CaMELS: In silico prediction of calmodulin binding proteins and their binding sites

Due to Ca²⁺‐dependent binding and the sequence diversity of Calmodulin (CaM) binding proteins, identifying CaM interactions and binding sites in the wet‐lab is tedious and costly. Therefore, computational methods for this purpose are crucial to the design of such wet‐lab experiments. We present an algorithm suite called CaMELS (CalModulin intEraction Learning System) for predicting proteins that interact with CaM as well as their binding sites using sequence information alone. CaMELS offers state of the art accuracy for both CaM interaction and binding site prediction and can aid biologists in studying CaM binding proteins. For CaM interaction prediction, CaMELS uses protein sequence features coupled with a large‐margin classifier. CaMELS models the binding site prediction problem using multiple instance machine learning with a custom optimization algorithm which allows more effective learning over imprecisely annotated CaM‐binding sites during training. CaMELS has been extensively benchmarked using a variety of data sets, mutagenic studies, proteome‐wide Gene Ontology enrichment analyses and protein structures. Our experiments indicate that CaMELS outperforms simple motif‐based search and other existing methods for interaction and binding site prediction. We have also found that the whole sequence of a protein, rather than just its binding site, is important for predicting its interaction with CaM. Using the machine learning model in CaMELS, we have identified important features of protein sequences for CaM interaction prediction as well as characteristic amino acid sub‐sequences and their relative position for identifying CaM binding sites. Python code for training and evaluating CaMELS together with a webserver implementation is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#camels .     

Interactions in native binding sites cause a large change in protein dynamics

Cellular functions are regulated by molecules that interact with proteins and alter their activities. To enable such control, protein activity, and therefore protein conformational distributions, must be susceptible to alteration by molecular interactions at functional sites. Here we investigate whether interactions at functional sites cause a large change in the protein conformational distribution. We apply a computational method, called dynamics perturbation analysis (DPA), to identify sites at which interactions have a large allosteric potential D(x), which is the Kullback-Leibler divergence between protein conformational distributions with and without an interaction. In DPA, a protein is decorated with surface points that interact with neighboring protein atoms, and D(x) is calculated for each of the points in a coarse-grained model of protein vibrations. We use DPA to examine hundreds of protein structures from a standard small-molecule docking test set, and find that ligand-binding sites have elevated values of D(x): for 95% of proteins, the probability of randomly obtaining values as high as those in the binding site is 10(-3) or smaller. We then use DPA to develop a computational method to predict functional sites in proteins, and find that the method accurately predicts ligand-binding-site residues for proteins in the test set. The performance of this method compares favorably with that of a cleft analysis method. The results confirm that interactions at small-molecule binding sites cause a large change in the protein conformational distribution, and motivate using DPA for large-scale prediction of functional sites in proteins. They also suggest that natural selection favors proteins whose activities are capable of being regulated by molecular interactions.     

Evaluation of an inverse molecular design algorithm in a model binding site

Computational molecular design is a useful tool in modern drug discovery. Virtual screening is an approach that docks and then scores individual members of compound libraries. In contrast to this forward approach, inverse approaches construct compounds from fragments, such that the computed affinity, or a combination of relevant properties, is optimized. We have recently developed a new inverse approach to drug design based on the dead-end elimination and A* algorithms employing a physical potential function. This approach has been applied to combinatorially constructed libraries of small-molecule ligands to design high-affinity HIV-1 protease inhibitors (Altman et al., J Am Chem Soc 2008;130:6099-6013). Here we have evaluated the new method using the well-studied W191G mutant of cytochrome c peroxidase. This mutant possesses a charged binding pocket and has been used to evaluate other design approaches. The results show that overall the new inverse approach does an excellent job of separating binders from nonbinders. For a few individual cases, scoring inaccuracies led to false positives. The majority of these involve erroneous solvation energy estimation for charged amines, anilinium ions, and phenols, which has been observed previously for a variety of scoring algorithms. Interestingly, although inverse approaches are generally expected to identify some but not all binders in a library, due to limited conformational searching, these results show excellent coverage of the known binders while still showing strong discrimination of the nonbinders.     

