Model for the irreversible dissociation kinetics of cooperatively bound protein–nucleic acid complexes

We present a quantitative model for the irreversible dissociation kinetics of cooperatively bound nonspecific protein–nucleic acid complexes. The model assumes that the major pathway of dissociation is via singly contiguously bound protein that “peels” off the ends of clusters of bound protein. It should therefore be most applicable for proteins that bind nucleic acids with high cooperativity (w > 10³). Furthermore, the model assumes that no redistribution of bound protein occurs during the time course of the dissociation. Solutions to the rate equations are presented for the entire time course of the dissociation. Under initial conditions such that the nucleic acid is less than fully saturated with protein, a single-exponential decay is predicted (if w is large). However, when the nucleic acid lattice is initially fully saturated, zero-order kinetics, corresponding to a constant rate of protein dissociation, is predicted. The experimental observation of zero-order dissociation kinetics in a cooperative protein–nucleic acid system is a good qualitative indicator for the dissociation mechanism discussed here. A discussion of the analysis of experimental data that enables one to extract molecular rate constants is presented. Furthermore, comparisons are made between the nonredistributing model presented here and Epstein's model [Epstein, I. R. (1979) Biopolymers 18 , 2037–2050] in which protein can translocate infinitely quickly while bound to the nucleic acid, and hence protein clusters redistribute during dissociation and maintain an equilibrium distribution on the nucleic acid at all times.     

Modeling the Hydration Layer around Proteins: HyPred

Protein hydration plays an integral role in determining protein function and stability. We develop a simple method with atomic level precision for predicting the solvent density near the surface of a protein. A set of proximal radial distribution functions are defined and calculated for a series of different atom types in proteins using all-atom, explicit solvent molecular dynamic simulations for three globular proteins. A major improvement in predicting the hydration layer is found when the protein is held immobile during the simulations. The distribution functions are used to develop a model for predicting the hydration layer with sub-1-Ångstrom resolution without the need for additional simulations. The model and the distribution functions for a given protein are tested in their ability to reproduce the hydration layer from the simulations for that protein, as well as those for other proteins and for simulations in which the protein atoms are mobile. Predictions for the density of water in the hydration shells are then compared with high occupancy sites observed in crystal structures. The accuracy of both tests demonstrates that the solvation model provides a basis for quantitatively understanding protein solvation and thereby predicting the hydration layer without additional simulations.     

基于点云卷积神经网络的蛋白质柔性预测

目的 蛋白质的柔性运动对生物体各种反应有着重要意义,基于蛋白质的空间结构预测其柔性运动是蛋白质结构-功能关系领域的重要问题.卷积神经网络(convolutional neural network,CNN)在蛋白质结构-功能关系研究中已有成功应用.方法 本研究借鉴计算机视觉研究中PointNet方法的思想,提出了一种蛋白...     

Localization of ligand binding site in proteins identified in silico

Knowledge-based models for protein folding assume that the early-stage structural form of a polypeptide is determined by the backbone conformation, followed by hydrophobic collapse. Side chain–side chain interactions, mostly of hydrophobic character, lead to the formation of the hydrophobic core, which seems to stabilize the structure of the protein in its natural environment. The fuzzy-oil-drop model is employed to represent the idealized hydrophobicity distribution in the protein molecule. Comparing it with the one empirically observed in the protein molecule reveals that they are not in agreement. It is shown in this study that the irregularity of hydrophobic distributions is aim-oriented. The character and strength of these irregularities in the organization of the hydrophobic core point to the specificity of a particular protein’s structure/function. When the location of these irregularities is determined versus the idealized fuzzy-oil-drop, function-related areas in the protein molecule can be identified. The presented model can also be used to identify ways in which protein–protein complexes can possibly be created. Active sites can be predicted for any protein structure according to the presented model with the free prediction server at . The implication based on the model presented in this work suggests the necessity of active presence of ligand during the protein folding process simulation. Figure Fuzzy-oil-drop model applied to identify the ligation site in lysozyme complexed with N-acetylglucosamine (PDB ID:1LMQ) in form of hydrophobicity deficiency (ΔH) profile and three-dimensional distribution of on protein surface     

