Analytical shape computation of macromolecules: I. molecular area and volume through alpha shape

The size and shape of macromolecules such as proteins and nucleic acids play an important role in their functions. Prior efforts to quantify these properties have been based on various discretization or tessellation procedures involving analytical or numerical computations. In this article, we present an analytically exact method for computing the metric properties of macromolecules based on the alpha shape theory. This method uses the duality between alpha complex and the weighted Voronoi decomposition of a molecule. We describe the intuitive ideas and concepts behind the alpha shape theory and the algorithm for computing areas and volumes of macromolecules. We apply our method to compute areas and volumes of a number of protein systems. We also discuss several difficulties commonly encountered in molecular shape computations and outline methods to overcome these problems. Proteins 33:1–17, 1998. © 1998 Wiley-Liss, Inc.     

Fast and robust two-dimensional inverse Laplace transformation of single-molecule fluorescence lifetime data

Fluorescence spectroscopy at the single-molecule scale has been indispensable for studying conformational dynamics and rare states of biological macromolecules. Single-molecule two-dimensional (2D) fluorescence lifetime correlation spectroscopy is an emerging technique that holds promise for the study of protein and nucleic acid dynamics, as the technique is 1) capable of resolving conformational dynamics using a single chromophore, 2) resolves forward and reverse transitions independently, and 3) has a dynamic window ranging from microseconds to seconds. However, the calculation of a 2D fluorescence relaxation spectrum requires an inverse Laplace transform (ILT), which is an ill-conditioned inversion that must be estimated numerically through a regularized minimization. Current methods for performing ILTs of fluorescence relaxation can be computationally inefficient, sensitive to noise corruption, and difficult to implement. Here, we adopt an approach developed for NMR spectroscopy (T1-T2 relaxometry) to perform one-dimensional (1D) and 2D-ILTs on single-molecule fluorescence spectroscopy data using singular-valued decomposition and Tikhonov regularization. This approach provides fast, robust, and easy to implement Laplace inversions of single-molecule fluorescence data. We compare this approach to the widely used maximal entropy method.     

The MIDAS display system

The Molecular Interactive Display and Simulation (MIDAS) system is designed to display and manipulate large macromolecules, such as proteins and nucleic acids. Several ancillary programs allow for such features as computing the surface of a molecule, selecting an active site region within a molecule, and computing electrostatic charge potentials. At the core of MIDAS is a hierarchical database system, designed specifically for macromolecules, that is both compact in its storage requirements and fast in its data access.     

Modeling nucleic acids

Nucleic acids are an important class of biological macromolecules that carry out a variety of cellular roles. For many functions, naturally occurring DNA and RNA molecules need to fold into precise three-dimensional structures. Due to their self-assembling characteristics, nucleic acids have also been widely studied in the field of nanotechnology, and a diverse range of intricate three-dimensional nanostructures have been designed and synthesized. Different physical terms such as base-pairing and stacking interactions, tertiary contacts, electrostatic interactions and entropy all affect nucleic acid folding and structure. Here we review general computational approaches developed to model nucleic acid systems. We focus on four key areas of nucleic acid modeling: molecular representation, potential energy function, degrees of freedom and sampling algorithm. Appropriate choices in each of these key areas in nucleic acid modeling can effectively combine to aid interpretation of experimental data and facilitate prediction of nucleic acid structure.     

Macromolecular therapeutics: emerging strategies for drug discovery in the postgenome era

The postgenome era offers a plethora of potential therapeutic targets. Many of these targets will be addressable using small organic molecules as drug candidates. However, certain aspects of cell function, particularly those that rely on protein-protein or protein-nucleic acid interactions, will be difficult to influence using small molecules. Thus, the possibility of using highly specific macromolecules as potential therapeutic agents is an intriguing concept. Recent developments in several areas of research have brought this possibility closer to fruition. Peptide and nucleic acid combinatorial libraries allow the generation of novel molecules having exquisite selectivity. Structural information and molecular modeling also contribute to the design of new macromolecules with therapeutic potential. Perhaps most importantly, approaches for delivering macromolecules into the cell interior have been developed and applied with considerable success. Thus, the therapeutic use of macromolecules, including oligonucleotides, peptides, and proteins, may be an idea whose time has come.     

Effect of high hydrostatic pressure on hydration and activity of ribozymes

Formation and stabilization of RNA structure in the cell depends on its interaction with solvent and metal ions. High hydrostatic pressure (HHP) is a convenient tool in an analysis of the role of small molecules in the structure stabilization of biological macromolecules. Analysis of HHP effect and various concentrations of ions showed that water induce formation of the active ribozyme structure. So, it is clear that water is the driving force of conformational changes of nucleic acid.     

