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INTRODUCTION: Cysteine cathepsins are implicated in breast cancer progression, produced by both transformed epithelial cells and infiltrated stromal cells in tumors, but to date, no cathepsin inhibitor has been approved for clinical use due to unexpected side effects. This study explores cellular feedback to cathepsin inhibitors that might yield non-intuitive responses, and uses computational models to determine underlying cathepsin-inhibitor dynamics. METHODS: MDA-MB-231 cells treated with E64 were tested by multiplex cathepsin zymography and immunoblotting to quantify total, active, and inactive cathepsins S and L. This data was used to parameterize mathematical models of intracellular free and inhibited cathepsins, and then applied to a dynamic model predicting cathepsin responses to other classes of cathepsin inhibitors that have also failed clinical trials. RESULTS: E64 treated cells exhibited increased amounts of active cathepsin S and reduced amount of active cathepsin L, although E64 binds tightly to both. This inhibitor response was not unique to cancer cells or any one cell type, suggesting an underlying fundamental mechanism of E64 preserving activity of cathepsin S, but not cathepsin L. Computational models were able to predict and differentiate between inhibitor-bound, active, and inactive cathepsin species and demonstrate how different classes of cathepsin inhibitors can have drastically divergent effects on active cathepsins located in different intracellular compartments. CONCLUSIONS: Together, this work has important implications for the development of mathematical model systems for protease inhibition in tissue destructive diseases, and consideration of preservation mechanisms by inhibitors that could alter perceived benefits of these treatment modalities. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s12195-019-00580-5) contains supplementary material, which is available to authorized users.

With the emergence of new pathogens, e.g., methicillin-resistant Staphylococcus aureus (MRSA), and the recent novel coronavirus pandemic, there has been an ever-increasing need for novel antimicrobial therapeutics. In this work, we have developed support vector machine (SVM) models to predict antiviral peptide sequences. Oscillations in physicochemical properties in protein sequences have been shown to be predictive of protein structure and function, and in the presented we work we have taken advantage of these known periodicities to develop models that predict antiviral peptide sequences. In developing the presented models, we first generated property factors by applying principal component analysis (PCA) to the AAindex dataset of 544 amino acid properties. We next converted peptide sequences into physicochemical vectors using 18 property factors resulting from the PCA. Fourier transforms were applied to the property factor vectors to measure the amplitude of the physicochemical oscillations, which served as the features to train our SVM models. To train and test the developed models we have used a publicly available database of antiviral peptides ( http://crdd.osdd.net/servers/avppred/ ), and we have used cross-validation to train and tune models based on multiple training and testing sets. To further understand the physicochemical properties of antiviral peptides we have also applied a previously developed feature selection algorithm. Future work will be aimed at computationally designing novel antiviral therapeutics based on the developed machine learning models.

In this article, we present (1) a feature selection algorithm based on nonlinear support vector machine (SVM) for fault detection and diagnosis in continuous processes and (2) results for the Tennessee Eastman benchmark process. The presented feature selection algorithm is derived from the sensitivity analysis of the dual C-SVM objective function. This enables simultaneous modeling and feature selection paving the way for simultaneous fault detection and diagnosis, where feature ranking guides fault diagnosis. We train fault-specific two-class SVM models to detect faulty operations, while using the feature selection algorithm to improve the accuracy and perform the fault diagnosis. Our results show that the developed SVM models outperform the available ones in the literature both in terms of detection accuracy and latency. Moreover, it is shown that the loss of information is minimized with the use of feature selection techniques compared to feature extraction techniques such as principal component analysis (PCA). This further facilitates a more accurate interpretation of the results.

Enzymes are catalysts in biochemical reactions that, by definition, increase rates of reactions without being altered or destroyed. However, when that enzyme is a protease, a subclass of enzymes that hydrolyze other proteins, and that protease is in a multiprotease system, protease-as-substrate dynamics must be included, challenging assumptions of enzyme inertness, shifting kinetic predictions of that system. Protease-on-protease inactivating hydrolysis can alter predicted protease concentrations used to determine pharmaceutical dosing strategies. Cysteine cathepsins are proteases capable of cathepsin cannibalism, where one cathepsin hydrolyzes another with substrate present, and misunderstanding of these dynamics may cause miscalculations of multiple proteases working in one proteolytic network of interactions occurring in a defined compartment. Once rates for individual protease-on-protease binding and catalysis are determined, proteolytic network dynamics can be explored using computational models of cooperative/competitive degradation by multiple proteases in one system, while simultaneously incorporating substrate cleavage. During parameter optimization, it was revealed that additional distraction reactions, where inactivated proteases become competitive inhibitors to remaining, active proteases, occurred, introducing another network reaction node. Taken together, improved predictions of substrate degradation in a multiple protease network were achieved after including reaction terms of autodigestion, inactivation, cannibalism, and distraction, altering kinetic considerations from other enzymatic systems, since enzyme can be lost to proteolytic degradation. We compiled and encoded these dynamics into an online platform ( https://plattlab.shinyapps.io/catKLS/ ) for individual users to test hypotheses of specific perturbations to multiple cathepsins, substrates, and inhibitors, and predict shifts in proteolytic network reactions and system dynamics.

