Exploring protein structural dissimilarity to facilitate structure classification

Background: Classification of newly resolved protein structures is important in understanding their architectural, evolutionary and functional relatedness to known protein structures. Among various efforts to improve the database of Structural Classification of Proteins (SCOP), automation has received particular attention. Herein, we predict the deepest SCOP structural level that an unclassified protein shares with classified proteins with an equal number of secondary structure elements (SSEs). Results: We compute a coefficient of dissimilarity (omega) between proteins, based on structural and sequence-based descriptors characterising the respective constituent SSEs. For a set of 1,661 pairs of proteins with sequence identity up to 35%, the performance of omega in predicting shared Class, Fold and Super-family levels is comparable to that of DaliLite Z score and shows a greater than four-fold increase in the true positive rate (TPR) for proteins sharing the Family level. On a larger set of 600 domains representing 200 families, the performance of Z score improves in predicting a shared Family, but still only achieves about half of the TPR of omega. The TPR for structures sharing a Superfamily is lower than in the first dataset, but omega performs slightly better than Z score. Overall, the sensitivity of omega in predicting common Fold level is higher than that of the DaliLite Z score. Conclusion: Classification to a deeper level in the hierarchy is specific and difficult. So the efficiency of omega may be attractive to the curators and the end-users of SCOP. We suggest omega may be a better measure for structure classification than the DaliLite Z score, with the caveat that currently we are restricted to comparing structures with equal number of SSEs

Prediction of glycosylation sites using random forests

<p>Abstract</p> <p>Background</p> <p>Post translational modifications (PTMs) occur in the vast majority of proteins and are essential for function. Prediction of the sequence location of PTMs enhances the functional characterisation of proteins. Glycosylation is one type of PTM, and is implicated in protein folding, transport and function.</p> <p>Results</p> <p>We use the random forest algorithm and pairwise patterns to predict glycosylation sites. We identify pairwise patterns surrounding glycosylation sites and use an odds ratio to weight their propensity of association with modified residues. Our prediction program, GPP (glycosylation prediction program), predicts glycosylation sites with an accuracy of 90.8% for Ser sites, 92.0% for Thr sites and 92.8% for Asn sites. This is significantly better than current glycosylation predictors. We use the trepan algorithm to extract a set of comprehensible rules from GPP, which provide biological insight into all three major glycosylation types.</p> <p>Conclusion</p> <p>We have created an accurate predictor of glycosylation sites and used this to extract comprehensible rules about the glycosylation process. GPP is available online at <url>http://comp.chem.nottingham.ac.uk/glyco/</url>.</p

Interpretable correlation descriptors for quantitative structure-activity relationships

<p>Abstract</p> <p>Background</p> <p>The topological maximum cross correlation (TMACC) descriptors are alignment-independent 2D descriptors for the derivation of QSARs. TMACC descriptors are generated using atomic properties determined by molecular topology. Previous validation (<it>J Chem Inf Model </it>2007, <b>47</b>: 626-634) of the TMACC descriptor suggests it is competitive with the current state of the art.</p> <p>Results</p> <p>Here, we illustrate the interpretability of the TMACC descriptors, through the analysis of the QSARs of inhibitors of angiotensin converting enzyme (ACE) and dihydrofolate reductase (DHFR). In the case of the ACE inhibitors, the TMACC interpretation shows features specific to C-domain inhibition, which have not been explicitly identified in previous QSAR studies.</p> <p>Conclusions</p> <p>The TMACC interpretation can provide new insight into the structure-activity relationships studied. Freely available, open source software for generating the TMACC descriptors can be downloaded from <url>http://comp.chem.nottingham.ac.uk</url>.</p

Computed optical spectra of SARS-CoV-2 proteins

© 2020 Elsevier B.V. Treatment for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes Covid-19, may well be predicated on knowledge of the structures of protein of this virus. However, often these cannot be determined easily or quickly. Herein, we provide calculated circular dichroism (CD) spectra in the far- and near-UV, and infra-red (IR) spectra in the amide I region for experimental structures and computational models of SARS-CoV-2 proteins. The near-UV CD spectra offer greatest sensitivity in assessing the accuracy of models

