Effect of milk pH at heating on protein complex formation and ultimate gel properties of free-fat yoghurt.

The effect of milk pH before heating on casein-whey protein interactions and ultimate gel properties of the free-fat yoghurt was investigated. Reconstituted skim milk at different pH values (6.4, 6.8 and 7.2) was heated at 80 °C for 30 min. The type of protein and size of casein micelle in milk were determined. The storage modulus (G'), loss tangent (tan δ), flow behaviour as well as microstructure, firmness and water holding capacity of the yoghurt samples were measured. Heating milk at pH 7.2 formed mostly soluble protein complexes whereas at pH 6.4 micelle bound complexes was dominant. However, heating milk at pH 6.8 resulted in a relatively compact protein network due to a balanced contribution from both soluble protein/κ-casein complexes and whey protein-casein micelle associated complexes. Yoghurt prepared with milk heated at pH 6.8 showed significantly higher G' values, shorter gelation times, higher water holding capacity, firmness and more compact protein network compared to those at pH 6.4, 7.2 and unheated milk. The obtained results demonstrated that milk pH adjustment before heating could be an important factor governing uniform quality yoghurt production.

Decoy selection for protein structure prediction via extreme gradient boosting and ranking.

BACKGROUND: Identifying one or more biologically-active/native decoys from millions of non-native decoys is one of the major challenges in computational structural biology. The extreme lack of balance in positive and negative samples (native and non-native decoys) in a decoy set makes the problem even more complicated. Consensus methods show varied success in handling the challenge of decoy selection despite some issues associated with clustering large decoy sets and decoy sets that do not show much structural similarity. Recent investigations into energy landscape-based decoy selection approaches show promises. However, lack of generalization over varied test cases remains a bottleneck for these methods. RESULTS: We propose a novel decoy selection method, ML-Select, a machine learning framework that exploits the energy landscape associated with the structure space probed through a template-free decoy generation. The proposed method outperforms both clustering and energy ranking-based methods, all the while consistently offering better performance on varied test-cases. Moreover, ML-Select shows promising results even for the decoy sets consisting of mostly low-quality decoys. CONCLUSIONS: ML-Select is a useful method for decoy selection. This work suggests further research in finding more effective ways to adopt machine learning frameworks in achieving robust performance for decoy selection in template-free protein structure prediction.

Graph-Based Community Detection for Decoy Selection in Template-Free Protein Structure Prediction.

Significant efforts in wet and dry laboratories are devoted to resolving molecular structures. In particular, computational methods can now compute thousands of tertiary structures that populate the structure space of a protein molecule of interest. These advances are now allowing us to turn our attention to analysis methodologies that are able to organize the computed structures in order to highlight functionally relevant structural states. In this paper, we propose a methodology that leverages community detection methods, designed originally to detect communities in social networks, to organize computationally probed protein structure spaces. We report a principled comparison of such methods along several metrics on proteins of diverse folds and lengths. We present a rigorous evaluation in the context of decoy selection in template-free protein structure prediction. The results make the case that network-based community detection methods warrant further investigation to advance analysis of protein structure spaces for automated selection of functionally relevant structures.

From mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes.

BACKGROUND: The protein energy landscape underscores the inherent nature of proteins as dynamic molecules interconverting between structures with varying energies. Reconstructing a protein's energy landscape holds the key to characterizing a protein's equilibrium conformational dynamics and its relationship to function. Many pathogenic mutations in protein sequences alter the equilibrium dynamics that regulates molecular interactions and thus protein function. In principle, reconstructing energy landscapes of a protein's healthy and diseased variants is a central step to understanding how mutations impact dynamics, biological mechanisms, and function. RESULTS: Recent computational advances are yielding detailed, sample-based representations of protein energy landscapes. In this paper, we propose and describe two novel methods that leverage computed, sample-based representations of landscapes to reconstruct them and extract from them informative local structures that reveal the underlying organization of an energy landscape. Such structures constitute landscape features that, as we demonstrate here, can be utilized to detect alterations of landscapes upon mutation. CONCLUSIONS: The proposed methods detect altered protein energy landscape features in response to sequence mutations. By doing so, the methods allow formulating hypotheses on the impact of mutations on specific biological activities of a protein. This work demonstrates that the availability of energy landscapes of healthy and diseased variants of a protein opens up new avenues to harness the quantitative information embedded in landscapes to summarize mechanisms via which mutations alter protein dynamics to percolate to dysfunction.

From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction.

