1
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Gu X, Schafer NP, Wang Q, Song SS, Chen M, Waxham MN, Wolynes PG. Exploring the F-actin/CPEB3 interaction and its possible role in the molecular mechanism of long-term memory. Proc Natl Acad Sci U S A 2020; 117:22128-22134. [PMID: 32848053 PMCID: PMC7486757 DOI: 10.1073/pnas.2012964117] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Dendritic spines are tiny membranous protrusions on the dendrites of neurons. Dendritic spines change shape in response to input signals, thereby strengthening the connections between neurons. The growth and stabilization of dendritic spines is thought to be essential for maintaining long-term memory. Actin cytoskeleton remodeling in spines is a key element of their formation and growth. More speculatively, the aggregation of CPEB3, a functional prion that binds RNA, has been reported to be involved in the maintenance of long-term memory. Here we study the interaction between actin and CPEB3 and propose a molecular model for the complex structure of CPEB3 and an actin filament (F-actin). The results of our computational modeling, including both energetic and structural analyses, are compared with novel data from peptide array experiments. Our model of the CPEB3/F-actin interaction suggests that F-actin potentially triggers the aggregation-prone structural transition of a short CPEB3 sequence by zipping it into a beta-hairpin form. We also propose that the CPEB3/F-actin interaction might be regulated by the SUMOylation of CPEB3, based on bioinformatic searches for potential SUMOylation sites as well as SUMO interacting motifs in CPEB3. On the basis of these results and the existing literature, we put forward a possible molecular mechanism underlying long-term memory that involves CPEB3's binding to actin, its aggregation, and its regulation by SUMOylation.
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Affiliation(s)
- Xinyu Gu
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005
- Department of Chemistry, Rice University, Houston, TX 77005
| | - Nicholas P Schafer
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005
- Department of Chemistry, Rice University, Houston, TX 77005
- Schafer Science, LLC, Houston, TX 77025
| | - Qian Wang
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005
| | - Sarah S Song
- Department of Neurobiology and Anatomy, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX 77030
| | - Mingchen Chen
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005
| | - M Neal Waxham
- Department of Neurobiology and Anatomy, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX 77030
| | - Peter G Wolynes
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005;
- Department of Chemistry, Rice University, Houston, TX 77005
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2
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Chen MC, Li Y, Zhu YH, Ge F, Yu DJ. SSCpred: Single-Sequence-Based Protein Contact Prediction Using Deep Fully Convolutional Network. J Chem Inf Model 2020; 60:3295-3303. [DOI: 10.1021/acs.jcim.9b01207] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ming-Cai Chen
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, P. R. China
| | - Yang Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, P. R. China
- Department of Computational Medicine and Bioinformatics, University of Michigan, Washtenaw 100, Ann Arbor, Michigan 48109-2218, United States
| | - Yi-Heng Zhu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, P. R. China
| | - Fang Ge
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, P. R. China
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing 210094, P. R. China
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3
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Perego C, Potestio R. Computational methods in the study of self-entangled proteins: a critical appraisal. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2019; 31:443001. [PMID: 31269476 DOI: 10.1088/1361-648x/ab2f19] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The existence of self-entangled proteins, the native structure of which features a complex topology, unveils puzzling, and thus fascinating, aspects of protein biology and evolution. The discovery that a polypeptide chain can encode the capability to self-entangle in an efficient and reproducible way during folding, has raised many questions, regarding the possible function of these knots, their conservation along evolution, and their role in the folding paradigm. Understanding the function and origin of these entanglements would lead to deep implications in protein science, and this has stimulated the scientific community to investigate self-entangled proteins for decades by now. In this endeavour, advanced experimental techniques are more and more supported by computational approaches, that can provide theoretical guidelines for the interpretation of experimental results, and for the effective design of new experiments. In this review we provide an introduction to the computational study of self-entangled proteins, focusing in particular on the methodological developments related to this research field. A comprehensive collection of techniques is gathered, ranging from knot theory algorithms, that allow detection and classification of protein topology, to Monte Carlo or molecular dynamics strategies, that constitute crucial instruments for investigating thermodynamics and kinetics of this class of proteins.
