OmniMerge: A Systematic Approach to Constrained Conformational Search

OmniMerge performs a systematic search to enumerate all conformations of a molecule (at a given level of torsion-angle resolution) that satisfy a set of local geometric constraints. Constraints would typically come from NMR experiments, but applications such as docking or homology modeling could also give rise to similar constraints. The molecule to be searched is partitioned into small subchains so that the set of possible conformations for the whole molecule may be constructed by merging the feasible conformations for the subchain parts. However, instead of using a binary tree for straightforward divide-and-conquer, OmniMerge defines a sub-problem for every possible subchain of the molecule. Searching every subchain provides a counter-intuitive advantage: with every possible subdivision available for merging, one may choose the most favorable merge for each subchain, particularly for the bottleneck chain(s). Improving the bottleneck step may therefore cause the whole search to be completed more quickly. Finally, to discard infeasible conformations more rapidly, OmniMerge filters the solution set of each subchain based on compatibility with the solutions sets of all overlapping subchains. These two innovations—choosing the most favorable merges and enforcing consistency between overlapping subchains—yield significant improvements in run time. By determining the extent of structural variability permitted by a set of constraints, OmniMerge offers the potential to aid error analysis and improve confidence for NMR results on peptides and moderate-sized molecules.Singapore-MIT Alliance (SMA

Systematic conformational search with constraint satisfaction

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 170-177).Determining the conformations of biological molecules is a high scientific priority for biochemists and for the pharmaceutical industry. This thesis describes a systematic method for conformational search, an application of the method to determining the structure of the formyl-Met-Leu-Phe-OH (fMLF)peptide by solid-state NMR spectroscopy, and a separate project to determine the structure of a protein-DNA complex by X-ray crystallography. The purpose of the systematic search method is to enumerate all conformations of a molecule (at a given level of torsion angle resolution) that satisfy a set of local geometric constraints. Constraints would typically come from NMR experiments, but applications such as docking or homology modelling could also give rise to similar constraints. The molecule to be searched is partitioned into small subchains so that the set of possible conformations for the whole molecule may be constructed by merging the feasible conformations for the parts. However, instead of using a binary tree for straightforward divide-and-conquer, four innovations are introduced: (1) OMNIMERGE searches a subproblem for every possible subchain of the molecule. Searching every subchain provides the advantage that every possible merge is available; by choosing the most favorable merge for each subchain, the bottleneck subchain(s) and therefore the whole search may be completed more efficiently. (2) A cost function evaluates alternative divide-and-conquer trees, provided that a preliminary OMNIMERGE search of the molecule has been completed. Then dynamic programming determines the optimal partitioning or "merge-tree" for the molecule; this merge-tree can be used to improve the efficiency of future searches.(cont.) (3) PROPAGATION shares information by enforcing arc consistency between the solution sets of overlapping subchains. By filtering the solution set of each subchain, infeasible conformations are discarded rapidly. (4) An A* function prioritizes each subchain based on estimated future costs. Subchains with sufficiently low priority can be skipped, which improves efficiency. A common theme of these four ideas is to make good choices about how to break the large search problem into lower-dimensional subproblems. These novel algorithms were implemented and the effectiveness of each is demonstrated on a well-constrained peptide with 40 degrees of freedom.by Lisa Tucker-Kellogg.Ph.D

Superpixel-based segmentation of muscle fibers in multi-channel microscopy

Background Confetti fluorescence and other multi-color genetic labelling strategies are useful for observing stem cell regeneration and for other problems of cell lineage tracing. One difficulty of such strategies is segmenting the cell boundaries, which is a very different problem from segmenting color images from the real world. This paper addresses the difficulties and presents a superpixel-based framework for segmentation of regenerated muscle fibers in mice. Results We propose to integrate an edge detector into a superpixel algorithm and customize the method for multi-channel images. The enhanced superpixel method outperforms the original and another advanced superpixel algorithm in terms of both boundary recall and under-segmentation error. Our framework was applied to cross-section and lateral section images of regenerated muscle fibers from confetti-fluorescent mice. Compared with “ground-truth” segmentations, our framework yielded median Dice similarity coefficients of 0.92 and higher. Conclusion Our segmentation framework is flexible and provides very good segmentations of multi-color muscle fibers. We anticipate our methods will be useful for segmenting a variety of tissues in confetti fluorecent mice and in mice with similar multi-color labels.National University of Singapore (Duke-NUS SRP Phase 2 Research Block Grant)Singapore. National Research Foundation (CREATE programme)Singapore-MIT Alliance for Research and Technology (SMART

