Discovery of binding proteins for a protein target using protein–protein docking‐based virtual screening

Target structure‐based virtual screening, which employs protein‐small molecule docking to identify potential ligands, has been widely used in small‐molecule drug discovery. In the present study, we used a protein–protein docking program to identify proteins that bind to a specific target protein. In the testing phase, an all‐to‐all protein–protein docking run on a large dataset was performed. The three‐dimensional rigid docking program SDOCK was used to examine protein–protein docking on all protein pairs in the dataset. Both the binding affinity and features of the binding energy landscape were considered in the scoring function in order to distinguish positive binding pairs from negative binding pairs. Thus, the lowest docking score, the average Z‐score, and convergency of the low‐score solutions were incorporated in the analysis. The hybrid scoring function was optimized in the all‐to‐all docking test. The docking method and the hybrid scoring function were then used to screen for proteins that bind to tumor necrosis factor‐α (TNFα), which is a well‐known therapeutic target for rheumatoid arthritis and other autoimmune diseases. A protein library containing 677 proteins was used for the screen. Proteins with scores among the top 20% were further examined. Sixteen proteins from the top‐ranking 67 proteins were selected for experimental study. Two of these proteins showed significant binding to TNFα in an in vitro binding study. The results of the present study demonstrate the power and potential application of protein–protein docking for the discovery of novel binding proteins for specific protein targets. Proteins 2014; 82:2472–2482. © 2014 Wiley Periodicals, Inc.     

Development of breeding values for susceptibility to virulent footrot in sheep: A strategy to accommodate variable disease progression at time of scoring

Genetic evaluations utilising footrot scores from industry flocks in their essence, incorporate data from a wide range of challenge environments, resulting in potentially large differences in means, variances and distribution of scores across challenges. The date that commencement of infection occurs is generally unknown, and progression of the infection varies with the prevailing environmental and management conditions, virulence of the bacterium Dichelobacter nodosus, as well as the genetic potential and (permanent) environmental ability of animals to resist footrot. In practice, animals are unlikely to be repeatedly scored to identify the best time for comparison, or monitor development of disease progression. Furthermore, field challenges are limited by the need to treat animals before their welfare is compromised. Therefore, the duration and intensity of infection varies and this affects comparisons between animals for their susceptibility. Diseases such as footrot are characterised by multiple categorical scores reflecting clinical stages that describe the progression and relative impact of the disease. This provides the opportunity for the transformation of the data to a standardised prevalence. Scoring events from multiple footrot field challenges under a standardised protocol were used to establish a series of transition matrices to describe disease progression between scores over time. These transition matrices were used to standardise challenge events to the more severe scoring events, observed later in the challenge. The accuracy of the transition technique was tested by comparing the ranking of animals and sires against the observed scores. Transitioning the data from low disease prevalence to the higher prevalence at the subsequent scoring event improved the correlations between the scoring events, at the animal level, by upwards of 0.10 across challenges. The utilisation of a transition matrix to transform low prevalence disease challenges by taking into account the natural biological rate of progression through the clinical stages of the disease provides a more accurate technique to account for variation in disease prevalence. The transition technique increases the acceptable range of disease expression targeted by producers when scoring virulent footrot challenges reducing the need for repeat scoring and allowing earlier treatment and reducing the impact of the disease on the host animal.     

Timing of Photoperiodic Flowering:Light Perception and Circadian Clock

Flowering symbolizes the transition of a plant from vegetative phase to reproductive phase and is controlled by fairly complex and highly coordinated regulatory pathways. Over the last decade, genetic studies in Arabidopsis have aided the discovery of many signaling components involved in these pathways. In this review, we discuss how the timing of flowering is regulated by photoperiod and the involvement of light perception and the circadian clock in this process. The specific regulatory mechanisms on CONSTANS expression and CONSTANS stability by the circadian clock and photoreceptors are described in detail. In addition, the roles of CONSTANS, FLOWERING LOCUS T, and several other light signaling and circadiandependent components in photoperiodic flowering are also highlighted.     

