A Structure-Based Model for Predicting Serum Albumin Binding

One of the many factors involved in determining the distribution and metabolism of a compound is the strength of its binding to human serum albumin. While experimental and QSAR approaches for determining binding to albumin exist, various factors limit their ability to provide accurate binding affinity for novel compounds. Thus, to complement the existing tools, we have developed a structure-based model of serum albumin binding. Our approach for predicting binding incorporated the inherent flexibility and promiscuity known to exist for albumin. We found that a weighted combination of the predicted logP and docking score most accurately distinguished between binders and nonbinders. This model was successfully used to predict serum albumin binding in a large test set of therapeutics that had experimental binding data.     

Ribosomal Protein S27-Like in Colorectal Cancer: A Candidate for Predicting Prognoses

Background

The development and progression of colorectal cancer (CRC) involve a complex process of multiple genetic changes. Tumor suppressor p53 is capable of determining the fate of CRC cells. However, the role of a p53-inducible modulator, ribosomal protein S27-like (RPS27L), in CRC is unknown.

Methods

Here, the differential expression of RPS27L was examined in the feces and colonic tissues of CRC patients, to explore its possible correlation with patient survival and to investigate the cellular mechanisms underlying their clinical outcomes. Eighty intermediate-stage CRC patients (42 at stage II and 38 at stage III) were divided into two groups according to their fecal RPS27L mRNA levels. The survival probabilities of the groups were estimated using the Kaplan–Meier method. The RPS27L protein in the colonic tissues of stage III patients with different prognoses was further examined immunohistochemically. RPS27L expression in LoVo cells was manipulated to examine the possible cellular responses in vitro.

Results

Elevated RPS27L expression, in either feces or tissues, was related to a better prognosis. In vitro, RPS27L-expressing LoVo cells ceased DNA synthesis and apoptotic activity while the expression of their DNA repair molecules was upregulated.

Conclusions

Elevated RPS27L may improve the prognoses of certain CRC patients by enhancing the DNA repair capacity of their colonic cells, and can be determined in feces. By integrating clinical, molecular, and cellular data, our study demonstrates that fecal RPS27L may be a useful index for predicting prognoses and guiding personalized therapeutic strategies, especially in patients with intermediate-stage CRC.     

A Self-Scaffolding Model for G Protein Signaling

Activation of heterotrimeric G proteins is generally believed to induce dissociation of Gα and Gβγ subunits, which are then free to bind to and change the catalytic activity of a variety of intracellular enzymes. We have previously found that in cells, Gαq subunits remain complexed with its major effector, phospholipase Cβ1, through the activation cycle. To determine whether this behavior may be operative in other systems, we carried out Förster resonance energy transfer studies and found that eYFP-Gαi and eCFP-Gβγ remain associated after stimulation in HEK293 cells. We also found that the level of Forster resonance energy transfer between Alexa546-phospholipase Cβ2 and eGFP-Gβγ is significant and unchanged upon activation in HEK293 cells, thus showing that these proteins can localize into stable signaling complexes. To understand the basis for this stabilization, we carried out in vitro studies using a series of single-Cys mutants labeled with fluorescence tags and monitored their interaction with Gβγ subunits and changes in their fluorescence properties and accessibility upon activation and Gβγ binding. Our studies suggest a significant change in the orientation between G protein subunits upon activation that allows the G proteins to remain complexed while activating effectors.     

Assessing Predictors of Changes in Protein Stability upon Mutation Using Self-Consistency

The ability to predict the effect of mutations on protein stability is important for a wide range of tasks, from protein engineering to assessing the impact of SNPs to understanding basic protein biophysics. A number of methods have been developed that make these predictions, but assessing the accuracy of these tools is difficult given the limitations and inconsistencies of the experimental data. We evaluate four different methods based on the ability of these methods to generate consistent results for forward and back mutations, and examine how this ability varies with the nature and location of the mutation. We find that, while one method seems to outperform the others, the ability of these methods to make accurate predictions is limited.     

A Neural Network for Predicting the Stability of DNA/DNA Duplexes

A back-propagation neural network method has been developed to predict the stability of DNA/DNA duplexes. Calculated T_m with the present parameters fits the experimental values within reasonable errors (AD = 1.86 K, SEP = 1.99151, R2 = 0.9894 for NN1; AD = 1.59667 K, SEP = 2.03824, R2 = 0.99371 for NN2), and it has the advantage that the determinations of thermodynamic parameters are not needed.     