The Self-Limiting Dynamics of TGF-β Signaling In Silico and In Vitro, with Negative Feedback through PPM1A Upregulation

The TGF-β/Smad signaling system decreases its activity through strong negative regulation. Several molecular mechanisms of negative regulation have been published, but the relative impact of each mechanism on the overall system is unknown. In this work, we used computational and experimental methods to assess multiple negative regulatory effects on Smad signaling in HaCaT cells. Previously reported negative regulatory effects were classified by time-scale: degradation of phosphorylated R-Smad and I-Smad-induced receptor degradation were slow-mode effects, and dephosphorylation of R-Smad was a fast-mode effect. We modeled combinations of these effects, but found no combination capable of explaining the observed dynamics of TGF-β/Smad signaling. We then proposed a negative feedback loop with upregulation of the phosphatase PPM1A. The resulting model was able to explain the dynamics of Smad signaling, under both short and long exposures to TGF-β. Consistent with this model, immuno-blots showed PPM1A levels to be significantly increased within 30 min after TGF-β stimulation. Lastly, our model was able to resolve an apparent contradiction in the published literature, concerning the dynamics of phosphorylated R-Smad degradation. We conclude that the dynamics of Smad negative regulation cannot be explained by the negative regulatory effects that had previously been modeled, and we provide evidence for a new negative feedback loop through PPM1A upregulation. This work shows that tight coupling of computational and experiments approaches can yield improved understanding of complex pathways.Singapore-MIT AllianceMechanobiology Institute, SingaporeInstitute of Bioengineering and Nanotechnology (Singapore)National University of Singapore (NUS Graduate School for Integrative Sciences and Engineering scholar)Singapore-MIT Alliance for Research and Technolog

Engrailed (Gln50→Lys) homeodomain–DNA complex at 1.9 Å resolution: structural basis for enhanced affinity and altered specificity

AbstractBackground: The homeodomain is one of the key DNA-binding motifs used in eukaryotic gene regulation, and homeodomain proteins play critical roles in development. The residue at position 50 of many homeodomains appears to determine the differential DNA-binding specificity, helping to distinguish among binding sites of the form TAATNN. However, the precise role(s) of residue 50 in the differential recognition of alternative sites has not been clear. None of the previously determined structures of homeodomain–DNA complexes has shown evidence for a stable hydrogen bond between residue 50 and a base, and there has been much discussion, based in part on NMR studies, about the potential importance of water-mediated contacts. This study was initiated to help clarify some of these issues.Results: The crystal structure of a complex containing the engrailed Gln50→Lys variant (QK50) with its optimal binding site TAATCC (versus TAATTA for the wild-type protein) has been determined at 1.9 Å resolution. The overall structure of the QK50 variant is very similar to that of the wild-type complex, but the sidechain of Lys50 projects directly into the major groove and makes several hydrogen bonds to the O6 and N7 atoms of the guanines at base pairs 5 and 6. Lys50 also makes an additional water-mediated contact with the guanine at base pair 5 and has an alternative conformation that allows a hydrogen bond with the O4 of the thymine at base pair 4.Conclusions: The structural context provided by the folding and docking of the engrailed homeodomain allows Lys50 to make remarkably favorable contacts with the guanines at base pairs 5 and 6 of the binding site. Although many different residues occur at position 50 in different homeodomains, and although numerous position 50 variants have been constructed, the most striking examples of altered specificity usually involve introducing or removing a lysine sidechain from position 50. This high-resolution structure also confirms the critical role of Asn51 in homeodomain–DNA recognition and further clarifies the roles of water molecules near residues 50 and 51

SPEDRE: a web server for estimating rate parameters for cell signaling dynamics in data-rich environments