静电纺丝纳米纤维体外释放与蛋白活性的研究

目的:通过选择不同的模型蛋白,探讨准确的研究静电纺丝纳米纤维支架的体外释放和快速的测定蛋白活性的方法.方法:通过O/W乳液法静电纺丝制备纳米纤维,并用扫描电镜对纳米纤维表面进行了表征.以GM-CSF为模型蛋白,采用ELISA双抗体夹心法考察纤维的体外释放行为;以BSA为模型蛋白,用SEC-H-PLC比较纤维制备前后蛋白的聚集情况;以β-半乳糖苷酶为模型蛋白,用ONPG法比较纤维制备前后酶的催化活性.结果:纤维表面平滑,直径均一,呈现互相连通的三维网状结构.纤维在5天内释放90％以上;纤维中回收的BSA单体比例为66.53％;β-半乳糖苷酶在纤维中的催化活性保持原活性的3.37％.结论:通过选择不同的模型蛋白,能够准确的测定静电纺丝纤维的体外释放,快速的考察纤维中的蛋白活性,对于更好的研究蛋白药物纳米纤维支架具有重要的参考价值.     

Mathematical Model of Growth and Heterologous Hantavirus Protein Production of the Recombinant Yeast Saccharomyces cerevisiae

A segregated mathematical model was developed for the analysis and interpretation of cultivation data of growth of the recombinant yeast Saccharomyces cerevisiae on multiple substrates (glucose, maltose, pyruvate, ethanol, acetate, and galactose). The model accounts for substrate consumption, plasmid stability, and production level of a model protein, a modified nucleocapsid protein of the Puumala virus. Recombinant nucleocapsid proteins from different Hantaviruses have previously been demonstrated as suitable antigens for diagnostics as well as for sero‐epidemiological studies. The model is based on a system of 10 nonlinear ordinary differential equations and accounts for the influence of various factors, e.g., selective pressure for enhancing plasmid stability by formaldehyde or the toxic effects of the intracellular accumulation of the heterologous protein on cell growth and product yield. The model allows the growth of two populations of cells to be simulated: plasmid‐bearing and plasmid‐free yeast cells, which have lost the plasmid during cultivation. Based on the model, sensitivity studies in respect to parameter changes were performed. These enabled, for example, the evaluation of the impact of an increase in the initial concentration of nutrients and growth factors (e.g., vitamins, microelements, etc.) on the biomass yield and the heterologous protein production level. As expected, the productivity of the heterologous protein in S. cerevisiae is closely correlated with plasmid stability. The 25 free model parameters, including the yield coefficients for different growth stages and dynamic constants, were estimated by nonlinear techniques, and the model was validated against a data set not used for parameter estimation. The simulation results were found to be in good agreement with the experimental data.     

Scoring predictive models using a reduced representation of proteins: model and energy definition

Background  

Reduced representations of proteins have been playing a keyrole in the study of protein folding. Many such models are available, with different representation detail. Although the usefulness of many such models for structural bioinformatics applications has been demonstrated in recent years, there are few intermediate resolution models endowed with an energy model capable, for instance, of detecting native or native-like structures among decoy sets. The aim of the present work is to provide a discrete empirical potential for a reduced protein model termed here PC2CA, because it employs a PseudoCovalent structure with only 2 Centers of interactions per Amino acid, suitable for protein model quality assessment.     

Discontinuous DNA replication: accumulation of Simian virus 40 DNA at specific stages in its replication.

The number of proline residues in a protein should have very marked consequences for the rates of protein unfolding and refolding according to the model proposed by Brandts et al. (1975). Kinetic simulations of this model indicate that the half-time for refolding of a polypeptide chain with 20 proline residues should be greater than 10 minutes and should increase by about an order of magnitude for each additional 10 proline residues. Various means are considered by which the rate of protein folding in vivo and in vitro might be increased.     

