In Silico Analysis of the Molecular-Level Impact of SMPD1 Variants on Niemann-Pick Disease Severity

Sphingomyelin phosphodiesterase (SMPD1) is a key enzyme in the sphingolipid metabolism. Genetic SMPD1 variants have been related to the Niemann-Pick lysosomal storage disorder, which has different degrees of phenotypic severity ranging from severe symptomatology involving the central nervous system (type A) to milder ones (type B). They have also been linked to neurodegenerative disorders such as Parkinson and Alzheimer. In this paper, we leveraged structural, evolutionary and stability information on SMPD1 to predict and analyze the impact of variants at the molecular level. We developed the SMPD1-ZooM algorithm, which is able to predict with good accuracy whether variants cause Niemann-Pick disease and its phenotypic severity; the predictor is freely available for download. We performed a large-scale analysis of all possible SMPD1 variants, which led us to identify protein regions that are either robust or fragile with respect to amino acid variations, and show the importance of aromatic-involving interactions in SMPD1 function and stability. Our study also revealed a good correlation between SMPD1-ZooM scores and in vitro loss of SMPD1 activity. The understanding of the molecular effects of SMPD1 variants is of crucial importance to improve genetic screening of SMPD1-related disorders and to develop personalized treatments that restore SMPD1 functionality.     

Amino acid sequence templates derived from recurrent turn motifs in proteins: critical evaluation of their predictive power

Amino acid sequence patterns suggested to characterize specificrecurrent turn conformations in proteins are tested as to theirpredictive power in a database containing 75 proteins of knownstructure. Many of these patterns are found to be associatedwith local structures that differ from the motifs originallyused to derive them. It is therefore concluded that, while theycould be useful for improving predictions made by other methods,their stand-alone predictive power is poor. The issue of derivingand validating consensus sequence patterns for use in proteinstructure prediction is raised.     

Revisiting the correlation between proteins' thermoresistance and organisms' thermophilicity

The possibility to rationally design protein mutants that remain structured and active at high temperatures strongly depends on a better understanding of the mechanisms of protein thermostability. Studies devoted to this issue often rely on the living temperature (T(env)) of the host organism rather than on the melting temperature (T(m)) of the analyzed protein. To investigate the scale of this approximation, we probed the relationship between T(m) and T(env) on a dataset of 127 proteins, and found a much weaker correlation than previously expected: the correlation coefficient is equal to 0.59 and the regression line is T(m) approximately 42.9 degrees C + 0.62T(env). To illustrate the effect of using T(env) rather than T(m) to analyze protein thermoresistance, we derive statistical distance potentials, describing Glu-Arg and Asp-Arg salt bridges, from protein structure sets with high or low T(m) or T(env). The results show that the more favorable nature of salt bridges, relative to other interactions, at high temperatures is more clear-cut when defining thermoresistance in terms of T(m). The T(env)-based sets nevertheless remain informative.     

Structural classification of alphabetabeta and betabetaalpha supersecondary structure units in proteins

We present a fully automatic structural classification of supersecondary structure units, consisting of two hydrogen-bonded beta strands, preceded or followed by an alpha helix. The classification is performed on the spatial arrangement of the secondary structure elements, irrespective of the length and conformation of the intervening loops. The similarity of the arrangements is estimated by a structure alignment procedure that uses as similarity measure the root mean square deviation of superimposed backbone atoms. Applied to a set of 141 well-resolved nonhomologous protein structures, the classification yields 11 families of recurrent arrangements. In addition, fragments that are structurally intermediate between the families are found; they reveal the continuity of the classification. The analysis of the families shows that the alpha helix and beta hairpin axes can adopt virtually all relative orientations, with, however, some preferable orientations; moreover, according to the orientation, preferences in the left/right handedness of the alpha-beta connection are observed. These preferences can be explained by favorable side by side packing of the alpha helix and the beta hairpin, local interactions in the region of the alpha-beta connection or stabilizing environments in the parent protein. Furthermore, fold recognition procedures and structure prediction algorithms coupled to database-derived potentials suggest that the preferable nature of these arrangements does not imply their intrinsic stability. They usually accommodate a large number of sequences, of which only a subset is predicted to stabilize the motif. The motifs predicted as stable could correspond to nuclei formed at the very beginning of the folding process.     

Are database-derived potentials valid for scoring both forward and inverted protein folding?

