Insight into the interaction sites between fatty acid binding proteins and their ligands

Fatty acid binding proteins (FABPs), are evolutionarily conserved small cytoplasmic proteins that occur in many tissue-specific types. One of their primary functions is to facilitate the clearance of the cytoplasmic matrix from free fatty acids and of other detergent-like compounds. Crystallographic studies of FABP proteins have revealed a well defined binding site located deep inside their β-clam structure that is hardly exposed to the bulk solution. However, NMR measurements revealed that, when the protein is equilibrated with its ligands, residues that are clearly located on the outer surface of the protein do interact with the ligand. To clarify this apparent contradiction we applied molecular dynamics simulations to follow the initial steps associated with the FABP–fatty acid interaction using, as a model, the interaction of toad liver basic FABP, or chicken liver bile acid binding protein, with a physiological concentration of palmitate ions. The simulations (~200 ns of accumulated time) show that fatty acid molecules interact, unevenly, with various loci on the protein surface, with the favored regions being the portal and the anti-portal domains. Random encounters with palmitate at these regions led to lasting adsorption to the surface, while encounters at the outer surface of the β-clam were transient. Therefore, we suggest that the protein surface is capable of sequestering free fatty acids from solution, where brief encounters evolve into adsorbed states, which later mature by migration of the ligand into a more specific binding site.     

South western blot mapping: a procedure for simultaneous characterization of DNA binding proteins and their specific genomic DNA target sites

A method called "South Western blot mapping" for rapid characterization of both DNA binding proteins and their specific sites on genomic DNA is described. Proteins are separated on a sodium dodecyl sulfate (SDS) polyacrylamide gel, renatured by removing SDS in the presence of urea, and blotted onto nitrocellulose by diffusion. The genomic DNA region of interest is digested by restriction enzymes selected to produce fragments of appropriate but different sizes, which are subsequently end-labeled and allowed to bind to the separated proteins. The specifically bound DNA is eluted from each individual protein-DNA complex and analyzed by acrylamide gel electrophoresis. Evidence that tissue-specific DNA binding proteins may be detected by this technique is presented. Moreover, their sequence-specific binding allows the purification of the corresponding selectively bound DNA fragments and may improve protein-mediated cloning of DNA regulatory sequences.     

The effects of local DNA sequence on the interaction of ligands with their preferred binding sites

We have examined the effects of local DNA sequence on the interaction of distamycin, Hoechst 33258, echinomycin, actinomycin and mithramycin with their preferred binding sites using a series of DNA fragments that contain every symmetrical hexanucleotide sequence. In several instances we find that the affinity for the ligands' preferred binding sites is affected by the hexanucleotide context in which they are located. The AT-selective minor groove binding ligand Hoechst 33258 shows a 200-fold difference in binding to the 16 different X(A/T)(4)Y sites; the strongest binding is to AAATTT and the weakest is to (G/C)TTAA(C/G). Although TTAA is generally a poor binding site, ATTAAT is better than TTTAAA and they are both much better than GTTAAC and CTTAAG. Similarly, TTATAA and ATATAT are better binding sites than GTATAC and CTATAG. In contrast, distamycin shows less discrimination between the various X(A/T)(4)Y sites, with a 20-fold difference between the best [(A/T)AATT(T/A)] and worst [GATATC and (G/C)TTAA(C/G)] sites. Although actinomycin binds to GpC it shows little or no interaction with any of the GGCC sites, yet shows only a six-fold variation in affinities for the other XYGCXY sites. Echinomycin binds to CpG yet shows no binding to TTCGAA, TGCGCA and AGCGCT, while the best binding is to AACGTT. The tetranucleotides CCGG and ACGT produce consistently good binding sites, irrespective of the surrounding sequences, while the interaction with TCGA and GCGC is sensitive to the hexanucleotide context. Hexanucleotides with a central GCGC, flanked by A and T are weaker echinomycin sites than those flanked by G and C, especially CGCGCG. The best X(G/C)(4)Y binding sites for mithramycin were located at AGCGCT and GGGCCC, and the worst at CCCGGG and TCCGGA. These footprinting fragments are valuable tools for comparing the binding of ligands to all the potential symmetrical hexanucleotides and provide insights into the effects of local DNA sequence on ligand-DNA interactions.     

PSSM-based prediction of DNA binding sites in proteins

Background  

Detection of DNA-binding sites in proteins is of enormous interest for technologies targeting gene regulation and manipulation. We have previously shown that a residue and its sequence neighbor information can be used to predict DNA-binding candidates in a protein sequence. This sequence-based prediction method is applicable even if no sequence homology with a previously known DNA-binding protein is observed. Here we implement a neural network based algorithm to utilize evolutionary information of amino acid sequences in terms of their position specific scoring matrices (PSSMs) for a better prediction of DNA-binding sites.     

