1
|
Zhang F, Li Z, Zhao K, Zhao P, Zhang G. Prediction of Inter-Residue Multiple Distances and Exploration of Protein Multiple Conformations by Deep Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:1731-1739. [PMID: 38857126 DOI: 10.1109/tcbb.2024.3411825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2024]
Abstract
AlphaFold2 has achieved a major breakthrough in end-to-end prediction for static protein structures. However, protein conformational change is considered to be a key factor in protein biological function. Inter-residue multiple distances prediction is of great significance for research on protein multiple conformations exploration. In this study, we proposed an inter-residue multiple distances prediction method, DeepMDisPre, based on an improved network which integrates triangle update, axial attention and ResNet to predict multiple distances of residue pairs. We built a dataset which contains proteins with a single structure and proteins with multiple conformations to train the network. We tested DeepMDisPre on 114 proteins with multiple conformations. The results show that the inter-residue distance distribution predicted by DeepMDisPre tends to have multiple peaks for flexible residue pairs than for rigid residue pairs. On two cases of proteins with multiple conformations, we modeled the multiple conformations relatively accurately by using the predicted inter-residue multiple distances. In addition, we also tested the performance of DeepMDisPre on 279 proteins with a single structure. Experimental results demonstrate that the average contact accuracy of DeepMDisPre is higher than that of the comparative method. In terms of static protein modeling, the average TM-score of the 3D models built by DeepMDisPre is also improved compared with the comparative method.
Collapse
|
2
|
Chandrasekhar G, Pengyong H, Pravallika G, Hailei L, Caixia X, Rajasekaran R. Defensin-based therapeutic peptide design in attenuating V30M TTR-induced Familial Amyloid Polyneuropathy. 3 Biotech 2023; 13:227. [PMID: 37304406 PMCID: PMC10250285 DOI: 10.1007/s13205-023-03646-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 05/24/2023] [Indexed: 06/13/2023] Open
Abstract
In the present study, we aimed to formulate an effective therapeutic candidate against V30M mutant transthyretin (TTR) protein to hinder its pathogenic misfolding. Nicotiana alata Defensin 1 (NaD1) Antimicrobial Peptide (AMP) was availed due to its tendency to aggregate, which may compete for aggregation-prone regions of pathogenic TTR protein. Based on NaD1's potential to bind to V30M TTR, we proposed NaD1-derived tetra peptides: CKTE and SKIL to be initial therapeutic candidates. Based on their association with mutant TTR protein, CKTE tetra peptide showed considerable interaction and curative potential as compared to SKIL tetra peptide. Further analyses from discrete molecular dynamics simulation corroborate CKTE tetra peptide's effectiveness as a 'beta-sheet breaker' against V30M TTR. Various post-simulation trajectory analyses suggested that CKTE tetra peptide alters the structural dynamics of pathogenic V30M TTR protein, thereby potentially attenuating its beta-sheets and impeding its aggregation. Normal mode analysis simulation corroborated that V30M TTR conformation is altered upon its interaction with CKTE peptide. Moreover, simulated thermal denaturation findings suggested that CKTE-V30M TTR complex is more susceptible to simulated denaturation, relative to pathogenic V30M TTR; further substantiating CKTE peptide's potential to alter V30M TTR's pathogenic conformation. Moreover, the residual frustration analysis augmented CKTE tetra peptide's proclivity in reorienting the conformation of V30M TTR. Therefore, we predicted that the tetra peptide, CKTE could be a promising therapeutic candidate in mitigating the amyloidogenic detrimental effects of V30M TTR-mediated familial amyloid polyneuropathy (FAP). Supplementary Information The online version contains supplementary material available at 10.1007/s13205-023-03646-4.