Database-derived potentials, compiled from frequencies of sequenceand structure features, are often used for scoring the compatibilityof protein sequences and conformations. It is often believedthat these scores correspond to differences in free energy with,in addition, a term containing the partition function of thesystem. Since this function does not depend on the conformation,the potentials are considered to be valid for scoring the compatibilityof different conformations with a given sequence (‘forwardfolding’), but not of sequences with a given structure(‘inverted folding’). This interpretation is questionedhere. It is argued that when many body-effects, which dominatefrequencies compiled from the protein database, are correctedfor, the potentials approximate a physically meaningful freeenergy difference from which the partition function term cancelsout It is the difference between the free energy of a givensequence in a specific conformation and that of the same sequencein a denatured-like state. Two examples of denatured-like statesare discussed. Depending on the considered state, the free energydifference reduces to the commonly used scoring scheme, or containsadditional terms that depend on the sequence. In both cases,all the terms can be derived from sequence-structure frequenciesin the database. Such free energy difference, commonly definedas the folding free energy, is a measure of protein stabilityand can be used for scoring both forward and inverted proteinfolding. The implications for the use of knowledge-based potentialsin protein structure prediction are described. Finally, thedifficulty of designing tests that could validate the proposedapproach, and the inherent limitations of such tests, are discussed     

Predicting protein stability changes upon mutation using database-derived potentials: solvent accessibility determines the importance of local versus non-local interactions along the sequence

For 238 mutations of residues totally or partially buried in the protein core, we estimate the folding free energy changes upon mutation using database-derived potentials and correlate them with the experimentally measured ones. Several potentials are tested, representing different kinds of interactions. Local interactions along the chain are described by torsion potentials, based on propensities of amino acids to be associated with backbone torsion angle domains. Non-local interactions along the sequence are represented by distance potentials, derived from propensities of amino acid pairs or triplets to be at a given spatial distance. We find that for the set of totally buried residues, the best performing potential is a combination of a distance potential and a torsion potential weighted by a factor of 0.4; it yields a correlation coefficient between computed and measured changes in folding free energy of 0.80. For mutations of partially buried residues, the best potential is a combination of a torsion potential and a distance potential weighted by a factor of 0.7, and for the previously analysed mutations of solvent accessible residues, it is a torsion potential taken individually; the respective correlation coefficients reach 0.82 and 0.87. These results show that distance potentials, dominated by hydrophobic interactions, represent best the main interactions stabilizing the protein core, whereas torsion potentials, describing local interactions along the chain, represent best the interactions at the protein surface. The prediction accuracy reached by the distance potentials is, however, lower than that of the torsion potentials. A possible reason for this is that distance potentials would not describe correctly the effect on protein stability due to cavity formation upon mutating a large into a small amino acid. Last but not least, our results indicate that although local interactions, responsible for secondary structure formation, do not dominate in the protein core, they are not negligible for all that. They have a significant weight in the delicate balance between all the interactions that ensure protein stability.     

Protein structure prediction by threading methods: evaluation of current techniques

This paper evaluates the results of a protein structure prediction contest. The predictions were made using threading procedures, which employ techniques for aligning sequences with 3D structures to select the correct fold of a given sequence from a set of alternatives. Nine different teams submitted 86 predictions, on a total of 21 target proteins with little or no sequence homology to proteins of known structure. The 3D structures of these proteins were newly determined by experimental methods, but not yet published or otherwise available to the predictors. The predictions, made from the amino acid sequence alone, thus represent a genuine test of the current performance of threading methods. Only a subset of all the predictions is evaluated here. It corresponds to the 44 predictions submitted for the 11 target proteins seen to adopt known folds. The predictions for the remaining 10 proteins were not analyzed, although weak similarities with known folds may also exist in these proteins. We find that threading methods are capable of identifying the correct fold in many cases, but not reliably enough as yet. Every team predicts correctly a different set of targets, with virtually all targets predicted correctly by at least one team. Also, common folds such as TIM barrels are recognized more readily than folds with only a few known examples. However, quite surprisingly, the quality of the sequence-structure alignments, corresponding to correctly recognized folds, is generally very poor, as judged by comparison with the corresponding 3D structure alignments. Thus, threading can presently not be relied upon to derive a detailed 3D model from the amino acid sequence. This raises a very intriguing question: how is fold recognition achieved? Our analysis suggests that it may be achieved because threading procedures maximize hydrophobic interactions in the protein core, and are reasonably good at recognizing local secondary structure.