OFraMP: a fragment-based tool to facilitate the parametrization of large molecules.

An Online tool for Fragment-based Molecule Parametrization (OFraMP) is described. OFraMP is a web application for assigning atomic interaction parameters to large molecules by matching sub-fragments within the target molecule to equivalent sub-fragments within the Automated Topology Builder (ATB, atb.uq.edu.au) database. OFraMP identifies and compares alternative molecular fragments from the ATB database, which contains over 890,000 pre-parameterized molecules, using a novel hierarchical matching procedure. Atoms are considered within the context of an extended local environment (buffer region) with the degree of similarity between an atom in the target molecule and that in the proposed match controlled by varying the size of the buffer region. Adjacent matching atoms are combined into progressively larger matched sub-structures. The user then selects the most appropriate match. OFraMP also allows users to manually alter interaction parameters and automates the submission of missing substructures to the ATB in order to generate parameters for atoms in environments not represented in the existing database. The utility of OFraMP is illustrated using the anti-cancer agent paclitaxel and a dendrimer used in organic semiconductor devices. OFraMP applied to paclitaxel (ATB ID 35922).

Systematic evaluation of particle loss during handling in the percutaneous transluminal angioplasty for eight different drug-coated balloons.

Paclitaxel drug coated balloons (DCBs) should provide optimal drug transfer exclusively to the target tissue. The aim of this study was to evaluate the particle loss by handling during angioplasty. A robotic arm was developed for systematic and reproducible drug abrasion experiments. The contact force on eight different commercially available DCB types was gradually increased, and high-resolution microscopic images of the deflated and inflated balloons were recorded. Three types of DCBs were classified: no abrasion of the drug in both statuses (deflated and inflated), significant abrasion only in the inflated status, and significant abrasion in both statuses. Quantitative measurements via image processing confirmed the qualitative classification and showed changes of the drug area between 2.25 and 45.73% (13.28 ± 14.29%) in the deflated status, and between 1.66 and 40.41% (21.43 ± 16.48%) in the inflated status. The structures and compositions of the DCBs are different, some are significantly more susceptible to drug loss. Particle loss by handling during angioplasty leads to different paclitaxel doses in the target regions for same DCB types. Susceptibility to involuntary drug loss may cause side effects, such as varying effective paclitaxel doses, which may explain variations in studies regarding the therapeutic outcome.

Automated partial atomic charge assignment for drug-like molecules: a fast knapsack approach.

A key factor in computational drug design is the consistency and reliability with which intermolecular interactions between a wide variety of molecules can be described. Here we present a procedure to efficiently, reliably and automatically assign partial atomic charges to atoms based on known distributions. We formally introduce the molecular charge assignment problem, where the task is to select a charge from a set of candidate charges for every atom of a given query molecule. Charges are accompanied by a score that depends on their observed frequency in similar neighbourhoods (chemical environments) in a database of previously parameterised molecules. The aim is to assign the charges such that the total charge equals a known target charge within a margin of error while maximizing the sum of the charge scores. We show that the problem is a variant of the well-studied multiple-choice knapsack problem and thus weakly NP -complete. We propose solutions based on Integer Linear Programming and a pseudo-polynomial time Dynamic Programming algorithm. We demonstrate that the results obtained for novel molecules not included in the database are comparable to the ones obtained performing explicit charge calculations while decreasing the time to determine partial charges for a molecule from hours or even days to below a second. Our software is openly available.

Exploring the Limits of the Geometric Copolymerization Model.

The geometric copolymerization model is a recently introduced statistical Markov chain model. Here, we investigate its practicality. First, several approaches to identify the optimal model parameters from observed copolymer fingerprints are evaluated using Monte Carlo simulated data. Directly optimizing the parameters is robust against noise but has impractically long running times. A compromise between robustness and running time is found by exploiting the relationship between monomer concentrations calculated by ordinary differential equations and the geometric model. Second, we investigate the applicability of the model to copolymerizations beyond living polymerization and show that the model is useful for copolymerizations involving termination and depropagation reactions.

Abundance correction for mass discrimination effects in polymer mass spectra.

RATIONALE: Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS) is frequently used to analyze homo- and copolymers, i.e. for computing copolymer fingerprints. However, the oligomer abundances are influenced by mass discrimination, i.e. mass- and composition-dependent ionization. We have developed a computational method to correct the abundance bias caused by the mass discrimination. METHODS: MALDI-TOFMS in combination with computational methods was used to investigate three random copolymers with different ratios of styrene and isoprene. Furthermore, equimolar high- and low-mass styrene and isoprene homopolymers (2500 and 4200 Da) were mixed and also analyzed by MALDI-TOFMS. The abundances of both copolymers and homopolymers were corrected for mass discrimination effects with our new method. RESULTS: The novel computational method was integrated into the existing COCONUT software. The method was demonstrated using the measured styrene and isoprene co- and homopolymers. First, the method was applied to homopolymer spectra. Subsequently, the copolymer fingerprint was computed from the copolymer MALDI mass spectra and the correcting function applied. The changes in the composition are plausible, indicating that correction of copolymer abundances was reasonable. CONCLUSIONS: Our computational method may help to avoid erroneous conclusions when analyzing copolymer MS spectra. The software is freely available and represents a step towards comprehensive computational support in polymer science. Copyright © 2016 John Wiley & Sons, Ltd.

New Statistical Models for Copolymerization.

For many years, copolymerization has been studied using mathematical and statistical models. Here, we present new Markov chain models for copolymerization kinetics: the Bernoulli and Geometric models. They model copolymer synthesis as a random process and are based on a basic reaction scheme. In contrast to previous Markov chain approaches to copolymerization, both models take variable chain lengths and time-dependent monomer probabilities into account and allow for computing sequence likelihoods and copolymer fingerprints. Fingerprints can be computed from copolymer mass spectra, potentially allowing us to estimate the model parameters from measured fingerprints. We compare both models against Monte Carlo simulations. We find that computing the models is fast and memory efficient.

COCONUT­An Efficient Tool for Estimating Copolymer Compositions from Mass Spectra.

The accurate characterization of synthetic polymer sequences represents a major challenge in polymer science. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is frequently used for the characterization of copolymer samples. We present the COCONUT software for estimating the composition distribution of the copolymer. Our method is based on Linear Programming and is capable of automatically resolving overlapping isotopes and isobaric ions. We demonstrate that COCONUT is well suited for analyzing complex copolymer MS spectra. COCONUT is freely available and provides a graphical user interface.