A simple physical model for binding energy hot spots in protein-protein complexes

Proscan: a structure-based proline design web server

The ability to control protein conformations and dynamics through structure-based design has been useful in various scenarios, including engineering of viral antigens for vaccines. One effective design strategy is the substitution of residues to proline amino acids, which due to its unique cyclic side chain can favor and rigidify key backbone conformations. To provide the community with a means to readily identify and explore proline designs for target proteins of interest, we developed the Proscan web server. Proscan provides assessment of backbone angles, energetic and deep learning-based favorability scores, and other parameters for proline substitutions at each position of an input structure, along with interactive visualization of backbone angles and candidate substitution sites on structures. It identifies known favorable proline substitutions for viral antigens, and was benchmarked against datasets of proline substitution stability effects from deep mutational scanning and thermodynamic measurements. This tool can enable researchers to identify and prioritize designs for prospective vaccine antigen targets, or other designs to favor stability of key protein conformations. Proscan is available at: https://proscan.ibbr.umd.edu.

Structural characterization and AlphaFold modeling of human T cell receptor recognition of NRAS cancer neoantigens

T cell receptors (TCRs) that recognize cancer neoantigens are important for anti-cancer immune responses and immunotherapy. Understanding the structural basis of TCR recognition of neoantigens provides insights into their exquisite specificity and can enable design of optimized TCRs. We determined crystal structures of a human TCR in complex with NRAS Q61K and Q61R neoantigen peptides and HLA-A1 MHC, revealing the molecular underpinnings for dual recognition and specificity versus wild-type NRAS peptide. We then used multiple versions of AlphaFold to model the corresponding complex structures, given the challenge of immune recognition for such methods. Interestingly, one implementation of AlphaFold2 (TCRmodel2) was able to generate accurate models of the complexes, while AlphaFold3 also showed strong performance, although success was lower for other complexes. This study provides insights into TCR recognition of a shared cancer neoantigen, as well as the utility and practical considerations for using AlphaFold to model TCR-peptide-MHC complexes.

Validation of aminodeoxychorismate synthase and anthranilate synthase as novel targets for bispecific antibiotics inhibiting conserved protein-protein interactions

Multi-resistant bacteria are a rapidly emerging threat to modern medicine. It is thus essential to identify and validate novel antibacterial targets that promise high robustness against resistance-mediating mutations. This can be achieved by simultaneously targeting several conserved function-determining protein-protein interactions in enzyme complexes from prokaryotic primary metabolism. Here, we selected two evolutionary related glutamine amidotransferase complexes, aminodeoxychorismate synthase and anthranilate synthase, that are required for the biosynthesis of folate and tryptophan in most prokaryotic organisms. Both enzymes rely on the interplay of a glutaminase and a synthase subunit that is conferred by a highly conserved subunit interface. Consequently, inhibiting subunit association in both enzymes by one competing bispecific inhibitor has the potential to suppress bacterial proliferation. We comprehensively verified two conserved interface hot-spot residues as potential inhibitor-binding sites in vitro by demonstrating their crucial role in subunit association and enzymatic activity. For in vivo target validation, we generated genomically modified Escherichia coli strains in which subunit association was disrupted by modifying these central interface residues. The growth of such strains was drastically retarded on liquid and solid minimal medium due to a lack of folate and tryptophan. Remarkably, the bacteriostatic effect was observed even in the presence of heat-inactivated human plasma, demonstrating that accessible host metabolite concentrations do not compensate for the lack of folate and tryptophan within the tested bacterial cells. We conclude that a potential inhibitor targeting both enzyme complexes will be effective against a broad spectrum of pathogens and offer increased resilience against antibiotic resistance.

IMPORTANCE

Antibiotics are indispensable for the treatment of bacterial infections in human and veterinary medicine and are thus a major pillar of modern medicine. However, the exposure of bacteria to antibiotics generates an unintentional selective pressure on bacterial assemblies that over time promotes the development or acquisition of resistance mechanisms, allowing pathogens to escape the treatment. In that manner, humanity is in an ever-lasting race with pathogens to come up with new treatment options before resistances emerge. In general, antibiotics with novel modes of action require more complex pathogen adaptations as compared to chemical derivates of existing entities, thus delaying the emergence of resistance. In this contribution, we use modified Escherichia coli strains to validate two novel targets required for folate and tryptophan biosynthesis that can potentially be targeted by one and the same bispecific protein-protein interaction inhibitor and promise increased robustness against bacterial resistances.

Cold Spot SCANNER: Colab Notebook for predicting cold spots in protein-protein interfaces

BACKGROUND

Protein-protein interactions (PPIs) are conveyed through binding interfaces or surface patches on proteins that become buried upon binding. Structural and biophysical analysis of many protein-protein interfaces revealed certain unique features of these surfaces that determine the energetics of interactions and play a critical role in protein evolution. One of the significant aspects of binding interfaces is the presence of binding hot spots, where mutations are highly deleterious for binding. Conversely, binding cold spots are positions occupied by suboptimal amino acids and several mutations in such positions could lead to affinity enhancement. While there are many software programs for identification of hot spot positions, there is currently a lack of software for cold spot detection.

RESULTS

In this paper, we present Cold Spot SCANNER, a Colab Notebook, which scans a PPI binding interface and identifies cold spots resulting from cavities, unfavorable charge-charge, and unfavorable charge-hydrophobic interactions. The software offers a Py3DMOL-based interface that allows users to visualize cold spots in the context of the protein structure and generates a zip file containing the results for easy download.

CONCLUSIONS

Cold spot identification is of great importance to protein engineering studies and provides a useful insight into protein evolution. Cold Spot SCANNER is open to all users without login requirements and can be accessible at: https://colab.

RESEARCH

google.com/github/sagagugit/Cold-Spot-Scanner/blob/main/Cold_Spot_Scanner.ipynb .

A systemic approach to identifying sequence frameworks that decrease mAb production in a transient Chinese hamster ovary cell expression system

Monoclonal antibodies (mAbs) are often engineered at the sequence level for improved clinical performance yet are rarely evaluated prior to candidate selection for their "developability" characteristics, namely expression, which can necessitate additional resource investments to improve the manufacturing processes for problematic mAbs. A strong relationship between primary sequence and expression has emerged, with slight differences in amino acid sequence resulting in titers differing by up to an order of magnitude. Previous work on these "difficult-to-express" (DTE) mAbs has shown that these phenotypes are driven by post-translational bottlenecks in antibody folding, assembly, and secretion processes. However, it has been difficult to translate these findings across cell lines and products. This work presents a systematic approach to study the impact of sequence variation on mAb expression at a larger scale and under more industrially relevant conditions. The analysis found 91 mutations that decreased transient expression of an IgG1κ in Chinese hamster ovary (CHO) cells and revealed that mutations at inaccessible residues, especially those leading to decreases in residue hydrophobicity, are not favorable for high expression. This workflow can be used to better understand sequence determinants of mAb expression to improve candidate selection procedures and reduce process development timelines.