The complement system is our first line of defense against foreign pathogens, but when it is not properly regulated, complement is implicated in the pathology of several autoimmune and inflammatory disorders. Compstatin is a peptidic complement inhibitor that acts by blocking the cleavage of complement protein C3 to the proinflammatory fragment C3a and opsonin fragment C3b. In this study, we aim to identify druglike small-molecule complement inhibitors with physicochemical, geometric, and binding properties similar to those of compstatin. We employed two approaches using various high-throughput virtual screening methods, which incorporate molecular dynamics (MD) simulations, pharmacophore model design, energy calculations, and molecular docking and scoring. We have generated a library of 274 chemical compounds with computationally predicted binding affinities for the compstatin binding site of C3. We have tested subsets of these chemical compounds experimentally for complement inhibitory activity, using hemolytic assays, and for binding affinity, using microscale thermophoresis. As a result, although none of the compounds showed inhibitory activity, compound 29 was identified to exhibit weak competitive binding against a potent compstatin analogue, therefore validating our computational approaches. Additional docking and MD simulation studies suggest that compound 29 interacts with C3 residues, which have been shown to be important in binding of compstatin to the C3c fragment of C3. Compound 29 is amenable to physicochemical optimization to acquire inhibitory properties. Additionally, it is possible that some of the untested compounds will demonstrate binding and inhibition in future experimental studies.

A surrogate-based optimization method is presented, which aims to locate the global optimum of box-constrained problems using input–output data. The method starts with a global search of the n-dimensional space, using a Smolyak (Sparse) grid which is constructed using Chebyshev extrema in the one-dimensional space. The collected samples are used to fit polynomial interpolants, which are used as surrogates towards the search for the global optimum. The proposed algorithm adaptively refines the grid by collecting new points in promising regions, and iteratively refines the search space around the incumbent sample until the search domain reaches a minimum hyper-volume and convergence has been attained. The algorithm is tested on a large set of benchmark problems with up to thirty dimensions and its performance is compared to a recent algorithm for global optimization of grey-box problems using quadratic, kriging and radial basis functions. It is shown that the proposed algorithm has a consistently reliable performance for the vast majority of test problems, and this is attributed to the use of Chebyshev-based Sparse Grids and polynomial interpolants, which have not gained significant attention in surrogate-based optimization thus far.

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.

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 data set which includes process data describing 22,200 batches with 15 faults. We train fault and time-specific models on the pre-aligned 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.

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. Funding Agencies|Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]; U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internship (SULI) program; U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Visiting Faculty Program (VFP); United States-Israel Binational Science Foundation (BSF) [2009432]; Israel Science Foundation (ISF) [1122/14]; National Institute of General Medical Sciences [R01GM093123, GM083107, GM116960]; Purdue University start-up funds; Ralph W. and Grace M. Showalter Trust Award; Jim and Diann Robbers Cancer Research Grant for New Investigators Award; Brazilian agency: FAPESP; Brazilian agency: CAPES; Brazilian agency: CNPq; NIH [GM-14312]; NSF [MCB-10-19767]; National Institutes of Medicine [GM11574901]; Swedish Research Council [2012-5270, 2016-05369]; Swedish e-Science Research Center; Polish National Science Center [UMO-2013/10/M/ST4/00640]; IISc Mathematical Initiative Assistantship; National Academy of Sciences, India; National Institutes of Health [R01-GM100701, R01GM052032]; National Science Foundation; National Science Foundation Graduate Research Fellowship [DGE-1148900]; Princeton Institute for Computational Science and Engineering (PICSciE); Princeton University Office of Information Technology; UK Engineering and Physical Sciences Research Council [EP/M020576/1, EP/N031962/1]; National Research Foundation of Korea [2016R1A2A1A05005485]

Rapid detection and identification of process faults in industrial applications is crucial to sustain a safe and profitable operation. Today, the advances in sensor technologies have facilitated large amounts of chemical process data collection in real time which subsequently broadened the use of data-driven process monitoring techniques via machine learning and multivariate statistical analysis. One of the well-known machine learning techniques is Support Vector Machines (SVM) which allows the use of high dimensional feature sets for learning problems such as classification and regression. In this paper, we present the application of a novel nonlinear (kernel-dependent) SVM-based feature selection algorithm to process monitoring and fault detection of continuous processes. The developed methodology is derived from sensitivity analysis of the dual SVM objective and utilizes existing and novel greedy algorithms to rank features that also guides fault diagnosis. Specifically, we train fault-specific two-class SVM models to detect faulty operations, while using the feature selection algorithm to improve the accuracy of the fault detection models and perform fault diagnosis. We present results for the Tennessee Eastman process as a case study and compare our approach to existing approaches for fault detection, diagnosis and identification.

Electric fields often play a role in guiding the association of protein complexes. Such interactions can be further engineered to accelerate complex association, resulting in protein systems with increased productivity. This is especially true for enzymes where reaction rates are typically diffusion limited. To facilitate quantitative comparisons of electrostatics in protein families and to describe electrostatic contributions of individual amino acids, we previously developed a computational framework called AESOP. We now implement this computational tool in Python with increased usability and the capability of performing calculations in parallel. AESOP utilizes PDB2PQR and Adaptive Poisson-Boltzmann Solver to generate grid-based electrostatic potential files for protein structures provided by the end user. There are methods within AESOP for quantitatively comparing sets of grid-based electrostatic potentials in terms of similarity or generating ensembles of electrostatic potential files for a library of mutants to quantify the effects of perturbations in protein structure and protein-protein association.

