV-A entry, while Cbl uptake was not affected. Collectively, our findings suggested that amino acids L55 and W69 of Tva were key for mediating virus entry. IMPORTANCE Retroviruses bind to cellular receptors through their envelope proteins, which is a crucial step in infection. While most retroviruses require two receptors for entry, ALV-A requires only one. Various Tva alleles conferring resistance to ALV-A, including Tva^r1 (C40W substitution), Tva^r2 (frame-shifting four-nucleotide insertion), Tva^r3, Tva^r4, Tva^r5, and Tva^r6 (deletion in the first intron), are known. However, the detailed entry mechanism of ALV-A in chickens remains to be explored. We demonstrated that Tva residues L55 and W69 were key for ALV-A entry and were important for correct interaction with ALV-A gp85. Soluble Tva and huCD320 harboring the Tva residues L55 and W69 effectively blocked ALV-A infection. Additionally, we constructed gene-edited cell lines targeting these two amino acids, which completely restricted ALV-A entry without affecting Cbl uptake. These findings contribute to a better understanding of the infection mechanism of ALV-A and provided novel insights into the prevention and control of ALV-A.

Solubility-Aware Protein Binding Peptide Design Using AlphaFold

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

Evolution of homo-oligomerization of methionine S-adenosyltransferases is replete with structure-function constrains

Homomers are prevalent in bacterial proteomes, particularly among core metabolic enzymes. Homomerization is often key to function and regulation, and interfaces that facilitate the formation of homomeric enzymes are subject to intense evolutionary change. However, our understanding of the molecular mechanisms that drive evolutionary variation in homomeric complexes is still lacking. How is the diversification of protein interfaces linked to variation in functional regulation and structural integrity of homomeric complexes? To address this question, we studied quaternary structure evolution of bacterial methionine S‐adenosyltransferases (MATs)—dihedral homotetramers formed along a large and conserved dimeric interface harboring two active sites, and a small, recently evolved, interdimeric interface. Here, we show that diversity in the physicochemical properties of small interfaces is directly linked to variability in the kinetic stability of MAT quaternary complexes and in modes of their functional regulation. Specifically, hydrophobic interactions within the small interface of Escherichia coli MAT render the functional homotetramer kinetically stable yet impose severe aggregation constraints on complex assembly. These constraints are alleviated by electrostatic interactions that accelerate dimer‐dimer assembly. In contrast, Neisseria gonorrhoeae MAT adopts a nonfunctional dimeric state due to the low hydrophobicity of its small interface and the high flexibility of its active site loops, which perturbs small interface integrity. Remarkably, in the presence of methionine and ATP, N. gonorrhoeae MAT undergoes substrate‐induced assembly into a functional tetrameric state. We suggest that evolution acts on the interdimeric interfaces of MATs to tailor the regulation of their activity and stability to unique organismal needs.

PDB Code(s): 7R2W;7R3B;

Insights Into the Role of Platelet-Derived Growth Factors: Implications for Parkinson’s Disease Pathogenesis and Treatment

Parkinson’s disease (PD), the second most common neurodegenerative disease after Alzheimer’s disease, commonly occurs in the elderly population, causing a significant medical and economic burden to the aging society worldwide. At present, there are few effective methods that achieve satisfactory clinical results in the treatment of PD. Platelet-derived growth factors (PDGFs) and platelet-derived growth factor receptors (PDGFRs) are important neurotrophic factors that are expressed in various cell types. Their unique structures allow for specific binding that can effectively regulate vital functions in the nervous system. In this review, we summarized the possible mechanisms by which PDGFs/PDGFRs regulate the occurrence and development of PD by affecting oxidative stress, mitochondrial function, protein folding and aggregation, Ca2+ homeostasis, and cell neuroinflammation. These modes of action mainly depend on the type and distribution of PDGFs in different nerve cells. We also summarized the possible clinical applications and prospects for PDGF in the treatment of PD, especially in genetic treatment. Recent advances have shown that PDGFs have contradictory roles within the central nervous system (CNS). Although they exert neuroprotective effects through multiple pathways, they are also associated with the disruption of the blood–brain barrier (BBB). Our recommendations based on our findings include further investigation of the contradictory neurotrophic and neurotoxic effects of the PDGFs acting on the CNS.

