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Biswas G, Mukherjee D, Basu S. Combining Complementarity and Binding Energetics in the Assessment of Protein Interactions: EnCPdock-A Practical Manual. J Comput Biol 2024. [PMID: 38885081 DOI: 10.1089/cmb.2024.0554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024] Open
Abstract
The combined effect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong determinant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and nonlocal complementarity (EC) in PPIs using the complementarity plot. It further derives an AI-based ΔGbinding with a prediction accuracy comparable to the state of the art. This book chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.
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Affiliation(s)
- Gargi Biswas
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | | | - Sankar Basu
- Department of Microbiology, Asutosh College, University of Calcutta, Kolkata, India
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2
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Korkosh VS, Tikhonov DB. Analysis of residue-residue interactions in the structures of ASIC1a suggests possible gating mechanisms. EUROPEAN BIOPHYSICS JOURNAL : EBJ 2023; 52:111-119. [PMID: 36690863 DOI: 10.1007/s00249-023-01628-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 12/27/2022] [Accepted: 12/29/2022] [Indexed: 01/25/2023]
Abstract
The gating mechanism of acid-sensitive ion channels (ASICs) remains unclear, despite the availability of atomic-scale structures in various functional states. The collapse of the acidic pocket and structural changes in the low-palm region are assumed to trigger activation. For the acidic pocket, protonation of some residues can minimize repulsion in the collapsed conformation. The relationship between low-palm rearrangements and gating is unknown. In this work, we performed a Monte Carlo energy optimization of known ASIC1a structures and determined the residue-residue interactions in different functional states. For rearrangements in the acidic pocket, our results are consistent with previously proposed mechanisms, although significant complexity was revealed for the residue-residue interactions. The data support the proposal of a gating mechanism in the low-palm region, in which residues E80 and E417 share a proton to activate the channel.
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Affiliation(s)
- Vyacheslav S Korkosh
- Sechenov Institute of Evolutionary Physiology and Biochemistry, St. Petersburg, Russia
| | - Denis B Tikhonov
- Sechenov Institute of Evolutionary Physiology and Biochemistry, St. Petersburg, Russia.
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3
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Multistep orthophosphate release tunes actomyosin energy transduction. Nat Commun 2022; 13:4575. [PMID: 35931685 PMCID: PMC9356070 DOI: 10.1038/s41467-022-32110-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 07/13/2022] [Indexed: 11/29/2022] Open
Abstract
Muscle contraction and a range of critical cellular functions rely on force-producing interactions between myosin motors and actin filaments, powered by turnover of adenosine triphosphate (ATP). The relationship between release of the ATP hydrolysis product ortophosphate (Pi) from the myosin active site and the force-generating structural change, the power-stroke, remains enigmatic despite its central role in energy transduction. Here, we present a model with multistep Pi-release that unifies current conflicting views while also revealing additional complexities of potential functional importance. The model is based on our evidence from kinetics, molecular modelling and single molecule fluorescence studies of Pi binding outside the active site. It is also consistent with high-speed atomic force microscopy movies of single myosin II molecules without Pi at the active site, showing consecutive snapshots of pre- and post-power stroke conformations. In addition to revealing critical features of energy transduction by actomyosin, the results suggest enzymatic mechanisms of potentially general relevance. Release of the ATP hydrolysis product orthophosphate (Pi) from the myosin active site is central in force generation but is poorly understood. Here, Moretto et al. present evidence for multistep Pi-release reconciling apparently contradictory results.
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4
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Yue K. Modeling protein structure as a stable static equilibrium. Phys Rev E 2022; 106:024410. [PMID: 36110022 DOI: 10.1103/physreve.106.024410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 07/24/2022] [Indexed: 06/15/2023]
Abstract
We present evidence that the protein structure can be modeled as a stable static equilibrium, determined mainly by compressive supports in the nonpolar interior. That is, protein structures derive their structural strength through the same mechanical principles as do conventional structures like bridges and buildings. This is based on the observation that the experimentally elucidated structural determinants, the interior nonpolar side chains, are engaged in strong compressions in static terms. At the same time, major substructures in proteins, helices and h-bonded strands, because of their geometry, inherently leave gaps in the space they occupy. Under the compressive force, nonpolar side chains from one substructure can protrude into the gaps of another neighboring substructure and block its motion. As a result, interlocking of substructures can form, which builds up the nonpolar core assembly. The native structure then is the one with the structurally most stable core assembly. While intuitively appealing, this is a radical departure from the prevailing thinking that protein native structure is determined by global energy minimum, which is founded on thermodynamic hypothesis. Furthermore, to develop an effective model for analyzing protein structure with conventional tools, a proper mechanical representation must be established. By proving that the stability of the equilibrium in compressive interactions is conditioned on a form of mechanical energy minimum, we show that our notion of native structure can be equally consistent with the thermodynamic hypothesis. By mathematically treating the blocking action, an interaction, as a bar, a physical object, we succeed in representing and analyzing the core assembly as truss, a conventional structure. In this paper we define and expound step-by-step increasingly integrated interlocking patterns. We then analyze the core assemblies of a large set of diverse protein database structures. A native structure can be distinguished from decoys by comparing the composition and strength of their core assemblies. We show the results for two sets of native structures vs decoys.
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Affiliation(s)
- Kaizhi Yue
- Conformational Search Solutions, Palo Alto, California 94306, USA
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5
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Akhter N, Kabir KL, Chennupati G, Vangara R, Alexandrov BS, Djidjev H, Shehu A. Improved Protein Decoy Selection via Non-Negative Matrix Factorization. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1670-1682. [PMID: 33400654 DOI: 10.1109/tcbb.2020.3049088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
A central challenge in protein modeling research and protein structure prediction in particular is known as decoy selection. The problem refers to selecting biologically-active/native tertiary structures among a multitude of physically-realistic structures generated by template-free protein structure prediction methods. Research on decoy selection is active. Clustering-based methods are popular, but they fail to identify good/near-native decoys on datasets where near-native decoys are severely under-sampled by a protein structure prediction method. Reasonable progress is reported by methods that additionally take into account the internal energy of a structure and employ it to identify basins in the energy landscape organizing the multitude of decoys. These methods, however, incur significant time costs for extracting basins from the landscape. In this paper, we propose a novel decoy selection method based on non-negative matrix factorization. We demonstrate that our method outperforms energy landscape-based methods. In particular, the proposed method addresses both the time cost issue and the challenge of identifying good decoys in a sparse dataset, successfully recognizing near-native decoys for both easy and hard protein targets.
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6
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Computational Analysis of the Crystal and Cryo-EM Structures of P-Loop Channels with Drugs. Int J Mol Sci 2021; 22:ijms22158143. [PMID: 34360907 PMCID: PMC8348670 DOI: 10.3390/ijms22158143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 07/26/2021] [Accepted: 07/26/2021] [Indexed: 12/01/2022] Open
Abstract
The superfamily of P-loop channels includes various potassium channels, voltage-gated sodium and calcium channels, transient receptor potential channels, and ionotropic glutamate receptors. Despite huge structural and functional diversity of the channels, their pore-forming domain has a conserved folding. In the past two decades, scores of atomic-scale structures of P-loop channels with medically important drugs in the inner pore have been published. High structural diversity of these complexes complicates the comparative analysis of these structures. Here we 3D-aligned structures of drug-bound P-loop channels, compared their geometric characteristics, and analyzed the energetics of ligand-channel interactions. In the superimposed structures drugs occupy most of the sterically available space in the inner pore and subunit/repeat interfaces. Cationic groups of some drugs occupy vacant binding sites of permeant ions in the inner pore and selectivity-filter region. Various electroneutral drugs, lipids, and detergent molecules are seen in the interfaces between subunits/repeats. In many structures the drugs strongly interact with lipid and detergent molecules, but physiological relevance of such interactions is unclear. Some eukaryotic sodium and calcium channels have state-dependent or drug-induced π-bulges in the inner helices, which would be difficult to predict. The drug-induced π-bulges may represent a novel mechanism of gating modulation.