SUMOylation, one of the most important protein post-translational modifications, plays critical roles in a variety of physiological and pathological processes. SENP (Sentrin/SUMO-specific protease), a family of SUMO-specific proteases, is responsible for the processing of pre-SUMO and removal of SUMO from conjugated substrates. SUMO4, the latest discovered member in the SUMO family, has been found as a type 1 diabetes susceptibility gene and its maturation is not understood so far. Despite the 14 amino acid differences between pre-SUMO4 and SUMO2, pre-SUMO4 is not processed by SENP2 but pre-SUMO2 does. A novel interdisciplinary approach involving computational modeling and a FRET-based protease assay was taken to engineer pre-SUMO4 as a substrate of SENP2. Given the difference in net charge between pre-SUMO4 and pre-SUMO2, the computational framework analysis of electrostatic similarities of proteins was applied to determine the contribution of each ionizable amino acid in a model of SENP2-(pre-SUMO4) binding, and to propose pre-SUMO4 mutations. The specificities of the SENP2 toward different pre-SUMO4 mutants were determined using a quantitative FRET assay by characterizing the catalytic efficiencies (kcat/KM). A single amino acid mutation made pre-SUMO4 amenable to SENP2 processing and a combination of two amino acid mutations made it highly accessible as SENP2 substrate. The combination of the two approaches provides a powerful protein engineering tool for future SUMOylation studies.

We describe the development and testing of ab initio derived, AMBER ff03 compatible charge parameters for a large library of 147 noncanonical amino acids including β- and N-methylated amino acids for use in applications such as protein structure prediction and de novo protein design. The charge parameter derivation was performed using the RESP fitting approach. Studies were performed assessing the suitability of the derived charge parameters in discriminating the activity/inactivity between 63 analogs of the complement inhibitor Compstatin on the basis of previously published experimental IC50 data and a screening procedure involving short simulations and binding free energy calculations. We found that both the approximate binding affinity (K*) and the binding free energy calculated through MM-GBSA are capable of discriminating between active and inactive Compstatin analogs, with MM-GBSA performing significantly better. Key interactions between the most potent Compstatin analog that contains a noncanonical amino acid are presented and compared to the most potent analog containing only natural amino acids and native Compstatin. We make the derived parameters and an associated web interface that is capable of performing modifications on proteins using Forcefield_NCAA and outputting AMBER-ready topology and parameter files freely available for academic use at http://selene.princeton.edu/FFNCAA . The forcefield allows one to incorporate these customized amino acids into design applications with control over size, van der Waals, and electrostatic interactions.

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. Proteins 2014; 82:794–814. © 2013 Wiley Periodicals, Inc.

Despite its sequence variability and structural flexibility, the V3 loop of the HIV-1 envelope glycoprotein gp120 is capable of recognizing cell-bound coreceptors CCR5 and CXCR4 and infecting cells. Viral selection of CCR5 is associated with the early stages of infection, and transition to selection of CXCR4 indicates disease progression. We have developed a predictive statistical model for coreceptor selectivity that uses the discrete property of net charge and the binary coreceptor preference markers of the N(6)X(7)[T/S](8)X(9) glycosylation motif and 11/24/25 positive amino acid rule. The model is based on analysis of 2,054 V3 loop sequences from patient data and allows us to infer the most likely state of the disease from physicochemical characteristics of the sequences. The performance of the model is comparable to established sequence-based predictive methods, and may be used in combination with other methods as a supportive diagnostic for coreceptor selection. This model may be used for personalized medical decisions in administering coreceptor-specific therapies.

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.

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/.

Targeting the complement component 3a receptor (C3aR) with selective agonists or antagonists is believed to be a viable therapeutic option for several diseases such as stroke, heart attack, reperfusion injuries, and rheumatoid arthritis. We designed a number of agonists, partial agonists, and antagonists of C3aR using our two-stage de novo protein design framework. Of the peptides tested using a degranulation assay in C3aR-transfected rat basophilic leukemia cells, two were prominent agonists (EC(50) values of 25.3 and 66.2 nM) and two others were partial agonists (IC(50) values of 15.4 and 26.1 nM). Further testing of these lead compounds in a calcium flux assay in U937 cells yielded similar results although with reduced potencies compared to transfected cells. The partial agonists also displayed full antagonist activity when tested in a C3aR inhibition assay. In addition, the electrostatic potential profile was shown to potentially discriminate between full agonists and partial agonists.

