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Ferranco A, Basak S, Lough A, Kraatz HB. Metal coordination of ferrocene–histidine conjugates. Dalton Trans 2017; 46:4844-4859. [PMID: 28349138 DOI: 10.1039/c7dt00456g] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Synthesis and complete structural characterization of ferrocene–histidine dipeptides including detailed analysis of the ligand–metal complexation.
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Affiliation(s)
- Annaleizle Ferranco
- Department of Physical and Environmental Sciences
- University of Toronto
- Toronto
- M1C 1A4 Canada
- Department of Chemistry
| | - Shibaji Basak
- Department of Physical and Environmental Sciences
- University of Toronto
- Toronto
- M1C 1A4 Canada
- Department of Chemistry
| | - Alan Lough
- Department of Chemistry
- University of Toronto
- Toronto
- M5S 3H6 Canada
| | - Heinz-Bernhard Kraatz
- Department of Physical and Environmental Sciences
- University of Toronto
- Toronto
- M1C 1A4 Canada
- Department of Chemistry
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2
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Three-dimensional protein structure prediction: Methods and computational strategies. Comput Biol Chem 2014; 53PB:251-276. [DOI: 10.1016/j.compbiolchem.2014.10.001] [Citation(s) in RCA: 121] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2014] [Revised: 10/03/2014] [Accepted: 10/07/2014] [Indexed: 01/01/2023]
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3
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Smadbeck J, Peterson MB, Khoury GA, Taylor MS, Floudas CA. Protein WISDOM: a workbench for in silico de novo design of biomolecules. J Vis Exp 2013. [PMID: 23912941 DOI: 10.3791/50476] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity. To disseminate these methods for broader use we present Protein WISDOM (http://www.proteinwisdom.org), a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.
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Affiliation(s)
- James Smadbeck
- Department of Chemical and Biological Engineering, Princeton University, USA
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4
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Bellows-Peterson ML, Fung HK, Floudas CA, Kieslich CA, Zhang L, Morikis D, Wareham KJ, Monk PN, Hawksworth OA, Woodruff T. De novo peptide design with C3a receptor agonist and antagonist activities: theoretical predictions and experimental validation. J Med Chem 2012; 55:4159-68. [PMID: 22500977 PMCID: PMC3349770 DOI: 10.1021/jm201609k] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
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.
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Affiliation(s)
| | - Ho Ki Fung
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ
| | | | - Chris A. Kieslich
- Department of Bioengineering, University of California at Riverside, Riverside, CA
| | - Li Zhang
- Department of Bioengineering, University of California at Riverside, Riverside, CA
| | - Dimitrios Morikis
- Department of Bioengineering, University of California at Riverside, Riverside, CA
| | - Kathryn J. Wareham
- Department of Infection and Immunity, The University of Sheffield Medical School, Sheffield, UK
| | - Peter N. Monk
- Department of Infection and Immunity, The University of Sheffield Medical School, Sheffield, UK
| | - Owen A. Hawksworth
- School of Biomedical Sciences, The University of Queensland, Brisbane, Australia
| | - Trent Woodruff
- School of Biomedical Sciences, The University of Queensland, Brisbane, Australia
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5
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Subramani A, Wei Y, Floudas CA. ASTRO-FOLD 2.0: an Enhanced Framework for Protein Structure Prediction. AIChE J 2012; 58:1619-1637. [PMID: 23049093 DOI: 10.1002/aic.12669] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
The three-dimensional (3-D) structure prediction of proteins, given their amino acid sequence, is addressed using the first principles-based approach ASTRO-FOLD 2.0. The key features presented are: (1) Secondary structure prediction using a novel optimization-based consensus approach, (2) β-sheet topology prediction using mixed-integer linear optimization (MILP), (3) Residue-to-residue contact prediction using a high-resolution distance-dependent force field and MILP formulation, (4) Tight dihedral angle and distance bound generation for loop residues using dihedral angle clustering and non-linear optimization (NLP), (5) 3-D structure prediction using deterministic global optimization, stochastic conformational space annealing, and the full-atomistic ECEPP/3 potential, (6) Near-native structure selection using a traveling salesman problem-based clustering approach, ICON, and (7) Improved bound generation using chemical shifts of subsets of heavy atoms, generated by SPARTA and CS23D. Computational results of ASTRO-FOLD 2.0 on 47 blind targets of the recently concluded CASP9 experiment are presented.