Combinatorially restricted computational design of protein-protein interfaces to produce IgG heterodimers

Redesigning protein-protein interfaces is an important tool for developing therapeutic strategies. Interfaces can be redesigned by in silico screening, which allows for efficient sampling of a large protein space before experimental validation. However, computational costs limit the number of combinations that can be reasonably sampled. Here, we present combinatorial tyrosine (Y)/serine (S) selection (combYSelect), a computational approach combining in silico determination of the change in binding free energy (ΔΔG) of an interface with a highly restricted library composed of just two amino acids, tyrosine and serine. We used combYSelect to design two immunoglobulin G (IgG) heterodimers-combYSelect1 (L368S/D399Y-K409S/T411Y) and combYSelect2 (D399Y/K447S-K409S/T411Y)-that exhibit near-optimal heterodimerization, without affecting IgG stability or function. We solved the crystal structures of these heterodimers and found that dynamic π-stacking interactions and polar contacts drive preferential heterodimeric interactions. Finally, we demonstrated the utility of our combYSelect heterodimers by engineering both a bispecific antibody and a cytokine trap for two unique therapeutic applications.

Environmentally relevant concentrations of 2,3,7,8-TCDD induced inhibition of multicellular alternative splicing and transcriptional dysregulation

Alternative splicing (AS), found in approximately 95 % of human genes, significantly amplifies protein diversity and is implicated in disease pathogenesis when dysregulated. However, the precise involvement of AS in the toxic mechanisms induced by TCDD (2,3,7,8-tetrachlorodibenzo-p-dioxin) remains incompletely elucidated. This study conducted a thorough global AS analysis in six human cell lines following TCDD exposure. Our findings revealed that environmentally relevant concentration (0.1 nM) of TCDD significantly suppressed AS events in all cell types, notably inhibiting diverse splicing events and reducing transcript diversity, potentially attributed to modifications in the splicing patterns of the inhibitory factor family, particularly hnRNP. And we identified 151 genes with substantial AS alterations shared among these cell types, particularly enriched in immune and metabolic pathways. Moreover, TCDD induced cell-specific changes in splicing patterns and transcript levels, with increased sensitivity notably in THP-1 monocyte, potentially linked to aberrant expression of pivotal genes within the spliceosome pathway (DDX5, EFTUD2, PUF60, RBM25, SRSF1, and CRNKL1). This study extends our understanding of disrupted alternative splicing and its relation to the multisystem toxicity of TCDD. It sheds light on how environmental toxins affect post-transcriptional regulatory processes, offering a fresh perspective for toxicology and disease etiology investigations.

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.

Streamlined Protein-Protein Interface Loop Mimicry

Cyclic peptides comprising endocyclic organic fragments, "cyclo-organopeptides", can be probes for perturbing protein-protein interactions (PPIs). Finding loop mimics is difficult because of high conformational variability amongst targets. Backbone Matching (BM), introduced here, helps solve this problem in the illustrative cases by facilitating efficient evaluation of virtual cyclo-organopeptide core-structure libraries. Thus, 86 rigid organic fragments were selected to build a library of 602 cyclo-organopeptides comprising Ala and organic parts: "cyclo-{-(Ala)n -organo-}". The central hypothesis is "hit" library members have accessible low energy conformers corresponding to backbone structures of target protein loops, while library members which cannot attain this conformation are probably unworthy of further evaluation. BM thereby prioritizes candidate loop mimics, so that less than 10 cyclo-organopeptides are needed to be prepared to find leads for two illustrative PPIs: iNOS ⋅ SPSB2, and uPA ⋅ uPAR.

Analysis of a Novel Peptide That Is Capable of Inhibiting the Enzymatic Activity of the Protein Kinase A Catalytic Subunit-Like Protein from Trypanosoma equiperdum

A 26-residue peptide possessing the αN-helix motif of the protein kinase A (PKA) regulatory subunit-like proteins from the Trypanozoom subgenera (VAP26, sequence = VAPYFEKSEDETALILKLLTYNVLFS), was shown to inhibit the enzymatic activity of the Trypanosoma equiperdum PKA catalytic subunit-like protein, in a similar manner that the mammalian heat-stable soluble PKA inhibitor known as PKI. However, VAP26 does not contain the PKI inhibitory sequence. Bioinformatics analyzes of the αN-helix motif from various Trypanozoon PKA regulatory subunit-like proteins suggested that the sequence could form favorable peptide-protein interactions of hydrophobic nature with the PKA catalytic subunit-like protein, which possibly may represent an alternative PKA inhibitory mechanism. The sequence of the αN-helix motif of the Trypanozoon proteins was shown to be highly homologous but significantly divergent from the corresponding αN-helix motifs of their Leishmania and mammalian counterparts. This sequence divergence contrasted with the proposed secondary structure of the αN-helix motif, which appeared conserved in every analyzed regulatory subunit-like protein. In silico mutation experiments at positions I234, L238 and F244 of the αN-helix motif from the Trypanozoon proteins destabilized both the specific motif and the protein. On the contrary, mutations at positions T239 and Y240 stabilized the motif and the protein. These results suggested that the αN-helix motif from the Trypanozoon proteins probably possessed a different evolutionary path than their Leishmania and mammalian counterparts. Moreover, finding stabilizing mutations indicated that new inhibitory peptides may be designed based on the αN-helix motif from the Trypanozoon PKA regulatory subunit-like proteins.

Quantification of biases in predictions of protein-protein binding affinity changes upon mutations

Understanding the impact of mutations on protein-protein binding affinity is a key objective for a wide range of biotechnological applications and for shedding light on disease-causing mutations, which are often located at protein-protein interfaces. Over the past decade, many computational methods using physics-based and/or machine learning approaches have been developed to predict how protein binding affinity changes upon mutations. They all claim to achieve astonishing accuracy on both training and test sets, with performances on standard benchmarks such as SKEMPI 2.0 that seem overly optimistic. Here we benchmarked eight well-known and well-used predictors and identified their biases and dataset dependencies, using not only SKEMPI 2.0 as a test set but also deep mutagenesis data on the severe acute respiratory syndrome coronavirus 2 spike protein in complex with the human angiotensin-converting enzyme 2. We showed that, even though most of the tested methods reach a significant degree of robustness and accuracy, they suffer from limited generalizability properties and struggle to predict unseen mutations. Interestingly, the generalizability problems are more severe for pure machine learning approaches, while physics-based methods are less affected by this issue. Moreover, undesirable prediction biases toward specific mutation properties, the most marked being toward destabilizing mutations, are also observed and should be carefully considered by method developers. We conclude from our analyses that there is room for improvement in the prediction models and suggest ways to check, assess and improve their generalizability and robustness.

