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I am currently trying to model a certain protein (Golgi Mannosidase II) and compare the induced fit of the inhibitor swainsonine. I would like to be able to analyse the distances between bonding interactions and the strengths of bonding regions. I was wondering if there was any type of software that would allow me to analyse this protein and its interactions with swainsonine? I have seen many images like the one below in research papers, but I'm never sure what software the researchers are using to generate these models.
Liberato, M. V. et al. Molecular characterization of a family 5 glycoside hydrolase suggests an induced-fit enzymatic mechanism. Sci. Rep. 6, 23473; doi: 10.1038/srep23473 (2016).
I don't know precisely what you mean by "analyze", which could be anything from simply "view" or "measure distances", to evaluating an energy function on a crystal structure or molecular dynamics simulation, to prediction of binding sites de novo. The tools are different for each case, though some can do several things.
For simply viewing, measuring distances, and creating "pretty pictures" like the ones you included with the question, PyMOL and UCSF Chimera are the most popular tools. Chimera is highly scriptable and can perform molecular dynamics simulations (i.e. simulate movement of atoms based on an energy function), though its capability in this regard is somewhat limited. The NAMD/VMD software suite (which was used in the study cited in the question) is more full-featured, as is CHARMM (the latter of which is also the name of an energy function). For prediction of binding sites de novo (without a bound experimental structure), i.e. "docking", there are DOCK and AutoDock (both with long academic histories) as well as more industry-oriented ones like Glide.
Be careful though what you're getting into - while simply viewing structures and making geometric statements by direct observation is well within reach of the average biochemist with reasonable knowledge of protein structure (provided you trust the lab that solved the structure in the first place!), simulation of proteins using energy functions requires considerable computational expertise, though the "black box" nature of some software glosses over this. It may be relatively easy to run a simulation using questionably appropriate parameters, but the adage "garbage in garbage out" applies very much here. This is somewhat true with docking as well.
I'm not an expert but a start could be PyMOL https://pymol.org/2/ Unfortunately, you need a license to use the latest version
An earlier (free) version can be found here:https://sourceforge.net/projects/pymol/files/latest/download
A tutorial can be found here:https://pymolwiki.org/index.php/Practical_Pymol_for_Beginners
Hope that helps to start!
Protein – ligand interactions
Not always, but sometimes one wants to flatten the interactions between a protein and a ligand. The aim is to unclutter the three-dimensional (3D) information to a 2D image. With such visualizations, the advantages is that one gets to see the various interactions without any of them getting buried, and concentrate on the crucial ones that are the key to protein-ligand interactions. The situations where these 2D representations are used are broadly of two areas:
- Plotting the interactions of protein-ligand complexes in the existing data (from PDB database)
- Plotting the interactions between a protein and a potential drug/small molecule from a molecular docking result. Again, the input could be from a single small molecule docking or from a virtual screening.
In this post, we will see three tools that help us in achieving the goal of plotting protein-ligand interactions.
LIGPLOT – For many years, Ligplot (1) has been the choice for plotting 2D interactions. Infact, the database pdbsum makes ligplot images for a given protein-ligand interactions. The main two things shown are the hydrogen bonds and hydrophobic interactions.
Hydrogen bonds are indicated by dashed lines between the atoms involved, while hydrophobic contacts are represented by an arc with spokes radiating towards the ligand atoms they contact. The contacted atoms are shown with spokes radiating back.
PoseView – This is a new tool that came out two years ago (2). It has Ligplot-like image generation but it has more features than Ligplot.
The 2D depiction shows hydrogen bonds as dashed lines between the interaction partners on either side. Hydrophobic interactions are illustrated as smooth contour lines between the respective amino acids and the ligand.
Recently PDB database incroporated poseview with the structures that are present. So, one can get the 2D plots straight away from PDB itself in the Ligand section of each protein, for example here. The web-interface for PoseView can be accessed here.
BINding ANAlyzer (BINANA) – This is probably the most recent protein-ligand representation tool (3). Although, not exactly a 2D plotting tool, it has more features than Ligplot or PoseView, namely it can plot electrostatic interactions, pi pi stacking, cation-pi interactions, and more. The only downside is that it needs the input in .PDBQT format. This can be obtained via AutoDock Tools. The output can be visualized via VMD, thus making the 2D back into 3D bu with distinguishable features.
