Microscale mesoarrays created by dip-pen nanolithography for screening of protein-protein interactions

Using microarrays to probe protein-protein interactions is becoming increasingly attractive due to their compatibility with highly sensitive detection techniques, selectivity of interaction, robustness and capacity for examining multiple proteins simultaneously. The major drawback to using this approach is the relatively large volumes and high concentrations necessary. Reducing the protein array spot size should allow for smaller volumes and lower concentrations to be used as well as opening the way for combination with more sensitive detection technologies. Dip-Pen Nanolithography (DPN) is a recently developed technique for structure creation on the nano to microscale with the capacity to create biological architectures. Here we describe the creation of miniaturised microarrays, 'mesoarrays', using DPN with protein spots 400× smaller by area compared to conventional microarrays. The mesoarrays were then used to probe the ERK2-KSR binding event of the Ras/Raf/MEK/ERK signalling pathway at a physical scale below that previously reported. Whilst the overall assay efficiency was determined to be low, the mesoarrays could detect KSR binding to ERK2 repeatedly and with low non-specific binding. This study serves as a first step towards an approach that can be used for analysis of proteins at a concentration level comparable to that found in the cellular environment.

Protein-protein interactions in ovalbumin solutions studied by small angle scattering: effect of ionic strength, and the chemical nature of cations

The influence of ionic strength and of the chemical nature of cations on the protein-protein interactions in ovalbumin solution was studied using small-angle X-ray and neutron scattering (SAXS/SANS). The globular protein ovalbumin is found in dimeric form in solutions as suggested by SANS/SAXS experiments. Due to the negative charge of the proteins at neutral pH, the protein-protein interactions without any salt addition are dominated by electrostatic repulsion. A structure factor related to screened Coulombic interactions together with an ellipsoid form factor was used to fit the scattering intensity. A monovalent salt (NaCl) and a trivalent salt (YCl3) were used to study the effect of the chemical nature of cations on the interaction in protein solutions. Upon addition of NaCl, with ionic strength below that of physiological conditions (150 mM), the effective interactions are still dominated by the surface charge of the proteins and the scattering data can be understood using the same model. When yttrium chloride was used, a reentrant condensation behavior, i.e., aggregation and subsequent redissolution of proteins with increasing salt concentration, was observed. SAXS measurements reveal a transition from effective repulsion to attraction with increasing salt concentration. The solutions in the reentrant regime become unstable after long times (several days). The results are discussed and compared with those from bovine serum albumin (BSA) in solutions.

Extracting protein-protein interactions from MEDLINE using the hidden vector state model

A major challenge in text mining for biomedicine is automatically extracting protein-protein interactions from the vast amount of biomedical literature. We have constructed an information extraction system based on the Hidden Vector State (HVS) model for protein-protein interactions. The HVS model can be trained using only lightly annotated data whilst simultaneously retaining sufficient ability to capture the hierarchical structure. When applied in extracting protein-protein interactions, we found that it performed better than other established statistical methods and achieved 61.5% in F-score with balanced recall and precision values. Moreover, the statistical nature of the pure data-driven HVS model makes it intrinsically robust and it can be easily adapted to other domains.

Ontology-based protein-protein interactions extraction from literature using the hidden vector state model

This paper proposes a novel framework of incorporating protein-protein interactions (PPI) ontology knowledge into PPI extraction from biomedical literature in order to address the emerging challenges of deep natural language understanding. It is built upon the existing work on relation extraction using the Hidden Vector State (HVS) model. The HVS model belongs to the category of statistical learning methods. It can be trained directly from un-annotated data in a constrained way whilst at the same time being able to capture the underlying named entity relationships. However, it is difficult to incorporate background knowledge or non-local information into the HVS model. This paper proposes to represent the HVS model as a conditionally trained undirected graphical model in which non-local features derived from PPI ontology through inference would be easily incorporated. The seamless fusion of ontology inference with statistical learning produces a new paradigm to information extraction.

From biomedical literature to knowledge:mining protein-protein interactions

To date, more than 16 million citations of published articles in biomedical domain are available in the MEDLINE database. These articles describe the new discoveries which accompany a tremendous development in biomedicine during the last decade. It is crucial for biomedical researchers to retrieve and mine some specific knowledge from the huge quantity of published articles with high efficiency. Researchers have been engaged in the development of text mining tools to find knowledge such as protein-protein interactions, which are most relevant and useful for specific analysis tasks. This chapter provides a road map to the various information extraction methods in biomedical domain, such as protein name recognition and discovery of protein-protein interactions. Disciplines involved in analyzing and processing unstructured-text are summarized. Current work in biomedical information extracting is categorized. Challenges in the field are also presented and possible solutions are discussed.

