Advances In Structural Modeling Research Articles

Benchmarking antibody clustering methods using sequence, structural, and machine learning similarity measures for antibody discovery applications.

Antibodies are proteins produced by our immune system that have been harnessed as biotherapeutics. The discovery of antibody-based therapeutics relies on analyzing large volumes of diverse sequences coming from phage display or animal immunizations. Identification of suitable therapeutic candidates is achieved by grouping the sequences by their similarity and subsequent selection of a diverse set of antibodies for further tests. Such groupings are typically created using sequence-similarity measures alone. Maximizing diversity in selected candidates is crucial to reducing the number of tests of molecules with near-identical properties. With the advances in structural modeling and machine learning, antibodies can now be grouped across other diversity dimensions, such as predicted paratopes or three-dimensional structures. Here we benchmarked antibody grouping methods using clonotype, sequence, paratope prediction, structure prediction, and embedding information. The results were benchmarked on two tasks: binder detection and epitope mapping. We demonstrate that on binder detection no method appears to outperform the others, while on epitope mapping, clonotype, paratope, and embedding clusterings are top performers. Most importantly, all the methods propose orthogonal groupings, offering more diverse pools of candidates when using multiple methods than any single method alone. To facilitate exploring the diversity of antibodies using different methods, we have created an online tool-CLAP-available at (clap.naturalantibody.com) that allows users to group, contrast, and visualize antibodies using the different grouping methods.

Module-Based Polyketide Synthase Engineering for de Novo Polyketide Biosynthesis.

Polyketide retrobiosynthesis, where the biosynthetic pathway of a given polyketide can be reversibly engineered due to the colinearity of the polyketide synthase (PKS) structure and function, has the potential to produce millions of organic molecules. Mixing and matching modules from natural PKSs is one of the routes to produce many of these molecules. Evolutionary analysis of PKSs suggests that traditionally used module boundaries may not lead to the most productive hybrid PKSs and that new boundaries around and within the ketosynthase domain may be more active when constructing hybrid PKSs. As this is still a nascent area of research, the generality of these design principles based on existing engineering efforts remains inconclusive. Recent advances in structural modeling and synthetic biology present an opportunity to accelerate PKS engineering by re-evaluating insights gained from previous engineering efforts with cutting edge tools.

Current structure predictors are not learning the physics of protein folding.

Summary Motivation. Predicting the native state of a protein has long been considered a gateway problem for understanding protein folding. Recent advances in structural modeling driven by deep learning have achieved unprecedented success at predicting a protein’s crystal structure, but it is not clear if these models are learning the physics of how proteins dynamically fold into their equilibrium structure or are just accurate knowledge-based predictors of the final state. Results. In this work, we compare the pathways generated by state-of-the-art protein structure prediction methods to experimental data about protein folding pathways. The methods considered were AlphaFold 2, RoseTTAFold, trRosetta, RaptorX, DMPfold, EVfold, SAINT2 and Rosetta. We find evidence that their simulated dynamics capture some information about the folding pathway, but their predictive ability is worse than a trivial classifier using sequence-agnostic features like chain length. The folding trajectories produced are also uncorrelated with experimental observables such as intermediate structures and the folding rate constant. These results suggest that recent advances in structure prediction do not yet provide an enhanced understanding of protein folding. Availability. The data underlying this article are available in GitHub at https://github.com/oxpig/structure-vs-folding/Supplementary information Supplementary data are available at Bioinformatics online.

Human Autoinflammatory Diseases Mediated by NLRP3-, Pyrin-, NLRP1-, and NLRC4-Inflammasome Dysregulation Updates on Diagnosis, Treatment, and the Respective Roles of IL-1 and IL-18.

Recent research has led to novel findings in inflammasome biology and genetics that altered the diagnosis and management of patients with autoinflammatory syndromes caused by NLRP3-, Pyrin-, NLRP1-, and NLRC4-inflammasomes and spurred the development of novel treatments. The use of next-generation sequencing in clinical practice allows for rapid diagnosis and the detection of somatic mutations that cause autoinflammatory diseases. Clinical differences in patients with NLRP3, pyrin, and NLRP1 inflammasomopathies, and the constitutive elevation of unbound free serum IL-18 that predisposes to the development of macrophage activation syndrome (MAS) in patients with gain-of function mutations in NLRC4 led to the screening and the characterization of novel diseases presenting with constitutively elevated serum IL-18 levels, and start to unravel the biology of “high IL-18 states” that translate into the use of biomarkers that improve diagnosis and monitoring of disease activity and investigations of treatments that target IL-18 and IFN-gamma which promise to improve the management and outcome of these conditions. Lastly, advances in structural modeling by cryo-electron microscopy (cryo-EM) of gasdermin, and of NLRP3- and NLRC4-inflammasome assembly, and the characterization of post-translational modifications (PTM) that regulate inflammasome activation, coupled with high-throughput screening (HTS) of libraries of inflammasome-inhibiting compounds, promise a new generation of treatments for patients with inflammasome-mediated diseases.

Targeting telomerase for cancer therapy.

Telomere maintenance via telomerase reactivation is a nearly universal hallmark of cancer cells which enables replicative immortality. In contrast, telomerase activity is silenced in most adult somatic cells. Thus, telomerase represents an attractive target for highly selective cancer therapeutics. However, development of telomerase inhibitors has been challenging and thus far there are no clinically approved strategies exploiting this cancer target. The discovery of prevalent mutations in the TERT promoter region in many cancers and recent advances in telomerase biology has led to a renewed interest in targeting this enzyme. Here we discuss recent efforts targeting telomerase, including immunotherapies and direct telomerase inhibitors, as well as emerging approaches such as targeting TERT gene expression driven by TERT promoter mutations. We also address some of the challenges to telomerase-directed therapies including potential therapeutic resistance and considerations for future therapeutic applications and translation into the clinical setting. Although much work remains to be done, effective strategies targeting telomerase will have a transformative impact for cancer therapy and the prospect of clinically effective drugs is boosted by recent advances in structural models of human telomerase.