PROTEUS: a suite of programs for prediction of structural features of proteins using an Apple lie

We have implemented several algorithms, developed by variousauthors for predicting structural features of proteins fromtheir primary structure, on an Apple lle and collected themin a suite, named PROTEUS. This suite incorporates: (i) methodsfor predicting secondary structure; (ii) the algorithm for computingthe hydropathy profile using one out of five available setsof parameters; (ii) the algorithms for calculating the hydrophobicmoment plot; and (iv) for performing the amphipathic analysisusing one out of four available sets of parameters. The suitehas a utility program for storing on a disk the sequence tobe analysed. As an example, we applied some of the methods includedin PROTEUS to predict the structure of a mitochondnal leaderpeptide. The results suggest the occurrence of structural featurespossibly related to the import of proteins into mitochondria. Received on April 30, 1987; accepted on July 21, 1987     

PI2PE: a suite of web servers for predictions ranging from protein structure to binding kinetics

PI²PE (http://pipe.sc.fsu.edu) is a suite of four web servers for predicting a variety of folding- and binding-related properties of proteins. These include the solvent accessibility of amino acids upon protein folding, the amino acids forming the interfaces of protein–protein and protein–nucleic acid complexes, and the binding rate constants of these complexes. Three of the servers debuted in 2007, and have garnered ～2,500 unique users and finished over 30,000 jobs. The functionalities of these servers are now enhanced, and a new sever, for predicting the binding rate constants, has been added. Together, these web servers form a pipeline from protein sequence to tertiary structure, then to quaternary structure, and finally to binding kinetics.     

DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins

This article describes DP-Bind, a web server for predicting DNA-binding sites in a DNA-binding protein from its amino acid sequence. The web server implements three machine learning methods: support vector machine, kernel logistic regression and penalized logistic regression. Prediction can be performed using either the input sequence alone or an automatically generated profile of evolutionary conservation of the input sequence in the form of PSI-BLAST position-specific scoring matrix (PSSM). PSSM-based kernel logistic regression achieves the accuracy of 77.2%, sensitivity of 76.4% and specificity of 76.6%. The outputs of all three individual methods are combined into a consensus prediction to help identify positions predicted with high level of confidence. AVAILABILITY: Freely available at http://lcg.rit.albany.edu/dp-bind. SUPPLEMENTARY INFORMATION: http://lcg.rit.albany.edu/dp-bind/dpbind_supplement.html.     

iLIR: A web resource for prediction of Atg8-family interacting proteins

Macroautophagy was initially considered to be a nonselective process for bulk breakdown of cytosolic material. However, recent evidence points toward a selective mode of autophagy mediated by the so-called selective autophagy receptors (SARs). SARs act by recognizing and sorting diverse cargo substrates (e.g., proteins, organelles, pathogens) to the autophagic machinery. Known SARs are characterized by a short linear sequence motif (LIR-, LRS-, or AIM-motif) responsible for the interaction between SARs and proteins of the Atg8 family. Interestingly, many LIR-containing proteins (LIRCPs) are also involved in autophagosome formation and maturation and a few of them in regulating signaling pathways. Despite recent research efforts to experimentally identify LIRCPs, only a few dozen of this class of—often unrelated—proteins have been characterized so far using tedious cell biological, biochemical, and crystallographic approaches. The availability of an ever-increasing number of complete eukaryotic genomes provides a grand challenge for characterizing novel LIRCPs throughout the eukaryotes. Along these lines, we developed iLIR, a freely available web resource, which provides in silico tools for assisting the identification of novel LIRCPs. Given an amino acid sequence as input, iLIR searches for instances of short sequences compliant with a refined sensitive regular expression pattern of the extended LIR motif (xLIR-motif) and retrieves characterized protein domains from the SMART database for the query. Additionally, iLIR scores xLIRs against a custom position-specific scoring matrix (PSSM) and identifies potentially disordered subsequences with protein interaction potential overlapping with detected xLIR-motifs. Here we demonstrate that proteins satisfying these criteria make good LIRCP candidates for further experimental verification. Domain architecture is displayed in an informative graphic, and detailed results are also available in tabular form. We anticipate that iLIR will assist with elucidating the full complement of LIRCPs in eukaryotes.     

