SiteComp: a server for ligand binding site analysis in protein structures.
ABSTRACT MOTIVATION: Computational characterization of ligand-binding sites in proteins provides preliminary information for functional annotation, protein design and ligand optimization. SiteComp implements binding site analysis for comparison of binding sites, evaluation of residue contribution to binding sites and identification of sub-sites with distinct molecular interaction properties. Availability and implementation: The SiteComp server and tutorials are freely available at http://sitecomp.sanchezlab.org.
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ABSTRACT: The combination of computational and directed evolution methods has proven a winning strategy for protein engineering. We refer to this approach as computer-aided protein directed evolution (CAPDE) and the review summarizes the recent developments in this rapidly growing field. We will restrict ourselves to overview the availability, usability and limitations of web servers, databases and other computational tools proposed in the last five years. The goal of this review is to provide concise information about currently available computational resources to assist the design of directed evolution based protein engineering experiment.Computational and structural biotechnology journal. 01/2012; 2:e201209008.
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ABSTRACT: With advancements in crystallographic technology and the increasing wealth of information populating structural databases, there is an increasing need for prediction tools based on spatial information that will support the characterization of proteins and protein-ligand interactions. Herein, a new web service is presented termed amino acid frequency around ligand (AFAL) for determining amino acids type and frequencies surrounding ligands within proteins deposited in the Protein Data Bank and for assessing the atoms and atom-ligand distances involved in each interaction (availability: http://structuralbio.utalca.cl/AFAL/index.html ). AFAL allows the user to define a wide variety of filtering criteria (protein family, source organism, resolution, sequence redundancy and distance) in order to uncover trends and evolutionary differences in amino acid preferences that define interactions with particular ligands. Results obtained from AFAL provide valuable statistical information about amino acids that may be responsible for establishing particular ligand-protein interactions. The analysis will enable investigators to compare ligand-binding sites of different proteins and to uncover general as well as specific interaction patterns from existing data. Such patterns can be used subsequently to predict ligand binding in proteins that currently have no structural information and to refine the interpretation of existing protein models. The application of AFAL is illustrated by the analysis of proteins interacting with adenosine-5'-triphosphate.Journal of Computer-Aided Molecular Design 08/2014; · 3.17 Impact Factor
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ABSTRACT: Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, proteinprotein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of proteinprotein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators.Molecular Informatics. 06/2014;
BIOINFORMATICS APPLICATIONS NOTE
Vol. 28 no. 8 2012, pages 1172–1173
SiteComp: a server for ligand binding site analysis in protein
Yingjie Lin, Seungyeul Yoo and Roberto Sanchez∗
Department of Structural and Chemical Biology, Mount Sinai School of Medicine, 1425 Madison Avenue, New York,
NY 10029, USA
Associate Editor: Anna Tramontano
Advance Access publication February 24, 2012
Motivation: Computational characterization of ligand-binding sites
in proteins provides preliminary information for functional annotation,
protein design and ligand optimization. SiteComp implements
binding site analysis for comparison of binding sites, evaluation of
residue contribution to binding sites and identification of sub-sites
with distinct molecular interaction properties.
Availability and implementation: The SiteComp server and tutorials
are freely available at http://sitecomp.sanchezlab.org
Contact: firstname.lastname@example.org; email@example.com
Supplementary information: Supplementary data are available at
Received on December 22, 2011; revised on February 13, 2012;
accepted on February 19, 2012
The interaction of proteins with their ligands (metabolites, proteins,
nucleic acids, lipids, etc.) is the most fundamental of all biological
mechanisms. These interactions are often specific and are the
consequence of distinct molecular interaction properties of the
binding sites. Hence, the analysis and comparison of binding site
properties can shed light on the basis of ligand affinity, selectivity
and ultimately the molecular underpinnings of protein function.
The most frequent questions that arise in binding site analysis
are: (i) Does a binding site contain regions (sub-sites) with special
formation of a binding site? (iii) What are the differences between
two similar binding sites? SiteComp is a webserver designed
to answer these questions, hence facilitating the design of new
experiments and the analysis of existing data in the context of
elucidating molecular mechanisms and drug design.
While tools for the characterization of sub-sites within a ligand-
binding region have been available since the development of the
GRID approach (Goodford, 1985), no freely available webservers
exist to carry out this type of analysis. Existing computational
methods have also achieved success in the identification of ligand-
binding sites (Ghersi and Sanchez, 2011), including detection
of local similarity (Kellenberger et al., 2008), or comparison
of interaction properties of complete proteins (Richter et al.,
2008). However, these methods are not well-suited for identifying
differences between similar binding sites, which can be exploited
to improve ligand selectivity. Methods that address the question of
∗To whom correspondence should be addressed.
(i) computational alanine scanning methods (Chong et al., 2006;
Kortemme et al., 2004; Kruger and Gohlke, 2010; Massova and
Kollman, 1999); and (ii) energy decomposition methods (Benedix
et al., 2009; Schymkowitz et al., 2005; Zoete and Michielin, 2007).
