On Evaluating MHC-II Binding Peptide Prediction Methods

Choice of one method over another for MHC-II binding peptide prediction is typically based on published reports of their estimated performance on standard benchmark datasets. We show that several standard benchmark datasets of unique peptides used in such studies contain a substantial number of peptides that share a high degree of sequence identity with one or more other peptide sequences in the same dataset. Thus, in a standard cross-validation setup, the test set and the training set are likely to contain sequences that share a high degree of sequence identity with each other, leading to overly optimistic estimates of performance. Hence, to more rigorously assess the relative performance of different prediction methods, we explore the use of similarity-reduced datasets. We introduce three similarity-reduced MHC-II benchmark datasets derived from MHCPEP, MHCBN, and IEDB databases. The results of our comparison of the performance of three MHC-II binding peptide prediction methods estimated using datasets of unique peptides with that obtained using their similarity-reduced counterparts shows that the former can be rather optimistic relative to the performance of the same methods on similarity-reduced counterparts of the same datasets. Furthermore, our results demonstrate that conclusions regarding the superiority of one method over another drawn on the basis of performance estimates obtained using commonly used datasets of unique peptides are often contradicted by the observed performance of the methods on the similarity-reduced versions of the same datasets. These results underscore the importance of using similarity-reduced datasets in rigorously comparing the performance of alternative MHC-II peptide prediction methods

Engineering T cells for cancer therapy

It is generally accepted that the immune system plays an important role in controlling tumour development. However, the interplay between tumour and immune system is complex, as demonstrated by the fact that tumours can successfully establish and develop despite the presence of T cells in tumour. An improved understanding of how tumours evade T-cell surveillance, coupled with technical developments allowing the culture and manipulation of T cells, has driven the exploration of therapeutic strategies based on the adoptive transfer of tumour-specific T cells. The isolation, expansion and re-infusion of large numbers of tumour-specific T cells generated from tumour biopsies has been shown to be feasible. Indeed, impressive clinical responses have been documented in melanoma patients treated with these T cells. These studies and others demonstrate the potential of T cells for the adoptive therapy of cancer. However, the significant technical issues relating to the production of natural tumour-specific T cells suggest that the application of this approach is likely to be limited at the moment. With the advent of retroviral gene transfer technology, it has become possible to efficiently endow T cells with antigen-specific receptors. Using this strategy, it is potentially possible to generate large numbers of tumour reactive T cells rapidly. This review summarises the current gene therapy approaches in relation to the development of adoptive T-cell-based cancer treatments, as these methods now head towards testing in the clinical trial setting

Triangle network motifs predict complexes by complementing high-error interactomes with structural information

BackgroundA lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.ResultsWe find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes.ConclusionGiven high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN

Soft imprinting of microstructured micro-prisms on optical fibre facets

Soft-imprint lithography is used to integrate optical gratings and polymer micro-prisms onto the tips of optical fibres. Light deflection and diffraction is demonstrated

Replication of 3-D micro-structures on optical fibre facets using soft-imprint lithography

We demonstrate the realisation and operation of optical gratings and micro-prisms integrated onto the tips of optical fibres using soft-imprint lithography

Surface Treatments Of Natural Fibres In Fibre Reinforced Composites: A Review

The use of natural fibres in fibre-reinforced composites comes with drawbacks. They are highly hydrophilic, leading to high moisture absorption and poor interfacial adhesion in matrix-reinforcement bonds. This affects the fibres’ thermal stability as well as mechanical properties, hence limiting their wider application. This paper reviewed different ways in which natural fibres have been treated to improve hydrophobicity, reinforcement-matrix interfacial adhesion and thermal stability. It will investigate. among others, treatments like alkali, acetylation, bleaching, silane, benzoylation and plasma, which have been found to improve fibre hydrophobicity. The literature reviewed showed that these methods work to improve mechanical, chemical, and morphological properties of natural fibres by removing the amorphous surface, thus allowing for more efficient load transfer on the fibre-matrix surface. Studies in the literature found alkali treatment to be the most common surface modification treatment due to its simplicity and effectiveness. However, plasma treatment has emerged due to its lower processing time and chemical consumption. A comparative analysis of other improved properties was also investigated

Techniques for Modelling and Optimizing the Mechanical Properties of Natural Fiber Composites: A Review

The study of natural fiber-based composites through the use of computational techniques for modelling and optimizing their properties has emerged as a fast-growing approach in recent years. Ecological concerns associated with synthetic fibers have made the utilisation of natural fibers as a reinforcing material in composites a popular approach. Computational techniques have become an important tool in the hands of many researchers to model and analyze the characteristics that influence the mechanical properties of natural fiber composites. This recent trend has led to the development of many advanced computational techniques and software for a profound understanding of the characteristics and performance behavior of composite materials reinforced with natural fibers. The large variations in the characteristics of natural fiber-based composites present a great challenge, which has led to the development of many computational techniques for composite materials analysis. This review seeks to infer, from conventional to contemporary sources, the computational techniques used in modelling, analyzing, and optimizing the mechanical characteristics of natural fiber reinforced composite materials

Sub-15nm Optical Fiber Nanoimprint Lithography: A Parallel Self-aligned and Portable Approach

We demonstrate the parallel patterning of multiple optical-fiber facets using nanoimprint lithography on a novel platform. A resolution of better than 15 nm is demonstrated and up to 40 optical-fiber facets have been imprinted in parallel. The lithography platform features a self-alignment mechanism that greatly relaxes the mechanical requirements, allowing the demonstration of a compact, portable imprinting-module and the accommodation of non-planar, biological molds. The imprinted fibers are metalized and employed as bi-directional probes for surface-enhanced Raman scattering

Large area annodised aluminium templates for nano-Imprinted optical fibre probes

Anodized aluminium nanostructures are employed as templates for performing nanoimprint lithography on an optical fibre platform. The cleaved endfaces of optical fibres serve as host to the replicated polymer nanostructures, which are metalized to create fibre-optic surface-enhanced Raman scattering probes

Understanding Structural Variations in Elastic Organic Crystals by in Situ High-Pressure Fourier Transform Infrared Spectroscopy and Nanoindentation Study

Organic crystals possessing elasticity are gaining wide attention due to their potential applications in technology. From a design perspective, it is of utmost importance to understand the mechanical behavior of these crystals and their ability to handle stress. In this paper, we present an in situ high-pressure Fourier transform infrared spectroscopy study on 2,5-dichloro-N-benzylidene-4-chloroaniline (DPA) and 2,6 dichloro-N-benzylidene-4-fluoro-3-nitro aniline (DFA) crystals at pressures ranging from ambient pressure to 21.5 and 14.5 GPa respectively along with nanoindentation studies, at room temperature. The infrared stretching wavenumber of the aromatic and aliphatic C-H, H-C=N, and C-Cl bands on compression showed blueshifts and increased widths, which reflect structure perturbation caused by changes in intermolecular interactions in the crystals. It was noted that both crystals DPA and DFA behave in a different fashion under high-pressure prompting the derivation of different models based on structural changes in the lattice. Further, nanoindentation studies corroborated pressure-induced molecular movement in both crystals. © Copyright 2019 American Chemical Society