Elastin-like Peptide amphiphiles form nanofibers with tunable length

Peptide amphiphiles (PAs) self-assemble nanostructures with potential applications in drug delivery and tissue engineering. Some PAs share environmentally responsive behavior with their peptide components. Here we report a new type of PAs biologically inspired from human tropoelastin. Above a lower critical solution temperature (LCST), elastin-like polypeptides (ELPs) undergo a reversible inverse phase transition. Similar to other PAs, elastin-like PAs (ELPAs) assemble micelles with fiber-like nanostructures. Similar to ELPs, ELPAs have inverse phase transition behavior. Here we demonstrate control over the ELPAs fiber length and cellular uptake. In addition, we observed that both peptide assembly and nanofiber phase separation are accompanied by a distinctive secondary structure attributed primarily to a type-1 β turn. We also demonstrate increased solubility of hydrophobic paclitaxel (PAX) in the presence of ELPAs. Due to their biodegradability, biocompatibility, and environmental responsiveness, elastin-inspired biopolymers are an emerging platform for drug and cell delivery; furthermore, the discovery of ELPAs may provide a new and useful approach to engineer these materials into stimuli-responsive gels and drug carriers.     

A proteome catalog of Drosophila melanogaster: an essential resource for targeted quantitative proteomics

Proteomic analyses are critically important for systems biology because important aspects related to the structure, function and control of biological systems are only amenable by direct protein measurements. It has become apparent that the current proteomics technologies are unlikely to allow routine, quantitative measurements of whole proteomes. We have therefore suggested and largely implemented a two-step strategy for quantitative proteome analysis. In a first step, the discovery phase, the proteome observable by mass spectrometry is extensively analyzed. The resulting proteome catalog can then be used to select peptides specific to only one protein, so-called proteotypic peptides (PTPs). It represents the basis to realize sensitive, robust and reproducible measurements based on targeted mass spectrometry of these PTPs in a subsequent scoring phase. In this Extra View we describe the need for such proteome catalogs and their multiple benefits for catalyzing the shift towards targeted quantitative proteomic analysis and beyond. We use the Insulin signaling cascade as a representative example to illustrate the limitations of currently used proteomics approaches for the specific analysis of individual pathway components, and describe how the recently published Drosophila proteome catalog already helped to overcome many of these limitations.     

Discrete molecular dynamics distinguishes nativelike binding poses from decoys in difficult targets

Virtual screening is one of the major tools used in computer-aided drug discovery. In structure-based virtual screening, the scoring function is critical to identifying the correct docking pose and accurately predicting the binding affinities of compounds. However, the performance of existing scoring functions has been shown to be uneven for different targets, and some important drug targets have proven especially challenging. In these targets, scoring functions cannot accurately identify the native or near-native binding pose of the ligand from among decoy poses, which affects both the accuracy of the binding affinity prediction and the ability of virtual screening to identify true binders in chemical libraries. Here, we present an approach to discriminating native poses from decoys in difficult targets for which several scoring functions failed to correctly identify the native pose. Our approach employs Discrete Molecular Dynamics simulations to incorporate protein-ligand dynamics and the entropic effects of binding. We analyze a collection of poses generated by docking and find that the residence time of the ligand in the native and nativelike binding poses is distinctly longer than that in decoy poses. This finding suggests that molecular simulations offer a unique approach to distinguishing the native (or nativelike) binding pose from decoy poses that cannot be distinguished using scoring functions that evaluate static structures. The success of our method emphasizes the importance of protein-ligand dynamics in the accurate determination of the binding pose, an aspect that is not addressed in typical docking and scoring protocols.     

