Determination of dendrigraft poly-L-lysine diffusion coefficients by taylor dispersion analysis

This work focuses on the physicochemical characterization of dendrigraft poly-L-lysines (DGLs) obtained by polymerization of N-carboxyanhydride in buffered water (pH 6.5). Diffusion coefficients (D) and hydrodynamic radii (Rh) of five successive DGL generations were determined by Taylor dispersion analysis (TDA). To our knowledge, this is the first experimental work using TDA for the characterization of dendrimer-like structures. Experimental Rh values obtained by TDA were compared to those derived from dynamic light scattering and size exclusion chromatography coupled to a triple detection (refractive index, viscosimetry, and static light scattering). Significant differences were obtained, especially for the highest generations, as a result of the inherent contribution of aggregates to the light scattering intensity. For that reason, TDA was found to be the most appropriate technique for determining the D values of these hyperbranched macromolecules. Regarding their physicochemical behavior, the experimental results confirm that DGLs are very similar to trifunctional dendrimers (exponential growth of the molar mass, almost linear variation of the hydrodynamic radius, high branching density, and maximum of the intrinsic viscosity or of the free volume fraction for generation 4).     

Using surface envelopes for discrimination of molecular models

Shape information about macromolecules is increasingly available but is difficult to use in modeling efforts. We demonstrate that shape information alone can often distinguish structural models of biological macromolecules. By using a data structure called a surface envelope (SE) to represent the shape of the molecule, we propose a method that generates a fitness score for the shape of a particular molecular model. This score correlates well with root mean squared deviation (RMSD) of the model to the known test structures and can be used to filter models in decoy sets. The scoring method requires both alignment of the model to the SE in three-dimensional space and assessment of the degree to which atoms in the model fill the SE. Alignment combines a hybrid algorithm using principal components and a previously published iterated closest point algorithm. We test our method against models generated from random atom perturbation from crystal structures, published decoy sets used in structure prediction, and models created from the trajectories of atoms in molecular modeling runs. We also test our alignment algorithm against experimental electron microscopic data from rice dwarf virus. The alignment performance is reliable, and we show a high correlation between model RMSD and score function. This correlation is stronger for molecular models with greater oblong character (as measured by the ratio of largest to smallest principal component).     

Three-dimensional structure for the beta 2 adrenergic receptor protein based on computer modeling studies.

Computer-aided model building techniques have been used to construct three-dimensional model structures for hamster beta 2 adrenergic receptor. Experimental data were used as constraints to guide the model building procedure, and a number of rather strict criteria were applied to assess the physical plausibility of model structures. We present details of our best model structure to date, which is consistent with a large body of experimental data. We also discuss in detail our model building procedures and evaluation criteria, which we believe may be of general utility in modeling projects.     

Generic technique to generate large branched DNA complexes

The inherent self-recognition properties of DNA have led to its use as a scaffold for various nanotechnology self-assembly applications, with macromolecular complexes, metallic and semiconducting nanoparticles, proteins, inter alia, being assembled onto a designed DNA scaffold. Such structures may typically comprise a number of DNA molecules organized into macromolecules. Many studies have used synthetic methods to produce the constituent DNA molecules, but this typically constrains the molecules to be no longer than around 100 base pairs (30 nm). However, applications that require larger self-assembling DNA complexes, several tens of nanometers or more, need to be generated by other techniques. Here, we present a generic technique to generate large linear, branched, and/or circular DNA macromolecular complexes. The effectiveness of this technique is demonstrated here by the use of Lambda Bacteriophage DNA as a template to generate single- and double-branched DNA structures approximately 120 nm in size.     