The complement system is a component of innate immunity and is activated by a cascade of protein interactions whose function is vital to our ability to fight infection. When proper regulation fails, the complement system is unable to recognize "self" from "nonself" and, therefore, attacks own tissues leading to autoimmune diseases. The central protein of the complement system is C3, which is the convergence point of three independently activated but communicating pathways. Regulation of C3 occurs through modular proteins which consist of many repeats of complement control protein (CCP) modules. CCP modules have diverse sequences, similar structures, and diverse physicochemical compositions, with excess of charge being a predominant characteristic. The goal of our study is to understand the electrostatic mechanism that underlies the interaction between the C3d domain of C3 and the fourth module of the complement regulator Factor H (FH4). We have performed a computational alanine scan in which we have replaced every ionizable amino acid, one at a time, with an alanine to generate a family of mutants for the C3d-FH4 complex. We have used Poisson-Boltzmann electrostatic calculations in combination with clustering of spatial distributions of electrostatic potentials and free energy calculations to delineate the contribution of each replaced amino acid to the C3d-FH4 interaction. We have analyzed our data in view of a two-step model which separates association into long-range recognition and short-range binding and we have identified key amino acids that contribute to association. We discuss the complex role of C3d in binding FH4 and the bacterial proteins Efb/Ehp from Staphylococcus aureus.

Virulence factors EfbC and Ehp from Staphylococcus aureus are potent inhibitors of complement activation. Both are excessively charged and bind to complement protein C3d at an acidic interface. We computationally generated single-alanine mutants of charged residues in the C3d–EfbC and C3d–Ehp complexes, and utilized electrostatic clustering and Poisson–Boltzmann free energy calculations to evaluate the role of electrostatics in association. Our results indicate that both interfacial electrostatic interactions and electrostatic potential distribution are crucial for C3d–EfbC and C3d–Ehp association. The results presented herein serve as a predictive tool in the selection of mutants with desired binding and immunological activity, and will narrow the search for viable candidates for further computational and experimental analyses. This study serves as the foundation for development of an inhibitor of the C3d–EfbC and C3d–Ehp interactions to combat bacterial infection and design of a complement inhibitor using EfbC and Ehp as a model.

Molecular similarity is an important tool in protein and drug design for analyzing the quantitative relationships between physicochemical properties of two molecules. We present a family of similarity measures which exploits the ability of wavelet transformation to analyze the spectral components of physicochemical properties and suggests a sensitive way for measuring similarities of biological molecules. In order to investigate how effective wavelet-based similarity measures were against conventional measures, we defined several patterns which involve scalar or topological changes in the distribution of electrostatic properties. The wavelet-based measures were more successful in discriminating these patterns in contrast to the current state-of-art similarity measures. We also present the validity of wavelet-based similarity measures through the hierarchical clustering of two protein datasets consisting of families of homologous domains and alanine scan mutants. This type of similarity analysis is useful for protein structure-function studies and protein design.

Factor H (FH) contributes to the regulation of the complement system by binding to polyanionic surfaces and the proteins C3b/C3c/C3d. This implicates charge and electrostatic interactions in recognition and binding of FH. Despite the large amount of experimental and pathology data the exact mechanism at molecular level is not yet known. We have implemented a computational framework for comparative analysis of the charge and electrostatic diversity of FH modules and C3b domains to identify electrostatic hotspots and predict potential binding sites. Our electrostatic potential clustering analysis shows that charge distributions and electrostatic potential distributions are more useful in understanding C3b–FH interactions than net charges alone. We present a model of non-specific electrostatic interactions of FH with polyanion-rich surfaces and specific interactions with C3b, using our computational data and existing experimental data. We discuss the electrostatic contributions to the formation of the C3b–FH complex and the competition between FH and Factor Bb (Bb) for binding to C3b. We also discuss the significance of mutations of charged amino acids in the pathobiology of FH-mediated disease, such as age-related macular degeneration, atypical hemolytic uremic syndrome, and dense deposit disease. Our data can be used to guide future experimental studies.

Electrostatically-driven association of proteins is important to many biological functions, and understanding which amino acid residues contribute to these interactions is crucial to protein design. Theoretical calculations that are used to elucidate the role of electrostatics in association are typically based on a single experimentally determined protein structure, while an underlying rigid-body assumption is adopted. The goal of this study was to investigate the role of conformational fluctuations on electrostatic interaction energies, as applied to the electrostatic analysis of barnase–barstar. For our calculations, we apply theoretical alanine-scan mutagenesis to introduce charge perturbations by replacing every ionizable residue with alanine, one at a time. Electrostatic clustering and free energy calculations based on the Poisson–Boltzmann method are used to evaluate the effects of each perturbation. Molecular dynamics simulations are performed for the barnase–barstar parent complex and seven experimental alanine mutations from the literature, in order to introduce relaxation before and after mutation. We discuss the effects of dynamics, in the form of pre- and post-mutation relaxation, on electrostatic clustering and free energies of association in light of experimental data. We also examine the utility of nine electrostatic similarity methods for clustering of barnase alanine-scan mutants. Our calculations suggest that the rigid-body assumption is reasonable for electrostatic calculations of barnase–barstar.