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Affiliation(s)
- Claudio Perego
- Max Panck Institute for Polymer Research, Ackermannweg 10, Mainz 55128, Germany
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4
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Yallapragada VVB, Walker SP, Devoy C, Buckley S, Flores Y, Tangney M. Function2Form Bridge-Toward synthetic protein holistic performance prediction. Proteins 2019; 88:462-475. [PMID: 31589780 DOI: 10.1002/prot.25825] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 09/02/2019] [Accepted: 09/17/2019] [Indexed: 11/06/2022]
Abstract
Protein engineering and synthetic biology stand to benefit immensely from recent advances in silico tools for structural and functional analyses of proteins. In the context of designing novel proteins, current in silico tools inform the user on individual parameters of a query protein, with output scores/metrics unique to each parameter. In reality, proteins feature multiple "parts"/functions and modification of a protein aimed at altering a given part, typically has collateral impact on other protein parts. A system for prediction of the combined effect of design parameters on the overall performance of the final protein does not exist. Function2Form Bridge (F2F-Bridge) attempts to address this by combining the scores of different design parameters pertaining to the protein being analyzed into a single easily interpreted output describing overall performance. The strategy comprises of (a) a mathematical strategy combining data from a myriad of in silico tools into an OP-score (a singular score informing on a user-defined overall performance) and (b) the F2F Plot, a graphical means of informing the wetlab biologist holistically on designed construct suitability in the context of multiple parameters, highlighting scope for improvement. F2F predictive output was compared with wetlab data from a range of synthetic proteins designed, built, and tested for this study. Statistical/machine learning approaches for predicting overall performance, for use alongside the F2F plot, were also examined. Comparisons between wetlab performance and F2F predictions demonstrated close and reliable correlations. This user-friendly strategy represents a pivotal enabler in increasing the accessibility of synthetic protein building and de novo protein design.
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Affiliation(s)
- Venkata V B Yallapragada
- Cancer Research at UCC, University College Cork, Cork, Ireland.,SynBioCentre, University College Cork, Cork, Ireland
| | - Sidney P Walker
- Cancer Research at UCC, University College Cork, Cork, Ireland.,SynBioCentre, University College Cork, Cork, Ireland.,APC Microbiome Ireland, University College Cork, Cork, Ireland.,School of Microbiology, University College Cork, Cork, Ireland
| | - Ciaran Devoy
- Cancer Research at UCC, University College Cork, Cork, Ireland.,SynBioCentre, University College Cork, Cork, Ireland
| | - Stephen Buckley
- Cancer Research at UCC, University College Cork, Cork, Ireland.,SynBioCentre, University College Cork, Cork, Ireland
| | - Yensi Flores
- Cancer Research at UCC, University College Cork, Cork, Ireland.,SynBioCentre, University College Cork, Cork, Ireland.,APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - Mark Tangney
- Cancer Research at UCC, University College Cork, Cork, Ireland.,SynBioCentre, University College Cork, Cork, Ireland.,APC Microbiome Ireland, University College Cork, Cork, Ireland
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5
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Chen M, Lin X, Lu W, Onuchic JN, Wolynes PG. Protein Folding and Structure Prediction from the Ground Up II: AAWSEM for α/β Proteins. J Phys Chem B 2016; 121:3473-3482. [PMID: 27797194 DOI: 10.1021/acs.jpcb.6b09347] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The atomistic associative memory, water mediated, structure and energy model (AAWSEM) is an efficient coarse-grained force field with transferable tertiary interactions that incorporates local in sequence energetic biases using structural information derived from all-atom simulations of long segments of the protein. For α helical proteins, the accuracy of structure prediction using AAWSEM has been established previously. In this article, we examine the capability of AAWSEM to predict the structure of α/β proteins. We also elaborate on an iterative approach that uses the structures from a first round of AAWSEM simulation as fragment memories. This iterative scheme improves the quality of the structure prediction and makes the free energy profile more funneled toward native configurations. We explore the use of clustering analyses as a way of evaluating the confidence in various structure prediction models. Clustering using a local relative order parameter (mutual Q) of the predicted structural ensemble turns out to be optimal. The tightest cluster according to mutual Q generally has the most correctly folded structure. Since there is no bioinformatic input, AAWSEM amounts to an ab initio protein structure prediction method that combines the efficiency of coarse-grained simulations with the local structural accuracy that can be achieved from all-atom simulations.
