mechanics and dynamics simulations use distance based
cutoff approximations for faster computation of pairwise van der Waals
and electrostatic energy terms. These approximations traditionally
use a precalculated and periodically updated list of interacting atom
pairs, known as the “nonbonded neighborhood lists” or
nblists, in order to reduce the overhead of finding atom pairs that
are within distance cutoff. The size of nblists grows linearly with
the number of atoms in the system and superlinearly with the distance
cutoff, and as a result, they require significant amount of memory
for large molecular systems. The high space usage leads to poor cache
performance, which slows computation for large distance cutoffs. Also,
the high cost of updates means that one cannot afford to keep the
data structure always synchronized with the configuration of the molecules
when efficiency is at stake. We propose a dynamic octree data structure
for implicit maintenance of nblists using space linear in the number
of atoms but independent of the distance cutoff. The list can be updated
very efficiently as the coordinates of atoms change during the simulation.
Unlike explicit nblists, a single octree works for all distance cutoffs.
In addition, octree is a cache-friendly data structure, and hence,
it is less prone to cache miss slowdowns on modern memory hierarchies
than nblists. Octrees use almost 2 orders of magnitude less memory,
which is crucial for simulation of large systems, and while they are
comparable in performance to nblists when the distance cutoff is small,
they outperform nblists for larger systems and large cutoffs. Our
tests show that octree implementation is approximately 1.5 times faster
in practical use case scenarios as compared to nblists.
Many proteins of widely differing functionality and structure are capable of binding heparin and heparan sulfate. Since crystallizing protein-heparin complexes for structure determination is generally difficult, computational docking can be a useful approach for understanding specific interactions. Previous studies used programs originally developed for docking small molecules to well-defined pockets, rather than for docking polysaccharides to highly charged shallow crevices that usually bind heparin. We have extended the program PIPER and the automated protein-protein docking server ClusPro to heparin docking. Using a molecular mechanics energy function for scoring and the fast Fourier transform correlation approach, the method generates and evaluates close to a billion poses of a heparin tetrasaccharide probe. The docked structures are clustered using pairwise root mean square deviations as the distance measure. It was shown that clustering of heparin molecules close to each other but having different orientations and selecting the clusters with the highest protein-ligand contacts reliably predicts the heparin binding site. In addition, the centers of the five most populated clusters include structures close to the native orientation of the heparin. These structures can provide starting points for further refinement by methods that account for flexibility such as molecular dynamics. The heparin docking method is available as an advanced option of the ClusPro server at http://cluspro.bu.edu/.
proteins of widely differing functionality and structure are
capable of binding heparin and heparan sulfate. Since crystallizing
protein–heparin complexes for structure determination is generally
difficult, computational docking can be a useful approach for understanding
specific interactions. Previous studies used programs originally developed
for docking small molecules to well-defined pockets, rather than for
docking polysaccharides to highly charged shallow crevices that usually
bind heparin. We have extended the program PIPER and the automated
protein–protein docking server ClusPro to heparin docking.
Using a molecular mechanics energy function for scoring and the fast
Fourier transform correlation approach, the method generates and evaluates
close to a billion poses of a heparin tetrasaccharide probe. The docked
structures are clustered using pairwise root-mean-square deviations
as the distance measure. It was shown that clustering of heparin molecules
close to each other but having different orientations and selecting
the clusters with the highest protein–ligand contacts reliably
predicts the heparin binding site. In addition, the centers of the
five most populated clusters include structures close to the native
orientation of the heparin. These structures can provide starting
points for further refinement by methods that account for flexibility
such as molecular dynamics. The heparin docking method is available
as an advanced option of the ClusPro server at http://cluspro.bu.edu/.
translation initiation factor 2B (eIF2B), the guanine
nucleotide exchange factor for the G-protein eIF2, is one of the main
targets for the regulation of protein synthesis. The eIF2B activity
is inhibited in response to a wide range of stress factors and diseases,
including viral infections, hypoxia, nutrient starvation, and heme
deficiency, collectively known as the integrated stress response.
