251
|
Gaillard T. Evaluation of AutoDock and AutoDock Vina on the CASF-2013 Benchmark. J Chem Inf Model 2018; 58:1697-1706. [DOI: 10.1021/acs.jcim.8b00312] [Citation(s) in RCA: 138] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Thomas Gaillard
- Laboratoire de Biochimie (CNRS UMR7654), Department of Biology, Ecole Polytechnique, 91128 Palaiseau, France
| |
Collapse
|
252
|
Skalic M, Varela-Rial A, Jiménez J, Martínez-Rosell G, De Fabritiis G. LigVoxel: inpainting binding pockets using 3D-convolutional neural networks. Bioinformatics 2018; 35:243-250. [DOI: 10.1093/bioinformatics/bty583] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 07/04/2018] [Indexed: 11/13/2022] Open
Affiliation(s)
- Miha Skalic
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB)
| | - Alejandro Varela-Rial
- Acellera, Barcelona Biomedical Research Park (PRBB), Doctor Aiguader 88, Barcelona, Spain
| | - José Jiménez
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB)
| | - Gerard Martínez-Rosell
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB)
| | - Gianni De Fabritiis
- Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB)
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Passeig Lluis Companys 23, Barcelona, Spain
| |
Collapse
|
253
|
In silico identification of AChE and PARP-1 dual-targeted inhibitors of Alzheimer’s disease. J Mol Model 2018; 24:151. [DOI: 10.1007/s00894-018-3696-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2017] [Accepted: 05/23/2018] [Indexed: 01/29/2023]
|
254
|
Gianti E, Carnevale V. Computational Approaches to Studying Voltage-Gated Ion Channel Modulation by General Anesthetics. Methods Enzymol 2018; 602:25-59. [DOI: 10.1016/bs.mie.2018.01.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|
255
|
Wang SH, Yu J. Structure-based design for binding peptides in anti-cancer therapy. Biomaterials 2017; 156:1-15. [PMID: 29182932 DOI: 10.1016/j.biomaterials.2017.11.024] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Revised: 10/30/2017] [Accepted: 11/21/2017] [Indexed: 12/18/2022]
Abstract
The conventional anticancer therapeutics usually lack cancer specificity, leading to damage of normal tissues that patients find hard to tolerate. Ideally, anticancer therapeutics carrying payloads of drugs equipped with cancer targeting peptides can act like "guided missiles" with the capacity of targeted delivery toward many types of cancers. Peptides are amenable for conjugation to nano drugs for functionalization, thereby improving drug delivery and cellular uptake in cancer-targeting therapies. Peptide drugs are often more difficult to design through molecular docking and in silico analysis than small molecules, because peptide structures are more flexible, possess intricate molecular conformations, and undergo complex interactions. In this review, the development and application of strategies for structure-based design of cancer-targeting peptides against GRP78 are discussed. This Review also covers topics related to peptide pharmacokinetics and targeting delivery, including molecular docking studies, features that provide advantages for in vivo use, and properties that influence the cancer-targeting ability. Some advanced technologies and special peptides that can overcome the pharmacokinetic challenges have also been included.
Collapse
Affiliation(s)
- Sheng-Hung Wang
- Institute of Stem Cell and Translational Cancer Research, Chang Gung Memorial Hospital at Linkou, Chang Gung University, Taoyuan 333, Taiwan
| | - John Yu
- Institute of Stem Cell and Translational Cancer Research, Chang Gung Memorial Hospital at Linkou, Chang Gung University, Taoyuan 333, Taiwan; Institute of Cellular and Organismic Biology, Academia Sinica, Taipei, Taiwan.