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Affiliation(s)
- Mingchen Chen
- Center for Theoretical Biological Physics, Rice University , 6100 Main St., Houston, Texas 77005-1892, United States.,Department of Bioengineering, Rice University , 6100 Main St., Houston, Texas 77005-1892, United States
| | - Xingcheng Lin
- Center for Theoretical Biological Physics, Rice University , 6100 Main St., Houston, Texas 77005-1892, United States.,Department of Physics and Astronomy, Rice University , 6100 Main St., Houston, Texas 77005-1892, United States
| | - Wei Lu
- Center for Theoretical Biological Physics, Rice University , 6100 Main St., Houston, Texas 77005-1892, United States.,Department of Physics and Astronomy, Rice University , 6100 Main St., Houston, Texas 77005-1892, United States
| | - José N Onuchic
- Center for Theoretical Biological Physics, Rice University , 6100 Main St., Houston, Texas 77005-1892, United States.,Department of Physics and Astronomy, Rice University , 6100 Main St., Houston, Texas 77005-1892, United States.,Department of Chemistry, Rice University , 6100 Main St., Houston, Texas 77005-1892, United States.,Department of Biosciences, Rice University , 6100 Main St., Houston, Texas 77005-1892, United States
| | - Peter G Wolynes
- Center for Theoretical Biological Physics, Rice University , 6100 Main St., Houston, Texas 77005-1892, United States.,Department of Physics and Astronomy, Rice University , 6100 Main St., Houston, Texas 77005-1892, United States.,Department of Chemistry, Rice University , 6100 Main St., Houston, Texas 77005-1892, United States.,Department of Biosciences, Rice University , 6100 Main St., Houston, Texas 77005-1892, United States
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6
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Lipska AG, Seidman SR, Sieradzan AK, Giełdoń A, Liwo A, Scheraga HA. Molecular dynamics of protein A and a WW domain with a united-residue model including hydrodynamic interaction. J Chem Phys 2016; 144:184110. [PMID: 27179474 PMCID: PMC4866947 DOI: 10.1063/1.4948710] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Accepted: 04/25/2016] [Indexed: 01/01/2023] Open
Abstract
The folding of the N-terminal part of the B-domain of staphylococcal protein A (PDB ID: 1BDD, a 46-residue three-α-helix bundle) and the formin-binding protein 28 WW domain (PDB ID: 1E0L, a 37-residue three-stranded anti-parallel β protein) was studied by means of Langevin dynamics with the coarse-grained UNRES force field to assess the influence of hydrodynamic interactions on protein-folding pathways and kinetics. The unfolded, intermediate, and native-like structures were identified by cluster analysis, and multi-exponential functions were fitted to the time dependence of the fractions of native and intermediate structures, respectively, to determine bulk kinetics. It was found that introducing hydrodynamic interactions slows down both the formation of an intermediate state and the transition from the collapsed structures to the final native-like structures by creating multiple kinetic traps. Therefore, introducing hydrodynamic interactions considerably slows the folding, as opposed to the results obtained from earlier studies with the use of Gō-like models.
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Affiliation(s)
- Agnieszka G Lipska
- Laboratory of Molecular Modeling, Faculty of Chemistry, University of Gdańsk, ul. Wita Stwosza 63, 80-308 Gdańsk, Poland
| | - Steven R Seidman
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853-1301, USA
| | - Adam K Sieradzan
- Laboratory of Molecular Modeling, Faculty of Chemistry, University of Gdańsk, ul. Wita Stwosza 63, 80-308 Gdańsk, Poland
| | - Artur Giełdoń
- Laboratory of Molecular Modeling, Faculty of Chemistry, University of Gdańsk, ul. Wita Stwosza 63, 80-308 Gdańsk, Poland
| | - Adam Liwo
- Laboratory of Molecular Modeling, Faculty of Chemistry, University of Gdańsk, ul. Wita Stwosza 63, 80-308 Gdańsk, Poland
| | - Harold A Scheraga
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853-1301, USA
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7
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Capelli R, Paissoni C, Sormanni P, Tiana G. Iterative derivation of effective potentials to sample the conformational space of proteins at atomistic scale. J Chem Phys 2014; 140:195101. [PMID: 24852563 DOI: 10.1063/1.4876219] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
The current capacity of computers makes it possible to perform simulations of small systems with portable, explicit-solvent potentials achieving high degree of accuracy. However, simplified models must be employed to exploit the behavior of large systems or to perform systematic scans of smaller systems. While powerful algorithms are available to facilitate the sampling of the conformational space, successful applications of such models are hindered by the availability of simple enough potentials able to satisfactorily reproduce known properties of the system. We develop an interatomic potential to account for a number of properties of proteins in a computationally economic way. The potential is defined within an all-atom, implicit solvent model by contact functions between the different atom types. The associated numerical values can be optimized by an iterative Monte Carlo scheme on any available experimental data, provided that they are expressible as thermal averages of some conformational properties. We test this model on three different proteins, for which we also perform a scan of all possible point mutations with explicit conformational sampling. The resulting models, optimized solely on a subset of native distances, not only reproduce the native conformations within a few Angstroms from the experimental ones, but show the cooperative transition between native and denatured state and correctly predict the measured free-energy changes associated with point mutations. Moreover, differently from other structure-based models, our method leaves a residual degree of frustration, which is known to be present in protein molecules.