Folding rate prediction using n-order contact distance for proteins with two- and three-state folding kinetics

It is a challenging task to understand the relationship between sequences and folding rates of proteins. Previous studies are found that one of contact order (CO), long-range order (LRO), total contact distance (TCD), chain topology parameter (CTP), and effective length (Leff) has a significant correlation with folding rate of proteins. In this paper, we introduce a new parameter called n-order contact distance (nOCD) and use it to predict folding rate of proteins with two- and three-state folding kinetics. A good linear correlation between the folding rate logarithm lnkf and nOCD with n=1.2, alpha=0.6 is found for two-state folders (correlation coefficient is -0.809, P-value<0.0001) and n=2.8, alpha=1.5 for three-state folders (correlation coefficient is -0.816, P-value<0.0001). However, this correlation is completely absent for three-state folders with n=1.2, alpha=0.6 (correlation coefficient is 0.0943, P-value=0.661) and for two-state folders with n=2.8, alpha=1.5 (correlation coefficient is -0.235, P-value=0.2116). We also find that the average number of contacts per residue Pm in the interval of m for two-state folders is smaller than that for three-state folders. The probability distribution P(gamma) of residue having gamma pairs of contacts fits a Gaussian distribution for both two- and three-state folders. We observe that the correlations between square radius of gyration S2 and number of residues for two- and three-state folders are both good, and the correlation coefficient is 0.908 and 0.901, and the slope of the fitting line is 1.202 and 0.795, respectively. Maybe three-state folders are more compact than two-state folders. Comparisons with nTCD and nCTP are also made, and it is found that nOCD is the best one in folding rate prediction.     

XtalPred: a web server for prediction of protein crystallizability

XtalPred is a web server for prediction of protein crystallizability. The prediction is made by comparing several features of the protein with distributions of these features in TargetDB and combining the results into an overall probability of crystallization. XtalPred provides: (1) a detailed comparison of the protein's features to the corresponding distribution from TargetDB; (2) a summary of protein features and predictions that indicate problems that are likely to be encountered during protein crystallization; (3) prediction of ligands; and (4) (optional) lists of close homologs from complete microbial genomes that are more likely to crystallize. AVAILABILITY: The XtalPred web server is freely available for academic users on http://ffas.burnham.org/XtalPred     

Using hydropathy features for function prediction of membrane proteins

A novel alignment-free method for computing functional similarity of membrane proteins based on features of hydropathy distribution is presented. The features of hydropathy distribution are used to represent protein families as hydropathy profiles. The profiles statistically summarize the hydropathy distribution of member proteins. The summation is made by using hydropathy features that numerically represent structurally/functionally significant portions of protein sequences. The hydropathy profiles are numerical vectors that are points in a high dimensional 'hydropathy' space. Their similarities are identified by projection of the space onto principal axes. Here, the approach is applied to the secondary transporters. The analysis using the presented approach is validated by the standard classification of the secondary transporters. The presented analysis allows for prediction of function attributes for proteins of uncharacterized families of secondary transporters. The results obtained using the presented analysis may help to characterize unknown function attributes of secondary transporters. They also show that analysis of hydropathy distribution can be used for function prediction of membrane proteins.     

Using hydropathy features for function prediction of membrane proteins

A novel alignment-free method for computing functional similarity of membrane proteins based on features of hydropathy distribution is presented. The features of hydropathy distribution are used to represent protein families as hydropathy profiles. The profiles statistically summarize the hydropathy distribution of member proteins. The summation is made by using hydropathy features that numerically represent structurally/functionally significant portions of protein sequences. The hydropathy profiles are numerical vectors that are points in a high dimensional ‘hydropathy’ space. Their similarities are identified by projection of the space onto principal axes. Here, the approach is applied to the secondary transporters. The analysis using the presented approach is validated by the standard classification of the secondary transporters. The presented analysis allows for prediction of function attributes for proteins of uncharacterized families of secondary transporters. The results obtained using the presented analysis may help to characterize unknown function attributes of secondary transporters. They also show that analysis of hydropathy distribution can be used for function prediction of membrane proteins.     

FITBAR: a web tool for the robust prediction of prokaryotic regulons

Background  

The binding of regulatory proteins to their specific DNA targets determines the accurate expression of the neighboring genes. The in silico prediction of new binding sites in completely sequenced genomes is a key aspect in the deeper understanding of gene regulatory networks. Several algorithms have been described to discriminate against false-positives in the prediction of new binding targets; however none of them has been implemented so far to assist the detection of binding sites at the genomic scale.     

Integrated prediction of one-dimensional structural features and their relationships with conformational flexibility in helical membrane proteins

Background  

Many structural properties such as solvent accessibility, dihedral angles and helix-helix contacts can be assigned to each residue in a membrane protein. Independent studies exist on the analysis and sequence-based prediction of some of these so-called one-dimensional features. However, there is little explanation of why certain residues are predicted in a wrong structural class or with large errors in the absolute values of these features. On the other hand, membrane proteins undergo conformational changes to allow transport as well as ligand binding. These conformational changes often occur via residues that are inherently flexible and hence, predicting fluctuations in residue positions is of great significance.     