eIF2B has five subunits (α–ε). The α, β,
and δ subunits are homologous to each other and form the eIF2B
regulatory subcomplex, which is believed to be a trimer consisting
of monomeric α, β, and δ subunits. Here we use a
combination of biophysical methods, site-directed mutagenesis, and
bioinformatics to show that the human eIF2Bα subunit is in fact
a homodimer, at odds with the current trimeric model for the eIF2Bα/β/δ
regulatory complex. eIF2Bα dimerizes using the same interface
that is found in the homodimeric archaeal eIF2Bα/β/δ
aIF2B and related metabolic enzymes. We also present evidence that
the eIF2Bβ/δ binding interface is similar to that in the
eIF2Bα2 homodimer. Mutations at the predicted eIF2Bβ/δ
dimer interface cause genetic neurological disorders in humans. We
propose that the eIF2B regulatory subcomplex is an α2β2δ2 hexamer, composed of one α2 homodimer and two βδ heterodimers. Our results
offer novel insights into the architecture of eIF2B and its interactions
with the G-protein eIF2.
The potential utility of synthetic macrocycles as drugs, particularly against low
druggability targets such as protein-protein interactions, has been widely discussed.
There is little information, however, to guide the design of macrocycles for good target
protein-binding activity or bioavailability. To address this knowledge gap we analyze the
binding modes of a representative set of macrocycle-protein complexes. The results,
combined with consideration of the physicochemical properties of approved macrocyclic
drugs, allow us to propose specific guidelines for the design of synthetic macrocycles
libraries possessing structural and physicochemical features likely to favor strong
binding to protein targets and also good bioavailability. We additionally provide evidence
that large, natural product derived macrocycles can bind to targets that are not druggable
by conventional, drug-like compounds, supporting the notion that natural product inspired
synthetic macrocycles can expand the number of proteins that are druggable by synthetic
druglikeness; druggability; ligand efficiency; binding mode; macrocyclic drugs
We propose a new stochastic global optimization method targeting protein docking problems. The method is based on finding a general convex polynomial underestimator to the binding energy function in a permissive subspace that possesses a funnel-like structure. We use Principal Component Analysis (PCA) to determine such permissive subspaces. The problem of finding the general convex polynomial underestimator is reduced into the problem of ensuring that a certain polynomial is a Sum-of-Squares (SOS), which can be done via semi-definite programming. The underestimator is then used to bias sampling of the energy function in order to recover a deep minimum. We show that the proposed method significantly improves the quality of docked conformations compared to existing methods.
In this paper we consider the problem of minimization of a cost function that depends on the location and poses of one or more rigid bodies, or bodies that consist of rigid parts hinged together. We present a unified setting for formulating this problem as an optimization on an appropriately defined manifold for which efficient manifold optimizations can be developed. This setting is based on a Lie group representation of the rigid movements of a body that is different from what is commonly used for this purpose. We illustrate this approach by using the steepest descent algorithm on the manifold of the search space and specify conditions for its convergence.
In screening a library of natural and synthetic products for eukaryotic translation modulators, we identified two natural products, isohymenialdisine and hymenialdisine, that exhibit stimulatory effects on translation. The characterization of these compounds lead to the insight that mRNA used to program the translation extracts during high throughput assay set-up was leading to phosphorylation of eIF2α, a potent negative regulatory event that is mediated by one of four kinases. We identified double-stranded RNA-dependent protein kinase (PKR) as the eIF2α kinase that was being activated by exogenously added mRNA template. Characterization of the mode of action of isohymenialdisine revealed that it directly acts on PKR by inhibiting autophosphorylation, perturbs the PKR-eIF2α phosphorylation axis, and can be modeled into the PKR ATP binding site. Our results identify a source of false positives for high throughput screening (HTS) campaigns using translation extracts, raising a cautionary note for this type of screen.
High Throughput Screens; Translation; PKR; eIF2α; Isohymenialdisine; Hymenialdisine
The protein docking server ClusPro has been participating in CAPRI since its introduction in 2004. This paper evaluates the performance of ClusPro 2.0 for targets 46–58 in rounds 22–27 of CAPRI. The analysis leads to a number of important observations. First, ClusPro reliably yields acceptable or medium accuracy models for targets of moderate difficulty that have also been successfully predicted by other groups, and fails only for targets that have few acceptable models submitted. Second, the quality of automated docking by ClusPro is very close to that of the best human predictor groups, including our own submissions. This is very important, because servers have to submit results within 48 hours and the predictions should be reproducible, whereas human predictors have several weeks and can use any type of information. Third, while we refined the ClusPro results for manual submission by running computationally costly Monte Carlo minimization simulations, we observed significant improvement in accuracy only for two of the six complexes correctly predicted by ClusPro. Fourth, new developments, not seen in previous rounds of CAPRI, are that the top ranked model provided by ClusPro was acceptable or better quality for all these six targets, and that the top ranked model was also the highest quality for five of the six, confirming that ranking models based on cluster size can reliably identify the best near-native conformations.