Cell signaling pathways and metabolic networks are often modeled using ordinary differential equations (ODEs) to represent the production/consumption of molecular species over time. Regardless whether a model is built de novo or adapted from previous models, there is a need to estimate kinetic rate constants based on time-series experimental measurements of molecular abundance. For data-rich cases such as proteomic measurements of all species, spline-based parameter estimation algorithms have been developed to avoid solving all the ODEs explicitly. We report the development of a web server for a spline-based method. Systematic Parameter Estimation for Data-Rich Environments (SPEDRE) estimates reaction rates for biochemical networks. As input, it takes the connectivity of the network and the concentrations of the molecular species at discrete time points. SPEDRE is intended for large sparse networks, such as signaling cascades with many proteins but few reactions per protein. If data are available for all species in the network, it provides global coverage of the parameter space, at low resolution and with approximate accuracy. The output is an optimized value for each reaction rate parameter, accompanied by a range and bin plot. SPEDRE uses tools from COPASI for pre-processing and post-processing. SPEDRE is a free service at http://LTKLab.org/SPEDRE.Singapore-MIT Alliance (IUP R-154-001-348-646

A classification model for non-alcoholic steatohepatitis (NASH) using confocal Raman micro-spectroscopy

We combined Raman micro-spectroscopy and machine learning techniques to develop a classification model based on a well-established non-alcoholic steatohepatitis (NASH) mouse model, using spectrum pre-processing, biochemical component analysis (BCA) and logistic regression

Membrane tension controls adhesion positioning at the leading edge of cells

Cell migration is dependent on adhesion dynamics and actin cytoskeleton remodeling at the leading edge. These events may be physically constrained by the plasma membrane. Here, we show that the mechanical signal produced by an increase in plasma membrane tension triggers the positioning of new rows of adhesions at the leading edge. During protrusion, as membrane tension increases, velocity slows, and the lamellipodium buckles upward in a myosin II-independent manner. The buckling occurs between the front of the lamellipodium, where nascent adhesions are positioned in rows, and the base of the lamellipodium, where a vinculin-dependent clutch couples actin to previously positioned adhesions. As membrane tension decreases, protrusion resumes and buckling disappears, until the next cycle. We propose that the mechanical signal of membrane tension exerts upstream control in mechanotransduction by periodically compressing and relaxing the lamellipodium, leading to the positioning of adhesions at the leading edge of cells

Predicting In Vivo Anti-Hepatofibrotic Drug Efficacy Based on In Vitro High-Content Analysis

Background/Aims Many anti-fibrotic drugs with high in vitro efficacies fail to produce significant effects in vivo. The aim of this work is to use a statistical approach to design a numerical predictor that correlates better with in vivo outcomes. Methods High-content analysis (HCA) was performed with 49 drugs on hepatic stellate cells (HSCs) LX-2 stained with 10 fibrotic markers. ~0.3 billion feature values from all cells in >150,000 images were quantified to reflect the drug effects. A systematic literature search on the in vivo effects of all 49 drugs on hepatofibrotic rats yields 28 papers with histological scores. The in vivo and in vitro datasets were used to compute a single efficacy predictor (Epredict). Results We used in vivo data from one context (CCl4 rats with drug treatments) to optimize the computation of Epredict. This optimized relationship was independently validated using in vivo data from two different contexts (treatment of DMN rats and prevention of CCl4 induction). A linear in vitro-in vivo correlation was consistently observed in all the three contexts. We used Epredict values to cluster drugs according to efficacy; and found that high-efficacy drugs tended to target proliferation, apoptosis and contractility of HSCs. Conclusions The Epredict statistic, based on a prioritized combination of in vitro features, provides a better correlation between in vitro and in vivo drug response than any of the traditional in vitro markers considered.Institute of Bioengineering and Nanotechnology (Singapore)Singapore. Biomedical Research CouncilSingapore. Agency for Science, Technology and ResearchSingapore-MIT Alliance for Research and Technology Center (C-185-000-033-531)Janssen Cilag (R-185-000-182-592)Singapore-MIT Alliance Computational and Systems Biology Flagship Project (C-382-641-001-091)Mechanobiology Institute, Singapore (R-714-001-003-271