SARS-CoV-2 infection establishes a stable and age-independent CD8+ T cell response against a dominant nucleocapsid epitope using restricted T cell receptors

The resolution of SARS-CoV-2 replication hinges on cell-mediated immunity, wherein CD8+ T cells play a vital role. Nonetheless, the characterization of the specificity and TCR composition of CD8+ T cells targeting non-spike protein of SARS-CoV-2 before and after infection remains incomplete. Here, we analyzed CD8+ T cells recognizing six epitopes from the SARS-CoV-2 nucleocapsid (N) protein and found that SARS-CoV-2 infection slightly increased the frequencies of N-recognizing CD8+ T cells but significantly enhanced activation-induced proliferation compared to that of the uninfected donors. The frequencies of N-specific CD8+ T cells and their proliferative response to stimulation did not decrease over one year. We identified the N222-230 peptide (LLLDRLNQL, referred to as LLL thereafter) as a dominant epitope that elicited the greatest proliferative response from both convalescent and uninfected donors. Single-cell sequencing of T cell receptors (TCR) from LLL-specific CD8+ T cells revealed highly restricted Vα gene usage (TRAV12-2) with limited CDR3α motifs, supported by structural characterization of the TCR-LLL-HLA-A2 complex. Lastly, transcriptome analysis of LLL-specific CD8+ T cells from donors who had expansion (expanders) or no expansion (non-expanders) after in vitro stimulation identified increased chromatin modification and innate immune functions of CD8+ T cells in non-expanders. These results suggests that SARS-CoV-2 infection induces LLL-specific CD8+ T cell responses with a restricted TCR repertoire.

FPGA Implementation of IIR Notch and Anti-Notch Filters With an Application to Localization of Protein Hot-Spots

In this paper, high-speed second-order infinite impulse response (IIR) notch filter (NF) and anti-notch filter (ANF) are designed and realized on hardware. The improvement in speed of operation for the NF is then achieved by using the re-timing concept. The ANF is designed to specify a stability margin and minimize the amplitude area. Next, an improved approach is proposed for the detection of protein hot-spot locations using the designed second-order IIR ANF. The analytical and experimental results reported in this paper show that the proposed approach provides better hot-spot prediction compared to the reported classical filtering techniques based on the IIR Chebyshev filter and S-transform. The proposed approach also yields consistency in prediction hot-spots compared to the results based on biological methodologies. Furthermore, the presented technique reveals some new "potential" hot-spots. The proposed filters are simulated and synthesized using the Xilinx Vivado 18.3 software platform with Zynq-7000 Series (ZedBoard Zynq Evaluation and Development Kit xc7z020clg484-1) FPGA family.

SARS-CoV-2 inhibitory activity of a short peptide derived from internal fusion peptide of S2 subunit of spike glycoprotein

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has posed a great concern in human population. To fight coronavirus emergence, we have dissected the conserved amino acid region of the internal fusion peptide in the S2 subunit of Spike glycoprotein of SARS-CoV-2 to design new inhibitory peptides. Among the 11 overlapping peptides (9-23-mer), PN19, a 19-mer peptide, exhibited a powerful inhibitory activity against different SARS-CoV-2 clinical isolate variants in absence of cytotoxicity. The PN19 inhibitory activity was found to be dependent on conservation of the central Phe and C-terminal Tyr residues in the peptide sequence. Circular dichroism spectra of the active peptide exhibited an alpha-helix propensity, confirmed by secondary structure prediction analysis. The PN19 inhibitory activity, exerted in the first step of virus infection, was reduced after peptide adsorption treatment with virus-cell substrate during fusion interaction. Additionally, PN19 inhibitory activity was reduced by adding S2 membrane-proximal region derived peptides. PN19 showed binding ability to the S2 membrane proximal region derived peptides, confirmed by molecular modelling, playing a role in the mechanism of action. Collectively, these results confirm that the internal fusion peptide region is a good candidate on which develop peptidomimetic anti SARS-CoV-2 antivirals.

Categorization of hotspots into three types - weak, moderate and strong to distinguish protein-protein versus protein-peptide interactions

Protein-protein and protein-peptide interactions (PPI and PPepI) belong to a similar category of interactions, yet seemingly subtle differences exist among them. To characterize differences between protein-protein (PP) and protein-peptide (PPep) interactions, we have focussed on two important classes of residues-hotspot and anchor residues. Using implicit solvation-based free energy calculations, a very large-scale alanine scanning has been performed on benchmark datasets, consisting of over 5700 interface residues. The differences in the two categories are more pronounced, if the data were divided into three distinct types, namely - weak hotspots (having binding free energy loss upon Ala mutation, ΔΔG, ∼2-10 kcal/mol), moderate hotspots (ΔΔG, ∼10-20 kcal/mol) and strong hotspots (ΔΔG ≥ ∼20 kcal/mol). The analysis suggests that for PPI, weak hotspots are predominantly populated by polar and hydrophobic residues. The distribution shifts towards charged and polar residues for moderate hotspot and charged residues (principally Arg) are overwhelmingly present in the strong hotspot. On the other hand, in the PPepI dataset, the distribution shifts from predominantly hydrophobic and polar (in the weak type) to almost similar preference for polar, hydrophobic and charged residues (in moderate type) and finally the charged residue (Arg) and Trp are mostly occupied in the strong type. The preferred anchor residues in both categories are Arg, Tyr and Leu, possessing bulky side chain and which also strike a delicate balance between side chain flexibility and rigidity. The present knowledge should aid in effective design of biologics, by augmentation or disruption of PPIs with peptides or peptidomimetics.Communicated by Ramaswamy H. Sarma.

Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex

Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a graphics processing unit (GPU)-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches─alchemical replica exchange and alchemical replica exchange with solute tempering─for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and is available at https://github.com/choderalab/perses.

Solvated interaction energy: from small-molecule to antibody drug design

Scoring functions are ubiquitous in structure-based drug design as an aid to predicting binding modes and estimating binding affinities. Ideally, a scoring function should be broadly applicable, obviating the need to recalibrate and refit its parameters for every new target and class of ligands. Traditionally, drugs have been small molecules, but in recent years biologics, particularly antibodies, have become an increasingly important if not dominant class of therapeutics. This makes the goal of having a transferable scoring function, i.e., one that spans the range of small-molecule to protein ligands, even more challenging. One such broadly applicable scoring function is the Solvated Interaction Energy (SIE), which has been developed and applied in our lab for the last 15 years, leading to several important applications. This physics-based method arose from efforts to understand the physics governing binding events, with particular care given to the role played by solvation. SIE has been used by us and many independent labs worldwide for virtual screening and discovery of novel small-molecule binders or optimization of known drugs. Moreover, without any retraining, it is found to be transferrable to predictions of antibody-antigen relative binding affinities and as accurate as functions trained on protein-protein binding affinities. SIE has been incorporated in conjunction with other scoring functions into ADAPT (Assisted Design of Antibody and Protein Therapeutics), our platform for affinity modulation of antibodies. Application of ADAPT resulted in the optimization of several antibodies with 10-to-100-fold improvements in binding affinity. Further applications included broadening the specificity of a single-domain antibody to be cross-reactive with virus variants of both SARS-CoV-1 and SARS-CoV-2, and the design of safer antibodies by engineering of a pH switch to make them more selective towards acidic tumors while sparing normal tissues at physiological pH.

AREA-AFFINITY: A Web Server for Machine Learning-Based Prediction of Protein-Protein and Antibody-Protein Antigen Binding Affinities

Protein-Protein binding affinity reflects the binding strength between the binding partners. The prediction of protein-protein binding affinity is important for elucidating protein functions and also for designing protein-based therapeutics. The geometric characteristics such as area (both interface and surface areas) in the structure of a protein-protein complex play an important role in determining protein-protein interactions and their binding affinity. Here, we present a free web server for academic use, AREA-AFFINITY, for prediction of protein-protein or antibody-protein antigen binding affinity based on interface and surface areas in the structure of a protein-protein complex. AREA-AFFINITY implements 60 effective area-based protein-protein affinity predictive models and 37 effective area-based models specific for antibody-protein antigen binding affinity prediction developed in our recent studies. These models take into consideration the roles of interface and surface areas in binding affinity by using areas classified according to different amino acid types with different biophysical nature. The models with the best performances integrate machine learning methods such as neural network or random forest. These newly developed models have superior or comparable performance compared to the commonly used existing methods. AREA-AFFINITY is available for free at: https://affinity.cuhk.edu.cn/.