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Affiliation(s)
- A Subramani
- Dept. of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544
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6
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Wei Y, Thompson J, Floudas CA. CONCORD: a consensus method for protein secondary structure prediction via mixed integer linear optimization. Proc Math Phys Eng Sci 2011. [DOI: 10.1098/rspa.2011.0514] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Most of the protein structure prediction methods use a multi-step process, which often includes secondary structure prediction, contact prediction, fragment generation, clustering, etc. For many years, secondary structure prediction has been the workhorse for numerous methods aimed at predicting protein structure and function. This paper presents a new mixed integer linear optimization (MILP)-based consensus method: a Consensus scheme based On a mixed integer liNear optimization method for seCOndary stRucture preDiction (CONCORD). Based on seven secondary structure prediction methods, SSpro, DSC, PROF, PROFphd, PSIPRED, Predator and GorIV, the MILP-based consensus method combines the strengths of different methods, maximizes the number of correctly predicted amino acids and achieves a better prediction accuracy. The method is shown to perform well compared with the seven individual methods when tested on the PDBselect25 training protein set using sixfold cross validation. It also performs well compared with another set of 10 online secondary structure prediction servers (including several recent ones) when tested on the CASP9 targets (
http://predictioncenter.org/casp9/
). The average Q3 prediction accuracy is 83.04 per cent for the sixfold cross validation of the PDBselect25 set and 82.3 per cent for the CASP9 targets. We have developed a MILP-based consensus method for protein secondary structure prediction. A web server, CONCORD, is available to the scientific community at
http://helios.princeton.edu/CONCORD
.
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Affiliation(s)
- Y. Wei
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA
| | - J. Thompson
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA
| | - C. A. Floudas
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA
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7
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Pan SJ, Cheung WL, Fung HK, Floudas CA, Link AJ. Computational design of the lasso peptide antibiotic microcin J25. Protein Eng Des Sel 2011; 24:275-82. [PMID: 21106549 PMCID: PMC3038460 DOI: 10.1093/protein/gzq108] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2010] [Revised: 10/04/2010] [Accepted: 10/26/2010] [Indexed: 11/12/2022] Open
Abstract
Microcin J25 (MccJ25) is a 21 amino acid (aa) ribosomally synthesized antimicrobial peptide with an unusual structure in which the eight N-terminal residues form a covalently cyclized macrolactam ring through which the remaining 13 aa tail is fed. An open question is the extent of sequence space that can occupy such an extraordinary, highly constrained peptide fold. To begin answering this question, here we have undertaken a computational redesign of the MccJ25 peptide using a two-stage sequence selection procedure based on both energy minimization and fold specificity. Eight of the most highly ranked sequences from the design algorithm, each of which contained two or three amino acid substitutions, were expressed in Escherichia coli and tested for production and antimicrobial activity. Six of the eight variants were successfully produced by E.coli at production levels comparable with that of the wild-type peptide. Of these six variants, three retain detectable antimicrobial activity, although this activity is reduced relative to wild-type MccJ25. The results here build upon previous findings that even rigid, constrained structures like the lasso architecture are amenable to redesign. Furthermore, this work provides evidence that a large amount of amino acid variation is tolerated by the lasso peptide fold.