A unified statistical potential reveals that amino acid stickiness governs nonspecific recruitment of client proteins into condensates

Membraneless organelles are cellular compartments that form by liquid-liquid phase separation of one or more components. Other molecules, such as proteins and nucleic acids, will distribute between the cytoplasm and the liquid compartment in accordance with the thermodynamic drive to lower the free energy of the system. The resulting distribution colocalizes molecular species to carry out a diversity of functions. Two factors could drive this partitioning: the difference in solvation between the dilute versus dense phase and intermolecular interactions between the client and scaffold proteins. Here, we develop a set of knowledge-based potentials that allow for the direct comparison between stickiness, which is dominated by desolvation energy, and pairwise residue contact propensity terms. We use these scales to examine experimental data from two systems: protein cargo dissolving within phase-separated droplets made from FG repeat proteins of the nuclear pore complex and client proteins dissolving within phase-separated FUS droplets. These analyses reveal a close agreement between the stickiness of the client proteins and the experimentally determined values of the partition coefficients (R > 0.9), while pairwise residue contact propensities between client and scaffold show weaker correlations. Hence, the stickiness of client proteins is sufficient to explain their differential partitioning within these two phase-separated systems without taking into account the composition of the condensate. This result implies that selective trafficking of client proteins to distinct membraneless organelles requires recognition elements beyond the client sequence composition. STATEMENT: Empirical potentials for amino acid stickiness and pairwise residue contact propensities are derived. These scales are unique in that they enable direct comparison of desolvation versus contact terms. We find that partitioning of a client protein to a condensate is best explained by amino acid stickiness.

Biochemical and computational approaches to understand venom toxin-toxin interaction

Snake venoms are mainly composed of proteins and peptides (venom toxins). The venom transcriptomes and proteomes have been extensively investigated; however, venom toxin-toxin interactions remain poorly characterized. We detected the interaction of venom Asp49-PLA₂ and 3FTx using biochemical and computational approaches. A stable structure of Asp49-PLA₂-3FTx was identified, and the interface of Asp49-PLA₂ and 3FTx was analyzed. The approaches will shed light on understanding the venom complexity and deciphering the synergistic effects of venom toxins.

Functional inhibition of c-Myc using novel inhibitors identified through “hot spot” targeting

Protein–protein interactions drive various biological processes in healthy as well as disease states. The transcription factor c-Myc plays a crucial role in maintaining cellular homeostasis, and its deregulated expression is linked to various human cancers; therefore, it can be considered a viable target for cancer therapeutics. However, the structural heterogeneity of c-Myc due to its disordered nature poses a major challenge to drug discovery. In the present study, we used an in silico alanine scanning mutagenesis approach to identify “hot spot” residues within the c-Myc/Myc-associated factor X interface, which is highly disordered and has not yet been systematically analyzed for potential small molecule binding sites. We then used the information gained from this analysis to screen potential inhibitors using a conformation ensemble approach. The fluorescence-based biophysical experiments showed that the identified hit molecules displayed noncovalent interactions with these hot spot residues, and further cell-based experiments showed substantial in vitro potency against diverse c-Myc-expressing cancer/stem cells by deregulating c-Myc activity. These biophysical and computational studies demonstrated stable binding of the hit compounds with the disordered c-Myc protein. Collectively, our data indicated effective drug targeting of the disordered c-Myc protein via the determination of hot spot residues in the c-Myc/Myc-associated factor X heterodimer.

The properties of human disease mutations at protein interfaces

The assembly of proteins into complexes and their interactions with other biomolecules are often vital for their biological function. While it is known that mutations at protein interfaces have a high potential to be damaging and cause human genetic disease, there has been relatively little consideration for how this varies between different types of interfaces. Here we investigate the properties of human pathogenic and putatively benign missense variants at homomeric (isologous and heterologous), heteromeric, DNA, RNA and other ligand interfaces, and at different regions in proteins with respect to those interfaces. We find that different types of interfaces vary greatly in their propensity to be associated with pathogenic mutations, with homomeric heterologous and DNA interfaces being particularly enriched in disease. We also find that residues that do not directly participate in an interface, but are close in three-dimensional space, show a significant disease enrichment. Finally, we observe that mutations at different types of interfaces tend to have distinct property changes when undergoing amino acid substitutions associated with disease, and that this is linked to substantial variability in their identification by computational variant effect predictors.

