Development and Evaluation of GlycanDock: A Protein-Glycoligand Docking Refinement Algorithm in Rosetta

Predictions from Deep Learning Propose Substantial Protein-Carbohydrate Interplay

It is a grand challenge to identify all the protein - carbohydrate interactions in an organism. Direct experiments would require extensive libraries of glycans to definitively distinguish binding from non-binding proteins. Computational screening of proteins for carbohydrate-binding provides an attractive and ultimately testable alternative. Recent computational techniques have focused primarily on which protein residues interact with carbohydrates or which carbohydrate species a protein binds to. Current estimates label 1.5 to 5% of proteins as carbohydrate-binding proteins; however, 50-70% of proteins are known to be glycosylated, suggesting a potential wealth of proteins that bind to carbohydrates. We therefore developed a novel dataset and neural network architecture, named Protein interaction of Carbohydrates Predictor (PiCAP), to predict whether a protein non-covalently binds to a carbohydrate. We trained PiCAP on a dataset of known carbohydrate binders, and we selected proteins that we identified as likely not to bind carbohydrates, including DNA-binding transcription factors, cytoskeletal components, selected antibodies, and selected small-molecule-binding proteins. PiCAP achieves a 90% balanced accuracy on protein-level predictions of carbohydrate binding/non-binding. Using the same dataset, we developed a model named Carbohydrate Protein Site Identifier 2 (CAPSIF2) to predict protein residues that interact non-covalently with carbohydrates. CAPSIF2 achieves a Dice coefficient of 0.57 on residue-level predictions on our independent test dataset, outcompeting all previous models for this task. To demonstrate the biological applicability of PiCAP and CAPSIF2, we investigated cell surface proteins of human neural cells and further predicted the likelihood of three proteomes, notably E. coli, M. musculus, and H. sapiens, to bind to carbohydrates. PiCAP predicts that approximately 35-40% of proteins in these proteomes bind carbohydrates, indicating a substantial interplay of protein-carbohydrate interactions for cellular functionality.

Tools for structural lectinomics: From structures to lectomes

Lectins are ubiquitous proteins that interact with glycans in a variety of molecular processes and as such, also play a role in diseases, whether infectious, chronic or cancer-related. The systematic study of lectins is therefore essential, in particular for understanding cell-cell communication. Accumulated protein three-dimensional structural data in the past decades boosted advance in AI-based prediction and opened up new options to characterise lectins that are known to often be multimeric and multivalent. This article reviews the methods to obtain structures of lectins, the current data available for lectin 3D structures and their interactions, how this knowledge is used to classify these proteins and shows that the combination of an array of bioinformatics tools should make the prediction of binding specificity possible in a near future.

Identification and Optimization of more Efficient Olivetolic Acid Synthases

Introduction: Olivetolic acid (OLA) is a key intermediate in cannabidiol (CBD) synthesis, and cannabinoids are important neuroactive drugs. However, the catalytic activity of olivetolic acid synthase (OLS), the key enzyme involved in OLA biosynthesis, remains low and its catalytic mechanism is unclear. Materials and Methods: In this study, we conducted a scrupulous screening of the pivotal rate-limiting enzyme and analyzed its amino acid sites that are critical to enzyme activity as validated by experiments. Results: Through stringent enzyme screening, we pinpointed a highly active OLS sequence, OLS4. Then, we narrowed down three critical amino acid sites (I258, D198, E196) that significantly influence the OLS activity. Conclusions: Our findings laid the groundwork for the efficient biosynthesis of OLA, and thereby facilitate the biosynthesis of CBD.