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Affiliation(s)
- Si Jia Pan
- Departments of Chemical and Biological Engineering and
| | | | - Ho Ki Fung
- Departments of Chemical and Biological Engineering and
| | | | - A. James Link
- Departments of Chemical and Biological Engineering and
- Molecular Biology, Princeton University, Princeton, NJ 08544, USA
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8
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Zhu L, Yang J, Song JN, Chou KC, Shen HB. Improving the accuracy of predicting disulfide connectivity by feature selection. J Comput Chem 2010; 31:1478-85. [PMID: 20127740 DOI: 10.1002/jcc.21433] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Disulfide bonds are primary covalent cross-links formed between two cysteine residues in the same or different protein polypeptide chains, which play important roles in the folding and stability of proteins. However, computational prediction of disulfide connectivity directly from protein primary sequences is challenging due to the nonlocal nature of disulfide bonds in the context of sequences, and the number of possible disulfide patterns grows exponentially when the number of cysteine residues increases. In the previous studies, disulfide connectivity prediction was usually performed in high-dimensional feature space, which can cause a variety of problems in statistical learning, such as the dimension disaster, overfitting, and feature redundancy. In this study, we propose an efficient feature selection technique for analyzing the importance of each feature component. On the basis of this approach, we selected the most important features for predicting the connectivity pattern of intra-chain disulfide bonds. Our results have shown that the high-dimensional features contain redundant information, and the prediction performance can be further improved when these high-dimensional features are reduced to a lower but more compact dimensional space. Our results also indicate that the global protein features contribute little to the formation and prediction of disulfide bonds, while the local sequential and structural information play important roles. All these findings provide important insights for structural studies of disulfide-rich proteins.
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Affiliation(s)
- Lin Zhu
- Department of Bioinformatics, Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, 800 Dongchuan Road, Shanghai 200240, China
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9
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New compstatin variants through two de novo protein design frameworks. Biophys J 2010; 98:2337-46. [PMID: 20483343 DOI: 10.1016/j.bpj.2010.01.057] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2009] [Revised: 01/21/2010] [Accepted: 01/25/2010] [Indexed: 11/22/2022] Open
Abstract
Two de novo protein design frameworks are applied to the discovery of new compstatin variants. One is based on sequence selection and fold specificity, whereas the other approach is based on sequence selection and approximate binding affinity calculations. The proposed frameworks were applied to a complex of C3c with compstatin variant E1 and new variants with improved binding affinities are predicted and experimentally validated. The computational studies elucidated key positions in the sequence of compstatin that greatly affect the binding affinity. Positions 4 and 13 were found to favor Trp, whereas positions 1, 9, and 10 are dominated by Asn, and position 11 consists mainly of Gln. A structural analysis of the C3c-bound peptide analogs is presented.
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10
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Rajgaria R, Wei Y, Floudas CA. Contact prediction for beta and alpha-beta proteins using integer linear optimization and its impact on the first principles 3D structure prediction method ASTRO-FOLD. Proteins 2010; 78:1825-46. [PMID: 20225257 PMCID: PMC2858251 DOI: 10.1002/prot.22696] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
An integer linear optimization model is presented to predict residue contacts in beta, alpha + beta, and alpha/beta proteins. The total energy of a protein is expressed as sum of a C(alpha)-C(alpha) distance dependent contact energy contribution and a hydrophobic contribution. The model selects contact that assign lowest energy to the protein structure as satisfying a set of constraints that are included to enforce certain physically observed topological information. A new method based on hydrophobicity is proposed to find the beta-sheet alignments. These beta-sheet alignments are used as constraints for contacts between residues of beta-sheets. This model was tested on three independent protein test sets and CASP8 test proteins consisting of beta, alpha + beta, alpha/beta proteins and it was found to perform very well. The average accuracy of the predictions (separated by at least six residues) was approximately 61%. The average true positive and false positive distances were also calculated for each of the test sets and they are 7.58 A and 15.88 A, respectively. Residue contact prediction can be directly used to facilitate the protein tertiary structure prediction. This proposed residue contact prediction model is incorporated into the first principles protein tertiary structure prediction approach, ASTRO-FOLD. The effectiveness of the contact prediction model was further demonstrated by the improvement in the quality of the protein structure ensemble generated using the predicted residue contacts for a test set of 10 proteins.
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Affiliation(s)
- R. Rajgaria
- Department of Chemical Engineering, Princeton University, Princeton, NJ 08544-5263, U.S.A
| | - Y. Wei
- Department of Chemical Engineering, Princeton University, Princeton, NJ 08544-5263, U.S.A
| | - C. A. Floudas
- Department of Chemical Engineering, Princeton University, Princeton, NJ 08544-5263, U.S.A
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11
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McAllister SR, Floudas CA. An improved hybrid global optimization method for protein tertiary structure prediction. COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2010; 45:377-413. [PMID: 20357906 PMCID: PMC2847311 DOI: 10.1007/s10589-009-9277-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
First principles approaches to the protein structure prediction problem must search through an enormous conformational space to identify low-energy, near-native structures. In this paper, we describe the formulation of the tertiary structure prediction problem as a nonlinear constrained minimization problem, where the goal is to minimize the energy of a protein conformation subject to constraints on torsion angles and interatomic distances. The core of the proposed algorithm is a hybrid global optimization method that combines the benefits of the αBB deterministic global optimization approach with conformational space annealing. These global optimization techniques employ a local minimization strategy that combines torsion angle dynamics and rotamer optimization to identify and improve the selection of initial conformations and then applies a sequential quadratic programming approach to further minimize the energy of the protein conformations subject to constraints. The proposed algorithm demonstrates the ability to identify both lower energy protein structures, as well as larger ensembles of low-energy conformations.