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7
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Alam FF, Shehu A. Unsupervised multi-instance learning for protein structure determination. J Bioinform Comput Biol 2021; 19:2140002. [PMID: 33568002 DOI: 10.1142/s0219720021400023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Many regions of the protein universe remain inaccessible by wet-laboratory or computational structure determination methods. A significant challenge in elucidating these dark regions in silico relates to the ability to discriminate relevant structure(s) among many structures/decoys computed for a protein of interest, a problem known as decoy selection. Clustering decoys based on geometric similarity remains popular. However, it is unclear how exactly to exploit the groups of decoys revealed via clustering to select individual structures for prediction. In this paper, we provide an intuitive formulation of the decoy selection problem as an instance of unsupervised multi-instance learning. We address the problem in three stages, first organizing given decoys of a protein molecule into bags, then identifying relevant bags, and finally drawing individual instances from these bags to offer as prediction. We propose both non-parametric and parametric algorithms for drawing individual instances. Our evaluation utilizes two datasets, one benchmark dataset of ensembles of decoys for a varied list of protein molecules, and a dataset of decoy ensembles for targets drawn from recent CASP competitions. A comparative analysis with state-of-the-art methods reveals that the proposed approach outperforms existing methods, thus warranting further investigation of multi-instance learning to advance our treatment of decoy selection.
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Affiliation(s)
- Fardina Fathmiul Alam
- Department of Computer Science, George Mason University, Fairfax, Virginia 22030, USA
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax, Virginia 22030, USA
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8
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Akhter N, Chennupati G, Djidjev H, Shehu A. Decoy selection for protein structure prediction via extreme gradient boosting and ranking. BMC Bioinformatics 2020; 21:189. [PMID: 33297949 PMCID: PMC7724862 DOI: 10.1186/s12859-020-3523-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 04/29/2020] [Indexed: 11/10/2022] Open
Abstract
Background Identifying one or more biologically-active/native decoys from millions of non-native decoys is one of the major challenges in computational structural biology. The extreme lack of balance in positive and negative samples (native and non-native decoys) in a decoy set makes the problem even more complicated. Consensus methods show varied success in handling the challenge of decoy selection despite some issues associated with clustering large decoy sets and decoy sets that do not show much structural similarity. Recent investigations into energy landscape-based decoy selection approaches show promises. However, lack of generalization over varied test cases remains a bottleneck for these methods. Results We propose a novel decoy selection method, ML-Select, a machine learning framework that exploits the energy landscape associated with the structure space probed through a template-free decoy generation. The proposed method outperforms both clustering and energy ranking-based methods, all the while consistently offering better performance on varied test-cases. Moreover, ML-Select shows promising results even for the decoy sets consisting of mostly low-quality decoys. Conclusions ML-Select is a useful method for decoy selection. This work suggests further research in finding more effective ways to adopt machine learning frameworks in achieving robust performance for decoy selection in template-free protein structure prediction.
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Affiliation(s)
- Nasrin Akhter
- Department of Computer Science, George Mason University, Fairfax, 22030, VA, USA
| | - Gopinath Chennupati
- Information Sciences (CCS-3) Group, Los Alamos National Laboratory, Bikini At al Rd., Los Alamos, 87545, USA.
| | - Hristo Djidjev
- Information Sciences (CCS-3) Group, Los Alamos National Laboratory, Bikini At al Rd., Los Alamos, 87545, USA
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax, 22030, VA, USA.,Department of Bioengineering, George Mason University, Fairfax, 22030, VA, USA.,School of Systems Biology, George Mason University, Manassas, 20110, VA, USA
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9
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Mulligan VK. The emerging role of computational design in peptide macrocycle drug discovery. Expert Opin Drug Discov 2020; 15:833-852. [PMID: 32345066 DOI: 10.1080/17460441.2020.1751117] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Drug discovery is a laborious process with rising cost per new drug. Peptide macrocycles are promising therapeutics, though conformational flexibility can reduce target affinity and specificity. Recent computational advancements address this problem by enabling rational design of rigidly folded peptide macrocycles. AREAS COVERED This review summarizes currently approved peptide macrocycle therapeutics and discusses advantages of mesoscale drugs over small molecules or protein therapeutics. It describes the history, rationale, and state of the art of computational tools, such as Rosetta, that allow the design of rigidly structured peptide macrocycles. The emerging pipeline for designing peptide macrocycle drugs is described, including current challenges in designing permeable molecules that can emulate the chameleonic behavior of natural macrocycles. Prospects for reducing computational cost and improving accuracy with emerging computational technologies are also discussed. EXPERT OPINION To embrace computational design of peptide macrocycle drugs, we must shift current attitudes regarding the role of computation in drug discovery, and move beyond Lipinski's rules. This technology has the potential to shift failures to earlier in silico stages of the drug discovery process, improving success rates in costly clinical trials. Given the available tools, now is the time for drug developers to incorporate peptide macrocycle design into drug discovery pipelines.
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Affiliation(s)
- Vikram K Mulligan
- Systems Biology, Center for Computational Biology, Flatiron Institute , New York, NY, USA
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10
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Akhter N, Chennupati G, Kabir KL, Djidjev H, Shehu A. Unsupervised and Supervised Learning over theEnergy Landscape for Protein Decoy Selection. Biomolecules 2019; 9:E607. [PMID: 31615116 PMCID: PMC6843838 DOI: 10.3390/biom9100607] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 10/03/2019] [Accepted: 10/04/2019] [Indexed: 11/17/2022] Open
Abstract
The energy landscape that organizes microstates of a molecular system and governs theunderlying molecular dynamics exposes the relationship between molecular form/structure, changesto form, and biological activity or function in the cell. However, several challenges stand in the wayof leveraging energy landscapes for relating structure and structural dynamics to function. Energylandscapes are high-dimensional, multi-modal, and often overly-rugged. Deep wells or basins inthem do not always correspond to stable structural states but are instead the result of inherentinaccuracies in semi-empirical molecular energy functions. Due to these challenges, energeticsis typically ignored in computational approaches addressing long-standing central questions incomputational biology, such as protein decoy selection. In the latter, the goal is to determine over apossibly large number of computationally-generated three-dimensional structures of a protein thosestructures that are biologically-active/native. In recent work, we have recast our attention on theprotein energy landscape and its role in helping us to advance decoy selection. Here, we summarizesome of our successes so far in this direction via unsupervised learning. More importantly, we furtheradvance the argument that the energy landscape holds valuable information to aid and advance thestate of protein decoy selection via novel machine learning methodologies that leverage supervisedlearning. Our focus in this article is on decoy selection for the purpose of a rigorous, quantitativeevaluation of how leveraging protein energy landscapes advances an important problem in proteinmodeling. However, the ideas and concepts presented here are generally useful to make discoveriesin studies aiming to relate molecular structure and structural dynamics to function.
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Affiliation(s)
- Nasrin Akhter
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA.
| | - Gopinath Chennupati
- Information Sciences (CCS-3) Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
| | - Kazi Lutful Kabir
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA.
| | - Hristo Djidjev
- Information Sciences (CCS-3) Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax, VA 22030, USA.
- Center for Adaptive Human-Machine Partnership, George Mason University, Fairfax, VA 22030, USA.
- Department of Bioengineering, George Mason University, Fairfax, VA 22030, USA.
- School of Systems Biology, George Mason University, Fairfax, VA 22030, USA.
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11
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Mirzaei S, Sidi T, Keasar C, Crivelli S. Purely Structural Protein Scoring Functions Using Support Vector Machine and Ensemble Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1515-1523. [PMID: 28113636 DOI: 10.1109/tcbb.2016.2602269] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
The function of a protein is determined by its structure, which creates a need for efficient methods of protein structure determination to advance scientific and medical research. Because current experimental structure determination methods carry a high price tag, computational predictions are highly desirable. Given a protein sequence, computational methods produce numerous 3D structures known as decoys. Selection of the best quality decoys is both challenging and essential as the end users can handle only a few ones. Therefore, scoring functions are central to decoy selection. They combine measurable features into a single number indicator of decoy quality. Unfortunately, current scoring functions do not consistently select the best decoys. Machine learning techniques offer great potential to improve decoy scoring. This paper presents two machine-learning based scoring functions to predict the quality of proteins structures, i.e., the similarity between the predicted structure and the experimental one without knowing the latter. We use different metrics to compare these scoring functions against three state-of-the-art scores. This is a first attempt at comparing different scoring functions using the same non-redundant dataset for training and testing and the same features. The results show that adding informative features may be more significant than the method used.