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Affiliation(s)
- Riccardo Capelli
- Department of Physics, Università degli Studi di Milano, via Celoria 16, 20133 Milano, Italy
| | - Cristina Paissoni
- Department of Chemistry, Università degli Studi di Milano, via Venezian 21, 20133 Milano, Italy
| | - Pietro Sormanni
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Guido Tiana
- Department of Physics, Università degli Studi di Milano and INFN, via Celoria 16, 20133 Milano, Italy
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8
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Schafer NP, Kim BL, Zheng W, Wolynes PG. Learning To Fold Proteins Using Energy Landscape Theory. Isr J Chem 2014; 54:1311-1337. [PMID: 25308991 PMCID: PMC4189132 DOI: 10.1002/ijch.201300145] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This review is a tutorial for scientists interested in the problem of protein structure prediction, particularly those interested in using coarse-grained molecular dynamics models that are optimized using lessons learned from the energy landscape theory of protein folding. We also present a review of the results of the AMH/AMC/AMW/AWSEM family of coarse-grained molecular dynamics protein folding models to illustrate the points covered in the first part of the article. Accurate coarse-grained structure prediction models can be used to investigate a wide range of conceptual and mechanistic issues outside of protein structure prediction; specifically, the paper concludes by reviewing how AWSEM has in recent years been able to elucidate questions related to the unusual kinetic behavior of artificially designed proteins, multidomain protein misfolding, and the initial stages of protein aggregation.
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Affiliation(s)
- N P Schafer
- Department of Physics, Rice University, Houston, TX 77005, USA ; Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA
| | - B L Kim
- Department of Chemistry, Rice University, Houston, TX 77005, USA ; Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA
| | - W Zheng
- Department of Chemistry, Rice University, Houston, TX 77005, USA ; Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA
| | - P G Wolynes
- Department of Physics, Rice University, Houston, TX 77005, USA ; Department of Chemistry, Rice University, Houston, TX 77005, USA ; Center for Theoretical Biological Physics, Rice University, Houston, TX 77005, USA
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9
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Molloy K, Shehu A. Elucidating the ensemble of functionally-relevant transitions in protein systems with a robotics-inspired method. BMC STRUCTURAL BIOLOGY 2013; 13 Suppl 1:S8. [PMID: 24565158 PMCID: PMC3952944 DOI: 10.1186/1472-6807-13-s1-s8] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background Many proteins tune their biological function by transitioning between different functional states, effectively acting as dynamic molecular machines. Detailed structural characterization of transition trajectories is central to understanding the relationship between protein dynamics and function. Computational approaches that build on the Molecular Dynamics framework are in principle able to model transition trajectories at great detail but also at considerable computational cost. Methods that delay consideration of dynamics and focus instead on elucidating energetically-credible conformational paths connecting two functionally-relevant structures provide a complementary approach. Effective sampling-based path planning methods originating in robotics have been recently proposed to produce conformational paths. These methods largely model short peptides or address large proteins by simplifying conformational space. Methods We propose a robotics-inspired method that connects two given structures of a protein by sampling conformational paths. The method focuses on small- to medium-size proteins, efficiently modeling structural deformations through the use of the molecular fragment replacement technique. In particular, the method grows a tree in conformational space rooted at the start structure, steering the tree to a goal region defined around the goal structure. We investigate various bias schemes over a progress coordinate for balance between coverage of conformational space and progress towards the goal. A geometric projection layer promotes path diversity. A reactive temperature scheme allows sampling of rare paths that cross energy barriers. Results and conclusions Experiments are conducted on small- to medium-size proteins of length up to 214 amino acids and with multiple known functionally-relevant states, some of which are more than 13Å apart of each-other. Analysis reveals that the method effectively obtains conformational paths connecting structural states that are significantly different. A detailed analysis on the depth and breadth of the tree suggests that a soft global bias over the progress coordinate enhances sampling and results in higher path diversity. The explicit geometric projection layer that biases the exploration away from over-sampled regions further increases coverage, often improving proximity to the goal by forcing the exploration to find new paths. The reactive temperature scheme is shown effective in increasing path diversity, particularly in difficult structural transitions with known high-energy barriers.