Automated real-space refinement of protein structures using a realistic backbone move set

Crystals of many important biological macromolecules diffract to limited resolution, rendering accurate model building and refinement difficult and time-consuming. We present a torsional optimization protocol that is applicable to many such situations and combines Protein Data Bank-based torsional optimization with real-space refinement against the electron density derived from crystallography or cryo-electron microscopy. Our method converts moderate- to low-resolution structures at initial (e.g., backbone trace only) or late stages of refinement to structures with increased numbers of hydrogen bonds, improved crystallographic R-factors, and superior backbone geometry. This automated method is applicable to DNA-binding and membrane proteins of any size and will aid studies of structural biology by improving model quality and saving considerable effort. The method can be extended to improve NMR and other structures. Our backbone score and its sequence profile provide an additional standard tool for evaluating structural quality.     

Linear polyethyleneimine grafted to a hyperbranched poly(ethylene glycol)-like core: a copolymer for gene delivery

A block copolymer of a hyperbranched poly(ethylene glycol)-like core and linear polyethylenimine (HBP) was synthesized by a facile synthetic route that included (1) a single-step cationic copolymerization of diepoxy and polyhydroxyl monomers, (2) derivatization of hydroxyl groups of the core HBPEG copolymer with either tosyl or chloromethylbenzoyl chlorides resulting in a corresponding macroinitiator, and (3) synthesis of HBPEG-block-poly(alkyl oxazolines). HBPEG-block-linear polyethyleneimine (HBP) was obtained by hydrolysis of HBPEG-block-poly(alkyl oxazolines). Linear PEI-bearing hyperbranched polycations (HBP) had lower inherent toxicity in cell culture than PEG-grafted linear polyethyleneimines (PEGLPEI). PEGLPEI formed a complex with DNA with an average diameter of 250 nm. The complexes were loosely condensed and formed aggregates and precipitates during storage. By contrast, hyperbranched polycations (HBP) formed approximately 50 nm nanocomplexes with DNA that were stable for several weeks and showed resistance to DNAse I-mediated degradation. The 'inverted' block copolymers showed several orders of magnitude higher transfection efficiency than PEGLPEI in vitro. Because of the biocompatibility and higher transfection efficiency, the 'inverted' block copolymer merits further investigation as a gene carrier.     

New opportunities in integrative structural modeling

Integrative structural modeling enables structure determination of macromolecules and their complexes by integrating data from multiple sources. It has been successfully used to characterize macromolecular structures when a single structural biology technique was insufficient. Recent developments in cellular structural biology, including in-cell cryo-electron tomography and artificial intelligence-based structure prediction, have created new opportunities for integrative structural modeling. Here, we will review these opportunities along with the latest developments in integrative modeling methods and their applications. We also highlight open challenges and directions for further development.     

Water-soluble degradable hyperbranched polyesters: novel candidates for drug delivery?

A novel approach to hyperbranched polymers is presented in this work. Hyperbranched polyesters with a large amount of terminal hydroxyl groups are prepared by a one-pot synthesis from commercially available AB-type and CD(n)-type monomers (n >/= 2). In this paper, Michael addition of diethanolamine (CD(2)) or N-methyl-d-glucamine (CD(5)) to methyl acrylate (AB) generates dominantly AD(n)-type intermediates. Further self-condensation of intermediates at higher temperature and in the presence of catalyst gives hyperbranched polyesters. Because of the tertiary amino groups in the backbone and the hydroxyl groups in the linear and terminal units, the resulting hyperbranched polyester is highly soluble in water. Furthermore, the hyperbranched polymer is degradable because of its ester units. So, the water-soluble hyperbranched polyesters might be applied as a novel material for drug delivery.     

Integrative structural modeling with small angle X-ray scattering profiles

Recent technological advances enabled high-throughput collection of Small Angle X-ray Scattering (SAXS) profiles of biological macromolecules. Thus, computational methods for integrating SAXS profiles into structural modeling are needed more than ever. Here, we review specifically the use of SAXS profiles for the structural modeling of proteins, nucleic acids, and their complexes. First, the approaches for computing theoretical SAXS profiles from structures are presented. Second, computational methods for predicting protein structures, dynamics of proteins in solution, and assembly structures are covered. Third, we discuss the use of SAXS profiles in integrative structure modeling approaches that depend simultaneously on several data types.     

