For optimal pancreatic cancer treatment, early and accurate diagnosis is vital. Blood-derived biomarkers and genetic predispositions can contribute to early diagnosis, but they often have limited accuracy or applicability. Here, we seek to exploit the synergy between them by combining the biomarker CA19-9 with RNA-based variants. We use deep sequencing and deep learning to improve differentiating pancreatic cancer and chronic pancreatitis. We obtained samples of nucleated cells found in peripheral blood from 268 patients suffering from resectable, non-resectable pancreatic cancer, and chronic pancreatitis. We sequenced RNA with high coverage and obtained millions of variants. The high-quality variants served as input together with CA19-9 values to deep learning models. Our model achieved an area under the curve (AUC) of 96% in differentiating resectable cancer from pancreatitis using a test cohort. Moreover, we identified variants to estimate survival in resectable cancer. We show that the blood transcriptome harbours variants, which can substantially improve noninvasive clinical diagnosis.

Summary: Biofilm‐forming benthic diatoms are key primary producers in coastal habitats, where they frequently dominate sunlit intertidal substrata. The development of gliding motility in raphid diatoms was a key molecular adaptation that contributed to their evolutionary success. However, the structure–function correlation between diatom adhesives utilized for gliding and their relationship to the extracellular matrix that constitutes the diatom biofilm is unknown. Here, we have used proteomics, immunolocalization, comparative genomics, phylogenetics and structural homology analysis to investigate the evolutionary history and function of diatom adhesive proteins. Our study identified eight proteins from the adhesive trails of Craspedostauros australis, of which four form a new protein family called Trailins that contain an enigmatic Choice‐of‐Anchor A (CAA) domain, which was acquired through horizontal gene transfer from bacteria. Notably, the CAA‐domain shares a striking structural similarity with one of the most widespread domains found in ice‐binding proteins (IPR021884). Our work offers new insights into the molecular basis for diatom biofilm formation, shedding light on the function and evolution of diatom adhesive proteins. This discovery suggests that there is a transition in the composition of biomolecules required for initial surface colonization and those utilized for 3D biofilm matrix formation.

Single mutations can confer resistance to antibiotics. Identifying such mutations can help to develop and improve drugs. Here, we systematically screen for candidate quinolone resistance-conferring mutations. We sequenced highly diverse wastewater E. coli and performed a genome-wide association study (GWAS) to determine associations between over 200,000 mutations and quinolone resistance phenotypes. We uncovered 13 statistically significant mutations including 1 located at the active site of the biofilm dispersal gene bdcA and 6 silent mutations in the aminoacyl-tRNA synthetase valS. The study also recovered the known mutations in the topoisomerases gyrase (gyrA) and topoisomerase IV (parC). In summary, we demonstrate that GWAS effectively and comprehensively identifies resistance mutations without a priori knowledge of targets and mode of action. The results suggest that mutations in the bdcA and valS genes, which are involved in biofilm dispersal and translation, may lead to novel resistance mechanisms.

Background: Structure-based drug repositioning has emerged as a promising alternative to conventional drug development. Regardless of the many success stories reported over the past years and the novel breakthroughs on the AI-based system AlphaFold for structure prediction, the availability of structural data for protein–drug complexes remains very limited. Whereas the chemical libraries contain millions of drug compounds, the vast majority of them do not have structures to crystallized targets,and it is, therefore, impossible to characterize their binding to targets from a structural view. However, the concept of building blocks offers a novel perspective on the structural problem. A drug compound is considered a complex of small chemical blocks or fragments, which confer the relevant properties to the drug and have a high proportion of functional groups involved in protein binding. Based on this, we propose a novel approach to expand the scope of structure-based repositioning approaches by transferring the structural knowledge from a fragment to a compound level. Results: We fragmented over 100,000 compounds in the Protein Data Bank (PDB) and characterized the structural binding mode of 153,000 fragments to their crystallized targets. Using the fragment’s data, we were able to artificially reconstruct the binding mode of over 7,800 complexes between ChEMBL compounds and their known targets, for which no structural data is available. We proved that the conserved binding tendency of fragments, when binding to the same targets, highly influences the drug’s binding specificity and carries the key information to reconstruct full drugs binding mode. Furthermore, our approach was able to reconstruct multiple compound-target pairs at optimal thresholds and high similarity to the actual binding mode. Conclusions: Such reconstructions are of great value and benefit structure-based drug repositioning since they automatically enlarge the technique’s scope and allow exploring the so far ‘unexplored compounds’ from a structural perspective. In general, the transfer of structural information is a promising technique that could be applied to any chemical library, to any compound that has no crystal structure available in PDB, and even to transfer any other feature that may be relevant for the drug discovery process and that due to data limitations is not yet fully available. In that sense, the results of this work document the full potential of structure-based screening even beyond PDB. Graphical

Summary: Biofilm-forming benthic diatoms are key primary producers in coastal habitats, where they frequently dominate sunlit submerged and intertidal substrata. The development of a unique form of gliding motility in raphid diatoms was a key molecular adaptation that contributed to their evolutionary success. Gliding motility is hypothesized to be driven by an intracellular actin-myosin motor and requires the secretion of polysaccharide- and protein-based adhesive materials. To date, the structure-function correlation between diatom adhesives utilized for gliding and their relationship to the extracellular matrix that constitutes the diatom biofilm is unknown. Proteomics analysis of the adhesive material from Craspedostauros australis revealed eight novel, diatom-specific proteins. Four of them constitute a new family of proteins, named Trailins, which contain an enigmatic domain termed Choice-of-Anchor-A (CAA). Immunostaining demonstrated that Trailins are only present in the adhesive trails required to generate traction on native substrata, but are absent from the extracellular matrix of biofilms. Phylogenetic analysis and Protein 3D structure prediction suggests that the CAA-domains in Trailins were obtained from bacteria by horizontal gene transfer, and exhibit a striking structural similarity to ice-binding proteins. Our work advances the understanding of the molecular basis for diatom underwater adhesion and biofilm formation providing evidence that there is a molecular switch between proteins required for initial surface colonization and those required for 3D biofilm matrix formation.

1 A protein can be represented by its primary, secondary, or tertiary structure. With recent advances in AI, there is now as much tertiary as primary structural data available. Fast and accurate search methods exist for both types of data, with searches over both representations being highly precise. However, primary structure data can sometimes be incomplete. As a result, tertiary structure has become the gold standard for remote homology detection. How does secondary structure perform in remote homology detection? Secondary structure interprets proteins as a sequence using an alphabet representing helices, strands, or loops. It shares its sequential nature with primary structure while retaining topological information similar to tertiary structure. To assess the effectiveness of secondary structure in remote homology detection, we devised a challenging classification task aimed at determining the superfamily membership of very distantly related protein domains. We used benchmarks from the CATH and SCOP databases and evaluated sequence and structure alignment algorithms on primary, secondary, and tertiary structures. As expected, both basic and advanced sequence alignment algorithms applied to primary structure achieved high precision, but their overall area under the curve was lower compared to the gold standard of structural alignment using tertiary structure. Surprisingly, a simple string comparison algorithm applied to secondary structure performed close to the gold standard. This result supports the hypothesis that key structural information is already encoded in secondary structure and suggests that secondary structure may be a promising representation to use when high-confidence structural data is unavailable, such as in cases involving protein flexibility and disorder.