| |
Collapse
|
256
|
Protein-Ligand Blind Docking Using QuickVina-W With Inter-Process Spatio-Temporal Integration. Sci Rep 2017; 7:15451. [PMID: 29133831 PMCID: PMC5684369 DOI: 10.1038/s41598-017-15571-7] [Citation(s) in RCA: 133] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Accepted: 10/25/2017] [Indexed: 12/31/2022] Open
Abstract
"Virtual Screening" is a common step of in silico drug design, where researchers screen a large library of small molecules (ligands) for interesting hits, in a process known as "Docking". However, docking is a computationally intensive and time-consuming process, usually restricted to small size binding sites (pockets) and small number of interacting residues. When the target site is not known (blind docking), researchers split the docking box into multiple boxes, or repeat the search several times using different seeds, and then merge the results manually. Otherwise, the search time becomes impractically long. In this research, we studied the relation between the search progression and Average Sum of Proximity relative Frequencies (ASoF) of searching threads, which is closely related to the search speed and accuracy. A new inter-process spatio-temporal integration method is employed in Quick Vina 2, resulting in a new docking tool, QuickVina-W, a suitable tool for "blind docking", (not limited in search space size or number of residues). QuickVina-W is faster than Quick Vina 2, yet better than AutoDock Vina. It should allow researchers to screen huge ligand libraries virtually, in practically short time and with high accuracy without the need to define a target pocket beforehand.
Collapse
|
257
|
Suzuki K, Stanfield JK, Shoji O, Yanagisawa S, Sugimoto H, Shiro Y, Watanabe Y. Control of stereoselectivity of benzylic hydroxylation catalysed by wild-type cytochrome P450BM3 using decoy molecules. Catal Sci Technol 2017. [DOI: 10.1039/c7cy01130j] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The benzylic hydroxylation of non-native substrates was catalysed by cytochrome P450BM3, wherein “decoy molecules” controlled the stereoselectivity of the reactions.
Collapse
Affiliation(s)
- Kazuto Suzuki
- Department of Chemistry
- Graduate School of Science
- Nagoya University
- Nagoya 464-8602
- Japan
| | - Joshua Kyle Stanfield
- Department of Chemistry
- Graduate School of Science
- Nagoya University
- Nagoya 464-8602
- Japan
| | - Osami Shoji
- Department of Chemistry
- Graduate School of Science
- Nagoya University
- Nagoya 464-8602
- Japan
| | - Sota Yanagisawa
- Department of Chemistry
- Graduate School of Science
- Nagoya University
- Nagoya 464-8602
- Japan
| | - Hiroshi Sugimoto
- Core Research for Evolutional Science and Technology (CREST)
- Japan Science and Technology Agency
- Tokyo
- Japan
- RIKEN SPring-8 Center
| | | | - Yoshihito Watanabe
- Research Center for Materials Science
- Nagoya University
- Nagoya 464-8602
- Japan
| |
Collapse
|
258
|
Choong YS, Lee YV, Soong JX, Law CT, Lim YY. Computer-Aided Antibody Design: An Overview. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 1053:221-243. [PMID: 29549642 DOI: 10.1007/978-3-319-72077-7_11] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The use of monoclonal antibody as the next generation protein therapeutics with remarkable success has surged the development of antibody engineering to design molecules for optimizing affinity, better efficacy, greater safety and therapeutic function. Therefore, computational methods have become increasingly important to generate hypotheses, interpret and guide experimental works. In this chapter, we discussed the overall antibody design by computational approches.
Collapse
Affiliation(s)
- Yee Siew Choong
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia.
