conSSert: Consensus SVM Model for Accurate Prediction of Ordered Secondary Structure

Combining Experimental Isotherms, Minimalistic Simulations, and a Model to Understand and Predict Chemical Adsorption onto Montmorillonite Clays

An attractive approach to minimize human and animal exposures to toxic environmental contaminants is the use of safe and effective sorbent materials to sequester them. Montmorillonite clays have been shown to tightly bind diverse toxic chemicals. Due to their promise as sorbents to mitigate chemical exposures, it is important to understand their function and rapidly screen and predict optimal clay-chemical combinations for further testing. We derived adsorption free-energy values for a structurally and physicochemically diverse set of toxic chemicals using experimental adsorption isotherms performed in the current and previous studies. We studied the diverse set of chemicals using minimalistic MD simulations and showed that their interaction energies with calcium montmorillonite clays calculated using simulation snapshots in combination with their net charge and their corresponding solvent's dielectric constant can be used as inputs to a minimalistic model to predict adsorption free energies in agreement with experiments. Additionally, experiments and computations were used to reveal structural and physicochemical properties associated with chemicals that can be adsorbed to calcium montmorillonite clay. These properties include positively charged groups, phosphine groups, halide-rich moieties, hydrogen bond donor/acceptors, and large, rigid structures. The combined experimental and computational approaches used in this study highlight the importance and potential applicability of analogous methods to study and design novel advanced sorbent systems in the future, broadening their applicability for environmental contaminants.

Computational approaches for inferring 3D conformations of chromatin from chromosome conformation capture data

Chromosome conformation capture (3C) and its variants are powerful experimental techniques for probing intra- and inter-chromosomal interactions within cell nuclei at high resolution and in a high-throughput, quantitative manner. The contact maps derived from such experiments provide an avenue for inferring the 3D spatial organization of the genome. This review provides an overview of the various computational methods developed in the past decade for addressing the very important but challenging problem of deducing the detailed 3D structure or structure population of chromosomal domains, chromosomes, and even entire genomes from 3C contact maps.

Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type

With close to 30 sequence-based predictors of RNA-binding residues (RBRs), this comparative survey aims to help with understanding and selection of the appropriate tools. We discuss past reviews on this topic, survey a comprehensive collection of predictors, and comparatively assess six representative methods. We provide a novel and well-designed benchmark dataset and we are the first to report and compare protein-level and datasets-level results, and to contextualize performance to specific types of RNAs. The methods considered here are well-cited and rely on machine learning algorithms on occasion combined with homology-based prediction. Empirical tests reveal that they provide relatively accurate predictions. Virtually all methods perform well for the proteins that interact with rRNAs, some generate accurate predictions for mRNAs, snRNA, SRP and IRES, while proteins that bind tRNAs are predicted poorly. Moreover, except for DRNApred, they confuse DNA and RNA-binding residues. None of the six methods consistently outperforms the others when tested on individual proteins. This variable and complementary protein-level performance suggests that users should not rely on applying just the single best dataset-level predictor. We recommend that future work should focus on the development of approaches that facilitate protein-level selection of accurate predictors and the consensus-based prediction of RBRs.

A Systematic Review on Popularity, Application and Characteristics of Protein Secondary Structure Prediction Tools

BACKGROUND

Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple Secondary Structure Prediction (SSP) options is challenging. The current study is an insight into currently favored methods and tools, within various contexts.

OBJECTIVE

A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools.

METHODS

Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of the 209 studies were finally found eligible to extract data.

RESULTS

Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating an SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields.

CONCLUSION

This study provides a comprehensive insight into the recent usage of SSP tools which could be helpful for selecting a proper tool.

Insights into the interactions of bisphenol and phthalate compounds with unamended and carnitine-amended montmorillonite clays

Montmorillonite clays could be promising sorbents to mitigate toxic compound exposures. Bisphenols A (BPA) and S (BPS) as well as phthalates, dibutyl phthalate (DBP) and di-2-ethylhexyl phthalate (DEHP), are ubiquitous environmental contaminants linked to adverse health effects. Here, we combined computational and experimental methods to investigate the ability of montmorillonite clays to sorb these compounds. Molecular dynamics simulations predicted that parent, unamended, clay has higher binding propensity for BPA and BPS than for DBP and DEHP; carnitine-amended clay improved BPA and BPS binding, through carnitine simultaneously anchoring to the clay through its quaternary ammonium cation and forming hydrogen bonds with BPA and BPS. Experimental isothermal analysis confirmed that carnitine-amended clay has enhanced BPA binding capacity, affinity and enthalpy. Our studies demonstrate how computational and experimental methods, combined, can characterize clay binding and sorption of toxic compounds, paving the way for future investigation of clays to reduce BPA and BPS exposure.