Timing of Photoperiodic Flowering：Light Perception and Circadian Clock

Flowering symbolizes the transition of s plant from vegetative phase to reproductive phase and is controlled by fairly complex and highly coordinated regulatory pathways. Over the last decade, genetic studies in Arabidopsis have aided the discovery of many signaling components involved in these pathways. In this review, we discuss how the timing of flowering is regulated by photoperiod and the involvement of light perception and the circadian clock in this process. The specific regulatory mechanisms on CONSTANS expression and CONSTANS stability by the circadian clock and photoreceptors are described in detail. In addition, the roles of CONSTANS, FLOWERING LOCUS T, and several other light signaling and circadian-dependent components in photoperiodic flowering are also highlighted.     

Scanning calorimetry reveals a new phase transition in L-alpha-dipalmitoylphosphatidylcholine.

We report a new phase transition in fully hydrated dispersions of dipalmitoylphosphatidylcholine (DPPC). This new transition, called the sub-subtransition, exhibits a transition enthalpy of 0.25 kcal/mol with a Tm at 6.8 degrees C. Unlike the subtransition, no extended low temperature incubation is required to observe the sub-subtransition. This new sub-subgel (SGII) phase may be a precursor to the subgel (SGI) phase, and this discovery is discussed in relation to the current knowledge regarding the polymorphic gel phases of both ester- and ether-linked lipids with identical acyl chains.     

How industry is approaching the search for new diagnostic markers and biomarkers

In the diagnostic and the pharmaceutical industry there is a constant need for new diagnostic markers and biomarkers with improved sensitivity and specificity. During the last 5 years, only a few novel diagnostic markers have been introduced into the market. Proteomics technologies are now offering unique chances to identify new candidate markers. Before a marker can be introduced into the market, three successive developmental phases have to be completed: the discovery phase, in which a variety of proteomics technologies are applied to identify marker candidates; the prototype developmental phase, in which immunological assays are established and validated in defined sample collectives; and finally the product development phase, with assay formats suitable for automated platforms. The hurdles that a potential candidate marker has to pass in each developmental phase before reaching the market are considerable. The costs are increasing from phase to phase, and in industry a number of questions concerning the medical need and the potential return on investment have to be answered before a proteomics discovery project is started. In this review, we will cover aspects of all three developmental phases including the repertoire of discovery tools for protein separation as well as giving an outline of modern principles of mass spectrometry for the identification of proteins.     

Differential scanning calorimetry of thermotropic phase transitions in vitaminylated lipids: aqueous dispersions of N-biotinyl phosphatidylethanolamines.

The thermotropic phase behavior of a homologous series of saturated diacyl phosphatidylethanolamines in which the headgroup is N-derivatized with biotin has been investigated by differential scanning calorimetry. In 1 M NaCl, derivatives with acyl chainlengths from C(12:0) to C(20:0) all exhibit sharp chain-melting phase transitions, which are reversible with a hysteresis of 1.5 degrees or less, except for the C(12:0) lipid which has a transition temperature below 0 degree C. The transition enthalpy and the transition entropy depend approximately linearly on the lipid chainlength, with incremental values per CH2 group that are very similar to those obtained for the corresponding underivatized phosphatidylethanolamines in aqueous dispersion. The chainlength-independent contribution to the transition enthalpy is significantly smaller than that for the underivatized phosphatidylethanolamines, and that for the transition entropy is much smaller; the latter suggesting that the N-biotinylated phosphatidylethanolamine headgroups are differently hydrated from those of the underivatized lipids. The gel-to-fluid phase transition temperatures of the N-biotinylated lipids are lower than those of the parent phosphatidylethanolamines, and their chainlength dependence conforms well with that predicted by assuming that the transition enthalpy and entropy are linearly dependent on chainlength. Although the chain-melting phase behavior is generally similar to that of the parent phosphatidylethanolamines, the gel phases (and the fluid phases in the case of chainlengths C(12:0) to C(16:0)) have a different lyotropic structure in the two cases, and this is reflected in the chainlength-independent contributions to the thermodynamic parameters. In the absence of salt, the thermotropic phase behavior of aqueous dispersions of the N-biotinyl phosphatidylethanolamines is considerably more complex.(ABSTRACT TRUNCATED AT 250 WORDS)     

