Protein and Peptide Letters - Volume 18, Issue 10, 2011

In recent decades, faced with global threats of SARS (severe acute respiratory syndrome) coronavirus and influenza A viruses on human life and health, scientists realize that studying methodologies for rapid and accurate structure determination of biomacromolecules is of utmost importance. The structural determination of viruses and their relevant enzymes and proteins will be helpful to understand the relationship between structures and functions, including the mechanisms of interactions between protein-protein, protein-DNA, and protein-ligands. Furthermore, it provides solid insights and footings for rational drug design. As is known, most membrane receptors are druggable target proteins, such as tyrosine kinase-linked receptors, peripheral membrane protein receptors, G-protein-coupled receptors, ion-channels and transporters. Many proteins, particularly membrane proteins are very difficult to crystallize for X-ray spectroscopy or solubilize for NMR spectroscopy to determine their 3D (three-dimensional) structures. Therefore, one of the highest priority tasks today for structural biologists is to timely and accurately determine the 3D structures of various biomacromolecules, either experimentally or theoretically, or by their combination. This special issue contains two systematic reviews, eight original research articles and a letter, reporting the recent progresses of this area from different angles. The review article by Dr. Guo-Ping Zhou addressed that the interactions between cGKIα and MBS proteins are closely related to the pathological mechanisms of cardiovascular diseases including hypertension and atherosclerosis [1]. A highresolution NMR facility and computational approaches were used in this study. Another remarkable feature in this review is that, rather than the traditional helix wheel diagrams widely used to represent helices in proteins, the wenxiang diagram [2,3] is for the first time adopted to study LZCC structures and LZCC-LZCC interaction mechanism, and stimulating insights are gained. The review article by Dr. Takeshi Kikuchi et al. have elucidated that decoding amino acid sequences to extract information on the principles of protein folding is a significant but difficult problem. Based on the technique used to study the statistical average distances between inter-residues in proteins, they have showed the possibility to predict some useful information about protein 3D structures. Meanwhile, some relevant results derived from their approach were reported as well. Dr. Chen-Yan Zhang et al. have developed an efficient approach to enhance the success rate of crystallizing proteins. According to their statistical analysis, a significant improvement was shown in crystal screening by using a convenient strategy called “composition modification”. Interestingly, with such a strategy, some new crystals were found in addition to a significant enhancement in the success rate of crystallisation. Dr. Jing He et al. report a novel geometric approach for predicting protein-ligand binding sites. The method may be of use for drug design. It is indicated in the article by Dr. Hui-Min Lv et al. that the accurate assessment of salvation free energy is important for molecules containing a lot of polar groups. Meanwhile, some examples are shown how the potential leading compounds bound to the protein target may affect the future drug design and screening. In the article by Dr. Khader Shameer et al., a novel method called “3Dswap-pred” was proposed for predicting the 3D information of proteins based on the Random Forest approach using a positive sequence dataset. The corresponding web-server is accessible to the public at http://caps.ncbs.res.in/3dswap-pred. Dr. Xu-Yuan Liu et al. have reported an interesting finding by means of molecular docking and molecular dynamics studies that the GlaxoSmithKline's compound sodelglitazar (GW677954) can activate PPARs (peroxisome proliferator-activated receptors), which play a critical role in treating metabolic diseases, especially the Type-2 diabetes mellitus. Dr. Xing Zhou et al. reported that, based on the analysis of the 3-D molecular modeling, SI and its mutants can synthesize α-arbutin, where some important phenylalanine residues have been found. This finding may pave a new way to change the selectivity and specificity of sucrose isomerase and other enzymes, thereby extending the application fields for these enzymes. The paper by Dr. Ibrahim et al. has presented a docking modeling of the protease MASPT-Mn2+. The model and structural analysis are very useful not only for the initial inferences about the enzyme's structure but also for the rational design of its improved derivatives.....

