Assembly of transmembrane helices of simple polytopic membrane proteins from sequence conservation patterns

The transmembrane (TM) domains of most membrane proteins consist of helix bundles. The seemingly simple task of TM helix bundle assembly has turned out to be extremely difficult. This is true even for simple TM helix bundle proteins, i.e., those that have the simple form of compact TM helix bundles. Herein, we present a computational method that is capable of generating native-like structural models for simple TM helix bundle proteins having modest numbers of TM helices based on sequence conservation patterns. Thus, the only requirement for our method is the presence of more than 30 homologous sequences for an accurate extraction of sequence conservation patterns. The prediction method first computes a number of representative well-packed conformations for each pair of contacting TM helices, and then a library of tertiary folds is generated by overlaying overlapping TM helices of the representative conformations. This library is scored using sequence conservation patterns, and a subsequent clustering analysis yields five final models. Assuming that neighboring TM helices in the sequence contact each other (but not that TM helices A and G contact each other), the method produced structural models of Calpha atom root-mean-square deviation (CA RMSD) of 3-5 A from corresponding crystal structures for bacteriorhodopsin, halorhodopsin, sensory rhodopsin II, and rhodopsin. In blind predictions, this type of contact knowledge is not available. Mimicking this, predictions were made for the rotor of the V-type Na(+)-adenosine triphosphatase without such knowledge. The CA RMSD between the best model and its crystal structure is only 3.4 A, and its contact accuracy reaches 55%. Furthermore, the model correctly identifies the binding pocket for sodium ion. These results demonstrate that the method can be readily applied to ab initio structure prediction of simple TM helix bundle proteins having modest numbers of TM helices.     

Prediction of beta-turns in proteins using neural networks

The use of neural networks to improve empirical secondary structure prediction is explored with regard to the identification of the position and conformational class of beta-turns, a four-residue chain reversal. Recently an algorithm was developed for beta-turn predictions based on the empirical approach of Chou and Fasman using different parameters for three classes (I, II and non-specific) of beta-turns. In this paper, using the same data, an alternative approach to derive an empirical prediction method is used based on neural networks which is a general learning algorithm extensively used in artificial intelligence. Thus the results of the two approaches can be compared. The most severe test of prediction accuracy is the percentage of turn predictions that are correct and the neural network gives an overall improvement from 20.6% to 26.0%. The proportion of correctly predicted residues is 71%, compared to a chance level of about 58%. Thus neural networks provide a method of obtaining more accurate predictions from empirical data than a simpler method of deriving propensities.     

Prediction of transmembrane helix orientation in polytopic membrane proteins

Background  

Membrane proteins compose up to 30% of coding sequences within genomes. However, their structure determination is lagging behind compared with soluble proteins due to the experimental difficulties. Therefore, it is important to develop reliable computational methods to predict structures of membrane proteins.     

Computational analysis of the transient movement of helices in sensory rhodopsin II

MD simulation of sensory rhodopsin II was executed for three intermediates (ground-state, K-state, M-state) appearing in its photocycle. We observed a large displacement of the cytoplasmic side of helixF only in M-state among the three intermediates. This displacement was transmitted to TM2, and the cytoplasmic side of TM2 rotated clockwise. These transient movements are in agreement with the results of an EPR experiment. That is, the early stage of signal transduction in a sRII-HtrII complex was successfully reproduced by the in silico MD simulation. By analyzing the structure of the sRII-HtrII complex, the following findings about the photocycle of sRII were obtained: (1) The hydrogen bonds between helixF and other helices determine the direction of the movement of helixF; (2) three amino acids (Arg162, Thr189, Tyr199) are essential for sRII-HtrII binding and contribute to the motion transfer from sRII to HtrII; (3) after the isomerization of retinal, a major conformational change of retinal was caused by proton transfer from Schiff base to Asp75, which, in turn, triggers the steric collision of retinal with Trp171. This is the main reason for the movement of the cytoplasmic side of helixF.     

Prediction of buried helices in multispan alpha helical membrane proteins

Analysis of a database of structures of membrane proteins shows that membrane proteins composed of 10 or more transmembrane (TM) helices often contain buried helices that are inaccessible to phospholipids. We introduce a method for identifying TM helices that are least phospholipid accessible and for prediction of fully buried TM helices in membrane proteins from sequence information alone. Our method is based on the calculation of residue lipophilicity and evolutionary conservation. Given that the number of buried helices in a membrane protein is known, our method achieves an accuracy of 78% and a Matthew's correlation coefficient of 0.68. A server for this tool (RANTS) is available online at http://gila.bioengr.uic.edu/lab/.     