Modeling Protein-Glycan Interactions with HADDOCK

The term glycan refers to a broad category of molecules composed of monosaccharide units linked to each other in a variety of ways, whose structural diversity is related to different functions in living organisms. Among others, glycans are recognized by proteins with the aim of carrying information and for signaling purposes. Determining the three-dimensional structures of protein-glycan complexes is essential both for the understanding of the mechanisms glycans are involved in and for applications such as drug design. In this context, molecular docking approaches are of undoubted importance as complementary approaches to experiments. In this study, we show how high ambiguity-driven DOCKing (HADDOCK) can be efficiently used for the prediction of protein-glycan complexes. Using a benchmark of 89 complexes, starting from their bound or unbound forms, and assuming some knowledge of the binding site on the protein, our protocol reaches a 70% and 40% top 5 success rate on bound and unbound data sets, respectively. We show that the main limiting factor is related to the complexity of the glycan to be modeled and the associated conformational flexibility.

Computational toolbox for the analysis of protein-glycan interactions

Protein-glycan interactions play pivotal roles in numerous biological processes, ranging from cellular recognition to immune response modulation. Understanding the intricate details of these interactions is crucial for deciphering the molecular mechanisms underlying various physiological and pathological conditions. Computational techniques have emerged as powerful tools that can help in drawing, building and visualising complex biomolecules and provide insights into their dynamic behaviour at atomic and molecular levels. This review provides an overview of the main computational tools useful for studying biomolecular systems, particularly glycans, both in free state and in complex with proteins, also with reference to the principles, methodologies, and applications of all-atom molecular dynamics simulations. Herein, we focused on the programs that are generally employed for preparing protein and glycan input files to execute molecular dynamics simulations and analyse the corresponding results. The presented computational toolbox represents a valuable resource for researchers studying protein-glycan interactions and incorporates advanced computational methods for building, visualising and predicting protein/glycan structures, modelling protein-ligand complexes, and analyse MD outcomes. Moreover, selected case studies have been reported to highlight the importance of computational tools in studying protein-glycan systems, revealing the capability of these tools to provide valuable insights into the binding kinetics, energetics, and structural determinants that govern specific molecular interactions.

In Vitro and in Silico Study of New Biscoumarin Glycosides from Paramignya trimera against Angiotensin-Converting Enzyme 2 (ACE-2) for Preventing SARS-CoV-2 Infection

In Vietnam, the stems and roots of the Rutaceous plant Paramignya trimera (Oliv.) Burkill (known locally as "Xáo tam phân") are widely used to treat liver diseases such as viral hepatitis and acute and chronic cirrhosis. In an effort to search for Vietnamese natural compounds capable of inhibiting coronavirus based on molecular docking screening, two new dimeric coumarin glycosides, namely cis-paratrimerin B (1) and cis-paratrimerin A (2), and two previously identified coumarins, the trans-isomers paratrimerin B (3) and paratrimerin A (4), were isolated from the roots of P. trimera and tested for their anti-angiotensin-converting enzyme 2 (ACE-2) inhibitory properties in vitro. It was discovered that ACE-2 enzyme was inhibited by cis-paratrimerin B (1), cis-paratrimerin A (2), and trans-paratrimerin B (3), with IC50 values of 28.9, 68, and 77 µM, respectively. Docking simulations revealed that four biscoumarin glycosides had good binding energies (∆G values ranging from -10.6 to -14.7 kcal/mol) and mostly bound to the S1' subsite of the ACE-2 protein. The key interactions of these natural ligands include metal chelation with zinc ions and multiple H-bonds with Ser128, Glu145, His345, Lys363, Thr371, Glu406, and Tyr803. Our findings demonstrated that biscoumarin glycosides from P. trimera roots occur naturally in both cis- and trans-diastereomeric forms. The biscoumarin glycosides Lys363, Thr371, Glu406, and Tyr803. Our findings demonstrated that biscoumarin glycosides from P. trimera roots hold potential for further studies as natural ACE-2 inhibitors for preventing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins

Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, degradation, and much more. To date, over 400 types of PTMs have been described, representing chemical diversity well beyond the genetically encoded amino acids. Such modifications pose a challenge to the successful design of proteins, but also represent a major opportunity to diversify the protein engineering toolbox. To this end, we first trained artificial neural networks (ANNs) to predict eighteen of the most abundant PTMs, including protein glycosylation, phosphorylation, methylation, and deamidation. In a second step, these models were implemented inside the computational protein modeling suite Rosetta, which allows flexible combination with existing protocols to model the modified sites and understand their impact on protein stability as well as function. Lastly, we developed a new design protocol that either maximizes or minimizes the predicted probability of a particular site being modified. We find that this combination of ANN prediction and structure-based design can enable the modification of existing, as well as the introduction of novel, PTMs. The potential applications of our work include, but are not limited to, glycan masking of epitopes, strengthening protein-protein interactions through phosphorylation, as well as protecting proteins from deamidation liabilities. These applications are especially important for the design of new protein therapeutics where PTMs can drastically change the therapeutic properties of a protein. Our work adds novel tools to Rosetta's protein engineering toolbox that allow for the rational design of PTMs.

Iterative Knowledge-Based Scoring Function for Protein-Ligand Interactions by Considering Binding Affinity Information

Scoring functions for protein-ligand interactions play a critical role in structure-based drug design. Owing to the good balance between general applicability and computational efficiency, knowledge-based scoring functions have obtained significant advancements and achieved many successes. Nevertheless, knowledge-based scoring functions face a challenge in utilizing the experimental affinity data and thus may not perform well in binding affinity prediction. Addressing the challenge, we have proposed an improved version of the iterative knowledge-based scoring function ITScore by considering binding affinity information, which is referred to as ITScoreAff, based on a large training set of 6216 protein-ligand complexes with both structures and affinity data. ITScoreAff was extensively evaluated and compared with ITScore, 33 traditional, and 6 machine learning scoring functions in terms of docking power, ranking power, and screening power on the independent CASF-2016 benchmark. It was shown that ITScoreAff obtained an overall better performance than the other 40 scoring functions and gave an average success rate of 85.3% in docking power, a correlation coefficient of 0.723 in scoring power, and an average rank correlation coefficient of 0.668 in ranking power. In addition, ITScoreAff also achieved the overall best screening power when the top 10% of the ranked database were considered. These results demonstrated the robustness of ITScoreAff and its improvement over existing scoring functions.

Structure-based neural network protein-carbohydrate interaction predictions at the residue level

Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate-binding sites on any given protein. Here, we present two deep learning (DL) models named CArbohydrate-Protein interaction Site IdentiFier (CAPSIF) that predicts non-covalent carbohydrate-binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate-binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2-predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein-carbohydrate structures.

Structure-Based Neural Network Protein-Carbohydrate Interaction Predictions at the Residue Level

Carbohydrates dynamically and transiently interact with proteins for cell-cell recognition, cellular differentiation, immune response, and many other cellular processes. Despite the molecular importance of these interactions, there are currently few reliable computational tools to predict potential carbohydrate binding sites on any given protein. Here, we present two deep learning models named CArbohydrate-Protein interaction Site IdentiFier (CAPSIF) that predict carbohydrate binding sites on proteins: (1) a 3D-UNet voxel-based neural network model (CAPSIF:V) and (2) an equivariant graph neural network model (CAPSIF:G). While both models outperform previous surrogate methods used for carbohydrate binding site prediction, CAPSIF:V performs better than CAPSIF:G, achieving test Dice scores of 0.597 and 0.543 and test set Matthews correlation coefficients (MCCs) of 0.599 and 0.538, respectively. We further tested CAPSIF:V on AlphaFold2-predicted protein structures. CAPSIF:V performed equivalently on both experimentally determined structures and AlphaFold2 predicted structures. Finally, we demonstrate how CAPSIF models can be used in conjunction with local glycan-docking protocols, such as GlycanDock, to predict bound protein-carbohydrate structures.