Physiologic relaxation of vascular smooth muscle is induced by the cyclic guanosine monophosphate (cGMP)- dependent protein kinase Iα enzyme (cGKIα), which activates myosin phosphatase (MLCP). This activation process is thought to occur through the interaction involving both N- and C-terminal leucine zipper coiled-coil (LZCC) domains of the kinase enzyme (cGKIα) with the myosin binding subunit (MBS) of MLCP. In this review, I summarize how to define the LZCC domains in both N-terminal cGKIα1-59and C-terminal MBS proteins using predictive and experimental methods, how to make a rapid and accurate structure determination of a cGKIα1-59 molecule using NMR's residual dipolar coupling (RDC) measurements, and how to indentify the existence of a weak protein interaction between N-terminal LZCC domain (cGKIα1-59) and a LZCC domain (MBSCT42) within the C-terminal MBS. In addition, the location and orientation of the residues in LZCC proteins can be readily visualized using a novel diagram, the so-called “wenxiang diagram”, which is more advantageous than traditional helical wheel diagrams in analyzing LZCC protein structures and their action mechanisms. Using the composed wenxiang diagrams, we have characterized the interaction between cGKIα1- 59 and another LZCC molecule (MBSCT42), and deduced that the most affected residues of these two LZCC molecules might be at the positions d, a, e and g. These studies and findings are also covered in this review. It is intriguing to see that the successful incorporation of wenxiang diagrams and NMR spectroscopy in the LZCC structural and functional studies may provide some insights into protein-protein interaction mechanisms.

One of the goals of molecular bioinformatics is decoding amino acid sequences to extract information on the principles of protein folding. However, this is difficult to perform with standard bioinformatics techniques such as multiple sequence alignment and so on. Thus, we propose a technique based on inter-residue average distance statistics to make predictions regarding the protein folding mechanisms of amino acid sequences. Our method involves constructing a kind of predicted contact map called an Average Distance Map (ADM) based on average distance statistics to pinpoint regions of possible folding nuclei for proteins. Only information on the amino acid sequence of a given protein is required for the present method. In this article, we summarize the results of studies using our method to analyze how specific protein sequences affect folding properties. In particular, we present studies on proteins in the phage lysozyme, such as the globin, fatty acid binding protein-like, and the cupredoxin-like fold families. In the present review, we characterize the 3D architectures of these proteins through the properties of the protein ADMs. Furthermore, we combine the information on the conserved residues within the regions predicted by the ADMs with our results obtained so far. Such information may help identify the folding characteristics of each protein. We discuss this possibility in the present review.

The nucleation zone has to be reached for any crystal to grow, and the search for crystallization conditions of new proteins is a trial and error process. Here a convenient screening strategy is studied in detail that varies the volume ratio of protein sample to the reservoir solution in the drop to initiate crystallization that is named “composition modification”. It is applied after the first screen and has been studied with twelve proteins. Statistical analysis shows a significant improvement in screening using this strategy. The average improvement of “hits” at different temperatures is between 32 and 42%, for examples, 41.8% ± 14.0% and 35.7% ± 12.4% (± standard deviation) at 288 K and 300 K, respectively. Remarkably, some new crystals were found by composition modification which increased the probability of reaching the nucleation zone to initiate crystallization. This was confirmed by a phase diagram study. It is also demonstrated that composition modification can further increase crystallisation success significantly (1.3 times) after the improvement of “hits” by temperature screening. The trajectories of different composition modifications during vapour diffusion were plotted, further demonstrating that protein crystallizability can be increased by hitting more parts of the nucleation zone. It was also found to facilitate the finding of initial crystals for proteins of low solubility. These proteins gradually become more concentrated during the vapour diffusion process starting from a larger protein solution ratio in the initial mixture.

Knowledge of protein-ligand binding sites is very important for structure-based drug designs. To get information on the binding site of a targeted protein with its ligand in a timely way, many scientists tried to resort to computational methods. Although several methods have been released in the past few years, their accuracy needs to be improved. In this study, based on the combination of incremental convex hull, traditional geometric algorithm, and solvent accessible surface of proteins, we developed a novel approach for predicting the protein-ligand binding sites. Using PDBbind database as a benchmark dataset and comparing the new approach with the existing methods such as POCKET, Q-SiteFinder, MOE-SiteFinder, and PASS, we found that the new method has the highest accuracy for the Top 2 and Top 3 predictions. Furthermore, our approach can not only successfully predict the protein-ligand binding sites but also provide more detailed information for the interactions between proteins and ligands. It is anticipated that the new method may become a useful tool for drug development, or at least play a complementary role to the other existing methods in this area.

Influenza viruses cause a significant level of morbidity and mortality in the population every year. Their resistance to current anti-influenza drugs increases the difficulty of flu treatment. Thus, development of new anti-influenza drugs is necessary in regards of prevent the tragedy of influenza pandemic. The Polymerase basic protein 2 (PB2) subunit of influenza virus RNA polymerase is one of potential targets for new drugs because the binding of PB2 with the 5' cap of the host pre-mRNAs is the initial step of the virus' protein synthesis. In this study, we compared the binding potency of PB2 cap binding domain with two small molecules, i.e., RO and PPT28, with that of PB2 with cap analog m7GTP. The calculated binding energies showed that RO and PPT28 had higher binding affinity with PB2. Further interaction analysis showed that the important parts for binding were the five- and six-member heterocyclic rings (the 6/5-member rings) of small molecules, as well as the hydrophobic parts of RO and PPT28 which had good interactions with the hydrophobic residues in the binding cavity. Thus, RO and PPT28 are two potential anti-influenza drugs targeted PB2, which may inhibit the growth of influenza virus by competitively binding with the cap structure binding domain of PB2.