Nearly all proteins interact with other molecules as part of their biological function. For example, proteins can interact with other copies of the same type of protein, with different proteins, with DNA, or with small ligand molecules. Many mutations at protein interfaces, the regions of proteins that interact with other molecules, are known to cause human genetic disease. In this study, we first investigate how different types of protein interfaces have different tendencies to be associated with disease. We also show that the closer a mutation is to an interface, the more likely it is to cause disease. Finally, we study how mutations at different types of interfaces tend to be associated with different changes in amino acid properties, which appears to influence our ability to computationally predict the effects of mutations. Ultimately, we hope that consideration of protein interface properties will eventually improve our ability to identify new disease-causing mutations.

Modeling the MreB-CbtA Interaction to Facilitate the Prediction and Design of Candidate Antibacterial Peptides

Protein-protein interactions (PPIs) have emerged as promising targets for PPI modulators as alternative drugs because they are essential for most biochemical processes in living organisms. In recent years, a spotlight has been put on the development of peptide-based PPI inhibitors as the next-generation therapeutics to combat antimicrobial resistance taking cognizance of protein-based PPI-modulators that interact with target proteins to inhibit function. Although protein-based PPI inhibitors are not effective therapeutic agents because of their high molecular weights, they could serve as sources for peptide-based pharmaceutics if the target-inhibitor complex is accessible and well characterized. The Escherichia coli (E. coli) toxin protein, CbtA, has been identified as a protein-based PPI modulator that binds to the bacterial actin homolog MreB leading to the perturbation of its polymerization dynamics; and consequently has been suggested to have antibacterial properties. Unfortunately, however, the three-dimensional structures of CbtA and the MreB-CbtA complex are currently not available to facilitate the optimization process of the pharmacological properties of CbtA. In this study, computer modeling strategies were used to predict key MreB-CbtA interactions to facilitate the design of antiMreB peptide candidates. A model of the E. coli CbtA was built using the trRosetta software and its stability was assessed through molecular dynamics (MD) simulations. The modeling and simulations data pointed to a model with reasonable quality and stability. Also, the HADDOCK software was used to predict a possible MreB-CbtA complex, which was characterized through MD simulations and compared with MreB-MreB dimmer. The results suggest that CbtA inhibits MreB through the competitive mechanism whereby CbtA competes with MreB monomers for the interprotofilament interface leading to interference with double protofilament formation. Additionally, by using the antiBP software to predict antibacterial peptides in CbtA, and the MreB-CbtA complex as the reference structure to determine important interactions and contacts, candidate antiMreB peptides were suggested. The peptide sequences could be useful in a rational antimicrobial peptide hybridization strategy to design novel antibiotics. All-inclusive, the data reveal the molecular basis of MreB inhibition by CbtA and can be incorporated in the design/development of the next-generation antibacterial peptides targeting MreB.

Rapid discovery of diverse neutralizing SARS-CoV-2 antibodies from large-scale synthetic phage libraries

Coronavirus disease 2019 (COVID-19) is an evolving global public health crisis in need of therapeutic options. Passive immunization of monoclonal antibodies (mAbs) represents a promising therapeutic strategy capable of conferring immediate protection from SARS-CoV-2 infection. Herein, we describe the discovery and characterization of neutralizing SARS-CoV-2 IgG and VHH antibodies from four large-scale phage libraries. Each library was constructed synthetically with shuffled complementarity-determining region loops from natural llama and human antibody repertoires. While most candidates targeted the receptor-binding domain of the S1 subunit of SARS-CoV-2 spike protein, we also identified a neutralizing IgG candidate that binds a unique epitope on the N-terminal domain. A select number of antibodies retained binding to SARS-CoV-2 variants Alpha, Beta, Gamma, Kappa and Delta. Overall, our data show that synthetic phage libraries can rapidly yield SARS-CoV-2 S1 antibodies with therapeutically desirable features, including high affinity, unique binding sites, and potent neutralizing activity in vitro, and a capacity to limit disease in vivo.

