The regulatory and effector functions of T cells are initiated by the binding of their cell-surface T cell receptor (TCR) to peptides presented by major histocompatibility complex (MHC) proteins on ...other cells. The specificity of TCR:peptide-MHC interactions, thus, underlies nearly all adaptive immune responses. Despite intense interest, generalizable predictive models of TCR:peptide-MHC specificity remain out of reach; two key barriers are the diversity of TCR recognition modes and the paucity of training data. Inspired by recent breakthroughs in protein structure prediction achieved by deep neural networks, we evaluated structural modeling as a potential avenue for prediction of TCR epitope specificity. We show that a specialized version of the neural network predictor AlphaFold can generate models of TCR:peptide-MHC interactions that can be used to discriminate correct from incorrect peptide epitopes with substantial accuracy. Although much work remains to be done for these predictions to have widespread practical utility, we are optimistic that deep learning-based structural modeling represents a path to generalizable prediction of TCR:peptide-MHC interaction specificity.
T-cell receptors (TCRs) encode clinically valuable information that reflects prior antigen exposure and potential future response. However, despite advances in deep repertoire sequencing, enormous ...TCR diversity complicates the use of TCR clonotypes as clinical biomarkers. We propose a new framework that leverages experimentally inferred antigen-associated TCRs to form meta-clonotypes - groups of biochemically similar TCRs - that can be used to robustly quantify functionally similar TCRs in bulk repertoires across individuals. We apply the framework to TCR data from COVID-19 patients, generating 1831 public TCR meta-clonotypes from the SARS-CoV-2 antigen-associated TCRs that have strong evidence of restriction to patients with a specific human leukocyte antigen (HLA) genotype. Applied to independent cohorts, meta-clonotypes targeting these specific epitopes were more frequently detected in bulk repertoires compared to exact amino acid matches, and 59.7% (1093/1831) were more abundant among COVID-19 patients that expressed the putative restricting HLA allele (false discovery rate FDR<0.01), demonstrating the potential utility of meta-clonotypes as antigen-specific features for biomarker development. To enable further applications, we developed an open-source software package,
that implements this framework and facilitates flexible workflows for distance-based TCR repertoire analysis.
T cells rely on their T cell receptors (TCRs) to discern foreign antigens presented by human leukocyte antigen (HLA) proteins. The TCRs of an individual contain a record of this individual's past ...immune activities, such as immune response to infections or vaccines. Mining the TCR data may recover useful information or biomarkers for immune related diseases or conditions. Some TCRs are observed only in the individuals with certain HLA alleles, and thus characterizing TCRs requires a thorough understanding of TCR-HLA associations. The extensive diversity of HLA alleles and the rareness of some HLA alleles present a formidable challenge for this task. Existing methods either treat HLA as a categorical variable or represent an HLA by its alphanumeric name, and have limited ability to generalize to the HLAs that are not seen in the training process. To address this challenge, we propose a neural network-based method named Deep learning Prediction of TCR-HLA association (DePTH) to predict TCR-HLA associations based on their amino acid sequences. We demonstrate that DePTH is capable of making reasonable predictions for TCR-HLA associations, even when neither the HLA nor the TCR have been included in the training dataset. Furthermore, we establish that DePTH can be used to quantify the functional similarities among HLA alleles, and that these HLA similarities are associated with the survival outcomes of cancer patients who received immune checkpoint blockade treatments.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Most biomolecular modeling energy functions for structure prediction, sequence design, and molecular docking have been parametrized using existing macromolecular structural data; this contrasts ...molecular mechanics force fields which are largely optimized using small-molecule data. In this study, we describe an integrated method that enables optimization of a biomolecular modeling energy function simultaneously against small-molecule thermodynamic data and high-resolution macromolecular structural data. We use this approach to develop a next-generation Rosetta energy function that utilizes a new anisotropic implicit solvation model, and an improved electrostatics and Lennard-Jones model, illustrating how energy functions can be considerably improved in their ability to describe large-scale energy landscapes by incorporating both small-molecule and macromolecule data. The energy function improves performance in a wide range of protein structure prediction challenges, including monomeric structure prediction, protein–protein and protein–ligand docking, protein sequence design, and prediction of the free energy changes by mutation, while reasonably recapitulating small-molecule thermodynamic properties.