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10
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Saleh S, Olson B, Shehu A. A population-based evolutionary search approach to the multiple minima problem in de novo protein structure prediction. BMC STRUCTURAL BIOLOGY 2013; 13 Suppl 1:S4. [PMID: 24565020 PMCID: PMC3953177 DOI: 10.1186/1472-6807-13-s1-s4] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Background Elucidating the native structure of a protein molecule from its sequence of amino acids, a problem known as de novo structure prediction, is a long standing challenge in computational structural biology. Difficulties in silico arise due to the high dimensionality of the protein conformational space and the ruggedness of the associated energy surface. The issue of multiple minima is a particularly troublesome hallmark of energy surfaces probed with current energy functions. In contrast to the true energy surface, these surfaces are weakly-funneled and rich in comparably deep minima populated by non-native structures. For this reason, many algorithms seek to be inclusive and obtain a broad view of the low-energy regions through an ensemble of low-energy (decoy) conformations. Conformational diversity in this ensemble is key to increasing the likelihood that the native structure has been captured. Methods We propose an evolutionary search approach to address the multiple-minima problem in decoy sampling for de novo structure prediction. Two population-based evolutionary search algorithms are presented that follow the basic approach of treating conformations as individuals in an evolving population. Coarse graining and molecular fragment replacement are used to efficiently obtain protein-like child conformations from parents. Potential energy is used both to bias parent selection and determine which subset of parents and children will be retained in the evolving population. The effect on the decoy ensemble of sampling minima directly is measured by additionally mapping a conformation to its nearest local minimum before considering it for retainment. The resulting memetic algorithm thus evolves not just a population of conformations but a population of local minima. Results and conclusions Results show that both algorithms are effective in terms of sampling conformations in proximity of the known native structure. The additional minimization is shown to be key to enhancing sampling capability and obtaining a diverse ensemble of decoy conformations, circumventing premature convergence to sub-optimal regions in the conformational space, and approaching the native structure with proximity that is comparable to state-of-the-art decoy sampling methods. The results are shown to be robust and valid when using two representative state-of-the-art coarse-grained energy functions.
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11
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Molloy K, Saleh S, Shehu A. Probabilistic search and energy guidance for biased decoy sampling in ab initio protein structure prediction. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2013; 10:1162-1175. [PMID: 24384705 DOI: 10.1109/tcbb.2013.29] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Adequate sampling of the conformational space is a central challenge in ab initio protein structure prediction. In the absence of a template structure, a conformational search procedure guided by an energy function explores the conformational space, gathering an ensemble of low-energy decoy conformations. If the sampling is inadequate, the native structure may be missed altogether. Even if reproduced, a subsequent stage that selects a subset of decoys for further structural detail and energetic refinement may discard near-native decoys if they are high energy or insufficiently represented in the ensemble. Sampling should produce a decoy ensemble that facilitates the subsequent selection of near-native decoys. In this paper, we investigate a robotics-inspired framework that allows directly measuring the role of energy in guiding sampling. Testing demonstrates that a soft energy bias steers sampling toward a diverse decoy ensemble less prone to exploiting energetic artifacts and thus more likely to facilitate retainment of near-native conformations by selection techniques. We employ two different energy functions, the associative memory Hamiltonian with water and Rosetta. Results show that enhanced sampling provides a rigorous testing of energy functions and exposes different deficiencies in them, thus promising to guide development of more accurate representations and energy functions.
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12
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Basin Hopping as a General and Versatile Optimization Framework for the Characterization of Biological Macromolecules. ACTA ACUST UNITED AC 2012. [DOI: 10.1155/2012/674832] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Since its introduction, the basin hopping (BH) framework has proven useful for hard nonlinear optimization problems with multiple variables and modalities. Applications span a wide range, from packing problems in geometry to characterization of molecular states in statistical physics. BH is seeing a reemergence in computational structural biology due to its ability to obtain a coarse-grained representation of
the protein energy surface in terms of local minima. In this paper, we show that the BH framework is general and versatile, allowing to address problems related to the characterization of protein structure, assembly, and motion due to its fundamental ability to sample minima in a high-dimensional variable space. We show how specific implementations of the main components in BH yield algorithmic realizations that attain state-of-the-art results in the context of ab initio protein structure prediction and rigid protein-protein docking. We also show that BH can map intermediate minima related with motions connecting diverse stable functionally relevant states in a protein molecule,
thus serving as a first step towards the characterization of transition trajectories connecting these states.
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13
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Olson B, Molloy K, Hendi SF, Shehu A. Guiding probabilistic search of the protein conformational space with structural profiles. J Bioinform Comput Biol 2012; 10:1242005. [PMID: 22809381 DOI: 10.1142/s021972001242005x] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The roughness of the protein energy surface poses a significant challenge to search algorithms that seek to obtain a structural characterization of the native state. Recent research seeks to bias search toward near-native conformations through one-dimensional structural profiles of the protein native state. Here we investigate the effectiveness of such profiles in a structure prediction setting for proteins of various sizes and folds. We pursue two directions. We first investigate the contribution of structural profiles in comparison to or in conjunction with physics-based energy functions in providing an effective energy bias. We conduct this investigation in the context of Metropolis Monte Carlo with fragment-based assembly. Second, we explore the effectiveness of structural profiles in providing projection coordinates through which to organize the conformational space. We do so in the context of a robotics-inspired search framework proposed in our lab that employs projections of the conformational space to guide search. Our findings indicate that structural profiles are most effective in obtaining physically realistic near-native conformations when employed in conjunction with physics-based energy functions. Our findings also show that these profiles are very effective when employed instead as projection coordinates to guide probabilistic search toward undersampled regions of the conformational space.