Collapse
Affiliation(s)
- G. Chandrasekhar
- Quantitative Biology Lab, Department of Integrative Biology, School of Bio Sciences and Technology, Vellore Institute of Technology (VIT, Deemed to Be University), Vellore, Tamil Nadu 632014 India
| | - H. Pengyong
- Changzhi Medical College, Changzhi, 046000 China
| | - G. Pravallika
- Quantitative Biology Lab, Department of Integrative Biology, School of Bio Sciences and Technology, Vellore Institute of Technology (VIT, Deemed to Be University), Vellore, Tamil Nadu 632014 India
| | - L. Hailei
- Changzhi Medical College, Changzhi, 046000 China
| | - X. Caixia
- Changzhi Medical College, Changzhi, 046000 China
| | - R. Rajasekaran
- Quantitative Biology Lab, Department of Integrative Biology, School of Bio Sciences and Technology, Vellore Institute of Technology (VIT, Deemed to Be University), Vellore, Tamil Nadu 632014 India
| |
Collapse
|
3
|
Wang W, Su X, Liu D, Zhang H, Wang X, Zhou Y. Predicting DNA-binding protein and coronavirus protein flexibility using protein dihedral angle and sequence feature. Proteins 2023; 91:497-507. [PMID: 36321218 PMCID: PMC9877568 DOI: 10.1002/prot.26443] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 09/07/2022] [Accepted: 10/20/2022] [Indexed: 11/07/2022]
Abstract
The flexibility of protein structure is related to various biological processes, such as molecular recognition, allosteric regulation, catalytic activity, and protein stability. At the molecular level, protein dynamics and flexibility are important factors to understand protein function. DNA-binding proteins and Coronavirus proteins are of great concern and relatively unique proteins. However, exploring the flexibility of DNA-binding proteins and Coronavirus proteins through experiments or calculations is a difficult process. Since protein dihedral rotational motion can be used to predict protein structural changes, it provides key information about protein local conformation. Therefore, this paper introduces a method to improve the accuracy of protein flexibility prediction, DihProFle (Prediction of DNA-binding proteins and Coronavirus proteins flexibility introduces the calculated dihedral Angle information). Based on protein dihedral Angle information, protein evolution information, and amino acid physical and chemical properties, DihProFle realizes the prediction of protein flexibility in two cases on DNA-binding proteins and Coronavirus proteins, and assigns flexibility class to each protein sequence position. In this study, compared with the flexible prediction using sequence evolution information, and physicochemical properties of amino acids, the flexible prediction accuracy based on protein dihedral Angle information, sequence evolution information and physicochemical properties of amino acids improved by 2.2% and 3.1% in the nonstrict and strict conditions, respectively. And DihProFle achieves better performance than previous methods for protein flexibility analysis. In addition, we further analyzed the correlation of amino acid properties and protein dihedral angles with residues flexibility. The results show that the charged hydrophilic residues have higher proportion in the flexible region, and the rigid region tends to be in the angular range of the protein dihedral angle (such as the ψ angle of amino acid residues is more flexible than rigid in the range of 91°-120°). Therefore, the results indicate that hydrophilic residues and protein dihedral angle information play an important role in protein flexibility.
Collapse
Affiliation(s)
- Wei Wang
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, China.,Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province, Xinxiang, China
| | - Xili Su
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, China
| | - Dong Liu
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, China
| | - Hongjun Zhang
- School of Computer Science and Technology, Anyang University, Anyang, China
| | - Xianfang Wang
- College of Computer Science and Technology Engineering, Henan Institute of Technology, Xinxiang, China
| | - Yun Zhou
- College of Computer and Information Engineering, Henan Normal University, Xinxiang, China
| |
Collapse
|
4
|
Reverse vaccinology assisted design of a novel multi-epitope vaccine to target Wuchereria bancrofti cystatin: An immunoinformatics approach. Int Immunopharmacol 2023; 115:109639. [PMID: 36586276 DOI: 10.1016/j.intimp.2022.109639] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/05/2022] [Accepted: 12/22/2022] [Indexed: 12/31/2022]
Abstract
Proteases are the critical mediators of immunomodulation exerted by the filarial parasites to bypass and divert host immunity. Cystatin is a small (∼15 kDa) immunomodulatory filarial protein and known to contribute in the immunomodulation strategy by inducing anti-inflammatory response through alternative activation of macrophages. Recently, Wuchereria bancrofti cystatin has been discovered as a ligand of human toll-like receptor 4 which is key behind the cystatin-induced anti-inflammatory response in major human antigen-presenting cells. Considering the pivotal role of cystatin in the immunobiology of filariasis, cystatin could be an efficacious target for developing vaccine. Herein, we present the design and in-silico analyses of a multi-epitope-based peptide vaccine to target W. bancrofti cystatin through immune-informatics approaches. The 262 amino acid long antigen construct comprises 9 MHC-I epitopes and MHC-II epitopes linked together by GPGPG peptide alongside an adjuvant (50S ribosomal protein L7/L12) at N terminus and 6 His tags at C terminus. Molecular docking study reveals that the peptide could trigger TLR4-MD2 to induce protective innate immune responses while the induced adaptive responses were found to be mediated by IgG, IgM and Th1 mediated responses. Notably, the designed vaccine exhibits high stability and no allergenicity in-silico. Furthermore, the muti epitope-vaccine was also predicted for its RNA structure and cloned in pET30ax for further experimental validation. Taken together, this study presents a novel multi-epitope peptide vaccine for triggering efficient innate and adaptive immune responses against W. bancrofti to intervene LF through immunotherapy.