Computationally restoring the potency of a clinical antibody against SARS-CoV-2 Omicron subvariants

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs1-3, but also revealed how quickly viral escape can curtail effective options4,5. With the emergence of the SARS-CoV-2 Omicron variant in late 2021, many clinically used antibody drug products lost potency, including Evusheld™ and its constituent, cilgavimab4,6. Cilgavimab, like its progenitor COV2-2130, is a class 3 antibody that is compatible with other antibodies in combination4 and is challenging to replace with existing approaches. Rapidly modifying such high-value antibodies with a known clinical profile to restore efficacy against emerging variants is a compelling mitigation strategy. We sought to redesign COV2-2130 to rescue in vivo efficacy against Omicron BA.1 and BA.1.1 strains while maintaining efficacy against the contemporaneously dominant Delta variant. Here we show that our computationally redesigned antibody, 2130-1-0114-112, achieves this objective, simultaneously increases neutralization potency against Delta and many variants of concern that subsequently emerged, and provides protection in vivo against the strains tested, WA1/2020, BA.1.1, and BA.5. Deep mutational scanning of tens of thousands pseudovirus variants reveals 2130-1-0114-112 improves broad potency without incurring additional escape liabilities. Our results suggest that computational approaches can optimize an antibody to target multiple escape variants, while simultaneously enriching potency. Because our approach is computationally driven, not requiring experimental iterations or pre-existing binding data, it could enable rapid response strategies to address escape variants or pre-emptively mitigate escape vulnerabilities.

Rapid Evaluation of Staple Placement in Stabilized α Helices Using Bacterial Surface Display

There are a wealth of proteins involved in disease that cannot be targeted by current therapeutics because they are inside cells, inaccessible to most macromolecules, and lack small-molecule binding pockets. Stapled peptides, where two amino acids are covalently linked, form a class of macrocycles that have the potential to penetrate cell membranes and disrupt intracellular protein-protein interactions. However, their discovery relies on solid-phase synthesis, greatly limiting queries into their complex design space involving amino acid sequence, staple location, and staple chemistry. Here, we use stabilized peptide engineering by Escherichia coli display (SPEED), which utilizes noncanonical amino acids and click chemistry for stabilization, to rapidly screen staple location and linker structure to accelerate peptide design. After using SPEED to confirm hotspots in the mdm2-p53 interaction, we evaluated different staple locations and staple chemistry to identify several novel nanomolar and sub-nanomolar antagonists. Next, we evaluated SPEED in the B cell lymphoma 2 (Bcl-2) protein family, which is responsible for regulating apoptosis. We report that novel staple locations modified in the context of BIM, a high affinity but nonspecific naturally occurring peptide, improve its specificity against the highly homologous proteins in the Bcl-2 family. These compounds demonstrate the importance of screening linker location and chemistry in identifying high affinity and specific peptide antagonists. Therefore, SPEED can be used as a versatile platform to evaluate multiple design criteria for stabilized peptide engineering.

Germline-encoded amino acid-binding motifs drive immunodominant public antibody responses

Despite the vast diversity of the antibody repertoire, infected individuals often mount antibody responses to precisely the same epitopes within antigens. The immunological mechanisms underpinning this phenomenon remain unknown. By mapping 376 immunodominant "public epitopes" at high resolution and characterizing several of their cognate antibodies, we concluded that germline-encoded sequences in antibodies drive recurrent recognition. Systematic analysis of antibody-antigen structures uncovered 18 human and 21 partially overlapping mouse germline-encoded amino acid-binding (GRAB) motifs within heavy and light V gene segments that in case studies proved critical for public epitope recognition. GRAB motifs represent a fundamental component of the immune system's architecture that promotes recognition of pathogens and leads to species-specific public antibody responses that can exert selective pressure on pathogens.

De novo design of modular peptide-binding proteins by superhelical matching

General approaches for designing sequence-specific peptide-binding proteins would have wide utility in proteomics and synthetic biology. However, designing peptide-binding proteins is challenging, as most peptides do not have defined structures in isolation, and hydrogen bonds must be made to the buried polar groups in the peptide backbone1-3. Here, inspired by natural and re-engineered protein-peptide systems4-11, we set out to design proteins made out of repeating units that bind peptides with repeating sequences, with a one-to-one correspondence between the repeat units of the protein and those of the peptide. We use geometric hashing to identify protein backbones and peptide-docking arrangements that are compatible with bidentate hydrogen bonds between the side chains of the protein and the peptide backbone12. The remainder of the protein sequence is then optimized for folding and peptide binding. We design repeat proteins to bind to six different tripeptide-repeat sequences in polyproline II conformations. The proteins are hyperstable and bind to four to six tandem repeats of their tripeptide targets with nanomolar to picomolar affinities in vitro and in living cells. Crystal structures reveal repeating interactions between protein and peptide interactions as designed, including ladders of hydrogen bonds from protein side chains to peptide backbones. By redesigning the binding interfaces of individual repeat units, specificity can be achieved for non-repeating peptide sequences and for disordered regions of native proteins.

Structural insights into protection against a SARS-CoV-2 spike variant by T cell receptor diversity

T cells play a crucial role in combatting SARS-CoV-2 and forming long-term memory responses to this coronavirus. The emergence of SARS-CoV-2 variants that can evade T cell immunity has raised concerns about vaccine efficacy and the risk of reinfection. Some SARS-CoV-2 T cell epitopes elicit clonally restricted CD8+ T cell responses characterized by T cell receptors (TCRs) that lack structural diversity. Mutations in such epitopes can lead to loss of recognition by most T cells specific for that epitope, facilitating viral escape. Here, we studied an HLA-A2-restricted spike protein epitope (RLQ) that elicits CD8+ T cell responses in COVID-19 convalescent patients characterized by highly diverse TCRs. We previously reported the structure of an RLQ-specific TCR (RLQ3) with greatly reduced recognition of the most common natural variant of the RLQ epitope (T1006I). Opposite to RLQ3, TCR RLQ7 recognizes T1006I with even higher functional avidity than the WT epitope. To explain the ability of RLQ7, but not RLQ3, to tolerate the T1006I mutation, we determined structures of RLQ7 bound to RLQ-HLA-A2 and T1006I-HLA-A2. These complexes show that there are multiple structural solutions to recognizing RLQ and thereby generating a clonally diverse T cell response to this epitope that assures protection against viral escape and T cell clonal loss.

