Track A Basic Science

Advancements in next-generation sequencing (NGS) technologies are generating a vast amount of data. This exacerbates the current challenge of translating NGS data into actionable clinical interpretations. We have comprehensively combined germline and somatic nonsynonymous single-nucleotide variations (nsSNVs) that affect drug binding sites in order to investigate their prevalence. The integrated data thus generated in conjunction with exome or whole-genome sequencing can be used to identify patients who may not respond to a specific drug because of alterations in drug binding efficacy due to nsSNVs in the target protein’s gene. To identify the nsSNVs that may affect drug binding, protein–drug complex structures were retrieved from Protein Data Bank (PDB) followed by identification of amino acids in the protein–drug binding sites using an occluded surface method. Then, the germline and somatic mutations were mapped to these amino acids to identify which of these alter protein–drug binding sites. Using this method we identified 12 993 amino acid–drug binding sites across 253 unique proteins bound to 235 unique drugs. The integration of amino acid–drug binding sites data with both germline and somatic nsSNVs data sets revealed 3133 nsSNVs affecting amino acid–drug binding sites. In addition, a comprehensive drug target discovery was conducted based on protein structure similarity and conservation of amino acid–drug binding sites. Using this method, 81 paralogs were identified that could serve as alternative drug targets. In addition, non-human mammalian proteins bound to drugs were used to identify 142 homologs in humans that can potentially bind to drugs. In the current protein–drug pairs that contain somatic mutations within their binding site, we identified 85 proteins with significant differential gene expression changes associated with specific cancer types. Information on protein–drug binding predicted drug target proteins and prevalence of both somatic and germline nsSNVs that disrupt these binding sites can provide valuable knowledge for personalized medicine treatment. A web portal is available where nsSNVs from individual patient can be checked by scanning against DrugVar to determine whether any of the SNVs affect the binding of any drug in the database.

Drug resistance mutations (DRMs) appear in HIV under treatment pressure. DRMs are commonly transmitted to naive patients. The standard approach to reveal new DRMs is to test for significant frequency differences of mutations between treated and naive patients. However, we then consider each mutation individually and cannot hope to study interactions between several mutations. Here, we aim to leverage the ever-growing quantity of high-quality sequence data and machine learning methods to study such interactions (i.e. epistasis), as well as try to find new DRMs.We trained classifiers to discriminate between Reverse Transcriptase Inhibitor (RTI)-experienced and RTI-naive samples on a large HIV-1 reverse transcriptase (RT) sequence dataset from the UK (n ≈ 55, 000), using all observed mutations as binary representation features. To assess the robustness of our findings, our classifiers were evaluated on independent data sets, both from the UK and Africa. Important representation features for each classifier were then extracted as potential DRMs. To find novel DRMs, we repeated this process by removing either features or samples associated to known DRMs.When keeping all known resistance signal, we detected sufficiently prevalent known DRMs, thus validating the approach. When removing features corresponding to known DRMs, our classifiers retained some prediction accuracy, and six new mutations significantly associated with resistance were identified. These six mutations have a low genetic barrier, are correlated to known DRMs, and are spatially close to either the RT active site or the regulatory binding pocket. When removing both known DRM features and sequences containing at least one known DRM, our classifiers lose all prediction accuracy. These results likely indicate that all mutations directly conferring resistance have been found, and that our newly discovered DRMs are accessory or compensatory mutations. Moreover, apart from the accessory nature of the relationships we found, we did not find any significant signal of further, more subtle epistasis combining several mutations which individually do not seem to confer any resistance.

Multidomain kinases such as c-Src and c-Abl are regulated by complex allosteric interactions involving their noncatalytic SH3 and SH2 domains. Here we show that enhancing natural allosteric control of kinase activity by SH3/linker engagement has long-range suppressive effects on the kinase activity of the c-Abl core. Surprisingly, enhanced SH3/linker interaction also dramatically sensitized the Bcr-Abl tyrosine kinase associated with chronic myelogenous leukemia to small molecule inhibitors that target either the active site or the myristic acid binding pocket in the kinase domain C-lobe. Dynamics analyses using hydrogen exchange mass spectrometry revealed a remarkable allosteric network linking the SH3 domain, the myristic acid binding pocket, and the active site of the c-Abl core, providing a structural basis for the biological observations. These results suggest a rational strategy for enhanced drug targeting of Bcr-Abl and other multidomain kinase systems that use multiple small molecules to exploit natural mechanisms of kinase control.