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12
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Wang X, Huang SY. Integrating Bonded and Nonbonded Potentials in the Knowledge-Based Scoring Function for Protein Structure Prediction. J Chem Inf Model 2019; 59:3080-3090. [DOI: 10.1021/acs.jcim.9b00057] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Affiliation(s)
- Xinxiang Wang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
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13
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Battisti A, Zamuner S, Sarti E, Laio A. Toward a unified scoring function for native state discrimination and drug-binding pocket recognition. Phys Chem Chem Phys 2019; 20:17148-17155. [PMID: 29900428 DOI: 10.1039/c7cp08170g] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Protein folding and receptor-ligand recognition are fundamental processes for any living organism. Although folding and ligand recognition are based on the same chemistry, the existing empirical scoring functions target just one problem: predicting the correct fold or the correct binding pose. We here introduce a statistical potential which considers moieties as fundamental units. The scoring function is able to deal with both folding and ligand pocket recognition problems with a performance comparable to the scoring functions specifically tailored for one of the two tasks. We foresee that the capability of the new scoring function to tackle both problems in a unified framework will be a key to deal with the induced fit phenomena, in which a target protein changes significantly its conformation upon binding. Moreover, the new scoring function might be useful in docking protocols towards intrinsically disordered proteins, whose flexibility cannot be handled with the available docking software.
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Affiliation(s)
- Anna Battisti
- International School for Advanced Studies (SISSA), Via Bonomea 265, I-34136 Trieste, Italy.
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14
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Khare S, Bhasin M, Sahoo A, Varadarajan R. Protein model discrimination attempts using mutational sensitivity, predicted secondary structure, and model quality information. Proteins 2019; 87:326-336. [PMID: 30615225 DOI: 10.1002/prot.25654] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 12/22/2018] [Accepted: 01/02/2019] [Indexed: 01/02/2023]
Abstract
Structure prediction methods often generate a large number of models for a target sequence. Even if the correct fold for the target sequence is sampled in this dataset, it is difficult to distinguish it from other decoy structures. An attempt to solve this problem using experimental mutational sensitivity data for the CcdB protein was described previously by exploiting the correlation of residue depth with mutational sensitivity (r ~ 0.6). We now show that such a correlation extends to four other proteins with localized active sites, and for which saturation mutagenesis datasets exist. We also examine whether incorporation of predicted secondary structure information and the DOPE model quality assessment score, in addition to mutational sensitivity, improves the accuracy of model discrimination using a decoy dataset of 163 targets from CASP. Although most CASP models would have been subjected to model quality assessment prior to submission, we find that the DOPE score makes a substantial contribution to the observed improvement. We therefore also applied the approach to CcdB and four other proteins for which reliable experimental mutational data exist and observe that inclusion of experimental mutational data results in a small qualitative improvement in model discrimination relative to that seen with just the DOPE score. This is largely because of our limited ability to quantitatively predict effects of point mutations on in vivo protein activity. Further improvements in the methodology are required to facilitate improved utilization of single mutant data.
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Affiliation(s)
- Shruti Khare
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Munmun Bhasin
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Anusmita Sahoo
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Raghavan Varadarajan
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.,Chemical Biology Unit, Jawaharlal Nehru Center for Advanced Scientific Research, Bangalore, India
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15
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An Energy Landscape Treatment of Decoy Selection in Template-Free Protein Structure Prediction. COMPUTATION 2018. [DOI: 10.3390/computation6020039] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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16
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Wang X, Zhang D, Huang SY. New Knowledge-Based Scoring Function with Inclusion of Backbone Conformational Entropies from Protein Structures. J Chem Inf Model 2018; 58:724-732. [DOI: 10.1021/acs.jcim.7b00601] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Affiliation(s)
- Xinxiang Wang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Di Zhang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
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17
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From Extraction of Local Structures of Protein Energy Landscapes to Improved Decoy Selection in Template-Free Protein Structure Prediction. Molecules 2018; 23:molecules23010216. [PMID: 29351266 PMCID: PMC6017496 DOI: 10.3390/molecules23010216] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 12/11/2017] [Indexed: 11/17/2022] Open
Abstract
Due to the essential role that the three-dimensional conformation of a protein plays in regulating interactions with molecular partners, wet and dry laboratories seek biologically-active conformations of a protein to decode its function. Computational approaches are gaining prominence due to the labor and cost demands of wet laboratory investigations. Template-free methods can now compute thousands of conformations known as decoys, but selecting native conformations from the generated decoys remains challenging. Repeatedly, research has shown that the protein energy functions whose minima are sought in the generation of decoys are unreliable indicators of nativeness. The prevalent approach ignores energy altogether and clusters decoys by conformational similarity. Complementary recent efforts design protein-specific scoring functions or train machine learning models on labeled decoys. In this paper, we show that an informative consideration of energy can be carried out under the energy landscape view. Specifically, we leverage local structures known as basins in the energy landscape probed by a template-free method. We propose and compare various strategies of basin-based decoy selection that we demonstrate are superior to clustering-based strategies. The presented results point to further directions of research for improving decoy selection, including the ability to properly consider the multiplicity of native conformations of proteins.
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18
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Rational design of proteins that exchange on functional timescales. Nat Chem Biol 2017; 13:1280-1285. [DOI: 10.1038/nchembio.2503] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 09/18/2017] [Indexed: 12/12/2022]
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19
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Wongrattanakamon P, Nimmanpipug P, Sirithunyalug B, Jiranusornkul S. Molecular modeling elucidates the cellular mechanism of synaptotagmin-SNARE inhibition: a novel plausible route to anti-wrinkle activity of botox-like cosmetic active molecules. Mol Cell Biochem 2017; 442:97-109. [DOI: 10.1007/s11010-017-3196-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Accepted: 10/05/2017] [Indexed: 11/30/2022]
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20
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Wongrattanakamon P, Nimmanpipug P, Sirithunyalug B, Chansakaow S, Jiranusornkul S. A significant mechanism of molecular recognition between bioflavonoids and P-glycoprotein leading to herb-drug interactions. Toxicol Mech Methods 2017; 28:1-11. [PMID: 28678657 DOI: 10.1080/15376516.2017.1351506] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Inhibition of P-glycoprotein (P-gp)'s function may conduct significant changes in the prescription drugs' pharmacokinetic profiles and escalate potential risks in taking place of drug/herb-drug interactions. Computational modeling was advanced to scrutinize some bioflavonoids which play roles in herb-drug interactions as P-gp inhibitors utilizing molecular docking and pharmacophore analyses. Twenty-five flavonoids were utilized as ligands for the modeling. The mouse P-gp (code: 4Q9H) was acquired from the PDB. The docking was operated utilizing AutoDock version 4.2.6 (Scripps Research Institute, La Jolla, CA) against the NBD2 of 4Q9H. The result illustrated the high correlation between the docking scores and observed activities of the flavonoids and the putative binding site of these flavonoids was proposed and compared with the site for ATP. To evaluate hotspot amino acid residues within the NBD2, Binding modes for the ligands were achieved using LigandScout to originate the NBD2-flavonoid pharmacophore models. The results asserted that these inhibitors competed with ATP for binding site in the NBD2 (as competitive inhibitors) including the hotspot residues which associated with electrostatic and van der Waals interactions with the flavonoids. In MD simulation of eight delegated complexes selected from the analyzed flavonoid subclasses, RMSD analysis of the trajectories indicated the residues were stable throughout the duration of simulations.
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Affiliation(s)
- Pathomwat Wongrattanakamon
- a Laboratory for Molecular Design and Simulation (LMDS), Department of Pharmaceutical Sciences, Faculty of Pharmacy , Chiang Mai University , Chiang Mai , Thailand
| | - Piyarat Nimmanpipug
- b Computational Simulation and Modelling Laboratory (CSML), Department of Chemistry, Faculty of Science , Chiang Mai University , Chiang Mai , Thailand
| | - Busaban Sirithunyalug
- c Department of Pharmaceutical Sciences, Faculty of Pharmacy , Chiang Mai University , Chiang Mai , Thailand
| | - Sunee Chansakaow
- c Department of Pharmaceutical Sciences, Faculty of Pharmacy , Chiang Mai University , Chiang Mai , Thailand
| | - Supat Jiranusornkul
- a Laboratory for Molecular Design and Simulation (LMDS), Department of Pharmaceutical Sciences, Faculty of Pharmacy , Chiang Mai University , Chiang Mai , Thailand
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Dutagaci B, Wittayanarakul K, Mori T, Feig M. Discrimination of Native-like States of Membrane Proteins with Implicit Membrane-based Scoring Functions. J Chem Theory Comput 2017; 13:3049-3059. [PMID: 28475346 DOI: 10.1021/acs.jctc.7b00254] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
A scoring protocol based on implicit membrane-based scoring functions and a new protocol for optimizing the positioning of proteins inside the membrane was evaluated for its capacity to discriminate native-like states from misfolded decoys. A decoy set previously established by the Baker lab (Proteins: Struct., Funct., Genet. 2006, 62, 1010-1025) was used along with a second set that was generated to cover higher resolution models. The Implicit Membrane Model 1 (IMM1), IMM1 model with CHARMM 36 parameters (IMM1-p36), generalized Born with simple switching (GBSW), and heterogeneous dielectric generalized Born versions 2 (HDGBv2) and 3 (HDGBv3) were tested along with the new HDGB van der Waals (HDGBvdW) model that adds implicit van der Waals contributions to the solvation free energy. For comparison, scores were also calculated with the distance-scaled finite ideal-gas reference (DFIRE) scoring function. Z-scores for native state discrimination, energy vs root-mean-square deviation (RMSD) correlations, and the ability to select the most native-like structures as top-scoring decoys were evaluated to assess the performance of the scoring functions. Ranking of the decoys in the Baker set that were relatively far from the native state was challenging and dominated largely by packing interactions that were captured best by DFIRE with less benefit of the implicit membrane-based models. Accounting for the membrane environment was much more important in the second decoy set where especially the HDGB-based scoring functions performed very well in ranking decoys and providing significant correlations between scores and RMSD, which shows promise for improving membrane protein structure prediction and refinement applications. The new membrane structure scoring protocol was implemented in the MEMScore web server ( http://feiglab.org/memscore ).