Kaposica, the complement regulator of Kaposi’s sarcoma-associated herpesvirus, inhibits complement by supporting factor I-mediated inactivation of the proteolytically activated form of C3 (C3b) and C4 (C4b) (cofactor activity [CFA]) and by accelerating the decay of classical and alternative pathway C3-convertases (decay-accelerating activity [DAA]). Previous data suggested that electrostatic interactions play a critical role in the binding of viral complement regulators to their targets, C3b and C4b. We therefore investigated how electrostatic potential on Kaposica influences its activities. We built a homology structure of Kaposica and calculated the electrostatic potential of the molecule, using the Poisson–Boltzmann equation. Mutants were then designed to alter the overall positive potential of the molecule or of each of its domains and linkers by mutating Lys/Arg to Glu/Gln, and the functional activities of the expressed mutants were analyzed. Our data indicate that 1) positive potential at specific sites and not the overall positive potential on the molecule guides the CFAs and classical pathway DAA; 2) positive potential around the linkers between complement control protein domains (CCPs) 1–2 and 2–3 is more important for DAAs than for CFAs; 3) positive potential in CCP1 is crucial for binding to C3b and C4b, and thereby its functional activities; 4) conversion to negative or enhancement of negative potential for CCPs 2–4 has a marked effect on C3b-linked activities as opposed to C4b-linked activities; and 5) reversal of the electrostatic potential of CCP4 to negative has a differential effect on classical and alternative pathway DAAs. Together, our data provide functional relevance to conservation of positive potential in CCPs 1 and 4 and the linkers of viral complement regulators.

In this work, we introduce Forcefield_PTM, a set of AMBER forcefield parameters consistent with ff03 for 32 common post-translational modifications. Partial charges were calculated through ab initio calculations and a two-stage RESP-fitting procedure in an ether-like implicit solvent environment. The charges were found to be generally consistent with others previously reported for phosphorylated amino acids, and trimethyllysine, using different parameterization methods. Pairs of modified and their corresponding unmodified structures were curated from the PDB for both single and multiple modifications. Background structural similarity was assessed in the context of secondary and tertiary structures from the global dataset. Next, the charges derived for Forcefield_PTM were tested on a macroscopic scale using unrestrained all-atom Langevin molecular dynamics simulations in AMBER for 34 (17 pairs of modified/unmodified) systems in implicit solvent. Assessment was performed in the context of secondary structure preservation, stability in energies, and correlations between the modified and unmodified structure trajectories on the aggregate. As an illustration of their utility, the parameters were used to compare the structural stability of the phosphorylated and dephosphorylated forms of OdhI. Microscopic comparisons between quantum and AMBER single point energies along key χ torsions on several PTMs were performed and corrections to improve their agreement in terms of mean squared errors and squared correlation coefficients were parameterized. This forcefield for post-translational modifications in condensed-phase simulations can be applied to a number of biologically relevant and timely applications including protein structure prediction, protein and peptide design, docking, and to study the effect of PTMs on folding and dynamics. We make the derived parameters and an associated interactive webtool capable of performing post-translational modifications on proteins using Forcefield_PTM available at http://selene.princeton.edu/FFPTM.

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.

HIV-1 entry into host cells is mediated by interactions between the V3-loop of viral glycoprotein gp120 and chemokine receptor CCR5 or CXCR4, collectively known as HIV-1 coreceptors. Accurate genotypic prediction of coreceptor usage is of significant clinical interest and determination of the factors driving tropism has been the focus of extensive study. We have developed a method based on nonlinear support vector machines to elucidate the interacting residue pairs driving coreceptor usage and provide highly accurate coreceptor usage predictions. Our models utilize centroid-centroid interaction energies from computationally derived structures of the V3-loop:coreceptor complexes as primary features, while additional features based on established rules regarding V3-loop sequences are also investigated. We tested our method on 2455 V3-loop sequences of various lengths and subtypes, and produce a median area under the receiver operator curve of 0.977 based on 500 runs of 10-fold cross validation. Our study is the first to elucidate a small set of specific interacting residue pairs between the V3-loop and coreceptors capable of predicting coreceptor usage with high accuracy across major HIV-1 subtypes. The developed method has been implemented as a web tool named CRUSH, CoReceptor USage prediction for HIV-1, which is available at http://ares.tamu.edu/CRUSH/.

Background The V3 loop of the glycoprotein gp120 of HIV-1 plays an important role in viral entry into cells by utilizing as coreceptor CCR5 or CXCR4, and is implicated in the phenotypic tropisms of HIV viruses. It has been hypothesized that the interaction between the V3 loop and CCR5 or CXCR4 is mediated by electrostatics. We have performed hierarchical clustering analysis of the spatial distributions of electrostatic potentials and charges of V3 loop structures containing consensus sequences of HIV-1 subtypes. Results Although the majority of consensus sequences have a net charge of +3, the spatial distribution of their electrostatic potentials and charges may be a discriminating factor for binding and infectivity. This is demonstrated by the formation of several small subclusters, within major clusters, which indicates common origin but distinct spatial details of electrostatic properties. Some of this information may be present, in a coarse manner, in clustering of sequences, but the spatial details are largely lost. We show the effect of ionic strength on clustering of electrostatic potentials, information that is not present in clustering of charges or sequences. We also make correlations between clustering of electrostatic potentials and net charge, coreceptor selectivity, global prevalence, and geographic distribution. Finally, we interpret coreceptor selectivity based on the N6X7T8|S8X9 sequence glycosylation motif, the specific positive charge location according to the 11/24/25 rule, and the overall charge and electrostatic potential distribution. Conclusions We propose that in addition to the sequence and the net charge of the V3 loop of each subtype, the spatial distributions of electrostatic potentials and charges may also be important factors for receptor recognition and binding and subsequent viral entry into cells. This implies that the overall electrostatic potential is responsible for long-range recognition of the V3 loop with coreceptors CCR5/CXCR4, whereas the charge distribution contributes to the specific short-range interactions responsible for the formation of the bound complex. We also propose a scheme for coreceptor selectivity based on the sequence glycosylation motif, the 11/24/25 rule, and net charge.