Prediction of mono- and di-nucleotide-specific DNA-binding sites in proteins using neural networks

Background  

DNA recognition by proteins is one of the most important processes in living systems. Therefore, understanding the recognition process in general, and identifying mutual recognition sites in proteins and DNA in particular, carries great significance. The sequence and structural dependence of DNA-binding sites in proteins has led to the development of successful machine learning methods for their prediction. However, all existing machine learning methods predict DNA-binding sites, irrespective of their target sequence and hence, none of them is helpful in identifying specific protein-DNA contacts. In this work, we formulate the problem of predicting specific DNA-binding sites in terms of contacts between the residue environments of proteins and the identity of a mononucleotide or a dinucleotide step in DNA. The aim of this work is to take a protein sequence or structural features as inputs and predict for each amino acid residue if it binds to DNA at locations identified by one of the four possible mononucleotides or one of the 10 unique dinucleotide steps. Contact predictions are made at various levels of resolution viz. in terms of side chain, backbone and major or minor groove atoms of DNA.     

The interplay between the clustering of transmembrane proteins and coupling of anchored membrane proteins

Clustering of membrane proteins plays an important role in many cellular activities such as protein sorting and signal transduction. In this study, we used dissipative particle dynamics simulation method to investigate the clustering of anchored membrane proteins (AMPs) in the presence of transmembrane proteins (TMPs). First, our simulation results show that clustering of AMPs and that of TMPs are in fact interdependent, and depending on their hydrophobic length, both protein mixing and protein demixing are observed. Especially, the protein demixing occurs only when the hydrophobic mismatch of TMPs is negative while that of AMPs is positive. Second, our simulation results indicate that the clustering of TMPs also modulates the coupling of the clustering of AMPs in both leaflets. On the one hand, the coupling between AMPs in different leaflets will be strongly restrained if TMPs form protein mixing with AMPs in one leaflet and protein demixing with AMPs in the other leaflet. On the other hand, the coupling between AMPs can be enhanced or mediated by TMPs when TMPs mix with AMPs in both leaflets. Our results may have some implications on our understanding of how different types of membrane proteins cluster and provide a possible explanation of how TMPs participate in signal transduction across cellular membranes.     

Predicting residue-residue contacts and helix-helix interactions in transmembrane proteins using an integrative feature-based random forest approach

Integral membrane proteins constitute 25-30% of genomes and play crucial roles in many biological processes. However, less than 1% of membrane protein structures are in the Protein Data Bank. In this context, it is important to develop reliable computational methods for predicting the structures of membrane proteins. Here, we present the first application of random forest (RF) for residue-residue contact prediction in transmembrane proteins, which we term as TMhhcp. Rigorous cross-validation tests indicate that the built RF models provide a more favorable prediction performance compared with two state-of-the-art methods, i.e., TMHcon and MEMPACK. Using a strict leave-one-protein-out jackknifing procedure, they were capable of reaching the top L/5 prediction accuracies of 49.5% and 48.8% for two different residue contact definitions, respectively. The predicted residue contacts were further employed to predict interacting helical pairs and achieved the Matthew's correlation coefficients of 0.430 and 0.424, according to two different residue contact definitions, respectively. To facilitate the academic community, the TMhhcp server has been made freely accessible at http://protein.cau.edu.cn/tmhhcp.     

Prediction of the location and type of beta-turns in proteins using neural networks.

A neural network has been used to predict both the location and the type of beta-turns in a set of 300 nonhomologous protein domains. A substantial improvement in prediction accuracy compared with previous methods has been achieved by incorporating secondary structure information in the input data. The total percentage of residues correctly classified as beta-turn or not-beta-turn is around 75% with predicted secondary structure information. More significantly, the method gives a Matthews correlation coefficient (MCC) of around 0.35, compared with a typical MCC of around 0.20 using other beta-turn prediction methods. Our method also distinguishes the two most numerous and well-defined types of beta-turn, types I and II, with a significant level of accuracy (MCCs 0.22 and 0.26, respectively).     

Transmembrane helices of membrane proteins may flex to satisfy hydrophobic mismatch

A novel mechanism for membrane modulation of transmembrane protein structure, and consequently function, is suggested in which mismatch between the hydrophobic surface of the protein and the hydrophobic interior of the lipid bilayer induces a flexing or bending of a transmembrane segment of the protein. Studies on model hydrophobic transmembrane peptides predict that helices tilt to submerge the hydrophobic surface within the lipid bilayer to satisfy the hydrophobic effect if the helix length exceeds the bilayer width. The hydrophobic surface of transmembrane helix 1 (TM1) of lactose permease, LacY, is accessible to the bilayer, and too long to be accommodated in the hydrophobic portion of a typical lipid bilayer if oriented perpendicular to the membrane surface. Hence, nuclear magnetic resonance (NMR) data and molecular dynamics simulations show that TM1 from LacY may flex as well as tilt to satisfy the hydrophobic mismatch with the bilayer. In an analogous study of the hydrophobic mismatch of TM7 of bovine rhodopsin, similar flexing of the transmembrane segment near the conserved NPxxY sequence is observed. As a control, NMR data on TM5 of lacY, which is much shorter than TM1, show that TM5 is likely to tilt, but not flex, consistent with the close match between the extent of hydrophobic surface of the peptide and the hydrophobic thickness of the bilayer. These data suggest mechanisms by which the lipid bilayer in which the protein is embedded modulates conformation, and thus function, of integral membrane proteins through interactions with the hydrophobic transmembrane helices.     