Real-time interactive frequency filtering of molecular dynamics trajectories

Molecular dynamics simulations of atomic motion in protein and nucleic acid molecules must be done on a femtosecond time-scale. Much of this rapid motion is unimportant for the slower changes that are most relevant to biological function (conformational changes, substrate binding, protein folding). The high-frequency motion makes simulations computationally expensive. More importantly, the high frequencies obscure visualization of the relevant dynamics processes. Sessions, Dauber-Osguthorpe and Osguthorpe presented a method for removing high-frequency motions from atomic co-ordinates of trajectories generated by simulation. While that study used fast Fourier methods and emphasized the use of filtering for analysis of trajectories, this communication describes a new method that makes it much easier to use frequency filtering in programs that display trajectories as a sequence of moving images. Tests of the method on systems extending from pure water to proteins and nucleic acid molecules in vacuo and in solution have demonstrated its general utility. Impressed with the power and simplicity of the new method, we wish to present it in sufficient detail to allow others to implement it themselves.     

Recent advances in the structural biology of the 26S proteasome

There is growing appreciation for the fundamental role of structural dynamics in the function of macromolecules. In particular, the 26S proteasome, responsible for selective protein degradation in an ATP dependent manner, exhibits dynamic conformational changes that enable substrate processing. Recent cryo-electron microscopy (cryo-EM) work has revealed the conformational dynamics of the 26S proteasome and established the function of the different conformational states. Technological advances such as direct electron detectors and image processing algorithms allowed resolving the structure of the proteasome at atomic resolution. Here we will review those studies and discuss their contribution to our understanding of proteasome function.     

A coarse-grained normal mode approach for macromolecules: an efficient implementation and application to Ca(2+)-ATPase

A block normal mode (BNM) algorithm, originally proposed by Tama et al., (Proteins Struct. Func. Genet. 41:1-7, 2000) was implemented into the simulation program CHARMM. The BNM approach projects the hessian matrix into local translation/rotation basis vectors and, therefore, dramatically reduces the size of the matrix involved in diagonalization. In the current work, by constructing the atomic hessian elements required in the projection operation on the fly, the memory requirement for the BNM approach has been significantly reduced from that of standard normal mode analysis and previous implementation of BNM. As a result, low frequency modes, which are of interest in large-scale conformational changes of large proteins or protein-nucleic acid complexes, can be readily obtained. Comparison of the BNM results with standard normal mode analysis for a number of small proteins and nucleic acids indicates that many properties dominated by low frequency motions are well reproduced by BNM; these include atomic fluctuations, the displacement covariance matrix, vibrational entropies, and involvement coefficients for conformational transitions. Preliminary application to a fairly large system, Ca(2+)-ATPase (994 residues), is described as an example. The structural flexibility of the cytoplasmic domains (especially domain N), correlated motions among residues on domain interfaces and displacement patterns for the transmembrane helices observed in the BNM results are discussed in relation to the function of Ca(2+)-ATPase. The current implementation of the BNM approach has paved the way for developing efficient sampling algorithms with molecular dynamics or Monte Carlo for studying long-time scale dynamics of macromolecules.     

Cellular delivery of impermeable effector molecules in the form of conjugates with peptides capable of mediating membrane translocation.

Most molecules that are not actively imported by living cells are impermeable to cell membranes, including practically all macromolecules and even many small molecules whose physicochemical properties prevent passive membrane diffusion. The use of peptide vectors capable of transporting such molecules into cells in the form of covalent conjugates has become an increasingly attractive solution to this problem. Not only has this technology permitted the study of modulating intracellular target proteins, but it has also gained importance as an alternative to conventional cellular transfection with oligonucleotides. Peptide vectors derived from viral, bacterial, insect, and mammalian proteins endowed with membrane translocation properties have now been proposed as delivery vectors. These are discussed comprehensively and critically in terms of relative utility, applications to compound classes and specific molecules, and relevant conjugation chemistry. Although in most cases the mechanisms of membrane translocation are still unclear, physicochemical studies have been carried out with a number of peptide delivery vectors. Unifying and distinguishing mechanistic features of the various vectors are discussed. Until a few years ago speculations that it might be possible to deliver peptides, proteins, oligonucleotides, and impermeable small molecules with the aid of cellular delivery peptides not only to target cells in vitro, but in vivo, was received with scepticism. However, the first studies showing pharmacological applications of conjugates between macromolecules and peptide delivery vectors are now being reported, and therapies based on such conjugates are beginning to appear feasible.     