Collapse
|
5
|
Li Q. Geometric basis of action potential of skeletal muscle cells and neurons. Open Life Sci 2022; 17:1191-1199. [PMID: 36185399 PMCID: PMC9482420 DOI: 10.1515/biol-2022-0488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 07/19/2022] [Accepted: 07/28/2022] [Indexed: 11/15/2022] Open
Abstract
Although we know something about single-cell neuromuscular junctions, it is still unclear how multiple skeletal muscle cells coordinate to complete intricate spatial curve movement. Here, we hypothesize that skeletal muscle cell populations with action potentials are aligned according to curved manifolds in space (a curved shape in space). When a specific motor nerve impulse is transmitted, the skeletal muscle also moves according to the corresponding shape (manifolds). The action potential of motor nerve fibers has the characteristics of a time curve manifold, and this time-manifold curve of motor nerve fibers comes from the visual cortex in which spatial geometric manifolds are formed within the synaptic connection of neurons. This spatial geometric manifold of the synaptic connection of neurons originates from spatial geometric manifolds outside nature that are transmitted to the brain through the cone cells and ganglion cells of the retina. The essence of life is that life is an object that can move autonomously, and the essence of life's autonomous movement is the movement of proteins. Theoretically, because of the infinite diversity of geometric manifold shapes in nature, the arrangement and combination of 20 amino acids should have infinite diversity, and the geometric manifold formed by the protein three-dimensional spatial structure should also have infinite diversity.
Collapse
Affiliation(s)
- Qing Li
- Department of Function, ShiJiaZhuang Traditional Chinese Medical Hospital, No. 233, ZhongShan West Road, ShiJiaZhuang, HeBei Province 050051, China
| |
Collapse
|
6
|
Carugo O. Uses and Abuses of the Atomic Displacement Parameters in Structural Biology. Methods Mol Biol 2022; 2449:281-298. [PMID: 35507268 DOI: 10.1007/978-1-0716-2095-3_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
B-factors determined with X-ray crystallographic analyses are commonly used to estimate the flexibility degree of atoms, residues, and molecular moieties in biological macromolecules. In this chapter, the most recent studies and applications of B-factors in protein engineering and structural biology are briefly summarized. Particular emphasis is given to the limitations in using B-factors, in order to prevent inappropriate applications. It is eventually predicted that future applications will involve anisotropically refined B-factors, deep learning, and data produced by cryo-EM.
Collapse
|
7
|
Wei H, Wang B, Yang J, Gao J. RNA Flexibility Prediction With Sequence Profile and Predicted Solvent Accessibility. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:2017-2022. [PMID: 31794403 DOI: 10.1109/tcbb.2019.2956496] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Structural flexibility plays an essential role in many biological processes. B-factor is an important indicator to measure the flexibility of protein or RNA structures. Many methods were developed to predict protein B-factors, but few studies have been done for RNA B-factor prediction. In this paper, we proposed a new method RNAbval to predict RNA B-factors using random forest. The method was developed using a comprehensive set of features, including the sequence profile and predicted solvent accessibility. RNAbval achieved an improvement of 9.2-20.5 percent over the state-of-the-art method on two benchmark test datasets. The proposed method is available at http://yanglab.nankai.edu.cn/RNAbval/.