The former have been developed exclusively for protein–protein
interaction surfaces. While the latter, which are relatively accurate,
require computationally expensive molecular dynamics or Monte
SiteComp complements the existing methods, bridging several
of the current gaps, by providing a web-based interface for
of sub-sites with different interaction properties and for fast (albeit
more approximate) calculations of residue contribution to binding
sites. It integrates these three modes of binding site analysis into an
easy to use interactive interface with graphical input and output.
SiteComp uses molecular interaction fields (MIFs) as descriptors of small-
molecule ligand binding sites. MIFs describe the spatial variation of the
interaction energy between a target molecule (e.g. a protein) and a probe,
which represents a specific chemical group or atom (Ghersi and Sanchez,
2009). SiteComp provides three types of MIF-based analyses:
(i) Binding site comparison identifies regions where two proteins exhibit
differences in ligand-binding properties. After superposition of the two
input proteins, a difference MIF is calculated and post-processed using
the SiteHound algorithm (Ghersi and Sanchez, 2009) to identify difference
clusters (see Supplementary Materials for details). These clusters identify
regions with more favorable probe interactions with one protein than the
other. The difference clusters can be used, for example, as guides to explain
or design ligand selectivity between two proteins (Fig. 1).
(ii) Binding site decomposition evaluates the contribution of specific side
chains to protein–ligand interaction regions. This is achieved by comparing
the MIFs of the wild-type protein with that of the same protein with one
or more residues mutated to alanine. Up to 10 residues can be selected in a
user-defined region of the protein. A single protein is required as input and
This type of analysis can be used to identify key residues in a previously
identified binding site and design mutations that disrupt binding.
(iii) Multi-probe characterization facilitates visual comparison of MIF
clusters detected in a single protein with different chemical probes. It also
cutoff) and clustering (algorithm). Hence, this type of analysis enables
an advanced characterization of the molecular interaction properties of
a user-defined region in one protein. One application of this analysis is
the identification of sub-sites with different interaction properties within
Types of SiteComp analyses
© The Author 2012. Published by Oxford University Press. All rights reserved. For Permissions, please email: firstname.lastname@example.org
Fig. 1. Exampleofbindingsitecomparison.Comparisonofthebindingsites
of two cyclooxygenase (COX) enzymes was carried out using SiteComp.
COXs are targets for non-steroidal anti-inflammatory drugs. (a) SiteComp
COX-1 (black sidechains). (b) The non-selective COX inhibitor Ibuprofen
(gray) does not take advantage of the difference region, while whereas the
selective COX-2 inhibitor Celecoxib (black) occupies most of the predicted
selectivity region (Wang, et al., 2010).
a larger binding site (Fig. 2). Visualization of the output in the server
facilitates comparison and combination of MIF clusters detected with
different parameters and probes.
The three types of SiteComp analyses can be integrated into a combined
analysis. For example, a difference region identified in binding site
comparison can be selected to be directly analyzed using binding site
decomposition to identify residues that are important contributors to that
region. Alternatively, it could be directed into multi-probe characterization
to provide detailed information about the molecular interaction properties
of the difference site. SiteComp is also integrated with the SiteHound-web
binding site identification server (Hernandez et al., 2009), which enables
seamless analysis of predicted binding sites using the SiteComp tools.
Integration of analyses
For each of the analyses, the user can upload PDB files or specify PDB codes
probe characterization, additional chains and ligands can be selected for
display only. Next, a region of interest, the calculation box, is defined using a
graphical user interface (GUI) based on the Jmol molecular structure viewer.
The center of the calculation box can be defined interactively by selecting an
atom in Jmol, entering a residue number or specifying coordinates. The box
dimensions can also be modified interactively. Subsequently, parameters for
MIF calculation and clustering are selected. Finally, the calculation is carried
out and the output is presented in a Jmol-based GUI. Runtime is usually less
than a few minutes, depending on the size of the calculation box.
The user can retrieve the results from the calculation at runtime or within
30 days after the calculation has completed using a unique and private URL
generated at the time of job submission. After 30 days the results and input
files are deleted from the server.
The SiteComp website includes step-by-step tutorials for each type of
tested on all major operating systems and web browsers.
Usage and output
Dr Dario Ghersi for help with EasyMIFs and SiteHound usage.
Fig. 2. Example of multi-probe characterization. Sub-sites in the active site
of adenylate kinase (ADK) were identified using SiteComp. ADK catalyzes
the phosphate transfer from ATP to AMP. The figure shows AP5A, an ADK
inhibitor (Abele and Schulz, 1995) that mimics the structure of the two
substrates in theADK active site. Sub-sites identified with the methyl carbon
probe (white surfaces) highlight the regions of the active site that recognize
the adenosine groups in the inhibitor and the substrates (thin lines), while
sub-sites identified with the phosphate oxygen probe (gray surface) delineate
the phosphate transfer region (thick lines).
Funding: National Institutes of Health (NIH) [HG004508,
Conflict of Interest: none declared.
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