dendPoint: a web resource for dendrimer pharmacokinetics investigation and prediction

Abstract: Nanomedicine development currently suffers from a lack of efficient tools to predict pharmacokinetic behavior without relying upon testing in large numbers of animals, impacting success rates and development costs. This work presents dendPoint, the first in silico model to predict the intravenous pharmacokinetics of dendrimers, a commonly explored drug vector, based on physicochemical properties. We have manually curated the largest relational database of dendrimer pharmacokinetic parameters and their structural/physicochemical properties. This was used to develop a machine learning-based model capable of accurately predicting pharmacokinetic parameters, including half-life, clearance, volume of distribution and dose recovered in the liver and urine. dendPoint successfully predicts dendrimer pharmacokinetic properties, achieving correlations of up to r = 0.83 and Q2 up to 0.68. dendPoint is freely available as a user-friendly web-service and database at http://biosig.unimelb.edu.au/dendpoint. This platform is ultimately expected to be used to guide dendrimer construct design and refinement prior to embarking on more time consuming and expensive in vivo testing

AI driven B-cell Immunotherapy Design

Antibodies, a prominent class of approved biologics, play a crucial role in detecting foreign antigens. The effectiveness of antigen neutralisation and elimination hinges upon the strength, sensitivity, and specificity of the paratope-epitope interaction, which demands resource-intensive experimental techniques for characterisation. In recent years, artificial intelligence and machine learning methods have made significant strides, revolutionising the prediction of protein structures and their complexes. The past decade has also witnessed the evolution of computational approaches aiming to support immunotherapy design. This review focuses on the progress of machine learning-based tools and their frameworks in the domain of B-cell immunotherapy design, encompassing linear and conformational epitope prediction, paratope prediction, and antibody design. We mapped the most commonly used data sources, evaluation metrics, and method availability and thoroughly assessed their significance and limitations, discussing the main challenges ahead

pdCSM-cancer: Using Graph-Based Signatures to Identify Small Molecules with Anticancer Properties.

The development of new, effective, and safe drugs to treat cancer remains a challenging and time-consuming task due to limited hit rates, restraining subsequent development efforts. Despite the impressive progress of quantitative structure-activity relationship and machine learning-based models that have been developed to predict molecule pharmacodynamics and bioactivity, they have had mixed success at identifying compounds with anticancer properties against multiple cell lines. Here, we have developed a novel predictive tool, pdCSM-cancer, which uses a graph-based signature representation of the chemical structure of a small molecule in order to accurately predict molecules likely to be active against one or multiple cancer cell lines. pdCSM-cancer represents the most comprehensive anticancer bioactivity prediction platform developed till date, comprising trained and validated models on experimental data of the growth inhibition concentration (GI50%) effects, including over 18,000 compounds, on 9 tumor types and 74 distinct cancer cell lines. Across 10-fold cross-validation, it achieved Pearson's correlation coefficients of up to 0.74 and comparable performance of up to 0.67 across independent, non-redundant blind tests. Leveraging the insights from these cell line-specific models, we developed a generic predictive model to identify molecules active in at least 60 cell lines. Our final model achieved an area under the receiver operating characteristic curve (AUC) of up to 0.94 on 10-fold cross-validation and up to 0.94 on independent non-redundant blind tests, outperforming alternative approaches. We believe that our predictive tool will provide a valuable resource to optimizing and enriching screening libraries for the identification of effective and safe anticancer molecules. To provide a simple and integrated platform to rapidly screen for potential biologically active molecules with favorable anticancer properties, we made pdCSM-cancer freely available online at http://biosig.unimelb.edu.au/pdcsm_cancer

Structure-guided machine learning prediction of drug resistance mutations in Abelson 1 kinase.