Systematic and Comparative Evaluation of Software Programs for Template‐Based Modeling of Protein Structures

Modeling protein structures is critical for understanding protein functions in various biological and biotechnological studies. Among representative protein structure modeling approaches, template‐based modeling (TBM) is by far the most reliable and most widely used approach to model protein structures. However, it still remains as a challenge to select appropriate software programs for pairwise alignments and model building, two major steps of the TBM. In this paper, pairwise alignment methods for TBM are first compared with respect to the quality of structure models built using these methods. This comparative study is conducted using comprehensive datasets, which cover 6185 domain sequences from Structural Classification of Proteins extended for soluble proteins, and 259 Protein Data Bank entries (whole protein sequences) from Orientations of Proteins in Membranes database for membrane proteins. Overall, a profile‐based method, especially PSI‐BLAST, consistently shows high performance across the datasets and model evaluation metrics used. Next, use of two model building programs, MODELLER and SWISS‐MODEL, does not seem to significantly affect the quality of protein structure models built except for the Hard group (a group of relatively less homologous proteins) of membrane proteins. The results presented in this study will be useful for more accurate implementation of TBM.     

Procedure for freeze-drying molecules adsorbed to mica flakes

The quick-freeze, deep-etch, rotary-replication technique is useful for visualizing cells and cell fractions but does not work with suspensions of macromolecules. These inevitably clump or collapse during deep-etching, presumably due to surface tension forces that develop during their transfer from ice to vacuum. Previous protocols have attempted to overcome such forces by attaching macromolecules to freshly cleaved mica before drying and replication. I describe here an adaptation of this procedure to the deep-etch technique as otherwise practiced. My innovation is to mix the molecules with an aqueous suspension of tiny flakes of mica and then to quick-freeze and freeze-fracture the suspension exactly as if one were dealing with cells. The fracture inevitably strikes the surfaces of many mica flakes and thereby cleaves the adsorbed macromolecules cleanly enough to reveal interesting substructure within them. The subsequent step of deep-etching exposes large expanses of unfractured mica and thus reveals intact macromolecules. These macromolecules are not obscured by salt deposits, even if they were frozen in hypertonic solutions, apparently because the fracturing step removes nearly all of the overlying electrolyte. Moreover, these macromolecules are minimally freeze-dried (since exposure is sufficient after only 3 min of etching at -102 degrees C) so they retain their three-dimensional topology. I show that molluscan hemocyanin is a good internal standard for this new technique. It is available commercially in stable solutions, mixes well with all sizes of macromolecules, and consists of particles that display distinct five-start surface helices, which have been measured carefully in the past and which possess a known handedness, useful for determining the orientation of micrographs when examining the various helical patterns possessed by most types of extended macromolecules. The fractured hemocyanin particles also display characteristic internal structures, which permit determination of the elevation of the "molecular cleavage" described above. Finally, molluscan hemocyanin is delicate enough to reflect bad freezing or poor replication, if these steps become a problem. A survey of several macromolecules is presented, including soluble enzymes, antibodies, filamentous proteins and nucleoproteins. These images, for the most part, correspond to those previously obtained by negative staining. New details of their structures are noted, and the images are used to illustrate both the advantages and drawbacks of the new procedure.     

A template-finding algorithm and a comprehensive benchmark for homology modeling of proteins

The first step in homology modeling is to identify a template protein for the target sequence. The template structure is used in later phases of the calculation to construct an atomically detailed model for the target. We have built from the Protein Data Bank (PDB) a large-scale learning set that includes tens of millions of pair matches that can be either a true template or a false one. Discriminatory learning (learning from positive and negative examples) is used to train a decision tree. Each branch of the tree is a mathematical programming model. The decision tree is tested on an independent set from PDB entries and on the sequences of CASP7. It provides significant enrichment of true templates (between 50 and 100%) when compared to PSI-BLAST. The model is further verified by building atomically detailed structures for each of the tentative true templates with modeller. The probability that a true match does not yield an acceptable structural model (within 6 A RMSD from the native structure) decays linearly as a function of the TM structural-alignment score.     