| | - Yie Vern Lee
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Jia Xin Soong
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Cheh Tat Law
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia
| | - Yee Ying Lim
- Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia
| |
Collapse
|
259
|
Paul DS, Gautham N. MOLS 2.0: software package for peptide modeling and protein-ligand docking. J Mol Model 2016; 22:239. [PMID: 27638416 DOI: 10.1007/s00894-016-3106-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Accepted: 09/01/2016] [Indexed: 11/25/2022]
Abstract
We previously developed an algorithm to perform conformational searches of proteins and peptides, and to perform the docking of ligands to protein receptors. In order to identify optimal conformations and docked poses, this algorithm uses mutually orthogonal Latin squares (MOLS) to rationally sample the vast conformational (or docking) space, and then analyzes this relatively small sample using a variant of mean field theory. The conformational search part of the algorithm was denoted MOLS 1.0. The docking portion of the algorithm, which allows only "flexible ligand/rigid receptor" docking, was denoted MOLSDOCK. Both are FORTRAN-based command-line-only molecular docking computer programs, though a GUI was developed later for MOLS 1.0. Both the conformational search and the rigid receptor docking parts of the algorithm have been extensively validated. We have now further enhanced the capabilities of the program by incorporating "induced fit" side-chain receptor flexibility for docking peptide ligands. Benchmarking and extensive testing is now being carried out for the flexible receptor portion of the docking. Additionally, to make both the peptide conformational search and docking algorithms (the latter including both flexible ligand/rigid receptor and flexible ligand/flexible receptor techniques) more accessible to the research community, we have developed MOLS 2.0, which incorporates a new Java-based graphical user interface (GUI). Here, we give a detailed description of MOLS 2.0. The source code and binary for MOLS 2.0 are distributed free (under a GNU Lesser General Public License) to the scientific community. They are freely available for download at https://sourceforge.net/projects/mols2-0/files/ .
Collapse
Affiliation(s)
- D Sam Paul
- Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai, 600025, India
| | - N Gautham
- Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai, 600025, India.
| |
Collapse
|
260
|
Ravindranath PA, Sanner MF. AutoSite: an automated approach for pseudo-ligands prediction-from ligand-binding sites identification to predicting key ligand atoms. Bioinformatics 2016; 32:3142-3149. [PMID: 27354702 DOI: 10.1093/bioinformatics/btw367] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2016] [Accepted: 06/06/2016] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION The identification of ligand-binding sites from a protein structure facilitates computational drug design and optimization, and protein function assignment. We introduce AutoSite: an efficient software tool for identifying ligand-binding sites and predicting pseudo ligand corresponding to each binding site identified. Binding sites are reported as clusters of 3D points called fills in which every point is labelled as hydrophobic or as hydrogen bond donor or acceptor. From these fills AutoSite derives feature points: a set of putative positions of hydrophobic-, and hydrogen-bond forming ligand atoms. RESULTS We show that AutoSite identifies ligand-binding sites with higher accuracy than other leading methods, and produces fills that better matches the ligand shape and properties, than the fills obtained with a software program with similar capabilities, AutoLigand In addition, we demonstrate that for the Astex Diverse Set, the feature points identify 79% of hydrophobic ligand atoms, and 81% and 62% of the hydrogen acceptor and donor hydrogen ligand atoms interacting with the receptor, and predict 81.2% of water molecules mediating interactions between ligand and receptor. Finally, we illustrate potential uses of the predicted feature points in the context of lead optimization in drug discovery projects. AVAILABILITY AND IMPLEMENTATION http://adfr.scripps.edu/AutoDockFR/autosite.html CONTACT: sanner@scripps.eduSupplementary information: Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Pradeep Anand Ravindranath
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Michel F Sanner
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| |
Collapse
|
261
|
Kmiecik S, Gront D, Kolinski M, Wieteska L, Dawid AE, Kolinski A. Coarse-Grained Protein Models and Their Applications. Chem Rev 2016; 116:7898-936. [DOI: 10.1021/acs.chemrev.6b00163] [Citation(s) in RCA: 555] [Impact Index Per Article: 69.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Sebastian Kmiecik