Finding composite regulatory patterns in DNA sequences

Pattern discovery in unaligned DNA sequences is a fundamental problem in computational biology with important applications in finding regulatory signals. Current approaches to pattern discovery focus on monad patterns that correspond to relatively short contiguous strings. However, many of the actual regulatory signals are composite patterns that are groups of monad patterns that occur near each other. A difficulty in discovering composite patterns is that one or both of the component monad patterns in the group may be 'too weak'. Since the traditional monad-based motif finding algorithms usually output one (or a few) high scoring patterns, they often fail to find composite regulatory signals consisting of weak monad parts. In this paper, we present a MITRA (MIsmatch TRee Algorithm) approach for discovering composite signals. We demonstrate that MITRA performs well for both monad and composite patterns by presenting experiments over biological and synthetic data.     

Markov clustering versus affinity propagation for the partitioning of protein interaction graphs

Background

Polypeptides are composed of amino acids covalently bonded via a peptide bond. The majority of peptide bonds in proteins is found to occur in the trans conformation. In spite of their infrequent occurrence, cis peptide bonds play a key role in the protein structure and function, as well as in many significant biological processes.

Results

We perform a systematic analysis of regions in protein sequences that contain a proline cis peptide bond in order to discover non-random associations between the primary sequence and the nature of proline cis/trans isomerization. For this purpose an efficient pattern discovery algorithm is employed which discovers regular expression-type patterns that are overrepresented (i.e. appear frequently repeated) in a set of sequences. Four types of pattern discovery are performed: i) exact pattern discovery, ii) pattern discovery using a chemical equivalency set, iii) pattern discovery using a structural equivalency set and iv) pattern discovery using certain amino acids' physicochemical properties. The extracted patterns are carefully validated using a specially implemented scoring function and a significance measure (i.e. log-probability estimate) indicative of their specificity. The score threshold for the first three types of pattern discovery is 0.90 while for the last type of pattern discovery 0.80. Regarding the significance measure, all patterns yielded values in the range [-9, -31] which ensure that the derived patterns are highly unlikely to have emerged by chance. Among the highest scoring patterns, most of them are consistent with previous investigations concerning the neighborhood of cis proline peptide bonds, and many new ones are identified. Finally, the extracted patterns are systematically compared against the PROSITE database, in order to gain insight into the functional implications of cis prolyl bonds.

Conclusion

Cis patterns with matches in the PROSITE database fell mostly into two main functional clusters: family signatures and protein signatures. However considerable propensity was also observed for targeting signals, active and phosphorylation sites as well as domain signatures.     

PRL-Dock: protein-ligand docking based on hydrogen bond matching and probabilistic relaxation labeling

Protein-ligand docking is widely applied to structure-based virtual screening for drug discovery. This article presents a novel docking technique, PRL-Dock, based on hydrogen bond matching and probabilistic relaxation labeling. It deals with multiple hydrogen bonds and can match many acceptors and donors simultaneously. In the matching process, the initial probability of matching an acceptor with a donor is estimated by an efficient scoring function and the compatibility coefficients are assigned according to the coexisting condition of two hydrogen bonds. After hydrogen bond matching, the geometric complementarity of the interacting donor and acceptor sites is taken into account for displacement of the ligand. It is reduced to an optimization problem to calculate the optimal translation and rotation matrixes that minimize the root mean square deviation between two sets of points, which can be solved using the Kabsch algorithm. In addition to the van der Waals interaction, the contribution of intermolecular hydrogen bonds in a complex is included in the scoring function to evaluate the docking quality. A modified Lennard-Jones 12-6 dispersion-repulsion term is used to estimate the van der Waals interaction to make the scoring function fairly "soft" so that ligands are not heavily penalized for small errors in the binding geometry. The calculation of this scoring function is very convenient. The evaluation is carried out on 278 rigid complexes and 93 flexible ones where there is at least one intermolecular hydrogen bond. The experiment results of docking accuracy and prediction of binding affinity demonstrate that the proposed method is highly effective.     