Due to the essential role that the three-dimensional conformation of a protein plays in regulating interactions with molecular partners, wet and dry laboratories seek biologically-active conformations of a protein to decode its function. Computational approaches are gaining prominence due to the labor and cost demands of wet laboratory investigations. Template-free methods can now compute thousands of conformations known as decoys, but selecting native conformations from the generated decoys remains challenging. Repeatedly, research has shown that the protein energy functions whose minima are sought in the generation of decoys are unreliable indicators of nativeness. The prevalent approach ignores energy altogether and clusters decoys by conformational similarity. Complementary recent efforts design protein-specific scoring functions or train machine learning models on labeled decoys. In this paper, we show that an informative consideration of energy can be carried out under the energy landscape view. Specifically, we leverage local structures known as basins in the energy landscape probed by a template-free method. We propose and compare various strategies of basin-based decoy selection that we demonstrate are superior to clustering-based strategies. The presented results point to further directions of research for improving decoy selection, including the ability to properly consider the multiplicity of native conformations of proteins.

Apurinic/Apyrimidinic Endonuclease 1/Redox Factor-1 (Ape1/Ref-1) Modulates Antigen Presenting Cell-mediated T Helper Cell Type 1 Responses.

Apurinic/apyrimidinic endonuclease 1/redox factor-1 (Ape1/Ref-1) is a multifunctional protein possessing DNA repair, redox control, and transcriptional regulatory activities. Although Ape1/Ref-1 plays multiple roles in the immune system, its functions in helper T (Th) cell activation and differentiation are largely unknown. In this study, the function of Ape1/Ref-1 in Th cell activation was analyzed using an Ape1/Ref-1 redox-specific inhibitor, E3330. When splenocytes from OT-II mice, which are ovalbumin (OVA)-specific T-cell receptor transgenic mice, were activated with OVA in the presence of E3330, the induction of IFN-γ-producing OT-II T cells was significantly increased. In contrast, E3330 did not enhance IFN-γ production from plate-bound anti-CD3 antibody-stimulated CD4+ T cells in the absence of antigen presenting cells (APCs). Furthermore, E3330-pretreated and OVA-pulsed APCs also enhanced the IFN-γ production from OT-II T cells. These results suggested that E3330 enhances Th1 responses by modifying APC function. E3330 did not alter the surface expression of MHC-II or the co-stimulatory molecules CD80 and CD86 on APCs. On the other hand, E3330 up-regulated the IL-12 p35 and p40 gene expression, and IL-12 surface retention, but decreased the IL-12 secretion from Toll-like receptor (TLR) ligand-stimulated APCs. These results were confirmed with Ape1/Ref-1 knockdown experiments. Taken together, our findings indicated that the suppression of Ape1/Ref-1 redox function leads to an increased cell surface retention of IL-12 and enhances Th1 responses. This is the first study to demonstrate that Ape1/Ref-1 modulates the IL-12 production and secretion from APCs and controls Th1 immune responses.

Improved Protein Decoy Selection via Non-Negative Matrix Factorization.

A central challenge in protein modeling research and protein structure prediction in particular is known as decoy selection. The problem refers to selecting biologically-active/native tertiary structures among a multitude of physically-realistic structures generated by template-free protein structure prediction methods. Research on decoy selection is active. Clustering-based methods are popular, but they fail to identify good/near-native decoys on datasets where near-native decoys are severely under-sampled by a protein structure prediction method. Reasonable progress is reported by methods that additionally take into account the internal energy of a structure and employ it to identify basins in the energy landscape organizing the multitude of decoys. These methods, however, incur significant time costs for extracting basins from the landscape. In this paper, we propose a novel decoy selection method based on non-negative matrix factorization. We demonstrate that our method outperforms energy landscape-based methods. In particular, the proposed method addresses both the time cost issue and the challenge of identifying good decoys in a sparse dataset, successfully recognizing near-native decoys for both easy and hard protein targets.

Anomaly Detection-Based Recognition of Near-Native Protein Structures.

The three-dimensional structures populated by a protein molecule determine to a great extent its biological activities. The rich information encoded by protein structure on protein function continues to motivate the development of computational approaches for determining functionally-relevant structures. The majority of structures generated in silico are not relevant. Discriminating relevant/native protein structures from non-native ones is an outstanding challenge in computational structural biology. Inherently, this is a recognition problem that can be addressed under the umbrella of machine learning. In this paper, based on the premise that near-native structures are effectively anomalies, we build on the concept of anomaly detection in machine learning. We propose methods that automatically select relevant subsets, as well as methods that select a single structure to offer as prediction. Evaluations are carried out on benchmark datasets and demonstrate that the proposed methods advance the state of the art. The presented results motivate further building on and adapting concepts and techniques from machine learning to improve recognition of near-native structures in protein structure prediction.

From molecular energy landscapes to equilibrium dynamics via landscape analysis and markov state models.

