Molecular subgroups of T-cell acute lymphoblastic leukemia in adults treated according to pediatric-based GMALL protocols.

In contrast to B-cell precursor acute lymphoblastic leukemia (ALL), molecular subgroups are less well defined in T-lineage ALL. Comprehensive studies on molecular T-ALL subgroups have been predominantly performed in pediatric ALL patients. Currently, molecular characteristics are rarely considered for risk stratification. Herein, we present a homogenously treated cohort of 230 adult T-ALL patients characterized on transcriptome, and partly on DNA methylation and gene mutation level in correlation with clinical outcome. We identified nine molecular subgroups based on aberrant oncogene expression correlating to four distinct DNA methylation patterns. The subgroup distribution differed from reported pediatric T-ALL cohorts with higher frequencies of prognostic unfavorable subgroups like HOXA or LYL1/LMO2. A small subset (3%) of HOXA adult T-ALL patients revealed restricted expression of posterior HOX genes with aberrant activation of lncRNA HOTTIP. With respect to outcome, TLX1 (n = 44) and NKX2-1 (n = 4) had an exceptionally favorable 3-year overall survival (3y-OS) of 94%. Within thymic T-ALL, the non TLX1 patients had an inferior but still good prognosis. To our knowledge this is the largest cohort of adult T-ALL patients characterized by transcriptome sequencing with meaningful clinical follow-up. Risk classification based on molecular subgroups might emerge and contribute to improvements in outcome.

The potential influence of genome-wide adaptive divergence on conservation translocation outcome in an isolated greater sage-grouse population.

Conservation translocations are an important conservation tool commonly employed to augment declining or reestablish extirpated populations. One goal of augmentation is to increase genetic diversity and reduce the risk of inbreeding depression (i.e., genetic rescue). However, introducing individuals from significantly diverged populations risks disrupting coadapted traits and reducing local fitness (i.e., outbreeding depression). Genetic data are increasingly more accessible for wildlife species and can provide unique insight regarding the presence and retention of introduced genetic variation from augmentation as an indicator of effectiveness and adaptive similarity as an indicator of source and recipient population suitability. We used 2 genetic data sets to evaluate augmentation of isolated populations of greater sage-grouse (Centrocercus urophasianus) in the northwestern region of the species range (Washington, USA) and to retrospectively evaluate adaptive divergence among source and recipient populations. We developed 2 statistical models for microsatellite data to evaluate augmentation outcomes. We used one model to predict genetic diversity after augmentation and compared these predictions with observations of genetic change. We used the second model to quantify the amount of observed reproduction attributed to transplants (proof of population integration). We also characterized genome-wide adaptive divergence among source and recipient populations. Observed genetic diversity (HO = 0.65) was higher in the recipient population than predicted had no augmentation occurred (HO = 0.58) but less than what was predicted by our model (HO = 0.75). The amount of shared genetic variation between the 2 geographically isolated resident populations increased, which is evidence of periodic gene flow previously assumed to be rare. Among candidate adaptive genes associated with elevated fixation index (FST) (143 genes) or local environmental variables (97 and 157 genes for each genotype-environment association method, respectively), we found clusters of genes with related functions that may influence the ability of transplants to use local resources and navigate unfamiliar environments and their reproductive potential, all possible reasons for low genetic retention from augmentation.