植物GRAS蛋白结构和功能研究进展

GRAS蛋白是一类植物特有的蛋白家族,是许多重要生长发育过程中的关键调控蛋白,如赤霉素信号转导、光信号转导、根的发育、根瘤和菌根形成以及分生组织形成等。从蛋白分子结构、分类及生理功能等方面综述了植物GRAS蛋白的最新研究进展,并对未来的研究方向进行了讨论。     

Predicting secondary structures of membrane proteins with neural networks

Back-propagation, feed-forward neural networks are used to predict the secondary structures of membrane proteins whose structures are known to atomic resolution. These networks are trained on globular proteins and can predict globular protein structures having no homology to those of the training set with correlation coefficients (C) of 0.45, 0.32 and 0.43 for a-helix, -strand and random coil structures, respectively. When tested on membrane proteins, neural networks trained on globular proteins do, on average, correctly predict (Q_i) 62%, 38% and 69% of the residues in the -helix, -strand and random coil structures. These scores rank higher than those obtained with the currently used statistical methods and are comparable to those obtained with the joint approaches tested so far on membrane proteins. The lower success score for -strand as compared to the other structures suggests that the sample of -strand patterns contained in the training set is less representative than those of a-helix and random coil. Our analysis, which includes the effects of the network parameters and of the structural composition of the training set on the prediction, shows that regular patterns of secondary structures can be successfully extrapolated from globular to membrane proteins. Correspondence to: R. Casadio     

Resonance electroconformational coupling: A proposed mechanism for energy and signal transductions by membrane proteins

Recent experiments show that membrane ATPases are capable of absorbing free energy from an applied oscillating electric field and converting it to chemical bond energy of ATP or chemical potential energy of concentration gradients. Presumably these enzymes would also respond to endogenous transmembrane electric fields of similar intensity and waveform. A mechanism is proposed in which energy coupling is achieved via Coulombic interaction of an electric field and the conformational equilibria of an ATPase. Analysis indicates that only an oscillating or fluctuating electric field can be used by an enzyme to drive a chemical reaction away from equilibrium.In vivo, the stationary transmembrane potential of a cell must be modulated to become locally oscillatory if it is to derive energy and signal transduction processes.     

14-3-3蛋白研究进展

14-3-3蛋白是高度保守的、所有真核生物细胞中都普遍存在的、在大多数生物物种中由一个基因家族编码的一类蛋白调控家族。它几乎参与生命体所有的生理反应过程,人们在各种组织细胞中发现了各种不同的14-3-3蛋白。作为与磷酸丝氨酸／苏氨酸结合的第一信号分子,14-3-3蛋白在细胞的信号转导中起着至关重要的作用,尤其是它直接参与调节蛋白激酶和蛋白磷酸化酶的活性,被称为蛋白质与蛋白质相互作用的”桥梁蛋白”;它可以与转录因子结合形成复合体,调节相关基因的表达。一些研究表明,14-3-3蛋白调控机制的紊乱可以直接导致疾病的发生,在临床上14-3-3蛋白常常可以作为诊断的标志物。     

Comparison of allosteric signaling in DnaK and BiP using mutual information between simulated residue conformations

The heat shock protein 70 kDa (Hsp70) chaperone system serves as a critical component of protein quality control across a wide range of prokaryotic and eukaryotic organisms. Divergent evolution and specialization to particular organelles have produced numerous Hsp70 variants which share similarities in structure and general function, but differ substantially in regulatory aspects, including conformational dynamics and activity modulation by cochaperones. The human Hsp70 variant BiP (also known as GRP78 or HSPA5) is of therapeutic interest in the context of cancer, neurodegenerative diseases, and viral infection, including for treatment of the pandemic virus SARS-CoV-2. Due to the complex conformational rearrangements and high sequential variance within the Hsp70 protein family, it is in many cases poorly understood which amino acid mutations are responsible for biochemical differences between protein variants. In this study, we predicted residues associated with conformational regulation of human BiP and Escherichia coli DnaK. Based on protein structure networks obtained from molecular dynamics simulations, we analyzed the shared information between interaction timelines to highlight residue positions with strong conformational coupling to their environment. Our predictions, which focus on the binding processes of the chaperone's substrate and cochaperones, indicate residues filling potential signaling roles specific to either DnaK or BiP. By combining predictions of individual residues into conformationally coupled chains connecting ligand binding sites, we predict a BiP specific secondary signaling pathway associated with substrate binding. Our study sheds light on mechanistic differences in signaling and regulation between Hsp70 variants, which provide insights relevant to therapeutic applications of these proteins.     