protein-protein docking; structure refinement; method development; CAPRI docking experiment; web based server; user community
Peptide-mediated interactions, in which a short linear motif binds to a globular domain, play major roles in cellular regulation. An accurate structural model of this type of interaction is an excellent starting point for the characterization of the binding specificity of a given peptide-binding domain. A number of different protocols have recently been proposed for the accurate modeling of peptide-protein complex structures, given the structure of the protein receptor and the binding site on its surface. When no information about the peptide binding site(s) is a priori available, there is a need for new approaches to locate peptide-binding sites on the protein surface. While several approaches have been proposed for the general identification of ligand binding sites, peptides show very specific binding characteristics, and therefore, there is a need for robust and accurate approaches that are optimized for the prediction of peptide-binding sites.
Here we present PeptiMap, a protocol for the accurate mapping of peptide binding sites on protein structures. Our method is based on experimental evidence that peptide-binding sites also bind small organic molecules of various shapes and polarity. Using an adaptation of ab initio ligand binding site prediction based on fragment mapping (FTmap), we optimize a protocol that specifically takes into account peptide binding site characteristics. In a high-quality curated set of peptide-protein complex structures PeptiMap identifies for most the accurate site of peptide binding among the top ranked predictions. We anticipate that this protocol will significantly increase the number of accurate structural models of peptide-mediated interactions.
protein peptide interactions; FFT sampling; binding site detection; mapping; PeptiDB
Most structure prediction algorithms consist of initial sampling of the conformational space, followed by re-scoring and possibly refinement of a number of selected structures. Here we focus on protein docking, and show that while decoupling sampling and scoring facilitates method development, integration of the two steps can lead to substantial improvements in docking results. Since decoupling is usually achieved by generating a decoy set containing both non-native and near-native docked structures, which are then used for scoring function construction, we first review the roles and potential pitfalls of decoys in protein-protein docking, and show that some type of decoys are better than others for method development. We then describe three case studies showing that complete decoupling of scoring from sampling is not optimal for solving realistic docking problems. Although some of the examples are based on our own experience, the results of the CAPRI docking and scoring experiments also show that decoupling leads to worse results. Next we investigate how the selection of training and decoy sets affects the performance of the scoring functions obtained. Finally, we discuss pathways to better integration of the two steps, and show some algorithms that achieve a certain level of integration. Although we focus on protein-protein docking, our observations also apply to other conformational search problems, including protein structure prediction and the docking of small molecules to proteins.
Molecular interaction; protein-protein docking; conformational search; structure refinement; CAPRI docking experiment; scoring function; molecular mechanics; Monte Carlo method; structure-based potential
Community-wide blind prediction experiments such as CAPRI and CASP provide an objective measure of the current state of predictive methodology. Here we describe a community-wide assessment of methods to predict the effects of mutations on protein-protein interactions. Twenty-two groups predicted the effects of comprehensive saturation mutagenesis for two designed influenza hemagglutinin binders and the results were compared with experimental yeast display enrichment data obtained using deep sequencing. The most successful methods explicitly considered the effects of mutation on monomer stability in addition to binding affinity, carried out explicit side chain sampling and backbone relaxation, and evaluated packing, electrostatic and solvation effects, and correctly identified around a third of the beneficial mutations. Much room for improvement remains for even the best techniques, and large-scale fitness landscapes should continue to provide an excellent test bed for continued evaluation of methodological improvement.
CAPRI; hemagglutinin; binding; deep mutational scanning; yeast display
Background: The use of alternative flame retardants has increased since the phase out of pentabromodiphenyl ethers (pentaBDEs). One alternative, Firemaster® 550 (FM550), induces obesity in rats. Triphenyl phosphate (TPP), a component of FM550, has a structure similar to that of organotins, which are obesogenic in rodents.