区间离散时滞广义系统的鲁棒稳定性

讨论了一类区间离散时滞广义系统的鲁棒稳定性问题,在给出区间离散时滞广义系统的等价描述之后,基于系统参数矩阵不等式,分别得出了时滞独立和时滞依赖情况下区间离散广义系统稳定的充分条件,数值例子说明了该方法的有效性．     

A Computational Model for Predicting RNase H Domain of Retrovirus

RNase H (RNH) is a pivotal domain in retrovirus to cleave the DNA-RNA hybrid for continuing retroviral replication. The crucial role indicates that RNH is a promising drug target for therapeutic intervention. However, annotated RNHs in UniProtKB database have still been insufficient for a good understanding of their statistical characteristics so far. In this work, a computational RNH model was proposed to annotate new putative RNHs (np-RNHs) in the retroviruses. It basically predicts RNH domains through recognizing their start and end sites separately with SVM method. The classification accuracy rates are 100%, 99.01% and 97.52% respectively corresponding to jack-knife, 10-fold cross-validation and 5-fold cross-validation test. Subsequently, this model discovered 14,033 np-RNHs after scanning sequences without RNH annotations. All these predicted np-RNHs and annotated RNHs were employed to analyze the length, hydrophobicity and evolutionary relationship of RNH domains. They are all related to retroviral genera, which validates the classification of retroviruses to a certain degree. In the end, a software tool was designed for the application of our prediction model. The software together with datasets involved in this paper can be available for free download at https://sourceforge.net/projects/rhtool/files/?source=navbar.     

A Phenomenological Model for Predicting Melting Temperatures of DNA Sequences

We report here a novel method for predicting melting temperatures of DNA sequences based on a molecular-level hypothesis on the phenomena underlying the thermal denaturation of DNA. The model presented here attempts to quantify the energetic components stabilizing the structure of DNA such as base pairing, stacking, and ionic environment which are partially disrupted during the process of thermal denaturation. The model gives a Pearson product-moment correlation coefficient (r) of ∼0.98 between experimental and predicted melting temperatures for over 300 sequences of varying lengths ranging from 15-mers to genomic level and at different salt concentrations. The approach is implemented as a web tool (www.scfbio-iitd.res.in/chemgenome/Tm_predictor.jsp) for the prediction of melting temperatures of DNA sequences.     

A Computational Model for Predicting Nanoparticle Accumulation in Tumor Vasculature

Vascular targeting of malignant tissues with systemically injected nanoparticles (NPs) holds promise in molecular imaging and anti-angiogenic therapies. Here, a computational model is presented to predict the development of tumor neovasculature over time and the specific, vascular accumulation of blood-borne NPs. A multidimensional tumor-growth model is integrated with a mesoscale formulation for the NP adhesion to blood vessel walls. The fraction of injected NPs depositing within the diseased vasculature and their spatial distribution is computed as a function of tumor stage, from 0 to day 24 post-tumor inception. As the malignant mass grows in size, average blood flow and shear rates increase within the tumor neovasculature, reaching values comparable with those measured in healthy, pre-existing vessels already at 10 days. The NP vascular affinity, interpreted as the likelihood for a blood-borne NP to firmly adhere to the vessel walls, is a fundamental parameter in this analysis and depends on NP size and ligand density, and vascular receptor expression. For high vascular affinities, NPs tend to accumulate mostly at the inlet tumor vessels leaving the inner and outer vasculature depleted of NPs. For low vascular affinities, NPs distribute quite uniformly intra-tumorally but exhibit low accumulation doses. It is shown that an optimal vascular affinity can be identified providing the proper balance between accumulation dose and uniform spatial distribution of the NPs. This balance depends on the stage of tumor development (vascularity and endothelial receptor expression) and the NP properties (size, ligand density and ligand-receptor molecular affinity). Also, it is demonstrated that for insufficiently developed vascular networks, NPs are transported preferentially through the healthy, pre-existing vessels, thus bypassing the tumor mass. The computational tool described here can effectively select an optimal NP formulation presenting high accumulation doses and uniform spatial intra-tumor distributions as a function of the development stage of the malignancy.     

Solvation Free Energy of the Peptide Group: Its Model Dependence and Implications for the Additive-Transfer Free-Energy Model of Protein Stability

The group-additive decomposition of the unfolding free energy of a protein in an osmolyte solution relative to that in water poses a fundamental paradox: whereas the decomposition describes the experimental results rather well, theory suggests that a group-additive decomposition of free energies is, in general, not valid. In a step toward resolving this paradox, here we study the peptide-group transfer free energy. We calculate the vacuum-to-solvent (solvation) free energies of (Gly)_n and cyclic diglycine (cGG) and analyze the data according to experimental protocol. The solvation free energies of (Gly)_n are linear in n, suggesting group additivity. However, the slope interpreted as the free energy of a peptide unit differs from that for cGG scaled by a factor of half, emphasizing the context dependence of solvation. However, the water-to-osmolyte transfer free energies of the peptide unit are relatively independent of the peptide model, as observed experimentally. To understand these observations, a way to assess the contribution to the solvation free energy of solvent-mediated correlation between distinct groups is developed. We show that linearity of solvation free energy with n is a consequence of uniformity of the correlation contributions, with apparent group-additive behavior in the water-to-osmolyte transfer arising due to their cancellation. Implications for inferring molecular mechanisms of solvent effects on protein stability on the basis of the group-additive transfer model are suggested.     