The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest ...and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem - designing an amino acid sequence that will fold into a specified three-dimensional structure - has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and the rapid growth in protein sequence and structure databases have fuelled the development of new data-intensive and computationally demanding approaches for structure prediction. New algorithms for designing protein folds and protein-protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties, as well as signalling proteins with therapeutic potential. In this Review, we describe current approaches for protein structure prediction and design and highlight a selection of the successful applications they have enabled.
Over the past decade, the Rosetta biomolecular modeling suite has informed diverse biological questions and engineering challenges ranging from interpretation of low-resolution structural data to ...design of nanomaterials, protein therapeutics, and vaccines. Central to Rosetta’s success is the energy function: a model parametrized from small-molecule and X-ray crystal structure data used to approximate the energy associated with each biomolecule conformation. This paper describes the mathematical models and physical concepts that underlie the latest Rosetta energy function, called the Rosetta Energy Function 2015 (REF15). Applying these concepts, we explain how to use Rosetta energies to identify and analyze the features of biomolecular models. Finally, we discuss the latest advances in the energy function that extend its capabilities from soluble proteins to also include membrane proteins, peptides containing noncanonical amino acids, small molecules, carbohydrates, nucleic acids, and other macromolecules.
DNA recognition by TAL effectors is mediated by tandem repeats, each 33 to 35 residues in length, that specify nucleotides via unique repeat-variable diresidues (RVDs). The crystal structure of ...PthXo1 bound to its DNA target was determined by high-throughput computational structure prediction and validated by heavy-atom derivatization. Each repeat forms a left-handed, two-helix bundle that presents an RVD-containing loop to the DNA. The repeats self-associate to form a right-handed superhelix wrapped around the DNA major groove. The first RVD residue forms a stabilizing contact with the protein backbone, while the second makes a base-specific contact to the DNA sense strand. Two degenerate amino-terminal repeats also interact with the DNA. Containing several RVDs and noncanonical associations, the structure illustrates the basis of TAL effector-DNA recognition.
Making the Forever War Mark Philip Bradley, Mary L. Dudziak / Mark Philip Bradley, Mary L. Dudziak
06/2021
eBook
The late historian Marilyn B. Young, a preeminent voice on the
history of U.S. military conflict, spent her career reassessing the
nature of American global power, its influence on domestic culture
...and politics, and the consequences felt by those on the receiving
end of U.S. military force. At the center of her inquiries was a
seeming paradox: How can the United States stay continually at war,
yet Americans pay so little attention to this militarism?
Making the Forever War brings Young's articles and essays
on American war together for the first time, including never before
published works. Moving from the first years of the Cold War to
Korea, Vietnam, and more recent "forever" wars in Iraq and
Afghanistan, Young reveals the ways in which war became
ever-present, yet more covert and abstract, particularly as aerial
bombings and faceless drone strikes have attained greater strategic
value. For Young, U.S. empire persisted because of, not despite,
the inattention of most Americans. The collection concludes with an
afterword by prominent military historian Andrew Bacevich.
► We review structure-based approaches for protein–DNA binding specificity prediction. ► Interface flexibility is necessary for accurate template-based predictions. ► Assessments on native complexes ...are likely biased toward conservative approaches. ► Applications to structure determination and protein design are discussed.
An accurate, predictive understanding of protein–DNA binding specificity is crucial for the successful design and engineering of novel protein–DNA binding complexes. In this review, we summarize recent studies that use atomistic representations of interfaces to predict protein–DNA binding specificity computationally. Although methods with limited structural flexibility have proven successful at recapitulating consensus binding sequences from wild-type complex structures, conformational flexibility is likely important for design and template-based modeling, where non-native conformations need to be sampled and accurately scored. A successful application of such computational modeling techniques in the construction of the TAL–DNA complex structure is discussed. With continued improvements in energy functions, solvation models, and conformational sampling, we are optimistic that reliable and large-scale protein–DNA binding prediction and engineering is a goal within reach.
The prediction of protein structure from amino acid sequence is a grand challenge of computational molecular biology. By using a combination of improved low- and high-resolution conformational ...sampling methods, improved atomically detailed potential functions that capture the jigsaw puzzle-like packing of protein cores, and high-performance computing, high-resolution structure prediction (<1.5 angstroms) can be achieved for small protein domains (<85 residues). The primary bottleneck to consistent high-resolution prediction appears to be conformational sampling.
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