Orthogonal threading-through-β-sheet design of lung cancer EGFR extracellular domain-derived peptidic mimotopes binding to anti-EGFR antibody

Human epidermal growth factor receptor (EGFR) has been established as a therapeutic target of lung cancer and other diverse tumors. The antibody drug Cetuximab has been developed to target the third subdomain III (TSDIII) of EGFR extracellular domain (ECD) by competitively inhibiting epidermal growth factor binding. In this study, we performed systematic investigation on the crystal complex structure of EGFR ECD domain with Cetuximab to create a residue importance profile for the TSDIII subdomain, based on which a number of U-shaped, double-stranded linear peptides were derived and cyclized to orthogonally thread through most hotspot residues and many responsible residues within the TSDIII β-sheet plane; they represent mimotopes of the key antibody-recognition site of TSDIII subdomain. Computational analyses revealed that these linear peptides cannot spontaneously fold to the desired conformation in free state due to their intrinsic flexibility. Cell-free assays confirmed that the stapling can considerably improve the binding affinity of linear peptides to Cetuximab by up to 18-fold. The cOrt1 [3-18] cyclic peptide was measured to have the highest affinity in all designed linear and cyclic peptides.

Identifying and overcoming the sampling challenges in relative binding free energy calculations of a model protein:protein complex

Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a GPU-accelerated opensource relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches-alchemical replica exchange and alchemical replica exchange with solute tempering-for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally-determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and available at https://github.com/choderalab/perses .

Design of a protease-activated PD-L1 inhibitor

Immune checkpoint inhibitors that bind to the cell surface receptor PD-L1 are effective anti-cancer agents but suffer from immune-related adverse events as PD-L1 is expressed on both healthy and cancer cells. To mitigate toxicity, researchers are testing prodrugs that have low affinity for checkpoint targets until activated with proteases enriched in the tumor microenvironment. Here, we engineer a prodrug form of a PD-L1 inhibitor. The inhibitor is a soluble PD-1 mimetic that was previously engineered to have high affinity for PD-L1. In the basal state, the binding surface of the PD-1 mimetic is masked by fusing it to a soluble variant of its natural ligand, PD-L1. Proteolytic cleavage of the linker that connects the mask to the inhibitor activates the molecule. To optimize the mask so that it effectively blocks binding to PD-L1 but releases upon cleavage, we tested a set of mutants with varied affinity for the inhibitor. The top-performing mask reduces the affinity of the prodrug for PD-L1 120-fold, and binding is nearly fully recovered upon cleavage. In a cell-based assay measuring inhibition of the PD-1:PD-L1 interaction on the surface of cells, the IC50s of the masked inhibitors were up to 40-fold higher than their protease-treated counterparts. The changes in activity we observe upon protease treatment are comparable to systems currently tested in the clinic and provide evidence that natural binding partners are an excellent starting point for creating a prodrug.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

Network of hotspot interactions cluster tau amyloid folds

Cryogenic electron microscopy has revealed unprecedented molecular insight into the conformations of β-sheet-rich protein amyloids linked to neurodegenerative diseases. It remains unknown how a protein can adopt a diversity of folds and form multiple distinct fibrillar structures. Here we develop an in silico alanine scan method to estimate the relative energetic contribution of each amino acid in an amyloid assembly. We apply our method to twenty-seven ex vivo and in vitro fibril structural polymorphs of the microtubule-associated protein tau. We uncover networks of energetically important interactions involving amyloid-forming motifs that stabilize the different fibril folds. We evaluate our predictions in cellular and in vitro aggregation assays. Using a machine learning approach, we classify the structures based on residue energetics to identify distinguishing and unifying features. Our energetic profiling suggests that minimal sequence elements control the stability of tau fibrils, allowing future design of protein sequences that fold into unique structures.

Predicting binding affinity changes from long-distance mutations using molecular dynamics simulations and Rosetta

Computationally modeling how mutations affect protein-protein binding not only helps uncover the biophysics of protein interfaces, but also enables the redesign and optimization of protein interactions. Traditional high-throughput methods for estimating binding free energy changes are currently limited to mutations directly at the interface due to difficulties in accurately modeling how long-distance mutations propagate their effects through the protein structure. However, the modeling and design of such mutations is of substantial interest as it allows for greater control and flexibility in protein design applications. We have developed a method that combines high-throughput Rosetta-based side-chain optimization with conformational sampling using classical molecular dynamics simulations, finding significant improvements in our ability to accurately predict long-distance mutational perturbations to protein binding. Our approach uses an analytical framework grounded in alchemical free energy calculations while enabling exploration of a vastly larger sequence space. When comparing to experimental data, we find that our method can predict internal long-distance mutational perturbations with a level of accuracy similar to that of traditional methods in predicting the effects of mutations at the protein-protein interface. This work represents a new and generalizable approach to optimize protein free energy landscapes for desired biological functions.

Effect of mutation of NS2B cofactor residues on Dengue 2 NS2B-NS3 protease complex - an insight to viral replication

Dengue fever is an endemic virus-borne disease that causes many severe ailments, including dengue hemorrhagic fever and dengue shock syndrome. NS2B-NS3 protease is present in all four strains of the dengue virus. NS2B-NS3 is a non-structural protein that performs three distinct functions: protease activity, helicase activity, and nucleoside triphosphatase activity. NS2B-NS3 pro-complex plays a crucial role in viral replication, and NS2B interacts with NS3 protease at a flat active site with an amino acid of the N-terminal region. NS2B acts as a cofactor for NS3 protease. In the current study, the conserved residues of NS2B were identified. Dengue virus-2 NS2B was mutated at the identified conserved amino acid region to investigate the role of NS2B on activation of NS3 pro. Molecular dynamics simulations were performed to investigate the mutated complex's changes in stability, conformation, and free energy. The EAG mutant complex exhibited more unstable conformation, less hydrogen bond formation, and high binding energy than wild type. This result suggests a vital role of E63, A65, G69 mutation in NS2B for the interruption of activation of the NS3.Communicated by Ramaswamy H. Sarma.

In silico engineering a CD80 variant with increased affinity to CTLA-4 and decreased affinity to CD28 for optimized cancer immunotherapy

CD80 or cluster of differentiation 80, also known as B7-1, is a member of the immunoglobulin super family, which binds to CTLA-4 and CD28 T cell receptors and induces inhibitory and inductive signals respectively. Although CTLA-4 and CD28 receptors belong to the same protein family, slight differences in their structures leads to CD80 having a higher binding affinity to CTLA-4 (-14.55 kcal/mol) compared with CD28(-12.51 kcal/mol). In this study, we constructed a variant of CD80 protein with increased binding affinity to CTLA-4 and decreased binding affinity to CD28. This variant has no signaling capability, and can act as a cap for these receptors to protect them from natural CD80 proteins existing in the body. The first step was the evolutionary and alanine scanning analysis of CD80 protein to determine conserved regions in this protein. Next, complex alanine scanning technique was employed to determine CD80 protein hotspots in CD80-CTLA-4 and CD80-CD28 protein complexes. This information was fed into a computational model developed in R for in silico mutagenesis and CD80 variant library construction. The 3D structures of variants were modeled using the Swiss model webserver. After modeling the 3D structures, HADDOCK server was employed to build all protein-protein complexes, which contain CTLA-4-CD80 variant complexes, Wild type CD80-CD28 complexes and CD28-CD80 variant complexes. Protein-protein binding free energy was determined using FoldX and the variant number 316 with mutations at 29, 31, 33 positions showed increased binding affinity to CTLA-4 (-21.43 kcal/mol) and decreased binding affinity to CD28 (- 9.54 kcal/mol). Finally, molecular dynamics (MD) simulations confirmed the stability of variant 316. In conclusion, we designed a new CD80 protein variant with potential immunotherapeutic applications.