Influencia potencial de la divergencia adaptativa a nivel genoma sobre el resultado de la reubicación para conservación en una población aislada de urogallo mayor Resumen Las reubicaciones para conservación son una herramienta importante que se usa con frecuencia para aumentar las poblaciones en declinación o reestablecer las poblaciones erradicadas. Una de las metas de este aumento es incrementar la diversidad genética y reducir el riesgo de depresión endogámica (es decir, rescate genético). Sin embargo, la introducción de individuos de una población con divergencia significativa puede perturbar los rasgos coadaptados y reducir la aptitud local (es decir, depresión exogámica). La información genética es cada vez más accesible para las especies silvestres y puede proporcionar conocimiento único con respecto a la presencia y retención de la variación genética introducida a partir del aumento como un indicador de eficiencia y las similitudes adaptativas como un indicador de la idoneidad de la población de origen y la receptora. Usamos dos conjuntos de datos genéticos para evaluar el aumento de las poblaciones aisladas del urogallo mayor (Centrocercus urophasianus) en la región noroeste de la distribución de la especie (Washington, EUA) y para evaluar de forma retrospectiva la divergencia adaptativa entre la población de origen y la receptora. Desarrollamos dos modelos estadísticos para los datos microsatelitales para así evaluar los resultados del aumento. Usamos un modelo para predecir la diversidad genética después del aumento y comparamos estas predicciones con observaciones del cambio genético. Usamos el segundo modelo para cuantificar el aumento de la reproducción observada atribuida a las reubicaciones (evidencia de la integración poblacional). También caracterizamos la divergencia adaptativa a nivel genoma entre la población de origen y la población receptora. La diversidad genética observada (HO = 0.65) fue mayor de lo que se predijo en la población receptora de no haber ocurrido el aumento (HO = 0.58) pero menor de lo que se predijo en nuestro modelo (HO = 0.75). El aumento de la variación genética compartida entre las dos poblaciones residentes geográficamente aisladas incrementó, lo cual es evidencia de un flujo génico periódico que antes se supuso casi no ocurría. Entre los genes adaptativos candidatos asociados a una FST elevada (143 genes) o a variables ambientales locales (97 y 157 genes para cada método de asociación entre el ambiente y el genotipo, respectivamente) encontramos grupos de genes con funciones relacionadas que pueden influir sobre la habilidad de cada reubicación para usar recursos locales y navegar ambientes desconocidos y su potencial reproductivo, todas posibles razones para la baja retención genética en el aumento.

Diagnosis and Treatment of Perioperative Allergic Complications: A Practical Review.

Background: Reported drug allergies are commonly encountered by surgeons and can lead to uncertainty in selecting an appropriate agent due to concerns of associated risks with related and cross-reactive drugs. This uncertainty can ultimately lead to increased infection rates. Methods: A literature review was conducted in PubMed using a combination of the terms "allergy," "allergic reaction," "anaphylaxis," and "surgery," "surgical," or "operating room" for articles published within the last 10 years. Publications identified with these search terms were then filtered for review articles, sorted by "best match," and a maximum of 100 articles were manually reviewed for each combination of search terms. Results: Search results yielded 46,484 articles, 676 of which were ultimately included for manual review, based on selection criteria. Specifically, articles selected for inclusion focused on surgical allergic reactions that were either related to mechanism of action, causative agent for the allergic reaction, timing of allergic reaction, or recommendations for appropriate management. Conclusions: Allergic reactions can be a common occurrence in the operative room. Knowledge of likely causative agents, timing of a reaction to various agents, and appropriate management in the immediate and delayed setting can improve outcomes and safety for plastic surgery patients.

Word-based GWAS harnesses the rich potential of genomic data for E. coli quinolone resistance.

Quinolone resistance presents a growing global health threat. We employed word-based GWAS to explore genomic data, aiming to enhance our understanding of this phenomenon. Unlike traditional variant-based GWAS analyses, this approach simultaneously captures multiple genomic factors, including single and interacting resistance mutations and genes. Analyzing a dataset of 92 genomic E. coli samples from a wastewater treatment plant in Dresden, we identified 54 DNA unitigs significantly associated with quinolone resistance. Remarkably, our analysis not only validated known mutations in gyrA and parC genes and the results of our variant-based GWAS but also revealed new (mutated) genes such as mdfA, the AcrEF-TolC multidrug efflux system, ptrB, and hisI, implicated in antibiotic resistance. Furthermore, our study identified joint mutations in 14 genes including the known gyrA gene, providing insights into potential synergistic effects contributing to quinolone resistance. These findings showcase the exceptional capabilities of word-based GWAS in unraveling the intricate genomic foundations of quinolone resistance.