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Affiliation(s)
- Brian Olson
- Department of Computer Science, George Mason University, 4400 University Drive Fairfax, VA 22030, USA
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14
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Goldstein M, Fredj E, Gerber RB. A new hybrid algorithm for finding the lowest minima of potential surfaces: Approach and application to peptides. J Comput Chem 2011; 32:1785-800. [DOI: 10.1002/jcc.21755] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2010] [Revised: 11/13/2010] [Accepted: 12/18/2010] [Indexed: 11/11/2022]
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15
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Klenin K, Strodel B, Wales DJ, Wenzel W. Modelling proteins: conformational sampling and reconstruction of folding kinetics. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2010; 1814:977-1000. [PMID: 20851219 DOI: 10.1016/j.bbapap.2010.09.006] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2010] [Revised: 09/03/2010] [Accepted: 09/05/2010] [Indexed: 01/08/2023]
Abstract
In the last decades biomolecular simulation has made tremendous inroads to help elucidate biomolecular processes in-silico. Despite enormous advances in molecular dynamics techniques and the available computational power, many problems involve long time scales and large-scale molecular rearrangements that are still difficult to sample adequately. In this review we therefore summarise recent efforts to fundamentally improve this situation by decoupling the sampling of the energy landscape from the description of the kinetics of the process. Recent years have seen the emergence of many advanced sampling techniques, which permit efficient characterisation of the relevant family of molecular conformations by dispensing with the details of the short-term kinetics of the process. Because these methods generate thermodynamic information at best, they must be complemented by techniques to reconstruct the kinetics of the process using the ensemble of relevant conformations. Here we review recent advances for both types of methods and discuss their perspectives to permit efficient and accurate modelling of large-scale conformational changes in biomolecules. This article is part of a Special Issue entitled: Protein Dynamics: Experimental and Computational Approaches.
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Affiliation(s)
- Konstantin Klenin
- Steinbuch Centre for Computing, Karlsruhe Institute of Technology, P.O. Box 3640, D-76021 Karlsruhe, Germany
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Prentiss MC, Wales DJ, Wolynes PG. The energy landscape, folding pathways and the kinetics of a knotted protein. PLoS Comput Biol 2010; 6:e1000835. [PMID: 20617197 PMCID: PMC2895632 DOI: 10.1371/journal.pcbi.1000835] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2009] [Accepted: 05/25/2010] [Indexed: 11/18/2022] Open
Abstract
The folding pathway and rate coefficients of the folding of a knotted protein are calculated for a potential energy function with minimal energetic frustration. A kinetic transition network is constructed using the discrete path sampling approach, and the resulting potential energy surface is visualized by constructing disconnectivity graphs. Owing to topological constraints, the low-lying portion of the landscape consists of three distinct regions, corresponding to the native knotted state and to configurations where either the N or C terminus is not yet folded into the knot. The fastest folding pathways from denatured states exhibit early formation of the N terminus portion of the knot and a rate-determining step where the C terminus is incorporated. The low-lying minima with the N terminus knotted and the C terminus free therefore constitute an off-pathway intermediate for this model. The insertion of both the N and C termini into the knot occurs late in the folding process, creating large energy barriers that are the rate limiting steps in the folding process. When compared to other protein folding proteins of a similar length, this system folds over six orders of magnitude more slowly.
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Affiliation(s)
- Michael C Prentiss
- Department of Chemistry, Center for Theoretical Biological Physics, University of California San Diego, La Jolla, California, United States of America.
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Oklejas V, Zong C, Papoian GA, Wolynes PG. Protein structure prediction: do hydrogen bonding and water-mediated interactions suffice? Methods 2010; 52:84-90. [PMID: 20561998 DOI: 10.1016/j.ymeth.2010.05.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2010] [Revised: 05/17/2010] [Accepted: 05/20/2010] [Indexed: 11/16/2022] Open
Abstract
The many-body physics of hydrogen bond formation in alpha-helices of globular proteins was investigated using a simple physics-based model. Specifically, a context-sensitive hydrogen bond potential, which depends on residue identity and degree of solvent exposure, was used in the framework of the Associated Memory Hamiltonian codes developed previously but without using local-sequence structure matches ("memories"). Molecular dynamics simulations employing the energy function using the context-sensitive hydrogen bond potential alone (the "amnesiac" model) were used to generate low energy structures for three alpha-helical test proteins. The resulting structures were compared to both the X-ray crystal structures of the test proteins and the results obtained using the full Associated Memory Hamiltonian previously used. Results show that the amnesiac Hamiltonian was able to generate structures with reasonably high structural similarity (Q approximately 0.4) to that of the native protein but only with the use of predicted secondary structure information encoding local steric signals. Low energy structures obtained using the amnesiac Hamiltonian without any a priori secondary structure information had considerably less similarity to the native protein structures (Q approximately 0.3). Both sets of results utilizing the amnesiac Hamiltonian are poorer than when local-sequence structure matches are used.
