Optimal Design of Energy Systems Using Constrained Grey-Box Multi-Objective Optimization.

The (global) optimization of energy systems, commonly characterized by high-fidelity and large-scale complex models, poses a formidable challenge partially due to the high noise and/or computational expense associated with the calculation of derivatives. This complexity is further amplified in the presence of multiple conflicting objectives, for which the goal is to generate trade-off compromise solutions, commonly known as Pareto-optimal solutions. We have previously introduced the p-ARGONAUT system, parallel AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems, which is designed to optimize general constrained single objective grey-box problems by postulating accurate and tractable surrogate formulations for all unknown equations in a computationally efficient manner. In this work, we extend p-ARGONAUT towards multi-objective optimization problems and test the performance of the framework, both in terms of accuracy and consistency, under many equality constraints. Computational results are reported for a number of benchmark multi-objective problems and a case study of an energy market design problem for a commercial building, while the performance of the framework is compared with other derivative-free optimization solvers.

Big Data Approach to Batch Process Monitoring: Simultaneous Fault Detection and Diagnosis Using Nonlinear Support Vector Machine-based Feature Selection.

This paper presents a novel data-driven framework for process monitoring in batch processes, a critical task in industry to attain a safe operability and minimize loss of productivity and profit. We exploit high dimensional process data with nonlinear Support Vector Machine-based feature selection algorithm, where we aim to retrieve the most informative process measurements for accurate and simultaneous fault detection and diagnosis. The proposed framework is applied to an extensive benchmark dataset which includes process data describing 22,200 batches with 15 faults. We train fault and time-specific models on the prealigned batch data trajectories via three distinct time horizon approaches: one-step rolling, two-step rolling, and evolving which varies the amount of data incorporation during modeling. The results show that two-step rolling and evolving time horizon approaches perform superior to the other. Regardless of the approach, proposed framework provides a promising decision support tool for online simultaneous fault detection and diagnosis for batch processes.

Princeton_TIGRESS 2.0: High refinement consistency and net gains through support vector machines and molecular dynamics in double-blind predictions during the CASP11 experiment.

Protein structure refinement is the challenging problem of operating on any protein structure prediction to improve its accuracy with respect to the native structure in a blind fashion. Although many approaches have been developed and tested during the last four CASP experiments, a majority of the methods continue to degrade models rather than improve them. Princeton_TIGRESS (Khoury et al., Proteins 2014;82:794-814) was developed previously and utilizes separate sampling and selection stages involving Monte Carlo and molecular dynamics simulations and classification using an SVM predictor. The initial implementation was shown to consistently refine protein structures 76% of the time in our own internal benchmarking on CASP 7-10 targets. In this work, we improved the sampling and selection stages and tested the method in blind predictions during CASP11. We added a decomposition of physics-based and hybrid energy functions, as well as a coordinate-free representation of the protein structure through distance-binning Cα-Cα distances to capture fine-grained movements. We performed parameter estimation to optimize the adjustable SVM parameters to maximize precision while balancing sensitivity and specificity across all cross-validated data sets, finding enrichment in our ability to select models from the populations of similar decoys generated for targets in CASPs 7-10. The MD stage was enhanced such that larger structures could be further refined. Among refinement methods that are currently implemented as web-servers, Princeton_TIGRESS 2.0 demonstrated the most consistent and most substantial net refinement in blind predictions during CASP11. The enhanced refinement protocol Princeton_TIGRESS 2.0 is freely available as a web server at http://atlas.engr.tamu.edu/refinement/. Proteins 2017; 85:1078-1098. © 2017 Wiley Periodicals, Inc.

COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming.

In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/.

Discovery of functionally selective C5aR2 ligands: novel modulators of C5a signalling.

The complement cascade is comprised of a highly sophisticated network of innate immune proteins that are activated in response to invading pathogens or tissue injury. The complement activation peptide, C5a, binds two seven transmembrane receptors, namely the C5a receptor 1 (C5aR1) and C5a receptor 2 (C5aR2, or C5L2). C5aR2 is a non-G-protein-signalling receptor whose biological role remains controversial. Some of this controversy arises owing to the lack of selective ligands for C5aR2. In this study, a library of 61 peptides based on the C-terminus of C5a was assayed for the ability to selectively modulate C5aR2 function. Two ligands (P32 and P59) were identified as functionally selective C5aR2 ligands, exhibiting selective recruitment of ß-arrestin 2 via C5aR2, partial inhibition of C5a-induced ERK1/2 activation and lipopolysaccharide-stimulated interleukin-6 release from human monocyte-derived macrophages. Importantly, neither ligand could induce ERK1/2 activation or inhibit C5a-induced ERK1/2 activation via C5aR1 directly. Finally, P32 inhibited C5a-mediated neutrophil mobilisation in wild-type, but not C5aR2(-/-) mice. These functionally selective ligands for C5aR2 are novel tools that can selectively modulate C5a activity in vitro and in vivo, and thus will be valuable tools to interrogate C5aR2 function.

