Insights of Expression Profile of Chemokine Family in Inflammatory Bowel Diseases and Carcinogenesis.

Chemokines are integral components of the immune system and deeply involved in the pathogenesis and progression of inflammatory bowel disease (IBD) and colorectal cancer (CRC). Although a considerable amount of transcriptome data has been accumulated on these diseases, most of them are limited to a specific stage of the disease. The purpose of this study is to visually demonstrate the dynamic changes in chemokines across various stages of bowel diseases by integrating relevant datasets. Integrating the existing datasets for IBD and CRC, we compare the expression changes of chemokines across different pathological stages. This study collected 11 clinical databases from various medical centers around the world. Patients: Data of patient tissue types were classified into IBD, colorectal adenoma, primary carcinoma, metastasis, and healthy control according to the publisher's annotation. The expression changes in chemokines in various pathological stages are statistically analyzed. The chemokines were clustered by different expression patterns. The chemokine family was clustered into four distinct expression patterns, which correspond to varying expression changes in different stages of colitis and tumor development. Certain chemokines and receptors associated with inflammation and tumorigenesis have been identified. Furthermore, it was confirmed that the 2,4,6-trinitrobenzenesulfonic acid (TNBS)-induced colitis model and the azoxymethane (AOM)/ dextran sulfate sodium (DSS)-induced colon cancer model shows stronger correlations with the clinical data in terms of chemokine expression levels. This study paints a panoramic picture of the expression profiles of chemokine families at multiple stages from IBD to advanced colon cancer, facilitating a comprehensive understanding of the regulation patterns of chemokines and guiding the direction of drug development. This study provides researchers with a clear atlas of chemokine expression in the pathological processes of inflammatory bowel disease and colon cancer.

ABTrans: A Transformer-based Model for Predicting Interaction between Anti-Aß Antibodies and Peptides.

Antibodies against Aß peptide have been recently approved to treat Alzheimer's disease, underscoring the importance of understanding their interactions for developing more potent treatments. Here we investigated the interaction between anti-Aß antibodies and various peptides using a deep learning model. Our model, ABTrans, was trained on dodecapeptide sequences from phage display experiments and known anti-Aß antibody sequences sourced from public sources. It classified the binding ability between anti-Aß antibodies and dodecapeptides into four levels: not binding, weak binding, medium binding, and strong binding, achieving an accuracy of 0.83. Using ABTrans, we examined the cross-reaction of anti-Aß antibodies with other human amyloidogenic proteins, revealing that Aducanumab and Donanemab exhibited the least cross-reactivity. Additionally, we systematically screened interactions between eleven selected anti-Aß antibodies and all human proteins to identify potential off-target candidates.

Computational investigation of the inhibitory interaction of IRF3 and SARS-CoV-2 accessory protein ORF3b.

ORF3b is one of the SARS-CoV-2 accessory proteins. Previous experimental study suggested that ORF3b prevents IRF3 translocating to nucleus. However, the biophysical mechanism of ORF3b-IRF3 interaction is elusive. Here, we explored the conformation ensemble of ORF3b using all-atom replica exchange molecular dynamics simulation. Disordered ORF3b has mixed α-helix, ß-turn and loop conformers. The potential ORF3b-IRF3 binding modes were searched by docking representative ORF3b conformers with IRF3, and 50 ORF3b-IRF3 complex poses were screened using molecular dynamics simulations ranging from 500 to 1000 ns. We found that ORF3b binds IRF3 predominantly on its CBP binding and phosphorylated pLxIS motifs, with CBP binding site has the highest binding affinity. The ORF3b-IRF3 binding residues are highly conserved in SARS-CoV-2. Our results provided biophysics insights into ORF3b-IRF3 interaction and explained its interferon antagonism mechanism.

Re-Engineering Therapeutic Anti-Aß Monoclonal Antibody to Target Amyloid Light Chain.