Importance of sequence specificity for predicting protein folding pathways: perturbed Gaussian chain model

Recent experimental and theoretical studies suggest that rates and pathways of protein folding are largely decided by topology of the native structures, at least for small proteins. However, some exceptions are known; for example, protein L and protein G have the same topology, but exhibit different characteristics of the TSE. Thus, folding pathways of some proteins are critically affected by detailed information on amino acid sequences. To investigate the sequence specificity, we calculate folding pathways of 20 small proteins using the perturbed Gaussian chain model developed by Portman et al. (Phys Rev Lett 1998;81:5237-5240; J Chem Phys 2001;114:5069-5081). Characteristics of the TSE predicted by the model are in good agreement with experimental phi-value data for many proteins at coarse-grained level. Especially, estimation of folding TSE for protein G and protein L based on both topology and additional sequence information are consistent with experimental phi-value data. With only topology information, however, the model predicts the TSE of protein G incorrectly. Moreover, the model that uses topology and sequence information describes free energy profiles of two-state and three-state folders consistently with experiment, whereas the topology only model predicts free energy profiles of some proteins incorrectly. This indicates that sequence specificity also has critical roles in determining the folding pathways for some proteins.     

Dilution of protein‐surfactant complexes: A fluorescence study

Dilution of protein–surfactant complexes is an integrated step in microfluidic protein sizing, where the contribution of free micelles to the overall fluorescence is reduced by dilution. This process can be further improved by establishing an optimum surfactant concentration and quantifying the amount of protein based on the fluorescence intensity. To this end, we study the interaction of proteins with anionic sodium dodecyl sulfate (SDS) and cationic hexadecyl trimethyl ammonium bromide (CTAB) using a hydrophobic fluorescent dye (sypro orange). We analyze these interactions fluourometrically with bovine serum albumin, carbonic anhydrase, and beta‐galactosidase as model proteins. The fluorescent signature of protein–surfactant complexes at various dilution points shows three distinct regions, surfactant dominant, breakdown, and protein dominant region. Based on the dilution behavior of protein–surfactant complexes, we propose a fluorescence model to explain the contribution of free and bound micelles to the overall fluorescence. Our results show that protein peak is observed at 3 mM SDS as the optimum dilution concentration. Furthermore, we study the effect of protein concentration on fluorescence intensity. In a single protein model with a constant dye quantum yield, the peak height increases with protein concentration. Finally, addition of CTAB to the protein–SDS complex at mole fractions above 0.1 shifts the protein peak from 3 mM to 4 mM SDS. The knowledge of protein–surfactant interactions obtained from these studies provides significant insights for novel detection and quantification techniques in microfluidics.     

The Interrelationship between Promoter Strength,Gene Expression,and Growth Rate

In exponentially growing bacteria, expression of heterologous protein impedes cellular growth rates. Quantitative understanding of the relationship between expression and growth rate will advance our ability to forward engineer bacteria, important for metabolic engineering and synthetic biology applications. Recently, a work described a scaling model based on optimal allocation of ribosomes for protein translation. This model quantitatively predicts a linear relationship between microbial growth rate and heterologous protein expression with no free parameters. With the aim of validating this model, we have rigorously quantified the fitness cost of gene expression by using a library of synthetic constitutive promoters to drive expression of two separate proteins (eGFP and amiE) in E. coli in different strains and growth media. In all cases, we demonstrate that the fitness cost is consistent with the previous findings. We expand upon the previous theory by introducing a simple promoter activity model to quantitatively predict how basal promoter strength relates to growth rate and protein expression. We then estimate the amount of protein expression needed to support high flux through a heterologous metabolic pathway and predict the sizable fitness cost associated with enzyme production. This work has broad implications across applied biological sciences because it allows for prediction of the interplay between promoter strength, protein expression, and the resulting cost to microbial growth rates.     

Exploring the folding energy landscapes of heme proteins using a hybrid AWSEM-heme model

Heme is an active center in many proteins. Here we explore computationally the role of heme in protein folding and protein structure. We model heme proteins using a hybrid model employing the AWSEM Hamiltonian, a coarse-grained forcefield for the protein chain along with AMBER, an all-atom forcefield for the heme. We carefully designed transferable force fields that model the interactions between the protein and the heme. The types of protein–ligand interactions in the hybrid model include thioester covalent bonds, coordinated covalent bonds, hydrogen bonds, and electrostatics. We explore the influence of different types of hemes (heme b and heme c) on folding and structure prediction. Including both types of heme improves the quality of protein structure predictions. The free energy landscape shows that both types of heme can act as nucleation sites for protein folding and stabilize the protein folded state. In binding the heme, coordinated covalent bonds and thioester covalent bonds for heme c drive the heme toward the native pocket. The electrostatics also facilitates the search for the binding site.