Objectives: We tested the hypothesis that components of FM550 are biologically active peroxisome proliferator-activated receptor γ (PPARγ) ligands and estimated indoor exposure to TPP.
Methods: FM550 and its components were assessed for ligand binding to and activation of human PPARγ. Solvent mapping was used to model TPP in the PPARγ binding site. Adipocyte and osteoblast differentiation were assessed in bone marrow multipotent mesenchymal stromal cell models. We estimated exposure of children to TPP using a screening-level indoor exposure model and house dust concentrations determined previously.
Results: FM550 bound human PPARγ, and binding appeared to be driven primarily by TPP. Solvent mapping revealed that TPP interacted with binding hot spots within the PPARγ ligand binding domain. FM550 and its organophosphate components increased human PPARγ1 transcriptional activity in a Cos7 reporter assay and induced lipid accumulation and perilipin protein expression in BMS2 cells. FM550 and TPP diverted osteogenic differentiation toward adipogenesis in primary mouse bone marrow cultures. Our estimates suggest that dust ingestion is the major route of exposure of children to TPP.
Conclusions: Our findings suggest that FM550 components bind and activate PPARγ. In addition, in vitro exposure initiated adipocyte differentiation and antagonized osteogenesis. TPP likely is a major contributor to these biological actions. Given that TPP is ubiquitous in house dust, further studies are warranted to investigate the health effects of FM550.
Citation: Pillai HK, Fang M, Beglov D, Kozakov D, Vajda S, Stapleton HM, Webster TF, Schlezinger JJ. 2014. Ligand binding and activation of PPARγ by Firemaster® 550: effects on adipogenesis and osteogenesis in vitro. Environ Health Perspect 122:1225–1232; http://dx.doi.org/10.1289/ehp.1408111
Many protein-protein interactions (PPIs) are compelling targets for drug discovery, and in a number of cases can be disrupted by small molecules. The main goal of this study is to examine the mechanism of binding site formation in the interface region of proteins that are PPI targets by comparing ligand-free and ligand-bound structures. To avoid any potential bias, we focus on ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) techniques and deposited in the Protein Data Bank, rather than on ensembles specifically generated for this study. The measures used for structure comparison are based on detecting binding hot spots, i.e., protein regions that are major contributors to the binding free energy. The main tool of the analysis is computational solvent mapping, which explores the surface of proteins by docking a large number of small “probe” molecules. Although we consider conformational ensembles obtained by NMR techniques, the analysis is independent of the method used for generating the structures. Finding the energetically most important regions, mapping can identify binding site residues using ligand-free models based on NMR data. In addition, the method selects conformations that are similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. This agrees with the conformational selection model of molecular recognition, which assumes such pre-existing conformations. The analysis also shows the maximum level of similarity between unbound and bound states that is achieved without any influence from a ligand. Further shift toward the bound structure assumes protein-peptide or protein-ligand interactions, either selecting higher energy conformations that are not part of the NMR ensemble, or leading to induced fit. Thus, forming the sites in protein-protein interfaces that bind peptides and can be targeted by small ligands always includes conformational selection, although other recognition mechanisms may also be involved.
Many protein-protein interfaces (PPIs) are biologically compelling drug targets. Disrupting the interaction between two large proteins by a small inhibitor requires forming a high affinity binding site in the interface that generally can bind both peptides and drug-like compounds. Here we investigate whether such sites are induced by peptide or ligand binding, or already exist in the unbound state. The analysis requires comparing ligand-free and ligand-bound structures. To avoid any potential bias, we study ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) rather than generated by simulations. The analysis is based on computational solvent mapping, which explores the surface of the target protein by docking a large number of small “probe” molecules. Results show that ensembles of ligand-free models always include conformations that are fairly similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. The analysis also identifies the models that are the most similar to a bound state, and shows the maximum level of similarity that is achieved without any influence from a ligand. While forming the binding site may require a combination of recognition mechanisms, there is preference for the spontaneous formation of bound-like structures.