Amyloidosis involves the deposition of misfolded proteins. Even though it is caused by different pathogenic mechanisms, in aggregate, it shares similar features. Here, we tested and confirmed a hypothesis that an amyloid antibody can be engineered by a few mutations to target a different species. Amyloid light chain (AL) and ß-amyloid peptide (Aß) are two therapeutic targets that are implicated in amyloid light chain amyloidosis and Alzheimer's disease, respectively. Though crenezumab, an anti-Aß antibody, is currently unsuccessful, we chose it as a model to computationally design and prepare crenezumab variants, aiming to discover a novel antibody with high affinity to AL fibrils and to establish a technology platform for repurposing amyloid monoclonal antibodies. We successfully re-engineered crenezumab to bind both Aß42 oligomers and AL fibrils with high binding affinities. It is capable of reversing Aß42-oligomers-induced cytotoxicity, decreasing the formation of AL fibrils, and alleviating AL-fibrils-induced cytotoxicity in vitro. Our research demonstrated that an amyloid antibody could be engineered by a few mutations to bind new amyloid sequences, providing an efficient way to reposition a therapeutic antibody to target different amyloid diseases.

Data Mining Suggests That CXCL14 Gene Silencing in Colon Cancer Is Due to Promoter Methylation.

CXCL14 is one of the most evolutionarily conserved members of the chemokine family and is constitutionally expressed in multiple organs, suggesting that it is involved in the homeostasis maintenance of the system. CXCL14 is highly expressed in colon epithelial cells and shows obvious gene silencing in clinical colon cancer samples, suggesting that its silencing is related to the immune escape of cancer cells. In this paper, we analyzed the expression profiles of multiple human clinical colon cancer datasets and mouse colon cancer models to reveal the variation trend of CXCL14 expression during colitis, colon polyps, primary colon cancer, and liver metastases. The relationship between CXCL14 gene silencing and promoter hypermethylation was revealed through the colorectal carcinoma methylation database. The results suggest that CXCL14 is a tumor suppressor gene in colorectal carcinoma which is activated first and then silenced during the process of tumor occurrence and deterioration. Promoter hypermethylation is the main cause of CXCL14 silencing. The methylation level of CXCL14 is correlated with the anatomic site of tumor occurrence, positively correlated with patient age, and associated with prognosis. Reversing the hypermethylation of CXCL14 may be an epigenetic therapy for colon cancer.

Evaluation of Molecular Simulations and Deep Learning Prediction of Antibodies' Recognition of TRBC1 and TRBC2.

T cell receptor ß-chain constant (TRBC) is a promising class of cancer targets consisting of two highly homologous proteins, TRBC1 and TRBC2. Developing targeted antibody therapeutics against TRBC1 or TRBC2 is expected to eradicate the malignant T cells and preserve half of the normal T cells. Recently, several antibody engineering strategies have been used to modulate the TRBC1 and TRBC2 specificity of antibodies. Here, we used molecular simulation and artificial intelligence methods to quantify the affinity difference in antibodies with various mutations for TRBC1 and TRBC2. The affinity of the existing mutants was verified by FEP calculations aided by the AI. We also performed long-time molecular dynamics simulations to reveal the dynamical antigen recognition mechanisms of the TRBC antibodies.

Recent Progress in Antibody Epitope Prediction.

Recent progress in epitope prediction has shown promising results in the development of vaccines and therapeutics against various diseases. However, the overall accuracy and success rate need to be improved greatly to gain practical application significance, especially conformational epitope prediction. In this review, we examined the general features of antibody-antigen recognition, highlighting the conformation selection mechanism in flexible antibody-antigen binding. We recently highlighted the success and warning signs of antibody epitope predictions, including linear and conformation epitope predictions. While deep learning-based models gradually outperform traditional feature-based machine learning, sequence and structure features still provide insight into antibody-antigen recognition problems.

Architectural basis for cylindrical self-assembly governing Plk4-mediated centriole duplication in human cells.

