PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences

The last few years have seen the development of numerous deep learning-based protein-ligand docking methods. They offer huge promise in terms of speed and accuracy. However, despite claims of state-of-the-art performance in terms of crystallographic root-mean-square deviation (RMSD), upon closer inspection, it has become apparent that they often produce physically implausible molecular structures. It is therefore not sufficient to evaluate these methods solely by RMSD to a native binding mode. It is vital, particularly for deep learning-based methods, that they are also evaluated on steric and energetic criteria. We present PoseBusters, a Python package that performs a series of standard quality checks using the well-established cheminformatics toolkit RDKit. Only methods that both pass these checks and predict native-like binding modes should be classed as having "state-of-the-art" performance. We use PoseBusters to compare five deep learning-based docking methods (DeepDock, DiffDock, EquiBind, TankBind, and Uni-Mol) and two well-established standard docking methods (AutoDock Vina and CCDC Gold) with and without an additional post-prediction energy minimisation step using a molecular mechanics force field. We show that both in terms of physical plausibility and the ability to generalise to examples that are distinct from the training data, no deep learning-based method yet outperforms classical docking tools. In addition, we find that molecular mechanics force fields contain docking-relevant physics missing from deep-learning methods. PoseBusters allows practitioners to assess docking and molecular generation methods and may inspire new inductive biases still required to improve deep learning-based methods, which will help drive the development of more accurate and more realistic predictions.Comment: 10 pages, 6 figures, version 2 added an additional filter to the PoseBusters Benchmark set to remove ligands with crystal contacts, version 3 corrected the description of the binding site used for Uni-Mo

PoseBusters: AI-based docking methods fail to generate physically valid poses or generalise to novel sequences

The last few years have seen the development of numerous deep learning-based protein-ligand docking methods. They offer huge promise in terms of speed and accuracy. However, despite claims of state-of-the-art performance in terms of crystallographic root-mean-square deviation (RMSD), upon closer inspection, it has become apparent that they often produce physically implausible molecular structures. It is therefore not sufficient to evaluate these methods solely by RMSD to a native binding mode. It is vital, particularly for deep learning-based methods, that they are also evaluated on steric and energetic criteria. We present PoseBusters, a Python package that performs a series of standard quality checks using the well-established cheminformatics toolkit RDKit. The PoseBusters test suite validates chemical and geometric consistency of a ligand including its stereochemistry, and the physical plausibility of intra- and intermolecular measurements such as the planarity of aromatic rings, standard bond lengths, and protein-ligand clashes. Only methods that both pass these checks and predict native-like binding modes should be classed as having "state-of-the-art" performance. We use PoseBusters to compare five deep learning-based docking methods (DeepDock, DiffDock, EquiBind, TankBind, and Uni-Mol) and two well-established standard docking methods (AutoDock Vina and CCDC Gold) with and without an additional post-prediction energy minimisation step using a molecular mechanics force field. We show that both in terms of physical plausibility and the ability to generalise to examples that are distinct from the training data, no deep learning-based method yet outperforms classical docking tools. In addition, we find that molecular mechanics force fields contain docking-relevant physics missing from deep-learning methods. PoseBusters allows practitioners to assess docking and molecular generation methods and may inspire new inductive biases still required to improve deep learning-based methods, which will help drive the development of more accurate and more realistic predictions

Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures

Protein-ligand binding site prediction is a useful tool for understanding the functional behaviour and potential drug-target interactions of a novel protein of interest. However, most binding site prediction methods are tested by providing crystallised ligand-bound (holo) structures as input. This testing regime is insufficient to understand the performance on novel protein targets where experimental structures are not available. An alternative option is to provide computationally predicted protein structures, but this is not commonly tested. However, due to the training data used, computationally-predicted protein structures tend to be extremely accurate, and are often biased toward a holo conformation. In this study we describe and benchmark IF-SitePred, a protein-ligand binding site prediction method which is based on the labelling of ESM-IF1 protein language model embeddings combined with point cloud annotation and clustering. We show that not only is IF-SitePred competitive with state-of-the-art methods when predicting binding sites on experimental structures, but it performs better on proxies for novel proteins where low accuracy has been simulated by molecular dynamics. Finally, IF-SitePred outperforms other methods if ensembles of predicted protein structures are generated

Choledochal cyst as a diagnostic pitfall: a case report

<p>Abstract</p> <p>Introduction</p> <p>Choledochal cysts are rare congenital anomalies. Their diagnosis is difficult, particulary in adults.</p> <p>Case presentation</p> <p>This case report demonstrates the diagnostic and therapeutic pitfalls.</p> <p>Conclusion</p> <p>To prevent cost-intensive and potentially life-threating complications, a choledochal cyst must be considered in the differential diagnosis whenever the rather common diagnosis of a hepatic cyst is considered.</p

Recurrent Hepatic Alveolar Echinococcosis: Report of The First Case in Korea with Unproven Infection Route

Human alveolar echinococcosis (AE), a hepatic disorder that resembles liver cancer, is a highly aggressive and lethal zoonotic infection caused by the larval stage of the fox tapeworm, Echinococcus multilocularis. E. multilocularis is widely distributed in the northern hemisphere; the disease-endemic area stretches from north America through Europe to central and east Asia, including northern parts of Japan, but it has not been reported in Korea. Herein, we represent a first case of AE in Korea. A 41-year-old woman was found to have a large liver mass on routine medical examination. The excised mass showed multinodular, necrotic, and spongiform appearance with small irregular pseudocystic spaces. Microscopically, the mass was composed of chronic granulomatous inflammation with extensive coagulation necrosis and parasite-like structure, which was revealed as parasitic vesicles and laminated layer delineated by periodic acid-Schiff (PAS) stain. Clinical and histologic features were consistent with AE. After 8 years, a new liver mass and multiple metastatic pulmonary nodules were found and the recurred mass showed similar histologic features to the initial mass. She had never visited endemic areas of AE, and thus the exact infection route is unclear

