Bond orders between molecular fragments

An extension of the Mayer bond order for the interaction between molecular fragments is presented. This approach allows the classical chemical concepts of bond order and valence to be utilised for fragments and the interactions between the fragments and symmetry-adapted linear combinations to be analysed. For high-symmetry systems, the approach allows the contribution from each irreducible representation to be assessed and provides a semiquantitative measure of the role of each bonding mode to interfragment interactions. The utility of this tool has been examined by a study of the bonding in symmetrical sandwich complexes. The validity of the frontier-orbital approach and the contributions from each frontier-orbital interaction can also be assessed within this model. As demonstrated by a study of a number of mixed-sandwich complexes, the model proves to be especially useful for low-symmetry systems in which separation of the sigma, pi and delta roles in bonding of the ligand is difficult to assess. The fragment bond order describes the interaction between preoptimized fragment orbitals and is independent of the charges that are placed on these fragments. Although the method allows the chemist to define fragments in any way they choose, most insight is gained by using the same frontier orbitals employed so successfully in perturbational molecular-orbital approaches. The results are free from the influence of the electron-counting method used to describe fragments, such as the rings and metals in sandwich complexes.     

Parametric method for computing the structure of the excited states and the vibronic spectra of complex molecules. Absorption and fluorescence spectra of perylene

The structures of the perylene molecule in the first excited 1¹ 0B_2u state and the band shape (vibrational structure) of its fluorescence and absorption spectra are computed by the parametric method. A fragmentary approach and the molecular fragments H/1C= with the parameters obtained for acenes and polyenes are used to form molecular models in the excited state. It is shown that a model that corresponds to the choice of fragments with the parameters of acenes is the most optimal. The theoretical spectra satisfactorily reproduce both qualitatively and quantitatively, the basic specific features of the vibrational structure of the experimental spectra. Calculation results show high degree of transfer of the parameters of the method in a series of related molecules not only for acenes with “linear” arrangement of the rings (benzene, naphthalene, anthracene, etc.) but also for more complex structures (perylene). It is shown that the parametric method developed is efficient for predicting the vibronic spectra and the structure of the excited states of complex molecules. Translated fromZhurnal Struktumoi Khimii, Vol.40, No. 2, pp. 242–250, March–April, 1999.     

AROHap: An effective algorithm for single individual haplotype reconstruction based on asexual reproduction optimization

In this paper, a method for single individual haplotype (SIH) reconstruction using Asexual reproduction optimization (ARO) is proposed. Haplotypes, as a set of genetic variations in each chromosome, contain vital information such as the relationship between human genome and diseases. Finding haplotypes in diploid organisms is a challenging task. Experimental methods are expensive and require special equipment. In SIH problem, we encounter with several fragments and each fragment covers some parts of desired haplotype. The main goal is bi-partitioning of the fragments with minimum error correction (MEC). This problem is addressed as NP-hard and several attempts have been made in order to solve it using heuristic methods. The current method, AROHap, has two main phases. In the first phase, most of the fragments are clustered based on a practical metric distance. In the second phase, ARO algorithm as a fast convergence bio-inspired method is used to improve the initial bi-partitioning of the fragments in the previous step. AROHap is implemented with several benchmark datasets. The experimental results demonstrate that satisfactory results were obtained, proving that AROHap can be used for SIH reconstruction problem.     

Correlations Between Amino Acids at Different Sites in Local Sequences of Protein Fragments with Given Structural Patterns

Ample evidence suggests that the local structures of peptide fragments in native proteins are to some extent encoded by their local sequences. Detecting such local correlations is important but it is still an open question what would be the most appropriate method. This is partly because conventional sequence analyses treat amino acid preferences at each site of a protein sequence independently, while it is often the inter-site interactions that bring about local sequence-structure correlations. Here a new scheme is introduced to capture the correlation between amino acid preferences at different sites for different local structure types. A library of nine-residue fragments is constructed, and the fragments are divided into clusters based on their local structures. For each local structure cluster or type, chi-square tests are used to identify correlated preferences of amino acid combinations at pairs of sites. A score function is constructed including both the single site amino acid preferences and the dual-site amino acid combination preferences, which can be used to identify whether a sequence fragment would have a strong tendency to form a particular local structure in native proteins. The results show that, given a local structure pattern, dual-site amino acid combinations contain different information from single site amino acid preferences. Representative examples show that many of the statistically identified correlations agree with previously-proposed heuristic rules about local sequence-structure correlations, or are consistent with physical-chemical interactions required to stabilize particular local structures. Results also show that such dual-site correlations in the score function significantly improves the Z-score matching a sequence fragment to its native local structure relative to nonnative local structures, and certain local structure types are highly predictable from the local sequence alone if inter-site correlations are considered.     