Molecular Interactions of the Copper Chaperone Atx1 of Paracoccidioides brasiliensis with Fungal Proteins Suggest a Crosstalk between Iron and Copper Homeostasis

Paracoccidioides spp. are endemic fungi from Latin America that cause Paracoccidioidomycosis, a systemic disease. These fungi present systems for high-affinity metal uptake, storage, and mobilization, which counteract host nutritional immunity and mitigate the toxic effects of metals. Regarding Cu mobilization, the metallochaperone Atx1 is regulated according to Cu bioavailability in Paracoccidioides spp., contributing to metal homeostasis. However, additional information in the literature on PbAtx1 is scarce. Therefore, in the present work, we aimed to study the PbAtx1 protein-protein interaction networks. Heterologous expressed PbAtx1 was used in a pull-down assay with Paracoccidioides brasiliensis cytoplasmic extract. Nineteen proteins that interacted with PbAtx1 were identified by HPLC-MS^E. Among them, a relevant finding was a Cytochrome b₅ (PbCyb5), regulated by Fe bioavailability in Aspergillus fumigatus and highly secreted by P. brasiliensis in Fe deprivation. We validated the interaction between PbAtx1-PbCyb5 through molecular modeling and far-Western analyses. It is known that there is a relationship between Fe homeostasis and Cu homeostasis in organisms. In this sense, would PbAtx1-PbCyb5 interaction be a new metal-sensor system? Would it be supported by the presence/absence of metals? We intend to answer those questions in future works to contribute to the understanding of the strategies employed by Paracoccidioides spp. to overcome host defenses.

Learning the relationship between nanoscale chemical patterning and hydrophobicity

The hydrophobicity of proteins and similar surfaces, which display chemical heterogeneity at the nanoscale, drives countless aqueous interactions and assemblies. However, predicting how surface chemical patterning influences hydrophobicity remains a challenge. Here, we address this challenge by using molecular simulations and machine learning to characterize and model the hydrophobicity of a diverse library of patterned surfaces, spanning a wide range of sizes, shapes, and chemical compositions. We find that simple models, based only on polar content, are inaccurate, whereas complex neural network models are accurate but challenging to interpret. However, by systematically incorporating chemical correlations between surface groups into our models, we are able to construct a series of minimal models of hydrophobicity, which are both accurate and interpretable. Our models highlight that the number of proximal polar groups is a key determinant of hydrophobicity and that polar neighbors enhance hydrophobicity. Although our minimal models are trained on particular patch size and shape, their interpretability enables us to generalize them to rectangular patches of all shapes and sizes. We also demonstrate how our models can be used to predict hot-spot locations with the largest marginal contributions to hydrophobicity and to design chemical patterns that have a fixed polar content but vary widely in their hydrophobicity. Our data-driven models and the principles they furnish for modulating hydrophobicity could facilitate the design of novel materials and engineered proteins with stronger interactions or enhanced solubilities.

Design of human immunodeficiency virus-1 neutralizing peptides targeting CD4-binding site: An integrative computational biologics approach

Peptide therapeutics have recently gained momentum in antiviral therapy due to their increased potency and cost-effectiveness. Interaction of the HIV-1 envelope gp120 with the host CD4 receptor is a critical step for viral entry, and therefore the CD4-binding site (CD4bs) of gp120 is a potential hotspot for blocking HIV-1 infection. The present study aimed to design short peptides from well-characterized CD4bs targeting broadly neutralizing antibodies (bNAbs), which could be utilized as bNAb mimetics for viral neutralization. Co-crystallized structures of HIV-1 gp120 in complex with CD4bs-directed bNAbs were used to derive hexameric peptides using the Rosetta Peptiderive protocol. Based on empirical insights into co-crystallized structures, peptides derived from the heavy chain alone were considered. The peptides were docked with both HIV-1 subtype B and C gp120, and the stability of the peptide–antigen complexes was validated using extensive Molecular Dynamics (MD) simulations. Two peptides identified in the study demonstrated stable intermolecular interactions with SER365, GLY366, and GLY367 of the PHE43 cavity in the CD4 binding pocket, and with ASP368 of HIV-1 gp120, thereby mimicking the natural interaction between ASP368gp120 and ARG59CD4–RECEPTOR. Furthermore, the peptides featured favorable physico-chemical properties for virus neutralization suggesting that these peptides may be highly promising bNAb mimetic candidates that may be taken up for experimental validation.

Molecular analysis of a public cross-neutralizing antibody response to SARS-CoV-2

As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concerns (VOCs) continue to emerge, cross-neutralizing antibody responses become key toward next-generation design of a more universal COVID-19 vaccine. By analyzing published data from the literature, we report here that the combination of germline genes IGHV2-5/IGLV2-14 represents a public antibody response to the receptor-binding domain (RBD) that potently cross-neutralizes a broad range of VOCs, including Omicron and its sub-lineages. Detailed molecular analysis shows that the complementarity-determining region H3 sequences of IGHV2-5/IGLV2-14-encoded RBD antibodies have a preferred length of 11 amino acids and a conserved HxIxxI motif. In addition, these antibodies have a strong allelic preference due to an allelic polymorphism at amino acid residue 54 of IGHV2-5, which is located at the paratope. These findings have important implications for understanding cross-neutralizing antibody responses to SARS-CoV-2 and its heterogenicity at the population level as well as the development of a universal COVID-19 vaccine.

Hybrid in vitro/in silico analysis of low-affinity protein-protein interactions that regulate signal transduction by Sema6D

Semaphorins constitute a large family of secreted and membrane-bound proteins that signal through cell-surface receptors, plexins. Semaphorins generally use low-affinity protein-protein interactions to bind with their specific plexin(s) and regulate distinct cellular processes such as neurogenesis, immune response, and organogenesis. Sema6D is a membrane-bound semaphorin that interacts with class A plexins. Sema6D exhibited differential binding affinities to class A plexins in prior cell-based assays, but the molecular mechanism underlying this selectivity is not well understood. Therefore, we performed hybrid in vitro/in silico analysis to examine the binding mode of Sema6D to class A plexins and to identify residues that give rise to the differential affinities and thus contribute to the selectivity within the same class of semaphorins. Our biophysical binding analysis indeed confirmed that Sema6D has a higher affinity for Plexin-A1 than for other class A plexins, consistent with the binding selectivity observed in the previous cell-based assays. Unexpectedly, our present crystallographic analysis of the Sema6D-Plexin-A1 complex showed that the pattern of polar interactions is not interaction-specific because it matches the pattern in the prior structure of the Sema6A-Plexin-A2 complex. Thus, we performed in silico alanine scanning analysis and discovered hotspot residues that selectively stabilized the Sema6D-Plexin-A1 pair via Van der Waals interactions. We then validated the contribution of these hotspot residues to the variation in binding affinity with biophysical binding analysis and molecular dynamics simulations on the mutants. Ultimately, our present results suggest that shape complementarity in the binding interfaces is a determinant for binding selectivity.

