Revolutionizing Peptide-Based Drug Discovery: Advances in the Post-AlphaFold Era.

Peptide-based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide-based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide-protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post-AF era, we are witnessing rapid progress with the potential to revolutionize peptide-based drug discovery such as the ability to rank peptide binders or classify them as binders/non-binders and the ability to design novel peptide sequences. However, AI-based methods are struggling due to the lack of well-curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics-based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD-based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics-based methods holds the promise of reshaping the landscape of peptide-based drug discovery.

A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries.

Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners but often include false positives. Furthermore, they provide no information about what the binding region is (e.g., the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competitive Binding Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment and the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies of AF and AF-CBA to help users identify scenarios where the approach will be most useful. Given the method's speed and accuracy, we anticipate its broad applicability to identify binding epitope regions among potential partners, setting the stage for experimental verification.

Sifting Through the Noise: A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries.

Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High-throughput experimental approaches narrow down possible binding partners, but often include false positives. Furthermore, they provide no information about what the binding region is (e.g. the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competition Assay (AF-CBA) to identify proteins that bind a target of interest from a pull-down experiment, along with the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide-protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies to AF and AF-CBA, to help users identify scenarios where the approach will be most useful. Given the speed and accuracy of the methodology, we expect it to be generally applicable to facilitate target selection for experimental verification starting from high-throughput protein libraries.

Structure and energetics guide dynamic behaviour in a T = 3 icosahedral virus capsid.

Although virus capsids appear as rigid, symmetric particles in experimentally determined structures; biochemical studies suggest a significant degree of structural flexibility in the particles. We carried out all-atom simulations on the icosahedral capsid of an insect virus, Flock House Virus, which show intriguing differences in the degree of flexibility of quasi-equivalent capsid subunits consistent with previously described biological behaviour. The flexibility of all the ß and γ subunits of the protein and RNA fragments is analysed and compared. Both γA subunit and RNA fragment exhibit higher flexibility than the γB and γC subunits. The capsid shell is permeable to the bidirectional movement of water molecules, and the movement is heavily influenced by the geometry of the capsid shell along specific symmetry axes. In comparison to the symmetry axes along I5 and I3, the I2 axis exhibits a slightly higher water content. This enriched water environment along I2 could play a pivotal role in facilitating the structural transitions necessary for RNA release, shedding some light on the intricate and dynamic processes underlying the viral life cycle. Our study suggests that the physical characterization of whole virus capsids is the key to identifying biologically relevant transition states in the virus life cycle and understanding the basis of virus infectivity.

FLAG based v/s Standard 3 + 7 induction therapy in treatment naïve Acute Myeloid Leukemia: Time to think "beyond anthracyclines".

Daunorubicin and Cytarabine (DA; 3 + 7) has been the standard frontline Acute Myeloid Leukemia (AML) induction regimen resulting in Complete Remission (CR) rates of 50-70%. It is associated with induction mortality of 15-30%. We report a comparative analysis of DA versus fludarabine, cytarabine, G-CSF (FLAG) + /- Venetoclax in resource constrained settings. We conducted a single center, retrospective analysis of 37 treatment naïve fit AML patients from May 2021 to December 2022 who received either standard DA regimen (Group 1) or FLAG + /- Venetoclax (Group 2). The median patient age was 36.6 years in DA arm (n = 18) as compared to 40.1 years in FLAG arm (n = 19). CR rates at day 28 were 55.5% in group 1 and 89.4% in group 2 (odds ratio [OR], 7.20; 95% confidence interval [CI], 1.274 -40.678; P = 0.012). Patients in FLAG based therapy arm had shorter duration of neutropenia (P = 0.003), fewer episodes of grade 3 febrile neutropenia (P = 0.0228), shorter duration of antibiotic therapy (P = 0.03), lesser need of 3rd line antibiotic therapy (P = 0.0228). Mortality rates were 16.6% (n = 3) in (group 1) and 0% (n = 0) in (group 2) (p = 0.105). Our analysis supports that FLAG based induction regimen is an effective and well-tolerated therapy in treatment naïve fit AML patients.

Towards More Predictive, Physiological and Animal-free In Vitro Models: Advances in Cell and Tissue Culture 2020 Conference Proceedings.

Experimental systems that faithfully replicate human physiology at cellular, tissue and organ level are crucial to the development of efficacious and safe therapies with high success rates and low cost. The development of such systems is challenging and requires skills, expertise and inputs from a diverse range of experts, such as biologists, physicists, engineers, clinicians and regulatory bodies. Kirkstall Limited, a biotechnology company based in York, UK, organised the annual conference, Advances in Cell and Tissue Culture (ACTC), which brought together people having a variety of expertise and interests, to present and discuss the latest developments in the field of cell and tissue culture and in vitro modelling. The conference has also been influential in engaging animal welfare organisations in the promotion of research, collaborative projects and funding opportunities. This report describes the proceedings of the latest ACTC conference, which was held virtually on 30th September and 1st October 2020, and included sessions on in vitro models in the following areas: advanced skin and respiratory models, neurological disease, cancer research, advanced models including 3-D, fluid flow and co-cultures, diabetes and other age-related disorders, and animal-free research. The roundtable session on the second day was very interactive and drew huge interest, with intriguing discussion taking place among all participants on the theme of replacement of animal models of disease.