R269C variant of ESR1: high prevalence and differential function in a subset of pancreatic cancers

Background

Estrogen receptor α (ESR1) plays a critical role in promoting growth of various cancers. Yet, its role in the development of pancreatic cancer is not well-defined. A less studied region of ESR1 is the hinge region, connecting the ligand binding and DNA domains. rs142712646 is a rare SNP in ESR1, which leads to a substitution of arginine to cysteine at amino acid 269 (R269C). The mutation is positioned in the hinge region of ESR1, hence may affect the receptor structure and function. We aimed to characterize the activity of R269C-ESR1 and study its role in the development of pancreatic cancer.

Methods

Transcriptional activity was evaluated by E2-response element (ERE) and AP1 –luciferase reporter assays and qRT-PCR. Proliferation and migration were assessed using MTT and wound healing assays. Gene-expression analysis was performed using RNAseq.

Results

We examined the presence of this SNP in various malignancies, using the entire database of FoundationOne and noted enrichment of it in a subset of pancreatic non-ductal adenocarcinoma (n = 2800) compared to pancreatic ductal adenocarcinoma (PDAC) as well as other tumor types (0.53% vs 0.29%, p = 0.02). Studies in breast and pancreatic cancer cells indicated cell type-dependent activity of ESR1 harboring R269C. Thus, expression of R269C-ESR1 enhanced proliferation and migration of PANC-1 and COLO-357 pancreatic cancer cells but not of MCF-7 breast cancer cells. Moreover, R269C-ESR1 enhanced E2-response elements (ERE) and AP1-dependent transcriptional activity and increased mRNA levels of ERE and AP1-regulated genes in pancreatic cancer cell lines, but had a modest effect on MCF-7 breast cancer cells. Accordingly, whole transcriptome analysis indicated alterations of genes associated with tumorigenicity in pancreatic cancer cells and upregulation of genes associated with cell metabolism and hormone biosynthesis in breast cancer cells.

Conclusions

Our study shed new light on the role of the hinge region in regulating transcriptional activity of the ER and indicates cell-type specific activity, namely increased activity in pancreatic cancer cells but reduced activity in breast cancer cells. While rare, the presence of rs142712646 may serve as a novel genetic risk factor, and a possible target for therapy in a subset of non-ductal pancreatic cancers.

Disorder-Order Transitions in Conformational Selection of a Peptide by Ebola Virus Nucleoprotein

This study presents parallel-tempering lattice Monte Carlo simulations based on the side-chain-only (SICHO) model for calculating the conformational landscape of a 28-residue intrinsically disordered peptide extracted from the Ebola virus protein VP35. The central issue is the applicability of the SICHO potential energy function and in general coarse-grained (CG) representations of intermediate resolution for modeling large-scale conformational heterogeneity that includes both folded and unstructured peptide states. Crystallographic data shows that the peptide folds in a 4₁₀-helix-turn-3₁₀-helix topology upon complex formation with the Ebola virus nucleoprotein, whereas in isolation, the peptide transitions to a disordered conformational ensemble as observed in circular dichroism experiments. The simulation reveals a potential of mean force that displays conformational diversity along the helix-forming reaction coordinate consistent with disorder-order transitions, yet unexpectedly the bound topology is poorly sampled, and a population shift to an unstructured state incurs a significant free-energy penalty. Applying an elastic network interpolation model suggests a hybrid binding mechanism through conformational selection of the 4₁₀-helix followed by an induced fit of the 3₁₀-helix. A comparison of the CG model with previously reported all-atom CHARMM-based simulations highlights a lattice-based approach that is computationally fast and with the correct parameterization yields good resolution to modeling conformational plasticity.

