The future of protein secondary structure prediction accuracy

Comparison of Peptide Ion Conformers Arising from Non-Helical and Helical Peptides Using Ion Mobility Spectrometry and Gas-Phase Hydrogen/Deuterium Exchange

The dominant gas-phase conformer of [M+3H]³⁺ ions of the model peptide acetyl-PSSSSKSSSSKSSSSKSSSSK has been examined with ion mobility spectrometry (IMS), gas-phase hydrogen deuterium exchange (HDX), and mass spectrometry (MS) techniques. The [M+3H]³⁺ peptide ions are observed predominantly as a relatively compact conformer type. Upon subjecting these ions to electron transfer dissociation (ETD), the level of protection for each amino acid residue in the peptide sequence is assessed. The overall per-residue deuterium uptake is observed to be relatively more efficient for the neutral residues than for the model peptide acetyl-PAAAAKAAAAKAAAAKAAAAK. In comparison, the N-terminal and C-terminal regions of the serine peptide show greater relative protection compared with interior residues. Molecular dynamics (MD) simulations have been used to generate candidate structures for collision cross section and HDX reactivity matching. Hydrogen accessibility scoring (HAS) for select structural candidates from MD simulations has been used to suggest conformer types that could contribute to the observed HDX patterns. The results are discussed with respect to recent studies employing extensive MD simulations of gas-phase structure establishment of a peptide system. Graphical Abstract ᅟ.

Molecular Dynamics Simulation of Cholera Toxin A-1 Polypeptide

A molecular dynamics (MD) simulation study of the enzymatic portion of cholera toxin; cholera toxin A-1 polypeptide (CTA1) was performed at 283, 310 and 323 K. From total energy analysis it was observed that this toxin is stable thermodynamically and these outcomes were likewise confirmed by root mean square deviations (RMSD) investigations. The Cα root mean square fluctuation (RMSF) examinations revealed that there are a number of residues inside CTA1, which can be used as target for designing and synthesizing inhibitory drugs, in order to inactivate cholera toxin inside the human body. The fluctuations in the radius of gyration and hydrogen bonding in CTA1 proved that protein unfolding and refolding were normal routine phenomena in its structure at all temperatures. Solvent accessible surface area study identified the hydrophilic nature of the CTA1, and due to this property it can be a potential biological weapon. The structural identification (STRIDE) algorithm for proteins was successfully used to determine the partially disordered secondary structure of CTA1. On account of this partially disordered secondary structure, it can easily deceive the proteolytic enzymes of the endoplasmic reticulum of host cells.

Characterization of non-trivial neighborhood fold constraints from protein sequences using generalized topohydrophobicity

Prediction of key features of protein structures, such as secondary structure, solvent accessibility and number of contacts between residues, provides useful structural constraints for comparative modeling, fold recognition, ab-initio fold prediction and detection of remote relationships. In this study, we aim at characterizing the number of non-trivial close neighbors, or long-range contacts of a residue, as a function of its “topohydrophobic” index deduced from multiple sequence alignments and of the secondary structure in which it is embedded. The “topohydrophobic” index is calculated using a two-class distribution of amino acids, based on their mean atom depths. From a large set of structural alignments processed from the FSSP database, we selected 1485 structural sub-families including at least 8 members, with accurate alignments and limited redundancy. We show that residues within helices, even when deeply buried, have few non-trivial neighbors (0–2), whereas β-strand residues clearly exhibit a multimodal behavior, dominated by the local geometry of the tetrahedron (3 non-trivial close neighbors associated with one tetrahedron; 6 with two tetrahedra). This observed behavior allows the distinction, from sequence profiles, between edge and central β-strands within β-sheets. Useful topological constraints on the immediate neighborhood of an amino acid, but also on its correlated solvent accessibility, can thus be derived using this approach, from the simple knowledge of multiple sequence alignments.

Protein secondary structure assignment revisited: a detailed analysis of different assignment methods

Background

A number of methods are now available to perform automatic assignment of periodic secondary structures from atomic coordinates, based on different characteristics of the secondary structures. In general these methods exhibit a broad consensus as to the location of most helix and strand core segments in protein structures. However the termini of the segments are often ill-defined and it is difficult to decide unambiguously which residues at the edge of the segments have to be included. In addition, there is a "twilight zone" where secondary structure segments depart significantly from the idealized models of Pauling and Corey. For these segments, one has to decide whether the observed structural variations are merely distorsions or whether they constitute a break in the secondary structure.

Methods

To address these problems, we have developed a method for secondary structure assignment, called KAKSI. Assignments made by KAKSI are compared with assignments given by DSSP, STRIDE, XTLSSTR, PSEA and SECSTR, as well as secondary structures found in PDB files, on 4 datasets (X-ray structures with different resolution range, NMR structures).

Results

A detailed comparison of KAKSI assignments with those of STRIDE and PSEA reveals that KAKSI assigns slightly longer helices and strands than STRIDE in case of one-to-one correspondence between the segments. However, KAKSI tends also to favor the assignment of several short helices when STRIDE and PSEA assign longer, kinked, helices. Helices assigned by KAKSI have geometrical characteristics close to those described in the PDB. They are more linear than helices assigned by other methods. The same tendency to split long segments is observed for strands, although less systematically. We present a number of cases of secondary structure assignments that illustrate this behavior.

Conclusion

Our method provides valuable assignments which favor the regularity of secondary structure segments.