Bacterial Infection of an Alveolar Echinococcus Cyst from C. perfringens Septicemia: A Case Report and Review of the Literature

Background and Objectives: Alveolar echinococcosis (AE) is a highly variable disease able to present as structurally diverse cysts in different organs based on the host’s immunological state as well as the time between diagnosis and the primary infection. Bacterial superinfections, especially with anaerobic pathogens from the Clostridiaceae genus, can further alter the radiological findings due to pneumobilia, newly formed abscess formations, and inflammatory changes. Materials and Methods: We present a case of a 71-year-old Caucasian male admitted to our intensive care unit with septic shock, pneumobilia, and a complex cyst of the liver with calcification, as shown by an initial CT. Because of the septic shock, the patient was started on broad-band antibiotics. Clostridiaceae infection was considered an important differential diagnosis due to the presence of pneumobilia observed in the initial CT, without a history of previous endoscopy. Furthermore, serology for echinococcus was positive, and blood cultures showed growth of C. perfringens. Therefore, the patient was additionally treated with albendazole. After recovery, further staging was conducted, showing complete remission of the cyst and a left-over lesion classified as Alveolar Echinococcosis Ulm Classification (AEUC) V. In summary, the patient had a pre-existing, controlled AE infection that became superinfected with C. perfringens, likely attributable to the anaerobic necrotic tissue, leading to septicemia. Results: The anaerobic tissue within the AE cyst provided an ideal medium for C. perfringens to replicate, leading to cyst infection, which subsequently caused septic shock and pneumobilia. The initial findings from CT and MRI were confounded by the superinfection, demonstrating the diagnostic challenges of AE, especially when presenting with complications. Conclusions: Diagnosing AE remains a demanding task, even with the excellent tools available through serology, coupled with CT, FDG-PET-CT, and MRI. Notably, older superinfected cysts can pose difficulties when integrated into the appropriate diagnostic context. Prompt diagnosis is critical for the accurate treatment of echinococcosis and its complications, such as bacterial superinfections. From a clinical perspective, septicemia from Clostridiaceae and infections with C. perfringens—pathogens capable of inducing pneumobilia—should be regarded as significant differential diagnoses for pneumobilia in the absence of a recent history of endoscopy

Interprofessional Therapeutic Drug Monitoring of Carbapenems Improves ICU Care and Guideline Adherence in Acute-on-Chronic Liver Failure

(1) Background: Acute-on-chronic liver failure (ACLF) is a severe, rapidly progressing disease in patients with liver cirrhosis. Meropenem is crucial for treating severe infections. Therapeutic drug monitoring (TDM) offers an effective means to control drug dosages, especially vital for bactericidal antibiotics like meropenem. We aimed to assess the outcomes of implementing TDM for meropenem using an innovative interprofessional approach in ACLF patients on a medical intensive care unit (ICU). (2) Methods: The retrospective study was conducted on a medical ICU. The outcomes of an interprofessional approach comprising physicians, hospital pharmacists, and staff nurses to TDM for meropenem in critically ill patients with ACLF were examined in 25 patients. Meropenem was administered continuously via an infusion pump after the application of an initial loading dose. TDM was performed weekly using high-performance liquid chromatography (HPLC). Meropenem serum levels, implementation of the recommendations of the interprofessional team, and meropenem consumption were analyzed. (3) Results: Initial TDM for meropenem showed a mean meropenem serum concentration of 20.9 � 9.6 mg/L in the 25 analyzed patients. Of note, in the initial TDM, only 16.0% of the patients had meropenem serum concentrations within the respective target range, while 84.0% exceeded this range. Follow-up TDM showed serum concentrations of 15.2 � 5.7 mg/L (9.0–24.6) in Week 2 and 11.9 � 2.3 mg/L (10.2–13.5) in Week 3. In Week 2, 41.7% of the patients had meropenem serum concentrations that were within the respective target range, while 58.3% of the patients were above this range. In Week 3, 50% of the analyzed serum concentrations of meropenem were within the targeted range, and 50% were above the range. In total, 100% of the advice given by the interprofessional team regarding meropenem dosing or a change in antibiotic therapy was implemented. During the intervention period, the meropenem application density was 37.9 recommended daily doses (RDD)/100 patient days (PD), compared to 42.1 RDD/100 PD in the control period, representing a 10.0% decrease. (4) Conclusions: Our interprofessional approach to TDM significantly reduced meropenem dosing, with all the team’s recommendations being implemented. This method not only improved patient safety but also considerably decreased the application density of meropenem

AWAKE: A Proton-Driven Plasma Wakefield Acceleration Experiment at CERN

The AWAKE Collaboration has been formed in order to demonstrate proton-driven plasma wakefield acceleration for the first time. This acceleration technique could lead to future colliders of high energy but of a much reduced length when compared to proposed linear accelerators. The CERN SPS proton beam in the CNGS facility will be injected into a 10 m plasma cell where the long proton bunches will be modulated into significantly shorter micro-bunches. These micro-bunches will then initiate a strong wakefield in the plasma with peak fields above 1 GV/m that will be harnessed to accelerate a bunch of electrons from about 20 MeV to the GeV scale within a few meters. The experimental program is based on detailed numerical simulations of beam and plasma interactions. The main accelerator components, the experimental area and infrastructure required as well as the plasma cell and the diagnostic equipment are discussed in detail. First protons to the experiment are expected at the end of 2016 and this will be followed by an initial three-four years experimental program. The experiment will inform future larger-scale tests of proton-driven plasma wakefield acceleration and applications to high energy colliders