1. Wallace AC, Laskowski RA, & Thornton JM (1995). LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions. Protein engineering, 8 (2), 127-34 PMID: 7630882
2. Stierand, K., & Rarey, M. (2010). Drawing the PDB: Protein−Ligand Complexes in Two Dimensions ACS Medicinal Chemistry Letters, 1 (9), 540-545 DOI: 10.1021/ml100164p
Ligands play a variety of roles in the regulation and expression of proteins. Currently, PDB has thousands of ligands and the majority of them bound non-covalently to various proteins. The non-covalent ligand binding occurs by intermolecular forces like hydrogen bonds, ionic bonds, hydrophobic-hydrophobic interaction, van der Waals forces, etc. 3D shape of the protein gets altered as a result of the ligand binding. These changes in the conformational state of the protein may activate or inhibit some specific function of the protein. Various methods have been developed to predict the binding affinity of ligands [1–9]. Many databases are also developed to summarize binding affinity of a diverse class of ligands [10, 11] or specific class of ligands [12, 13].
Ligands have high or low binding with specific amino acids depending on various factors (e.g. shape, charge, surface area). ATP has significantly higher interaction with glycine and least interaction with leucine . Various studies have been performed to understand the binding behaviour of ligands with the amino acids in a protein. Many machine learning methods have also been developed to predict the preference of interacting and non-interacting amino acids with various ligands [15–24].
However, binding preference analysis between different ligands and protein was not carried out on a large dataset. Considering this, we performed a rigorous study to understand the binding behaviour of various ligands with different amino acids. This information can be used to either enhance or diminish the binding strength of the given ligand by mutating unfavourable residue with preferred residue at the site of binding. In addition, we developed a web-based platform for the analysis of amino acid preference for all the ligand present in PDB.
Related Research Articles
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In the field of molecular modeling, docking is a method which predicts the preferred orientation of one molecule to a second when bound to each other to form a stable complex. Knowledge of the preferred orientation in turn may be used to predict the strength of association or binding affinity between two molecules using, for example, scoring functions.
Macromolecular docking is the computational modelling of the quaternary structure of complexes formed by two or more interacting biological macromolecules. Protein–protein complexes are the most commonly attempted targets of such modelling, followed by protein–nucleic acid complexes.
Protein–ligand docking is a molecular modelling technique. The goal of protein–ligand docking is to predict the position and orientation of a ligand when it is bound to a protein receptor or enzyme. Pharmaceutical research employs docking techniques for a variety of purposes, most notably in the virtual screening of large databases of available chemicals in order to select likely drug candidates.
In molecular modelling, docking is a method which predicts the preferred orientation of one molecule to another when bound together in a stable complex. In the case of protein docking, the search space consists of all possible orientations of the protein with respect to the ligand. Flexible docking in addition considers all possible conformations of the protein paired with all possible conformations of the ligand.
In the fields of computational chemistry and molecular modelling, scoring functions are mathematical functions used to approximately predict the binding affinity between two molecules after they have been docked. Most commonly one of the molecules is a small organic compound such as a drug and the second is the drug's biological target such as a protein receptor. Scoring functions have also been developed to predict the strength of intermolecular interactions between two proteins or between protein and DNA.
Critical Assessment of PRediction of Interactions (CAPRI) is a community-wide experiment in modelling the molecular structure of protein complexes, otherwise known as protein–protein docking.
AutoDock is molecular modeling simulation software. It is especially effective for protein-ligand docking. AutoDock 4 is available under the GNU General Public License. AutoDock is one of the most cited docking software applications in the research community. It is a base for the [email protected] and OpenPandemics - COVID-19 projects run at World Community Grid, to search for antivirals against HIV/AIDS and COVID-19. In February 2007, a search of the ISI Citation Index showed more than 1,100 publications had been cited using the primary AutoDock method papers. As of 2009, this number surpassed 1,200.
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David S. Goodsell, is an associate professor at the Scripps Research Institute and research professor at Rutgers University, New Jersey. He is especially known for his watercolor paintings of cell interiors.
Schellman loops are commonly occurring structural features of proteins and polypeptides. Each has six amino acid residues with two specific inter-mainchain hydrogen bonds and a characteristic main chain dihedral angle conformation. The CO group of residue i is hydrogen-bonded to the NH of residue i+5, and the CO group of residue i+1 is hydrogen-bonded to the NH of residue i+4. Residues i+1, i+2, and i+3 have negative φ (phi) angle values and the phi value of residue i+4 is positive. Schellman loops incorporate a three amino acid residue RL nest, in which three mainchain NH groups form a concavity for hydrogen bonding to carbonyl oxygens. About 2.5% of amino acids in proteins belong to Schellman loops. Two websites are available for examining small motifs in proteins, Motivated Proteins: or PDBeMotif:.