Concepts and schemes for the re-engineering of physical protein modules: generating nanodevices via targeted replacements with constrained amino acids

Physically building complex multi-molecular structures from naturally occurring biological macromolecules has aroused a great deal of interest. Here we focus on nanostructures composed of re-engineered, natural 'foldamer' building blocks. Our aim is to provide some of the underlying concepts and schemes for crafting structures utilizing such conformationally relatively stable molecular components. We describe how, via chemical biology strategies, it is further possible to chemically manipulate the foldamer building blocks toward specific shape-driven structures, which in turn could be used toward potential-designed functions. We outline the criteria in choosing candidate foldamers from the vast biological repertoire, and how to enhance their stability through selected targeted replacements by non-proteinogenic conformationally constrained amino acids. These approaches combine bioinformatics, high performance computations and mathematics with synthetic organic chemistry. The resulting artificially engineered self-organizing molecular scale structures take advantage of nature's nanobiology toolkit and at the same time improve on it, since their new targeted function differs from that optimized by evolution. The major challenge facing nanobiology is to be able to exercise fine control over the performance of these target-specific molecular machines.     

MMB-GUI: a fast morphing method demonstrates a possible ribosomal tRNA translocation trajectory

Easy-to-use macromolecular viewers, such as UCSF Chimera, are a standard tool in structural biology. They allow rendering and performing geometric operations on large complexes, such as viruses and ribosomes. Dynamical simulation codes enable modeling of conformational changes, but may require considerable time and many CPUs. There is an unmet demand from structural and molecular biologists for software in the middle ground, which would allow visualization combined with quick and interactive modeling of conformational changes, even of large complexes. This motivates MMB-GUI. MMB uses an internal-coordinate, multiscale approach, yielding as much as a 2000-fold speedup over conventional simulation methods. We use Chimera as an interactive graphical interface to control MMB. We show how this can be used for morphing of macromolecules that can be heterogeneous in biopolymer type, sequence, and chain count, accurately recapitulating structural intermediates. We use MMB-GUI to create a possible trajectory of EF-G mediated gate-passing translocation in the ribosome, with all-atom structures. This shows that the GUI makes modeling of large macromolecules accessible to a wide audience. The morph highlights similarities in tRNA conformational changes as tRNA translocates from A to P and from P to E sites and suggests that tRNA flexibility is critical for translocation completion.     

Computational reconstruction of multidomain proteins using atomic force microscopy data

Classical structural biology techniques face a great challenge to determine the structure at the atomic level of large and flexible macromolecules. We present a novel methodology that combines high-resolution AFM topographic images with atomic coordinates of proteins to assemble very large macromolecules or particles. Our method uses a two-step protocol: atomic coordinates of individual domains are docked beneath the molecular surface of the large macromolecule, and then each domain is assembled using a combinatorial search. The protocol was validated on three test cases: a simulated system of antibody structures; and two experimentally based test cases: Tobacco mosaic virus, a rod-shaped virus; and Aquaporin Z, a bacterial membrane protein. We have shown that AFM-intermediate resolution topography and partial surface data are useful constraints for building macromolecular assemblies. The protocol is applicable to multicomponent structures connected in the polypeptide chain or as disjoint molecules. The approach effectively increases the resolution of AFM beyond topographical information down to atomic-detail structures.     

X-ray solution scattering (SAXS) combined with crystallography and computation: defining accurate macromolecular structures, conformations and assemblies in solution