An human immunodeficiency virus (HIV)-1 integrase (IN) genotyping assay was developed to evaluate clinical samples from patients infected with HIV-1 subtype AE, a subtype highly prevalent in Asia. The HIV-1 IN gene was amplified from plasma-derived HIV-1 viral RNA via reverse transcription polymerase chain reaction followed by population sequencing. Assay sensitivity was also evaluated using serially diluted plasma samples. The genotyping assay successfully amplified the IN gene from patient plasma samples with HIV-1 RNA as low as 500 copies/mL. This assay is suitable for IN genotyping to identify IN drug resistance mutations in HIV-1 patients harboring subtype AE virus.

Current knowledge on HIV-1 resistance to integrase inhibitors (INIs) is based mostly on subtype B strains. This contrasts with the increasing use of INIs in low- and middle-income countries, where non-B subtypes predominate. HIV-1 drug resistance genotyping was performed in 30 HIV-1-infected individuals undergoing virological failure to raltegravir. Drug resistance mutations (DRMs) and HIV-1 subtype were characterized using Stanford HIVdb and phylogenetic analyses. Of the 30 integrase (IN) sequences, 14 were characterized as subtype F (47%), 8 as subtype B (27%), 7 as BF recombinants (23%) and 1 as a putative CRF05_DF (3%). In 25 cases (83%), protease and reverse transcriptase (PR-RT) sequences from the same individuals confirmed the presence of different BF recombinants. Stanford HIVdb genotyping was concordant with phylogenetic inference in 70% of IN and 60% of PR-RT sequences. INI DRMs differed between B and F IN subtypes, with Q148K/R/H, G140S and E138K/A being more prevalent in subtype B (63% versus 0%, P = 0.0021; 50% versus 0%, P = 0.0096; and 50% versus 0%, P = 0.0096, respectively). These differences were independent of the time on raltegravir therapy or viral load at the time of genotyping. INI DRMs in subtype F IN genomes predicted a lower level of resistance to raltegravir and no cross-resistance to second-generation INIs. Alternative resistance pathways to raltegravir develop in subtypes B and F IN genomes, with implications for clinical practice. Evaluating the role of HIV-1 subtype in development and persistence of mutations that confer resistance to INIs will be important to improve algorithms for resistance testing and optimize the use of INIs.

PurposeHIV drug resistance is increasing globally, especially in resource-limited settings. NNRTIs, commonly used as first-line ART, have a low genetic barrier and are prone to resistance mutations. Lifelong ART contributes to the accumulation of drug resistance mutations (DRMs), compromising treatment efficacy. Genotypic resistance testing is essential before initiating or modifying ART. However, the Sanger sequencing method relies on successful PCR amplification, which is often suboptimal in low viral load (VL) samples, limiting sensitivity and coverage.Patients and MethodsWe developed an optimized primer system targeting conserved regions in the protease (PR), reverse transcriptase (RT), and integrase (IN) genes, covering PR aa 1–99, RT aa 1–410 (including NNRTI resistance sites Y318 and N348), and IN aa 1–288. A total of 2,071 HIV-positive plasma samples collected in China (Jan 2023–Dec 2024) were analyzed using a PCR-Sanger sequencing method. Subtyping was performed using BLAST, COMET 2.4, and the HIV-1 Gene Sequences Database (China). Amplification success rates and mutation detection were evaluated across VL levels.ResultsThe overall amplification success rates were 87.40% (1,810/2,071) for the PR/RT region and 87.06% (1,803/2,071) for the IN region. In samples with VLs of 50–200 copies/mL, the success rates remained above 80% for PR/RT and 78.10% for IN. For samples with VL ≥1000 copies/mL, both regions achieved amplification rates above 99%. Among eight samples harboring Y318 or N348 mutations, all were successfully amplified at 1000, 400, 200, and 100 copies/mL. Three of them consistently yielded detectable mutations across all gradients. Subtyping revealed CRF01_AE and CRF07_BC as the predominant strains, consistent with national epidemiological trends.ConclusionThe optimized system improves amplification sensitivity and mutation coverage, especially in low-VL samples. It enables stable detection of key NNRTI resistance mutations and shows strong subtype compatibility, supporting its utility in clinical resistance surveillance and early detection.