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Affiliation(s)
- Bercem Dutagaci
- Department of Biochemistry and Molecular Biology, Michigan State University , East Lansing, Michigan, United States
| | - Kitiyaporn Wittayanarakul
- Department of Natural Resource and Environmental Management, Faculty of Applied Science and Engineering, Khon Kaen University , Nong Khai Campus, Nong Khai 43000, Thailand
| | - Takaharu Mori
- Theoretical Molecular Science Laboratory, RIKEN , Wako-shi, Japan
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University , East Lansing, Michigan, United States
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Bochicchio A, Jordaan S, Losasso V, Chetty S, Perera RC, Ippoliti E, Barth S, Carloni P. Designing the Sniper: Improving Targeted Human Cytolytic Fusion Proteins for Anti-Cancer Therapy via Molecular Simulation. Biomedicines 2017; 5:E9. [PMID: 28536352 PMCID: PMC5423494 DOI: 10.3390/biomedicines5010009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2016] [Revised: 01/27/2017] [Accepted: 02/10/2017] [Indexed: 12/19/2022] Open
Abstract
Targeted human cytolytic fusion proteins (hCFPs) are humanized immunotoxins for selective treatment of different diseases including cancer. They are composed of a ligand specifically binding to target cells genetically linked to a human apoptosis-inducing enzyme. hCFPs target cancer cells via an antibody or derivative (scFv) specifically binding to e.g., tumor associated antigens (TAAs). After internalization and translocation of the enzyme from endocytosed endosomes, the human enzymes introduced into the cytosol are efficiently inducing apoptosis. Under in vivo conditions such enzymes are subject to tight regulation by native inhibitors in order to prevent inappropriate induction of cell death in healthy cells. Tumor cells are known to upregulate these inhibitors as a survival mechanism resulting in escape of malignant cells from elimination by immune effector cells. Cytosolic inhibitors of Granzyme B and Angiogenin (Serpin P9 and RNH1, respectively), reduce the efficacy of hCFPs with these enzymes as effector domains, requiring detrimentally high doses in order to saturate inhibitor binding and rescue cytolytic activity. Variants of Granzyme B and Angiogenin might feature reduced affinity for their respective inhibitors, while retaining or even enhancing their catalytic activity. A powerful tool to design hCFPs mutants with improved potency is given by in silico methods. These include molecular dynamics (MD) simulations and enhanced sampling methods (ESM). MD and ESM allow predicting the enzyme-protein inhibitor binding stability and the associated conformational changes, provided that structural information is available. Such "high-resolution" detailed description enables the elucidation of interaction domains and the identification of sites where particular point mutations may modify those interactions. This review discusses recent advances in the use of MD and ESM for hCFP development from the viewpoints of scientists involved in both fields.
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Affiliation(s)
- Anna Bochicchio
- German Research School for Simulation Sciences, Forschungszentrum Jülich, Jülich 52425, Germany.
- Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich, Jülich 52425, Germany.
- Department of Physics, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen 52062, Germany.
| | - Sandra Jordaan
- Department of Integrative Biomedical Sciences, Institute for Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town 7701, South Africa.
| | - Valeria Losasso
- Scientific Computing Department, Science and Technology Facilities Council, Daresbury Laboratory, Warrington WA4 4AD, UK.
| | - Shivan Chetty
- Department of Integrative Biomedical Sciences, Institute for Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town 7701, South Africa.
| | - Rodrigo Casasnovas Perera
- Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich, Jülich 52425, Germany.
| | - Emiliano Ippoliti
- Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich, Jülich 52425, Germany.
| | - Stefan Barth
- Department of Integrative Biomedical Sciences, Institute for Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town 7701, South Africa.
| | - Paolo Carloni
- Computational Biomedicine, Institute for Advanced Simulation IAS-5 and Institute of Neuroscience and Medicine INM-9, Forschungszentrum Jülich, Jülich 52425, Germany.
- Department of Physics, Rheinisch-Westfälische Technische Hochschule Aachen, Aachen 52062, Germany.
- JARA-HPC, Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH, Jülich 52425, Germany.
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Wongrattanakamon P, Lee VS, Nimmanpipug P, Sirithunyalug B, Chansakaow S, Jiranusornkul S. Insight into the molecular mechanism of P-glycoprotein mediated drug toxicity induced by bioflavonoids: an integrated computational approach. Toxicol Mech Methods 2017; 27:253-271. [PMID: 27996361 DOI: 10.1080/15376516.2016.1273428] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
In this work, molecular docking, pharmacophore modeling and molecular dynamics (MD) simulation were rendered for the mouse P-glycoprotein (P-gp) (code: 4Q9H) and bioflavonoids; amorphigenin, chrysin, epigallocatechin, formononetin and rotenone including a positive control; verapamil to identify protein-ligand interaction features including binding affinities, interaction characteristics, hot-spot amino acid residues and complex stabilities. These flavonoids occupied the same binding site with high binding affinities and shared the same key residues for their binding interactions and the binding region of the flavonoids was revealed that overlapped the ATP binding region with hydrophobic and hydrophilic interactions suggesting a competitive inhibition mechanism of the compounds. Root mean square deviations (RMSDs) analysis of MD trajectories of the protein-ligand complexes and NBD2 residues, and ligands pointed out these residues were stable throughout the duration of MD simulations. Thus, the applied preliminary structure-based molecular modeling approach of interactions between NBD2 and flavonoids may be gainful to realize the intimate inhibition mechanism of P-gp at NBD2 level and on the basis of the obtained data, it can be concluded that these bioflavonoids have the potential to cause herb-drug interactions or be used as lead molecules for the inhibition of P-gp (as anti-multidrug resistance agents) via the NBD2 blocking mechanism in future.
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Affiliation(s)
- Pathomwat Wongrattanakamon
- a Laboratory for Molecular Design and Simulation (LMDS), Department of Pharmaceutical Sciences, Faculty of Pharmacy , Chiang Mai University , Chiang Mai , Thailand
| | - Vannajan Sanghiran Lee
- b Department of Chemistry, Faculty of Science , University of Malaya , Kuala Lumpur , Malaysia
| | - Piyarat Nimmanpipug
- c Computational Simulation and Modelling Laboratory (CSML), Department of Chemistry, Faculty of Science , Chiang Mai University , Chiang Mai , Thailand
| | - Busaban Sirithunyalug
- d Department of Pharmaceutical Sciences, Faculty of Pharmacy , Chiang Mai University , Chiang Mai , Thailand
| | - Sunee Chansakaow
- d Department of Pharmaceutical Sciences, Faculty of Pharmacy , Chiang Mai University , Chiang Mai , Thailand
| | - Supat Jiranusornkul
- a Laboratory for Molecular Design and Simulation (LMDS), Department of Pharmaceutical Sciences, Faculty of Pharmacy , Chiang Mai University , Chiang Mai , Thailand
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Ojewole A, Lowegard A, Gainza P, Reeve SM, Georgiev I, Anderson AC, Donald BR. OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design. Methods Mol Biol 2017; 1529:291-306. [PMID: 27914058 PMCID: PMC5192561 DOI: 10.1007/978-1-4939-6637-0_15] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Drug resistance in protein targets is an increasingly common phenomenon that reduces the efficacy of both existing and new antibiotics. However, knowledge of future resistance mutations during pre-clinical phases of drug development would enable the design of novel antibiotics that are robust against not only known resistant mutants, but also against those that have not yet been clinically observed. Computational structure-based protein design (CSPD) is a transformative field that enables the prediction of protein sequences with desired biochemical properties such as binding affinity and specificity to a target. The use of CSPD to predict previously unseen resistance mutations represents one of the frontiers of computational protein design. In a recent study (Reeve et al. Proc Natl Acad Sci U S A 112(3):749-754, 2015), we used our OSPREY (Open Source Protein REdesign for You) suite of CSPD algorithms to prospectively predict resistance mutations that arise in the active site of the dihydrofolate reductase enzyme from methicillin-resistant Staphylococcus aureus (SaDHFR) in response to selective pressure from an experimental competitive inhibitor. We demonstrated that our top predicted candidates are indeed viable resistant mutants. Since that study, we have significantly enhanced the capabilities of OSPREY with not only improved modeling of backbone flexibility, but also efficient multi-state design, fast sparse approximations, partitioned continuous rotamers for more accurate energy bounds, and a computationally efficient representation of molecular-mechanics and quantum-mechanical energy functions. Here, using SaDHFR as an example, we present a protocol for resistance prediction using the latest version of OSPREY. Specifically, we show how to use a combination of positive and negative design to predict active site escape mutations that maintain the enzyme's catalytic function but selectively ablate binding of an inhibitor.