Side-chain positioning (SCP) is an important component of computational protein docking methods. Existing SCP methods and available software have been designed for protein folding applications where side-chain positioning is also important. As a result they do not take into account significant special structure that SCP for docking exhibits. We propose a new algorithm which poses SCP as a Maximum Weighted Independent Set (MWIS) problem on an appropriately constructed graph. We develop an approximate algorithm which solves a relaxation of the MWIS and then rounds the solution to obtain a high-quality feasible solution to the problem. The algorithm is fully distributed and can be executed on a large network of processing nodes requiring only local information and message-passing between neighboring nodes. Motivated by the special structure in docking, we establish optimality guarantees for a certain class of graphs. Our results on a benchmark set of enzyme-inhibitor protein complexes show that our predictions are close to the native structure and are comparable to the ones obtained by a state-of-the-art method. The results are substantially improved if rotamers from unbound protein structures are included in the search. We also establish that the use of our SCP algorithm substantially improves docking results.
Our work is motivated by energy minimization of biological macromolecules, an essential step in computational docking. By allowing some ligand flexibility, we generalize a recently introduced novel representation of rigid body minimization as an optimization on the SO(3)×R3 manifold, rather than on the commonly used Special Euclidean group SE(3). We show that the resulting flexible docking can also be formulated as an optimization on a Lie group that is the direct product of simpler Lie groups for which geodesics and exponential maps can be easily obtained. Our computational results for a local optimization algorithm developed based on this formulation show that it is about an order of magnitude faster than the state-of-the-art local minimization algorithms for computational protein-small molecule docking.
Computational solvent mapping finds binding hot spots, determines their druggability and provides information for drug design. While mapping of a ligand-bound structure yields more accurate results, usually the apo structure serves as the starting point in design. The FTFlex algorithm, implemented as a server, can modify an apo structure to yield mapping results that are similar to those of the respective bound structure. Thus, FTFlex is an extension of our FTMap server, which only considers rigid structures. FTFlex identifies flexible residues within the binding site and determines alternative conformations using a rotamer library. In cases where the mapping results of the apo structure were in poor agreement with those of the bound structure, FTFlex was able to yield a modified apo structure, which lead to improved FTMap results. In cases where the mapping results of the apo and bound structures were in good agreement, no new structure was predicted.
Availability: FTFlex is freely available as a web-based server at http://ftflex.bu.edu/.
firstname.lastname@example.org or email@example.com
Supplementary data are available at Bioinformatics online.
We report a comprehensive analysis of binding energy hot spots at the protein-protein interaction (PPI) interface between NF-κB Essential Modulator (NEMO) and IκB kinase subunit β (IKKβ), an interaction that is critical for NF-κB pathway signaling, using experimental alanine scanning mutagenesis and also the FTMap method for computational fragment screening. The experimental results confirm that the previously identified NBD region of IKKβ contains the highest concentration of hot spot residues, the strongest of which are W739, W741 and L742 (ΔΔG = 4.3, 3.5 and 3.2 kcal/mol, respectively). The region occupied by these residues defines a potentially druggable binding site on NEMO that extends for ~16 Å to additionally include the regions that bind IKKβ L737 and F734. NBD residues D738 and S740 are also important for binding but do not make direct contact with NEMO, instead likely acting to stabilize the active conformation of surrounding residues. We additionally found two previously unknown hot spot regions centered on IKKβ residues L708/V709 and L719/I723. The computational approach successfully identified all three hot spot regions on IKKβ. Moreover, the method was able to accurately quantify the energetic importance of all hot spots residues involving direct contact with NEMO. Our results provide new information to guide the discovery of small molecule inhibitors that target the NEMO/IKKβ interaction. They additionally clarify the structural and energetic complementarity between “pocket-forming” and “pocket occupying” hot spot residues, and further validate computational fragment mapping as a method for identifying hot spots at PPI interfaces.
IKKγ; alanine scanning mutagenesis; protein-protein interactions; IKKγ; fluorescence polarization; fluorescence anisotropy
Our work is motivated by energy minimization in the space of rigid affine transformations of macromolecules, an essential step in computational protein-protein docking. We introduce a novel representation of rigid body motion that leads to a natural formulation of the energy minimization problem as an optimization on the SO(3)×R3 manifold, rather than the commonly used SE(3). The new representation avoids the complications associated with optimization on the SE(3) manifold and provides additional flexibilities for optimization not available in that formulation. The approach is applicable to general rigid body minimization problems. Our computational results for a local optimization algorithm developed based on the new approach show that it is about an order of magnitude faster than a state of art local minimization algorithms for computational protein-protein docking.