Preoperative prognostication of short-term postoperative mortality in patients with spinal metastatic disease can improve shared decision making around end-of-life care. To (1) develop machine learning algorithms for prediction of short-term mortality and (2) deploy these models in an open access web application. The American College of Surgeons, National Surgical Quality Improvement Program was used to identify patients that underwent operative intervention for metastatic disease. Four machine learning algorithms were developed, and the algorithm with the best performance across discrimination, calibration, and overall performance was integrated into an open access web application. The 30-d mortality for the 1790 patients undergoing surgery for spinal metastatic disease was 8.49%. Preoperative factors used for prognostication were albumin, functional status, white blood cell count, hematocrit, alkaline phosphatase, spinal location (cervical, thoracic, lumbosacral), and severity of comorbid systemic disease (American Society of Anesthesiologist Class). In this population, machine learning algorithms developed to predict 30-d mortality performed well on discrimination (c-statistic), calibration (assessed by calibration slope and intercept), Brier score, and decision analysis. An open access web application was developed for the best performing model and this web application can be found here: https://sorg-apps.shinyapps.io/spinemets/. Machine learning algorithms are promising for prediction of postoperative outcomes in spinal oncology and these algorithms can be integrated into clinically useful decision tools. As the volume of data in oncology continues to grow, creation of learning systems and deployment of these systems as accessible tools may significantly enhance prognostication and management.

The HIV-1 integrase viral protein is responsible for incorporating the viral DNA into the genomic DNA. The inhibition of viral integration into host cell DNA is part of recent therapeutic procedures. Combination therapy with protease and reverse transcriptase inhibitors has demonstrated good synergistic results in reducing viral replication. The purpose of this study is to assess the occurrence of integrase drug resistance mutations from the period comprising 2013 through 2018 in Puerto Rico (PR). We analyzed 131 nucleotide sequences available in our HIV genotyping database, and we performed drug resistance mutation analyses using the Stanford HIV Drug Resistance Database. Twenty-one sequences (16.03%) harbored major or resistance-associated mutations. We identified the Q148HKR, G140S, Y143R, N155H, S147G, and E138EA major drug resistance mutations and the D232DN, T97TA, E157Q, G163GART accessory mutations. We detected high-level drug resistance to Elvitegravir and Raltegravir (76.19% and 85.71%). Moreover, we identified sequences harboring drug resistance mutations that could provide resistance to Dolutegravir. The transmission of strains with integrase antiretroviral resistance has been previously documented in treatment naïve patients. Given the increase of patients treated with integrase inhibitors, surveillance of drug resistance mutations is an essential aspect of PR’s clinical management of HIV infection.

Mutation N155H in HIV-2 integrase confers high phenotypic resistance to raltegravir and impairs replication capacity

The influenza A M2 channel in the viral envelope is a pH-regulated proton channel that is crucial for viral infection and replication. Amantadine and rimantadine are two M2 inhibitors that have been widely used as anti-influenza drugs. However, due to naturally occurring drug-resistant mutations, their inhibition ability has gradually decreased. These drug-resistant mutations are found at various positions on the transmembrane domain of the M2 protein and could be categorized to three types: mutations close to the drug-binding site located at the pore-facing positions (V27A, A30T, S31N, and G34E); mutations at the interhelical interfaces at the N-terminal half of the channel (L26F); and mutations outside the drug-binding site lying at the interhelical interfaces (L38F, D44A). Investigating the structures and the M2-inhibitor interactions of these mutants would illuminate drug inhibition and drug resistance mechanisms and guide the design of novel anti-influenza drugs targeting these drug-resistant mutants. In this study, we chose four mutations at different positions (V27A, S31N, L26F, L38F) and conducted molecular dynamics simulations on both the apo-form and the drug-bound forms. The protein structures as well as the water structure in the channel pore were analyzed. Stable water clusters facilitating drug binding were found. Both the protein pore radius profiles and the structure of the water clusters were sensitive to the mutations. Based on our simulations, we compared the structures of the mutated proteins and proposed possible mechanisms for drug resistance of these mutations.