Proper organization of intracellular assemblies is fundamental for efficient promotion of biochemical processes and optimal assembly functionality. Although advances in imaging technologies have shed light on how the centrosome is organized, how its constituent proteins are coherently architected to elicit downstream events remains poorly understood. Using multidisciplinary approaches, we showed that two long coiled-coil proteins, Cep63 and Cep152, form a heterotetrameric building block that undergoes a stepwise formation into higher molecular weight complexes, ultimately generating a cylindrical architecture around a centriole. Mutants defective in Cep63•Cep152 heterotetramer formation displayed crippled pericentriolar Cep152 organization, polo-like kinase 4 (Plk4) relocalization to the procentriole assembly site, and Plk4-mediated centriole duplication. Given that the organization of pericentriolar materials (PCM) is evolutionarily conserved, this work could serve as a model for investigating the structure and function of PCM in other species, while offering a new direction in probing the organizational defects of PCM-related human diseases.

Accelerating antibody discovery and design with artificial intelligence: Recent advances and prospects.

Therapeutic antibodies are the largest class of biotherapeutics and have been successful in treating human diseases. However, the design and discovery of antibody drugs remains challenging and time-consuming. Recently, artificial intelligence technology has had an incredible impact on antibody design and discovery, resulting in significant advances in antibody discovery, optimization, and developability. This review summarizes major machine learning (ML) methods and their applications for computational predictors of antibody structure and antigen interface/interaction, as well as the evaluation of antibody developability. Additionally, this review addresses the current status of ML-based therapeutic antibodies under preclinical and clinical phases. While many challenges remain, ML may offer a new therapeutic option for the future direction of fully computational antibody design.

Molecular Insights into the Self-Assembly of a Full-Length hIAPP Trimer: ß-Protofibril Formed by ß-Hairpin Lateral or Longitudinal Association.

The fibrillar protein deposits of the human islet amyloid polypeptide (hIAPP) in the pancreatic islet of Langerhans are pathological hallmark of type II diabetes. Extensive experimental studies have revealed that the oligomeric formations of the hIAPP are more toxic than the mature fibrils. Exploring the oligomeric conformations in the early aggregation state is valuable for effective therapeutics. In this work, using the all-atom explicit-solvent replica exchange molecular dynamic (REMD) simulations, we investigated the structural features and the assembly mechanisms of the full-length hIAPP trimer in solution. The hIAPP trimer adopted more ß-sheets than a-helix conformations, and three types of ordered conformations including open ß-barrel, single-layer, and double-layer U-shaped ß-sheet structures with five ß-strands were captured in our simulations. A representative single-layer ß-sheet conformation with a CCS value of 1400 Å2 in our simulations matches exactly the experimentally ESI-IMS-MS-derived hIAPP trimer sample. These five ß-strand conformations formed via the ß-hairpin lateral and longitudinal association, respectively, showing two ß-protofibril formation models. To the best of our knowledge, it is the first time to reveal two routes to ß-sheet formation in the hIAPP trimers on the atomic level. The contact probabilities between pairs of the ß-stranded residue show that the hydrophobic interactions between the residues F15 ∼ V17 and A25 ∼ L27 are responsible for the inter- and intra-peptide ß-hairpin formations. All of these results indicate that the ß-sheet formation is the first step in the conformational changes toward pathological aggregation and provides evidence of the ß-sheet assembly mechanism into hIAPP aggregation.

Precision Therapy of Recurrent Breast Cancer through Targeting Different Malignant Tumor Cells with a HER2/CD44-Targeted Hydrogel Nanobot.