The base composition of the genes is correlated with the secondary structures of the encoded proteins

The analysis of a non-redundant set of human proteins, for which both the crystallographic structures and the corresponding gene sequences are available, show that bases at third codon position are non-uniformly distributed along the coding sequences. Significant compositional differences are found by comparing the gene regions corresponding to the different secondary structures of the proteins. Inter-and intra-structure differences were most pronounced in the GC-richest genes. These results are not compatible with any proposed hypotheses based on a neutral process of formation/maintenance of the high GC(3) levels of the genes localized in the GC-richest isochores of the human genome.

Multiple alignment of complete sequences (MACS) in the post-genomic era

Multiple alignment, since its introduction in the early seventies, has become a cornerstone of modern molecular biology. It has traditionally been used to deduce structure / function by homology, to detect conserved motifs and in phylogenetic studies. There has recently been some renewed interest in the development of multiple alignment techniques, with current opinion moving away from a single all-encompassing algorithm to iterative and / or co-operative strategies. The exploitation of multiple alignments in genome annotation projects represents a qualitative leap in the functional analysis process, opening the way to the study of the co-evolution of validated sets of proteins and to reliable phylogenomic analysis. However, the alignment of the highly complex proteins detected by today's advanced database search methods is a daunting task. In addition, with the explosion of the sequence databases and with the establishment of numerous specialized biological databases, multiple alignment programs must evolve if they are to successfully rise to the new challenges of the post-genomic era. The way forward is clearly an integrated system bringing together sequence data, knowledge-based systems and prediction methods with their inherent unreliability. The incorporation of such heterogeneous, often non-consistent, data will require major changes to the fundamental alignment algorithms used to date. Such an integrated multiple alignment system will provide an ideal workbench for the validation, propagation and presentation of this information in a format that is concise, clear and intuitive.

Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks

By using an unsupervised cluster analyzer, we have identified a local structural alphabet composed of 16 folding patterns of five consecutive C(alpha) ("protein blocks"). The dependence that exists between successive blocks is explicitly taken into account. A Bayesian approach based on the relation protein block-amino acid propensity is used for prediction and leads to a success rate close to 35%. Sharing sequence windows associated with certain blocks into "sequence families" improves the prediction accuracy by 6%. This prediction accuracy exceeds 75% when keeping the first four predicted protein blocks at each site of the protein. In addition, two different strategies are proposed: the first one defines the number of protein blocks in each site needed for respecting a user-fixed prediction accuracy, and alternatively, the second one defines the different protein sites to be predicted with a user-fixed number of blocks and a chosen accuracy. This last strategy applied to the ubiquitin conjugating enzyme (alpha/beta protein) shows that 91% of the sites may be predicted with a prediction accuracy larger than 77% considering only three blocks per site. The prediction strategies proposed improve our knowledge about sequence-structure dependence and should be very useful in ab initio protein modelling.

Structure-based conformational preferences of amino acids

Proteins can be very tolerant to amino acid substitution, even within their core. Understanding the factors responsible for this behavior is of critical importance for protein engineering and design. Mutations in proteins have been quantified in terms of the changes in stability they induce. For example, guest residues in specific secondary structures have been used as probes of conformational preferences of amino acids, yielding propensity scales. Predicting these amino acid propensities would be a good test of any new potential energy functions used to mimic protein stability. We have recently developed a protein design procedure that optimizes whole sequences for a given target conformation based on the knowledge of the template backbone and on a semiempirical potential energy function. This energy function is purely physical, including steric interactions based on a Lennard-Jones potential, electrostatics based on a Coulomb potential, and hydrophobicity in the form of an environment free energy based on accessible surface area and interatomic contact areas. Sequences designed by this procedure for 10 different proteins were analyzed to extract conformational preferences for amino acids. The resulting structure-based propensity scales show significant agreements with experimental propensity scale values, both for alpha-helices and beta-sheets. These results indicate that amino acid conformational preferences are a natural consequence of the potential energy we use. This confirms the accuracy of our potential and indicates that such preferences should not be added as a design criterion.

Protein structure prediction

Genome sequencing projects continue to provide a flood of new protein sequences, and prediction methods remain an important means of adding structural information. Recently, there have been advances in secondary structure prediction, which feed, in turn, into improved fold recognition algorithms. Finally, there have been technical improvements in comparative modelling, and studies of the expected accuracy of three-dimensional structural models built by this method.

A helix propensity scale based on experimental studies of peptides and proteins

The average globular protein contains 30% alpha-helix, the most common type of secondary structure. Some amino acids occur more frequently in alpha-helices than others; this tendency is known as helix propensity. Here we derive a helix propensity scale for solvent-exposed residues in the middle positions of alpha-helices. The scale is based on measurements of helix propensity in 11 systems, including both proteins and peptides. Alanine has the highest helix propensity, and, excluding proline, glycine has the lowest, approximately 1 kcal/mol less favorable than alanine. Based on our analysis, the helix propensities of the amino acids are as follows (kcal/mol): Ala = 0, Leu = 0.21, Arg = 0.21, Met = 0.24, Lys = 0.26, Gln = 0.39, Glu = 0.40, Ile = 0.41, Trp = 0.49, Ser = 0.50, Tyr = 0. 53, Phe = 0.54, Val = 0.61, His = 0.61, Asn = 0.65, Thr = 0.66, Cys = 0.68, Asp = 0.69, and Gly = 1.