1
|
Abuhammad A, Taha M. Innovative computer-aided methods for the discovery of new kinase ligands. Future Med Chem 2016; 8:509-526. [PMID: 27105126 DOI: 10.4155/fmc-2015-0003] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Accepted: 02/02/2016] [Indexed: 07/10/2024] Open
Abstract
Recent evidence points to significant roles played by protein kinases in cell signaling and cellular proliferation. Faulty protein kinases are involved in cancer, diabetes and chronic inflammation. Efforts are continuously carried out to discover new inhibitors for selected protein kinases. In this review, we discuss two new computer-aided methodologies we developed to mine virtual databases for new bioactive compounds. One method is ligand-based exploration of the pharmacophoric space of inhibitors of any particular biotarget followed by quantitative structure-activity relationship-based selection of the best pharmacophore(s). The second approach is structure-based assuming that potent ligands come into contact with binding site spots distinct from those contacted by weakly potent ligands. Both approaches yield pharmacophores useful as 3D search queries for the discovery of new bioactive (kinase) inhibitors.
Collapse
Affiliation(s)
- Areej Abuhammad
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, 11942, Amman, Jordan
| | - Mutasem Taha
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, The University of Jordan, 11942, Amman, Jordan
| |
Collapse
|
2
|
Tang K, Wong SWK, Liu JS, Zhang J, Liang J. Conformational sampling and structure prediction of multiple interacting loops in soluble and β-barrel membrane proteins using multi-loop distance-guided chain-growth Monte Carlo method. Bioinformatics 2015; 31:2646-52. [PMID: 25861965 DOI: 10.1093/bioinformatics/btv198] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2014] [Accepted: 04/03/2015] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Loops in proteins are often involved in biochemical functions. Their irregularity and flexibility make experimental structure determination and computational modeling challenging. Most current loop modeling methods focus on modeling single loops. In protein structure prediction, multiple loops often need to be modeled simultaneously. As interactions among loops in spatial proximity can be rather complex, sampling the conformations of multiple interacting loops is a challenging task. RESULTS In this study, we report a new method called multi-loop Distance-guided Sequential chain-Growth Monte Carlo (M-DiSGro) for prediction of the conformations of multiple interacting loops in proteins. Our method achieves an average RMSD of 1.93 Å for lowest energy conformations of 36 pairs of interacting protein loops with the total length ranging from 12 to 24 residues. We further constructed a data set containing proteins with 2, 3 and 4 interacting loops. For the most challenging target proteins with four loops, the average RMSD of the lowest energy conformations is 2.35 Å. Our method is also tested for predicting multiple loops in β-barrel membrane proteins. For outer-membrane protein G, the lowest energy conformation has a RMSD of 2.62 Å for the three extracellular interacting loops with a total length of 34 residues (12, 12 and 10 residues in each loop). AVAILABILITY AND IMPLEMENTATION The software is freely available at: tanto.bioe.uic.edu/m-DiSGro. CONTACT jinfeng@stat.fsu.edu or jliang@uic.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Ke Tang
- Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, Chicago, IL
| | - Samuel W K Wong
- Department of Statistics, University of Florida, Gainesville, FL
| | - Jun S Liu
- Department of Statistics, Harvard University, Science Center, Cambridge, MA and
| | - Jinfeng Zhang
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Jie Liang
- Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, Chicago, IL
| |
Collapse
|
3
|
Liang S, Zhang C, Zhou Y. LEAP: highly accurate prediction of protein loop conformations by integrating coarse-grained sampling and optimized energy scores with all-atom refinement of backbone and side chains. J Comput Chem 2014; 35:335-41. [PMID: 24327406 PMCID: PMC4125323 DOI: 10.1002/jcc.23509] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2013] [Revised: 10/06/2013] [Accepted: 11/24/2013] [Indexed: 11/11/2022]
Abstract
Prediction of protein loop conformations without any prior knowledge (ab initio prediction) is an unsolved problem. Its solution will significantly impact protein homology and template-based modeling as well as ab initio protein-structure prediction. Here, we developed a coarse-grained, optimized scoring function for initial sampling and ranking of loop decoys. The resulting decoys are then further optimized in backbone and side-chain conformations and ranked by all-atom energy scoring functions. The final integrated technique called loop prediction by energy-assisted protocol achieved a median value of 2.1 Å root mean square deviation (RMSD) for 325 12-residue test loops and 2.0 Å RMSD for 45 12-residue loops from critical assessment of structure-prediction techniques (CASP) 10 target proteins with native core structures (backbone and side chains). If all side-chain conformations in protein cores were predicted in the absence of the target loop, loop-prediction accuracy only reduces slightly (0.2 Å difference in RMSD for 12-residue loops in the CASP target proteins). The accuracy obtained is about 1 Å RMSD or more improvement over other methods we tested. The executable file for a Linux system is freely available for academic users at http://sparks-lab.org.