Fluorescence correlation spectroscopy as a new method for the investigation of aptamer/target interactions

Fluorescence correlation spectroscopy is an attractive tool for monitoring molecular interactions in solution. We report here a new and highly sensitive method for studying the interaction of aptamers with their targets using this technique. In vitro selection technology is a combinatorial method for the generation of nucleic acid receptors (aptamers) that are capable of binding to various target molecules. Using the in vitro selection approach we isolated RNAs which bind to the antibiotic moenomycin with high affinity. The formation of RNA-moenomycin complexes was studied by fluorescence correlation spectroscopy with a tetramethylrhodamine-labeled derivative of moenomycin.     

Interaction of the putative anticancer alkaloid chelerythrine with nucleic acids: biophysical perspectives

Alkaloids represent an important group of molecules that have immense pharmacological potential. Benzophenanthridine alkaloids are one such class of alkaloids known for their myriad pharmacological activities that include potential anticancer activities. Chelerythrine is a premier member of the benzophenanthridine family of the isoquinoline group. This alkaloid is endowed with excellent medicinal properties and exhibits antibacterial, antimicrobial and anti-inflammatory properties. The molecular basis of its therapeutic activity is considered due to its nucleic acid binding capabilities. This review focuses on consolidating the current status on the nucleic acid binding properties of chelerythrine that is essential for the rational design and development of this alkaloid as a potential drug. This work reviews the interaction of chelerythrine with different natural and synthetic nucleic acids like double- and single-stranded DNAs, heat-denatured DNA, quadruplex DNA, double- and single-stranded RNA, tRNA and triplex and quadruplex RNA. The review emphasizes on the mode, specificity, conformational aspects and energetics of the binding that is particularly helpful for developing nucleic acid targeted therapeutics. The fundamental results discussed in this review will greatly benefit drug development for many diseases and serve as a database for the design of futuristic benzophenanthridine-based therapeutics.     

De novo design of sequences for nucleic acid structural engineering

An interactive procedure has been developed to assign sequences for the design of nucleic acid secondary structure. The primary goal of the procedure is to facilitate macromolecular architecture studies through the design of branched nucleic acid mono- and oligo-junction constructs in a convenient fashion. The essential feature of the sequence-symmetry minimization algorithm employed is the treatment of short sequences as vocabulary elements whose repetition decreases control over the resulting secondary structure. Both manual and semi-automatic application of this approach are available. The design of linear nucleic acid molecules or molecules containing single-stranded loops or connectors is also possible through application of the procedure.     

A versatile photocleavable bifunctional linker for facile synthesis of substrate-DNA conjugates for the selection of nucleic acid catalysts

Covalent photocleavable attachment of small molecules or peptides to oligonucleotides is an integral strategic element in the selection of novel nucleic acid enzymes. Here, we report the synthesis of a multipurpose, photocleavable bifunctional linker (PCBL) suitable for nucleic acid selections and other biotechnology applications. PCBL contains a photocleavable O-nitrobenzyl group flanked on one side by an N-hydroxysuccinimidyl ester (reactive toward primary amines) and on the other side by a sulfhydryl. To demonstrate the utility of PCBL, the linker was used to couple an analog of the antibiotic chloramphenicol (Cam) to the 5' end of an amino-modified 8-mer DNA oligo. Coupling was confirmed by MALDI-TOF spectrophotometry. Decoupling was performed by irradiating the coupled species with near-UV light (approximately 360 nm), regenerating the original amino-modified oligo. Ligation of the Cam-PCBL-DNA conjugate to random-sequence RNA generated a diversity library appropriate for the selection of new ribozymes that catalyze reactions involving the tethered substrate. Coupling and decoupling of the Cam analog from the library was monitored on a trilayered organomercurial polyacrylamide gel. The coupling/decoupling strategy described here is readily generalized to many combinations of macromolecules and small molecules. For example, analogs of this small molecule-DNA conjugate can be generated as synthons for ligation to nucleic acid diversity libraries during each round of novel ribozyme selections, or they can be immobilized onto chips for addresssably reversible microarray analysis.     