Funder: State Government of VictoriaKinases play crucial roles in cellular signalling and biological processes with their dysregulation associated with diseases, including cancers. Kinase inhibitors, most notably those targeting ABeLson 1 (ABL1) kinase in chronic myeloid leukemia, have had a significant impact on cancer survival, yet emergence of resistance mutations can reduce their effectiveness, leading to therapeutic failure. Limited effort, however, has been devoted to developing tools to accurately identify ABL1 resistance mutations, as well as providing insights into their molecular mechanisms. Here we investigated the structural basis of ABL1 mutations modulating binding affinity of eight FDA-approved drugs. We found mutations impair affinity of type I and type II inhibitors differently and used this insight to developed a novel web-based diagnostic tool, SUSPECT-ABL, to pre-emptively predict resistance profiles and binding free-energy changes (ΔΔG) of all possible ABL1 mutations against inhibitors with different binding modes. Resistance mutations in ABL1 were successfully identified, achieving a Matthew's Correlation Coefficient of up to 0.73 and the resulting change in ligand binding affinity with a Pearson's correlation of up to 0.77, with performances consistent across non-redundant blind tests. Through an in silico saturation mutagenesis, our tool has identified possibly emerging resistance mutations, which offers opportunities for in vivo experimental validation. We believe SUSPECT-ABL will be an important tool not just for improving precision medicine efforts, but for facilitating the development of next-generation inhibitors that are less prone to resistance. We have made our tool freely available at http://biosig.unimelb.edu.au/suspect_abl/

Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches

Funder: Medical Research Council; doi: http://dx.doi.org/10.13039/501100000265Funder: National Health and Medical Research Council; doi: http://dx.doi.org/10.13039/501100000925Abstract: Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce protein affinities within the RNA polymerase complex, subsequently reducing nucleic acid affinity. Here, we have used these insights to develop a computational rifampicin resistance predictor capable of identifying resistant mutations even outside the well-defined rifampicin resistance determining region (RRDR), using clinical M. tuberculosis sequencing information. Our tool successfully identified up to 90.9% of M. tuberculosis rpoB variants correctly, with sensitivity of 92.2%, specificity of 83.6% and MCC of 0.69, outperforming the current gold-standard GeneXpert-MTB/RIF. We show our model can be translated to other clinically relevant organisms: M. leprae, P. aeruginosa and S. aureus, despite weak sequence identity. Our method was implemented as an interactive tool, SUSPECT-RIF (StrUctural Susceptibility PrEdiCTion for RIFampicin), freely available at https://biosig.unimelb.edu.au/suspect_rif/

Mitochondrial Data in Monocot Phylologenetics

Mitochondrial sequences are an important source of data in animal phylogenetics, equivalent in importance to plastid sequences in plants. However, in recent years plant systematists have begun exploring the mitochondrial genome as a source of phylogenetically useful characters. The plant mitochondrial genome is renowned for its variability in size, structure, and gene organization, but this need not be of concern for the application of sequence data in phylogenetics. However, the incorporation of reverse transcribed mitochondrial genes ( processed paralogs ) and the recurring transfer of genes from the mitochondrion to the nucleus are evolutionary events that must be taken into account. RNA editing of mitochondrial genes is sometimes considered a problem in phylogenetic reconstruction, but we regard it only as a mechanism that may increase variability at edited sites and change the codon position bias accordingly. Additionally, edited sites may prove a valuable tool in identifying processed paralogs. An overview of genes and sequences used in phylogenetic studies of angiosperms is presented. In the monocots, a large amount of mitochondrial sequence data is being collected together with sequence data from plastid and nuclear genes, thus offering an opportunity to compare data from different genomic compartments. The mitochondrial and plastid data are incongruent when organelle gene trees are reconstructed. Possible reasons for the observed incongruence involve sampling of paralogous sequences and highly divergent substitution rates, potentially leading to longbranch attraction. The above problems are addressed in Acorales, Alismatales, Poales, Liliaceae, the Anthericum clade (in Agavaceae), and in some achlorophyllous taxa

A structural biology community assessment of AlphaFold2 applications

Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank. AF2-based predictions of protein disorder and complexes surpass dedicated tools, and AF2 models can be used across diverse applications equally well compared with experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life-science research

Taking the First Steps towards a Standard for Reporting on Phylogenies: Minimum Information about a Phylogenetic Analysis (MIAPA)

In the eight years since phylogenomics was introduced as the intersection of genomics and phylogenetics, the field has provided fundamental insights into gene function, genome history and organismal relationships. The utility of phylogenomics is growing with the increase in the number and diversity of taxa for which whole genome and large transcriptome sequence sets are being generated. We assert that the synergy between genomic and phylogenetic perspectives in comparative biology would be enhanced by the development and refinement of minimal reporting standards for phylogenetic analyses. Encouraged by the development of the Minimum Information About a Microarray Experiment (MIAME) standard, we propose a similar roadmap for the development of a Minimal Information About a Phylogenetic Analysis (MIAPA) standard. Key in the successful development and implementation of such a standard will be broad participation by developers of phylogenetic analysis software, phylogenetic database developers, practitioners of phylogenomics, and journal editors. This paper is part of the special issue of OMICS on data standards.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63208/1/omi.2006.10.231.pd

Tumour risks and genotype-phenotype correlations associated with germline variants in succinate dehydrogenase subunit genes SDHB, SDHC and SDHD.