Heterogeneity and drug resistance of tumor cells are the leading causes of incurability and poor survival for patients with recurrent breast cancer. In order to accurately deliver the biological anticancer drugs to different subtypes of malignant tumor cells for omnidirectional targeted treatment of recurrent breast cancer, a distinct design is demonstrated by embedding liposome-based nanocomplexes containing pro-apoptotic peptide and survivin siRNA drugs (LPR) into Herceptin/hyaluronic acid cross-linked nanohydrogels (Herceptin-HA) to fabricate a HER2/CD44-targeted hydrogel nanobot (named as ALPR). ALPR delivered cargoes to the cells overexpressing CD44 and HER2, followed by Herceptin-HA biodegradation, subsequently, the exposed lipid component containing DOPE fused with the endosomal membrane and released peptide and siRNA into the cytoplasm. These experiments indicated that ALPR can specifically deliver Herceptin, peptide, and siRNA drugs to HER2-positive SKBR-3, triple-negative MDA-MB-231, and HER2-negative drug-resistant MCF-7 human breast cancer cells. ALPR completely inhibited the growth of heterogeneous breast tumors via multichannel synergistic effects: disrupting mitochondria, downregulating the survivin gene, and blocking HER2 receptors on the surface of HER2-positive cells. The present design overcomes the chemical drug resistance and opens a feasible route for the combinative treatment of recurrent breast cancer, even other solid tumors, utilizing different kinds of biological drugs.

PGLYRP1-mIgG2a-Fc inhibits macrophage activation via AKT/NF-κB signaling and protects against fatal lung injury during bacterial infection.

Severe bacterial pneumonia leads to acute respiratory distress syndrome (ARDS), with a high incidence rate and mortality. It is well-known that continuous and dysregulated macrophage activation is vital for aggravating the progression of pneumonia. Here, we designed and produced an antibody-like molecule, peptidoglycan recognition protein 1-mIgG2a-Fc (PGLYRP1-Fc). PGLYRP1 was fused to the Fc region of mouse IgG2a with high binding to macrophages. We demonstrated that PGLYRP1-Fc ameliorated lung injury and inflammation in ARDS, without affecting bacterial clearance. Besides, PGLYRP1-Fc reduced AKT/nuclear factor kappa-B (NF-κB) activation via the Fc segment bound Fc gamma receptor (FcγR)-dependent mechanism, making macrophage unresponsive, and immediately suppressed proinflammatory response upon bacteria or lipopolysaccharide (LPS) stimulus in turn. These results confirm that PGLYRP1-Fc protects against ARDS by promoting host tolerance with reduced inflammatory response and tissue damage, irrespective of the host's pathogen burden, and provide a promising therapeutic strategy for bacterial infection.

A supramolecular assembly strategy for hydrophilic drug delivery towards synergistic cancer treatment.

To improve the drug loading, tumor targeting, and delivery simplicity of hydrophilic drugs, we propose a supramolecular assembly strategy that potentially benefits a wide range of hydrophilic drug delivery. Firstly, we choose a hydrophilic drug (tirapazamine) as a model drug to directly co-assemble with chlorin e6 (Ce6) at different molar ratios, and systematically evaluate the resultant Ce6-tirapazamine nanoparticles (CT NPs) in aspects of size distribution, polydispersity, morphology, optical properties and molecular dynamics simulation. Based on the assembling facts between Ce6 and tirapazamine, we summarize a plausible rule of the supramolecular assembly for hydrophilic drugs. To validate our findings, more drugs with increasing hydrophilicity, such as temozolomide, gemcitabine hydrochloride and 5-azacytidine, successfully co-assemble with Ce6 into nanostructures by following similar assembling behaviors, demonstrating that our assembling rule may guide a wide range of hydrophilic drug delivery. Next, the combination of Ce6 and tirapazamine was chosen as the representative to investigate the anti-tumor activities of the supramolecular assemblies. CT NPs showed synergistic anti-tumor efficacy, increased tumor accumulation and significant tumor progression and metastasis inhibition in tumor-bearing mice. We anticipate that the supramolecular assembly mechanism will provide broad guidance for developing easy-to-make but functional nanomedicines. STATEMENT OF SIGNIFICANCE: Although thousands of nanomedicines have been developed, only a few have been approved for clinical use. The manufacturing complexity significantly hinders the "bench-to-bed" translation of nanomedicines. Hence, we need to rethink how to conduct research on translational nanomedicines by avoiding more and more complex chemistry and complicated nanostructures. Here, we summarize a plausible rule according to multiple supramolecular assembly pairs and propose a supramolecular assembly strategy that can improve the drug loading, tumor targeting, and manufacturing simplicity of nanomedicine for hydrophilic drugs. The supramolecular assembly strategy would guide a broader range of drug delivery to provide a new paradigm for developing easy-to-make but multifunctional nanoformulations for synergistic cancer treatment.

