Integration of the Rosetta suite with the python software stack via reproducible packaging and core programming interfaces for distributed simulation

Leveraging artificial intelligence in vaccine development: A narrative review

Vaccine development stands as a cornerstone of public health efforts, pivotal in curbing infectious diseases and reducing global morbidity and mortality. However, traditional vaccine development methods are often time-consuming, costly, and inefficient. The advent of artificial intelligence (AI) has ushered in a new era in vaccine design, offering unprecedented opportunities to expedite the process. This narrative review explores the role of AI in vaccine development, focusing on antigen selection, epitope prediction, adjuvant identification, and optimization strategies. AI algorithms, including machine learning and deep learning, leverage genomic data, protein structures, and immune system interactions to predict antigenic epitopes, assess immunogenicity, and prioritize antigens for experimentation. Furthermore, AI-driven approaches facilitate the rational design of immunogens and the identification of novel adjuvant candidates with optimal safety and efficacy profiles. Challenges such as data heterogeneity, model interpretability, and regulatory considerations must be addressed to realize the full potential of AI in vaccine development. Integrating emerging technologies, such as single-cell omics and synthetic biology, promises to enhance vaccine design precision and scalability. This review underscores the transformative impact of AI on vaccine development and highlights the need for interdisciplinary collaborations and regulatory harmonization to accelerate the delivery of safe and effective vaccines against infectious diseases.

De novo design of immunoglobulin-like domains

Antibodies, and antibody derivatives such as nanobodies, contain immunoglobulin-like (Ig) β-sandwich scaffolds which anchor the hypervariable antigen-binding loops and constitute the largest growing class of drugs. Current engineering strategies for this class of compounds rely on naturally existing Ig frameworks, which can be hard to modify and have limitations in manufacturability, designability and range of action. Here, we develop design rules for the central feature of the Ig fold architecture—the non-local cross-β structure connecting the two β-sheets—and use these to design highly stable Ig domains de novo, confirm their structures through X-ray crystallography, and show they can correctly scaffold functional loops. Our approach opens the door to the design of antibody-like scaffolds with tailored structures and superior biophysical properties.

The immunoglobulin domain framework of antibodies has been a long standing design challenge. Here, the authors describe design rules for tailoring these domains and show they can be accurately designed, de novo, with high stability and the ability to scaffold functional loops.

The Intracellular Interaction of Porcine β-Defensin 2 with VASH1 Alleviates Inflammation via Akt Signaling Pathway

Defensins are a major class of antimicrobial peptides that facilitate the immune system to resist pathogen infection. To date, only β-defensins have been identified in pigs. In our previous studies, porcine β-defensin 2 (PBD-2) was shown to have both bactericidal activity and modulatory roles on inflammation. PBD-2 can interact with the cell surface TLR4 and interfere with the NF-κB signaling pathway to suppress the inflammatory response. In this study, the intracellular functions of PBD-2 were investigated. The fluorescently labeled PBD-2 could actively enter mouse macrophage cells. Proteomic analysis indicated that 37 proteins potentially interacted with PBD-2, among which vasohibin-1 (VASH1) was further tested. LPS, an inflammation inducer, suppressed the expression of VASH1, whereas PBD-2 inhibited this effect. PBD-2 inhibited LPS-induced activation of Akt, expression and release of the inflammatory mediators vascular endothelial growth factor and NO, and cell damage. A follow-up VASH1 knockdown assay validated the specificity of the above observations. In addition, PBD-2 inhibited LPS-induced NF-κB activation via Akt. The inhibition effects of PBD-2 on LPS triggered suppression of VASH1 and activation of Akt, and NF-κB and inflammatory cytokines were also confirmed using pig alveolar macrophage 3D4/21 cells. Therefore, the data indicate that PBD-2 interacts with intracellular VASH1, which inhibits the LPS-induced Akt/NF-κB signaling pathway, resulting in suppression of inflammatory responses. Together with our previous findings, we conclude that PBD-2 interacts with both the cell surface receptor (TLR4) and also with the intracellular receptor (VASH1) to control inflammation, thereby providing insights into the immunomodulatory roles of defensins.

A local platform for user-friendly FAIR data management and reproducible analytics

Collaborative research is common practice in modern life sciences. For most projects several researchers from multiple universities collaborate on a specific topic. Frequently, these research projects produce a wealth of data that requires central and secure storage, which should also allow for easy sharing among project participants. Only under best circumstances, this comes with minimal technical overhead for the researchers. Moreover, the need for data to be analyzed in a reproducible way often poses a challenge for researchers without a data science background and thus represents an overly time-consuming process. Here, we report on the integration of CyVerse Austria (CAT), a new cyberinfrastructure for a local community of life science researchers and provide two examples how it can be used to facilitate FAIR data management and reproducible analytics for teaching and research. In particular, we describe in detail how CAT can be used (i) as a teaching platform with a defined software environment and data management/sharing possibilities, and (ii) to build a data analysis pipeline using the Docker technology tailored to the needs and interests of the researcher.

Anchor extension: a structure-guided approach to design cyclic peptides targeting enzyme active sites

Despite recent success in computational design of structured cyclic peptides, de novo design of cyclic peptides that bind to any protein functional site remains difficult. To address this challenge, we develop a computational "anchor extension" methodology for targeting protein interfaces by extending a peptide chain around a non-canonical amino acid residue anchor. To test our approach using a well characterized model system, we design cyclic peptides that inhibit histone deacetylases 2 and 6 (HDAC2 and HDAC6) with enhanced potency compared to the original anchor (IC50 values of 9.1 and 4.4 nM for the best binders compared to 5.4 and 0.6 µM for the anchor, respectively). The HDAC6 inhibitor is among the most potent reported so far. These results highlight the potential for de novo design of high-affinity protein-peptide interfaces, as well as the challenges that remain.