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Affiliation(s)
- Vanessa Oklejas
- Department of Chemistry and Biochemistry, University of California, La Jolla, CA 92093-0371, United States.
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Zong W, Liu R, Sun F, Wang M, Zhang P, Liu Y, Tian Y. Cyclic voltammetry: a new strategy for the evaluation of oxidative damage to bovine insulin. Protein Sci 2010; 19:263-8. [PMID: 20027620 DOI: 10.1002/pro.313] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Research on protein oxidative damage may give insight into the nature of protein functions and pathological conditions. In this work, the oxidative damage of bovine insulin on Au electrode was investigated by cyclic voltammetry (CV). The experimental results show that there are two anodic peaks for the oxidative damage of bovine insulin, which arise from the oxidation of the exposed disulfide bond S--S(CYS7A,CYS7B), forming sulfenic acid RSOH (1.20 V, vs. SCE), sulfinic acid RSO(2)H and sulfonic acid RSO(3)H (1.35 V, vs. SCE). These in vitro findings not only demonstrate the applicability of CV in simulating/evaluating the oxidative damage of nonredox proteins but also find two promising candidates (two anodic peaks) for measuring insulin.
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Affiliation(s)
- Wansong Zong
- School of Environmental Science and Engineering, Shandong University, Jinan 250100, People's Republic of China
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19
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Protein Structure Prediction Using an Associated Memory Hamiltonian and All-Atom Molecular Dynamics Simulations. B KOREAN CHEM SOC 2008. [DOI: 10.5012/bkcs.2008.29.11.2172] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Prentiss MC, Wales DJ, Wolynes PG. Protein structure prediction using basin-hopping. J Chem Phys 2008; 128:225106. [PMID: 18554063 DOI: 10.1063/1.2929833] [Citation(s) in RCA: 30] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Associative memory Hamiltonian structure prediction potentials are not overly rugged, thereby suggesting their landscapes are like those of actual proteins. In the present contribution we show how basin-hopping global optimization can identify low-lying minima for the corresponding mildly frustrated energy landscapes. For small systems the basin-hopping algorithm succeeds in locating both lower minima and conformations closer to the experimental structure than does molecular dynamics with simulated annealing. For large systems the efficiency of basin-hopping decreases for our initial implementation, where the steps consist of random perturbations to the Cartesian coordinates. We implemented umbrella sampling using basin-hopping to further confirm when the global minima are reached. We have also improved the energy surface by employing bioinformatic techniques for reducing the roughness or variance of the energy surface. Finally, the basin-hopping calculations have guided improvements in the excluded volume of the Hamiltonian, producing better structures. These results suggest a novel and transferable optimization scheme for future energy function development.
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Affiliation(s)
- Michael C Prentiss
- Center for Theoretical Biological Physics, University of California at San Diego, La Jolla, California 92093, USA.
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21
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Shen T, Zong C, Portman JJ, Wolynes PG. Variationally determined free energy profiles for structural models of proteins: characteristic temperatures for folding and trapping. J Phys Chem B 2008; 112:6074-82. [PMID: 18376882 DOI: 10.1021/jp076280n] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Characterizing the phase diagram for proteins is important both for laboratory studies and for the development of structure prediction algorithms. Using a variational scheme, we calculated the generic features of the protein thermostability over a large range of temperatures for a set of more than 50 different proteins using a model based on native structure alone. Focusing on a specific system, protein G, we further examined, using a more realistic model that includes the nonnative interaction, the thermostability of both the native state and a collection of trap structures. By surveying the native structures for many proteins and by paying closer attention to the various trap structures of protein G, we obtained an overall understanding of the folding dynamics far from the conditions usually focused on; namely, those near the folding temperature alone. Two characteristic temperatures (shown to scale with folding temperature in general) signal drastic changes in the folding mechanism. The variational calculations suggest that most proteins would, indeed, fold in a barrierless manner below a critical temperature analogous to a spinodal in crystallization. For fixed interaction strengths, this temperature, however, seems to be generally very low, approximately 50% of the equilibrium folding temperature. Likewise, native proteins, in general, would unfold in a completely barrierless way at a temperature 25% above folding temperature according to these variational calculations. We also studied the distribution of free energy profiles for escape from a set of trap structures generated by simulations.