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Affiliation(s)
- Adegoke Ojewole
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, 27708, USA
| | - Anna Lowegard
- Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, 27708, USA
| | - Pablo Gainza
- Department of Computer Science, Duke University, Durham, NC, 27708, USA
| | - Stephanie M Reeve
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT, 06269, USA
| | - Ivelin Georgiev
- Department of Computer Science, Duke University, Durham, NC, 27708, USA
- Vaccine Research Center, National Institute of Allergy and Infectious Diseases, Bethesda, MD, 20892, USA
| | - Amy C Anderson
- Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT, 06269, USA
| | - Bruce R Donald
- Department of Computer Science, Duke University, Durham, NC, 27708, USA.
- Department of Biochemistry, Duke University, Durham, NC, 27708, USA.
- Department of Chemistry, Duke University, Durham, NC, 27708, USA.
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25
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Urquiza-Carvalho GA, Fragoso WD, Rocha GB. Assessment of semiempirical enthalpy of formation in solution as an effective energy function to discriminate native-like structures in protein decoy sets. J Comput Chem 2016; 37:1962-72. [DOI: 10.1002/jcc.24415] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Revised: 04/29/2016] [Accepted: 05/11/2016] [Indexed: 11/09/2022]
Affiliation(s)
- Gabriel Aires Urquiza-Carvalho
- Departamento De QúImica; CCEN, Universidade Federal Da ParáIba; Jõao, Pessoa/PB, Caixa Postal: 5093 CEP: 58051-970 Brazil
| | - Wallace Duarte Fragoso
- Departamento De QúImica; CCEN, Universidade Federal Da ParáIba; Jõao, Pessoa/PB, Caixa Postal: 5093 CEP: 58051-970 Brazil
| | - Gerd Bruno Rocha
- Departamento De QúImica; CCEN, Universidade Federal Da ParáIba; Jõao, Pessoa/PB, Caixa Postal: 5093 CEP: 58051-970 Brazil
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26
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Xu L, Zheng J, Margittai M, Nussinov R, Ma B. How Does Hyperphopsphorylation Promote Tau Aggregation and Modulate Filament Structure and Stability? ACS Chem Neurosci 2016; 7:565-75. [PMID: 26854860 PMCID: PMC7831686 DOI: 10.1021/acschemneuro.5b00294] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Tau proteins are hyperphosphorylated at common sites in their N- and C-terminal domains in at least three neurodegenerative diseases, Parkinson, dementia with Lewy bodies, and Alzheimer's, suggesting specific pathology but general mechanism. Full-length human tau filament comprises a rigid core and a two-layered fuzzy coat. Tau is categorized into two groups of isoforms, with either four repeats (R1-R4) or three repeats (R1, R3, and R4); their truncated constructs are respectively called K18 and K19. Using multiscale molecular dynamics simulations, we explored the conformational consequences of hyperhposphorylation on tau's repeats. Our lower conformational energy filament models suggest a rigid filament core with a radius of ∼30 to 40 Å and an outer layer with a thickness of ∼140 Å consisting of a double-layered polyelectrolyte. The presence of the phosphorylated terminal domains alters the relative stabilities in the K18 ensemble, thus shifting the populations of the full-length filaments. However, the structure with the straight repeats in the core region is still the most stable, similar to the truncated K18 peptide species without the N- and C-terminus. Our simulations across different scales of resolution consistently reveal that hyperphosphorylation of the two terminal domains decreases the attractive interactions among the N- and C-terminus and repeat domain. To date, the relationship on the conformational level between phosphorylation and aggregation has not been understood. Our results suggest that the exposure of the repeat domain upon hyperphosphorylation could enhance tau filament aggregation. Thus, we discovered that even though these neurodegenerative diseases vary and their associated tau filaments are phosphorylated to different extents, remarkably, the three pathologies appear to share a common tau aggregation mechanism.
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Affiliation(s)
- Liang Xu
- School of Chemistry, Dalian University of Technology, Dalian 116024, China
| | - Jie Zheng
- Department of Chemical & Biomolecular Engineering, The University of Akron, Akron, Ohio 44325, United States
| | - Martin Margittai
- Department of Chemistry and Biochemistry, University of Denver, Denver, Colorado 80208, United States
| | - Ruth Nussinov
- Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
- Basic Research Program, Leidos Biomedical Research, Inc., Cancer and Inflammation Program, Frederick National Laboratory for Cancer Research, NCI, Frederick, Maryland 21702, United States
| | - Buyong Ma
- Basic Research Program, Leidos Biomedical Research, Inc., Cancer and Inflammation Program, Frederick National Laboratory for Cancer Research, NCI, Frederick, Maryland 21702, United States
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27
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Kumar A, Campitelli P, Thorpe MF, Ozkan SB. Partial unfolding and refolding for structure refinement: A unified approach of geometric simulations and molecular dynamics. Proteins 2015; 83:2279-92. [PMID: 26476100 DOI: 10.1002/prot.24947] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2015] [Revised: 09/11/2015] [Accepted: 09/29/2015] [Indexed: 12/26/2022]
Abstract
The most successful protein structure prediction methods to date have been template-based modeling (TBM) or homology modeling, which predicts protein structure based on experimental structures. These high accuracy predictions sometimes retain structural errors due to incorrect templates or a lack of accurate templates in the case of low sequence similarity, making these structures inadequate in drug-design studies or molecular dynamics simulations. We have developed a new physics based approach to the protein refinement problem by mimicking the mechanism of chaperons that rehabilitate misfolded proteins. The template structure is unfolded by selectively (targeted) pulling on different portions of the protein using the geometric based technique FRODA, and then refolded using hierarchically restrained replica exchange molecular dynamics simulations (hr-REMD). FRODA unfolding is used to create a diverse set of topologies for surveying near native-like structures from a template and to provide a set of persistent contacts to be employed during re-folding. We have tested our approach on 13 previous CASP targets and observed that this method of folding an ensemble of partially unfolded structures, through the hierarchical addition of contact restraints (that is, first local and then nonlocal interactions), leads to a refolding of the structure along with refinement in most cases (12/13). Although this approach yields refined models through advancement in sampling, the task of blind selection of the best refined models still needs to be solved. Overall, the method can be useful for improved sampling for low resolution models where certain of the portions of the structure are incorrectly modeled.
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Affiliation(s)
- Avishek Kumar
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona
| | - Paul Campitelli
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona
| | - M F Thorpe
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona.,Rudolf Peierls Center for Theoretical Physics, University of Oxford, Oxford, OX1 3NP, United Kingdom
| | - S Banu Ozkan
- Department of Physics and Center for Biological Physics, Arizona State University, Tempe, Arizona
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Zhang J, Barz B, Zhang J, Xu D, Kosztin I. Selective refinement and selection of near-native models in protein structure prediction. Proteins 2015; 83:1823-35. [PMID: 26214389 PMCID: PMC4700123 DOI: 10.1002/prot.24866] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Revised: 06/22/2015] [Accepted: 07/21/2015] [Indexed: 11/07/2022]
Abstract
In recent years in silico protein structure prediction reached a level where fully automated servers can generate large pools of near-native structures. However, the identification and further refinement of the best structures from the pool of models remain problematic. To address these issues, we have developed (i) a target-specific selective refinement (SR) protocol; and (ii) molecular dynamics (MD) simulation based ranking (SMDR) method. In SR the all-atom refinement of structures is accomplished via the Rosetta Relax protocol, subject to specific constraints determined by the size and complexity of the target. The best-refined models are selected with SMDR by testing their relative stability against gradual heating through all-atom MD simulations. Through extensive testing we have found that Mufold-MD, our fully automated protein structure prediction server updated with the SR and SMDR modules consistently outperformed its previous versions.