Decoding the Fucose Migration Product during Mass-Spectrometric analysis of Blood Group Epitopes

Fucose is a signaling carbohydrate that is attached at the end of glycan processing. It is involved in a range of processes, such as the selectin-dependent leukocyte adhesion or pathogen-receptor interactions. Mass-spectrometric techniques, which are commonly used to determine the structure of glycans, frequently show fucose-containing chimeric fragments that obfuscate the analysis. The rearrangement leading to these fragments—often referred to as fucose migration—has been known for more than 25 years, but the chemical identity of the rearrangement product remains unclear. In this work, we combine ion-mobility spectrometry, radical-directed dissociation mass spectrometry, cryogenic IR spectroscopy of ions, and density-functional theory calculations to deduce the product of the rearrangement in the model trisaccharides Lewis x and blood group H2. The structural search yields the fucose moiety attached to the galactose with an α(1→6) glycosidic bond as the most likely product.     

Model for the fast estimation of basis set superposition error in biomolecular systems

Basis set superposition error (BSSE) is a significant contributor to errors in quantum-based energy functions, especially for large chemical systems with many molecular contacts such as folded proteins and protein-ligand complexes. While the counterpoise method has become a standard procedure for correcting intermolecular BSSE, most current approaches to correcting intramolecular BSSE are simply fragment-based analogues of the counterpoise method which require many (two times the number of fragments) additional quantum calculations in their application. We propose that magnitudes of both forms of BSSE can be quickly estimated by dividing a system into interacting fragments, estimating each fragment's contribution to the overall BSSE with a simple statistical model, and then propagating these errors throughout the entire system. Such a method requires no additional quantum calculations, but rather only an analysis of the system's interacting fragments. The method is described herein and is applied to a protein-ligand system, a small helical protein, and a set of native and decoy protein folds.     

A growing string method for determining transition states: comparison to the nudged elastic band and string methods

Interpolation methods such as the nudged elastic band and string methods are widely used for calculating minimum energy pathways and transition states for chemical reactions. Both methods require an initial guess for the reaction pathway. A poorly chosen initial guess can cause slow convergence, convergence to an incorrect pathway, or even failed electronic structure force calculations along the guessed pathway. This paper presents a growing string method that can find minimum energy pathways and transition states without the requirement of an initial guess for the pathway. The growing string begins as two string fragments, one associated with the reactants and the other with the products. Each string fragment is grown separately until the fragments converge. Once the two fragments join, the full string moves toward the minimum energy pathway according to the algorithm for the string method. This paper compares the growing string method to the string method and to the nudged elastic band method using the alanine dipeptide rearrangement as an example. In this example, for which the linearly interpolated guess is far from the minimum energy pathway, the growing string method finds the saddle point with significantly fewer electronic structure force calculations than the string method or the nudged elastic band method.     

Molecular fragments and the hybrid basis

A method of performing ab initio calculations of the electronic structure of fragments within a larger molecule is reported. The method uses the self-consistent group formalism of McWeeny together with the systematic use of a hybrid atomic orbital basis. The mutual compatibility of these two ideas is stressed and results are given for model calculations on fragments in saturated hydrocarbon chains and rings. © 1996 John Wiley & Sons, Inc.     