Hidden partners: Using cross‐docking calculations to predict binding sites for proteins with multiple interactions

Protein‐protein interactions control a large range of biological processes and their identification is essential to understand the underlying biological mechanisms. To complement experimental approaches, in silico methods are available to investigate protein‐protein interactions. Cross‐docking methods, in particular, can be used to predict protein binding sites. However, proteins can interact with numerous partners and can present multiple binding sites on their surface, which may alter the binding site prediction quality. We evaluate the binding site predictions obtained using complete cross‐docking simulations of 358 proteins with 2 different scoring schemes accounting for multiple binding sites. Despite overall good binding site prediction performances, 68 cases were still associated with very low prediction quality, presenting individual area under the specificity‐sensitivity ROC curve (AUC) values below the random AUC threshold of 0.5, since cross‐docking calculations can lead to the identification of alternate protein binding sites (that are different from the reference experimental sites). For the large majority of these proteins, we show that the predicted alternate binding sites correspond to interaction sites with hidden partners, that is, partners not included in the original cross‐docking dataset. Among those new partners, we find proteins, but also nucleic acid molecules. Finally, for proteins with multiple binding sites on their surface, we investigated the structural determinants associated with the binding sites the most targeted by the docking partners.     

Automatic identification and representation of protein binding sites for molecular docking.

Molecular docking is a popular way to screen for novel drug compounds. The method involves aligning small molecules to a protein structure and estimating their binding affinity. To do this rapidly for tens of thousands of molecules requires an effective representation of the binding region of the target protein. This paper presents an algorithm for representing a protein's binding site in a way that is specifically suited to molecular docking applications. Initially the protein's surface is coated with a collection of molecular fragments that could potentially interact with the protein. Each fragment, or probe, serves as a potential alignment point for atoms in a ligand, and is scored to represent that probe's affinity for the protein. Probes are then clustered by accumulating their affinities, where high affinity clusters are identified as being the "stickiest" portions of the protein surface. The stickiest cluster is used as a computational binding "pocket" for docking. This method of site identification was tested on a number of ligand-protein complexes; in each case the pocket constructed by the algorithm coincided with the known ligand binding site. Successful docking experiments demonstrated the effectiveness of the probe representation.     

Mutation in a flexible linker modulates binding affinity for modular complexes

Tandem beta zippers are modular complexes formed between repeated linear motifs and tandemly arrayed domains of partner proteins in which β-strands form upon binding. Studies of such complexes, formed by LIM domain proteins and linear motifs in their intrinsically disordered partners, revealed spacer regions between the linear motifs that are relatively flexible but may affect the overall orientation of the binding modules. We demonstrate that mutation of a solvent exposed side chain in the spacer region of an LHX4–ISL2 complex has no significant effect on the structure of the complex, but decreases binding affinity, apparently by increasing flexibility of the linker.     

Crucial stages of protein folding through a solvable model: predicting target sites for enzyme-inhibiting drugs

An exactly solvable model based on the topology of a protein native state is applied to identify bottlenecks and key sites for the folding of human immunodeficiency virus type 1 (HIV-1) protease. The predicted sites are found to correlate well with clinical data on resistance to Food and Drug Administration-approved drugs. It has been observed that the effects of drug therapy are to induce multiple mutations on the protease. The sites where such mutations occur correlate well with those involved in folding bottlenecks identified through the deterministic procedure proposed in this study. The high statistical significance of the observed correlations suggests that the approach may be promisingly used in conjunction with traditional techniques to identify candidate locations for drug attacks.     

Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition

With the explosive growth of protein sequences entering into protein data banks in the post-genomic era, it is highly demanded to develop automated methods for rapidly and effectively identifying the protein–protein binding sites (PPBSs) based on the sequence information alone. To address this problem, we proposed a predictor called iPPBS-PseAAC, in which each amino acid residue site of the proteins concerned was treated as a 15-tuple peptide segment generated by sliding a window along the protein chains with its center aligned with the target residue. The working peptide segment is further formulated by a general form of pseudo amino acid composition via the following procedures: (1) it is converted into a numerical series via the physicochemical properties of amino acids; (2) the numerical series is subsequently converted into a 20-D feature vector by means of the stationary wavelet transform technique. Formed by many individual “Random Forest” classifiers, the operation engine to run prediction is a two-layer ensemble classifier, with the 1st-layer voting out the best training data-set from many bootstrap systems and the 2nd-layer voting out the most relevant one from seven physicochemical properties. Cross-validation tests indicate that the new predictor is very promising, meaning that many important key features, which are deeply hidden in complicated protein sequences, can be extracted via the wavelets transform approach, quite consistent with the facts that many important biological functions of proteins can be elucidated with their low-frequency internal motions. The web server of iPPBS-PseAAC is accessible at http://www.jci-bioinfo.cn/iPPBS-PseAAC, by which users can easily acquire their desired results without the need to follow the complicated mathematical equations involved.     