Adeno-associated virus serotype 2 capsid variants for improved liver-directed gene therapy

BACKGROUND AND AIMS

Current liver-directed gene therapies look for adeno-associated virus (AAV) vectors with improved efficacy. With this background, capsid engineering is explored. Whereas shuffled capsid library screenings have resulted in potent liver targeting variants with one first vector in human clinical trials, modifying natural serotypes by peptide insertion has so far been less successful. Here, we now report on two capsid variants, MLIV.K and MLIV.A, both derived from a high-throughput in vivo AAV peptide display selection screen in mice.

APPROACH AND RESULTS

The variants transduce primary murine and human hepatocytes at comparable efficiencies, a valuable feature in clinical development, and show significantly improved liver transduction efficacy, thereby allowing a dose reduction, and outperform parental AAV2 and AAV8 in targeting human hepatocytes in humanized mice. The natural heparan sulfate proteoglycan binding ability is markedly reduced, a feature that correlates with improved hepatocyte transduction. A further property that might contribute to the improved transduction efficacy is the lower capsid melting temperature. Peptide insertion also caused a moderate change in sensitivity to human sera containing anti-AAV2 neutralizing antibodies, revealing the impact of epitopes located at the basis of the AAV capsid protrusions.

CONCLUSIONS

In conclusion, MLIV.K and MLIV.A are AAV peptide display variants selected in immunocompetent mice with improved hepatocyte tropism and transduction efficiency. Because these features are maintained across species, MLIV variants provide remarkable potential for translation of therapeutic approaches from mice to men.

Multifaceted Computational Modeling in Glycoscience

Glycoscience assembles all the scientific disciplines involved in studying various molecules and macromolecules containing carbohydrates and complex glycans. Such an ensemble involves one of the most extensive sets of molecules in quantity and occurrence since they occur in all microorganisms and higher organisms. Once the compositions and sequences of these molecules are established, the determination of their three-dimensional structural and dynamical features is a step toward understanding the molecular basis underlying their properties and functions. The range of the relevant computational methods capable of addressing such issues is anchored by the specificity of stereoelectronic effects from quantum chemistry to mesoscale modeling throughout molecular dynamics and mechanics and coarse-grained and docking calculations. The Review leads the reader through the detailed presentations of the applications of computational modeling. The illustrations cover carbohydrate-carbohydrate interactions, glycolipids, and N- and O-linked glycans, emphasizing their role in SARS-CoV-2. The presentation continues with the structure of polysaccharides in solution and solid-state and lipopolysaccharides in membranes. The full range of protein-carbohydrate interactions is presented, as exemplified by carbohydrate-active enzymes, transporters, lectins, antibodies, and glycosaminoglycan binding proteins. A final section features a list of 150 tools and databases to help address the many issues of structural glycobioinformatics.

CSM-carbohydrate: protein-carbohydrate binding affinity prediction and docking scoring function

Protein-carbohydrate interactions are crucial for many cellular processes but can be challenging to biologically characterise. To improve our understanding and ability to model these molecular interactions, we used a carefully curated set of 370 protein-carbohydrate complexes with experimental structural and biophysical data in order to train and validate a new tool, cutoff scanning matrix (CSM)-carbohydrate, using machine learning algorithms to accurately predict their binding affinity and rank docking poses as a scoring function. Information on both protein and carbohydrate complementarity, in terms of shape and chemistry, was captured using graph-based structural signatures. Across both training and independent test sets, we achieved comparable Pearson's correlations of 0.72 under cross-validation [root mean square error (RMSE) of 1.58 Kcal/mol] and 0.67 on the independent test (RMSE of 1.72 Kcal/mol), providing confidence in the generalisability and robustness of the final model. Similar performance was obtained across mono-, di- and oligosaccharides, further highlighting the applicability of this approach to the study of larger complexes. We show CSM-carbohydrate significantly outperformed previous approaches and have implemented our method and make all data freely available through both a user-friendly web interface and application programming interface, to facilitate programmatic access at http://biosig.unimelb.edu.au/csm_carbohydrate/. We believe CSM-carbohydrate will be an invaluable tool for helping assess docking poses and the effects of mutations on protein-carbohydrate affinity, unravelling important aspects that drive binding recognition.

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.