Quantitative predictions for DNA two-dimensional display according to size and nucleotide sequence composition

2-D DNA display is a simple separation method that provides a fast and economical way of visualizing polymorphism and comparing genomes. The DNA fragments are separated first according to their size by standard gel electrophoresis and then according to their sequence composition using denaturing gradient gel electrophoresis. First developed by Fischer and Lerman (Cell 1979, 16, 191-200), this method has recently been used to distinguish strains within a bacterial species. The genomic restriction fragments are displayed as spots on a 2-D surface. Although most of the relevant physical mechanisms are understood, this technique is mostly empirical and remains essentially qualitative. In view of optimizing this procedure, we combine our understanding of the different physical mechanisms at play to develop a complete numerical model to predict the relative coordinates of the spots as a function of the corresponding DNA sequence and of the experimental conditions. We experimentally validate our model by predicting the outcome of a 2-D display of the lambda phage genome. It thus becomes possible to optimize in silico the experimental parameters, to predict whether specific mutations as well as yet undescribed genetic polymorphisms can be resolved, and to assist in interpreting the experimental data.     

Development of non-peptide ACE inhibitors as novel and potent cardiovascular therapeutics: An in silico modelling approach

Angiotensin-converting enzyme (ACE) inhibitors have been acknowledged as first-line agents for the treatment of hypertension and a variety of cardiovascular disorders. In this context, quantitative structure–activity relationship (QSAR) models for a series of non-peptide compounds as ACE inhibitors are developed based on Simplified Molecular Input-Line Entry System (SMILES) notation and local graph invariants. Three random splits into the training and test sets are used. The Monte Carlo method is applied for model development. Molecular docking studies are used for the final assessment of the developed QSAR model and the design of novel inhibitors. The statistical quality of the developed model is good. Molecular fragments responsible for the increase/decrease of the studied activity are calculated. The computer-aided design of new compounds, as potential ACE inhibitors, is presented. The predictive potential of the applied approach is tested, and the robustness of the model is proven using different methods. The results obtained from molecular docking studies are in excellent correlation with the results from QSAR studies. The presented study may be useful in the search for novel cardiovascular therapeutics based on ACE inhibition.     

Fast and accurate methods for predicting short-range constraints in protein models

Protein modeling tools utilize many kinds of structural information that may be predicted from amino acid sequence of a target protein or obtained from experiments. Such data provide geometrical constraints in a modeling process. The main aim is to generate the best possible consensus structure. The quality of models strictly depends on the imposed conditions. In this work we present an algorithm, which predicts short-range distances between Cα atoms as well as a set of short structural fragments that possibly share structural similarity with a query sequence. The only input of the method is a query sequence profile. The algorithm searches for short protein fragments with high sequence similarity. As a result a statistics of distances observed in the similar fragments is returned. The method can be used also as a scoring function or a short-range knowledge-based potential based on the computed statistics.     

Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation

We have developed a method that uses energetic analysis of structure-based fragment docking to elucidate key features for molecular recognition. This hybrid ligand- and structure-based methodology uses an atomic breakdown of the energy terms from the Glide XP scoring function to locate key pharmacophoric features from the docked fragments. First, we show that Glide accurately docks fragments, producing a root mean squared deviation (RMSD) of <1.0 Å for the top scoring pose to the native crystal structure. We then describe fragment-specific docking settings developed to generate poses that explore every pocket of a binding site while maintaining the docking accuracy of the top scoring pose. Next, we describe how the energy terms from the Glide XP scoring function are mapped onto pharmacophore sites from the docked fragments in order to rank their importance for binding. Using this energetic analysis we show that the most energetically favorable pharmacophore sites are consistent with features from known tight binding compounds. Finally, we describe a method to use the energetically selected sites from fragment docking to develop a pharmacophore hypothesis that can be used in virtual database screening to retrieve diverse compounds. We find that this method produces viable hypotheses that are consistent with known active compounds. In addition to retrieving diverse compounds that are not biased by the co-crystallized ligand, the method is able to recover known active compounds from a database screen, with an average enrichment of 8.1 in the top 1% of the database.     