Complementing structural information of modular proteins with small angle neutron scattering and contrast variation

Many macromolecules in the cell function by forming multi-component assemblies. We have applied the technique of small angle neutron scattering to study a nucleic acid-protein complex and a multi-protein complex. The results illustrate the versatility and applicability of the method to study macromolecular assemblies. The neutron scattering experiments, complementing X-ray solution scattering data, reveal that the conserved catalytic domain of RNase E, an essential ribonuclease in Escherichia coli (E. coli), undergoes a marked conformational change upon binding a 5'monophosphate-RNA substrate analogue. This provides the first evidence in support of an allosteric mechanism that brings about RNA substrate cleavage. Neutron contrast variation of the multi-protein TIM10 complex, a mitochondrial chaperone assembly comprising the subunits Tim9 and Tim10, has been used to determine a low-resolution shape reconstruction of the complex, highlighting the integral subunit organization. It shows characteristic features involving protrusions that could be assigned to the six subunits forming the complex.     

Human hnRNP protein A1: A model polypeptide for a structural and genetic investigation of a broad family of RNA binding proteins

The hnRNP fiber is the substrate on which pre-mRNA processing occurs. The protein moiety of the fiber (hnRNP proteins) constitutes a broad family of RNA binding proteins that revealed, upon molecular analysis, a number of interesting features.Heterogeneous nuclear ribonucleoprotein A1 is a major component of the human hnRNP complex. In recent years this protein has attracted great attention because of several emerging evidences of its direct involvement in pre-mRNA processing and it has become one of the best characterized RNA binding proteins. Detailed knowledge of the structure of protein A1 has laid the basis for the understanding of its function, and for this reason A1 can be considered as a model polypeptide for the investigation of a large number of RNA binding proteins.In this work we report recent findings regarding the binding properties of protein A1 as well as new data on the gene structure of A1 and of its closely related hnRNP protein A2. Our results show that a single A1 molecule contains the determinants for simultaneous binding of two single-stranded nucleic acid molecules and we demonstrate that the glycine-rich domain of A1, isolated from the rest of the molecule, is capable of sustaining protein-protein interactions. These features probably account for the reannealing activity of the protein and for its capacity to modulate the binding of snRNPs to intron sequencesin vitro. Comparison of A1 and A2 gene sequences revealed a remarkable conservation of the overall structural organization, suggesting important functions for the different structural elements.     

In silico investigation of conformational motions in superfamily 2 helicase proteins

Helicases are motor proteins that play a central role in the metabolism of DNA and RNA in biological cells. Using the energy of ATP molecules, they are able to translocate along the nucleic acids and unwind their duplex structure. They have been extensively characterized in the past and grouped into superfamilies based on structural similarities and sequential motifs. However, their functional aspects and the mechanism of their operation are not yet well understood. Here, we consider three helicases from the major superfamily 2--Hef, Hel308 and XPD--and study their conformational dynamics by using coarse-grained relaxational elastic network models. Specifically, their responses to mechanical perturbations are analyzed. This enables us to identify robust and ordered conformational motions which may underlie the functional activity of these proteins. As we show, such motions are well-organized and have large amplitudes. Their possible roles in the processing of nucleic substrate are discussed.     

Stopped-flow fluorescence resonance energy transfer for analysis of nucleosome dynamics

Macromolecular assemblies and machines undergo large-scale conformational changes as essential features of their normal function. Modern stopped-flow instrumentation and biotechnology combine to provide a powerful tool for characterizing the rates and natures of these conformational changes. Standard commercially available instruments provide extraordinary sensitivity and speed, allowing analysis of millisecond or longer timescale processes, with concentrations as low as a few nanomolar and volumes of just a few hundred microliters. One can now place specific dyes anywhere desired on a nucleic acid, and often on a protein as well. This ability allows the use of fluorescence resonance energy transfer experiments for detailed conformational analyses, even as the system is evolving rapidly over time following the initiation of a reaction. This approach is ideally suited for analysis of intrinsic properties of chromatin and of the machines that control chromatin assembly, disassembly, and function.     

Basic module for an adaptive control system based on neurone information processing

The design of such devices as robotic aids for handicapped people, powered prostheses and manipulative aids such as page turners would benefit from the use of an adaptive control system. Much recent work on adaptive networks has been based on simplified models of the information processing capabilities of neurones. Neurones are now known to be capable of association learning and memory and this study incorporates these features in a neurone model. A single neuronal input system, the NMDA-type glutamate receptor, is modelled by deriving finite difference equations from its reaction dynamics so that the concentration of several molecules in the receptor can be plotted as a function of time. The model shows association learning taking place at the glutamate receptor. A whole neurone with ten glutamate receptor regions is also modelled and shows that a neurone should be capable of recognizing patterns of inputs. As the neurone model is complicated and slow to run, a much simplified form of the model is described which embodies the basic features of neurone information processing in a simple algorithm.