The V3 loop of the glycoprotein 120 (gp120) is a contact point for cell entry of HIV-1 leading to infection. Despite sequence variability and lack of specific structure, the highly flexible V3 loop possesses a well-defined role in recognizing and selecting cell-bound coreceptors CCR5 and CXCR4 through a mechanism of charge complementarity. We have performed two independent molecular dynamics (MD) simulations to gain insights into the dynamic character of two V3 loops with slightly different sequences, but significantly different starting crystallographic structures. We have identified highly populated trajectory-specific salt bridges between oppositely charged stem residues Arg9 and Glu25 or Asp29. The two trajectories share nearly identical correlated motions within the simulations, despite their different overall structures. High occupancy salt bridges play a key role in the major cross-correlated motions in both trajectories, and may be responsible for transient structural stability in preparation for coreceptor binding. In addition, the two V3 loops visit conformations with similarities in spatial distributions of electrostatic potentials, despite their inherent flexibility, which may play a role in coreceptor recognition. It is plausible that cooperativity between overall electrostatic potential, charged residue interactions, and correlated motions could be associated with a coreceptor selection and binding.

The interaction between complement fragment C3d and complement receptor 2 (CR2) is a key aspect of complement immune system activation, and is a component in a link between innate and adaptive immunities. The complement immune system is an ancient mechanism for defense, and can be found in species that have been on Earth for the last 600 million years. However, the link between the complement system and adaptive immunity, which is formed through the association of the B-cell co-receptor complex, including the C3d-CR2 interaction, is a much more recent adaptation. Human C3d and CR2 have net charges of −1 and +7 respectively, and are believed to have evolved favoring the role of electrostatics in their functions. To investigate the role of electrostatics in the function and evolution of human C3d and CR2, we have applied electrostatic similarity methods to identify regions of evolutionarily conserved electrostatic potential based on 24 homologues of complement C3d and 4 homologues of CR2. We also examine the effects of structural perturbation, as introduced through molecular dynamics and mutations, on spatial distributions of electrostatic potential to identify perturbation resistant regions, generated by so-called electrostatic “hot-spots”. Distributions of electrostatic similarity based on families of perturbed structures illustrate the presence of electrostatic “hot-spots” at the two functional sites of C3d, while the surface of CR2 lacks electrostatic “hot-spots” despite its excessively positive nature. We propose that the electrostatic “hot-spots” of C3d have evolved to optimize its dual-functionality (covalently attaching to pathogen surfaces and interaction with CR2), which are both necessary for the formation B-cell co-receptor complexes. Comparison of the perturbation resistance of the electrostatic character of the homologues of C3d suggests that there was an emergence of a new role of electrostatics, and a transition in the function of C3d, after the divergence of jawless fish., Author Summary Complement fragment C3d is a thioester-containing protein that is a key component/domain in the complement system, an ancient line of defense, due to its ability to covalently attach to pathogen cell surfaces, such as bacteria. As the immune system evolved in complexity, from acellular defense mechanisms to multicellular systems with memory, so has the function of C3d. In humans, but not lower species such as invertebrates, C3d attached to pathogen surfaces binds B-cell co-receptor CR2, in conjunction with an antibody/antigen complex, forming a link between the innate and adaptive immune systems. The C3d-CR2 interaction ultimately increases B-cell sensitivity to the C3d tagged pathogen by 1,000–10,000 fold, and is known to be driven by electrostatic forces. Since electrostatics are crucial to the C3d-CR2 interaction, it is likely that probing the evolution of the electrostatics of C3d and CR2 will provide insight into this gained function. To this end, we employ a novel computational approach for identifying the electrostatic “hot-spots” of C3d and CR2, which are produced by clusters of like-charged residues found on the surface of the protein. Electrostatic “hot-spots” are often evolutionarily favored and in this study provide new insight into the evolution of C3d in its role in a link between innate and adaptive immunity.

For systems involving highly and oppositely charged proteins, electrostatic forces dominate association and contribute to biomolecular complex stability. Using experimental or theoretical alanine-scanning mutagenesis, it is possible to elucidate the contribution of individual ionizable amino acids to protein association. We evaluated our electrostatic free energy calculations by comparing calculated and experimental data for alanine mutants of five protein complexes. We calculated Poisson–Boltzmann electrostatic free energies based on a thermodynamic cycle, which incorporates association in a reference (Coulombic) and solvated (solution) state, as well as solvation effects. We observe that Coulombic and solvation free energy values correlate with experimental data in highly and oppositely charged systems, but not in systems comprised of similarly charged proteins. We also observe that correlation between solution and experimental free energies is dependent on dielectric coefficient selection for the protein interior. Free energy correlations improve as protein dielectric coefficient increases, suggesting that the protein interior experiences moderate dielectric screening, despite being shielded from solvent. We propose that higher dielectric coefficients may be necessary to more accurately predict protein–protein association. Additionally, our data suggest that Coulombic potential calculations alone may be sufficient to predict relative binding of protein mutants. © 2011 Wiley Periodicals, Inc. Biopolymers 95: 746-754, 2011.