Biomolecular Topology: Modelling and Analysis

With the great advancement of experimental tools, a tremendous amount of biomolecular data has been generated and accumulated in various databases. The high dimensionality, structural complexity, the nonlinearity, and entanglements of biomolecular data, ranging from DNA knots, RNA secondary structures, protein folding configurations, chromosomes, DNA origami, molecular assembly, to others at the macromolecular level, pose a severe challenge in their analysis and characterization. In the past few decades, mathematical concepts, models, algorithms, and tools from algebraic topology, combinatorial topology, computational topology, and topological data analysis, have demonstrated great power and begun to play an essential role in tackling the biomolecular data challenge. In this work, we introduce biomolecular topology, which concerns the topological problems and models originated from the biomolecular systems. More specifically, the biomolecular topology encompasses topological structures, properties and relations that are emerged from biomolecular structures, dynamics, interactions, and functions. We discuss the various types of biomolecular topology from structures (of proteins, DNAs, and RNAs), protein folding, and protein assembly. A brief discussion of databanks (and databases), theoretical models, and computational algorithms, is presented. Further, we systematically review related topological models, including graphs, simplicial complexes, persistent homology, persistent Laplacians, de Rham-Hodge theory, Yau-Hausdorff distance, and the topology-based machine learning models.

Hom-Complex-Based Machine Learning (HCML) for the Prediction of Protein-Protein Binding Affinity Changes upon Mutation

Protein-protein interactions (PPIs) are involved in almost all biological processes in the cell. Understanding protein-protein interactions holds the key for the understanding of biological functions, diseases and the development of therapeutics. Recently, artificial intelligence (AI) models have demonstrated great power in PPIs. However, a key issue for all AI-based PPI models is efficient molecular representations and featurization. Here, we propose Hom-complex-based PPI representation, and Hom-complex-based machine learning models for the prediction of PPI binding affinity changes upon mutation, for the first time. In our model, various Hom complexes Hom(G₁, G) can be generated for the graph representation G of protein-protein complex by using different graphs G₁, which reveal G₁-related inner connections within the graph representation G of protein-protein complex. Further, for a specific graph G₁, a series of nested Hom complexes are generated to give a multiscale characterization of the PPIs. Its persistent homology and persistent Euler characteristic are used as molecular descriptors and further combined with the machine learning model, in particular, gradient boosting tree (GBT). We systematically test our model on the two most-commonly used data sets, that is, SKEMPI and AB-Bind. It has been found that our model outperforms all the existing models as far as we know, which demonstrates the great potential of our model for the analysis of PPIs. Our model can be used for the analysis and design of efficient antibodies for SARS-CoV-2.

Dynamic stability of salt stable cowpea chlorotic mottle virus capsid protein dimers and pentamers of dimers

Intermediates of the self-assembly process of the salt stable cowpea chlorotic mottle virus (ss-CCMV) capsid can be modelled atomistically on realistic computational timescales either by studying oligomers in equilibrium or by focusing on their dissociation instead of their association. Our previous studies showed that among the three possible dimer interfaces in the icosahedral capsid, two are thermodynamically relevant for capsid formation. The aim of the current study is to evaluate the relative structural stabilities of the three different ss-CCMV dimers and to find and understand the conditions that lead to their dissociation. Long timescale molecular dynamics simulations at 300 K of the various dimers and of the pentamer of dimers underscore the importance of large contact surfaces on stabilizing the capsid subunits within an oligomer. Simulations in implicit solvent show that at higher temperature (350 K), the N-terminal tails of the protein units act as tethers, delaying dissociation for all but the most stable interface. The pentamer of dimers is also found to be stable on long timescales at 300 K, with an inherent flexibility of the outer protein chains.

Identification of Small-Molecule Inhibitors of Fibroblast Growth Factor 23 Signaling via In Silico Hot Spot Prediction and Molecular Docking to α-Klotho

Fibroblast growth factor 23 (FGF23) is a therapeutic target for treating hereditary and acquired hypophosphatemic disorders, such as X-linked hypophosphatemic (XLH) rickets and tumor-induced osteomalacia (TIO), respectively. FGF23-induced hypophosphatemia is mediated by signaling through a ternary complex formed by FGF23, the FGF receptor (FGFR), and α-Klotho. Currently, disorders of excess FGF23 are treated with an FGF23-blocking antibody, burosumab. Small-molecule drugs that disrupt protein/protein interactions necessary for the ternary complex formation offer an alternative to disrupting FGF23 signaling. In this study, the FGF23:α-Klotho interface was targeted to identify small-molecule protein/protein interaction inhibitors since it was computationally predicted to have a large fraction of hot spots and two druggable residues on α-Klotho. We further identified Tyr433 on the KL1 domain of α-Klotho as a promising hot spot and α-Klotho as an appropriate drug-binding target at this interface. Subsequently, we performed in silico docking of ∼5.5 million compounds from the ZINC database to the interface region of α-Klotho from the ternary crystal structure. Following docking, 24 and 20 compounds were in the final list based on the lowest binding free energies to α-Klotho and the largest number of contacts with Tyr433, respectively. Five compounds were assessed experimentally by their FGF23-mediated extracellular signal-regulated kinase (ERK) activities in vitro, and two of these reduced activities significantly. Both these compounds were predicted to have favorable binding affinities to α-Klotho but not have a large number of contacts with the hot spot Tyr433. ZINC12409120 was found experimentally to disrupt FGF23:α-Klotho interaction to reduce FGF23-mediated ERK activities by 70% and have a half maximal inhibitory concentration (IC₅₀) of 5.0 ± 0.23 μM. Molecular dynamics (MD) simulations of the ZINC12409120:α-Klotho complex starting from in silico docking poses reveal that the ligand exhibits contacts with residues on the KL1 domain, the KL1-KL2 linker, and the KL2 domain of α-Klotho simultaneously, thereby possibly disrupting the regular function of α-Klotho and impeding FGF23:α-Klotho interaction. ZINC12409120 is a candidate for lead optimization.

Rational design of AAVrh10-vectored ACE2 functional domain to broadly block the cell entry of SARS-CoV-2 variants

The frequently emerging SARS-CoV-2 variants have weakened the effectiveness of existing COVID-19 vaccines and neutralizing antibody therapy. Nevertheless, the infections of SARS-CoV-2 variants still depend on angiotensin-converting enzyme 2 (ACE2) receptor-mediated cell entry, and thus the soluble human ACE2 (shACE2) is a potential decoy for broadly blocking SARS-CoV-2 variants. In this study, we firstly generated the recombinant AAVrh10-vectored shACE2 constructs, a kind of adeno-associated virus (AAV) serotype with pulmonary tissue tropism, and then validated its inhibition capacity against SARS-CoV-2 infection. To further optimize the minimized ACE2 functional domain candidates, a comprehensive analysis was performed to clarify the interactions between the ACE2 orthologs from various species and the receptor binding domain (RBD) of SARS-CoV-2 spike (S) protein. Based on the key interface amino acids, we designed a series of truncated ACE2 orthologs, and then assessed their potential affinity to bind to SARS-CoV-2 variants RBD in silico. Of note, we found that the 24-83aa fragment of dog ACE2 (dACE2_24-83) had a higher affinity to the RBD of SARS-CoV-2 variants than that of human ACE2. Importantly, AAVrh10-vectored shACE2 or dACE2_24-83 constructs exhibited a broadly blockage breadth against SARS-CoV-2 prototype and variants in vitro and ex vivo. Collectively, these data highlighted a promising therapeutic strategy against SARS-CoV-2 variants.