Mathematical modelling of fluid flow and solute transport to define operating parameters for in vitro perfusion cell culture systems.

In recent years, there has been a move away from the use of static in vitro two-dimensional cell culture models for testing the chemical safety and efficacy of drugs. Such models are increasingly being replaced by more physiologically relevant cell culture systems featuring dynamic flow and/or three-dimensional structures of cells. While it is acknowledged that such systems provide a more realistic environment within which to test drugs, progress is being hindered by a lack of understanding of the physical and chemical environment that the cells are exposed to. Mathematical and computational modelling may be exploited in this regard to unravel the dependency of the cell response on spatio-temporal differences in chemical and mechanical cues, thereby assisting with the understanding and design of these systems. In this paper, we present a mathematical modelling framework that characterizes the fluid flow and solute transport in perfusion bioreactors featuring an inlet and an outlet. To demonstrate the utility of our model, we simulated the fluid dynamics and solute concentration profiles for a variety of different flow rates, inlet solute concentrations and cell types within a specific commercial bioreactor chamber. Our subsequent analysis has elucidated the basic relationship between inlet flow rate and cell surface flow speed, shear stress and solute concentrations, allowing us to derive simple but useful relationships that enable prediction of the behaviour of the system under a variety of experimental conditions, prior to experimentation. We describe how the model may used by experimentalists to define operating parameters for their particular perfusion cell culture systems and highlight some operating conditions that should be avoided. Finally, we critically comment on the limitations of mathematical and computational modelling in this field, and the challenges associated with the adoption of such methods.

The Utility of Dual Energy Computed Tomography in Musculoskeletal Imaging.

The objective of this article is to review the mechanisms, advantages and disadvantages of dual energy computed tomography (DECT) over conventional tomography (CT) in musculoskeletal imaging as DECT provides additional information about tissue composition and artifact reduction. This provides clinical utility in detection of urate crystals, bone marrow edema, reduction of beam hardening metallic artifact, and ligament and tendon analysis.

Rationale of Using Dynamic Imaging for Characterization of Suspicious Lung Masses into Benign or Malignant on Contrast Enhanced Multi Detector Computed Tomography.

OBJECTIVES: To assess the utility of dynamic imaging namely, wash-in and wash-out characteristics through multidetector contrast-enhanced computed tomography in differentiating benign and malignant pulmonary masses. MATERIALS AND METHODS: Seventy-three patients who were suspected to have malignant pulmonary mass on the basis of clinical symptoms and chest radiograph were included in the study. All the patients underwent multidetector computed tomography scanning, and three series of images were obtained for each patient-noncontrast, early enhanced, and 15 min delayed enhanced scans. Computed tomography (CT) findings were assessed in terms of washin, absolute, and relative percentage washout of contrast. Biopsy of the mass was done and sent for histopathological evaluation. Sensitivity, specificity, and area under curve for diagnosing malignancy in the lung masses were calculated by considering both the wash-in and wash-out characteristics at dynamic CT and plotting the receiver operating curve after the final diagnosis which was obtained by histopathological evaluation. RESULTS: Threshold net enhancement (washin) value of >22.5 HU had sensitivity, specificity, and diagnostic accuracy of 88.5%, 57.1%, and 82%, respectively, in predicting malignancy. Threshold relative percentage washout of <16.235% had 98.1%, 85.7%, and 94% sensitivity, specificity, and diagnostic accuracy, respectively, and threshold absolute percentage washout of <42.72% had 98.1%, 95.2%, and 95% sensitivity, specificity, and diagnostic accuracy, respectively, in predicting malignancy. CONCLUSION: Threshold net enhancement (washin), absolute and relative washout percentages can be used to predict malignancy with very high diagnostic yield, and possibly obviate the need of invasive procedures for diagnosis of bronchogenic carcinoma.

Imaging microscopic distribution of antifungal agents in dandruff treatments with stimulated Raman scattering microscopy.

Treatment of dandruff condition usually involves use of antidandruff shampoos containing antifungal agents. Different antifungal agents show variable clinical efficacy based on their cutaneous distribution and bioavailability. Using stimulated Raman scattering (SRS), we mapped the distribution of unlabeled low-molecular weight antifungal compounds zinc pyrithione (ZnPT) and climbazole (CBZ) on the surface of intact porcine skin with cellular precision. SRS has sufficient chemical selectivity and sensitivity to detect the agents on the skin surface based on their unique chemical motifs that do not occur naturally in biological tissues. Moreover, SRS is able to correlate the distribution of the agents with the morphological features of the skin using the CH 2 stretch mode, which is abundant in skin lipids. This is a significant strength of the technique since it allows the microscopic accumulation of the agents to be correlated with physiological features and their chemical environment without the use of counter stains. Our findings show that due to its lower solubility, ZnPT coats the surface of the skin with a sparse layer of crystals in the size range of 1 to 4 ?? ? m . This is consistent with the current understanding of the mode of action of ZnPT. In contrast, CBZ being more soluble and hydrophobic resulted in diffuse homogeneous distribution. It predominantly resided in microscopic lipid-rich crevasses and penetrated up to 60 ?? ? m into the infundibular spaces surrounding the hair shaft. The ability of the SRS to selectively map the distribution of agents on the skin's surface has the potential to provide insight into the mechanisms underpinning the topical application of antifungal or skin-active agents that could lead to the rational engineering of enhanced formulations.