Collapse
|
8
|
Dalwani S, Lampela O, Leprovost P, Schmitz W, Juffer A, Wierenga RK, Venkatesan R. Substrate specificity and conformational flexibility properties of the Mycobacterium tuberculosis β-oxidation trifunctional enzyme. J Struct Biol 2021; 213:107776. [PMID: 34371166 DOI: 10.1016/j.jsb.2021.107776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/30/2021] [Accepted: 08/04/2021] [Indexed: 10/20/2022]
Abstract
The Mycobacterium tuberculosis trifunctional enzyme (MtTFE) is an α2β2 tetrameric enzyme. The α -chain harbors the 2E-enoyl-CoA hydratase (ECH) and 3S-hydroxyacyl-CoA dehydrogenase (HAD) activities and the β -chain provides the 3-ketoacyl-CoA thiolase (KAT) activity. Enzyme kinetic data reported here show that medium and long chain enoyl-CoA molecules are preferred substrates for MtTFE. Modelling studies indicate how the linear medium and long chain acyl chains of these substrates can bind to each of the active sites. In addition, crystallographic binding studies have identified three new CoA binding sites which are different from the previously known CoA binding sites of the three TFE active sites. Structure comparisons provide new insights into the properties of ECH, HAD and KAT active sites of MtTFE. The interactions of the adenine moiety of CoA with loop-2 of the ECH active site cause a conformational change of this loop by which a competent ECH active site is formed. The NAD+ binding domain (domain C) of the HAD part of MtTFE has only a few interactions with the rest of the complex and adopts a range of open conformations, whereas the A-domain of the ECH part is rigidly fixed with respect to the HAD part. Two loops, the CB1-CA1 region and the catalytic CB4-CB5 loop, near the thiolase active site and the thiolase dimer interface, have high B-factors. Structure comparisons suggest that a competent and stable thiolase dimer is formed only when complexed with the α -chains, highlighting the importance of the assembly for the proper functioning of the complex.
Collapse
Affiliation(s)
- Subhadra Dalwani
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
| | - Outi Lampela
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland; Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Pierre Leprovost
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland; Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Werner Schmitz
- Theoder-Boveri-Institut für Biowissenschaften der Universität Würzburg, Würzburg, Germany
| | - Andre Juffer
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland; Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Rik K Wierenga
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland; Biocenter Oulu, University of Oulu, Oulu, Finland
| | - Rajaram Venkatesan
- Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland.
| |
Collapse
|
9
|
Schlick T, Portillo-Ledesma S, Myers CG, Beljak L, Chen J, Dakhel S, Darling D, Ghosh S, Hall J, Jan M, Liang E, Saju S, Vohr M, Wu C, Xu Y, Xue E. Biomolecular Modeling and Simulation: A Prospering Multidisciplinary Field. Annu Rev Biophys 2021; 50:267-301. [PMID: 33606945 PMCID: PMC8105287 DOI: 10.1146/annurev-biophys-091720-102019] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners.
Collapse
Affiliation(s)
- Tamar Schlick
- Department of Chemistry, New York University, New York, New York 10003, USA;
- Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA
- New York University-East China Normal University Center for Computational Chemistry, New York University Shanghai, Shanghai 200122, China
| | | | - Christopher G Myers
- Department of Chemistry, New York University, New York, New York 10003, USA;
| | - Lauren Beljak
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Justin Chen
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Sami Dakhel
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Daniel Darling
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Sayak Ghosh
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Joseph Hall
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Mikaeel Jan
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Emily Liang
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Sera Saju
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Mackenzie Vohr
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Chris Wu
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Yifan Xu
- College of Arts and Science, New York University, New York, New York 10003, USA
| | - Eva Xue
- College of Arts and Science, New York University, New York, New York 10003, USA
| |
Collapse
|
10
|
Vander Meersche Y, Cretin G, de Brevern AG, Gelly JC, Galochkina T. MEDUSA: Prediction of Protein Flexibility from Sequence. J Mol Biol 2021; 433:166882. [PMID: 33972018 DOI: 10.1016/j.jmb.2021.166882] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 02/12/2021] [Accepted: 02/13/2021] [Indexed: 12/11/2022]
Abstract
Information on the protein flexibility is essential to understand crucial molecular mechanisms such as protein stability, interactions with other molecules and protein functions in general. B-factor obtained in the X-ray crystallography experiments is the most common flexibility descriptor available for the majority of the resolved protein structures. Since the gap between the number of the resolved protein structures and available protein sequences is continuously growing, it is important to provide computational tools for protein flexibility prediction from amino acid sequence. In the current study, we report a Deep Learning based protein flexibility prediction tool MEDUSA (https://www.dsimb.inserm.fr/MEDUSA). MEDUSA uses evolutionary information extracted from protein homologous sequences and amino acid physico-chemical properties as input for a convolutional neural network to assign a flexibility class to each protein sequence position. Trained on a non-redundant dataset of X-ray structures, MEDUSA provides flexibility prediction in two, three and five classes. MEDUSA is freely available as a web-server providing a clear visualization of the prediction results as well as a standalone utility (https://github.com/DSIMB/medusa). Analysis of the MEDUSA output allows a user to identify the potentially highly deformable protein regions and general dynamic properties of the protein.