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Affiliation(s)
- Jiong Zhang
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri 65211
| | - Bagdan Barz
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri 65211
| | - Jingfen Zhang
- Department of Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211
| | - Dong Xu
- Department of Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211
| | - Ioan Kosztin
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri 65211
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Naiyer A, Hassan MI, Islam A, Sundd M, Ahmad F. Structural characterization of MG and pre-MG states of proteins by MD simulations, NMR, and other techniques. J Biomol Struct Dyn 2015; 33:2267-84. [PMID: 25586676 DOI: 10.1080/07391102.2014.999354] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Almost all proteins fold via a number of partially structured intermediates such as molten globule (MG) and pre-molten globule states. Understanding the structure of these intermediates at atomic level is often a challenge, as these states are observed under extreme conditions of pH, temperature, and chemical denaturants. Furthermore, several other processes such as chemical modification, site-directed mutagenesis (or point mutation), and cleavage of covalent bond of natural proteins often lead to MG like partially unfolded conformation. However, the dynamic nature of proteins in these states makes them unsuitable for most structure determination at atomic level. Intermediate states studied so far have been characterized mostly by circular dichroism, fluorescence, viscosity, dynamic light scattering measurements, dye binding, infrared techniques, molecular dynamics simulations, etc. There is a limited amount of structural data available on these intermediate states by nuclear magnetic resonance (NMR) and hence there is a need to characterize these states at the molecular level. In this review, we present characterization of equilibrium intermediates by biophysical techniques with special reference to NMR.
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Affiliation(s)
- Abdullah Naiyer
- a Centre for Interdisciplinary Research in Basic Sciences , Jamia Millia Islamia , Jamia Nagar, New Delhi - 110025 , India
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30
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D. Lokits A, Koehler Leman J, E. Kitko K, S. Alexander N, E. Hamm H, Meiler J. A survey of conformational and energetic changes in G protein signaling. AIMS BIOPHYSICS 2015. [DOI: 10.3934/biophy.2015.4.630] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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Manavalan B, Lee J, Lee J. Random forest-based protein model quality assessment (RFMQA) using structural features and potential energy terms. PLoS One 2014; 9:e106542. [PMID: 25222008 PMCID: PMC4164442 DOI: 10.1371/journal.pone.0106542] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2014] [Accepted: 08/06/2014] [Indexed: 01/28/2023] Open
Abstract
Recently, predicting proteins three-dimensional (3D) structure from its sequence information has made a significant progress due to the advances in computational techniques and the growth of experimental structures. However, selecting good models from a structural model pool is an important and challenging task in protein structure prediction. In this study, we present the first application of random forest based model quality assessment (RFMQA) to rank protein models using its structural features and knowledge-based potential energy terms. The method predicts a relative score of a model by using its secondary structure, solvent accessibility and knowledge-based potential energy terms. We trained and tested the RFMQA method on CASP8 and CASP9 targets using 5-fold cross-validation. The correlation coefficient between the TM-score of the model selected by RFMQA (TMRF) and the best server model (TMbest) is 0.945. We benchmarked our method on recent CASP10 targets by using CASP8 and 9 server models as a training set. The correlation coefficient and average difference between TMRF and TMbest over 95 CASP10 targets are 0.984 and 0.0385, respectively. The test results show that our method works better in selecting top models when compared with other top performing methods. RFMQA is available for download from http://lee.kias.re.kr/RFMQA/RFMQA_eval.tar.gz.
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Affiliation(s)
- Balachandran Manavalan
- Center for In Silico Protein Science, School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea
| | - Juyong Lee
- Center for In Silico Protein Science, School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea
| | - Jooyoung Lee
- Center for In Silico Protein Science, School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea
- * E-mail:
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33
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Yadahalli S, Hemanth Giri Rao VV, Gosavi S. Modeling Non-Native Interactions in Designed Proteins. Isr J Chem 2014. [DOI: 10.1002/ijch.201400035] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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35
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Tian Y, Schwieters CD, Opella SJ, Marassi FM. A practical implicit solvent potential for NMR structure calculation. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2014; 243:54-64. [PMID: 24747742 PMCID: PMC4037354 DOI: 10.1016/j.jmr.2014.03.011] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2014] [Revised: 03/18/2014] [Accepted: 03/20/2014] [Indexed: 05/30/2023]
Abstract
The benefits of protein structure refinement in water are well documented. However, performing structure refinement with explicit atomic representation of the solvent molecules is computationally expensive and impractical for NMR-restrained structure calculations that start from completely extended polypeptide templates. Here we describe a new implicit solvation potential, EEFx (Effective Energy Function for XPLOR-NIH), for NMR-restrained structure calculations of proteins in XPLOR-NIH. The key components of EEFx are an energy term for solvation energy that works together with other nonbonded energy functions, and a dedicated force field for conformational and nonbonded protein interaction parameters. The initial results obtained with EEFx show that significant improvements in structural quality can be obtained. EEFx is computationally efficient and can be used both to fold and refine structures. Overall, EEFx improves the quality of protein conformation and nonbonded atomic interactions. Moreover, such benefits are accompanied by enhanced structural precision and enhanced structural accuracy, reflected in improved agreement with the cross-validated dipolar coupling data. Finally, implementation of EEFx calculations is straightforward and computationally efficient. Overall, EEFx provides a useful method for the practical calculation of experimental protein structures in a physically realistic environment.
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Affiliation(s)
- Ye Tian
- Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA; Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0307, USA
| | - Charles D Schwieters
- Division of Computational Bioscience, Building 12A, Center for Information Technology, National Institutes of Health, Bethesda, MD 20892-5624, USA
| | - Stanley J Opella
- Department of Chemistry and Biochemistry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0307, USA
| | - Francesca M Marassi
- Sanford-Burnham Medical Research Institute, 10901 North Torrey Pines Road, La Jolla, CA 92037, USA.
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Improvement in low-homology template-based modeling by employing a model evaluation method with focus on topology. PLoS One 2014; 9:e89935. [PMID: 24587135 PMCID: PMC3935967 DOI: 10.1371/journal.pone.0089935] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2013] [Accepted: 01/24/2014] [Indexed: 01/22/2023] Open
Abstract
Many template-based modeling (TBM) methods have been developed over the recent years that allow for protein structure prediction and for the study of structure-function relationships for proteins. One major problem all TBM algorithms face, however, is their unsatisfactory performance when proteins under consideration are low-homology. To improve the performance of TBM methods for such targets, a novel model evaluation method was developed here, and named MEFTop. Our novel method focuses on evaluating the topology by using two novel groups of features. These novel features included secondary structure element (SSE) contact information and 3-dimensional topology information. By combining MEFTop algorithm with FR-t5, a threading program developed by our group, we found that this modified TBM program, which was named FR-t5-M, exhibited significant improvements in predictive abilities for low-homology protein targets. We further showed that the MEFTop could be a generalized method to improve threading programs for low-homology protein targets. The softwares (FR-t5-M and MEFTop) are available to non-commercial users at our website: http://jianglab.ibp.ac.cn/lims/FRt5M/FRt5M.html.
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Tonddast-Navaei S, Stan G. Mechanism of transient binding and release of substrate protein during the allosteric cycle of the p97 nanomachine. J Am Chem Soc 2013; 135:14627-36. [PMID: 24007343 DOI: 10.1021/ja404051b] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
ATPases associated with various cellular activities (AAA+) form a superfamily of ring-shaped motor proteins that utilize cyclical allosteric motions to remodel or translocate substrate proteins (SP) through a narrow central pore. The p97 ATPase is a homohexameric, double-ring member of this superfamily that encloses a central channel with nonuniform width. A narrow compartment is present within the D1 ring and a larger cavity within the D2 ring, separated by a constriction formed by six His amino acids. We use molecular dynamics simulations to probe the interaction between p97 and an extended peptide substrate. Mechanical pulling of the substrate through the p97 pore reveals that smaller work is required for translocation from the D1 toward the D2 compartment than in the opposite direction. These distinct energetic requirements originate in structural aspects and chemical properties of the pore lining. Whereas van der Waals interactions are dominant within the D1 pore, interaction within the D2 pore are strongly electrostatic. Two charged amino acids in the D2 pore, Arg599 and Glu554, provide the largest contribution to the interaction and hinder translocation from the D2 pore. SP threading requires smaller forces when the SP is pulled from the D1 side due to lower barrier to rotation of the His side chains in the direction of the D2 pore. Based on additional simulations of SP binding to two allosteric conformations of p97, we propose that transient binding and release of SP from the pore involves a lever mechanism. Binding to the open pore conformation of p97 occurs primarily at the Arg599 side chain, where the SP backbone is engaged through electrostatic interactions and hydrogen bonds. ATP-driven conformational transitions within the D2 ring alter the chemical environment inside the p97 cavity in the closed pore state. In this state, Glu554 side chains project further into the pore and interacts strongly through van der Waals contacts with the SP backbone. Based on mutations at the two sites in each of the states we identify a specific requirement of these side chains for interaction with the substrate.