An outstanding challenge has been to understand the mechanism whereby proteins associate. We report here the results of exhaustively sampling the conformational space in protein–protein association using a physics-based energy function. The agreement between experimental intermolecular paramagnetic relaxation enhancement (PRE) data and the PRE profiles calculated from the docked structures shows that the method captures both specific and non-specific encounter complexes. To explore the energy landscape in the vicinity of the native structure, the nonlinear manifold describing the relative orientation of two solid bodies is projected onto a Euclidean space in which the shape of low energy regions is studied by principal component analysis. Results show that the energy surface is canyon-like, with a smooth funnel within a two dimensional subspace capturing over 75% of the total motion. Thus, proteins tend to associate along preferred pathways, similar to sliding of a protein along DNA in the process of protein-DNA recognition.
Proteins rarely act alone. Instead, they tend to bind to other proteins to form structures known as complexes. When two proteins come together to form a complex, they twist and turn through a series of intermediate states before they form the actual complex. These intermediate states are difficult to study because they don’t last for very long, which means that our knowledge of how complexes are formed remains incomplete.
One promising approach for studying the formation of complexes is called paramagnetic relaxation enhancement. In this technique certain areas in one of the proteins are labelled with magnetic particles, which produce signals when the two proteins are close to each other. Repeating the measurement several times with the magnetic particles in different positions provides information about the overall structure of the complex. Computational modelling can then be used to work out the fine details of the structure, including the shapes of the intermediate structures made by the proteins as they interact.
A computer method called docking can be used to predict the most favourable positions that the proteins can take, relative to one another, in a complex. This involves calculating the energy contained in the system, with the correct structure having the lowest energy. Docking methods also predict protein models with slightly higher energies, but with structures that are radically different. Modellers usually ignore these structures, but comparing the docking results to paramagnetic relaxation enhancement data, Kozakov et al. found that these structures actually represent the intermediate states.
Analysing the structure of the intermediate states revealed that the movement of the two proteins relative to one another is severely restricted as they form the final complex. Kozakov et al. found that proteins associate along preferred pathways, similar to the way a protein slides along DNA in the process of protein-DNA recognition. Knowing that the movement of the proteins is restricted in this way will enable researchers to improve the efficiency of docking calculations.
encounter landscapes; FFT sampling; protein–protein interactions; none
Initiation of transcription in human mitochondria involves two factors, TFAM and TFB2M, in addition to the mitochondrial RNA polymerase, POLRMT. We have investigated the organization of the human mitochondrial transcription initiation complex on the light-strand promoter (LSP) through solution X-ray scattering, electron microscopy (EM) and biochemical studies. Our EM results demonstrate a compact organization of the initiation complex, suggesting that protein–protein interactions might help mediate initiation. We demonstrate that, in the absence of DNA, only POLRMT and TFAM form a stable interaction, albeit one with low affinity. This is consistent with the expected transient nature of the interactions necessary for initiation and implies that the promoter DNA acts as a scaffold that enables formation of the full initiation complex. Docking of known crystal structures into our EM maps results in a model for transcriptional initiation that strongly correlates with new and existing biochemical observations. Our results reveal the organization of TFAM, POLRMT and TFB2M around the LSP and represent the first structural characterization of the entire mitochondrial transcriptional initiation complex.
Virtually all docking methods include some local continuous minimization of an energy/scoring function in order to remove steric clashes and obtain more reliable energy values. In this paper, we describe an efficient rigid-body optimization algorithm that, compared to the most widely used algorithms, converges approximately an order of magnitude faster to conformations with equal or slightly lower energy. The space of rigid body transformations is a nonlinear manifold, namely, a space which locally resembles a Euclidean space. We use a canonical parametrization of the manifold, called the exponential parametrization, to map the Euclidean tangent space of the manifold onto the manifold itself. Thus, we locally transform the rigid body optimization to an optimization over a Euclidean space where basic optimization algorithms are applicable. Compared to commonly used methods, this formulation substantially reduces the dimension of the search space. As a result, it requires far fewer costly function and gradient evaluations and leads to a more efficient algorithm. We have selected the LBFGS quasi-Newton method for local optimization since it uses only gradient information to obtain second order information about the energy function and avoids the far more costly direct Hessian evaluations. Two applications, one in protein-protein docking, and the other in protein-small molecular interactions, as part of macromolecular docking protocols are presented. The code is available to the community under open source license, and with minimal effort can be incorporated into any molecular modeling package.