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12
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Subramani A, DiMaggio PA, Floudas CA. Selecting high quality protein structures from diverse conformational ensembles. Biophys J 2009; 97:1728-36. [PMID: 19751678 DOI: 10.1016/j.bpj.2009.06.046] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2009] [Revised: 06/15/2009] [Accepted: 06/30/2009] [Indexed: 01/01/2023] Open
Abstract
Protein structure prediction encompasses two major challenges: 1), the generation of a large ensemble of high resolution structures for a given amino-acid sequence; and 2), the identification of the structure closest to the native structure for a blind prediction. In this article, we address the second challenge, by proposing what is, to our knowledge, a novel iterative traveling-salesman problem-based clustering method to identify the structures of a protein, in a given ensemble, which are closest to the native structure. The method consists of an iterative procedure, which aims at eliminating clusters of structures at each iteration, which are unlikely to be of similar fold to the native, based on a statistical analysis of cluster density and average spherical radius. The method, denoted as ICON, has been tested on four data sets: 1), 1400 proteins with high resolution decoys; 2), medium-to-low resolution decoys from Decoys 'R' Us; 3), medium-to-low resolution decoys from the first-principles approach, ASTRO-FOLD; and 4), selected targets from CASP8. The extensive tests demonstrate that ICON can identify high-quality structures in each ensemble, regardless of the resolution of conformers. In a total of 1454 proteins, with an average of 1051 conformers per protein, the conformers selected by ICON are, on an average, in the top 3.5% of the conformers in the ensemble.
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Affiliation(s)
- Ashwin Subramani
- Department of Chemical Engineering, Princeton University, Princeton, New Jersey, USA
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13
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Rajgaria R, McAllister SR, Floudas CA. Distance dependent centroid to centroid force fields using high resolution decoys. Proteins 2008; 70:950-70. [PMID: 17847088 DOI: 10.1002/prot.21561] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Simplified force fields play an important role in protein structure prediction and de novo protein design by requiring less computational effort than detailed atomistic potentials. A side chain centroid based, distance dependent pairwise interaction potential has been developed. A linear programming based formulation was used in which non-native "decoy" conformers are forced to take a higher energy compared with the corresponding native structure. This model was trained on an enhanced and diverse protein set. High quality decoy structures were generated for approximately 1400 nonhomologous proteins using torsion angle dynamics along with restricted variations of the hydrophobic cores of the native structure. The resulting decoy set was used to train the model yielding two different side chain centroid based force fields that differ in the way distance dependence has been used to calculate energy parameters. These force fields were tested on an independent set of 148 test proteins with 500 decoy structures for each protein. The side chain centroid force fields were successful in correctly identifying approximately 86% native structures. The Z-scores produced by the proposed centroid-centroid distance dependent force fields improved compared with other distance dependent C(alpha)-C(alpha) or side chain based force fields.