Conserved charged amino acids are key determinants for fatty acid binding proteins (FABPs)-membrane interactions. A multi-methodological computational approach

Based on the analysis of the mechanism of ligand transfer to membranes employing in vitro methods, Fatty Acid Binding Protein (FABP) family has been divided in two subgroups: collisional and diffusional FABPs. Although the collisional mechanism has been well characterized employing in vitro methods, the structural features responsible for the difference between collisional and diffusional mechanisms remain uncertain. In this work, we have identified the amino acids putatively responsible for the interaction with membranes of both, collisional and diffusional, subgroups of FABPs. Moreover, we show how specific changes in FABPs’ structure could change the mechanism of interaction with membranes. We have computed protein–membrane interaction energies for members of each subgroup of the family, and performed Molecular Dynamics simulations that have shown different configurations for the initial interaction between FABPs and membranes. In order to generalize our hypothesis, we extended the electrostatic and bioinformatics analysis over FABPs of different mammalian genus. Also, our methodological approach could be used for other systems involving protein–membrane interactions.     

Interaction of opioid peptides and other drugs with multiple delta ncx binding sites in rat brain: further evidence for heterogeneity.

Recent pharmacological data strongly support the hypothesis of δ receptor subtypes as mediators of both supraspinal and spinal antinociception (δ₁ and δ₂ receptors). In vitro ligand binding data, which are fully supportive of the in vivo data, are still lacking. A previous study indicated that [³H][ -Ala², -Leu⁵]enkephalin labels two binding sites in membranes depleted of μ binding sites by pretreatment with the site-directed acylating agent, 2-(p-ethoxybenzyl)-1-diethylaminoethyl-5-isothiocyanatobenzimidazole-HCI (BIT). The main goal of the present study was to develop a ligand-selectivity profile of the two δ_ncx binding sites. The data indicated that naltrindole and oxymorphindole were relatively selective for site 1 (20-fold). [ -Ser²,Thr⁶]Enkephalin and deltorphin-II were only 2.7-fold and 2.2-fold selective for site 1. [ -Pen², -Pen⁵]Enkephalin and deltorphin-I were 80-fold and 38-fold selective for site 2.3-Iodo-Tyr- -Ala-Gly-Phe- -Leu was 52-fold selective for site 1. Morphine had moderate affinity for site 1 (K_i = 16 nM), and was about 11-fold selective for site 1. Thus, of the 10 drugs studied, only DPDPE and DELT-I were selective for site 2. Viewed collectively with other data, it is likely that the δ₁ receptor and the δ_ncx binding site are synonymous.     

Combining features in a graphical model to predict protein binding sites

Large efforts have been made in classifying residues as binding sites in proteins using machine learning methods. The prediction task can be translated into the computational challenge of assigning each residue the label binding site or non‐binding site. Observational data comes from various possibly highly correlated sources. It includes the structure of the protein but not the structure of the complex. The model class of conditional random fields (CRFs) has previously successfully been used for protein binding site prediction. Here, a new CRF‐approach is presented that models the dependencies of residues using a general graphical structure defined as a neighborhood graph and thus our model makes fewer independence assumptions on the labels than sequential labeling approaches. A novel node feature “change in free energy” is introduced into the model, which is then denoted by ΔF‐CRF. Parameters are trained with an online large‐margin algorithm. Using the standard feature class relative accessible surface area alone, the general graph‐structure CRF already achieves higher prediction accuracy than the linear chain CRF of Li et al. ΔF‐CRF performs significantly better on a large range of false positive rates than the support‐vector‐machine‐based program PresCont of Zellner et al. on a homodimer set containing 128 chains. ΔF‐CRF has a broader scope than PresCont since it is not constrained to protein subgroups and requires no multiple sequence alignment. The improvement is attributed to the advantageous combination of the novel node feature with the standard feature and to the adopted parameter training method. Proteins 2015; 83:844–852. © 2015 Wiley Periodicals, Inc.