Collapse
Affiliation(s)
- Shide Liang
- Systems Immunology Lab, Immunology Frontier Research Center, Osaka University, Suita, Osaka, 565-0871, Japan
| | - Chi Zhang
- School of Biological Sciences, Center for Plant Science and Innovation, University of Nebraska, Lincoln, NE, 68588, USA
| | - Yaoqi Zhou
- School of Informatics, Indiana University Purdue University at Indianapolis, Indianapolis, IN 46202, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Institute for Glycomics and School of Informatics and Communication Technology, Griffith University, Parklands Drive, Southport Qld 4222, Australia
| |
Collapse
|
4
|
Understanding Protein–Protein Interactions Using Local Structural Features. J Mol Biol 2013; 425:1210-24. [DOI: 10.1016/j.jmb.2013.01.014] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2012] [Revised: 01/08/2013] [Accepted: 01/14/2013] [Indexed: 11/21/2022]
|
5
|
Guardiani C, Procacci P. The conformational landscape of tartrate-based inhibitors of the TACE enzyme as revealed by Hamiltonian Replica Exchange simulation. Phys Chem Chem Phys 2013; 15:9186-96. [DOI: 10.1039/c3cp00108c] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
6
|
Danielson ML, Lill MA. Predicting flexible loop regions that interact with ligands: the challenge of accurate scoring. Proteins 2011; 80:246-60. [PMID: 22072600 DOI: 10.1002/prot.23199] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2011] [Revised: 09/06/2011] [Accepted: 09/13/2011] [Indexed: 01/12/2023]
Abstract
Flexible loop regions play a critical role in the biological function of many proteins and have been shown to be involved in ligand binding. In the context of structure-based drug design, using or predicting an incorrect loop configuration can be detrimental to the study if the loop is capable of interacting with the ligand. Three protein systems, each with at least one flexible loop region in close proximity to the known binding site, were selected for loop prediction using the CorLps program; a six residue loop region from phosphoribosylglycinamide formyltransferase (GART), two nine residue loop regions from cytochrome P450 (CYP) 119, and an 11 residue loop region from enolase were selected for loop prediction. The results of this study indicate that the statistically based DFIRE scoring function implemented in the CorLps program did not accurately rank native-like predicted loop configurations in any protein system. In an attempt to improve the ranking of the native-like predicted loop configurations, the MM/GBSA and the optimized MM/GBSA-dsr scoring functions were used to re-rank the predicted loops with and without bound ligand. In general, single snapshot MM/GBSA scoring provided the best ranking of native-like loop configurations. Based on the scoring function analyses presented, the optimal ranking of native-like loop configurations is still a difficult challenge and the choice of the "best" scoring function appears to be system dependent.
Collapse
Affiliation(s)
- Matthew L Danielson
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN 47907, USA
| | | |
Collapse
|
7
|
Integrating structure-based and ligand-based approaches for computational drug design. Future Med Chem 2011; 3:735-50. [PMID: 21554079 DOI: 10.4155/fmc.11.18] [Citation(s) in RCA: 103] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Methods utilized in computer-aided drug design can be classified into two major categories: structure based and ligand based, using information on the structure of the protein or on the biological and physicochemical properties of bound ligands, respectively. In recent years there has been a trend towards integrating these two methods in order to enhance the reliability and efficiency of computer-aided drug-design approaches by combining information from both the ligand and the protein. This trend resulted in a variety of methods that include: pseudoreceptor methods, pharmacophore methods, fingerprint methods and approaches integrating docking with similarity-based methods. In this article, we will describe the concepts behind each method and selected applications.
Collapse
|
8
|
Sircar A, Sanni KA, Shi J, Gray JJ. Analysis and modeling of the variable region of camelid single-domain antibodies. THE JOURNAL OF IMMUNOLOGY 2011; 186:6357-67. [PMID: 21525384 DOI: 10.4049/jimmunol.1100116] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Camelids have a special type of Ab, known as heavy chain Abs, which are devoid of classical Ab light chains. Relative to classical Abs, camelid heavy chain Abs (cAbs) have comparable immunogenicity, Ag recognition diversity and binding affinities, higher stability and solubility, and better manufacturability, making them promising candidates for alternate therapeutic scaffolds. Rational engineering of cAbs to improve therapeutic function requires knowledge of the differences of sequence and structural features between cAbs and classical Abs. In this study, amino acid sequences of 27 cAb variable regions (V(H)H) were aligned with the respective regions of 54 classical Abs to detect amino acid differences, enabling automatic identification of cAb V(H)H CDRs. CDR analysis revealed that the H1 often (and sometimes the H2) adopts diverse conformations not classifiable by established canonical rules. Also, although the cAb H3 is much longer than classical H3 loops, it often contains common structural motifs and sometimes a disulfide bond to the H1. Leveraging these observations, we created a Monte Carlo-based cAb V(H)H structural modeling tool, where the CDR H1 and H2 loops exhibited a median root-mean-square deviation to natives of 3.1 and 1.5 Å, respectively. The protocol generated 8-12, 14-16, and 16-24 residue H3 loops with a median root-mean-square deviation to natives of 5.7, 4.5, and 6.8 Å, respectively. The large deviation of the predicted loops underscores the challenge in modeling such long loops. cAb V(H)H homology models can provide structural insights into interaction mechanisms to enable development of novel Abs for therapeutic and biotechnological use.