The chemistry of snake venom and its medicinal potential

The fascination and fear of snakes dates back to time immemorial, with the first scientific treatise on snakebite envenoming, the Brooklyn Medical Papyrus, dating from ancient Egypt. Owing to their lethality, snakes have often been associated with images of perfidy, treachery and death. However, snakes did not always have such negative connotations. The curative capacity of venom has been known since antiquity, also making the snake a symbol of pharmacy and medicine. Today, there is renewed interest in pursuing snake-venom-based therapies. This Review focuses on the chemistry of snake venom and the potential for venom to be exploited for medicinal purposes in the development of drugs. The mixture of toxins that constitute snake venom is examined, focusing on the molecular structure, chemical reactivity and target recognition of the most bioactive toxins, from which bioactive drugs might be developed. The design and working mechanisms of snake-venom-derived drugs are illustrated, and the strategies by which toxins are transformed into therapeutics are analysed. Finally, the challenges in realizing the immense curative potential of snake venom are discussed, and chemical strategies by which a plethora of new drugs could be derived from snake venom are proposed.

An Evolutionary Conservation and Druggability Analysis of Enzymes Belonging to the Bacterial Shikimate Pathway

Enzymes belonging to the shikimate pathway have long been considered promising targets for antibacterial drugs because they have no counterpart in mammals and are essential for bacterial growth and virulence. However, despite decades of research, there are currently no clinically relevant antibacterial drugs targeting any of these enzymes, and there are legitimate concerns about whether they are sufficiently druggable, i.e., whether they can be adequately modulated by small and potent drug-like molecules. In the present work, in silico analyses combining evolutionary conservation and druggability are performed to determine whether these enzymes are candidates for broad-spectrum antibacterial therapy. The results presented here indicate that the substrate-binding sites of most enzymes in this pathway are suitable drug targets because of their reasonable conservation and druggability scores. An exception was the substrate-binding site of 3-deoxy-D-arabino-heptulosonate-7-phosphate synthase, which was found to be undruggable because of its high content of charged residues and extremely high overall polarity. Although the presented study was designed from the perspective of broad-spectrum antibacterial drug development, this workflow can be readily applied to any antimicrobial target analysis, whether narrow- or broad-spectrum. Moreover, this research also contributes to a deeper understanding of these enzymes and provides valuable insights into their properties.

In Silico Optimized Stapled Peptides Targeting WASF3 in Breast Cancer

Wiskott-Aldrich Syndrome Protein Family (WASF) members regulate actin cytoskeletal dynamics, and WASF3 is directly associated with breast cancer metastasis and invasion. WASF3 forms a heteropentameric complex with CYFIP, NCKAP, ABI, and BRK1, called the WASF Regulatory Complex (WRC), which cooperatively regulates actin nucleation by WASF3. Since aberrant deployment of the WRC is observed in cancer metastasis and invasion, its disruption provides a novel avenue for targeting motility in breast cancer cells. Here, we report the development of a second generation WASF3 mimetic peptide, WAHMIS-2, which was designed using a combination of structure-guided design, homology modeling, and in silico optimization to disrupt binding of WASF3 to the WRC. WAHMIS-2 was found to permeate cells and inhibit cell motility, invasion, and MMP9 expression with greater potency than its predecessor, WAHM1. Targeted disruption of WASF3 from the WRC may serve as a useful strategy for suppression of breast cancer metastasis.

Persistent spectral based ensemble learning (PerSpect-EL) for protein-protein binding affinity prediction

Protein-protein interactions (PPIs) play a significant role in nearly all cellular and biological activities. Data-driven machine learning models have demonstrated great power in PPIs. However, the design of efficient molecular featurization poses a great challenge for all learning models for PPIs. Here, we propose persistent spectral (PerSpect) based PPI representation and featurization, and PerSpect-based ensemble learning (PerSpect-EL) models for PPI binding affinity prediction, for the first time. In our model, a sequence of Hodge (or combinatorial) Laplacian (HL) matrices at various different scales are generated from a specially designed filtration process. PerSpect attributes, which are statistical and combinatorial properties of spectrum information from these HL matrices, are used as features for PPI characterization. Each PerSpect attribute is input into a 1D convolutional neural network (CNN), and these CNN networks are stacked together in our PerSpect-based ensemble learning models. We systematically test our model on the two most commonly used datasets, i.e. SKEMPI and AB-Bind. It has been found that our model can achieve state-of-the-art results and outperform all existing models to the best of our knowledge.

Veratridine Can Bind to a Site at the Mouth of the Channel Pore at Human Cardiac Sodium Channel NaV1.5

The cardiac sodium ion channel (Na_V1.5) is a protein with four domains (DI-DIV), each with six transmembrane segments. Its opening and subsequent inactivation results in the brief rapid influx of Na⁺ ions resulting in the depolarization of cardiomyocytes. The neurotoxin veratridine (VTD) inhibits Na_V1.5 inactivation resulting in longer channel opening times, and potentially fatal action potential prolongation. VTD is predicted to bind at the channel pore, but alternative binding sites have not been ruled out. To determine the binding site of VTD on Na_V1.5, we perform docking calculations and high-throughput electrophysiology experiments in the present study. The docking calculations identified two distinct binding regions. The first site was in the pore, close to the binding site of Na_V1.4 and Na_V1.5 blocking drugs in experimental structures. The second site was at the “mouth” of the pore at the cytosolic side, partly solvent-exposed. Mutations at this site (L409, E417, and I1466) had large effects on VTD binding, while residues deeper in the pore had no effect, consistent with VTD binding at the mouth site. Overall, our results suggest a VTD binding site close to the cytoplasmic mouth of the channel pore. Binding at this alternative site might indicate an allosteric inactivation mechanism for VTD at Na_V1.5.

PPI-HotspotDB: Database of Protein-Protein Interaction Hot Spots

Single-point mutations of certain residues (so-called hot spots) impair/disrupt protein-protein interactions (PPIs), leading to pathogenesis and drug resistance. Conventionally, a PPI-hot spot is identified when its replacement decreased the binding free energy significantly, generally by ≥2 kcal/mol. The relatively few mutations with such a significant binding free energy drop limited the number of distinct PPI-hot spots. By defining PPI-hot spots based on mutations that have been manually curated in UniProtKB to significantly impair/disrupt PPIs in addition to binding free energy changes, we have greatly expanded the number of distinct PPI-hot spots by an order of magnitude. These experimentally determined PPI-hot spots along with available structures have been collected in a database called PPI-Hotspot^DB. We have applied the PPI-Hotspot^DB to create a nonredundant benchmark, PPI-Hotspot+PDB^BM, for assessing methods to predict PPI-hot spots using the free structure as input. PPI-Hotspot^DB will benefit the design of mutagenesis experiments and development of PPI-hot spot prediction methods. The database and benchmark are freely available at https://ppihotspot.limlab.dnsalias.org.