Protein-protein interactions classification from text via local learning with class priors

Text classification is essential for narrowing down the number of documents relevant to a particular topic for further pursual, especially when searching through large biomedical databases. Protein-protein interactions are an example of such a topic with databases being devoted specifically to them. This paper proposed a semi-supervised learning algorithm via local learning with class priors (LL-CP) for biomedical text classification where unlabeled data points are classified in a vector space based on their proximity to labeled nodes. The algorithm has been evaluated on a corpus of biomedical documents to identify abstracts containing information about protein-protein interactions with promising results. Experimental results show that LL-CP outperforms the traditional semisupervised learning algorithms such as SVMand it also performs better than local learning without incorporating class priors.

Protein interactions studied by SAXS:effect of ionic strength and protein concentration for BSA in aqueous solutions

We have studied a series of samples of bovine serum albumin (BSA) solutions with protein concentration, c, ranging from 2 to 500 mg/mL and ionic strength, I, from 0 to 2 M by small-angle X-ray scattering (SAXS). The scattering intensity distribution was compared to simulations using an oblate ellipsoid form factor with radii of 17 x 42 x 42 A, combined with either a screened Coulomb, repulsive structure factor, S-SC(q), or an attractive square-well structure factor, S-SW(q). At pH = 7, BSA is negatively charged. At low ionic strength, I <0.3 M, the total interaction exhibits a decrease of the repulsive interaction when compared to the salt-free solution, as the net surface charge is screened, and the data can be fitted by assuming an ellipsoid form factor and screened Coulomb interaction. At moderate ionic strength (0.3-0.5 M), the interaction is rather weak, and a hard-sphere structure factor has been used to simulate the data with a higher volume fraction. Upon further increase of the ionic strength (I >= 1.0 M), the overall interaction potential was dominated by an additional attractive potential, and the data could be successfully fitted by an ellipsoid form factor and a square-well potential model. The fit parameters, well depth and well width, indicate that the attractive potential caused by a high salt concentration is weak and long-ranged. Although the long-range, attractive potential dominated the protein interaction, no gelation or precipitation was observed in any of the samples. This is explained by the increase of a short-range, repulsive interaction between protein molecules by forming a hydration layer with increasing salt concentration. The competition between long-range, attractive and short-range, repulsive interactions accounted for the stability of concentrated BSA solution at high ionic strength.

Synthesis, self-assembly and (absence of) protein interactions of poly(glycerol methacrylate)-silicone macro-amphiphiles

The present study focuses on the synthesis of amphiphilic block copolymers containing poly(glycerol monomethacrylate) (PGMMA), showing the advantages of a protection/deprotection strategy based on silyl groups. PGMMA blocks were synthesized via ATRP started by a double functional poly(dimethyl siloxane) (PDMS) macroinitiator of molecular weight ≈7000 g mol-1. The resulting triblock copolymers were characterized by low polydispersity (generally ≤1.1) and their aggregation concentration in water was essentially dominated by the PDMS block length (critical aggregation concentration substantially invariant for GMMA degree of polymerization ≥30). For GMMA blocks with DP > 50, the self-assembly in water produced 35-50 nm spherical micelles, while shorter hydrophilic chains produced larger aggregates apparently displaying worm-like morphologies. Block copolymers with long GMMA chains (DP ≈ 200) produced particularly stable micellar aggregates, which were then selected for a preliminary assessment of the possibility of adsorption of plasma proteins (albumin and fibrinogen); using diffusion NMR as an analytical technique, no significant adsorption was recorded both on micelles and on soluble PGMMA employed as a control, indicating the possibility of a "stealth" behaviour. This journal is © 2013 The Royal Society of Chemistry.

Immunological protein-protein interactions:a critical reflection

Life, and the biochemistry of which it is ultimately comprised, is built from the interactions of proteins, and the study of protein-protein interactions is fast becoming a central feature of molecular bioscience. This is as true of immunobiology as it is of other areas of the wider biological milieu. Protein-protein interactions within an immunological setting comprise both the kind familiar from other areas of biology and instantiations of protein-protein interactions special to the immune arena. Of the generic kind of protein-protein interaction, co-stimulatory receptors, such as CD28, and the interaction of accessory proteins, such as CD4 or CD8, are amongst the most prevalent and apposite of examples. The key examples of special immunological instantiations of protein-protein interactions are the binding of antigens by antibodies and the formation of peptide-MHC-TCR complexes; both prime examples of vital molecular recognition events mediated by protein-protein interactions. In this brief review, and within the context of this burgeoning field, we examine immunological protein-protein interactions, focussing on the problematic nature of defining such interactions. © 2011 by Nova Science Publishers, Inc. All rights reserved.

Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation

Background: DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation. There have been several computational methods proposed in the literature to deal with the DNA-binding protein identification. However, most of them can't provide an invaluable knowledge base for our understanding of DNA-protein interactions. Results: We firstly presented a new protein sequence encoding method called PSSM Distance Transformation, and then constructed a DNA-binding protein identification method (SVM-PSSM-DT) by combining PSSM Distance Transformation with support vector machine (SVM). First, the PSSM profiles are generated by using the PSI-BLAST program to search the non-redundant (NR) database. Next, the PSSM profiles are transformed into uniform numeric representations appropriately by distance transformation scheme. Lastly, the resulting uniform numeric representations are inputted into a SVM classifier for prediction. Thus whether a sequence can bind to DNA or not can be determined. In benchmark test on 525 DNA-binding and 550 non DNA-binding proteins using jackknife validation, the present model achieved an ACC of 79.96%, MCC of 0.622 and AUC of 86.50%. This performance is considerably better than most of the existing state-of-the-art predictive methods. When tested on a recently constructed independent dataset PDB186, SVM-PSSM-DT also achieved the best performance with ACC of 80.00%, MCC of 0.647 and AUC of 87.40%, and outperformed some existing state-of-the-art methods. Conclusions: The experiment results demonstrate that PSSM Distance Transformation is an available protein sequence encoding method and SVM-PSSM-DT is a useful tool for identifying the DNA-binding proteins. A user-friendly web-server of SVM-PSSM-DT was constructed, which is freely accessible to the public at the web-site on http://bioinformatics.hitsz.edu.cn/PSSM-DT/.

The redoxomics of PTEN: walking a fine line between damage and signaling:mass spectrometry-based approaches to study the effect of oxidation on PTEN function, structure, and protein-protein interactions

The research described in this PhD thesis focuses on proteomics approaches to study the effect of oxidation on the modification status and protein-protein interactions of PTEN, a redox-sensitive phosphatase involved in a number of cellular processes including metabolism, apoptosis, cell proliferation, and survival. While direct evidence of a redox regulation of PTEN and its downstream signaling has been reported, the effect of cellular oxidative stress or direct PTEN oxidation on PTEN structure and interactome is still poorly defined. In a first study, GST-tagged PTEN was directly oxidized over a range of hypochlorous acid (HOCl) concentration, assayed for phosphatase activity, and oxidative post-translational modifications (oxPTMs) were quantified using LC-MS/MS-based label-free methods. In a second study, GSTtagged PTEN was prepared in a reduced and reversibly H2O2-oxidized form, immobilized on a resin support and incubated with HCT116 cell lysate to capture PTEN interacting proteins, which were analyzed by LC-MS/MS and comparatively quantified using label-free methods. In parallel experiments, HCT116 cells transfected with a GFP-tagged PTEN were treated with H2O2 and PTENinteracting proteins immunoprecipitated using standard methods. Several high abundance HOCl-induced oxPTMs were mapped, including those taking place at amino acids known to be important for PTEN phosphatase activity and protein-protein interactions, such as Met35, Tyr155, Tyr240 and Tyr315. A PTEN redox interactome was also characterized, which identified a number of PTEN-interacting proteins that vary with the reversible inactivation of PTEN caused by H2O2 oxidation. These included new PTEN interactors as well as the redox proteins peroxiredoxin-1 (Prdx1) and thioredoxin (Trx), which are known to be involved in the recycling of PTEN active site following H2O2-induced reversible inactivation. The results suggest that the oxidative modification of PTEN causes functional alterations in PTEN structure and interactome, with fundamental implications for the PTEN signaling role in many cellular processes, such as those involved in the pathophysiology of disease and ageing.

The binding of nuclear factors to the as-1 element in the CaMV 35S promoter is affected by cytosine methylation in vitro

Transcriptional gene silencing (TCS) is often associated with an increased level of cytosine methylation in the affected promoters. The effect of methylation of the cauliflower mosaic virus (CaMV) 35S promoter sequence on its binding to factors present in the nuclei was analyzed by electrophoretic mobility shift assays using extracts of petunia flowers. Specific DNA-protein interactions were detected in the region of the CaMV 35S promoter that contains the as-1 element and the region between -345 and -208. The binding of protein factor(s) to the as-1 element was influenced by cytosine methylation, whereas the binding to the region between -345 and -208 was unaffected. The results suggest that cytosine methylation of the as-1 element potentially affects the activity of the CaMV 35S promoter. © Georg Thieme Verlag KG Stuttgart.

