Toward Waveguide-Based Optical Chromatography

We report analytical expressions for optical forces acting on particles inside waveguides. The analysis builds on our previously reported Fourier Transform method to obtain Beam Shape Coefficients for any beam. Here we develop analytical expressions for the Beam Shape Coefficients in cylindrical and rectangular metallic waveguides. The theory is valid for particle radius a ranging from the Rayleigh regime to large microparticles, such as aerosols like virus loaded droplets. The theory is used to investigate how optical forces within hollow waveguides can be used to sort particles in “optical chromatography” experiments in which particles are optically propelled along a hollow-core waveguide. For Rayleigh particles, the axial force is found to scale with a6, while the radial force, which prevents particles from crashing into the waveguide walls, scales with a3. For microparticles, narrow Mie resonances create a strong wavelength dependence of the optical force, enabling more selective sorting. Several beam parameters, such as power, wavelength, polarization state and waveguide modes can be tuned to optimize the sorting performance. The analysis focuses on cylindrical waveguides, where meter-long liquid waveguides in the form of hollow-core photonic crystal fibers are readily available. The modes of such fibers are well-approximated by the cylindrical waveguide modes considered in the theory.</jats:p

Raman, hyper-Raman, hyper-Rayleigh, two-photon luminescence and morphology-dependent resonance modes in a single optical tweezers system

We present a setup of optical tweezers combined with linear and nonlinear microspectroscopies that enhances the capabilities of capture and analysis of both techniques. We can use either a continuous-wave (cw) Ti:sapphire laser for Raman measurements or a pulsed femtosecond Ti:sapphire laser that permitted the observation of nonlinear results such as hyper-Raman, hyper-Rayleigh, and two-photon luminescence. Only the high peak intensity of the femtosecond laser allows the observation of all these nonlinear spectroscopies. The sensitivity of our system also permitted the observation of morphology-dependent resonance (MDR) modes of a single stained trapped microsphere of 6 mu m. The possibility of performing spectroscopy in a living microorganism optically trapped in any desired neighborhood would mean that one can dynamically observe the chemical reactions and/or mechanical properties changing in real time.721

Identification of hot-spot residues in protein-protein interactions by computational docking

<p>Abstract</p> <p>Background</p> <p>The study of protein-protein interactions is becoming increasingly important for biotechnological and therapeutic reasons. We can define two major areas therein: the structural prediction of protein-protein binding mode, and the identification of the relevant residues for the interaction (so called 'hot-spots'). These hot-spot residues have high interest since they are considered one of the possible ways of disrupting a protein-protein interaction. Unfortunately, large-scale experimental measurement of residue contribution to the binding energy, based on alanine-scanning experiments, is costly and thus data is fairly limited. Recent computational approaches for hot-spot prediction have been reported, but they usually require the structure of the complex.</p> <p>Results</p> <p>We have applied here normalized interface propensity (<it>NIP</it>) values derived from rigid-body docking with electrostatics and desolvation scoring for the prediction of interaction hot-spots. This parameter identifies hot-spot residues on interacting proteins with predictive rates that are comparable to other existing methods (up to 80% positive predictive value), and the advantage of not requiring any prior structural knowledge of the complex.</p> <p>Conclusion</p> <p>The <it>NIP </it>values derived from rigid-body docking can reliably identify a number of hot-spot residues whose contribution to the interaction arises from electrostatics and desolvation effects. Our method can propose residues to guide experiments in complexes of biological or therapeutic interest, even in cases with no available 3D structure of the complex.</p

Design and Analysis of Rhesus Cytomegalovirus IL-10 Mutants as a Model for Novel Vaccines against Human Cytomegalovirus

Human cytomegalovirus (HCMV) expresses a viral ortholog (CMVIL-10) of human cellular interleukin-10 (cIL-10). Despite only ∼26% amino acid sequence identity, CMVIL-10 exhibits comparable immunosuppressive activity with cIL-10, attenuates HCMV antiviral immune responses, and contributes to lifelong persistence within infected hosts. The low sequence identity between CMVIL-10 and cIL-10 suggests vaccination with CMVIL-10 may generate antibodies that specifically neutralize CMVIL-10 biological activity, but not the cellular cytokine, cIL-10. However, immunization with functional CMVIL-10 might be detrimental to the host because of its immunosuppressive properties.Structural biology was used to engineer biologically inactive mutants of CMVIL-10 that would, upon vaccination, elicit a potent immune response to the wild-type viral cytokine. To test the designed proteins, the mutations were incorporated into the rhesus cytomegalovirus (RhCMV) ortholog of CMVIL-10 (RhCMVIL-10) and used to vaccinate RhCMV-infected rhesus macaques. Immunization with the inactive RhCMVIL-10 mutants stimulated antibodies against wild-type RhCMVIL-10 that neutralized its biological activity, but did not cross-react with rhesus cellular IL-10.This study demonstrates an immunization strategy to neutralize RhCMVIL-10 biological activity using non-functional RhCMVIL-10 antigens. The results provide the methodology for targeting CMVIL-10 in vaccine, and therapeutic strategies, to nullify HCMV's ability to (1) skew innate and adaptive immunity, (2) disseminate from the site of primary mucosal infection, and (3) establish a lifelong persistent infection

Chagas Cardiomiopathy: The Potential of Diastolic Dysfunction and Brain Natriuretic Peptide in the Early Identification of Cardiac Damage