     

Homology Modeling and Functional Characterization of PR-1a Protein of Hordeum vulgare subsp. Vulgare

Pathogenesis-related protein 1a of Hordeum vulgare subsp. Vulgare (HvPR-1a) is induced by various pathogens and stress related factors. It plays important roles in plant defense system. Since the discovery of HvPR-1a a great deal of research has been focused on its isolation and characterization. However, three dimensional structure of HvPR-1a is still unknown. 3D structure can be used for determining protein function, and identifying novel protein folds and potential targets for regulation. The protein model was developed using MODELLER 9v10. Physicochemical characterization and functional annotation of the model carried out with Expasy''s ProtParam server and three different conserved domain finding programs including InterProScan, Proteins Families Database (Pfam), and NCBI Conserved Domains Database (NCBI-CDD). Applying validation programs revealed that the model has good quality and the RMSD value is 0.7. The predicted model submitted in Protein Model Database, PMDB for public use. This model will be used in wide range of studies for functional analysis and improvement activity of the protein.     

Rational design of the survivin/CDK4 complex by combining protein–protein docking and molecular dynamics simulations

Survivin, the smallest inhibitor of apoptosis protein (IAP), is a valid target for cancer research. It mediates both the apoptosis pathway and the cell cycle and has been proposed to form a complex with the cyclin-dependent kinase protein CDK4. The resulting complex transports CDK4 from the cytosol to the nucleus, where CDK4 participates in cell division. Survivin has been recognized as a node protein that interacts with several partners; disruption of the formed complexes can lead to new anticancer compounds. We propose a rational model of the survivin/CDK4 complex that fulfills the experimental evidence and that can be used for structure-based design of inhibitors modifying its interface recognition. In particular, the suggested complex involves the alpha helical domain of survivin and resembles the mode of binding of survivin in the survivin/borealin X-ray structure. The proposed model has been obtained by combining protein–protein docking, fractal-based shape complementarity, electrostatics studies and extensive molecular dynamics simulations.

Requirements for Hirano Body Formation

Hirano bodies are paracrystalline F-actin-rich structures associated with diverse conditions, including neurodegeneration and aging. Generation of model Hirano bodies using altered forms of Dictyostelium 34-kDa actin-bundling protein allows studies of their physiological function and mechanism of formation. We describe a novel 34-kDa protein mutant, E60K, with a point mutation within the inhibitory domain of the 34-kDa protein. Expression of E60K in Dictyostelium induces the formation of model Hirano bodies. The E60K protein has activated actin binding and is calcium regulated, unlike other forms of the 34-kDa protein that induce Hirano bodies and that have activated actin binding but lack calcium regulation. Actin filaments in the presence of E60K in vitro show enhanced resistance to disassembly induced by latrunculin B. Actin filaments in model Hirano bodies are also protected from latrunculin-induced depolymerization. We used nocodazole and blebbistatin to probe the role of the microtubules and myosin II, respectively, in the formation of model Hirano bodies. In the presence of these inhibitors, model Hirano bodies can form but are smaller than controls at early times of formation. The ultrastructure of model Hirano bodies did not reveal any major difference in structure and organization in the presence of inhibitors. In summary, these results support the conclusion that formation of model Hirano bodies is promoted by gain-of-function actin filament bundling, which enhances actin filament stabilization. Microtubules and myosin II contribute to but are not required for formation of model Hirano bodies.     

Protein complex detection with semi-supervised learning in protein interaction networks

Background

Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes. The systematic analysis of PPI networks can enable a great understanding of cellular organization, processes and function. In this paper, we investigate the problem of protein complex detection from noisy protein interaction data, i.e., finding the subsets of proteins that are closely coupled via protein interactions. However, protein complexes are likely to overlap and the interaction data are very noisy. It is a great challenge to effectively analyze the massive data for biologically meaningful protein complex detection.