Visualizing RNA conformational and architectural heterogeneity in solution.

RNA flexibility is reflected in its heterogeneous conformation. Through direct visualization using atomic force microscopy (AFM) and the adenosylcobalamin riboswitch aptamer domain as an example, we show that a single RNA sequence folds into conformationally and architecturally heterogeneous structures under near-physiological solution conditions. Recapitulated 3D topological structures from AFM molecular surfaces reveal that all conformers share the same secondary structural elements. Only a population-weighted cohort, not any single conformer, including the crystal structure, can account for the ensemble behaviors observed by small-angle X-ray scattering (SAXS). All conformers except one are functionally active in terms of ligand binding. Our findings provide direct visual evidence that the sequence-structure relationship of RNA under physiologically relevant solution conditions is more complex than the one-to-one relationship for well-structured proteins. The direct visualization of conformational and architectural ensembles at the single-molecule level in solution may suggest new approaches to RNA structural analyses.

Development of ultra-high affinity bivalent ligands targeting the polo-like kinase 1.

The polo-like kinase 1 (Plk1) is an important mediator of cell cycle regulation and a recognized anti-cancer molecular target. In addition to its catalytic kinase domain (KD), Plk1 contains a polo-box domain (PBD), which engages in protein-protein interactions (PPIs) essential to proper Plk1 function. We have developed a number of extremely high-affinity PBD-binding peptide inhibitors. However, we have reached an apparent limit to increasing the affinities of these monovalent ligands. Accordingly, we undertook an extensive investigation of bivalent ligands, designed to engage both KD and PBD regions of Plk1. This has resulted in bivalent constructs exhibiting more than 100-fold Plk1 affinity enhancement relative to the best monovalent PBD-binding ligands. Startlingly, and in contradiction to widely accepted notions of KD-PBD interactions, we have found that full affinities can be retained even with minimal linkers between KD and PBD-binding components. In addition to significantly advancing the development of PBD-binding ligands, our findings may cause a rethinking of the structure - function of Plk1.

Fewer Dimensions, More Structures for Improved Discrete Models of Dynamics of Free versus Antigen-Bound Antibody.

Over the past decade, Markov State Models (MSM) have emerged as powerful methodologies to build discrete models of dynamics over structures obtained from Molecular Dynamics trajectories. The identification of macrostates for the MSM is a central decision that impacts the quality of the MSM but depends on both the selected representation of a structure and the clustering algorithm utilized over the featurized structures. Motivated by a large molecular system in its free and bound state, this paper investigates two directions of research, further reducing the representation dimensionality in a non-parametric, data-driven manner and including more structures in the computation. Rigorous evaluation of the quality of obtained MSMs via various statistical tests in a comparative setting firmly shows that fewer dimensions and more structures result in a better MSM. Many interesting findings emerge from the best MSM, advancing our understanding of the relationship between antibody dynamics and antibody-antigen recognition.

IKKα-deficient lung adenocarcinomas generate an immunosuppressive microenvironment by overproducing Treg-inducing cytokines.