Rapid Rational Design of Cyclic Peptides Mimicking Protein-Protein Interfaces

The cPEPmatch approach is a rapid computational methodology for the rational design of cyclic peptides to target desired regions of protein-protein interfaces. The method selects cyclic peptides that structurally match backbone structures of short segments at a protein-protein interface. In a second step, the cyclic peptides act as templates for designed binders by adapting the amino acid side chains to the side chains found in the target complex. A link to access the different tools that comprise the cPEPmatch method and a detailed step-by-step guide is provided. We outline the protocol by following the application to a trypsin protease in complex with the bovine inhibitor protein (BPTI). An extension of our original approach is also presented, where we give a detailed description of the usage of the cPEPmatch methodology focusing on identifying hot regions of protein-protein interfaces prior to the matching. This extension allows one to reduce the amount of evaluated putative cyclic peptides and to specifically design only those that compete with the strongest protein-protein binding regions. It is illustrated by an application to an MHC class I protein complex.

Prediction of Protein-Protein Binding Affinities from Unbound Protein Structures

Proteins are the workhorses of cells to carry out sophisticated and complex cellular processes. Such processes require a coordinated and regulated interactions between proteins that are both time and location specific. The strength, or binding affinity, of protein-protein interactions ranges between the micro- and the nanomolar association constant, often dictating the molecular mechanisms underlying the interaction and the longevity of the complex, i.e., transient or permanent. In consequence, there is a need to quantify the strength of protein-protein interactions for biological, biomedical, and biotechnological applications. While experimental methods are labor intensive and costly, computational ones are useful tools to predict the affinity of protein-protein interactions. In this chapter, we review the methods developed by us to address this question. We briefly present two methods to comprehend the structure of the protein complex derived by either comparative modeling or docking. Then we introduce BADOCK, a method to predict the binding energy without requiring the structure of the protein complex, thus overcoming one of the major limitations of structure-based methods for the prediction of binding affinity. BADOCK utilizes the structure of unbound proteins and the protein docking sampling space to predict protein-protein binding affinities. We present step-by-step protocols to utilize these methods, describing the inputs and potential pitfalls as well as their respective strengths and limitations.

Biochemical propensity mapping for structural and functional anatomy of importin α IBB domain

Importin α has been described as a nuclear protein transport receptor that enables proteins synthesized in the cytoplasm to translocate into the nucleus. Besides its function in nuclear transport, an increasing number of studies have examined its non-nuclear transport functions. In both nuclear transport and non-nuclear transport, a functional domain called the IBB domain (importin β binding domain) plays a key role in regulating importin α behavior, and is a common interacting domain for multiple binding partners. However, it is not yet fully understood how the IBB domain interacts with multiple binding partners, which leads to the switching of importin α function. In this study, we have distinguished the location and propensities of amino acids important for each function of the importin α IBB domain by mapping the biochemical/physicochemical propensities of evolutionarily conserved amino acids of the IBB domain onto the structure associated with each function. We found important residues that are universally conserved for IBB functions across species and family members, in addition to those previously known, as well as residues that are presumed to be responsible for the differences in complex-forming ability among family members and for functional switching.

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

We present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70-75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70-80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.

1H R1ρ relaxation dispersion experiments in aromatic side chains

Aromatic side chains are attractive probes of protein dynamic, since they are often key residues in enzyme active sites and protein binding sites. Dynamic processes on microsecond to millisecond timescales can be studied by relaxation dispersion experiments that attenuate conformational exchange contributions to the transverse relaxation rate by varying the refocusing frequency of applied radio-frequency fields implemented as either CPMG pulse trains or continuous spin-lock periods. Here we present an aromatic ¹H R_1ρ relaxation dispersion experiment enabling studies of two to three times faster exchange processes than achievable by existing experiments for aromatic side chains. We show that site-specific isotope labeling schemes generating isolated ¹H-¹³C spin pairs with vicinal ²H-¹²C moieties are necessary to avoid anomalous relaxation dispersion profiles caused by Hartmann-Hahn matching due to the ³J_HH couplings and limited chemical shift differences among ¹H spins in phenylalanine, tyrosine and the six-ring moiety of tryptophan. This labeling pattern is sufficient in that remote protons do not cause additional complications. We validated the approach by measuring ring-flip kinetics in the small protein GB1. The determined rate constants, k_flip, agree well with previous results from ¹³C R_1ρ relaxation dispersion experiments, and yield ¹H chemical shift differences between the two sides of the ring in good agreement with values measured under slow-exchange conditions. The aromatic¹H R_1ρ relaxation dispersion experiment in combination with the site-selective ¹H-¹³C/²H-¹²C labeling scheme enable measurement of exchange rates up to k_ex = 2k_flip = 80,000 s^-1, and serve as a useful complement to previously developed ¹³C-based methods.