Collapse
Affiliation(s)
- Yann Vander Meersche
- Université de Paris, Inserm UMR_S 1134 - BIGR, INTS, 6 rue Alexandre Cabanel, 75015 Paris, France; Laboratoire d'Excellence GR-Ex, 75015 Paris, France
| | - Gabriel Cretin
- Université de Paris, Inserm UMR_S 1134 - BIGR, INTS, 6 rue Alexandre Cabanel, 75015 Paris, France; Laboratoire d'Excellence GR-Ex, 75015 Paris, France
| | - Alexandre G de Brevern
- Université de Paris, Inserm UMR_S 1134 - BIGR, INTS, 6 rue Alexandre Cabanel, 75015 Paris, France; Laboratoire d'Excellence GR-Ex, 75015 Paris, France
| | - Jean-Christophe Gelly
- Université de Paris, Inserm UMR_S 1134 - BIGR, INTS, 6 rue Alexandre Cabanel, 75015 Paris, France; Laboratoire d'Excellence GR-Ex, 75015 Paris, France.
| | - Tatiana Galochkina
- Université de Paris, Inserm UMR_S 1134 - BIGR, INTS, 6 rue Alexandre Cabanel, 75015 Paris, France; Laboratoire d'Excellence GR-Ex, 75015 Paris, France.
| |
Collapse
|
11
|
Marks C, Shi J, Deane CM. Predicting loop conformational ensembles. Bioinformatics 2018; 34:949-956. [PMID: 29136084 DOI: 10.1093/bioinformatics/btx718] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Accepted: 11/09/2017] [Indexed: 12/23/2022] Open
Abstract
Motivation Protein function is often facilitated by the existence of multiple stable conformations. Structure prediction algorithms need to be able to model these different conformations accurately and produce an ensemble of structures that represent a target's conformational diversity rather than just a single state. Here, we investigate whether current loop prediction algorithms are capable of this. We use the algorithms to predict the structures of loops with multiple experimentally determined conformations, and the structures of loops with only one conformation, and assess their ability to generate and select decoys that are close to any, or all, of the observed structures. Results We find that while loops with only one known conformation are predicted well, conformationally diverse loops are modelled poorly, and in most cases the predictions returned by the methods do not resemble any of the known conformers. Our results contradict the often-held assumption that multiple native conformations will be present in the decoy set, making the production of accurate conformational ensembles impossible, and hence indicating that current methodologies are not well suited to prediction of conformationally diverse, often functionally important protein regions. Contact marks@stats.ox.ac.uk. Supplementary information Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Claire Marks
- Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
| | - Jiye Shi
- Department of Chemistry, UCB Pharma, Slough SL1 3WE, UK
| | | |
Collapse
|
12
|
Vera R, Synsmir-Zizzamia M, Ojinnaka S, Snyder DA. Prediction of protein flexibility using a conformationally restrained contact map. Proteins 2018; 86:1111-1116. [DOI: 10.1002/prot.25591] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2017] [Revised: 06/29/2018] [Accepted: 08/05/2018] [Indexed: 12/30/2022]
Affiliation(s)
- Rebecca Vera
- Department of Biology and Physical Sciences; Passaic County Community College; Paterson New Jersey
- Department of Biological Sciences; Rutgers University; Newark New Jersey
| | - Melissa Synsmir-Zizzamia
- Department of Chemistry; Union County College; Cranford New Jersey
- Department of Chemistry; The College of New Jersey; Ewing Township New Jersey
| | - Sarah Ojinnaka
- Department of Chemistry, College of Science and Health; William Paterson University of New Jersey; Wayne New Jersey
| | - David A. Snyder
- Department of Chemistry, College of Science and Health; William Paterson University of New Jersey; Wayne New Jersey
| |
Collapse
|
13
|
Siebert DCB, Wieder M, Schlener L, Scholze P, Boresch S, Langer T, Schnürch M, Mihovilovic MD, Richter L, Ernst M, Ecker GF. SAR-Guided Scoring Function and Mutational Validation Reveal the Binding Mode of CGS-8216 at the α1+/γ2- Benzodiazepine Site. J Chem Inf Model 2018; 58:1682-1696. [PMID: 30028134 DOI: 10.1021/acs.jcim.8b00199] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The structural resolution of a bound ligand-receptor complex is a key asset to efficiently drive lead optimization in drug design. However, structural resolution of many drug targets still remains a challenging endeavor. In the absence of structural knowledge, scientists resort to structure-activity relationships (SARs) to promote compound development. In this study, we incorporated ligand-based knowledge to formulate a docking scoring function that evaluates binding poses for their agreement with a known SAR. We showcased this protocol by identifying the binding mode of the pyrazoloquinolinone (PQ) CGS-8216 at the benzodiazepine binding site of the GABAA receptor. Further evaluation of the final pose by molecular dynamics and free energy simulations revealed a close proximity between the pendent phenyl ring of the PQ and γ2D56, congruent with the low potency of carboxyphenyl analogues. Ultimately, we introduced the γ2D56A mutation and in fact observed a 10-fold potency increase in the carboxyphenyl analogue, providing experimental evidence in favor of our binding hypothesis.