Evaluation of the scale-consistent UNRES force field in template-free prediction of protein structures in the CASP13 experiment

The recent NEWCT-9P version of the coarse-grained UNRES force field for proteins, with scale-consistent formulas for the local and correlation terms, has been tested in the CASP13 experiment of the blind-prediction of protein structure, in the ab initio, contact-assisted, and data-assisted modes. Significant improvement of the performance has been observed with respect to the CASP11 and CASP12 experiments (by over 10 GDT_TS units for the ab initio mode predictions and by over 15 GDT_TS units for the contact-assisted prediction, respectively), which is a result of introducing scale-consistent terms and improved handling of contact-distance restraints. As in previous CASP exercises, UNRES ranked higher in the free modeling category than in the general category that included template based modeling targets. Use of distance restraints from the predicted contacts, albeit many of them were wrong, resulted in the increase of GDT_TS by over 8 units on average and introducing sparse restraints from small-angle X-ray/neutron scattering and chemical cross-link-mass-spectrometry experiments, and ambiguous restraints from nuclear magnetic resonance experiments has also improved the predictions by 8.6, 9.7, and 10.7 GDT_TS units on average, respectively.

Introduction of a bounded penalty function in contact-assisted simulations of protein structures to omit false restraints

Contact-assisted simulations, the contacts being predicted or determined experimentally, have become very important in the determination of the structures of proteins and other biological macromolecules. In this work, the effect of contact-distance restraints on the simulated structures was investigated with the use of multiplexed replica exchange simulations with the coarse-grained UNRES force field. A modified bounded flat-bottom restraint function that does not generate a gradient when a restraint cannot be satisfied was implemented. Calculations were run with (i) a set of four small proteins, with contact restraints derived from experimental structures, and (ii) selected CASP11 and CASP12 targets, with restraints as used at prediction time. The bounded penalty function largely omitted false contacts, which were usually inconsistent. It was found that at least 20% of correct contacts must be present in the restraint set to improve model quality with respect to unrestrained simulations. © 2019 Wiley Periodicals, Inc.

Advances with support vector machines for novel drug discovery

INTRODUCTION

Novel drug discovery remains an enormous challenge, with various computer-aided drug design (CADD) approaches having been widely employed for this purpose. CADD, specifically the commonly used support vector machines (SVMs), can employ machine learning techniques. SVMs and their variations offer numerous drug discovery applications, which range from the classification of substances (as active or inactive) to the construction of regression models and the ranking/virtual screening of databased compounds. Areas covered: Herein, the authors consider some of the applications of SVMs in medicinal chemistry, illustrating their main advantages and disadvantages, as well as trends in their utilization, via the available published literature. The aim of this review is to provide an up-to-date review of the recent applications of SVMs in drug discovery as described by the literature, thereby highlighting their strengths, weaknesses, and future challenges. Expert opinion: Techniques based on SVMs are considered as powerful approaches in early drug discovery. The ability of SVMs to classify active or inactive compounds has enabled the prioritization of substances for virtual screening. Indeed, one of the main advantages of SVMs is related to their potential in the analysis of nonlinear problems. However, despite successes in employing SVMs, the challenges of improving accuracy remain.

Conformational Selection of a Polyproline Peptide by Ebola Virus VP30

An adaptive temperature-based replica-exchange simulation of a peptide extracted from the Ebola virus nucleoprotein containing a polyproline sequence motif is reported. The simulation results of applying the CHARMM36m force field with a generalized Born solvent model is presented. Conformational heterogeneity is described by potentials of mean force (PMFs) for a set of reaction coordinates that define the topological fold space. Starting from an extended backbone conformation of the peptide observed in an X-ray crystallographic assembly with the Ebola virus protein VP30, the PMFs report a conformational landscape populated by chain excursions to collapsed states with limited transitions to either an extended fold or a canonical polyproline type II helix. Clustering of the conformations and applying an elastic network interpolation model yield a multistep pathway of conformational selection that minimizes the net transition-state cost from the population hub to the bound state. Related difference between the pathway endpoints taken from the PMFs reveal a significant free-energy penalty in reaching a population shift. To evaluate sequence fitness of the Ebola virus peptide in generating probability distributions, two human sequence variants are modeled and are found to produce profiles that show extensive deviations, thus suggesting either dissimilar binding mechanisms or the lack of recognition by VP30.

Chronic Obstructive Pulmonary Disease Molecular Subtyping and Pathway Deviation-Based Candidate Gene Identification

OBJECTIVE

The aim of this study was to identify the molecular subtypes of chronic obstructive pulmonary disease (COPD) and to prioritize COPD candidate genes using bioinformatics methods.