WebAllergen: a web server for predicting allergenic proteins

WebAllergen is a web server that predicts the potential allergenicity of proteins. The query protein will be compared against a set of prebuilt allergenic motifs that have been obtained from 664 known allergen proteins. The query will also be compared with known allergens that do not have detectable allergenic motifs. Moreover, users are allowed to upload their own allergens as alternative training sequences on which a new set of allergenic motifs will be built. The query sequences can also be compared with these motifs. AVAILABILITY: http://weballergen.bii.a-star.edu.sg/     

Bhageerath: an energy based web enabled computer software suite for limiting the search space of tertiary structures of small globular proteins

We describe here an energy based computer software suite for narrowing down the search space of tertiary structures of small globular proteins. The protocol comprises eight different computational modules that form an automated pipeline. It combines physics based potentials with biophysical filters to arrive at 10 plausible candidate structures starting from sequence and secondary structure information. The methodology has been validated here on 50 small globular proteins consisting of 2-3 helices and strands with known tertiary structures. For each of these proteins, a structure within 3-6 A RMSD (root mean square deviation) of the native has been obtained in the 10 lowest energy structures. The protocol has been web enabled and is accessible at http://www.scfbio-iitd.res.in/bhageerath.     

Effects of unilateral castration on morphologic characteristics of the testis in one-, two-, and three-year-old stallions

The effects of unilateral castration on testicular compensatory hypertrophy were measured in 12 Morgan stallions, four each at one, two, and three years of age. They were randomized within age to intact (IN) or unilaterally castrated (UC) groups. Allotment and surgery were in January 1983 and total castration was in June 1983, 150 d after unilateral castration. Testis weight increased linearly with age (P < 0.01) and was increased by unilateral castration (P < 0.07). Epididymal weight also increased linearly with age (P < 0.05) and was heavier in UC animals (P = 0.15). Tubule diameter (P < 0.10) and epithelial height (P < 0.03) were greater in UC than in IN stallions. In conclusion, testes of stallions underwent compensatory hypertrophy after unilateral castration.     

FLEXc: protein flexibility prediction using context-based statistics,predicted structural features,and sequence information

Background

The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions.

Results

In this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context.The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor.

Conclusions

Incorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called “FLEXc” and available online at: http://hpcr.cs.odu.edu/flexc.

     

Calculations of one-, two- and three-bond nuclear spin-spin couplings in a model peptide and correlations with experimental data

Summary We present ab initio calculations of the Fermi contact term and experimental correlations of six coupling constants, ³J_H ^N _H , ¹J_{C H} , ²J_CH , ¹J_{C N}, ²J_{C N} and ¹J_CN, in a peptide as functions of the backbone dihedral angles, and . Given estimates of experimental uncertainties, we find semiquantitative experimental correlations for ³J_H ^N _H , ¹J_{C N} and ²J_{C N}, qualitative correlations for ¹J_{C H} and ²J_CH , but no experimental correlations of practical utility for ¹J_CN, owing to its complex dependence on at least four dihedral angles. Errors in the estimation of dihedral angles from X-ray crystallographic data for proteins, which result from uncertainties in atom-to-atom distances, place substantial limitations on the quantitative reliability of coupling constant calculations fitted to such data. In the accompanying paper [Edison, A.S. et al., J. Biomol. NMR, 4, 543–551] we apply the results of the coupling constant calculations presented here to the estimation of and angles in staphylococcal nuclease from experimental coupling constants.Abbreviations AO atomic orbital - BPTI basic pancreatic trypsin inhibitor (bovine) - CI-2 chymotrypsin inhibitor 2 - E.COSY exclusive correlation spectroscopy (Griesinger et al., 1986) - ⁿJ_AB single bond (n=1), geminal (n=2), or vicinal (n=3) coupling constant between nuclei A and B - LCAO linear combination of atomic orbitals - NBO natural bond orbital - n lone pair orbitals - bonding orbitals - ^* antibonding orbitals - dihedral angle or molecular orbital wave function; r², correlation coefficient - RHF restricted Hartree-Fock; rmsd, root-mean-square deviation - 3-21G and 6-31G^* molecular orbital basis set designations (Hehre et al., 1986)     

MutaProt: a web interface for structural analysis of point mutations

A web-based tool, termed 'MutaProt', is described which analyses pairs of PDB files whose members differ in one, or two, amino acids. MutaProt examines the micro environment surrounding the exchanged residue(s) and can be searched by specifying a PDB ID, keywords, or any pair of amino acids. Detailed information about accessibility of the exchanged residue(s) and its atomic contacts are provided based on CSU software (Sobolev et al., Bioinformatics, 15, 327-332, 1999). An interactive 3D presentation of the superimposed regions around the mutation(s) is included. MutaProt is updated weekly.