Diatom adhesive trail proteins acquired by horizontal gene transfer from bacteria serve as primers for marine biofilm formation.

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.

Netrank: network-based approach for biomarker discovery.

BACKGROUND: Integrating multi-omics data is fast becoming a powerful approach for predicting disease progression and treatment outcomes. In light of that, we introduce a modified version of the NetRank algorithm, a network-based algorithm for biomarker discovery that incorporates the protein associations, co-expressions, and functions with its phenotypic association to differentiate different types of cancer. NetRank is introduced here as a robust feature selection method for biomarker selection in cancer prediction. We assess the robustness and suitability of the RNA gene expression data through scanning genomic data for 19 cancer types with more than 3000 patients from The Cancer Genome Atlas (TCGA). RESULTS: The results of evaluating different cancer type profiles from the TCGA data demonstrate the strength of our approach to identifying interpretable biomarker signatures for cancer outcome prediction. NetRank's biomarkers segregate most cancer types with an area under the curve (AUC) above 90% using compact signatures. CONCLUSION: In this paper we provide a fast and efficient implementation of NetRank, with a case study from The Cancer Genome Atlas, to assess the performance. We incorporated complete functionality for pre and post-processing for RNA-seq gene expression data with functions for building protein-protein interaction networks. The source code of NetRank is freely available (at github.com/Alfatlawi/Omics-NetRank) with an installable R library. We also deliver a comprehensive practical user manual with examples and data attached to this paper.

Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances.

INTRODUCTION: Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks. AREAS COVERED: This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets. EXPERT OPINION: Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.

An interaction-based drug discovery screen explains known SARS-CoV-2 inhibitors and predicts new compound scaffolds.

The recent outbreak of the COVID-19 pandemic caused by severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) has shown the necessity for fast and broad drug discovery methods to enable us to react quickly to novel and highly infectious diseases. A well-known SARS-CoV-2 target is the viral main 3-chymotrypsin-like cysteine protease (Mpro), known to control coronavirus replication, which is essential for the viral life cycle. Here, we applied an interaction-based drug repositioning algorithm on all protein-compound complexes available in the protein database (PDB) to identify Mpro inhibitors and potential novel compound scaffolds against SARS-CoV-2. The screen revealed a heterogeneous set of 692 potential Mpro inhibitors containing known ones such as Dasatinib, Amodiaquine, and Flavin mononucleotide, as well as so far untested chemical scaffolds. In a follow-up evaluation, we used publicly available data published almost two years after the screen to validate our results. In total, we are able to validate 17% of the top 100 predictions with publicly available data and can furthermore show that predicted compounds do cover scaffolds that are yet not associated with Mpro. Finally, we detected a potentially important binding pattern consisting of 3 hydrogen bonds with hydrogen donors of an oxyanion hole within the active side of Mpro. Overall, these results give hope that we will be better prepared for future pandemics and that drug development will become more efficient in the upcoming years.

FAT1 expression in T-cell acute lymphoblastic leukemia (T-ALL) modulates proliferation and WNT signaling.

FAT atypical cadherin 1 (FAT1), a transmembrane protein, is frequently mutated in various cancer types and has been described as context-dependent tumor suppressor or oncogene. The FAT1 gene is mutated in 12-16% of T-cell acute leukemia (T-ALL) and aberrantly expressed in about 54% of T-ALL cases contrasted with absent expression in normal T-cells. Here, we characterized FAT1 expression and profiled the methylation status from T-ALL patients. In our T-ALL cohort, 53% of patient samples were FAT1 positive (FAT1pos) compared to only 16% FAT1 positivity in early T-ALL patient samples. Aberrant expression of FAT1 was strongly associated with FAT1 promotor hypomethylation, yet a subset, mainly consisting of TLX1-driven T-ALL patient samples showed methylation-independent high FAT1 expression. Genes correlating with FAT1 expression revealed enrichment in WNT signaling genes representing the most enriched single pathway. FAT1 knockdown or knockout led to impaired proliferation and downregulation of WNT pathway target genes (CCND1, MYC, LEF1), while FAT1 overexpressing conveyed a proliferative advantage. To conclude, we characterized a subtype pattern of FAT1 gene expression in adult T-ALL patients correlating with promotor methylation status. FAT1 dependent proliferation and WNT signaling discloses an impact on deeper understanding of T-ALL leukemogenesis as a fundament for prospective therapeutic strategies.