Crystallography supplies unparalleled detail on structural information critical for mechanistic analyses; however, it is restricted to describing low energy conformations of macromolecules within crystal lattices. Small angle X-ray scattering (SAXS) offers complementary information about macromolecular folding, unfolding, aggregation, extended conformations, flexibly linked domains, shape, conformation, and assembly state in solution, albeit at the lower resolution range of about 50 A to 10 A resolution, but without the size limitations inherent in NMR and electron microscopy studies. Together these techniques can allow multi-scale modeling to create complete and accurate images of macromolecules for modeling allosteric mechanisms, supramolecular complexes, and dynamic molecular machines acting in diverse processes ranging from eukaryotic DNA replication, recombination and repair to microbial membrane secretion and assembly systems. This review addresses both theoretical and practical concepts, concerns and considerations for using these techniques in conjunction with computational methods to productively combine solution scattering data with high-resolution structures. Detailed aspects of SAXS experimental results are considered with a focus on data interpretation tools suitable to model protein and nucleic acid macromolecular structures, including membrane protein, RNA, DNA, and protein-nucleic acid complexes. The methods discussed provide the basis to examine molecular interactions in solution and to study macromolecular flexibility and conformational changes that have become increasingly relevant for accurate understanding, simulation, and prediction of mechanisms in structural cell biology and nanotechnology.     

How growth affects the fate of cellular metabolites

Cellular metabolites frequently have more than a single function in the cell. For example they may be sources of energy as well as building blocks for several macromolecules. The relative cellular needs for these different functions depend on environmental and intracellular factors. The intermediary products of phosphorylation of pyruvate by mitochondria, for example, are used for growth, while the released ATP is used for both growth and maintenance. Since maintenance has priority over growth, and maintenance is proportional to a cell’s mass, a cell’s need for ATP vs. building blocks depends on the growth rate, and hence on substrate availability. We show how the concept of Synthesising Units (SUs) in linear and cyclic pathways takes care of the correct variation of the ATP/building block ratio in the context of the Dynamic Energy Budget (DEB) theory. This can only be achieved by an interaction between subsequent SUs in transferring metabolites. Apart from this interaction we also needed an essential feature of the performance of the pathway in the DEB context: the relative amount of enzymes varies with the growth rate in a special way.We solved an important consistency problem between the DEB model at the whole-cell level and a model for pathway dynamics. We observe that alternative whole-cell models, such as the Marr-Pirt model, that keep the relative amount of enzymes constant, and hence independent of the growth rate, will have problems in explaining how pathways can meet cells’ growth-dependent needs for building blocks vs. ATP.     

Developments in describing equilibrium phase transitions of multivalent associative macromolecules

Biomolecular condensates are distinct cellular bodies that form and dissolve reversibly to organize cellular matter and biochemical reactions in space and time. Condensates are thought to form and dissolve under the influence of spontaneous and driven phase transitions of multivalent associative macromolecules. These include phase separation, which is defined by segregation of macromolecules from the solvent or from one another, and percolation or gelation, which is an inclusive networking transition driven by reversible associations among multivalent macromolecules. Considerable progress has been made to model sequence-specific phase transitions, especially for intrinsically disordered proteins. Here, we summarize the state-of-the-art of theories and computations aimed at understanding and modeling sequence-specific, thermodynamically controlled, coupled associative and segregative phase transitions of archetypal multivalent macromolecules.     

Artificial neural networks modeling gene-environment interaction

ABSTRACT: BACKGROUND: Gene-environment interactions play an important role in the etiological pathway of complex diseases. An appropriate statistical method for handling a wide variety of complex situations involving interactions between variables is still lacking, especially when continuous variables are involved. The aim of this paper is to explore the ability of neural networks to model different structures of gene-environment interactions. A simulation study is set up to compare neural networks with standard logistic regression models. Eight different structures of gene-environment interactions are investigated. These structures are characterized by penetrance functions that are based on sigmoid functions or on combinations of linear and non-linear effects of a continuous environmental factor and a genetic factor with main effect or with a masking effect only. RESULTS: In our simulation study, neural networks are more successful in modeling gene-environment interactions than logistic regression models. This outperfomance is especially pronounced when modeling sigmoid penetrance functions, when distinguishing between linear and nonlinear components, and when modeling masking effects of the genetic factor. CONCLUSION: Our study shows that neural networks are a promising approach for analyzing gene-environment interactions. Especially, if no prior knowledge of the correct nature of the relationship between co-variables and response variable is present, neural networks provide a valuable alternative to regression methods that are limited to the analysis of linearly separable data.