Lily stigma/style cysteine-rich adhesin (SCA), a plant lipid transfer protein (LTP) which is secreted into the extracellular matrix, functions in pollen tube guidance in fertilization. A gain-of-function mutant (ltp5-1) for Arabidopsis LTP5, an SCA-like molecule, was recently shown to display defects in sexual reproduction. In the current study, it is reported that ltp5-1 plants have dwarfed primary shoots, delayed hypocotyl elongation, various abnormal tissue fusions, and display multibranching. These mutant phenotypes in vegetative growth are recessive. No abnormality was found in ltp5-1/+ plants. In a phylogenetic analysis of plant LTPs, SCA-like Arabidopsis LTPs were classified with conventional plant LTPs. Homology modelling-based electrostatic similarity index (ESI) clustering was used to show diversity in spatial distributions of electrostatic potentials of SCA-like LTPs, suggestive of their various roles in interaction in the extracellular matrix space. β-Glucuronidase (GUS) analysis showed that SCA-like Arabidopsis LTP genes are diversely present in various tissues. LTP4 was found specifically in the guard cells and LTP6 in trichomes as well as in other tissues. LTP1 levels were specifically abundant in the stigma, and both LTP3 and LTP6 in the ovules. LTP2 and LTP4 gene levels were up-regulated in whole seedlings with 20% polyethylene glycol (PEG) and 300 mM NaCl treatments, respectively. LTP5 was up-regulated in the hypocotyl with 3 d dark growth conditions. LTP6 was specifically expressed in the tip of the cotyledon under drought stress conditions. The results suggest that SCA-like Arabidopsis LTPs are multifunctional, with diversified roles in plant growth and reproduction.

Electrostatic interactions are ubiquitous in proteins and dictate stability and function. In this review, we discuss several methods for the analysis of electrostatics in protein–protein interactions. We discuss alanine-scanning mutagenesis, Poisson–Boltzmann electrostatics, free energy calculations, electrostatic similarity distances, and hierarchical clustering of electrostatic potentials. Our recently developed computational framework, known as Analysis of Electrostatic Similarities Of Proteins (AESOP), incorporates these tools to efficiently elucidate the role of electrostatic potentials in protein interactions. We present the application of AESOP to several proteins and protein complexes, for which charge is purported to facilitate protein association. Specifically, we illustrate how recent work has shaped the formulation of electrostatic calculations, the correlation of electrostatic free energies and electrostatic potential clustering results with experimental binding and activity data, the pH dependence of protein stability and association, the design of mutant proteins with enhanced immunological activity, and how AESOP can expose deficiencies in structural models and experimental data. This integrative approach can be utilized to develop mechanistic models and to guide experimental studies by predicting mutations with desired physicochemical properties and function. Alteration of the electrostatic properties of proteins offers a basis for the design of proteins with optimized binding and activity.

Charge plays an important role in protein-protein interactions. In the case of excessively charged proteins, their electrostatic potentials contribute to the processes of recognition and binding with other proteins or ligands. We present an automated computational framework for determining the contribution of each charged amino acid to the electrostatic properties of proteins, at atomic resolution level. This framework involves computational alanine scans, calculation of Poisson-Boltzmann electrostatic potentials, calculation of electrostatic similarity distances (ESDs), hierarchical clustering analysis of ESDs, calculation of solvation free energies of association, and visualization of the spatial distributions of electrostatic potentials. The framework is useful to classify families of mutants with similar electrostatic properties and to compare them with the parent proteins in the complex. The alanine scan mutants introduce perturbations in the local electrostatic properties of the proteins and aim in delineating the contribution of each mutated amino acid in the spatial distribution of electrostatic potential, and in biological function when electrostatics is a dominant contributing factor in protein-protein interactions. The framework can be used to design new proteins with tailored electrostatic properties, such as immune system regulators, inhibitors, and vaccines, and in guiding experimental studies. We present an example for the interaction of the immune system protein C3d (the d-fragment of complement protein C3) with its receptor CR2, and we discuss our data in view of a binding site controversy.

The complement system is an integral part of the innate immune system that participates in the clearance of pathogens from the body. The association between complement protein fragment C3d and B or T cell-receptor complement receptor (CR) 2 represents a crucial link between innate and adaptive immunities. The goal of this study is to predict association abilities of C3d and CR2 mutants by theoretically calculating electrostatic free energies of association and to assess the importance of solvation effects in the calculations. We demonstrate that calculated solvation free energy differences and Coulombic free energies of association are more sensitive than electrostatic free energies of association in solution and, thus, more accurate in predicting previously published experimental data for the association abilities (relative to the parent proteins) of specific C3d and CR2 mutants. We show that a proportional relationship exists between the predicted solvation free energy differences and the experimental data, while an inversely proportional relationship exists between the predicted Coulombic free energies of association and the experimental data. Our results yield new insights into the physicochemical properties underlying C3d-CR2 association. We discuss the predictive validity of Coulombic, solvation, and solution electrostatic free energies of association and the generalization of our method for theoretical mutagenesis studies of other systems. This is a basic study, aimed toward improving our understanding of the theoretical basis of immune system regulation at the molecular level. Such insight can serve as the groundwork for the design of regulators with tailored properties, vaccines, and other biotechnology products.