Emati: a recommender system for biomedical literature based on supervised learning.

The scientific literature continues to grow at an ever-increasing rate. Considering that thousands of new articles are published every week, it is obvious how challenging it is to keep up with newly published literature on a regular basis. Using a recommender system that improves the user experience in the online environment can be a solution to this problem. In the present study, we aimed to develop a web-based article recommender service, called Emati. Since the data are text-based by nature and we wanted our system to be independent of the number of users, a content-based approach has been adopted in this study. A supervised machine learning model has been proposed to generate article recommendations. Two different supervised learning approaches, namely the naïve Bayes model with Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer and the state-of-the-art language model bidirectional encoder representations from transformers (BERT), have been implemented. In the first one, a list of documents is converted into TF-IDF-weighted features and fed into a classifier to distinguish relevant articles from irrelevant ones. Multinomial naïve Bayes algorithm is used as a classifier since, along with the class label, it also gives the probability that the input belongs to this class. The second approach is based on fine-tuning the pretrained state-of-the-art language model BERT for the text classification task. Emati provides a weekly updated list of article recommendations and presents it to the user, sorted by probability scores. New article recommendations are also sent to users' email addresses on a weekly basis. Additionally, Emati has a personalized search feature to search online services' (such as PubMed and arXiv) content and have the results sorted by the user's classifier. Database URL: https://emati.biotec.tu-dresden.de.

Open Versus Endoscopic Carpal Tunnel Release: A Comparison of Opioid Prescription Patterns and Occupational Therapy Referrals.

BACKGROUND: Endoscopic and open carpal tunnel releases (ECTR and OCTR) are safe and effective operations. We compared the approaches in terms of postoperative opioid refills and occupational therapy (OT) referrals. METHODS: We conducted a retrospective study of patients with carpal tunnel syndrome (CTS) treated with ECTR or OCTR. Patients with isolated idiopathic CTS were included; patients undergoing simultaneous bilateral carpal tunnel release (CTR), revision CTR, and additional procedures at time of CTR were excluded. Outcomes included number of patients requiring an opioid refill and/or an OT referral within 6 months of surgery. RESULTS: A total of 1125 patients met inclusion criteria. Endoscopic release was performed in 634 (56%) cases and open release in 491 (44%). Unadjusted analysis revealed no difference in number of patients requiring refills (6.0% vs 7.1%, P = .44), mean number of refills among those requiring one (1.29 vs 1.23, P = .69), total oral morphine equivalents (45.1 vs 44.7, P = .84), number of patients calling regarding pain (12.8% vs 14.7%, P = .36), OT referrals (12.1% vs 11.4%, P = .71), or average number of OT visits (4.5 vs 4.2, P = .74) for endoscopic and open techniques, respectively. Adjusted analysis revealed lower age, lower body mass index, and history of muscle relaxant as predictors of opioid refills, and in contrast to the unadjusted analysis, operating surgeon and surgical technique were predictors of referral to OT. CONCLUSION: Endoscopic CTR and OCTR did not differ in terms of unadjusted postoperative patient calls for pain, number of opioid refills, or OT referrals. After correcting for individual surgeon practice, endoscopic was associated with decreased odds of requiring postoperative OT.