Unfolding Dynamics of a Photoswitchable Helical Peptide

We present an atomistic force field for the azo-moiety of the photoswitchable FK-11-X peptide. We use the parameters to study the unfolding of the peptide through molecular dynamics simulations. The unfolded ensemble contains many different structures, ranging from a partially unfolded peptide to a fully unfolded structure. The averaged computed far-ultraviolet circular dichroism (CD) spectrum of the set of structures, which was simulated using the newly developed force field, agrees well with experiment. The rate of the simulated unfolding process was estimated to have a time constant of 5.80 ± 0.03 ns from the time evolution of the CD spectrum of the peptide, computed from the backbone conformations sampled over 40 simulated trajectories. Our estimated time constant is faster than, but not inconsistent with, previous experimental estimates from time-resolved infrared and optical rotatory dispersion spectroscopy

Making It Easier To Be Green: A Single Case Demonstration of the Effects of Computer Defaults To Conserve Energy in a University Computer Lab

This is the publisher's version, also available electronically from http://online.liebertpub.com/doi/abs/10.1089/SUS.2013.9827Educational buildings and university campuses represent some of the most computer-dense settings in the United States. Unfortunately, the administrators and users in these settings often lack proper energy saving strategies, resulting in excessive energy waste. Research in behavioral economics has reliably shown that effort is an inhibitory factor in changing a variety of behaviors. That is, humans have a tendency to choose the option that requires the least amount of effort, regardless of whether that option is the best one. Thus, it might be inferred that interventions requiring greater effort for computer users to conserve energy are unlikely to be effective. This study highlights a successful cost-cutting application of default energy savings settings in a campus computer-testing laboratory. Default settings applied by the research team did not require effort on the part of users and resulted in computers powering-down after a relatively short period of inactivity. A cost analysis revealed modest fiscal and electricity savings among the small number of computers included in the study. However, extrapolating these modest savings across the many hundreds of work stations typically found on university campuses suggests a substantial savings would result from the adoption of the intervention described herein. Implications for practice and future research are discussed

ProCKSI: a decision support system for Protein (Structure) Comparison, Knowledge, Similarity and Information

Background: We introduce the decision support system for Protein (Structure) Comparison, Knowledge, Similarity and Information (ProCKSI). ProCKSI integrates various protein similarity measures through an easy to use interface that allows the comparison of multiple proteins simultaneously. It employs the Universal Similarity Metric (USM), the Maximum Contact Map Overlap (MaxCMO) of protein structures and other external methods such as the DaliLite and the TM-align methods, the Combinatorial Extension (CE) of the optimal path, and the FAST Align and Search Tool (FAST). Additionally, ProCKSI allows the user to upload a user-defined similarity matrix supplementing the methods mentioned, and computes a similarity consensus in order to provide a rich, integrated, multicriteria view of large datasets of protein structures. Results: We present ProCKSI's architecture and workflow describing its intuitive user interface, and show its potential on three distinct test-cases. In the first case, ProCKSI is used to evaluate the results of a previous CASP competition, assessing the similarity of proposed models for given targets where the structures could have a large deviation from one another. To perform this type of comparison reliably, we introduce a new consensus method. The second study deals with the verification of a classification scheme for protein kinases, originally derived by sequence comparison by Hanks and Hunter, but here we use a consensus similarity measure based on structures. In the third experiment using the Rost and Sander dataset (RS126), we investigate how a combination of different sets of similarity measures influences the quality and performance of ProCKSI's new consensus measure. ProCKSI performs well with all three datasets, showing its potential for complex, simultaneous multi-method assessment of structural similarity in large protein datasets. Furthermore, combining different similarity measures is usually more robust than relying on one single, unique measure. Conclusion: Based on a diverse set of similarity measures, ProCKSI computes a consensus similarity profile for the entire protein set. All results can be clustered, visualised, analysed and easily compared with each other through a simple and intuitive interface

Force fields for macromolecular assemblies containing Diketopyrrolopyrrole and Thiophene