During compatible pollination of the angiosperms, pollen tubes grow in the pistil transmitting tract (TT) and are guided to the ovule for fertilization. Lily (Lilium longiflorum) stigma/style Cys-rich adhesin (SCA), a plant lipid transfer protein (LTP), is a small, secreted peptide involved in pollen tube adhesion-mediated guidance. Here, we used a reverse genetic approach to study biological roles of Arabidopsis thaliana LTP5, a SCA-like LTP. The T-DNA insertional gain-of-function mutant plant for LTP5 (ltp5-1) exhibited ballooned pollen tubes, delayed pollen tube growth, and decreased numbers of fertilized eggs. Our reciprocal cross-pollination study revealed that ltp5-1 results in both male and female partial sterility. RT-PCR and β-glucuronidase analyses showed that LTP5 is present in pollen and the pistil TT in low levels. Pollen-targeted overexpression of either ltp5-1 or wild-type LTP5 resulted in defects in polar tip growth of pollen tubes and thereby decreased seed set, suggesting that mutant ltp5-1 acts as a dominant-active form of wild-type LTP5 in pollen tube growth. The ltp5-1 protein has additional hydrophobic C-terminal sequences, compared with LTP5. In our structural homology/molecular dynamics modeling, Tyr-91 in ltp5-1, replacing Val-91 in LTP5, was predicted to interact with Arg-45 and Tyr-81, which are known to interact with a lipid ligand in maize (Zea mays) LTP. Thus, Arabidopsis LTP5 plays a significant role in reproduction.

rratum rratum to “Electrostatic exploration of the C3d–FH4 interaction using a omputational alanine scan” [Mol. Immunol. 48 (2011) 1844–1850] tlal M. El-Assaada, Chris A. Kieslichb, Ronald D. Gorham Jr. b, Dimitrios Morikisb,∗ Department of Computer Science and Engineering, University of California, Riverside, CA 92521, United States Department of Bioengineering, University of California, Riverside, CA 92521, United States

Complement fragment C3d, a key component of the complement immune system, is involved in the opsonization of foreign pathogens (as a domain of C3), and is a link between innate and adaptive immunities. The complement immune system is an ancient mechanism for defense, and can be found in species that have been on the earth for the last 600 million years; however, the link between the complement system and adaptive immunity is a much more recent adaptation. Human C3d has high charge content, as do most complement proteins, and is believed to have evolved favoring the role of electrostatics in its function. To investigate the role of electrostatics in the function and evolution of human C3d, we have constructed 23 homology models of C3d homologues from various animal species with sequence similarities in the range of ∼30-80%. Electrostatic potentials of each homologue were calculated and electrostatic similarity methods were employed to identify conserved electrostatic “hotspots”. Electrostatic similarity methods were also utilized to analyze the effects of structural perturbations on the electrostatic character of the C3d homologues. Distributions of electrostatic similarity, based on families of perturbed structures produced either through an MD simulation or theoretical alanine-scan mutagenesis, illustrate that the electrostatic character of the functional sites of human C3d are resistant to change. Electrostatic similarity analysis of complement C3d identifies the binding sites for both host and pathogenic ligands, and such analysis could serve as a guide in computational drug design targeting C3d.

Sumoylation of cellular proteins by the ubiquitin-like protein, SUMO, has been found to be one of the essential regulation mechanisms in signal transduction and genome integrity. SENP2, an endopeptidase, is responsible for both maturation of SUMO-1 into its conjugatable form, and the deconjugation of SUMO-1 containing species. Due to the excessive charge of SUMO-1 and SENP2, it has been proposed that electrostatics is important for the association of SUMO-1 and SENP2. In the current study we have used computational methods to investigate the possible role of electrostatics in the formation of the SUMO-1:SENP2 complex. Here we introduce a newly developed computational protocol, AESOP (Analysis of Electrostatic Similarities Of Proteins), which provides a systematic analysis of the contributions of each ionizable residue to the spatial distribution of electrostatic potential and their implied role in protein association. The AESOP protocol performs computational alanine scans, by mutating each ionizable residue within a protein or protein complex to alanine, one at a time. AESOP utilizes Poisson-Boltzmann electrostatic calculations to obtain the spatial distributions of electrostatic potential of proteins or protein mutants at atomic resolution. Electrostatic free energies of association, electrostatic similarity indices, and clustering methods then provide a quantitative comparison of the effects of each alanine mutation, leading to the prediction of key residues in protein association. The data of the current study provide a comprehensive comparison of several electrostatic clustering schemes that have been incorporated into the AESOP protocol. The data also depict important interactions for both SUMO1:SENP2 binding and the stability of the individual components of the complex. The produced predictions provide physicochemical insight into the mechanism of SUMO-1:SENP2 binding and will be used to guide mutagenesis experiments.