NetRank Recovers Known Cancer Hallmark Genes as Universal Biomarker Signature for Cancer Outcome Prediction.

Gene expression can serve as a powerful predictor for disease progression and other phenotypes. Consequently, microarrays, which capture gene expression genome-wide, have been used widely over the past two decades to derive biomarker signatures for tasks such as cancer grading, prognosticating the formation of metastases, survival, and others. Each of these signatures was selected and optimized for a very specific phenotype, tissue type, and experimental set-up. While all of these differences may naturally contribute to very heterogeneous and different biomarker signatures, all cancers share characteristics regardless of particular cell types or tissue as summarized in the hallmarks of cancer. These commonalities could give rise to biomarker signatures, which perform well across different phenotypes, cell and tissue types. Here, we explore this possibility by employing a network-based approach for pan-cancer biomarker discovery. We implement a random surfer model, which integrates interaction, expression, and phenotypic information to rank genes by their suitability for outcome prediction. To evaluate our approach, we assembled 105 high-quality microarray datasets sampled from around 13,000 patients and covering 13 cancer types. We applied our approach (NetRank) to each dataset and aggregated individual signatures into one compact signature of 50 genes. This signature stands out for two reasons. First, in contrast to other signatures of the 105 datasets, it is performant across nearly all cancer types and phenotypes. Second, It is interpretable, as the majority of genes are linked to the hallmarks of cancer in general and proliferation specifically. Many of the identified genes are cancer drivers with a known mutation burden linked to cancer. Overall, our work demonstrates the power of network-based approaches to compose robust, compact, and universal biomarker signatures for cancer outcome prediction.

A multiscale model of the regulation of aquaporin 2 recycling.

The response of cells to their environment is driven by a variety of proteins and messenger molecules. In eukaryotes, their distribution and location in the cell are regulated by the vesicular transport system. The transport of aquaporin 2 between membrane and storage region is a crucial part of the water reabsorption in renal principal cells, and its malfunction can lead to Diabetes insipidus. To understand the regulation of this system, we aggregated pathways and mechanisms from literature and derived three models in a hypothesis-driven approach. Furthermore, we combined the models to a single system to gain insight into key regulatory mechanisms of Aquaporin 2 recycling. To achieve this, we developed a multiscale computational framework for the modeling and simulation of cellular systems. The analysis of the system rationalizes that the compartmentalization of cAMP in renal principal cells is a result of the protein kinase A signalosome and can only occur if specific cellular components are observed in conjunction. Endocytotic and exocytotic processes are inherently connected and can be regulated by the same protein kinase A signal.

A genome-wide scan of wastewater E. coli for genes under positive selection: focusing on mechanisms of antibiotic resistance.

Antibiotic resistance is a global health threat and consequently, there is a need to understand the mechanisms driving its emergence. Here, we hypothesize that genes and mutations under positive selection may contribute to antibiotic resistance. We explored wastewater E. coli, whose genomes are highly diverse. We subjected 92 genomes to a statistical analysis for positively selected genes. We obtained 75 genes under positive selection and explored their potential for antibiotic resistance. We found that eight genes have functions relating to antibiotic resistance, such as biofilm formation, membrane permeability, and bacterial persistence. Finally, we correlated the presence/absence of non-synonymous mutations in positively selected sites of the genes with a function in resistance against 20 most prescribed antibiotics. We identified mutations associated with antibiotic resistance in two genes: the porin ompC and the bacterial persistence gene hipA. These mutations are located at the surface of the proteins and may hence have a direct effect on structure and function. For hipA, we hypothesize that the mutations influence its interaction with hipB and that they enhance the capacity for dormancy as a strategy to evade antibiotics. Overall, genomic data and positive selection analyses uncover novel insights into mechanisms driving antibiotic resistance.

Impact of Opioid Restriction Legislation on Prescribing Practices for Outpatient Plastic and Reconstructive Surgery.