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Affiliation(s)
- R Rajgaria
- Department of Chemical Engineering, Princeton University, Princeton, New Jersey 08544-5263, USA
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14
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Fung HK, Welsh WJ, Floudas CA. Computational De Novo Peptide and Protein Design: Rigid Templates versus Flexible Templates. Ind Eng Chem Res 2008. [DOI: 10.1021/ie071286k] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Ho Ki Fung
- Department of Chemical Engineering, Princeton University, Princeton, New Jersey 08544-5263, and Department of Pharmacology, University of Medicine & Dentistry of New Jersey (UMDNJ), Robert Wood Johnson Medical School, and the Informatics Institute of UMDNJ, Piscataway, New Jersey 08854
| | - William J. Welsh
- Department of Chemical Engineering, Princeton University, Princeton, New Jersey 08544-5263, and Department of Pharmacology, University of Medicine & Dentistry of New Jersey (UMDNJ), Robert Wood Johnson Medical School, and the Informatics Institute of UMDNJ, Piscataway, New Jersey 08854
| | - Christodoulos A. Floudas
- Department of Chemical Engineering, Princeton University, Princeton, New Jersey 08544-5263, and Department of Pharmacology, University of Medicine & Dentistry of New Jersey (UMDNJ), Robert Wood Johnson Medical School, and the Informatics Institute of UMDNJ, Piscataway, New Jersey 08854
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15
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16
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Abstract
This review presents the advances in protein structure prediction from the computational methods perspective. The approaches are classified into four major categories: comparative modeling, fold recognition, first principles methods that employ database information, and first principles methods without database information. Important advances along with current limitations and challenges are presented.
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Affiliation(s)
- C A Floudas
- Department of Chemical Engineering, Princeton University, Princeton, New Jersey 08544-5263, USA.
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17
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McAllister SR, Mickus BE, Klepeis JL, Floudas CA. Novel approach for alpha-helical topology prediction in globular proteins: generation of interhelical restraints. Proteins 2007; 65:930-52. [PMID: 17029234 DOI: 10.1002/prot.21095] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The protein folding problem represents one of the most challenging problems in computational biology. Distance constraints and topology predictions can be highly useful for the folding problem in reducing the conformational space that must be searched by deterministic algorithms to find a protein structure of minimum conformational energy. We present a novel optimization framework for predicting topological contacts and generating interhelical distance restraints between hydrophobic residues in alpha-helical globular proteins. It should be emphasized that since the model does not make assumptions about the form of the helices, it is applicable to all alpha-helical proteins, including helices with kinks and irregular helices. This model aims at enhancing the ASTRO-FOLD protein folding approach of Klepeis and Floudas (Journal of Computational Chemistry 2003;24:191-208), which finds the structure of global minimum conformational energy via a constrained nonlinear optimization problem. The proposed topology prediction model was evaluated on 26 alpha-helical proteins ranging from 2 to 8 helices and 35 to 159 residues, and the best identified average interhelical distances corresponding to the predicted contacts fell below 11 A in all 26 of these systems. Given the positive results of applying the model to several protein systems, the importance of interhelical hydrophobic-to-hydrophobic contacts in determining the folding of alpha-helical globular proteins is highlighted.
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Affiliation(s)
- S R McAllister
- Department of Chemical Engineering, Princeton University, Princeton, New Jersey 08544-5263, USA
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18
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Ho BK, Dill KA. Folding very short peptides using molecular dynamics. PLoS Comput Biol 2006; 2:e27. [PMID: 16617376 PMCID: PMC1435986 DOI: 10.1371/journal.pcbi.0020027] [Citation(s) in RCA: 54] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2005] [Accepted: 02/20/2005] [Indexed: 11/29/2022] Open
Abstract
Peptides often have conformational preferences. We simulated 133 peptide 8-mer fragments from six different proteins, sampled by replica-exchange molecular dynamics using Amber7 with a GB/SA (generalized-Born/solvent-accessible electrostatic approximation to water) implicit solvent. We found that 85 of the peptides have no preferred structure, while 48 of them converge to a preferred structure. In 85% of the converged cases (41 peptides), the structures found by the simulations bear some resemblance to their native structures, based on a coarse-grained backbone description. In particular, all seven of the β hairpins in the native structures contain a fragment in the turn that is highly structured. In the eight cases where the bioinformatics-based I-sites library picks out native-like structures, the present simulations are largely in agreement. Such physics-based modeling may be useful for identifying early nuclei in folding kinetics and for assisting in protein-structure prediction methods that utilize the assembly of peptide fragments. To carry out specific biochemical reactions, proteins must adopt precise three-dimensional conformations. During the folding of a protein, the protein picks out the right conformation out of billions of other conformations. It is not yet possible to do this computationally. Picking out the native conformation using physics-based atomically detailed models, sampled by molecular dynamics, is presently beyond the reach of computer methods. How can we speed up computational protein-structure prediction? One idea is that proteins start folding at specific parts of a chain that kink up early in the folding process. If we can identify these kinks, we should be able to speed up protein-structure prediction. Previous studies have identified likely kinks through bioinformatic analysis of existing protein structures. The goal of the authors here is to identify these putative folding initiation sites with a physical model instead. In this study, Ho and Dill show that, by chopping a protein chain into peptide pieces, then simulating the pieces in molecular dynamics, they can identify those peptide fragments that have conformational biases. These peptides identify the kinks in the protein chain.