Results

Many people try to solve the problem by using the traditional unsupervised graph clustering methods. Here, we stand from a different point of view, redefining the properties and features for protein complexes and designing a “semi-supervised” method to analyze the problem. In this paper, we utilize the neural network with the “semi-supervised” mechanism to detect the protein complexes. By retraining the neural network model recursively, we could find the optimized parameters for the model, in such a way we can successfully detect the protein complexes. The comparison results show that our algorithm could identify protein complexes that are missed by other methods. We also have shown that our method achieve better precision and recall rates for the identified protein complexes than other existing methods. In addition, the framework we proposed is easy to be extended in the future.

Conclusions

Using a weighted network to represent the protein interaction network is more appropriate than using a traditional unweighted network. In addition, integrating biological features and topological features to represent protein complexes is more meaningful than using dense subgraphs. Last, the “semi-supervised” learning model is a promising model to detect protein complexes with more biological and topological features available.

     

AbsoluRATE: An in-silico method to predict the aggregation kinetics of native proteins

Protein aggregation has two aspects, namely, mechanistic and kinetics. Understanding protein aggregation kinetics is critical for prediction of progression of diseases caused by amyloidosis, accumulation of aggregates in biotherapeutics during storage and engineering commercial nano-biomaterials. In this work, we have collected experimentally determined absolute protein aggregation rates and developed an SVM based regression model to predict absolute rates of protein and peptide aggregation near-physiological conditions. The regression model achieved a correlation coefficient of 0.72 with MAE of 0.91 (natural log of k_app, where k_app is in hour^?1) using leave-one-out cross-validation on a dataset of 82 non-redundant proteins/peptides. The model accounts for the experimental conditions (such as temperature, pH, ionic and protein concentration) and sequence-based properties. The amino acid sequence features revealed by this model as being important for aggregation kinetics, are also associated with the aggregation mechanism. In particular, inherent aggregation propensity of the protein/peptide sequence and number of aggregation prone regions (APRs) unpunctuated by the gatekeeping residues, were found to play important roles in the prediction of the absolute aggregation rates. This analysis shows that mechanism and kinetics of protein aggregation are coupled via common sequence attributes. The aggregation kinetic prediction method developed in this work is available at https://web.iitm.ac.in/bioinfo2/absolurate-pred/index.html.     

The Path from the RNA World

We describe a sequential (step by step) Darwinian model for the evolution of life from the late stages of the RNA world through to the emergence of eukaryotes and prokaryotes. The starting point is our model, derived from current RNA activity, of the RNA world just prior to the advent of genetically-encoded protein synthesis. By focusing on the function of the protoribosome we develop a plausible model for the evolution of a protein-synthesizing ribosome from a high-fidelity RNA polymerase that incorporated triplets of oligonucleotides. With the standard assumption that during the evolution of enzymatic activity, catalysis is transferred from RNA → RNP → protein, the first proteins in the ``breakthrough organism' (the first to have encoded protein synthesis) would be nonspecific chaperone-like proteins rather than catalytic. Moreover, because some RNA molecules that pre-date protein synthesis under this model now occur as introns in some of the very earliest proteins, the model predicts these particular introns are older than the exons surrounding them, the ``introns-first' theory. Many features of the model for the genome organization in the final RNA world ribo-organism are more prevalent in the eukaryotic genome and we suggest that the prokaryotic genome organization (a single, circular genome with one center of replication) was derived from a ``eukaryotic-like' genome organization (a fragmented linear genome with multiple centers of replication). The steps from the proposed ribo-organism RNA genome → eukaryotic-like DNA genome → prokaryotic-like DNA genome are all relatively straightforward, whereas the transition prokaryotic-like genome → eukaryotic-like genome appears impossible under a Darwinian mechanism of evolution, given the assumption of the transition RNA → RNP → protein. A likely molecular mechanism, ``plasmid transfer,' is available for the origin of prokaryotic-type genomes from an eukaryotic-like architecture. Under this model prokaryotes are considered specialized and derived with reduced dependence on ssRNA biochemistry. A functional explanation is that prokaryote ancestors underwent selection for thermophily (high temperature) and/or for rapid reproduction (r selection) at least once in their history. Received: 14 January 1997 / Accepted: 19 May 1997