Solvation Free Energy of the Peptide Group: Its Model Dependence and Implications for the Additive-Transfer Free-Energy Model of Protein Stability

The group-additive decomposition of the unfolding free energy of a protein in an osmolyte solution relative to that in water poses a fundamental paradox: whereas the decomposition describes the experimental results rather well, theory suggests that a group-additive decomposition of free energies is, in general, not valid. In a step toward resolving this paradox, here we study the peptide-group transfer free energy. We calculate the vacuum-to-solvent (solvation) free energies of (Gly)_n and cyclic diglycine (cGG) and analyze the data according to experimental protocol. The solvation free energies of (Gly)_n are linear in n, suggesting group additivity. However, the slope interpreted as the free energy of a peptide unit differs from that for cGG scaled by a factor of half, emphasizing the context dependence of solvation. However, the water-to-osmolyte transfer free energies of the peptide unit are relatively independent of the peptide model, as observed experimentally. To understand these observations, a way to assess the contribution to the solvation free energy of solvent-mediated correlation between distinct groups is developed. We show that linearity of solvation free energy with n is a consequence of uniformity of the correlation contributions, with apparent group-additive behavior in the water-to-osmolyte transfer arising due to their cancellation. Implications for inferring molecular mechanisms of solvent effects on protein stability on the basis of the group-additive transfer model are suggested.     

A Maltose-Binding Protein Fusion Construct Yields a Robust Crystallography Platform for MCL1

Crystallization of a maltose-binding protein MCL1 fusion has yielded a robust crystallography platform that generated the first apo MCL1 crystal structure, as well as five ligand-bound structures. The ability to obtain fragment-bound structures advances structure-based drug design efforts that, despite considerable effort, had previously been intractable by crystallography. In the ligand-independent crystal form we identify inhibitor binding modes not observed in earlier crystallographic systems. This MBP-MCL1 construct dramatically improves the structural understanding of well-validated MCL1 ligands, and will likely catalyze the structure-based optimization of high affinity MCL1 inhibitors.     

MOTIPS: Automated Motif Analysis for Predicting Targets of Modular Protein Domains

Background  

Many protein interactions, especially those involved in signaling, involve short linear motifs consisting of 5-10 amino acid residues that interact with modular protein domains such as the SH3 binding domains and the kinase catalytic domains. One straightforward way of identifying these interactions is by scanning for matches to the motif against all the sequences in a target proteome. However, predicting domain targets by motif sequence alone without considering other genomic and structural information has been shown to be lacking in accuracy.     

A Model for CDK2 in Maintaining Genomic Stability

Abundant CDK2/cyclin A activity is present in human cancer cells, suggesting that rapid S phase CDK2 inhibition would be an effective anti-cancer approach. The dynamic change of chromatin-loading and -dissociation of MCM proteins requires S phase CDK2 activity. CDK2 inhibition during replication leads to increased MCM complex association with DNA and triggers rereplication. Overreplication-induced DSB and RPA-ssDNA intermediates activate ATM and ATR, resulting in a p53 response which selectively deletes cells with unresolved rereplication.     

A Robust Feedforward Model of the Olfactory System

Most natural odors have sparse molecular composition. This makes the principles of compressed sensing potentially relevant to the structure of the olfactory code. Yet, the largely feedforward organization of the olfactory system precludes reconstruction using standard compressed sensing algorithms. To resolve this problem, recent theoretical work has shown that signal reconstruction could take place as a result of a low dimensional dynamical system converging to one of its attractor states. However, the dynamical aspects of optimization slowed down odor recognition and were also found to be susceptible to noise. Here we describe a feedforward model of the olfactory system that achieves both strong compression and fast reconstruction that is also robust to noise. A key feature of the proposed model is a specific relationship between how odors are represented at the glomeruli stage, which corresponds to a compression, and the connections from glomeruli to third-order neurons (neurons in the olfactory cortex of vertebrates or Kenyon cells in the mushroom body of insects), which in the model corresponds to reconstruction. We show that should this specific relationship hold true, the reconstruction will be both fast and robust to noise, and in particular to the false activation of glomeruli. The predicted connectivity rate from glomeruli to third-order neurons can be tested experimentally.     

A Model for Shaping Membrane Sheets by Protein Scaffolds

Membranes of peripheral endoplasmic reticulum form intricate morphologies consisting of tubules and sheets as basic elements. The physical mechanism of endoplasmic-reticulum shaping has been suggested to originate from the elastic behavior of the sheet edges formed by linear arrays of oligomeric protein scaffolds. The heart of this mechanism, lying in the relationships between the structure of the protein scaffolds and the effective intrinsic shapes and elastic properties of the sheets’ edges, has remained hypothetical. Here we provide a detailed computational analysis of these issues. By minimizing the elastic energy of membrane bending, we determine the effects of a rowlike array of semicircular arclike membrane scaffolds on generation of a membrane fold, which shapes the entire membrane surface into a flat double-membrane sheet. We show, quantitatively, that the sheet’s edge line tends to adopt a positive or negative curvature depending on the scaffold’s geometrical parameters. We compute the effective elastic properties of the sheet edge and analyze the dependence of the equilibrium distance between the scaffolds along the edge line on the scaffold geometry.