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Affiliation(s)
- Bosco K Ho
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA.
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19
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Abstract
The structure prediction of loops with flexible stem residues is addressed in this article. While the secondary structure of the stem residues is assumed to be known, the geometry of the protein into which the loop must fit is considered to be unknown in our methodology. As a consequence, the compatibility of the loop with the remainder of the protein is not used as a criterion to reject loop decoys. The loop structure prediction with flexible stems is more difficult than fitting loops into a known protein structure in that a larger conformational space has to be covered. The main focus of the study is to assess the precision of loop structure prediction if no information on the protein geometry is available. The proposed approach is based on (1) dihedral angle sampling, (2) structure optimization by energy minimization with a physically based energy function, (3) clustering, and (4) a comparison of strategies for the selection of loops identified in (3). Steps (1) and (2) have similarities to previous approaches to loop structure prediction with fixed stems. Step (3) is based on a new iterative approach to clustering that is tailored for the loop structure prediction problem with flexible stems. In this new approach, clustering is not only used to identify conformers that are likely to be close to the native structure, but clustering is also employed to identify far-from-native decoys. By discarding these decoys iteratively, the overall quality of the ensemble and the loop structure prediction is improved. Step (4) provides a comparative study of criteria for loop selection based on energy, colony energy, cluster density, and a hybrid criterion introduced here. The proposed method is tested on a large set of 3215 loops from proteins in the Pdb-Select25 set and to 179 loops from proteins from the Casp6 experiment.
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Affiliation(s)
- M Mönnigmann
- Department of Chemical Engineering, Princeton University, Princeton, New Jersey 08544-5263, USA
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20
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Floudas C, Fung H, McAllister S, Mönnigmann M, Rajgaria R. Advances in protein structure prediction and de novo protein design: A review. Chem Eng Sci 2006. [DOI: 10.1016/j.ces.2005.04.009] [Citation(s) in RCA: 175] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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21
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22
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Floudas CA. Research challenges, opportunities and synergism in systems engineering and computational biology. AIChE J 2005. [DOI: 10.1002/aic.10620] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Grossmann IE. Challenges in the new millennium: product discovery and design, enterprise and supply chain optimization, global life cycle assessment. Comput Chem Eng 2004. [DOI: 10.1016/j.compchemeng.2004.07.016] [Citation(s) in RCA: 99] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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24
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Nölting B, Jülich D, Vonau W, Andert K. Evolutionary Computer Programming of Protein Folding and Structure Predictions. J Theor Biol 2004; 229:13-8. [PMID: 15178181 DOI: 10.1016/j.jtbi.2004.02.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2003] [Revised: 02/09/2004] [Accepted: 02/25/2004] [Indexed: 10/26/2022]
Abstract
In order to understand the mechanism of protein folding and to assist the rational de-novo design of fast-folding, non-aggregating and stable artificial enzymes it is very helpful to be able to simulate protein folding reactions and to predict the structures of proteins and other biomacromolecules. Here, we use a method of computer programming called "evolutionary computer programming" in which a program evolves depending on the evolutionary pressure exerted on the program. In the case of the presented application of this method on a computer program for folding simulations, the evolutionary pressure exerted was towards faster finding deep minima in the energy landscape of protein folding. Already after 20 evolution steps, the evolved program was able to find deep minima in the energy landscape more than 10 times faster than the original program prior to the evolution process.
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Affiliation(s)
- Bengt Nölting
- Prussian Private Institute of Technology at Berlin, Am Schlosspark 30, Berlin D-13187, Germany.