The effect of bacteriorhodopsin, detergent and hydration on the cubic-to-lamellar phase transition in the monoolein-distearoyl phosphatidyl glycerol-water system

The cubic phase of monoolein (MO) has successfully been used for crystallization of membrane proteins. It is likely that the transition to a lamellar phase upon dehydration is important for the crystallization process, and that the internal dimensions of the lipid phases (i.e., water pore diameter) are crucial for the inclusion and the diffusion of membrane proteins. In the present study, we investigated the cubic-to-lamellar phase transitions in the MO-water and the MO-distearoyl phosphatidyl glycerol (DSPG) systems. The MO-water system was investigated by means of isothermal sorption and desorption microcalorimetry. We show that the transition from cubic to lamellar phase induced by desorption is driven by entropy. At 25 degrees C, this occurs at a water activity of 0.98 with a transition enthalpy of 860 J/mol (MO). The phase behavior was also investigated in the presence of a small amount of the transmembrane protein bacteriorhodopsin (bR), and a detergent, octyl glucoside (OG), and it was shown that both bR and OG stabilize the lamellar phase. Analogous results were obtained for the MO-DSPG-water system. The latter system resembles the MO-water system in that a cubic-to-lamellar phase transition is induced by dehydration, although the structural properties of these phases are slightly different. Finally, we demonstrate that bR can be crystallized from a cubic phase of MO-DSPG-buffer.     

Phasing in on the cell cycle

Just like all matter, proteins can also switch between gas, liquid and solid phases. Protein phase transition has claimed the spotlight in recent years as a novel way of how cells compartmentalize and regulate biochemical reactions. Moreover, this discovery has provided a new framework for the study of membrane-less organelle biogenesis and protein aggregation in neurodegenerative disorders. We now argue that this framework could be useful in the study of cell cycle regulation and cancer. Based on our work on phase transitions of arginine-rich proteins in neurodegeneration, via combining mass spectroscopy with bioinformatics analyses, we found that also numerous proteins involved in the regulation of the cell cycle can undergo protein phase separation. Indeed, several proteins whose function affects the cell cycle or are associated with cancer, have been recently found to phase separate from the test tube to cells. Investigating the role of this process for cell cycle proteins and understanding its molecular underpinnings will provide pivotal insights into the biology of cell cycle progression and cancer.     

Getting stress out of stressed‐out stress granules

Amyotrophic lateral sclerosis (ALS) pathology is linked to the aberrant aggregation of specific proteins, including TDP‐43, FUS, and SOD1, but it is not clear why these aggregation events cause ALS. In this issue of The EMBO Journal, Mateju et al (2017) report a direct link between misfolded proteins accumulating in stress granules and the phase transition of these stress granules from liquid to solid. This discovery provides a model connecting protein aggregation to stress granule dysfunction.     

Prioritization of Cancer Marker Candidates Based on the Immunohistochemistry Staining Images Deposited in the Human Protein Atlas

Cancer marker discovery is an emerging topic in high-throughput quantitative proteomics. However, the omics technology usually generates a long list of marker candidates that requires a labor-intensive filtering process in order to screen for potentially useful markers. Specifically, various parameters, such as the level of overexpression of the marker in the cancer type of interest, which is related to sensitivity, and the specificity of the marker among cancer groups, are the most critical considerations. Protein expression profiling on the basis of immunohistochemistry (IHC) staining images is a technique commonly used during such filtering procedures. To systematically investigate the protein expression in different cancer versus normal tissues and cell types, the Human Protein Atlas is a most comprehensive resource because it includes millions of high-resolution IHC images with expert-curated annotations. To facilitate the filtering of potential biomarker candidates from large-scale omics datasets, in this study we have proposed a scoring approach for quantifying IHC annotation of paired cancerous/normal tissues and cancerous/normal cell types. We have comprehensively calculated the scores of all the 17219 tested antibodies deposited in the Human Protein Atlas based on their accumulated IHC images and obtained 457110 scores covering 20 different types of cancers. Statistical tests demonstrate the ability of the proposed scoring approach to prioritize cancer-specific proteins. Top 100 potential marker candidates were prioritized for the 20 cancer types with statistical significance. In addition, a model study was carried out of 1482 membrane proteins identified from a quantitative comparison of paired cancerous and adjacent normal tissues from patients with colorectal cancer (CRC). The proposed scoring approach demonstrated successful prioritization and identified four CRC markers, including two of the most widely used, namely CEACAM5 and CEACAM6. These results demonstrate the potential of this scoring approach in terms of cancer marker discovery and development. All the calculated scores are available at http://bal.ym.edu.tw/hpa/.     