Protein Assembly by Design

Proteins are nature's primary building blocks for the construction of sophisticated molecular machines and dynamic materials, ranging from protein complexes such as photosystem II and nitrogenase that drive biogeochemical cycles to cytoskeletal assemblies and muscle fibers for motion. Such natural systems have inspired extensive efforts in the rational design of artificial protein assemblies in the last two decades. As molecular building blocks, proteins are highly complex, in terms of both their three-dimensional structures and chemical compositions. To enable control over the self-assembly of such complex molecules, scientists have devised many creative strategies by combining tools and principles of experimental and computational biophysics, supramolecular chemistry, inorganic chemistry, materials science, and polymer chemistry, among others. Owing to these innovative strategies, what started as a purely structure-building exercise two decades ago has, in short order, led to artificial protein assemblies with unprecedented structures and functions and protein-based materials with unusual properties. Our goal in this review is to give an overview of this exciting and highly interdisciplinary area of research, first outlining the design strategies and tools that have been devised for controlling protein self-assembly, then describing the diverse structures of artificial protein assemblies, and finally highlighting the emergent properties and functions of these assemblies.

Extended antibody-framework-to-antigen distance observed exclusively with broad HIV-1-neutralizing antibodies recognizing glycan-dense surfaces

Antibody-Framework-to-Antigen Distance (AFAD) - the distance between the body of an antibody and a protein antigen - is an important parameter governing antibody recognition. Here, we quantify AFAD for ~2,000 non-redundant antibody-protein-antigen complexes in the Protein Data Bank. AFADs showed a gaussian distribution with mean of 16.3 Å and standard deviation (σ) of 2.4 Å. Notably, antibody-antigen complexes with extended AFADs (>3σ) were exclusively human immunodeficiency virus-type 1 (HIV-1)-neutralizing antibodies. High correlation (R² = 0.8110) was observed between AFADs and glycan coverage, as assessed by molecular dynamics simulations of the HIV-1-envelope trimer. Especially long AFADs were observed for antibodies targeting the glycosylated trimer apex, and we tested the impact of introducing an apex-glycan hole (N160K); the cryo-EM structure of the glycan hole-targeting HIV-1-neutralizing antibody 2909 in complex with an N160K-envelope trimer revealed a substantially shorter AFAD. Overall, extended AFADs exclusively recognized densely glycosylated surfaces, with the introduction of a glycan hole enabling closer recognition.

Exploring the Nature of Hydrogen Bonding between RNA and Proteins: A Comprehensive Analysis of RNA : Protein Complexes

A nonredundant dataset of ∼300 high (up to 2.5 Å) resolution X-ray structures of RNA:protein complexes were analyzed for hydrogen bonds between amino-acid residues and canonical ribonucleotides (rNs). The identified 17100 contacts were classified based on the identity (rA, rC, rG or rU) and interacting fragment (base, sugar, or ribose) of the rN, the nature (polar or nonpolar) and interacting moiety (main chain or side chain) of the amino-acid residue, as well as the rN and amino-acid atoms participating in the hydrogen bonding. 80 possible hydrogen-bonding combinations (4 (rNs) X 20 (amino acids)) involve a wide variety of RNA and protein types and are present in multiple occurrences in almost all PDB files. Comparison with the analogously-selected DNA:protein complexes reveals that the absence of 2'-OH group in DNA mainly accounts for the differences in DNA:protein and RNA:protein hydrogen bonding. Search for intrinsically-stable base:amino acid pairs containing single or multiple hydrogen bonds reveals 37 unique pairs, which may act as well-defined RNA:protein interaction motifs. Overall, our work collectively analyzes the largest set of nucleic acid-protein hydrogen bonds to date, and therefore highlights several trends that may help frame structural rules governing the physiochemical characteristics of RNA:protein recognition.