Collapse
Affiliation(s)
- David C B Siebert
- Institute of Applied Synthetic Chemistry , TU Wien , Getreidemarkt 9/163 , 1060 Vienna , Austria
| | - Marcus Wieder
- Department of Pharmaceutical Chemistry , University of Vienna , Althanstrasse 14 , 1090 Vienna , Austria.,Faculty of Chemistry, Department of Computational Biological Chemistry , University of Vienna , Währingerstrasse 17 , 1090 Vienna , Austria
| | - Lydia Schlener
- Department of Pharmaceutical Chemistry , University of Vienna , Althanstrasse 14 , 1090 Vienna , Austria
| | - Petra Scholze
- Department of Pathobiology of the Nervous System, Center for Brain Research , Medical University of Vienna , Spitalgasse 4 , 1090 Vienna , Austria
| | - Stefan Boresch
- Faculty of Chemistry, Department of Computational Biological Chemistry , University of Vienna , Währingerstrasse 17 , 1090 Vienna , Austria
| | - Thierry Langer
- Department of Pharmaceutical Chemistry , University of Vienna , Althanstrasse 14 , 1090 Vienna , Austria
| | - Michael Schnürch
- Institute of Applied Synthetic Chemistry , TU Wien , Getreidemarkt 9/163 , 1060 Vienna , Austria
| | - Marko D Mihovilovic
- Institute of Applied Synthetic Chemistry , TU Wien , Getreidemarkt 9/163 , 1060 Vienna , Austria
| | - Lars Richter
- Department of Pharmaceutical Chemistry , University of Vienna , Althanstrasse 14 , 1090 Vienna , Austria
| | - Margot Ernst
- Department of Molecular Neurosciences, Center for Brain Research , Medical University of Vienna , Spitalgasse 4 , 1090 Vienna , Austria
| | - Gerhard F Ecker
- Department of Pharmaceutical Chemistry , University of Vienna , Althanstrasse 14 , 1090 Vienna , Austria
| |
Collapse
|
14
|
Guruge I, Taherzadeh G, Zhan J, Zhou Y, Yang Y. B
-factor profile prediction for RNA flexibility using support vector machines. J Comput Chem 2017; 39:407-411. [DOI: 10.1002/jcc.25124] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Accepted: 11/07/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Ivantha Guruge
- School of Information and Communication Technology and Institue for Glycomics; Griffith University, Parklands Drive; Southport Queensland 4215 Australia
| | - Ghazaleh Taherzadeh
- School of Information and Communication Technology and Institue for Glycomics; Griffith University, Parklands Drive; Southport Queensland 4215 Australia
| | - Jian Zhan
- School of Information and Communication Technology and Institue for Glycomics; Griffith University, Parklands Drive; Southport Queensland 4215 Australia
| | - Yaoqi Zhou
- School of Information and Communication Technology and Institue for Glycomics; Griffith University, Parklands Drive; Southport Queensland 4215 Australia
| | - Yuedong Yang
- School of Information and Communication Technology and Institue for Glycomics; Griffith University, Parklands Drive; Southport Queensland 4215 Australia
- School of Data and Computer Science; Sun Yat-sen University; Guangzhou 510275 China
| |
Collapse
|