PyRosetta Jupyter Notebooks Teach Biomolecular Structure Prediction and Design

Biomolecular structure drives function, and computational capabilities have progressed such that the prediction and computational design of biomolecular structures is increasingly feasible. Because computational biophysics attracts students from many different backgrounds and with different levels of resources, teaching the subject can be challenging. One strategy to teach diverse learners is with interactive multimedia material that promotes self-paced, active learning. We have created a hands-on education strategy with a set of sixteen modules that teach topics in biomolecular structure and design, from fundamentals of conformational sampling and energy evaluation to applications like protein docking, antibody design, and RNA structure prediction. Our modules are based on PyRosetta, a Python library that encapsulates all computational modules and methods in the Rosetta software package. The workshop-style modules are implemented as Jupyter Notebooks that can be executed in the Google Colaboratory, allowing learners access with just a web browser. The digital format of Jupyter Notebooks allows us to embed images, molecular visualization movies, and interactive coding exercises. This multimodal approach may better reach students from different disciplines and experience levels as well as attract more researchers from smaller labs and cognate backgrounds to leverage PyRosetta in their science and engineering research. All materials are freely available at https://github.com/RosettaCommons/PyRosetta.notebooks.

Toward complete rational control over protein structure and function through computational design

The grand challenge of protein design is a general method for producing a polypeptide with arbitrary functionality, conformation, and biochemical properties. To that end, a wide variety of methods have been developed for the improvement of native proteins, the design of ideal proteins de novo, and the redesign of suboptimal proteins with better-performing substructures. These methods employ informatic comparisons of function-structure-sequence relationships as well as knowledge-based evaluation of protein properties to narrow the immense protein sequence search space down to an enumerable and often manually evaluable set of structures that meet specified criteria. While arbitrary manipulation of protein-protein interfaces and molecular catalysis remains an unsolved problem, and no protein shape or behavior manipulation algorithm is universally applicable, the promising results thus far are a strong indicator that a general approach to the arbitrary manipulation of polypeptides is within reach.

Structural characterization and computational analysis of PDZ domains in Monosiga brevicollis

Identification of the molecular networks that facilitated the evolution of multicellular animals from their unicellular ancestors is a fundamental problem in evolutionary cellular biology. Choanoflagellates are recognized as the closest extant nonmetazoan ancestors to animals. These unicellular eukaryotes can adopt a multicellular-like "rosette" state. Therefore, they are compelling models for the study of early multicellularity. Comparative studies revealed that a number of putative human orthologs are present in choanoflagellate genomes, suggesting that a subset of these genes were necessary for the emergence of multicellularity. However, previous work is largely based on sequence alignments alone, which does not confirm structural nor functional similarity. Here, we focus on the PDZ domain, a peptide-binding domain which plays critical roles in myriad cellular signaling networks and which underwent a gene family expansion in metazoan lineages. Using a customized sequence similarity search algorithm, we identified 178 PDZ domains in the Monosiga brevicollis proteome. This includes 11 previously unidentified sequences, which we analyzed using Rosetta and homology modeling. To assess conservation of protein structure, we solved high-resolution crystal structures of representative M. brevicollis PDZ domains that are homologous to human Dlg1 PDZ2, Dlg1 PDZ3, GIPC, and SHANK1 PDZ domains. To assess functional conservation, we calculated binding affinities for mbGIPC, mbSHANK1, mbSNX27, and mbDLG-3 PDZ domains from M. brevicollis. Overall, we find that peptide selectivity is generally conserved between these two disparate organisms, with one possible exception, mbDLG-3. Overall, our results provide novel insight into signaling pathways in a choanoflagellate model of primitive multicellularity.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

Integration of the Rosetta suite with the python software stack via reproducible packaging and core programming interfaces for distributed simulation

The Rosetta software suite for macromolecular modeling is a powerful computational toolbox for protein design, structure prediction, and protein structure analysis. The development of novel Rosetta-based scientific tools requires two orthogonal skill sets: deep domain-specific expertise in protein biochemistry and technical expertise in development, deployment, and analysis of molecular simulations. Furthermore, the computational demands of molecular simulation necessitate large scale cluster-based or distributed solutions for nearly all scientifically relevant tasks. To reduce the technical barriers to entry for new development, we integrated Rosetta with modern, widely adopted computational infrastructure. This allows simplified deployment in large-scale cluster and cloud computing environments, and effective reuse of common libraries for simulation execution and data analysis. To achieve this, we integrated Rosetta with the Conda package manager; this simplifies installation into existing computational environments and packaging as docker images for cloud deployment. Then, we developed programming interfaces to integrate Rosetta with the PyData stack for analysis and distributed computing, including the popular tools Jupyter, Pandas, and Dask. We demonstrate the utility of these components by generating a library of a thousand de novo disulfide-rich miniproteins in a hybrid simulation that included cluster-based design and interactive notebook-based analyses. Our new tools enable users, who would otherwise not have access to the necessary computational infrastructure, to perform state-of-the-art molecular simulation and design with Rosetta.