Improved prediction of the number of residue contacts in proteins by recurrent neural networks

Knowing the number of residue contacts in a protein is crucial for deriving constraints useful in modeling protein folding, protein structure, and/or scoring remote homology searches. Here we use an ensemble of bi-directional recurrent neural network architectures and evolutionary information to improve the state-of-the-art in contact prediction using a large corpus of curated data. The ensemble is used to discriminate between two different states of residue contacts, characterized by a contact number higher or lower than the average value of the residue distribution. The ensemble achieves performances ranging from 70.1% to 73.1% depending on the radius adopted to discriminate contacts (6Ato 12A). These performances represent gains of 15% to 20% over the base line statistical predictors always assigning an aminoacid to the most numerous state, 3% to 7% better than any previous method. Combination of different radius predictors further improves the performance. SERVER: http://promoter.ics.uci.edu/BRNN-PRED/.     

Prediction of ubiquitin proteins using artificial neural networks, hidden markov model and support vector machines

Ubiquitin functions to regulate protein turnover in a cell by closely regulating the degradation of specific proteins. Such a regulatory role is very important, and thus I have analyzed the proteins that are ubiquitin-like, using an artificial neural network, support vector machines and a hidden Markov model (HMM). The methods were trained and tested on a set of 373 ubiquitin proteins and 373 non-ubiquitin proteins, obtained from Entrez protein database. The artificial neural network and support vector machine are trained and tested using both the physicochemical properties and PSSM matrices generated from PSI-BLAST, while in the HMM based method direct sequences are used for training-testing procedures. Further, the performance measures of the methods are calculated for test sequences, i.e. accuracy, specificity, sensitivity and Matthew's correlation coefficients of the methods are calculated. The highest accuracy of 90.2%, specificity of 87.04% and sensitivity of 94.08% was achieved using the support vector machine model with PSSM matrices. While accuracies of 86.82%, 83.37%, 80.18% and 72.11% were obtained for the support vector machine with physicochemical properties, neural network with PSSM matrices, neural networks with physicochemical properties, and hidden Markov model, respectively. As the accuracy for SVM model is better both using physicochemical properties and the PSSM matrices, it is concluded that kernel methods such as SVM outperforms neural networks and hidden Markov models.     

Helix dynamics in a membrane transport protein: comparative simulations of the glycerol-3-phosphate transporter and its constituent helices

The glycerol-3-phosphate transporter (GlpT) is a member of the major facilitator superfamily (MFS). GlpT is an organic phosphate/inorganic phosphate antiporter. It shares a similar fold with other MFS transporters (e.g. LacY and EmrD) consisting of 12 transmembrane (TM) helices which form two domains (each of six TM helices) surrounding a central ligand-binding cavity. The TM helices (especially the cavity-lining helices) contain a large number of proline and glycine residues, which may aid in the conformational changes believed to underline the transport mechanism. Molecular dynamics simulations in a phospholipid bilayer have been used to compare the conformational properties of the isolated TM helices with those in the intact GlpT protein. Analysis of these simulations focuses on the role of proline-induced flexibility in the TM helices. Our results are consistent with the proposed rocker switch mechanism for transport by GlpT. In particular, the simulations highlight the cavity-lining helices (H4, H5, H10 and H11) as being significantly flexible, suggesting that the transport mechanism may involve intra-helix motions in addition to pseudo-rigid body motions of the N- and C-terminal domains relative to one another.     

Structural and dynamics analysis of matrix metalloproteinases MMP-2 complexed with chemically modified tetracyclines (CMTs)

Matrix metalloproteinases (MMPs) play a critical role in physiological processes and pathological conditions such tumor invasion and metastasis. In recent years, a number of MMP inhibitors have been proposed, including the chemically modified tetracyclines (CMTs), which have been evaluated in preclinical cancer models showing promising results. This work provides insights into the structure and dynamics of the MMP-2 catalytic domain complexed with seven CMT (CMT-n), based on the analysis of molecular dynamics trajectories in solution. The comparative analysis of various relevant molecular aspects of the different complexes of MMP-2 and CMT-n derivatives was performed aiming to elucidate the effect of ligands on the enzyme structure. These include the radial distribution function of the water molecules around the catalytic zinc, the solvent accessible surface area for the inhibitors and the root-mean-square fluctuation for all amino acid residues. The results help to understand the differences in the binding modes of related compounds and, therefore, add to further design of novel tetracycline-based inhibitors for MMP enzymes.