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

Accurate prediction of protein secondary structure remains a crucial step in most approaches to the protein-folding problem, yet the prediction of ordered secondary structure, specifically beta-strands, remains a challenge. We developed a consensus secondary structure prediction method, conSSert, which is based on support vector machines (SVM) and provides exceptional accuracy for the prediction of beta-strands with QE accuracy of over 0.82 and a Q2-EH of 0.86. conSSert uses as input probabilities for the three types of secondary structure (helix, strand, and coil) that are predicted by four top performing methods: PSSpred, PSIPRED, SPINE-X, and RAPTOR. conSSert was trained/tested using 4261 protein chains from PDBSelect25, and 8632 chains from PISCES. Further validation was performed using targets from CASP9, CASP10, and CASP11. Our data suggest that poor performance in strand prediction is likely a result of training bias and not solely due to the nonlocal nature of beta-sheet contacts. conSSert is freely available for noncommercial use as a webservice: http://ares.tamu.edu/conSSert/.

De novo design and experimental characterization of ultrashort self-associating peptides.

Self-association is a common phenomenon in biology and one that can have positive and negative impacts, from the construction of the architectural cytoskeleton of cells to the formation of fibrils in amyloid diseases. Understanding the nature and mechanisms of self-association is important for modulating these systems and in creating biologically-inspired materials. Here, we present a two-stage de novo peptide design framework that can generate novel self-associating peptide systems. The first stage uses a simulated multimeric template structure as input into the optimization-based Sequence Selection to generate low potential energy sequences. The second stage is a computational validation procedure that calculates Fold Specificity and/or Approximate Association Affinity (K*association) based on metrics that we have devised for multimeric systems. This framework was applied to the design of self-associating tripeptides using the known self-associating tripeptide, Ac-IVD, as a structural template. Six computationally predicted tripeptides (Ac-LVE, Ac-YYD, Ac-LLE, Ac-YLD, Ac-MYD, Ac-VIE) were chosen for experimental validation in order to illustrate the self-association outcomes predicted by the three metrics. Self-association and electron microscopy studies revealed that Ac-LLE formed bead-like microstructures, Ac-LVE and Ac-YYD formed fibrillar aggregates, Ac-VIE and Ac-MYD formed hydrogels, and Ac-YLD crystallized under ambient conditions. An X-ray crystallographic study was carried out on a single crystal of Ac-YLD, which revealed that each molecule adopts a ß-strand conformation that stack together to form parallel ß-sheets. As an additional validation of the approach, the hydrogel-forming sequences of Ac-MYD and Ac-VIE were shuffled. The shuffled sequences were computationally predicted to have lower K*association values and were experimentally verified to not form hydrogels. This illustrates the robustness of the framework in predicting self-associating tripeptides. We expect that this enhanced multimeric de novo peptide design framework will find future application in creating novel self-associating peptides based on unnatural amino acids, and inhibitor peptides of detrimental self-aggregating biological proteins.

Princeton_TIGRESS: protein geometry refinement using simulations and support vector machines.

Protein structure refinement aims to perform a set of operations given a predicted structure to improve model quality and accuracy with respect to the native in a blind fashion. Despite the numerous computational approaches to the protein refinement problem reported in the previous three CASPs, an overwhelming majority of methods degrade models rather than improve them. We initially developed a method tested using blind predictions during CASP10 which was officially ranked in 5th place among all methods in the refinement category. Here, we present Princeton_TIGRESS, which when benchmarked on all CASP 7,8,9, and 10 refinement targets, simultaneously increased GDT_TS 76% of the time with an average improvement of 0.83 GDT_TS points per structure. The method was additionally benchmarked on models produced by top performing three-dimensional structure prediction servers during CASP10. The robustness of the Princeton_TIGRESS protocol was also tested for different random seeds. We make the Princeton_TIGRESS refinement protocol freely available as a web server at http://atlas.princeton.edu/refinement. Using this protocol, one can consistently refine a prediction to help bridge the gap between a predicted structure and the actual native structure.