Structure-based prediction of DNA target sites by regulatory proteins

Regulatory proteins play a critical role in controlling complex spatial and temporal patterns of gene expression in higher organism, by recognizing multiple DNA sequences and regulating multiple target genes. Increasing amounts of structural data on the protein-DNA complex provides clues for the mechanism of target recognition by regulatory proteins. The analyses of the propensities of base-amino acid interactions observed in those structural data show that there is no one-to-one correspondence in the interaction, but clear preferences exist. On the other hand, the analysis of spatial distribution of amino acids around bases shows that even those amino acids with strong base preference such as Arg with G are distributed in a wide space around bases. Thus, amino acids with many different geometries can form a similar type of interaction with bases. The redundancy and structural flexibility in the interaction suggest that there are no simple rules in the sequence recognition, and its prediction is not straightforward. However, the spatial distributions of amino acids around bases indicate a possibility that the structural data can be used to derive empirical interaction potentials between amino acids and bases. Such information extracted from structural databases has been successfully used to predict amino acid sequences that fold into particular protein structures. We surmised that the structures of protein-DNA complexes could be used to predict DNA target sites for regulatory proteins, because determining DNA sequences that bind to a particular protein structure should be similar to finding amino acid sequences that fold into a particular structure. Here we demonstrate that the structural data can be used to predict DNA target sequences for regulatory proteins. Pairwise potentials that determine the interaction between bases and amino acids were empirically derived from the structural data. These potentials were then used to examine the compatibility between DNA sequences and the protein-DNA complex structure in a combinatorial "threading" procedure. We applied this strategy to the structures of protein-DNA complexes to predict DNA binding sites recognized by regulatory proteins. To test the applicability of this method in target-site prediction, we examined the effects of cognate and noncognate binding, cooperative binding, and DNA deformation on the binding specificity, and predicted binding sites in real promoters and compared with experimental data. These results show that target binding sites for several regulatory proteins are successfully predicted, and our data suggest that this method can serve as a powerful tool for predicting multiple target sites and target genes for regulatory proteins.     

GYF domain proteomics reveals interaction sites in known and novel target proteins

GYF domains are conserved eukaryotic adaptor domains that recognize proline-rich sequences. Although the structure and function of the prototypic GYF domain from the human CD2BP2 protein have been characterized in detail, very little is known about GYF domains from other proteins and species. Here we describe the binding properties of four GYF domains of various origins. Phage display in combination with SPOT analysis revealed the PPG(F/I/L/M/V) motif as a general recognition signature. Based on these results, the proteomes of human, yeast, and Arabidopsis thaliana were searched for potential interaction sites. Binding of several candidate proteins was confirmed by pull-down experiments or yeast two-hybrid analysis. The binding epitope of the GYF domain from the yeast SMY2 protein was mapped by NMR spectroscopy and led to a structural model that accounts for the different binding properties of SMY2-type GYF domains and the CD2BP2-GYF domain.     

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 .     

Biosensor measurement of the interaction kinetics between insulin-like growth factors and their binding proteins.

The binding kinetics of human insulin-like growth factor binding protein (IGFBP) 1-6 for recombinant human insulin-like growth factor (IGF) I and II were measured and compared in the present study using surface plasmon resonance biosensor technique. Different concentrations of IGFBPs (5-100 nM) were allowed to interact with the immobilized IGF-I or IGF-II on sensor chip surface. Both des(1-3)IGF-I and insulin are known to bind weakly to the IGFBPs and therefore are used as negative controls for the binding experiments. The resultant sensorgrams were analyzed by using simple 1:1 binding model to derive both the association rate (k(a)) and dissociation rate (k(d)) constants for IGFBP-IGF interactions. The k(a) values of IGFBPs are in the range of 1x10(4) to 9x10(5) M(-1) s(-1) for IGF-I and 7x10(3) to 1.7x10(6) M(-1) s(-1) for IGF-II, respectively. The orders of k(a) for both IGF-I and IGF-II are IGFBP-3>IGFBP-5>IGFBP-6>IGFBP-4>IGFBP-2>++ +IGFBP-1. The k(d) values of IGFBPs are in the range of 1.5x10(-5) to 2x10(-4) s(-1) for IGF-I and 3.6x10(-5) to 3.7x10(-4) s(-1) for IGF-II, respectively. The order of k(d) for IGF-I is IGFBP-6>IGFBP-5>IGFBP-4>IGFBP-3>IGFBP-2>++ +IGFBP-1 and that for IGF-II is IGFBP-5>IGFBP-6>IGFBP-2>IGFBP-4>IGFBP-3>++ +IGFBP-1, respectively. The equilibrium affinity constants (K(A)) were calculated based on the ratio of k(a)/k(d) and were more precise than the published literature values based on competitive radioligand binding assays. The systematic study enables a direct comparison on the IGF-binding properties among the various IGFBPs, and the kinetic data provide additional information to delineate the physiological role of different IGFBPs in vivo.     

The taxonomy of binding sites in proteins

Summary Conservation of polypeptide fold and mode of ligand binding is frequently found within proteins of related function. Examples illustrating this phenomenon are taken from NAD linked enzymes, nucleotide binding proteins, polysaccharide binding proteins, heme binding proteins and enzymes with essential Fe-S complexes or zinc atoms.     

Searching target sites on DNA by proteins: Role of DNA dynamics under confinement

DNA-binding proteins (DBPs) rapidly search and specifically bind to their target sites on genomic DNA in order to trigger many cellular regulatory processes. It has been suggested that the facilitation of search dynamics is achieved by combining 3D diffusion with one-dimensional sliding and hopping dynamics of interacting proteins. Although, recent studies have advanced the knowledge of molecular determinants that affect one-dimensional search efficiency, the role of DNA molecule is poorly understood. In this study, by using coarse-grained simulations, we propose that dynamics of DNA molecule and its degree of confinement due to cellular crowding concertedly regulate its groove geometry and modulate the inter-communication with DBPs. Under weak confinement, DNA dynamics promotes many short, rotation-decoupled sliding events interspersed by hopping dynamics. While this results in faster 1D diffusion, associated probability of missing targets by jumping over them increases. In contrast, strong confinement favours rotation-coupled sliding to locate targets but lacks structural flexibility to achieve desired specificity. By testing under physiological crowding, our study provides a plausible mechanism on how DNA molecule may help in maintaining an optimal balance between fast hopping and rotation-coupled sliding dynamics, to locate target sites rapidly and form specific complexes precisely.