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Affiliation(s)
- Sam Tonddast-Navaei
- Department of Chemistry, University of Cincinnati , Cincinnati, Ohio 45221, United States
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He Z, Alazmi M, Zhang J, Xu D. Protein structural model selection by combining consensus and single scoring methods. PLoS One 2013; 8:e74006. [PMID: 24023923 PMCID: PMC3759460 DOI: 10.1371/journal.pone.0074006] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2013] [Accepted: 07/26/2013] [Indexed: 01/28/2023] Open
Abstract
Quality assessment (QA) for predicted protein structural models is an important and challenging research problem in protein structure prediction. Consensus Global Distance Test (CGDT) methods assess each decoy (predicted structural model) based on its structural similarity to all others in a decoy set and has been proved to work well when good decoys are in a majority cluster. Scoring functions evaluate each single decoy based on its structural properties. Both methods have their merits and limitations. In this paper, we present a novel method called PWCom, which consists of two neural networks sequentially to combine CGDT and single model scoring methods such as RW, DDFire and OPUS-Ca. Specifically, for every pair of decoys, the difference of the corresponding feature vectors is input to the first neural network which enables one to predict whether the decoy-pair are significantly different in terms of their GDT scores to the native. If yes, the second neural network is used to decide which one of the two is closer to the native structure. The quality score for each decoy in the pool is based on the number of winning times during the pairwise comparisons. Test results on three benchmark datasets from different model generation methods showed that PWCom significantly improves over consensus GDT and single scoring methods. The QA server (MUFOLD-Server) applying this method in CASP 10 QA category was ranked the second place in terms of Pearson and Spearman correlation performance.
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Affiliation(s)
- Zhiquan He
- Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Missouri, United States of America
| | - Meshari Alazmi
- Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Missouri, United States of America
| | - Jingfen Zhang
- Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Missouri, United States of America
| | - Dong Xu
- Department of Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Missouri, United States of America
- * E-mail:
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40
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Bruhova I, Gregg T, Auerbach A. Energy for wild-type acetylcholine receptor channel gating from different choline derivatives. Biophys J 2013; 104:565-74. [PMID: 23442907 DOI: 10.1016/j.bpj.2012.11.3833] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2012] [Revised: 11/25/2012] [Accepted: 11/27/2012] [Indexed: 02/01/2023] Open
Abstract
Agonists, including the neurotransmitter acetylcholine (ACh), bind at two sites in the neuromuscular ACh receptor channel (AChR) to promote a reversible, global change in protein conformation that regulates the flow of ions across the muscle cell membrane. In the synaptic cleft, ACh is hydrolyzed to acetate and choline. Replacement of the transmitter's ester acetyl group with a hydroxyl (ACh→choline) results in a + 1.8 kcal/mol reduction in the energy for gating generated by each agonist molecule from a low- to high-affinity change of the transmitter binding site (ΔG(B)). To understand the distinct actions of structurally related agonist molecules, we measured ΔG(B) for 10 related choline derivatives. Replacing the hydroxyl group of choline with different substituents, such as hydrogen, chloride, methyl, or amine, increased the energy for gating (i.e., it made ΔG(B) more negative relative to choline). Extending the ethyl hydroxide tail of choline to propyl and butyl hydroxide also increased this energy. Our findings reveal the amount of energy that is available for the AChR conformational change provided by different, structurally related agonists. We speculate that a hydrogen bond between the choline hydroxyl and the backbone carbonyl of αW149 positions this agonist's quaternary ammonium group so as to reduce the cation-π interaction between this moiety and the aromatic groups at the binding site.
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Affiliation(s)
- Iva Bruhova
- Department of Physiology and Biophysics, SUNY at Buffalo, Buffalo, New York, USA
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41
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The Suitability of Water Molecule Models for Constructing the Equivalent Potential of Water for the Electronic Structures of Proteins. J SOLUTION CHEM 2013. [DOI: 10.1007/s10953-013-9979-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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42
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On the transferability of fractional contributions to the hydration free energy of amino acids. Theor Chem Acc 2013. [DOI: 10.1007/s00214-013-1343-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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43
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Gainza P, Roberts KE, Georgiev I, Lilien RH, Keedy DA, Chen CY, Reza F, Anderson AC, Richardson DC, Richardson JS, Donald BR. OSPREY: protein design with ensembles, flexibility, and provable algorithms. Methods Enzymol 2013; 523:87-107. [PMID: 23422427 DOI: 10.1016/b978-0-12-394292-0.00005-9] [Citation(s) in RCA: 96] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
UNLABELLED We have developed a suite of protein redesign algorithms that improves realistic in silico modeling of proteins. These algorithms are based on three characteristics that make them unique: (1) improved flexibility of the protein backbone, protein side-chains, and ligand to accurately capture the conformational changes that are induced by mutations to the protein sequence; (2) modeling of proteins and ligands as ensembles of low-energy structures to better approximate binding affinity; and (3) a globally optimal protein design search, guaranteeing that the computational predictions are optimal with respect to the input model. Here, we illustrate the importance of these three characteristics. We then describe OSPREY, a protein redesign suite that implements our protein design algorithms. OSPREY has been used prospectively, with experimental validation, in several biomedically relevant settings. We show in detail how OSPREY has been used to predict resistance mutations and explain why improved flexibility, ensembles, and provability are essential for this application. AVAILABILITY OSPREY is free and open source under a Lesser GPL license. The latest version is OSPREY 2.0. The program, user manual, and source code are available at www.cs.duke.edu/donaldlab/software.php. CONTACT osprey@cs.duke.edu.
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Affiliation(s)
- Pablo Gainza
- Department of Computer Science, Duke University, Durham, North Carolina, USA
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44
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Ab initio simulation of the effect of the potential of water on the electronic structure of arginine. J Mol Model 2012; 18:859-70. [DOI: 10.1007/s00894-011-1125-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2011] [Accepted: 05/11/2011] [Indexed: 10/18/2022]
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45
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Pritchard-Bell A, Shell MS. Smoothing protein energy landscapes by integrating folding models with structure prediction. Biophys J 2011; 101:2251-9. [PMID: 22067165 DOI: 10.1016/j.bpj.2011.09.036] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2011] [Revised: 09/13/2011] [Accepted: 09/19/2011] [Indexed: 10/15/2022] Open
Abstract
Decades of work has investigated the energy landscapes of simple protein models, but what do the landscapes of real, large, atomically detailed proteins look like? We explore an approach to this problem that systematically extracts simple funnel models of actual proteins using ensembles of structure predictions and physics-based atomic force fields and sampling. Central to our effort are calculations of a quantity called the relative entropy, which quantifies the extent to which a given set of structure decoys and a putative native structure can be projected onto a theoretical funnel description. We examine 86 structure prediction targets and one coupled folding-binding system, and find that in a majority of cases the relative entropy robustly signals which structures are nearest to native (i.e., which appear to lie closest to a funnel bottom). Importantly, the landscape model improves substantially upon purely energetic measures in scoring decoys. Our results suggest that physics-based models-including both folding theories and all-atom force fields-may be successfully integrated with structure prediction efforts. Conversely, detailed predictions of structures and the relative entropy approach enable one to extract coarse topographic features of protein landscapes that may enhance the development and application of simpler folding models.