Prediction of homology model quality with multivariate regression

A new method has been developed for prediction of homology model quality directly from the sequence alignment, using multivariate regression. Hence, the expected quality of future homology models can be estimated using only information about the primary structure. This method has been applied to protein kinases and can easily be extended to other protein families. Homology model quality for a reference set of homology models was verified by comparison to experimental structures, by calculation of root-mean-square deviations (RMSDs) and comparison of interresidue contact areas. The homology model quality measures were then used as dependent variables in a Partial Least Squares (PLS) regression, using a matrix of alignment score profiles found from the Point Accepted Mutation (PAM) 250 similarity matrix as independent variables. This resulted in a regression model that can be used to predict the accuracy of future homology models from the sequence alignment. Using this method, one can identify the target-template combinations that are most likely to give homology models of sufficient quality. Hence, this method can be used to effectively choose the optimal templates to use for the homology modeling. The method's ability to guide the choice of homology modeling templates was verified by comparison of success rates to those obtained using BLAST scores and target-template sequence identities, respectively. The results indicate that the method presented here performs best in choosing the optimal homology modeling templates. Using this method, the optimal template was chosen in 86% of the cases, as compared to 62% using BLAST scores, and 57% using sequence identities. The method presented here can also be used to identify regions of the protein structure that are difficult to model, as well as alignment errors. Hence, this method is a useful tool for ensuring that the best possible homology model is generated.     

Architecture of intracellular networks in plant matrices

Pectin is an integral component of plant cell walls. It is believed to form an interconnected network structure independent of the cellulose–xyloglucan network structure. Pectin gels are often used as a model for the pectin network structure within the plant cell wall. The middle lamella pectin can be extracted with chelating agents and is believed to be associated through cooperative binding of calcium ions in the so-called egg-box junction zones. Although a great deal is known about the nature of the junction zones in pectin gels, less is known about the long-range structure within calcium-set gels. Two plausible alternative models for long-range order in these gels are a pseudo rubber-like structure and a fibrous network structure. Atomic force microscopy studies of calcium-induced gel precursors, and fragments released from gels, suggest that association leads to a branched fibrous structure within the gels. Enzymatic de-esterification of high methoxy pectin in the presence of calcium ions can induce gelation of the pectin. Thus pectin gel networks may provide a model for a self-assembled network structure within the middle lamella region of the plant cell wall.     

Enhanced Wang Landau sampling of adsorbed protein conformations

Using computer simulations to model the folding of proteins into their native states is computationally expensive due to the extraordinarily low degeneracy of the ground state. In this paper, we develop an efficient way to sample these folded conformations using Wang Landau sampling coupled with the configurational bias method (which uses an unphysical "temperature" that lies between the collapse and folding transition temperatures of the protein). This method speeds up the folding process by roughly an order of magnitude over existing algorithms for the sequences studied. We apply this method to study the adsorption of intrinsically disordered hydrophobic polar protein fragments on a hydrophobic surface. We find that these fragments, which are unstructured in the bulk, acquire secondary structure upon adsorption onto a strong hydrophobic surface. Apparently, the presence of a hydrophobic surface allows these random coil fragments to fold by providing hydrophobic contacts that were lost in protein fragmentation.     

A versatile method for systematic conformational searches: application to CheY

A novel molecular structure prediction method, the Z Method, is described. It provides a versatile platform for the development and use of systematic, grid‐based conformational search protocols, in which statistical information (i.e., rotamers) can also be included. The Z Method generates trial structures by applying many changes of the same type to a single starting structure, thereby sampling the conformation space in an unbiased way. The method, implemented in the CHARMM program as the Z Module, is applied here to an illustrative model problem in which rigid, systematic searches are performed in a 36‐dimensional conformational space that describes the relative positions of the 10 secondary structural elements of the protein CheY. A polar hydrogen representation with an implicit solvation term (EEF1) is used to evaluate successively larger fragments of the protein generated in a hierarchical build‐up procedure. After a final refinement stage, and a total computational time of about two‐and‐a‐half CPU days on AMD Opteron processors, the prediction is within 1.56 Å of the native structure. The errors in the predicted backbone dihedral angles are found to approximately cancel. Monte Carlo and simulated annealing trials on the same or smaller versions of the problem, using the same atomic model and energy terms, are shown to result in less accurate predictions. Although the problem solved here is a limited one, the findings illustrate the utility of systematic searches with atom‐based models for macromolecular structure prediction and the importance of unbiased sampling in structure prediction methods. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011     