Utilizing a force-matching procedure, we parametrize new force fields systematically for large conjugated systems. We model both conjugated polymers and molecular crystals that contain diketopyrrolopyrrole, thiophene, and thieno[3,2-b]thiophene units. These systems have recently been found to have low band gaps, which exhibit high efficiency for photovoltaic devices. The equilibrium structures, forces, and energies of the building block chromophores, diketopyrrolopyrrole, thiophene, and thieno[3,2-b]thiophene computed using our parameters are comparable to those computed using the reference electronic structure method. We assess the suitability of this new force field for electronic property calculations by comparing the electronic excitation properties computed along classical and ab initio molecular dynamics trajectories. For both trajectories, we find similar distributions of TDDFT-calculated excitation energies and oscillator strengths for the building block chromophore diketopyrrolopyrrole-thieno[3,2-b]thiophene. The structural, dynamic, and electronic properties of the macromolecular assemblies built upon these chromophores are characterized. For both polymers and molecular crystals, pronounced peaks around 0° or 180° are observed for the torsions between chromophores under ambient conditions. The high planarity in these systems can promote local ordering and π–π stacking, thereby potentially facilitating charge transport across these materials. For the model conducting polymers, we found that the fluctuations in the density of states per chain per monomer is negligibly small and does not vary significantly with chains comprising 20–40 monomers. Analysis of the electron–hole distributions and the transition density matrices indicates that the delocalized length is approximately 4–6 monomers, which is in good agreement with other theoretical and experimental studies of different conducting polymers. For the molecular crystals, our investigation of the characteristic time scale of the fluctuation in the excitonic couplings shows that a low-frequency vibration below 100 cm–1 is observed for the nearest neighbors. These observations are in line with previous studies on other molecular crystals, in which low-frequency vibrations are believed to be responsible for the large modulation of the excitonic coupling. Thus, our approach and the new force fields provide a direct route for studying the structure–property relations and the molecular level origins of the high efficiency of these classes of materials

Structure–Property Relationships in Amorphous Thieno[3,2-b]thiophene–Diketopyrrolopyrrole–Thiophene-Containing Polymers

(Figure Presented) We present calculations of electronic structure properties of disordered conducting polymers containing thieno[3,2b]thiophene, diketopyrrolopyrrole, and thiophene. Atomistic force field parameters for the polymer were optimized to minimize the difference between the ab initio and empirical potential energy surfaces and their corresponding first derivatives. These new force fields are employed to propagate the nuclear dynamics, and the equilibrium trajectories are sampled for subsequent electronic structure calculations. We found that the fluctuations of the bulk density of states are negligibly small and do not vary significantly with the length of the backbone and the side-chains. The localization length near the band gap is between 8 and 12 Å, which is about half of the length of the monomer and significantly less than the length of the extended polymer (∼200−400 Å). This indicates that the orbital localization is not affected by the length of the polymer. The inter-chain excitonic couplings are usually smaller than 5 meV, suggesting that the transport mechanism across chains is described by incoherent hopping, and excitons mainly move along the chain. Furthermore, thermal fluctuations cause the evolution of the excitons along the chain. Characterization of the relationships between the geometric disorder of the polymers and the distributions of the lowest excited states reveals that the low-energy excitons tend to localize in regions that are more planar and less folded. However, some excitons are also spread over defects. Thus, our theoretical calculations and the new force fields provide a direct route for characterizing the structure−property relationships and helpful information for constructing more realistic models for the exciton dynamics study of this class of polymeric materials

Möbius and Hückel Cyclacenes with Dewar and Ladenburg Defects

Copyright © 2020 American Chemical Society. Cyclacene nanobelts have not been synthesized in over 60 years and remain one of the last unsynthesized building blocks of carbon nanotubes. Recent work has predicted that Hückel-cyclacenes containing Dewar benzenoid ring isomers are the most stable isomeric forms for several of the smaller sizes of cyclacene belts. Here, we give a more complete picture of the isomers that are possible within these nanobelt systems by simulating embedded Ladenburg (prismane) benzenoid rings in Hückel-[n]cyclacenes (n = 5-14) and embedded Dewar benzenoid rings in twisted Möbius-[n]cyclacenes (n = 9-14). The Möbius-[9]cyclacene isomer containing one Dewar benzenoid defect and the Hückel-[5]cyclacene isomer containing two maximally spaced Ladenburg benzenoid defects are found to be more stable than their conventional Kekulé benzenoid ring counterparts. The isomers that contain Dewar and Ladenburg benzenoid rings have larger electronic singlet-triplet energy gaps and lower polyradical character when compared with the conventional isomers