BACKGROUND: Germline pathogenic variants in SDHB/SDHC/SDHD are the most frequent causes of inherited phaeochromocytomas/paragangliomas. Insufficient information regarding penetrance and phenotypic variability hinders optimum management of mutation carriers. We estimate penetrance for symptomatic tumours and elucidate genotype-phenotype correlations in a large cohort of SDHB/SDHC/SDHD mutation carriers. METHODS: A retrospective survey of 1832 individuals referred for genetic testing due to a personal or family history of phaeochromocytoma/paraganglioma. 876 patients (401 previously reported) had a germline mutation in SDHB/SDHC/SDHD (n=673/43/160). Tumour risks were correlated with in silico structural prediction analyses. RESULTS: Tumour risks analysis provided novel penetrance estimates and genotype-phenotype correlations. In addition to tumour type susceptibility differences for individual genes, we confirmed that the SDHD:p.Pro81Leu mutation has a distinct phenotype and identified increased age-related tumour risks with highly destabilising SDHB missense mutations. By Kaplan-Meier analysis, the penetrance (cumulative risk of clinically apparent tumours) in SDHB and (paternally inherited) SDHD mutation-positive non-probands (n=371/67 with detailed clinical information) by age 60 years was 21.8% (95% CI 15.2% to 27.9%) and 43.2% (95% CI 25.4% to 56.7%), respectively. Risk of malignant disease at age 60 years in non-proband SDHB mutation carriers was 4.2%(95% CI 1.1% to 7.2%). With retrospective cohort analysis to adjust for ascertainment, cumulative tumour risks for SDHB mutation carriers at ages 60 years and 80 years were 23.9% (95% CI 20.9% to 27.4%) and 30.6% (95% CI 26.8% to 34.7%). CONCLUSIONS: Overall risks of clinically apparent tumours for SDHB mutation carriers are substantially lower than initially estimated and will improve counselling of affected families. Specific genotype-tumour risk associations provides a basis for novel investigative strategies into succinate dehydrogenase-related mechanisms of tumourigenesis and the development of personalised management for SDHB/SDHC/SDHD mutation carriers

A Gammaherpesvirus Cooperates with Interferon-alpha/beta-Induced IRF2 to Halt Viral Replication, Control Reactivation, and Minimize Host Lethality

The gammaherpesviruses, including Epstein-Barr virus (EBV) and Kaposi's sarcoma-associated herpesvirus (KSHV), establish latency in memory B lymphocytes and promote lymphoproliferative disease in immunocompromised individuals. The precise immune mechanisms that prevent gammaherpesvirus reactivation and tumorigenesis are poorly defined. Murine gammaherpesvirus 68 (MHV68) is closely related to EBV and KSHV, and type I (alpha/beta) interferons (IFNαβ) regulate MHV68 reactivation from both B cells and macrophages by unknown mechanisms. Here we demonstrate that IFNβ is highly upregulated during latent infection, in the absence of detectable MHV68 replication. We identify an interferon-stimulated response element (ISRE) in the MHV68 M2 gene promoter that is bound by the IFNαβ-induced transcriptional repressor IRF2 during latency in vivo. The M2 protein regulates B cell signaling to promote establishment of latency and reactivation. Virus lacking the M2 ISRE (ISREΔ) overexpresses M2 mRNA and displays uncontrolled acute replication in vivo, higher latent viral load, and aberrantly high reactivation from latency. These phenotypes of the ISREΔ mutant are B-cell-specific, require IRF2, and correlate with a significant increase in virulence in a model of acute viral pneumonia. We therefore identify a mechanism by which a gammaherpesvirus subverts host IFNαβ signaling in a surprisingly cooperative manner, to directly repress viral replication and reactivation and enforce latency, thereby minimizing acute host disease. Since we find ISREs 5′ to the major lymphocyte latency genes of multiple rodent, primate, and human gammaherpesviruses, we propose that cooperative subversion of IFNαβ-induced IRFs to promote latent infection is an ancient strategy that ensures a stable, minimally-pathogenic virus-host relationship