BACKGROUND: Overprescription of opioids for acute postoperative pain, plastic surgery procedures included, is contributing to the pervasive opioid epidemic in the United States. This study examines the effect of a statewide legislation limiting postoperative opioids on opioid prescription behavior among providers following outpatient plastic surgery procedures at a high-volume academic center. METHODS: Retrospective review of all outpatient surgical encounters between June 1, 2016, and November 30, 2018, was performed. Encounters were grouped into two cohorts: prepolicy and postpolicy. Primary outcomes included total oral morphine equivalents prescribed on the day of surgery and proportion of patients prescribed greater than 210 oral morphine equivalents. Secondary outcomes included proportion of patients requiring an opioid refill within 30 days following surgery, and number of refills required. RESULTS: The mean oral morphine equivalents prescribed on the day of surgery was reduced from 271.8 to 150.37 oral morphine equivalents ( p < 0.001) following implementation of the legislation, with an associated decrease in the standard deviation of oral morphine equivalents prescribed from 225.35 to 196.71 ( p < 0.001), suggesting a decrease in the variability of prescriber practices. Time series analysis demonstrated the decrease in oral morphine equivalents remained significant when accounting for baseline level of change in opioid prescription patterns. CONCLUSION: This study provides evidence that legislation at the state level restricting postoperative opioid prescriptions is associated with a decrease in opioid prescriptions without an increase in the need for refills in the acute postoperative setting following outpatient plastic surgery procedures.

An alternative CYB5A transcript is expressed in aneuploid ALL and enriched in relapse.

BACKGROUND: B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is a genetically heterogenous malignancy with poor prognosis in relapsed adult patients. The genetic basis for relapse in aneuploid subtypes such as near haploid (NH) and high hyperdiploid (HeH) BCP-ALL is only poorly understood. Pathogenic genetic alterations remain to be identified. To this end, we investigated the dynamics of genetic alterations in a matched initial diagnosis-relapse (ID-REL) BCP-ALL cohort. Here, we firstly report the identification of the novel genetic alteration CYB5Aalt, an alternative transcript of CYB5A, in two independent cohorts. METHODS: We identified CYB5alt in the RNAseq-analysis of a matched ID-REL BCP-ALL cohort with 50 patients and quantified its expression in various molecular BCP-ALL subtypes. Findings were validated in an independent cohort of 140 first diagnosis samples from adult BCP-ALL patients. Derived from patient material, the alternative open reading frame of CYB5Aalt was cloned (pCYB5Aalt) and pCYB5Aalt or the empty vector were stably overexpressed in NALM-6 cells. RNA sequencing was performed of pCYB5Aalt clones and empty vector controls followed by differential expression analysis, gene set enrichment analysis and complementing cell death and viability assays to determine functional implications of CYB5Aalt. RESULTS: RNAseq data analysis revealed non-canonical exon usage of CYB5Aalt starting from a previously undescribed transcription start site. CYB5Aalt expression was increased in relapsed BCP-ALL and its occurrence was specific towards the shared gene expression cluster of NH and HeH BCP-ALL in independent cohorts. Overexpression of pCYB5Aalt in NALM-6 cells induced a distinct transcriptional program compared to empty vector controls with downregulation of pathways related to reported functions of CYB5A wildtype. Interestingly, CYB5A wildtype expression was decreased in CYB5Aalt samples in silico and in vitro. Additionally, pCYB5Aalt NALM-6 elicited a more resistant drug response. CONCLUSIONS: Across all age groups, CYB5Aalt was the most frequent secondary genetic event in relapsed NH and HeH BCP-ALL. In addition to its high subgroup specificity, CYB5Aalt is a novel candidate to be potentially implicated in therapy resistance in NH and HeH BCP-ALL. This is underlined by overexpressing CYB5Aalt providing first evidence for a functional role in BCL2-mediated apoptosis.

Decomposing compounds enables reconstruction of interaction fingerprints for structure-based drug screening.

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.