3D domain swapping is a protein structural phenomenon that mediates the formation of the higher order oligomers in a variety of proteins with different structural and functional properties. 3D domain swapping is associated with a variety of biological functions ranging from oligomerization to pathological conformational diseases. 3D domain swapping is realised subsequent to structure determination where the protein is observed in the swapped conformation in the oligomeric state. This is a limiting step to understand this important structural phenomenon in a large scale from the growing sequence data. A new machine learning approach, 3dswap-pred, has been developed for the prediction of 3D domain swapping in protein structures from mere sequence data using the Random Forest approach. 3Dswap-pred is implemented using a positive sequence dataset derived from literature based structural curation of 297 structures. A negative sequence dataset is obtained from 462 SCOP domains using a new sequence data mining approach and a set of 126 sequencederived features. Statistical validation using an independent dataset of 68 positive sequences and 313 negative sequences revealed that 3dswap-pred achieved an accuracy of 63.8%. A webserver is also implemented using the 3dswap-pred Random Forest model. The server is available from the URL: http://caps.ncbs.res.in/3dswap-pred

PPAR (peroxisome proliferator-activated receptor) pan agonists play a critical role in treating metabolic diseases, especially the Type-2 diabetes mellitus (T2DM). GlaxoSmithKline's sodelglitazar (GW677954) is one of the potent PPAR pan agonists, which is currently being investigated in Phase II clinical trials for the treatment of T2DM and its complications. The present study was aimed at investigation into the effect of sodelglitazar at the binding pockets of PPARs. The Schrodinger Suite program (2009) was used for the molecular docking, while the GROMACS program used for the molecular dynamics (MD) simulations. The results thus obtained showed that sodelglitazar being docked well in the active site of PPARs. It was revealed by the MD simulations that the structures of the receptors remained quite stable during the simulations and that the important AF-2 helix showed less flexibility after binding with sodelglitazar. Also, it was observed that sodelglitazar could periodically form hydrogen bonds with the AF-2 helix of PPARs to stabilize the AF-2 helix in an active conformation. Our findings have confirmed that GlaxoSmithKline's sodelglitazar can activate the PPARs, which is quite consistent with the previous biological studies.

Sucrose isomerase (SI) from Erwinia rhapontici is an intramolecular isomerase that is normally used to synthesise isomaltulose from sucrose by a mechanism of intramolecular transglycosylation. In this study, it was found that SI could synthesise α-arbutin using hydroquinone and sucrose as substrates, via an intermolecular transglycosylation reaction. Five phenylalanine residues (F185, F186, F205, F297, and F321) in the catalytic pocket of SI were chosen for sitedirected mutagenesis. Mutants F185I, F321I, and F321W, whose hydrolytic activities were enhanced after the mutation, could synthesise α-arbutin through intermolecular transglycosylation with a more than two-fold increase in the molar transfer ratio compared with wild type SI. The F297A mutant showed a strong ability to synthesise a novel α-arbutin derivative and a four-fold increase in its specific activity for intermolecular transglycosylation over the wild type. Our findings may lead to a new way to synthesise novel glucoside products such as α-arbutin derivatives by simply manipulating the Phe residues in the catalytic pocket. From the structure superposition, our strategy of manipulating these Phe residues may be applicable to other similar transglycosylating enzymes.

Leather industries release a large amount of pollution-causing chemicals which creates one of the major industrial pollutions. The development of enzyme based processes as a potent alternative to pollution-causing chemicals is useful to overcome this issue. Proteases are enzymes which have extensive applications in leather processing and in several bioremediation processes due to their high alkaline protease activity and dehairing efficacy. In the present study, we report cloning, characterization of a Mn2+ dependent alkaline serine protease gene (MASPT) of Bacillus pumilus TMS55. The gene encoding the protease from B. pumilus TMS55 was cloned and its nucleotide sequence was determined. This gene has an open reading frame (ORF) of 1,149 bp that encodes a polypeptide of 383 amino acid residues. Our analysis showed that this polypeptide is composed of 29 residues N-terminal signal peptide, a propeptide of 79 residues and a mature protein of 275 amino acids. We performed bioinformatics analysis to compare MASPT enzyme with other proteases. Homology modeling was employed to model three dimensional structure for MASPT. Structural analysis showed that MASPT structure is composed of nine α-helices and nine β-strands. It has 3 catalytic residues and 14 metal binding residues. Docking analysis showed that residues S223, A260, N263, T328 and S329 interact with Mn2+. This study allows initial inferences about the structure of the protease and will allow the rational design of its derivatives for structure-function studies and also for further improvement of the enzyme.