PSI-blast based secondary structure PREDiction (PSIPRED) is a method used to investigate protein structure. It uses artificial neural network machine learning methods in its algorithm. It is a server-side program, featuring a website serving as a front-end interface, which can predict a protein's secondary structure from the primary sequence.
Jeffrey Skolnick is an American computational biologist. He is currently a Georgia Institute of Technology School of Biology Professor, the Director of the Center for the Study of Systems Biology, the Mary and Maisie Gibson Chair, the Georgia Research Alliance Eminent Scholar in Computational Systems Biology, the Director of the Integrative BioSystems Institute, and was previously the Scientific Advisor at Intellimedix.
In mass spectrometry, de novo peptide sequencing is the method in which a peptide amino acid sequence is determined from tandem mass spectrometry.
Osnat Penn is an Israeli computational biologist. Penn is the third Israeli scientist in three years to win the UNESCO-L’Oréal fellowship, which she received in 2013 for her work on the genetic origins of autism. Penn is currently at the University of Washington in Seattle.
FlexAID is a molecular docking software that can use small molecules and peptides as ligands and proteins and nucleic acids as docking targets. As the name suggests, FlexAID supports full ligand flexibility as well side-chain flexibility of the target. It does using a soft scoring function based on the complementarity of the two surfaces.
Zhiping Weng is the Li Weibo Professor of biomedical research and chair of the program in integrative biology and bioinformatics at the University of Massachusetts Medical School. She was awarded Fellowship of the International Society for Computational Biology (ISCB) in 2020 for outstanding contributions to computational biology and bioinformatics.
TargetS – Predictor for Targeting Protein-ligand Binding Sites
TargetS is a new ligand-specific template-free predictor for targeting protein-ligand binding sites from primary sequences.
:: MORE INFORMATION
IEEE/ACM Trans Comput Biol Bioinform. 2013 Jul-Aug10(4):994-1008. doi: 10.1109/TCBB.2013.104.
Designing template-free predictor for targeting protein-ligand binding sites with classifier ensemble and spatial clustering.
Dong-Jun Yu, Jun Hu, Jing Yang, Hong-Bin Shen, Jinhui Tang, and Jing-Yu Yang,
Analysis of Protein Stability and Ligand Interactions by Thermal Shift Assay
Purification of recombinant proteins for biochemical assays and structural studies is time-consuming and presents inherent difficulties that depend on the optimization of protein stability. The use of dyes to monitor thermal denaturation of proteins with sensitive fluorescence detection enables rapid and inexpensive determination of protein stability using real-time PCR instruments. By screening a wide range of solution conditions and additives in a 96-well format, the thermal shift assay easily identifies conditions that significantly enhance the stability of recombinant proteins. The same approach can be used as an initial low-cost screen to discover new protein-ligand interactions by capitalizing on increases in protein stability that typically occur upon ligand binding. This unit presents a methodological workflow for small-scale, high-throughput thermal denaturation of recombinant proteins in the presence of SYPRO Orange dye. © 2015 by John Wiley & Sons, Inc.
We report a template-based method, LT-scanner, which scans the human proteome using protein structural alignment to identify proteins that are likely to bind ligands that are present in experimentally determined complexes. A scoring function that rapidly accounts for binding site similarities between the template and the proteins being scanned is a crucial feature of the method. The overall approach is first tested based on its ability to predict the residues on the surface of a protein that are likely to bind small-molecule ligands. The algorithm that we present, LBias, is shown to compare very favorably to existing algorithms for binding site residue prediction. LT-scanner’s performance is evaluated based on its ability to identify known targets of Food and Drug Administration (FDA)-approved drugs and it too proves to be highly effective. The specificity of the scoring function that we use is demonstrated by the ability of LT-scanner to identify the known targets of FDA-approved kinase inhibitors based on templates involving other kinases. Combining sequence with structural information further improves LT-scanner performance. The approach we describe is extendable to the more general problem of identifying binding partners of known ligands even if they do not appear in a structurally determined complex, although this will require the integration of methods that combine protein structure and chemical compound databases.
Computational methods that match small ligands to specific proteins they bind have many practical applications including protein function annotation and drug discovery/repurposing. Underlying these goals are related but distinct algorithmic challenges including the following: (i) given a protein, where on its surface does it bind small molecules (ii) given a protein, what small molecules will it bind (iii) given a small molecule, what proteins will it bind. There is a large literature on some of these subjects, and this paper is intended to add to this literature. However, as we discuss below, the methods we introduce have distinct features that enable us to account for protein–ligand interactions in the binding site while still allowing large-scale, genome-wide predictions to be made in a relatively limited amount of time on a modern computer cluster.