Motivation: An effective docking algorithm for antibody–protein antigen complex prediction is an important first step toward design of biologics and vaccines. We have recently developed a new class of knowledge-based interaction potentials called Decoys as the Reference State (DARS) and incorporated DARS into the docking program PIPER based on the fast Fourier transform correlation approach. Although PIPER was the best performer in the latest rounds of the CAPRI protein docking experiment, it is much less accurate for docking antibody–protein antigen pairs than other types of complexes, in spite of incorporating sequence-based information on the location of the paratope. Analysis of antibody–protein antigen complexes has revealed an inherent asymmetry within these interfaces. Specifically, phenylalanine, tryptophan and tyrosine residues highly populate the paratope of the antibody but not the epitope of the antigen.
Results: Since this asymmetry cannot be adequately modeled using a symmetric pairwise potential, we have removed the usual assumption of symmetry. Interaction statistics were extracted from antibody–protein complexes under the assumption that a particular atom on the antibody is different from the same atom on the antigen protein. The use of the new potential significantly improves the performance of docking for antibody–protein antigen complexes, even without any sequence information on the location of the paratope. We note that the asymmetric potential captures the effects of the multi-body interactions inherent to the complex environment in the antibody–protein antigen interface.
Availability: The method is implemented in the ClusPro protein docking server, available at http://cluspro.bu.edu.
firstname.lastname@example.org or email@example.com
Supplementary data are available at Bioinformatics online.
In the context of protein-protein interactions, the term “hot spot” refers to a residue or cluster of residues that makes a major contribution to the binding free energy, as determined by alanine scanning mutagenesis. In contrast, in pharmaceutical research a hot spot is a site on a target protein that has high propensity for ligand binding and hence is potentially important for drug discovery. Here we examine the relationship between these two hot spot concepts by comparing alanine scanning data for a set of 15 proteins with results from mapping the protein surfaces for sites that can bind fragment-sized small molecules. We find the two types of hot spots are largely complementary; the residues protruding into hot spot regions identified by computational mapping or experimental fragment screening are almost always themselves hot spot residues as defined by alanine scanning experiments. Conversely, a residue that is found by alanine scanning to contribute little to binding rarely interacts with hot spot regions on the partner protein identified by fragment mapping. In spite of the strong correlation between the two hot spot concepts, they fundamentally differ, however. In particular, while identification of a hot spot by alanine scanning establishes the potential to generate substantial interaction energy with a binding partner, there are additional topological requirements to be a hot spot for small molecule binding. Hence, only a minority of hot spots identified by alanine scanning represent sites that are potentially useful for small inhibitor binding, and it is this subset that is identified by experimental or computational fragment screening.
The goal of this paper is to reduce the complexity of the side chain search within docking problems. We apply six methods of generating side chain conformers to unbound protein structures, and determine their ability of obtaining the bound conformation in small ensembles of conformers. Methods are evaluated in terms of the positions of side chain end groups. Results for 68 protein complexes yield two important observations. First, the end group positions change less than 1 Å upon association for over 60% of interface side chains. Thus, the unbound protein structure carries substantial information about the side chains in the bound state, and the inclusion of the unbound conformation into the ensemble of conformers is very beneficial. Second, considering each surface side chain separately in its protein environment, small ensembles of low energy states include the bound conformation for a large fraction of side chains. In particular, the ensemble consisting of the unbound conformation and the two highest probability predicted conformers includes the bound conformer with an accuracy of 1 Å for 78% of interface side chains. Since more than 60% of the interface side chains have only one conformer and many others only a few, these ensembles of low energy states substantially reduce the complexity of side chain search in docking problems. This approach was already used for finding pockets in protein-protein interfaces that can bind small molecules to potentially disrupt protein-protein interactions. Side chain search with the reduced search space will also be incorporated into protein docking algorithms.
rotamer libraries; side chain flexibility; protein binding; structure prediction; preexisting ensemble of conformers