MATERIALS AND METHODS

In this bioinformatics study, the gene expression dataset GSE76705 (including 229 COPD samples) and known COPD-related genes (candidate genes) were downloaded from the Gene Expression Omnibus (GEO) and the Online Mendelian Inheritance in Man (OMIM) databases respectively. Based on the expression values of the candidate genes, COPD samples were divided into molecular subtypes through hierarchical clustering analysis. Candidate genes were accordingly allocated into the defined molecular subtypes and functional enrichment analysis was undertaken. Pathway deviation scores were then analyzed, followed by the analysis of clinical indicators (FEV1, FEV1/FVC, age and gender) of COPD patients in each subtype, and prediction models were constructed. Furthermore, the gene expression dataset GSE71220 was used to bioinformatically validate our results.

RESULTS

A total of 213 COPD-related genes were identified, which divided samples into three subtypes based on the gene expression values. After intersection analysis, 160 common genes including transforming growth factor β1 (TGFB1), epidermal growth factor receptor (EGFR) and interleukin 13 (IL13) were obtained. Functional enrichment analysis identified 22 pathways such as 'hsa04060: cytokine-cytokine receptor interaction pathways, 'hsa04110: cell cycle' and 'hsa05222: small cell lung cancer'. Pathways in subtype 2 had higher deviation scores. Furthermore, three receiver operating characteristic (ROC) curves (accuracies >80%) were constructed. The three subtypes in COPD samples were also identified in the validation dataset GSE71220.

CONCLUSION

COPD may be further subdivided into several molecular subtypes, which may be useful in improving COPD therapy based on the molecular subtype of a patient.

Prediction of disulfide dihedral angles using chemical shifts

Cystine residues result from the formation of disulfide bonds between pairs of cysteine residues. This cross linking of the backbone is essential for the structure and activity of peptides and proteins. The conformation of a cystine side chain can be described using five dihedral angles, χ1, χ2, χ3, χ2', and χ1', with cystines favouring certain combinations of these angles. 2D NMR spectroscopy is ideally suited for structure determination of disulfide-rich peptides, because of their small size and constrained nature. However, only limited information of the cystine side chain conformation can be determined by NMR spectroscopy, leading to ambiguity in the deduced 3D structures. Resolving accurate structures is important as disulfide-rich peptides have proven to be promising drug candidates in a number of fields, either as bioactive leads or scaffolds. Using a database of NMR chemical shifts combined with crystallographic structures, we have developed a method called DISH that uses support vector machines to predict the dihedral angles of cysteine side chains. It is able to successfully predict χ2 angles with 91% accuracy, and has improved performance over existing prediction methods for χ1 angles, with 87% accuracy. For 81% of cysteine residues, DISH successfully predicted both the χ1 and χ2 angles. By revisiting published solution structures of peptides determined using NMR spectroscopy, we assessed the impact of additional cystine dihedral restraints on the quality of 3D models. DISH improved the resolution and accuracy, highlighting the potential for improving the understanding of structure-activity relationships and rational development of peptide drugs.

An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research.

Assessment of contact predictions in CASP12: Co-evolution and deep learning coming of age

Following up on the encouraging results of residue-residue contact prediction in the CASP11 experiment, we present the analysis of predictions submitted for CASP12. The submissions include predictions of 34 groups for 38 domains classified as free modeling targets which are not accessible to homology-based modeling due to a lack of structural templates. CASP11 saw a rise of coevolution-based methods outperforming other approaches. The improvement of these methods coupled to machine learning and sequence database growth are most likely the main driver for a significant improvement in average precision from 27% in CASP11 to 47% in CASP12. In more than half of the targets, especially those with many homologous sequences accessible, precisions above 90% were achieved with the best predictors reaching a precision of 100% in some cases. We furthermore tested the impact of using these contacts as restraints in ab initio modeling of 14 single-domain free modeling targets using Rosetta. Adding contacts to the Rosetta calculations resulted in improvements of up to 26% in GDT_TS within the top five structures.