Chagas disease remains a major cause of morbidity and mortality in several countries of Latin America and has become a potential public health problem in countries where the disease is not endemic as a result of migration flows. Cardiac involvement represents the main cause of mortality, but its diagnosis is still based on nonspecific criteria with poor sensitivity. Early identification of patients with cardiac damage is desirable, since early treatment may improve prognosis. Diastolic dysfunction and elevated brain natriuretic peptide levels are present in different cardiomyopathies and in advanced phases of Chagas disease. However, there are scarce data about the role of these parameters in earlier forms of the disease. We conducted a study to assess the diastolic function, regional systolic abnormalities and brain natriuretic peptide levels in the different forms of Chagas disease. The main finding of our investigation is that diastolic dysfunction occurs before any cardiac dilatation or motion abnormality. In addition, BNP levels identify patients with diastolic dysfunction and Chagas disease with high specificity. The results reported in this study could help to early diagnose myocardial involvement and better stratify patients with Chagas disease

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Background: Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual aminoacids are systematically mutated to alanine and changes in free energy of binding (Delta Delta G) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition.Results: We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which Delta Delta G >= 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%.Conclusion: We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to biomolecular problems, the results of our investigation indicate that there are substantial benefits to be gained by their integration

Protein binding hot spots and the residue-residue pairing preference: a water exclusion perspective

<p>Abstract</p> <p>Background</p> <p>A protein binding hot spot is a small cluster of residues tightly packed at the center of the interface between two interacting proteins. Though a hot spot constitutes a small fraction of the interface, it is vital to the stability of protein complexes. Recently, there are a series of hypotheses proposed to characterize binding hot spots, including the pioneering O-ring theory, the insightful 'coupling' and 'hot region' principle, and our 'double water exclusion' (DWE) hypothesis. As the perspective changes from the O-ring theory to the DWE hypothesis, we examine the physicochemical properties of the binding hot spots under the new hypothesis and compare with those under the O-ring theory.</p> <p>Results</p> <p>The requirements for a cluster of residues to form a hot spot under the DWE hypothesis can be mathematically satisfied by a biclique subgraph if a vertex is used to represent a residue, an edge to indicate a close distance between two residues, and a bipartite graph to represent a pair of interacting proteins. We term these hot spots as DWE bicliques. We identified DWE bicliques from crystal packing contacts, obligate and non-obligate interactions. Our comparative study revealed that there are abundant <it>unique </it>bicliques to the biological interactions, indicating specific biological binding behaviors in contrast to crystal packing. The two sub-types of biological interactions also have their own signature bicliques. In our analysis on residue compositions and residue pairing preferences in DWE bicliques, the focus was on interaction-preferred residues (ipRs) and interaction-preferred residue pairs (ipRPs). It is observed that hydrophobic residues are heavily involved in the ipRs and ipRPs of the obligate interactions; and that aromatic residues are in favor in the ipRs and ipRPs of the biological interactions, especially in those of the non-obligate interactions. In contrast, the ipRs and ipRPs in crystal packing are dominated by hydrophilic residues, and most of the anti-ipRs of crystal packing are the ipRs of the obligate or non-obligate interactions.</p> <p>Conclusions</p> <p>These ipRs and ipRPs in our DWE bicliques describe a diverse binding features among the three types of interactions. They also highlight the specific binding behaviors of the biological interactions, sharply differing from the artifact interfaces in the crystal packing. It can be noted that DWE bicliques, especially the unique bicliques, can capture deep insights into the binding characteristics of protein interfaces.</p

Prediction of binding hot spot residues by using structural and evolutionary parameters

In this work, we present a method for predicting hot spot residues by using a set of structural and evolutionary parameters. Unlike previous studies, we use a set of parameters which do not depend on the structure of the protein in complex, so that the predictor can also be used when the interface region is unknown. Despite the fact that no information concerning proteins in complex is used for prediction, the application of the method to a compiled dataset described in the literature achieved a performance of 60.4%, as measured by F-Measure, corresponding to a recall of 78.1% and a precision of 49.5%. This result is higher than those reported by previous studies using the same data set

Beauty Is in the Eye of the Beholder: Proteins Can Recognize Binding Sites of Homologous Proteins in More than One Way

Understanding the mechanisms of protein–protein interaction is a fundamental problem with many practical applications. The fact that different proteins can bind similar partners suggests that convergently evolved binding interfaces are reused in different complexes. A set of protein complexes composed of non-homologous domains interacting with homologous partners at equivalent binding sites was collected in 2006, offering an opportunity to investigate this point. We considered 433 pairs of protein–protein complexes from the ABAC database (AB and AC binary protein complexes sharing a homologous partner A) and analyzed the extent of physico-chemical similarity at the atomic and residue level at the protein–protein interface. Homologous partners of the complexes were superimposed using Multiprot, and similar atoms at the interface were quantified using a five class grouping scheme and a distance cut-off. We found that the number of interfacial atoms with similar properties is systematically lower in the non-homologous proteins than in the homologous ones. We assessed the significance of the similarity by bootstrapping the atomic properties at the interfaces. We found that the similarity of binding sites is very significant between homologous proteins, as expected, but generally insignificant between the non-homologous proteins that bind to homologous partners. Furthermore, evolutionarily conserved residues are not colocalized within the binding sites of non-homologous proteins. We could only identify a limited number of cases of structural mimicry at the interface, suggesting that this property is less generic than previously thought. Our results support the hypothesis that different proteins can interact with similar partners using alternate strategies, but do not support convergent evolution