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Affiliation(s)
- Tongye Shen
- Department of Chemistry & Biochemistry, University of California at San Diego, and Center for Theoretical Biological Physics, La Jolla, California 92093-0371, USA
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Lätzer J, Shen T, Wolynes PG. Conformational switching upon phosphorylation: a predictive framework based on energy landscape principles. Biochemistry 2008; 47:2110-22. [PMID: 18198897 DOI: 10.1021/bi701350v] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We investigate how post-translational phosphorylation modifies the global conformation of a protein by changing its free energy landscape using two test proteins, cystatin and NtrC. We first examine the changes in a free energy landscape caused by phosphorylation using a model containing information about both structural forms. For cystatin the free energy cost is fairly large indicating a low probability of sampling the phosphorylated conformation in a perfectly funneled landscape. The predicted barrier for NtrC conformational transition is several times larger than the barrier for cystatin, indicating that the switch protein NtrC most probably follows a partial unfolding mechanism to move from one basin to the other. Principal component analysis and linear response theory show how the naturally occurring conformational changes in unmodified proteins are captured and stabilized by the change of interaction potential. We also develop a partially guided structure prediction Hamiltonian which is capable of predicting the global structure of a phosphorylated protein using only knowledge of the structure of the unphosphorylated protein or vice versa. This algorithm makes use of a generic transferable long-range residue contact potential along with details of structure short range in sequence. By comparing the results obtained with this guided transferable potential to those from the native-only, perfectly funneled Hamiltonians, we show that the transferable Hamiltonian correctly captures the nature of the global conformational changes induced by phosphorylation and can sample substantially correct structures for the modified protein with high probability.
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Affiliation(s)
- Joachim Lätzer
- Department of Chemistry & Biochemistry, University of California, San Diego, NSF Center for Theoretical Biological Physics, La Jolla, California 92093-0365, USA
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Chu JW, Ayton GS, Izvekov S, Voth GA. Emerging methods for multiscale simulation of biomolecular systems. Mol Phys 2007. [DOI: 10.1080/00268970701256696] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Liwo A, Khalili M, Czaplewski C, Kalinowski S, Ołdziej S, Wachucik K, Scheraga HA. Modification and optimization of the united-residue (UNRES) potential energy function for canonical simulations. I. Temperature dependence of the effective energy function and tests of the optimization method with single training proteins. J Phys Chem B 2007; 111:260-85. [PMID: 17201450 PMCID: PMC3236617 DOI: 10.1021/jp065380a] [Citation(s) in RCA: 159] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We report the modification and parametrization of the united-residue (UNRES) force field for energy-based protein structure prediction and protein folding simulations. We tested the approach on three training proteins separately: 1E0L (beta), 1GAB (alpha), and 1E0G (alpha + beta). Heretofore, the UNRES force field had been designed and parametrized to locate native-like structures of proteins as global minima of their effective potential energy surfaces, which largely neglected the conformational entropy because decoys composed of only lowest-energy conformations were used to optimize the force field. Recently, we developed a mesoscopic dynamics procedure for UNRES and applied it with success to simulate protein folding pathways. However, the force field turned out to be largely biased toward -helical structures in canonical simulations because the conformational entropy had been neglected in the parametrization. We applied the hierarchical optimization method, developed in our earlier work, to optimize the force field; in this method, the conformational space of a training protein is divided into levels, each corresponding to a certain degree of native-likeness. The levels are ordered according to increasing native-likeness; level 0 corresponds to structures with no native-like elements, and the highest level corresponds to the fully native-like structures. The aim of optimization is to achieve the order of the free energies of levels, decreasing as their native-likeness increases. The procedure is iterative, and decoys of the training protein(s) generated with the energy function parameters of the preceding iteration are used to optimize the force field in a current iteration. We applied the multiplexing replica-exchange molecular dynamics (MREMD) method, recently implemented in UNRES, to generate decoys; with this modification, conformational entropy is taken into account. Moreover, we optimized the free-energy gaps between levels at temperatures corresponding to a predominance of folded or unfolded structures, as well as to structures at the putative folding-transition temperature, changing the sign of the gaps at the transition temperature. This enabled us to obtain force fields characterized by a single peak in the heat capacity at the transition temperature. Furthermore, we introduced temperature dependence to the UNRES force field; this is consistent with the fact that it is a free-energy and not a potential energy function. beta
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Affiliation(s)
- Adam Liwo
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, N.Y., 14853-1301, U.S.A
| | - Mey Khalili
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, N.Y., 14853-1301, U.S.A
| | - Cezary Czaplewski
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, N.Y., 14853-1301, U.S.A
| | - Sebastian Kalinowski
- Faculty of Chemistry, University of Gdańsk, Sobieskiego 18, 80-952 Gdańsk, Poland
| | - Stanisław Ołdziej
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, N.Y., 14853-1301, U.S.A
| | - Katarzyna Wachucik
- Faculty of Chemistry, University of Gdańsk, Sobieskiego 18, 80-952 Gdańsk, Poland
| | - Harold A. Scheraga
- Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca, N.Y., 14853-1301, U.S.A
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