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Editor's Notes

• #3: 1
• #5: Experimental data can aid the structure prediction process. Some of these are: Disulphide bonds, which provide tight restraints on the location of cysteines in space Spectroscopic data, which can give you and idea as to the secondary structure content of your protein Site directed mutagenesis studies, which can give insights as to residues involved in active or binding sites Knowledge of proteolytic cleavage sites, post-translational modifictions, such as phosphorylation or glycosylation can suggest residues that must be accessible Protein Sequence: Transmembrane? Coil-coil? Does your protein contain regions of low complexity? Proteins frequently contain runs of poly-glutamine or poly-serine, which do not predict well (SEG program). If the answer to any of the above questions is yes, then it is worthwhile trying to break your sequence into pieces, or ignore particular sections of the sequence, etc. This is related to the problem of locating domains .
• #7: Fig.:Coverage for each species is reported as the fraction of the residues in the proteome that are annotated . Structural annotation is an homology to a known structure. Functional annotation is when there is no structural annotation but there is an homology to a sequence database entry that has a useful description. Homology denotes a sequence similarity to a structurally or functionally un-annotated protein, such as one described as hypothetical. Non-globular denotes remaining sequence regions that were predicted as transmembrane, signal peptide, coiled-coils or low-complexity. Remaining residues are classified as orphans.
• #9: Analysis of the frequency with which different amino acids are found in different types of secondary structure shows some general preferences. For example, long side chains such as those of leucine, methionine, glutamine and glutatamic acid are often found in helices, presumably because these extended side chains can project out away from the crowded central region of the helical cylinder. In contrast, residues whose side chains are branched at the beta carbon , such as valine, isoleucine and phenylalanine are more often found in beta sheets, because every other side chain in a sheet is pointing in the opposite direction, leaving room for beta-branched side chains to pack. Such tendencies underlie various empirical rules for the prediction of secondary structure from sequence, such as those of Chou and Fasman. In the Chou-Fasman and other statistical methods of predicting secondary structure, the assumption is made that local effects predominate in determining whether a stretch of sequence will be helical, form a turn, compose a beta strand, or adopt an irregular conformation. This assumption is probably only partially valid, which may account for the failure of such methods to achieve close to 100% success in secondary structure prediction. The methods take proteins of known three-dimensional structure and tabulate the preferences of individual amino acids for various structural elements. By comparing these values with what might be expected randomly, conformational preferences can be assigned to each amino acid. To apply these preferences to a sequence of unknown structure, a moving window of about five residues is scanned along a sequence, and the average preferences are tallied. Empirical rules are then applied to assign secondary structural features based on the average preferences. Unfortunately, these tendencies are only very rough, and there are many exceptions. It is probably more useful to consider which side chains are disfavored in particular types of secondary structures. With specialized exceptions Proline is disfavored in both helices and sheets because it has no backbone N-H group to participate in hydrogen bonding. Glycine is also less commonly found in helices and sheets, in part because it lacks a side chain and therefore can adopt a much wider range of phi, psi torsion angles in peptides. These two residues are, however, strongly associated with beta turns, and sequences such as Pro-Gly and Gly-Pro are sometimes considered diagnostic for turns. Although predictive schemes based on residue preferences have some value, none is completely accurate, and the one rule that seems to be most reliable is that any amino acid can be found in any type of secondary structure, if only infrequently. Proline, for instance, is sometimes found in alpha helices; when it is, it simply interrupts the helical hydrogen-bonding network and produces a kink in the helix.
• #11: 1974 Chou and Fasman propose a statistical method based on the propensities of amino acids to adopt secondary structures based on the observation of their location in 15 protein structures determined by X-ray diffraction. Clearly these statistics derive from the particular stereochemical and physicochemical properties of the amino acids. See for example, glycine and proline. These statistics have been refined over the years by a number of authors (including Chou and Fasman themselves) using a larger set of proteins. Rather than a position by position analysis the propensity of a position is calculated using an average over 5 or 6 residues surrounding each position. On a larger set of 62 proteins the base method reports a success rate of 50%. 1978 Garnier improved the method by using statistically significant pair-wise interactions as a determinant of the statistical significance. This improved the success rate to 62% 1993 Levin improved the prediction level by using multiple sequence alignments. The reasoning is as follows. Conserved regions in a multiple sequence alignment provides a strong evolutionary indicator of a role in the function of the protein. Those regions are also likely to have conserved structure, including secondary structure and strengthen the prediction by their joint propensities. This improved the success rate to 69%. 1994 Rost and Sander combined neural networks with multiple sequence alignments. The idea of a neural net is to create a complex network of interconnected nodes, where progress from one node to the next depends on satisfying a weighted function that has been derived by training the net with data of known results, in this case protein sequences with known secondary structures. The success rate is 72%.