Energy-based graph convolutional networks for scoring protein docking models

Structural information about protein-protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein docking provides a computational alternative for such information. However, ranking near-native docked models high among a large number of candidates, often known as the scoring problem, remains a critical challenge. Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking. In this study, the two challenging problems in protein docking are regarded as relative and absolute scoring, respectively, and addressed in one physics-inspired deep learning framework. We represent protein and complex structures as intra- and inter-molecular residue contact graphs with atom-resolution node and edge features. And we propose a novel graph convolutional kernel that aggregates interacting nodes’ features through edges so that generalized interaction energies can be learned directly from 3D data. The resulting energy-based graph convolutional networks (EGCN) with multihead attention are trained to predict intra- and inter-molecular energies, binding affinities, and quality measures (interface RMSD) for encounter complexes. Compared to a state-of-the-art scoring function for model ranking, EGCN significantly improves ranking for a critical assessment of predicted interactions (CAPRI) test set involving homology docking; and is comparable or slightly better for Score_set, a CAPRI benchmark set generated by diverse community-wide docking protocols not known to training data. For Score_set quality assessment, EGCN shows about 27% improvement to our previous efforts. Directly learning from 3D structure data in graph representation, EGCN represents the first successful development of graph convolutional networks for protein docking.     

Growth Conditions and Cell Cycle Phase Modulate Phase Transition Temperatures in RBL-2H3 Derived Plasma Membrane Vesicles

Giant plasma membrane vesicle (GPMV) isolated from a flask of RBL-2H3 cells appear uniform at physiological temperatures and contain coexisting liquid-ordered and liquid-disordered phases at low temperatures. While a single GPMV transitions between these two states at a well-defined temperature, there is significant vesicle-to-vesicle heterogeneity in a single preparation of cells, and average transition temperatures can vary significantly between preparations. In this study, we explore how GPMV transition temperatures depend on growth conditions, and find that average transition temperatures are negatively correlated with average cell density over 15°C in transition temperature and nearly three orders of magnitude in average surface density. In addition, average transition temperatures are reduced by close to 10°C when GPMVs are isolated from cells starved of serum overnight, and elevated transition temperatures are restored when serum-starved cells are incubated in serum-containing media for 12h. We also investigated variation in transition temperature of GPMVs isolated from cells synchronized at the G1/S border through a double Thymidine block and find that average transition temperatures are systematically higher in GPMVs produced from G1 or M phase cells than in GPMVs prepared from S or G1 phase cells. Reduced miscibility transition temperatures are also observed in GPMVs prepared from cells treated with TRAIL to induce apoptosis or sphingomyelinase, and in some cases a gel phase is observed at temperatures above the miscibility transition in these vesicles. We conclude that at least some variability in GPMV transition temperature arises from variation in the local density of cells and asynchrony of the cell cycle. It is hypothesized that GPMV transition temperatures are a proxy for the magnitude of lipid-mediated membrane heterogeneity in intact cell plasma membranes at growth temperatures. If so, these results suggest that cells tune their plasma membrane composition in order to control the magnitude of membrane heterogeneity in response to different growth conditions.