Problem i, the prediction of residues on a protein surface that bind ligands, has been widely studied. Predicted ligand-binding residues can be used to guide in silico screening of chemical libraries using docking or other approaches. Existing structure-based methods for binding site prediction fall into distinct categories. One involves the identification of binding pockets on the protein surface based for example on surface curvature (1, 2). However, since there can be concave regions on a protein surface that do not bind small molecules, or conversely, convex/flat regions that do, programs such as ConCavity (3) and LIGSITE CSC (4) combine pocket finding algorithms with sequence conservation information. FTsite (5) uses docking to probe a protein surface with various types of chemical groups and uses an empirical scoring function to identify surface patches that might favorably interact with those groups. MetaPocket 2.0 (6) and COACH (7) are “metaservers” that combine results from a range of structure-based approaches using machine learning.
Although pocket finding and sequence-based methods are often highly successful, they may miss binding sites that do not display the expected curvature or sequence characteristics. Template-based methods rely on the observation (8) that two proteins that share structural similarity will likely bind ligands at similar geometric locations on their surfaces. This is true even for remotely related proteins (i.e., different SCOP fold) (9), thus enabling the exploitation of both close and distant structural homologs in binding site prediction. Similar observations for protein–protein binding sites led to the development of the PredUs server, which has been shown to be extremely effective in predicting regions on a protein surface that bind other proteins (10 ⇓ –12).
A number of template-based programs that predict ligand-binding site residues have been reported in the past few years. A common feature is the use of geometric alignments to superimpose the structure of a template with a bound ligand (“holo” structure) on a query structure without ligands (“apo” structure). Algorithms such as 3DLigandsite (13) and FINDSITE (14, 15) score residues based in part on the number of superimposed ligands within a fixed distance from that residue. Hybrid methods have also been developed in particular, the COACH metaserver (7) combines a number of template-based methods, sequence conservation information, and ConCavity.
Here, we report a template-based method, “ligand binding site analysis” (LBias). As in other template-based methods, LBias first identifies proteins structurally similar to a query protein that contains a ligand and then places these structures and their ligands in the coordinate system of the query (Fig. 1A). LBias has a unique scoring function that reflects whether the specific types of interactions between the template and its ligand could also form with the query. As will be discussed, LBias performance is found to compare very favorably to existing state-of-the-art methods, in part due to its use of binding site similarity in weighting the contribution of a given template.
Overview of LBias and LT-scanner methods. (A) For a given query protein (shown in green) LBias collects and superposes structure neighbors A, B, and C (shown in yellow) that are cocrystalized with their bound ligands (shown in blue). Then LBias predicts the most likely ligand-binding residues (shown in red and yellow) on the query protein based on collective contact information that the superposed ligands make. (B) For a given template cocrystal structure of a drug (shown in blue) and a template protein (shown in green), LT-scanner scans through protein A, B, and C (shown in orange) for by superposing the template structure onto each protein so as to create interaction models. Then LT-scanner calculates the SimLT-scanner interaction similarity score (shown as SimLT) between the interaction models of the query–drug complex and the interactions in the binding site of the template.
The success of LBias suggests that its representation of specific types of protein–ligand interactions might be effective in the prediction of the proteins that bind to a particular ligand (the ligand’s “targets”). With this goal in mind, we developed ligand–target scanner (LT-scanner) a method to predict, on a genome-wide scale, target proteins for a given ligand based on the LBias scoring function. LT-scanner takes a ligand–protein complex structure as input and scans through a protein structure database to identify proteins that might bind to that ligand (Fig. 1B). Several computational approaches have been developed previously for target protein prediction. A number of methods use binding site similarities to predict targets (16, 17). Others involve ligand-based quantitative structure–activity relationships (18 ⇓ ⇓ ⇓ –22), although a recently developed approach, FINDSITE comb (23), combines both template-based and chemical similarity-based approaches. LT-scanner was used to predict known target human proteins of 200 Food and Drug Administration (FDA)-approved drugs that were extracted from drug–target databases (24 ⇓ ⇓ –27). Its encouraging performance and its ability to account for binding specificity among closely related proteins suggests that the method can be used effectively for both drug repurposing and “off-target” prediction (i.e., unintended targets of a given drug). Notably, using a naive Bayesian network to combine LT-scanner with a sequence-based approach yielded further improvement in performance.
