No abstract

Motivation: Understanding the relationship between protein structure and function is a fundamental problem in protein science. Given a protein of unknown function, fast identification of similar protein structures from the Protein Data Bank (PDB) is a critical step for inferring its biological function. Such structural neighbors can provide evolutionary insights into protein conformation, interfaces and binding sites that are not detectable from sequence similarity. However, the computational cost of performing pairwise structural alignment against all structures in PDB is prohibitively expensive. Alignment-free approaches have been introduced to enable fast but coarse comparisons by representing each protein as a vector of structure features or fingerprints and only computing similarity between vectors. As a notable example, FragBag represents each protein by a "bag of fragments", which is a vector of frequencies of contiguous short backbone fragments from a predetermined library. Results: Here we present a new approach to learning effective structural motif presentations using deep learning. We develop DeepFold, a deep convolutional neural network model to extract structural motif features of a protein structure. Similar to FragBag, DeepFold represents each protein structure or fold using a vector of learned structural motif features. We demonstrate that DeepFold substantially outperforms FragBag on protein structural search on a non-redundant protein structure database and a set of newly released structures. Remarkably, DeepFold not only extracts meaningful backbone segments but also finds important long-range interacting motifs for structural comparison. We expect that DeepFold will provide new insights into the evolution and hierarchical organization of protein structural motifs.

Safe and efficient delivery of blood-brain barrier (BBB)–impermeable cargos into the brain through intravenous injection remains a challenge. Here, we developed a previously unknown class of neurotransmitter–derived lipidoids (NT-lipidoids) as simple and effective carriers for enhanced brain delivery of several BBB-impermeable cargos. Doping the NT-lipidoids into BBB-impermeable lipid nanoparticles (LNPs) gave the LNPs the ability to cross the BBB. Using this brain delivery platform, we successfully delivered amphotericin B (AmB), antisense oligonucleotides (ASOs) against tau, and genome-editing fusion protein (−27)GFP-Cre recombinase into the mouse brain via systemic intravenous administration. We demonstrated that the NT-lipidoid formulation not only facilitates cargo crossing of the BBB, but also delivery of the cargo into neuronal cells for functional gene silencing or gene recombination. This class of brain delivery lipid formulations holds great potential in the treatment of central nervous system diseases or as a tool to study the brain function.

Approximately 15-40% of all cancers develop metastases in the central nervous system (CNS), yet few therapeutic options exist to treat them. Cancer therapies based on monoclonal antibodies are widely successful, yet have limited efficacy against CNS metastases, owing to the low levels of the drug reaching the tumour site. Here, we show that the encapsulation of rituximab within a crosslinked zwitterionic polymer layer leads to the sustained release of rituximab as the crosslinkers are gradually hydrolyzed, enhancing by approximately 10-fold the CNS levels of the antibody with respect to the administration of naked rituximab. When the nanocapsules are functionalized with CXCL13, the ligand for the chemokine receptor CXCR5 frequently found on B-cell lymphoma, a single dose led to improved control of CXCR5-expressing metastases in a murine xenograft model of non-Hodgkin lymphoma, and eliminated lymphoma in a xenografted humanized bone-marrow-liver-thymus mouse model. Encapsulation and molecular targeting of therapeutic antibodies could become an option for the treatment of cancers with CNS metastases. Treatments for cancer metastases, especially those of the central nervous system (CNS), are less successful than those for primary tumors 1. Approximately 15%−40% of all cancers develop a CNS metastasis 2,3 , which most commonly arises from lung cancer, melanoma, breast cancer, and colorectal cancer. Therapeutic monoclonal antibodies (mAbs) have revolutionized the treatment of cancer; however, their efficacy is limited in patients with CNS metastases due to insufficient mAb CNS delivery-typically 0.1% of the levels in plasma 4. By bypassing the blood-brain barrier (BBB) through intrathecal or intraventricular administration, mAb therapy has shown some effectiveness against CNS tumor metastases 4-10. However, direct CNS administration is invasive, with potential for neurotoxicity, and is limited by rapid efflux of antibodies from the CNS within hours 5,10,11. Therefore, novel approaches for mAbs delivery are preferable to maintain systemic therapeutic effect in the CNS with improved efficiency.

CRISPR/Cas9 ribonucleoprotein (RNP) complexes with transient therapeutic activity and minimum off-target effects have attracted tremendous attention in recent years for genome editing and have been successfully employed in diverse targets. One ongoing challenge is how to transport structurally and functionally intact Cas9 protein and guide RNA molecules into cells efficiently and safely. Here we report a combinatorial library of disulfide bond-containing cationic lipidoid nanoparticles (LNPs) as carrier systems for intracellular Cas9/sgRNA delivery and subsequent genome editing. Nanoparticles with high efficacies of targeted gene knockout as well as relatively low cytotoxicities have been identified through in vitro screening. The in vivo biodistribution profiles were studied utilizing fluorescent dye labeled and RNP complexed LNPs. Results from this study may shed some light on the design of effective cationic lipidoids for intracellular delivery of genome editing platforms, as well as optimizing the nanoparticle formulations for further disease modeling and therapeutic applications.

MotivationGiven a protein of unknown function, fast identification of similar protein structures from the Protein Data Bank (PDB) is a critical step for inferring its biological function. Such structural neighbors can provide evolutionary insights into protein conformation, interfaces and binding sites that are not detectable from sequence similarity. However, the computational cost of performing pairwise structural alignment against all structures in PDB is prohibitively expensive. Alignment-free approaches have been introduced to enable fast but coarse comparisons by representing each protein as a vector of structure features or fingerprints and only computing similarity between vectors. As a notable example, FragBag represents each protein by a ‘bag of fragments’, which is a vector of frequencies of contiguous short backbone fragments from a predetermined library. Despite being efficient, the accuracy of FragBag is unsatisfactory because its backbone fragment library may not be optimally constructed and long-range interacting patterns are omitted.ResultsHere we present a new approach to learning effective structural motif presentations using deep learning. We develop DeepFold, a deep convolutional neural network model to extract structural motif features of a protein structure. We demonstrate that DeepFold substantially outperforms FragBag on protein structural search on a non-redundant protein structure database and a set of newly released structures. Remarkably, DeepFold not only extracts meaningful backbone segments but also finds important long-range interacting motifs for structural comparison. We expect that DeepFold will provide new insights into the evolution and hierarchical organization of protein structural motifs.Availability and implementation https://github.com/largelymfs/DeepFold

In situ vaccination is a promising strategy for cancer immunotherapy owing to its convenience and the ability to induce numerous tumor antigens. However, the advancement of in situ vaccination techniques has been hindered by low cross-presentation of tumor antigens and the immunosuppressive tumor microenvironment. To balance the safety and efficacy of in situ vaccination, we designed a lipidoid nanoparticle (LNP) to achieve simultaneously enhancing cross-presentation and STING activation. From combinatorial library screening, we identified 93-O17S-F, which promotes both the cross-presentation of tumor antigens and the intracellular delivery of cGAMP (STING agonist). Intratumor injection of 93-O17S-F/cGAMP in combination with pretreatment with doxorubicin exhibited excellent antitumor efficacy, with 35% of mice exhibiting total recovery from a primary B16F10 tumor and 71% of mice with a complete recovery from a subsequent challenge, indicating the induction of an immune memory against the tumor. This study provides a promising strategy for in situ cancer vaccination.

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