Deep Learning Insights into Lanthanides Complexation Chemistry

Modern structure–property models are widely used in chemistry; however, in many cases, they are still a kind of a “black box” where there is no clear path from molecule structure to target property. Here we present an example of deep learning usage not only to build a model but also to determine key structural fragments of ligands influencing metal complexation. We have a series of chemically similar lanthanide ions, and we have collected data on complexes’ stability, built models, predicting stability constants and decoded the models to obtain key fragments responsible for complexation efficiency. The results are in good correlation with the experimental ones, as well as modern theories of complexation. It was shown that the main influence on the constants had a mutual location of the binding centers.     

Structural alternatives for the formation of halogenophosphine-phosphenium complexes

NMR studies of reactions between some N-heterocyclic and acyclic diamino phosphenium ions (R2N)2P+ and P-chlorophosphines (R2N)2PCl suggest that the reactants interact via chloride scrambling rather than by formation of P-P bonded phosphenium-phosphine complexes. Computational studies of reactions between model ions (R'2N)2P+ and neutral phosphines (R'2N)2PX (X = F, Cl, Br) confirm that in the gas phase the formation of halide-bridged adducts is indeed preferred and only for the most electrophilic cation an alternative but energetically less favorable P-P bonded structure was found. The halide-bridged adducts feature nearly C2-symmetrical P...X...P arrays (for X = Cl, Br) or are loose molecular complexes arising from electrostatic interaction between nearly unperturbed fragments (for X = F). In the latter case, a P...F...P-bridged structure was located as a transition state of a fluoride transfer reaction. The formation of the adducts appears to be controlled by electrostatic rather than orbital interactions. Consideration of solvent effects by a polarizable continuum model indicates a destabilization of the adducts versus the isolated fragments and suggests that in solution extensive dissociation occurs. The computations further reveal a large solvent-induced lengthening of the P-Cl bonds in N-heterocyclic halogenophosphines which implies that the unusual P-Cl distances observed for these species are, to a large part, attributable to intermolecular influences.     

PEO-PPO block copolymer vectors do not interact directly with DNA but with lipid membranes

Small angle neutron (SANS) and light scattering was used to study the interaction between fragments of double stranded deoxyribonucleic acid (DNA) and a synthetic triblock [poly(ethylene oxide)-poly(propylene oxide)-poly(ethylene oxide)] amphiphilic polymer, known as L64, a potential vector for gene therapy. The mechanism of action of this vector is yet unknown. The contrast variation method was used to separate the partial structure factors of the different components in mixtures of triblock and DNA. It has been found that the copolymer and DNA molecules exhibit repulsive interactions. Further, the interaction between the copolymer and a model lipid membrane was investigated in order to explain the action of the vector. Electrical measurements on black lipid membranes indicated that the main effect of L64 as a vector is to permeabilize the cell's membrane.     

A distance-dependent parameterization of the extended Hubbard model for conjugated and aromatic hydrocarbons derived from stretched ethene

The Hubbard model, which is widely used in physics but is mostly unfamiliar to chemists, provides an attractive yet simple model for chemistry beyond the self consistent field molecular orbital approximation. The Hubbard model adds an effective electron-electron repulsion when two electrons occupy the same atomic orbital to the familiar Hückel Hamiltonian. Thus it breaks the degeneracy between excited singlet and triplet states and allows an explicit treatment of electron correlation. We show how to evaluate the parameters of the model from high-level ab initio calculations on two-atom fragments and then to transfer the parameters to large molecules and polymers where accurate ab initio calculations are difficult or impossible. The recently developed MS-RASPT2 method is used to generate accurate potential energy curves for ethene as a function of carbon-carbon bond length, which are used to parameterize the model for conjugated hydrocarbons. Test applications to several conjugated/aromatic molecules show that even though the model is very simple, it is capable of reasonably accurate predictions for bond lengths, and predicts molecular excitation energies in reasonable agreement with those from the MS-RASPT2 method.