The tumor microenvironment (TME) provides potential targets for cancer therapy. However, how signals originating in cancer cells affect tumor-directed immunity is largely unknown. Deletions in the CHUK locus, coding for IκB kinase α (IKKα), correlate with reduced lung adenocarcinoma (ADC) patient survival and promote KrasG12D-initiated ADC development in mice, but it is unknown how reduced IKKα expression affects the TME. Here, we report that low IKKα expression in human and mouse lung ADC cells correlates with increased monocyte-derived macrophage and regulatory T cell (Treg) scores and elevated transcription of genes coding for macrophage-recruiting and Treg-inducing cytokines (CSF1, CCL22, TNF, and IL-23A). By stimulating recruitment of monocyte-derived macrophages from the bone marrow and enforcing a TNF/TNFR2/c-Rel signaling cascade that stimulates Treg generation, these cytokines promote lung ADC progression. Depletion of TNFR2, c-Rel, or TNF in CD4+ T cells or monocyte-derived macrophages dampens Treg generation and lung tumorigenesis. Treg depletion also attenuates carcinogenesis. In conclusion, reduced cancer cell IKKα activity enhances formation of a protumorigenic TME through a pathway whose constituents may serve as therapeutic targets for KRAS-initiated lung ADC.

Computational Construction of a Single-Chain Bi-Paratopic Antibody Allosterically Inhibiting TCR-Staphylococcal Enterotoxin B Binding.

Staphylococcal enterotoxin B (SEB) simultaneously crosslinks MHC class II antigen and TCR, promoting proliferation of T cells and releasing a large number of toxic cytokines. In this report, we computationally examined the possibility of using a single-chain biparatopic bispecific antibody to target SEB and prevent TCR binding. The design was inspired by the observation that mixing two anti-SEB antibodies 14G8 and 6D3 can block SEB-TCR activation, and we used 14G8-6D3-SEB tertiary crystal structure as a template. Twelve simulation systems were constructed to systematically examine the effects of the designed bispecific scFV MB102a, including isolated SEB, MB102a with different linkers, MB102a-SEB complex, MB102a-SEB-TCRß complex, MB102a-SEB-TCR-MHC II complex, and MB102a-SEB-MHC II. Our all atom molecular dynamics simulations (total 18,900 ns) confirmed that the designed single-chain bispecific antibody may allosterically prevent SEB-TCRß chain binding and inhibit SEB-TCR-MHC II formation. Subsequent analysis indicated that the binding of scFV to SEB correlates with SEB-TCR binding site motion and weakens SEB-TCR interactions.

Conformational Ensemble of TteAdoCbl Riboswitch Provides Stable Structural Elements for Conformation Selection and Population Shift in Cobalamin Recognition.

Cobalamin riboswitch is a cis-regulatory element widely found in the 5'-UTRs of the vitamin B12-associated genes in bacteria, resulting in modulation and production of a particular protein. Thermoanaerobacter tengcongensis (Tte) AdoCbl riboswitches are the largest of the known riboswitches with 210 nucleotides, partially due to its long peripheral P6-extension, which enable high affinity of AdoCbl. Two structural elements, T-loop/T-looplike motif and kissing loop are key to the global folding of the RNA. While the structure of the TteAdoCbl riboswitch complex is known, we still do not understand the structure and conformation before AdoCbl ligand recognition. In order to delineate the conformational changes and the stabilities of long-range interactions, we have performed extensive all-atom replica-exchange molecular dynamics simulations of the TteAdoCbl riboswitch with a total simulation time of 2296 ns. We found that both the T-loop/T-looplike motif and kissing loop are very stable with ligand binding. The gating conformation changes of P6-extension allow the ligand to bind to the preorganized kissing loop binding pocket. The T-loop/T-looplike motif has much more hydrogen bonds than observed in TteAdoCbl riboswitch complex crystal structure, indicating an allosteric response of the T-loop/T-looplike motif. Our study demonstrated that the conformational ensemble of TteAdoCbl riboswitch provides stable structural elements for conformation selection and population shift in cobalamin recognition.