Statistical clustering techniques for the analysis of long molecular dynamics trajectories: analysis of 2.2-ns trajectories of YPGDV.

The microscopic interactions and mechanisms leading to nascent protein folding events are generally unknown. While such short time-scale events are difficult to study experimentally, molecular dynamics simulations of peptides can provide a useful model for studying events related to protein folding initiation. Recently, two extremely long molecular dynamics simulations (2.2 ns each) were carried out on the pentapeptide Tyr-Pro-Gly-Asp-Val [Tobias, D. J., Mertz, J. E., & Brooks, C. L., III (1991) Biochemistry 30, 6054-6058] that forms stable reverse turns in solution. Tobias et al. examined folding events in this large system (approximately 30,000 conformations) using traditional methods of trajectory analysis. The shear magnitude of this problem prompted us to develop an automated approach, based on self-organizing neural nets, to extract the key features of the molecular dynamics trajectory. The neural net is used to perform conformational clustering, which reduces the complexity of a system while minimizing the loss of information. The conformations were grouped together using distances in dihedral angle space as a measure of conformational similarity. The resulting clusters represent "conformational states", and transitions between these states were examined to identify mechanisms of conformational change. Many conformational changes involved the rotation of only a single dihedral angle, but concerted angle changes were also found. Most of the conformational information in the 30,000 samples from the full trajectories was retained in the relatively few resultant clusters, providing a powerful tool for analysis of an expanding base of large molecular simulations.

Differences in the amino acid distributions of 3(10)-helices and alpha-helices.

Local determinants of 3(10)-helix stabilization have been ascertained from the analysis of the crystal structure data base. We have clustered all 5-length substructures from 51 nonhomologous proteins into classes based on the conformational similarity of their backbone dihedral angles. Several clusters, derived from 3(10)-helices and multiple-turn conformations, had strong amino acid sequence patterns not evident among alpha-helices. Aspartate occurred over twice as frequently in the N-cap position of 3(10)-helices as in the N-cap position of alpha-helices. Unlike alpha-helices, 3(10)-helices had few C-termini ending in a left-handed alpha conformation; most 3(10) C-caps adopted an extended conformation. Differences in the distribution of hydrophobic residues among 3(10)- and alpha-helices were also apparent, producing amphipathic 3(10)-helices. Local interactions that stabilize 3(10)-helices can be inferred both from the strong amino acid preferences found for these short helices, as well as from the existence of substructures in which tertiary interactions replace consensus local interactions. Because the folding and unfolding of alpha-helices have been postulated to proceed through reverse-turn and 3(10)-helix intermediates, sequence differences between 3(10)- and alpha-helices can also lend insight into factors influencing alpha-helix initiation and propagation.

A common pentapeptide conformation occurs in viral acid proteases and other proteins.

We found a pentapeptide conformation, termed a type I twist, which has a strikingly high propensity (56%) for aspartic acid in the first position. Type I twists include the active site loops from cellular and viral aspartic proteases, with the catalytic Asp in the first position. Fifteen other type I twists, from non-homologous proteins, were found among high-resolution structures in the Protein Data Bank using a comparison method based on main-chain torsion angles. We propose that the Asp affects electrostatic interactions and thus plays a major structural role in the formation of this recurring motif, in addition to its catalytic role in the aspartic proteases.

Comparing short protein substructures by a method based on backbone torsion angles.

An efficient algorithm was characterized that determines the similarity in main chain conformation between short protein substructures. The algorithm computes delta t, the root mean square difference in phi and psi torsion angles over a small number of amino acids (typically 3-5). Using this algorithm, large numbers of protein substructure comparisons were feasible. The parameter delta t was sensitive to variations in local protein conformation, and it correlates with delta r, the root mean square deviation in atomic coordinates. Values for delta t were obtained that define similarity thresholds, which determine whether two substructures are considered structurally similar. To set a lower bound on the similarity threshold, we estimated the component of delta t due to measurement noise from comparisons of independently refined coordinates of the same protein. A sample distribution of delta t from nonhomologous protein comparisons identified an upper bound on the similarity threshold, one that refrains from incorporating large numbers of nonmatching comparisons. Unlike methods based on C alpha atoms alone, delta t was sensitive to rotations in the peptide plane, shown to occur in several proteins. Comparisons of homologous proteins by delta t showed that the active site torsion angles are highly conserved. The delta t method was applied to the alpha-chain of human hemoglobin, where it readily demonstrated the local differences in the structures of different ligation states.