Collapse
Affiliation(s)
- Aroop Sircar
- Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | | | | | | |
Collapse
|
9
|
Xu M, Lill MA. Significant enhancement of docking sensitivity using implicit ligand sampling. J Chem Inf Model 2011; 51:693-706. [PMID: 21375306 DOI: 10.1021/ci100457t] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
The efficient and accurate quantification of protein-ligand interactions using computational methods is still a challenging task. Two factors strongly contribute to the failure of docking methods to predict free energies of binding accurately: the insufficient incorporation of protein flexibility coupled to ligand binding and the neglected dynamics of the protein-ligand complex in current scoring schemes. We have developed a new methodology, named the 'ligand-model' concept, to sample protein conformations that are relevant for binding structurally diverse sets of ligands. In the ligand-model concept, molecular-dynamics (MD) simulations are performed with a virtual ligand, represented by a collection of functional groups that binds to the protein and dynamically changes its shape and properties during the simulation. The ligand model essentially represents a large ensemble of different chemical species binding to the same target protein. Representative protein structures were obtained from the MD simulation, and docking was performed into this ensemble of protein conformation. Similar binding poses were clustered, and the averaged score was utilized to rerank the poses. We demonstrate that the ligand-model approach yields significant improvements in predicting native-like binding poses and quantifying binding affinities compared to static docking and ensemble docking simulations into protein structures generated from an apo MD simulation.
Collapse
Affiliation(s)
- Mengang Xu
- Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, West Lafayette, Indiana 47907, United States
| | | |
Collapse
|
10
|
Arnautova YA, Abagyan RA, Totrov M. Development of a new physics-based internal coordinate mechanics force field and its application to protein loop modeling. Proteins 2011; 79:477-98. [PMID: 21069716 PMCID: PMC3057902 DOI: 10.1002/prot.22896] [Citation(s) in RCA: 61] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
We report the development of internal coordinate mechanics force field (ICMFF), new force field parameterized using a combination of experimental data for crystals of small molecules and quantum mechanics calculations. The main features of ICMFF include: (a) parameterization for the dielectric constant relevant to the condensed state (ε = 2) instead of vacuum, (b) an improved description of hydrogen-bond interactions using duplicate sets of van der Waals parameters for heavy atom-hydrogen interactions, and (c) improved backbone covalent geometry and energetics achieved using novel backbone torsional potentials and inclusion of the bond angles at the C(α) atoms into the internal variable set. The performance of ICMFF was evaluated through loop modeling simulations for 4-13 residue loops. ICMFF was combined with a solvent-accessible surface area solvation model optimized using a large set of loop decoys. Conformational sampling was carried out using the biased probability Monte Carlo method. Average/median backbone root-mean-square deviations of the lowest energy conformations from the native structures were 0.25/0.21 Å for four residues loops, 0.84/0.46 Å for eight residue loops, and 1.16/0.73 Å for 12 residue loops. To our knowledge, these results are significantly better than or comparable with those reported to date for any loop modeling method that does not take crystal packing into account. Moreover, the accuracy of our method is on par with the best previously reported results obtained considering the crystal environment. We attribute this success to the high accuracy of the new ICM force field achieved by meticulous parameterization, to the optimized solvent model, and the efficiency of the search method.
Collapse
Affiliation(s)
- Yelena A Arnautova
- Molsoft LLC, 3366 North Torrey Pines Court, Suite 300, La Jolla, California 92037, USA
| | | | | |
Collapse
|
11
|
Abstract
Loop modeling is crucial for high-quality homology model construction outside conserved secondary structure elements. Dozens of loop modeling protocols involving a range of database and ab initio search algorithms and a variety of scoring functions have been proposed. Knowledge-based loop modeling methods are very fast and some can successfully and reliably predict loops up to about eight residues long. Several recent ab initio loop simulation methods can be used to construct accurate models of loops up to 12-13 residues long, albeit at a substantial computational cost. Major current challenges are the simulations of loops longer than 12-13 residues, the modeling of multiple interacting flexible loops, and the sensitivity of the loop predictions to the accuracy of the loop environment.
Collapse
|
12
|
Lill MA, Danielson ML. Computer-aided drug design platform using PyMOL. J Comput Aided Mol Des 2010; 25:13-9. [DOI: 10.1007/s10822-010-9395-8] [Citation(s) in RCA: 254] [Impact Index Per Article: 18.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2010] [Accepted: 10/20/2010] [Indexed: 12/29/2022]
|