Molecular dynamics (MD) simulation software allows probing the equilibrium structural dynamics of a molecule of interest, revealing how a molecule navigates its structure space one structure at a time. To obtain a broader view of dynamics, typically one needs to launch many such simulations, obtaining many trajectories. A summarization of the equilibrium dynamics requires integrating the information in the various trajectories, and Markov State Models (MSM) are increasingly being used for this task. At its core, the task involves organizing the structures accessed in simulation into structural states, and then constructing a transition probability matrix revealing the transitions between states. While now considered a mature technology and widely used to summarize equilibrium dynamics, the underlying computational process in the construction of an MSM ignores energetics even though the transition of a molecule between two nearby structures in an MD trajectory is governed by the corresponding energies. In this paper, we connect theory with simulation and analysis of equilibrium dynamics. A molecule navigates the energy landscape underlying the structure space. The structural states that are identified via off-the-shelf clustering algorithms need to be connected to thermodynamically-stable and semi-stable (macro)states among which transitions can then be quantified. Leveraging recent developments in the analysis of energy landscapes that identify basins in the landscape, we evaluate the hypothesis that basins, directly tied to stable and semi-stable states, lead to better models of dynamics. Our analysis indicates that basins lead to MSMs of better quality and thus can be useful to further advance this widely-used technology for summarization of molecular equilibrium dynamics.

Learning Organizations of Protein Energy Landscapes: An Application on Decoy Selection in Template-Free Protein Structure Prediction.

The protein energy landscape, which lifts the protein structure space by associating energies with structures, has been useful in improving our understanding of the relationship between structure, dynamics, and function. Currently, however, it is challenging to automatically extract and utilize the underlying organization of an energy landscape to the link structural states it houses to biological activity. In this chapter, we first report on two computational approaches that extract such an organization, one that ignores energies and operates directly in the structure space and another that operates on the energy landscape associated with the structure space. We then describe two complementary approaches, one based on unsupervised learning and another based on supervised learning. Both approaches utilize the extracted organization to address the problem of decoy selection in template-free protein structure prediction. The presented results make the case that learning organizations of protein energy landscapes advances our ability to link structures to biological activity.

Unsupervised and Supervised Learning over theEnergy Landscape for Protein Decoy Selection.

The energy landscape that organizes microstates of a molecular system and governs theunderlying molecular dynamics exposes the relationship between molecular form/structure, changesto form, and biological activity or function in the cell. However, several challenges stand in the wayof leveraging energy landscapes for relating structure and structural dynamics to function. Energylandscapes are high-dimensional, multi-modal, and often overly-rugged. Deep wells or basins inthem do not always correspond to stable structural states but are instead the result of inherentinaccuracies in semi-empirical molecular energy functions. Due to these challenges, energeticsis typically ignored in computational approaches addressing long-standing central questions incomputational biology, such as protein decoy selection. In the latter, the goal is to determine over apossibly large number of computationally-generated three-dimensional structures of a protein thosestructures that are biologically-active/native. In recent work, we have recast our attention on theprotein energy landscape and its role in helping us to advance decoy selection. Here, we summarizesome of our successes so far in this direction via unsupervised learning. More importantly, we furtheradvance the argument that the energy landscape holds valuable information to aid and advance thestate of protein decoy selection via novel machine learning methodologies that leverage supervisedlearning. Our focus in this article is on decoy selection for the purpose of a rigorous, quantitativeevaluation of how leveraging protein energy landscapes advances an important problem in proteinmodeling. However, the ideas and concepts presented here are generally useful to make discoveriesin studies aiming to relate molecular structure and structural dynamics to function.

IL-21 Attenuates FITC-Induced Contact Hypersensitivity Response via Regulation of Dendritic Cell Function.

IL-21 is mainly produced by activated CD4+ T cells and is involved in the activation of immune cells such as T cells and macrophages. In contrast, IL-21 suppresses dendritic cell maturation. We studied the effect of IL-21 in a mouse model of FITC-induced contact hypersensitivity using IL-21 isoform transgenic (IL-21iso-Tg) mice. Tissue inflammation at 24 hours after elicitation in IL-21iso-Tg mice was significantly weaker than that in wild-type mice. In agreement with tissue inflammation, recruitment of CD4+ and CD8+ T cells, neutrophils, and macrophages into the inflamed tissue was decreased in IL-21iso-Tg mice. In addition, both mRNA expression and protein production of inflammatory cytokines were lower in IL-21iso-Tg mice. In the skin, T cells were activated at inducible skin-associated lymphoid tissue, which is likely a gut-associated lymphoid tissue. The mRNA level of CXCL2, an essential chemokine for inducible skin-associated lymphoid tissue formation, was significantly lower in IL-21iso-Tg mice, and histological analysis showed that dendritic cell clustering, a preliminary step in inducible skin-associated lymphoid tissue formation, was impaired. Our study showed that IL-21 down-regulated inducible skin-associated lymphoid tissue formation and reduced contact hypersensitivity response.