• #22: Simulate the brain. Selection of training sets is extremely important. Different protein families, only one or two representative from each family.
• #23: Jpred: (http://www.dl.ac.uk/CCP/CCP11/newsletter/vol2_4/jpred_ccp11/) Jpred runs DSC (5), PHD (1,2), PREDATOR (3,4) and NNSSP (6) in parallel to build its consensus prediction, but predictions from slightly less accurate algorithms MULPRED (8) and ZPRED (7) are also included in the final output.������ These methods were chosen as representatives of current state-of-the-art secondary structure predictions methods that exploit the evolutionary information from multiple sequences.�� Each derives its prediction using a different heuristic, based upon nearest neighbours (NNSSP), jury decision neural networks (PHD), linear discrimination (DSC), consensus single sequence method (MULPRED), hydrogen bonding propensities (PREDATOR), or conservation number weighted prediction (ZPRED).�������� The consensus is constructed using a simple majority wins combination of DSC, PHD, PREDATOR and NNSSP, relying on the PHD prediction if there is a a tie.�� In our study, we found this combination to be optimal.��
• #24: Flowchart of EVA. Every day, EVA downloads the newest protein structures from PDB [1] . The structures are added to mySQL databases, sequences are extracted for every protein chain, and are sent to each prediction server by META-PredictProtein [2] . META-PP collects the results and sends them to EVA. Every week, EVA runs alignment programs for searching sequence (iterated PSI-BLAST [3] , MaxHom [4] ) and structure (CE [5] , ProSub [6] ) databases to determine homologues. Predictions of secondary structure and inter-residue contacts, as well as, comparative modelling are evaluated at the EVA satellites at Columbia University, Rockefeller University, and CNB Madrid. Goals: CASP addresses the question ‘how well can experts predict protein structure if given sufficient incentive to do so?’. In contrast, the question addressed by EVA is ‘how well could molecular biologists predict protein structure, if they simply take the output from the programs out there?’. Thus, the goals are: Provide a continuous, fully automated, and statistically significant analysis of structure prediction servers. As has been shown by many of us, predictions based on small numbers of samples are NOT representative. EVA running for a year could produce a fairly representative picture. Even running for a month EVA could produce more reliable estimates than CASP can do in 2 years (at least, for answering the particular, restricted — but important - question ‘how well do servers do’). EVA will NOT answer to requests of users!! It will NOT be a meta-server, rather it will simply sit there and evaluate servers based on known structures. EVA will NOT evaluate any server without the consent of the author.
• #26: SSE – secondary structure elements Perutz (1990) showed (while working with hemoglobin and myoglobin) that amphipathicity can be detected in the sequnce: non polar residues can appears every 3.6 approx. in a linear sequence, making one side of the helix hydrophobic.
• #27: An example of the prediction of secondary structure from sequence for a protein of unknown function from the Enterococcus faecalis genome. What is striking is that all of the schemes agree on the approximate locations of the alpha helices (h) and beta strands (e), but they disagree considerably on the lengths and end positions of these segments. Note also that the probable positions of loops (indicated by a c) and turns (indicated by a t) are very inconsistently predicted. Such results are typical, but the application of many methods is clearly more informative than the use of a single one. The bottom line shows the consensus prediction.
• #28: The upper one is by Jpred and the lower one is by GOR
• #40: 1wqa, 1tx4, 1grn, 1tad and 1gfi. -> only 1tx4, 1grn, 1tad Note that some amino acids may appear in yellow once a molecule has been loaded. It signifies that their sidechain has been reconstructed during the loading process because some atoms were lacking. When all sidechain atoms are lacking, a rotamer library is searched until the rotamer that generate a maximum of H-bonds and a minimum of steric hindrances is found. If only some sidechain atoms are lacking, the rotamer that gives the lowest RMS when fitted to the partial sidechain is taken. In any case, you may try to find a better sidechain manually with the mutation tool. If you want to act on a complete column , simply hold down the shift key while clicking in a column Note: if a little earth icon is shown below the first tool, the rotation takes place in absolute coordinates. Otherwise (little protein icon) molecules are rotated around their centrotid. Hence the first option allows you to rotate the molecule around any atom, providing that this atom has previously been centered (translated to the (0,0,0) coordinate). Note: if "caps lock" is down, you can measure several distances or angles successively. To exit the "repeated" measurement mode, you can either depress "caps lock" or hit "esc".
• #53: Устные пояснения.