WeFold: a coopetition for protein structure prediction.

The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social-media based worldwide collaborative effort, named WeFold, was undertaken by 13 labs. During the collaboration, the laboratories were simultaneously competing with each other. Here, we present the first attempt at "coopetition" in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org.

Proteomics for the discovery of biomarkers and diagnosis of periodontitis: a critical review.

Periodontitis is a common chronic and destructive disease whose pathogenetic mechanisms remain unclear. Due to their sensitivity and global scale, proteomics studies offer the opportunity to uncover critical host and pathogen activity indicators and can elucidate clinically applicable biomarkers for improved diagnosis and treatment of the disease. This review summarizes the literature of proteomics studies on periodontitis and comprehensively discusses commonly found candidate biomarkers. Key considerations in the design of an experimental proteomics platform are also outlined. The applicability of protein biomarkers across the progression of periodontitis and unexplored areas of research are highlighted.

Elucidating a key component of cancer metastasis: CXCL12 (SDF-1α) binding to CXCR4.

The chemotactic signaling induced by the binding of chemokine CXCL12 (SDF-1α) to chemokine receptor CXCR4 is of significant biological importance and is a potential therapeutic axis against HIV-1. However, as CXCR4 is overexpressed in certain cancer cells, the CXCL12:CXCR4 signaling is involved in tumor metastasis, progression, angiogenesis, and survival. Motivated by the pivotal role of the CXCL12:CXCR4 axis in cancer, we employed a comprehensive set of computational tools, predominantly based on free energy calculations and molecular dynamics simulations, to obtain insights into the molecular recognition of CXCR4 by CXCL12. We report, what is to our knowledge, the first computationally derived CXCL12:CXCR4 complex structure which is in remarkable agreement with experimental findings and sheds light into the functional role of CXCL12 and CXCR4 residues which are associated with binding and signaling. Our results reveal that the CXCL12 N-terminal domain is firmly bound within the CXCR4 transmembrane domain, and the central 24-50 residue domain of CXCL12 interacts with the upper N-terminal domain of CXCR4. The stability of the CXCL12:CXCR4 complex structure is attributed to an abundance of nonpolar and polar intermolecular interactions, including salt bridges formed between positively charged CXCL12 residues and negatively charged CXCR4 residues. The success of the computational protocol can mainly be attributed to the nearly exhaustive docking conformational search, as well as the heterogeneous dielectric implicit water-membrane-water model used to simulate and select the optimum conformations. We also recently utilized this protocol to elucidate the binding of an HIV-1 gp120 V3 loop in complex with CXCR4, and a comparison between the molecular recognition of CXCR4 by CXCL12 and the HIV-1 gp120 V3 loop shows that both CXCL12 and the HIV-1 gp120 V3 loop share the same CXCR4 binding pocket, as they mostly interact with the same CXCR4 residues.

Molecular recognition of CXCR4 by a dual tropic HIV-1 gp120 V3 loop.

HIV-1 cell entry is initiated by the interaction of the viral envelope glycoprotein gp120 with CD4, and chemokine coreceptors CXCR4 and CCR5. The molecular recognition of CXCR4 or CCR5 by the HIV-1 gp120 is mediated through the V3 loop, a fragment of gp120. The binding of the V3 loop to CXCR4 or CCR5 determines the cell tropism of HIV-1 and constitutes a key step before HIV-1 cell entry. Thus, elucidating the molecular recognition of CXCR4 by the V3 loop is important for understanding HIV-1 viral infectivity and tropism, and for the design of HIV-1 inhibitors. We employed a comprehensive set of computational tools, predominantly based on free energy calculations and molecular-dynamics simulations, to investigate the molecular recognition of CXCR4 by a dual tropic V3 loop. We report what is, to our knowledge, the first HIV-1 gp120 V3 loop:CXCR4 complex structure. The computationally derived structure reveals an abundance of polar and nonpolar intermolecular interactions contributing to the HIV-1 gp120:CXCR4 binding. Our results are in remarkable agreement with previous experimental findings. Therefore, this work sheds light on the functional role of HIV-1 gp120 V3 loop and CXCR4 residues associated with HIV-1 coreceptor activity.