Parallel Tempering of Dark Matter from the Ebola Virus Proteome: Comparison of CHARMM36m and CHARMM22 Force Fields with Implicit Solvent

Intrinsically disordered proteins are characterized by their large manifold of thermally accessible conformations and their related statistical weights, making them an interesting target of simulation studies. To assess the development of a computational framework for modeling this distinct class of proteins, this work examines temperature-based replica-exchange simulations to generate a conformational ensemble of a 28-residue peptide from the Ebola virus protein VP35. Starting from a prefolded helix-β-turn-helix topology observed in a crystallographic assembly, the simulation strategy tested is the recently refined CHARMM36m force field combined with a generalized Born solvent model. A comparison of two replica-exchange methods is provided, where one is a traditional approach with a fixed set of temperatures and the other is an adaptive scheme in which the thermal windows are allowed to move in temperature space. The assessment is further extended to include a comparison with equivalent CHARMM22 simulation data sets. The analysis finds CHARMM36m to shift the minimum in the potential of mean force (PMF) to a lower fractional helicity compared with CHARMM22, while the latter showed greater conformational plasticity along the helix-forming reaction coordinate. Among the simulation models, only the adaptive tempering method with CHARMM36m found an ensemble of conformational heterogeneity consisting of transitions between α-helix-β-hairpin folds and unstructured states that produced a PMF of fractional fold propensity in qualitative agreement with circular dichroism experiments reporting a disordered peptide.

Structure-based design for binding peptides in anti-cancer therapy

The conventional anticancer therapeutics usually lack cancer specificity, leading to damage of normal tissues that patients find hard to tolerate. Ideally, anticancer therapeutics carrying payloads of drugs equipped with cancer targeting peptides can act like "guided missiles" with the capacity of targeted delivery toward many types of cancers. Peptides are amenable for conjugation to nano drugs for functionalization, thereby improving drug delivery and cellular uptake in cancer-targeting therapies. Peptide drugs are often more difficult to design through molecular docking and in silico analysis than small molecules, because peptide structures are more flexible, possess intricate molecular conformations, and undergo complex interactions. In this review, the development and application of strategies for structure-based design of cancer-targeting peptides against GRP78 are discussed. This Review also covers topics related to peptide pharmacokinetics and targeting delivery, including molecular docking studies, features that provide advantages for in vivo use, and properties that influence the cancer-targeting ability. Some advanced technologies and special peptides that can overcome the pharmacokinetic challenges have also been included.

On the Helix Propensity in Generalized Born Solvent Descriptions of Modeling the Dark Proteome

Intrinsically disordered proteins that populate the so-called “Dark Proteome” offer challenging benchmarks of atomistic simulation methods to accurately model conformational transitions on a multidimensional energy landscape. This work explores the application of parallel tempering with implicit solvent models as a computational framework to capture the conformational ensemble of an intrinsically disordered peptide derived from the Ebola virus protein VP35. A recent X-ray crystallographic study reported a protein-peptide interface where the VP35 peptide underwent a folding transition from a disordered form to a helix-β-turn-helix topological fold upon molecular association with the Ebola protein NP. An assessment is provided of the accuracy of two generalized Born solvent models (GBMV2 and GBSW2) using the CHARMM force field and applied with temperature-based replica exchange dynamics to calculate the disorder propensity of the peptide and its probability density of states in a continuum solvent. A further comparison is presented of applying an explicit/implicit solvent hybrid replica exchange simulation of the peptide to determine the effect of modeling water interactions at the all-atom resolution.

Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine

BACKGROUND

Elimination half-life is an important pharmacokinetic parameter that determines exposure duration to approach steady state of drugs and regulates drug administration. The experimental evaluation of half-life is time-consuming and costly. Thus, it is attractive to build an accurate prediction model for half-life.

METHODS

In this study, several machine learning methods, including gradient boosting machine (GBM), support vector regressions (RBF-SVR and Linear-SVR), local lazy regression (LLR), SA, SR, and GP, were employed to build high-quality prediction models. Two strategies of building consensus models were explored to improve the accuracy of prediction. Moreover, the applicability domains (ADs) of the models were determined by using the distance-based threshold.

RESULTS

Among seven individual models, GBM showed the best performance (R(2)=0.820 and RMSE=0.555 for the test set), and Linear-SVR produced the inferior prediction accuracy (R(2)=0.738 and RMSE=0.672). The use of distance-based ADs effectively determined the scope of QSAR models. However, the consensus models by combing the individual models could not improve the prediction performance. Some essential descriptors relevant to half-life were identified and analyzed.

CONCLUSIONS

An accurate prediction model for elimination half-life was built by GBM, which was superior to the reference model (R(2)=0.723 and RMSE=0.698).

GENERAL SIGNIFICANCE

Encouraged by the promising results, we expect that the GBM model for elimination half-life would have potential applications for the early pharmacokinetic evaluations, and provide guidance for designing drug candidates with favorable in vivo exposure profile. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.