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Dominik Gront
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| | - Michal Kolinski
- Bioinformatics
Laboratory, Mossakowski Medical Research Center of the Polish Academy of Sciences, Pawinskiego 5, 02-106 Warsaw, Poland
| | - Lukasz Wieteska
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
- Department
of Medical Biochemistry, Medical University of Lodz, Mazowiecka 6/8, 92-215 Lodz, Poland
| | | | - Andrzej Kolinski
- Faculty
of Chemistry, University of Warsaw, Pasteura 1, 02-093 Warsaw, Poland
| |
Collapse
|
262
|
Lee A, Lee K, Kim D. Using reverse docking for target identification and its applications for drug discovery. Expert Opin Drug Discov 2016; 11:707-15. [PMID: 27186904 DOI: 10.1080/17460441.2016.1190706] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
INTRODUCTION In contrast to traditional molecular docking, inverse or reverse docking is used for identifying receptors for a given ligand among a large number of receptors. Reverse docking can be used to discover new targets for existing drugs and natural compounds, explain polypharmacology and the molecular mechanism of a substance, find alternative indications of drugs through drug repositioning, and detecting adverse drug reactions and drug toxicity. AREAS COVERED In this review, the authors examine how reverse docking methods have evolved over the past fifteen years and how they have been used for target identification and related applications for drug discovery. They discuss various aspects of target databases, reverse docking tools and servers. EXPERT OPINION There are several issues related to reverse docking methods such as target structure dataset construction, computational efficiency, how to include receptor flexibility, and most importantly, how to properly normalize the docking scores. In order for reverse docking to become a truly useful tool for the drug discovery, these issues need to be adequately resolved.
Collapse
Affiliation(s)
- Aeri Lee
- a Department of Bio and Brain Engineering , KAIST , Daejeon , South Korea
| | - Kyoungyeul Lee
- a Department of Bio and Brain Engineering , KAIST , Daejeon , South Korea
| | - Dongsup Kim
- a Department of Bio and Brain Engineering , KAIST , Daejeon , South Korea
| |
Collapse
|
263
|
Clark AJ, Tiwary P, Borrelli K, Feng S, Miller EB, Abel R, Friesner RA, Berne BJ. Prediction of Protein-Ligand Binding Poses via a Combination of Induced Fit Docking and Metadynamics Simulations. J Chem Theory Comput 2016; 12:2990-8. [PMID: 27145262 DOI: 10.1021/acs.jctc.6b00201] [Citation(s) in RCA: 146] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Ligand docking is a widely used tool for lead discovery and binding mode prediction based drug discovery. The greatest challenges in docking occur when the receptor significantly reorganizes upon small molecule binding, thereby requiring an induced fit docking (IFD) approach in which the receptor is allowed to move in order to bind to the ligand optimally. IFD methods have had some success but suffer from a lack of reliability. Complementing IFD with all-atom molecular dynamics (MD) is a straightforward solution in principle but not in practice due to the severe time scale limitations of MD. Here we introduce a metadynamics plus IFD strategy for accurate and reliable prediction of the structures of protein-ligand complexes at a practically useful computational cost. Our strategy allows treating this problem in full atomistic detail and in a computationally efficient manner and enhances the predictive power of IFD methods. We significantly increase the accuracy of the underlying IFD protocol across a large data set comprising 42 different ligand-receptor systems. We expect this approach to be of significant value in computationally driven drug design.
Collapse
Affiliation(s)
- Anthony J Clark
- Department of Chemistry, Columbia University , New York, New York 10027, United States
| | - Pratyush Tiwary
- Department of Chemistry, Columbia University , New York, New York 10027, United States
| | - Ken Borrelli
- Schrödinger, Inc. , 120 West 45th Street, New York, New York 10036, United States
| | - Shulu Feng
- Schrödinger, Inc. , 120 West 45th Street, New York, New York 10036, United States
| | - Edward B Miller
- Schrödinger, Inc. , 120 West 45th Street, New York, New York 10036, United States
| | - Robert Abel
- Schrödinger, Inc. , 120 West 45th Street, New York, New York 10036, United States
| | - Richard A Friesner
- Department of Chemistry, Columbia University , New York, New York 10027, United States
| | - B J Berne
- Department of Chemistry, Columbia University , New York, New York 10027, United States
| |
Collapse
|