Management of an inventory data within an agency requires an interactive management system to provide ease and informative data presentation. Data processing using an interactive system makes it easy for users to understand and interact with data, especially if the agency concerned is an agency that is in charge of long-term data storage, complex data processing, data collection and data distribution processes, controlling inventory availability, and data collection conducted every day. Data storage and conventional data processing has several weaknesses such as high error rate in data processing, the system is not interactive so that it troubles and impedes the inventory data collection process by users, and can produce data ambiguity that can cause misinformation. By using the inventory control model Just-In-Time (JIT) Inventory method that allows the compilation of data collection and inventory stock requests as needed so that it can easily distribute and minimize wasted stock, the processing, storage, and data collection of a particular data inventory can be controlled well and can provide clearer information. As a result, implementing inventory control methods into a web application can provide web applications that have better functionality in processing, storing, and collecting data.

tert-Butoxy-(4-phenyl-quinolin-3-yl)-acetic acids (tBPQA) are a new class of HIV-1 integrase (IN) inhibitors that are structurally distinct from IN strand transfer inhibitors but analogous to LEDGINs. LEDGINs are a class of potent antiviral compounds that interacts with the lens epithelium-derived growth factor (LEDGF) binding pocket on IN and were identified through competition binding against LEDGF. LEDGF tethers IN to the host chromatin and enables targeted integration of viral DNA. The prevailing understanding of the antiviral mechanism of LEDGINs is that they inhibit LEDGF binding to IN, which prevents targeted integration of HIV-1. We showed that in addition to the properties already known for LEDGINs, the binding of tBPQAs to the IN dimer interface inhibits IN enzymatic activity in a LEDGF-independent manner. Using the analysis of two long terminal repeat junctions in HIV-infected cells, we showed that the inhibition by tBPQAs occurs at or prior to the viral DNA 3'-processing step. Biochemical studies revealed that this inhibition operates by compound-induced conformational changes in the IN dimer that prevent proper assembly of IN onto viral DNA. For the first time, tBPQAs were demonstrated to be allosteric inhibitors of HIV-1 IN displaying a dual mode of action: inhibition of IN-viral DNA assembly and inhibition of IN-LEDGF interaction.

Over 125 million individuals worldwide suffer from psoriasis, a chronic inflammatory skin condition that is immune-mediated. It is often linked to systemic comorbidities such psoriatic arthritis, cardiovascular disease, and mental health conditions. It is characterised by erythematous plaques, itching, and scaling. Psoriasis is caused by a complex interaction of environmental factors, genetic predisposition, and dysregulated immune responses, which leads to chronic inflammation and keratinocyte hyperproliferation. This review examines the treatment potential of important protein targets linked to psoriasis. Transcriptional regulators that affect inflammatory cascades and immune cell behavior include ACKR2, NFKBIZ, and TNIP1. Proteins like PK2 and KPNA2 affect the growth and differentiation of keratinocytes, and kynurenine pathway enzymes like KYNU and IDO lead to immunological imbalance. Inflammatory mediators like CXCL10 and CYR61, as well as antimicrobial peptides like LL37 and S100A15, are significantly increased in psoriatic lesions and maintain the chronic inflammatory state. Using information from the Protein Data Bank (PDB), the review also looks at the structural biology of these proteins, providing insights into molecular interactions and drug-binding sites. The study highlights the importance of these proteins in promoting individualised psoriasis treatment by combining molecular targeting techniques with new biologic treatments. This review surveys molecular proteins implicated in psoriasis, focusing on their potential as biomarkers or therapeutic targets. While many, such as CXCL10 or S100A15, are broadly involved in inflammatory cascades, others like LL37, TNIP1, and RORγt exhibit roles more uniquely tied to psoriatic pathology and may underpin future personalized treatment approaches.

Unity in Variety in website aesthetics: A systematic inquiry

The PDBSite database provides comprehensive structural and functional information on various protein sites (post-translational modification, catalytic active, organic and inorganic ligand binding, protein–protein, protein–DNA and protein–RNA interactions) in the Protein Data Bank (PDB). The PDBSite is available online at http://wwwmgs.bionet.nsc.ru/mgs/gnw/pdbsite/. It consists of functional sites extracted from PDB using the SITE records and of an additional set containing the protein interaction sites inferred from the contact residues in heterocomplexes. The PDBSite was set up by automated processing of the PDB. The PDBSite database can be queried through the functional description and the structural characteristics of the site and its environment. The PDBSite is integrated with the PDBSiteScan tool allowing structural comparisons of a protein against the functional sites. The PDBSite enables the recognition of functional sites in protein tertiary structures, providing annotation of function through structure. The PDBSite is updated after each new PDB release.