In the past decade, when compared to models describing the folding rates of two-state proteins, models describing the folding mechanism of three-state proteins remain quite limited due to the complexity present in the folding mechanism and lack in their experimental data. In the present work, rate-limiting long-range contacts were classified into various bins based on sequence separation distance between the contacting residues and the role of these bins were analyzed for their importance in a data set of 35 three-state proteins. Predicting the folding rates of these proteins have been carried out by relating experimental folding rates and long-range contacts obtained from various sequence separation bins. For comparison, using the present model, prediction of the folding rates of 45 two-state proteins also resulted with good accuracy. Our method shows that long-range contacts observed in the final 3-D structure of proteins at various sequence separation bins are found to be an important descriptor in explaining the folding rates of three-state proteins and suggest that formation of contacts between residues present at these sequence separation distance may be a crucial factor in deciding structure formation and folding rates of these proteins. The aim of our present work is not to construct a new descriptor for the folding rates of three-state proteins, nor is to provide improved means of folding-rate prediction for these proteins. We tend to elucidate that how long-range contacts play a crucial role in the folding mechanism of three state proteins belonging to three major structural classes and implication of these observations due to rate-limiting long-range contacts has been discussed in the light of other experimental studies of protein folding.

In this study, we attempted to use the neural network to model a quantitative structure-Km (Michaelis-Menten constant) relationship for beta-glucosidase, which is an important enzyme to cut the beta-bond linkage in glucose while Km is a very important parameter in enzymatic reactions. Eight feedforward backpropagation neural networks with different layers and neurons were applied for the development of predictive model, and twenty-five different features of amino acids were chosen as predictors one by one. The results show that the 20-1 feedforward backpropagation neural network can serve as a predictive model while the normalized polarizability index as well as the amino-acid distribution probability can serve as the predictors. This study threw lights on the possibility of predicting the Km in beta-glucosidases based on their amino-acid features.

Galanin is a neuropeptide that is widely distributed in the central and peripheral nervous systems. Some small cell lung carcinoma (SCLC) cell lines such as SBC-3A release only the high-molecular-mass form, with lower molecular mass forms being undetectable. To investigate the mechanism of processing of progalanin to active peptide, we studied galanin-LI in both the culture media of SBC-3A cells and in extracts from in vivo mouse SBC-3A tumors. SBC-3A cells were found to release high molecular mass galanin, but did not release active peptides. In contrast, tumor extract contained both high-molecular-mass galanin, and a cleaved lower-molecular-mass form of the peptide (8, 5 and 2 kDa). The lower-molecular-mass peptide was identified as galanin(1-20) by MALDI-TOF mass spectrometry. We then looked at MMP-2 and MMP-9 release from SBC-3A cells and tumor tissue treated with galanin and progalanin, as revealed by gelatin zymography. Galanin elicited pro-MMP-2 and pro-MMP-9 release from SBC-3A cells and tumor tissue; however, recombinant progalanin induced pro-MMP-2 and pro-MMP-9 release from tumor tissue only. This study has shown that the galanin-LI released from SCLC SBC-3A cells consisted of the high-molecular-mass peptide form, and was processed extracellularly to galanin(1-20). Furthermore, galanin was seen to induce pro-MMP-2 and pro-MMP-9 release from SBC-3A cells.

We have investigated the dependence of peptide oligomerization on intermolecular interaction in terms of both energetic and structural effect by PFGNMR. Three peptides, NPY[20-36], Pro34-NPY[20-36] and NPY[21-31], which are related to human NPY, were synthesized as models in this work. In contrast to NPY[20-36], both Pro34-NPY[20-36] and NPY[21-31] were found with descendent affinity with TFE cluster and continuous dissociating with increased temperature. The observed results can be accounted by the entropic change with temperature and the varied hydrophobic interactions between species due to the differed structures of peptides from each other. The removal of helical secondary structure or residues from C-terminal region may increase the energetic difference between peptide-peptide self-associating and peptidesolvent binding. This increased energetic difference leads to larger dependence of association-dissociation equilibrium on temperature and entropic increase while dissociating.