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Affiliation(s)
- Ari Pritchard-Bell
- Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, California, USA
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46
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Zhang J, Wang Q, Vantasin K, Zhang J, He Z, Kosztin I, Shang Y, Xu D. A multilayer evaluation approach for protein structure prediction and model quality assessment. Proteins 2011; 79 Suppl 10:172-84. [PMID: 21997706 DOI: 10.1002/prot.23184] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2011] [Revised: 08/26/2011] [Accepted: 09/05/2011] [Indexed: 01/03/2023]
Abstract
Protein tertiary structures are essential for studying functions of proteins at molecular level. An indispensable approach for protein structure solution is computational prediction. Most protein structure prediction methods generate candidate models first and select the best candidates by model quality assessment (QA). In many cases, good models can be produced, but the QA tools fail to select the best ones from the candidate model pool. Because of incomplete understanding of protein folding, each QA method only reflects partial facets of a structure model and thus has limited discerning power with no one consistently outperforming others. In this article, we developed a set of new QA methods, including two QA methods for evaluating target/template alignments, a molecular dynamics (MD)-based QA method, and three consensus QA methods with selected references to reveal new facets of protein structures complementary to the existing methods. Moreover, the underlying relationship among different QA methods were analyzed and then integrated into a multilayer evaluation approach to guide the model generation and model selection in prediction. All methods are integrated and implemented into an innovative and improved prediction system hereafter referred to as MUFOLD. In CASP8 and CASP9, MUFOLD has demonstrated the proof of the principles in terms of both QA discerning power and structure prediction accuracy.
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Affiliation(s)
- Jingfen Zhang
- Department of Computer Science, University of Missouri, Columbia, MO, USA
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47
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Shi X, Zhang J, He Z, Shang Y, Xu D. A sampling-based method for ranking protein structural models by integrating multiple scores and features. Curr Protein Pept Sci 2011; 12:540-8. [PMID: 21787308 PMCID: PMC4368063 DOI: 10.2174/138920311796957658] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2011] [Revised: 04/01/2011] [Accepted: 05/04/2011] [Indexed: 11/22/2022]
Abstract
One of the major challenges in protein tertiary structure prediction is structure quality assessment. In many cases, protein structure prediction tools generate good structural models, but fail to select the best models from a huge number of candidates as the final output. In this study, we developed a sampling-based machine-learning method to rank protein structural models by integrating multiple scores and features. First, features such as predicted secondary structure, solvent accessibility and residue-residue contact information are integrated by two Radial Basis Function (RBF) models trained from different datasets. Then, the two RBF scores and five selected scoring functions developed by others, i.e., Opus-CA, Opus-PSP, DFIRE, RAPDF, and Cheng Score are synthesized by a sampling method. At last, another integrated RBF model ranks the structural models according to the features of sampling distribution. We tested the proposed method by using two different datasets, including the CASP server prediction models of all CASP8 targets and a set of models generated by our in-house software MUFOLD. The test result shows that our method outperforms any individual scoring function on both best model selection, and overall correlation between the predicted ranking and the actual ranking of structural quality.
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Affiliation(s)
- Xiaohu Shi
- College of Computer Science and Technology, Jilin University, Jilin, Changchun 130012, China
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48
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Huang SY, Zou X. Statistical mechanics-based method to extract atomic distance-dependent potentials from protein structures. Proteins 2011; 79:2648-61. [PMID: 21732421 PMCID: PMC11108592 DOI: 10.1002/prot.23086] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2011] [Revised: 04/21/2011] [Accepted: 05/09/2011] [Indexed: 12/25/2022]
Abstract
In this study, we have developed a statistical mechanics-based iterative method to extract statistical atomic interaction potentials from known, nonredundant protein structures. Our method circumvents the long-standing reference state problem in deriving traditional knowledge-based scoring functions, by using rapid iterations through a physical, global convergence function. The rapid convergence of this physics-based method, unlike other parameter optimization methods, warrants the feasibility of deriving distance-dependent, all-atom statistical potentials to keep the scoring accuracy. The derived potentials, referred to as ITScore/Pro, have been validated using three diverse benchmarks: the high-resolution decoy set, the AMBER benchmark decoy set, and the CASP8 decoy set. Significant improvement in performance has been achieved. Finally, comparisons between the potentials of our model and potentials of a knowledge-based scoring function with a randomized reference state have revealed the reason for the better performance of our scoring function, which could provide useful insight into the development of other physical scoring functions. The potentials developed in this study are generally applicable for structural selection in protein structure prediction.
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Affiliation(s)
- Sheng-You Huang
- Department of Physics and Astronomy, Department of Biochemistry, Dalton Cardiovascular Research Center, and Informatics Institute, University of Missouri, Columbia, MO 65211
| | - Xiaoqin Zou
- Department of Physics and Astronomy, Department of Biochemistry, Dalton Cardiovascular Research Center, and Informatics Institute, University of Missouri, Columbia, MO 65211
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49
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Raghav PK, Verma YK, Gangenahalli GU. Molecular dynamics simulations of the Bcl-2 protein to predict the structure of its unordered flexible loop domain. J Mol Model 2011; 18:1885-906. [DOI: 10.1007/s00894-011-1201-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2010] [Accepted: 07/24/2011] [Indexed: 12/20/2022]
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50
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Notomista E, Scognamiglio R, Troncone L, Donadio G, Pezzella A, Di Donato A, Izzo V. Tuning the specificity of the recombinant multicomponent toluene o-xylene monooxygenase from Pseudomonas sp. strain OX1 for the biosynthesis of tyrosol from 2-phenylethanol. Appl Environ Microbiol 2011; 77:5428-37. [PMID: 21666013 PMCID: PMC3147462 DOI: 10.1128/aem.00461-11] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2011] [Accepted: 06/01/2011] [Indexed: 12/27/2022] Open
Abstract
Biocatalysis is today a standard technology for the industrial production of several chemicals, and the number of biotransformation processes running on a commercial scale is constantly increasing. Among biocatalysts, bacterial multicomponent monooxygenases (BMMs), a diverse group of nonheme diiron enzymes that activate dioxygen, are of primary interest due to their ability to catalyze a variety of complex oxidations, including reactions of mono- and dihydroxylation of phenolic compounds. In recent years, both directed evolution and rational design have been successfully used to identify the molecular determinants responsible for BMM regioselectivity and to improve their activity toward natural and nonnatural substrates. Toluene o-xylene monooxygenase (ToMO) is a BMM isolated from Pseudomonas sp. strain OX1 which hydroxylates a wide spectrum of aromatic compounds. In this work we investigate the use of recombinant ToMO for the biosynthesis in recombinant cells of Escherichia coli strain JM109 of 4-hydroxyphenylethanol (tyrosol), an antioxidant present in olive oil, from 2-phenylethanol, a cheap and commercially available substrate. We initially found that wild-type ToMO is unable to convert 2-phenylethanol to tyrosol. This was explained by using a computational model which analyzed the interactions between ToMO active-site residues and the substrate. We found that residue F176 is the major steric hindrance for the correct positioning of the reaction intermediate leading to tyrosol production into the active site of the enzyme. Several mutants were designed and prepared, and we found that the combination of different mutations at position F176 with mutation E103G allows ToMO to convert up to 50% of 2-phenylethanol into tyrosol in 2 h.
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Affiliation(s)
- Eugenio Notomista
- Dipartimento di Biologia Strutturale e Funzionale, Università di Napoli Federico II, Via Cinthia, I-80126 Naples, and CEINGE-Biotecnologie Avanzate s.c.ar.l., Naples, Italy
| | - Roberta Scognamiglio
- Dipartimento di Biologia Strutturale e Funzionale, Università di Napoli Federico II, Via Cinthia, I-80126 Naples, and CEINGE-Biotecnologie Avanzate s.c.ar.l., Naples, Italy
| | - Luca Troncone
- Dipartimento di Biologia Strutturale e Funzionale, Università di Napoli Federico II, Via Cinthia, I-80126 Naples, and CEINGE-Biotecnologie Avanzate s.c.ar.l., Naples, Italy
| | - Giuliana Donadio
- Dipartimento di Biologia Strutturale e Funzionale, Università di Napoli Federico II, Via Cinthia, I-80126 Naples, and CEINGE-Biotecnologie Avanzate s.c.ar.l., Naples, Italy
| | - Alessandro Pezzella
- Dipartimento di Chimica Organica e Biochimica, Università di Napoli Federico II, Via Cinthia, 80126 Naples, Italy
| | - Alberto Di Donato
- Dipartimento di Biologia Strutturale e Funzionale, Università di Napoli Federico II, Via Cinthia, I-80126 Naples, and CEINGE-Biotecnologie Avanzate s.c.ar.l., Naples, Italy
| | - Viviana Izzo
- Dipartimento di Biologia Strutturale e Funzionale, Università di Napoli Federico II, Via Cinthia, I-80126 Naples, and CEINGE-Biotecnologie Avanzate s.c.ar.l., Naples, Italy
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