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25
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Klepeis JL, Floudas CA. ASTRO-FOLD: a combinatorial and global optimization framework for Ab initio prediction of three-dimensional structures of proteins from the amino acid sequence. Biophys J 2004; 85:2119-46. [PMID: 14507680 PMCID: PMC1303441 DOI: 10.1016/s0006-3495(03)74640-2] [Citation(s) in RCA: 67] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The field of computational biology has been revolutionized by recent advances in genomics. The completion of a number of genome projects, including that of the human genome, has paved the way toward a variety of challenges and opportunities in bioinformatics and biological systems engineering. One of the first challenges has been the determination of the structures of proteins encoded by the individual genes. This problem, which represents the progression from sequence to structure (genomics to structural genomics), has been widely known as the structure-prediction-in-protein-folding problem. We present the development and application of ASTRO-FOLD, a novel and complete approach for the ab initio prediction of protein structures given only the amino acid sequences of the proteins. The approach exhibits many novel components and the merits of its application are examined for a suite of protein systems, including a number of targets from several critical-assessment-of-structure-prediction experiments.
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Affiliation(s)
- J L Klepeis
- Department of Chemical Engineering, Princeton University, Princeton, New Jersey 10036, USA.
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Klepeis JL, Pieja MJ, Floudas CA. Hybrid global optimization algorithms for protein structure prediction: alternating hybrids. Biophys J 2003; 84:869-82. [PMID: 12547770 PMCID: PMC1302666 DOI: 10.1016/s0006-3495(03)74905-4] [Citation(s) in RCA: 58] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2002] [Accepted: 10/25/2002] [Indexed: 10/21/2022] Open
Abstract
Hybrid global optimization methods attempt to combine the beneficial features of two or more algorithms, and can be powerful methods for solving challenging nonconvex optimization problems. In this paper, novel classes of hybrid global optimization methods, termed alternating hybrids, are introduced for application as a tool in treating the peptide and protein structure prediction problems. In particular, these new optimization methods take the form of hybrids between a deterministic global optimization algorithm, the alphaBB, and a stochastically based method, conformational space annealing (CSA). The alphaBB method, as a theoretically proven global optimization approach, exhibits consistency, as it guarantees convergence to the global minimum for twice-continuously differentiable constrained nonlinear programming problems, but can benefit from computationally related enhancements. On the other hand, the independent CSA algorithm is highly efficient, though the method lacks theoretical guarantees of convergence. Furthermore, both the alphaBB method and the CSA method are found to identify ensembles of low-energy conformers, an important feature for determining the true free energy minimum of the system. The proposed hybrid methods combine the desirable features of efficiency and consistency, thus enabling the accurate prediction of the structures of larger peptides. Computational studies for met-enkephalin and melittin, employing sequential and parallel computing frameworks, demonstrate the promise for these proposed hybrid methods.
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Affiliation(s)
- J L Klepeis
- Department of Chemical Engineering, Princeton University, Princeton, New Jersey 08544-5263, USA
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Klepeis JL, Floudas CA. Prediction of beta-sheet topology and disulfide bridges in polypeptides. J Comput Chem 2003; 24:191-208. [PMID: 12497599 DOI: 10.1002/jcc.10167] [Citation(s) in RCA: 35] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
An ab initio method has been developed to predict beta architectures in polypeptides. The approach predicts the topology of beta-sheets and disulfide bridges through a novel superstructure-based mathematical framework originally established for chemical process synthesis problems. Two types of superstructure are introduced, both of which emanate from the principle that hydrophobic interactions drive the formation of a beta-structure. The mathematical formulation of the problem results in a set of integer linear programming (ILP) problems that can be solved to global optimality to identify the optimal beta-configuration. These (ILP) models can also predict a ranked ordered list of the best, second-best, third-best, etc., topologies of beta-sheets and disulfide bridges. The approach is shown to perform very well for several benchmark polypeptide systems, as well as polypeptides exhibiting challenging nonsequential beta-sheet topologies folds (56 to 187 amino acids).
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Affiliation(s)
- J L Klepeis
- Department of Chemical Engineering, Princeton University, Princeton, New Jersey 08544-5263, USA
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Challenges in the new millennium: Product discovery and design, enterprise and supply chain optimization, global life cycle assessment. ACTA ACUST UNITED AC 2003. [DOI: 10.1016/s1570-7946(03)80525-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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