Comparison of promoter-specific events during transcription initiation in mycobacteria

DNA protein interactions that occur during transcription initiation play an important role in regulating gene expression. To initiate transcription, RNA polymerase (RNAP) binds to promoters in a sequence-specific fashion. This is followed by a series of steps governed by the equilibrium binding and kinetic rate constants, which in turn determine the overall efficiency of the transcription process. We present here the first detailed kinetic analysis of promoter RNAP interactions during transcription initiation in the sigma(A)-dependent promoters P-rrnAPCL1, P-rrnB and P-gyr of Mycobacterium smegmatis. The promoters show comparable equilibrium binding affinity but differ significantly in open complex formation, kinetics of isomerization and promoter clearance. Furthermore, the two rrn promoters exhibit varied kinetic properties during transcription initiation and appear to be subjected to different modes of regulation. In addition to distinct kinetic patterns, each one of the housekeeping promoters studied has its own rate-limiting step in the initiation pathway, indicating the differences in their regulation.

Study of influence of regulatory polymorphisms of expression in development of breast cancer

The human genome has millions of genetics variants that can affect gene expression. These variants are known as cis-regulatory variants and are responsible for intra-species phenotypic differences and individual susceptibility to disease. One of the diseases affected by cis-regulatory variants is breast cancer. Breast cancer is one of the most common cancers, with approximately 4500 new cases each year in Portugal. Breast cancer has many genes mutated and TP53 has been shown to be relevant for this disease. TP53 is one of the most commonly mutated genes in human cancer and it is involved in cell cycle regulation and apoptosis. Previous work by Maia et al has shown that TP53 has differential allelic expression (DAE), which suggests that this gene may be under the influence of cis-regulatory variants. Also, its DAE pattern is totally altered in breast tumours with normal copy number. We hypothesized that cis-regulatory variants affecting TP53 may have a role in breast cancer development and treatment. The present work aims to identify the cis-regulatory variants playing a role in TP53 expression, using in silico, in vitro and in vivo approaches. By bioinformatic tools we have identified candidate cis-regulatory variants and predicted the possible transcription factor binding sites that they affect. By EMSA we studied DNA-protein interactions in this region of TP53. The in silico analysis allowed us to identified three candidate cis-regulatory SNPs which may affect the binding of seven transcription factors. However, the EMSA experiments have not been conclusive and we have not yet confirmed whether any of the identified SNPs are associated with gene expression control of TP53. We will carry out further experiments to validate our findings.

Transcriptional regulation of cell lineage-specific expression of the murine type I collagen genes and of osteoblast differentiation

The aim of my project is to examine the mechanisms of cell lineage-specific transcriptional regulation of the two type I collagen genes by characterizing critical cis-acting elements and trans-acting factors. I hypothesize that the transcription factors that are involved in the cell lineage-specific expression of these genes may have a larger essential role in cell lineage commitment and differentiation. I first examined the proximal promoters of the proα1(I) and the proα2(I) collagen genes for cell type-specific DNA-protein interactions, using in vitro DNaseI and in vivo DMS footprinting. These experiments demonstrated that the cis-acting elements in these promoters are accessible to ubiquitous DNA-binding proteins in fibroblasts that express these genes, but not in other cells that do not express these genes. I speculate that in type I collagen-expressing cells, cell type-specific enhancer elements facilitate binding of ubiquitous proteins to the proximal promoters of these genes. Subsequently, examination of the upstream promoter of the proα(I) collagen gene by transgenic mice experiments delineated a 117 bp sequence (-1656 to -1540 bp) as the minimum element required for osteoblast-specific expression. This 117 bp element contained two segments that appeared to have different functions: (1) the A-segment, which was necessary to obtain osteoblast-specific expression and (2) the C-segment, which was dispensable for osteoblast-specific expression, but was necessary to obtain high-level expression. In experiments to identify trans-acting factors that bind to the 117 bp element, I have demonstrated that the cell lineage-restricted homeodomain proteins, Dlx2, Dlx5 and mHOX, bound to the A-segment and that the ubiquitous transcription factor, Sp1, bound to the C-segment of this element. These results suggested a model where the binding of cell lineage-restricted proteins to the A-segment and of ubiquitous proteins to the C-segment of the 117 bp element of the proα1 (I) collagen gene activated this gene in osteoblasts. These results, combined with additional evidence that Dlx2, Dlx5 and mHOX are probably involved in osteoblast differentiation, support my hypothesis that the transcription factors involved in osteoblast-specific expression of type I collagen genes may have essential role in osteoblast lineage commitment and differentiation. ^