Protein-ligand interaction analysis using LigPlot+
In our last article, we explained the installation of LigPlot +  on Ubuntu. In this article, we will perform protein complex analysis using LigPlot + .
Please refer to our last tutorial for executing LigPlot + on Ubuntu. If you are using the Windows version, then double-click the executable jar file named “LigPlus”.
Prepare a PDB file of a protein-ligand complex as an input to LigPlot + . You can use Pymol for this as shown below:
- Open Pymol --> File --> Open --> Select .pdb file of protein
- Go to File --> Open --> Select output file of docking
- Choose an appropriate pose of the ligand.
- Go to File --> Export Molecule --> Select 'PDB Options' --> Select 'Write CONECT records for all bonds'
- Click 'Save' --> Change 'Save as type' to 'PDB' --> Click 'Save'
This will save your protein-ligand file in PDB format. Here, we have saved it as ‘input.pdb‘.
Analyzing on LigPlot +
After opening LigPlot + , follow the steps mentioned below:
- Go to File --> Open --> Browse --> Select 'input.pdb' file . It will display how many ligands and chains this protein-ligand file contains. You can also select a range of residues for analysis including several other options.
- It also displays DIMPLOT and Antibody tabs but in this article, we are dealing with LigPlot only.
- Click 'Run'
It will display a 2D plot showing interactions between the protein and ligand. You can select different colors, and move atoms or residues, and so on.
Software to model and analyse protein-ligand interactions - Biology
Table of Contents for Model Analysis Applications
3DNA : Nucleic acid structure analysis and visualization
ACCESS : Surface Accessibility
APROPOS : Calculate pockets on the surface of a molecule
ASC: Analytic Surface Calculation
AutoDock : Program for the Automated Docking of Flexible Ligands to Macromolecules
AQUA : Check Geometry in NMR Protein Structures
CCP4 Program Suite : Structure Refinement
CHI: Computational searching of protein helix interactions
CNS : Crystallography and NMR System (Local Only)
CoordComp : Compare Side Chain Movements
CURVES : Helical Analysis of Irregular Nucleic Acids
DALI Server Comparing protein structures in 3D
DEFINE_S : Secondary Structure Analysis and Plot
DELPHI : Calculate Charge Distribution
Difference Distance Matrix Plot (DDMP) : Compare C-alpha positions
Dials and Windows : Display of DNA structure and MD simulations
DialX : Dials plots from X-PLOR trajectories
DOCK : Most Favorable Ligand/Receptor Binding
DSSP Program : Secondary Structure Calculation
DSSP Database Secondary Structure Assignments
FREEHELIX : Analysis of radically bent and kinked DNA
gnuplot: Function plotting program
GRASP : Calculate surface areas
HBPLUS : Hydrogen Bond Calculation
HELANAL : A program to characterize overall geometry of an alpha helix.
HOLE : Analyze pores and channels
LIGPLOT : 2D schematic diagrams of protein-ligand interactions
MOLEMAN2: Manipulation and analysis of PDB files
MSMS : Solevent Excluded Surface and Volume Calculation
MSP : Molecular Surface Package : Surfaces and Volumes
OS : Occluded Surface : Packing Analysis
PROCHECK : Check Geometry in Crystal Protein Structures
PROCHECK-NMR : Check Geometry in NMR Protein Structures
PROFILE : Eisenberg 3D Environment Analysis
PyMOL : Molecular Graphics and Animations
ROTAMER : Side Chain Rotamer Analysis
SPOCK : Molecular Graphics
Structural Classification of Proteins Families, Superfamilies and Folds
SURFNET : Surface Gaps, Clefts and Voids
SurfVol : Mark Gerstein's Surface Area and Volume Programs
Swiss-PDBViewer : Friendly interface to analyse several proteins at the same time
Uppsala Software Factory: Gerard Kleywegt's Programs
VOIDOO : Cavity and volume calculation, surface generation
VOLUME : Residue Voronoi Volumes
WHAT_IF: CHECK Residue environments
Xmgr : X, Y Plotting and Regression
Xplor : Structure Refinement
Software to model and analyse protein-ligand interactions - Biology
Last update: October 9, 2011
|Reviews and fundamental papers|
|Archakov, A.I. Govorun, V.M. Dubanov, A.V. Ivanov, Y.D. Veselovsky, A.V. Lewi, P. Janssen, P. Proteomics 2003, 3(4), 380-391. Protein-protein interactions as a target for drugs in proteomics.|
|Prediction contest CAPRI|
|CAPRI - Critical Assessment of PRedicted Interactions|
|Meeting report 2001: Modeling of protein interactions in genomes|