Novel compstatin family peptides inhibit complement activation by drusen-like deposits in human retinal pigmented epithelial cell cultures.

We have used a novel human retinal pigmented epithelial (RPE) cell-based model that mimics drusen biogenesis and the pathobiology of age-related macular degeneration to evaluate the efficacy of newly designed peptide inhibitors of the complement system. The peptides belong to the compstatin family and, compared to existing compstatin analogs, have been optimized to promote binding to their target, complement protein C3, and to enhance solubility by improving their polarity/hydrophobicity ratios. Based on analysis of molecular dynamics simulation data of peptide-C3 complexes, novel binding features were designed by introducing intermolecular salt bridge-forming arginines at the N-terminus and at position -1 of N-terminal dipeptide extensions. Our study demonstrates that the RPE cell assay has discriminatory capability for measuring the efficacy and potency of inhibitory peptides in a macular disease environment.

Predictive framework for shape-selective separations in three-dimensional zeolites and metal-organic frameworks.

With the growing number of zeolites and metal-organic frameworks (MOFs) available, computational methods are needed to screen databases of structures to identify those most suitable for applications of interest. We have developed novel methods based on mathematical optimization to predict the shape selectivity of zeolites and MOFs in three dimensions by considering the energy costs of transport through possible pathways. Our approach is applied to databases of over 1800 microporous materials including zeolites, MOFs, zeolitic imidazolate frameworks, and hypothetical MOFs. New materials are identified for applications in gas separations (CO2/N2, CO2/CH4, and CO2/H2), air separation (O2/N2), and chemicals (propane/propylene, ethane/ethylene, styrene/ethylbenzene, and xylenes).

Cost-effective CO2 capture based on in silico screening of zeolites and process optimization.

A hierarchical computational approach is introduced that combines materials screening with process optimization. This approach leads to novel materials for cost-effective CO2 capture. Zeolites are screened using shape, size, and adsorption selectivities. Next, process optimization is introduced to generate a rank-ordered list based on total cost of capture and compression. We not only select the most cost-effective materials, but we also attain the optimal process conditions while satisfying purity, recovery, and other process constraints. The top ten zeolites (AHT, NAB, MVY, ABW, AWO, WEI, VNI, TON, OFF and ITW) can capture and compress CO2 to 150 bar from a mixture of 14% CO2 and 86% N2 at less than $30 per ton of CO2 captured. Several zeolites have moderate selectivities, yet they cost-effectively capture CO2 with 90% purity and 90% recovery using a 4-step adsorption process. Such nonintuitive selection demonstrates the necessity of combining materials-centric and process-centric viewpoints.

Discovery of biomarker combinations that predict periodontal health or disease with high accuracy from GCF samples based on high-throughput proteomic analysis and mixed-integer linear optimization.

AIM: To identify optimal combination(s) of proteomic based biomarkers in gingival crevicular fluid (GCF) samples from chronic periodontitis (CP) and periodontally healthy individuals and validate the predictions through known and blind test sets. MATERIALS AND METHODS: GCF samples were collected from 96 CP and periodontally healthy subjects and analysed using high-performance liquid chromatography, tandem mass spectrometry and the PILOT_PROTEIN algorithm. A mixed-integer linear optimization (MILP) model was then developed to identify the optimal combination of biomarkers which could clearly distinguish a blind subject sample as healthy or diseased. RESULTS: A thorough cross-validation of the MILP model capability was performed on a training set of 55 samples and greater than 99% accuracy was consistently achieved when annotating the testing set samples as healthy or diseased. The model was then trained on all 55 samples and tested on two different blind test sets, and using an optimal combination of 7 human proteins and 3 bacterial proteins, the model was able to correctly predict 40 out of 41 healthy and diseased samples. CONCLUSIONS: The proposed large-scale proteomic analysis and MILP model led to the identification of novel combinations of biomarkers for consistent diagnosis of periodontal status with greater than 95% predictive accuracy.

Estimation of diffusion anisotropy in microporous crystalline materials and optimization of crystal orientation in membranes.

The complex nature of the porous networks in microporous materials is primarily responsible for a high degree of intracrystalline diffusion anisotropy. Although this is a well-understood phenomenon, little attention has been paid in the literature with regards to classifying such anisotropy and elucidating its effect on the performance of membrane-based separation systems. In this paper, we develop a novel methodology to estimate full diffusion tensors based on the detailed description of the porous network geometry through our recent advances for the characterization of such networks. The proposed approach explicitly accounts for the tortuosity and complex connectivity of the porous framework, as well as for the variety of diffusion regimes that may be experienced by a guest molecule while it travels through the different localities of the crystal. Results on the diffusion of light gases in silicalite demonstrate good agreement with results from experiments and other computational techniques that have been reported in the literature. A comprehensive computational study involving 183 zeolite frameworks classifies these structures in terms of a number of anisotropy metrics. Finally, we utilize the computed diffusion tensors in a membrane optimization model that determines optimal crystal orientations. Application of the model in the context of separating carbon dioxide from nitrogen demonstrates that optimizing crystal orientation can offer significant benefit to membrane-based separation processes.

PILOT_PROTEIN: identification of unmodified and modified proteins via high-resolution mass spectrometry and mixed-integer linear optimization.

A novel protein identification framework, PILOT_PROTEIN, has been developed to construct a comprehensive list of all unmodified proteins that are present in a living sample. It uses the peptide identification results from the PILOT_SEQUEL algorithm to initially determine all unmodified proteins within the sample. Using a rigorous biclustering approach that groups incorrect peptide sequences with other homologous sequences, the number of false positives reported is minimized. A sequence tag procedure is then incorporated along with the untargeted PTM identification algorithm, PILOT_PTM, to determine a list of all modification types and sites for each protein. The unmodified protein identification algorithm, PILOT_PROTEIN, is compared to the methods SEQUEST, InsPecT, X!Tandem, VEMS, and ProteinProspector using both prepared protein samples and a more complex chromatin digest. The algorithm demonstrates superior protein identification accuracy with a lower false positive rate. All materials are freely available to the scientific community at http://pumpd.princeton.edu.

Stereochemically consistent reaction mapping and identification of multiple reaction mechanisms through integer linear optimization.

Reaction mappings are of fundamental importance to researchers studying the mechanisms of chemical reactions and analyzing biochemical pathways. We have developed an automated method based on integer linear optimization, ILP, to identify optimal reaction mappings that minimize the number of bond changes. An alternate objective function is also proposed that minimizes the number of bond order changes. In contrast to previous approaches, our method produces mappings that respect stereochemistry. We also show how to locate multiple reaction mappings efficiently and determine which of those mappings correspond to distinct reaction mechanisms by automatically detecting molecular symmetries. We demonstrate our techniques through a number of computational studies on the GRI-Mech, KEGG LIGAND, and BioPath databases. The computational studies indicate that 99% of the 8078 reactions tested can be addressed within 1 CPU hour. The proposed framework has been incorporated into the Web tool DREAM ( http://selene.princeton.edu/dream/ ), which is freely available to the scientific community.

Novel protein identification methods for biomarker discovery via a proteomic analysis of periodontally healthy and diseased gingival crevicular fluid samples.

AIM: To identify possible novel biomarkers in gingival crevicular fluid (GCF) samples from chronic periodontitis (CP) and periodontally healthy individuals using high-throughput proteomic analysis. MATERIALS AND METHODS: Gingival crevicular fluid samples were collected from 12 CP and 12 periodontally healthy subjects. Samples were trypically digested with trypsin, eluted using high-performance liquid chromatography, and fragmented using tandem mass spectrometry (MS/MS). MS/MS spectra were analysed using PILOT_PROTEIN to identify all unmodified proteins within the samples. RESULTS: Using the database derived from Homo sapiens taxonomy and all bacterial taxonomies, 432 human (120 new) and 30 bacterial proteins were identified. The human proteins, angiotensinogen, clusterin and thymidine phosphorylase were identified as biomarker candidates based on their high-scoring only in samples from periodontal health. Similarly, neutrophil defensin-1, carbonic anhydrase-1 and elongation factor-1 gamma were associated with CP. Candidate bacterial biomarkers include 33 kDa chaperonin, iron uptake protein A2 and phosphoenolpyruvate carboxylase (health-associated) and ribulose biphosphate carboxylase, a probable succinyl-CoA:3-ketoacid-coenzyme A transferase, or DNA-directed RNA polymerase subunit beta (CP-associated). Most of these human and bacterial proteins have not been previously evaluated as biomarkers of periodontal conditions and require further investigation. CONCLUSIONS: The proposed methods for large-scale comprehensive proteomic analysis may lead to the identification of novel biomarkers of periodontal health or disease.