Cooperative approach for the protein fold recognition

Conformational study of intermediate in the unfolding of PcoC

PcoC is a small soluble protein and is considered as a kind of copper carrier in the periplasm. The PcoC protein from E. coli possesses a β-barrel fold with two metal-binding sites of Cu²⁺ and Cu⁺. In this work, different spectroscopic techniques were adopted to clarify the stability of PcoC and metals' binding property. As demonstrated in results, Ag⁺ and Cu²⁺ are capable of binding with PcoC in a proportion of 1:1. The constant for PcoC and Cu²⁺ was (7.27 ± 0.21) × 10¹³ L/mol. In addition, we have explored how the cofactors affect the PcoC stability, finding that Cu²⁺ coordination affects both protein stability and unfolding pathway. The intermediate appeared during PcoC-Cu²⁺ unfolding. Further, the intermediate could be formed as CTAB interacted with PcoC. As found, the intermediate's C-terminal structure was unfolded, whereas the N-terminal was almost unaffected. Furthermore, the capability of the different unfolding degree protein with Cu²⁺ also indicated that the N-terminal exhibited a strong stability. Based on the anisotropy decay, tryptophan moved at a higher concentration of urea, also showing that the N-terminal was highly stable. In addition, the steered molecular dynamics simulations were performed, showing the rigidness of the N-terminal.

Rapid Assessment of Protein Structural Stability and Fold Validation via NMR

In structural proteomics, it is necessary to efficiently screen in a high-throughput manner for the presence of stable structures in proteins that can be subjected to subsequent structure determination by X-ray or NMR spectroscopy. Here we illustrate that the (1)H chemical distribution in a protein as detected by (1)H NMR spectroscopy can be used to probe protein structural stability (e.g., the presence of stable protein structures) of proteins in solution. Based on experimental data obtained on well-structured proteins and proteins that exist in a molten globule state or a partially folded alpha-helical state, a well-defined threshold exists that can be used as a quantitative benchmark for protein structural stability (e.g., foldedness) in solution. Additionally, in this chapter we describe a largely automated strategy for rapid fold validation and structure-based backbone signal assignment. Our methodology is based on a limited number of NMR experiments (e.g., HNCA and 3D NOESY-HSQC) and performs a Monte Carlo-type optimization. The novel feature of the method is the opportunity to screen for structural fragments (e.g., template scanning). The performance of this new validation tool is demonstrated with applications to a diverse set of proteins.

A novel fold recognition method using composite predicted secondary structures

In this work, we introduce a new method for fold recognition using composite secondary structures assembled from different secondary structure prediction servers for a given target sequence. An automatic, complete, and robust way of finding all possible combinations of predicted secondary structure segments (SSS) for the target sequence and clustering them into a few flexible clusters, each containing patterns with the same number of SSS, is developed. This program then takes two steps in choosing plausible homologues: (i) a SSS-based alignment excludes impossible templates whose SSS patterns are very different from any of those of the target; (ii) a residue-based alignment selects good structural templates based on sequence similarity and secondary structure similarity between the target and only those templates left in the first stage. The secondary structure of each residue in the target is selected from one of the predictions to find the best match with the template. Truncation is applied to a target where different predictions vary. In most cases, a target is also divided into N-terminal and C-terminal fragments, each of which is used as a separate subsequence. Our program was tested on the fold recognition targets from CASP3 with known PDB codes and some available targets from CASP4. The results are compared with a structural homologue list for each target produced by the CE program (Shindyalov and Bourne, Protein Eng 1998;11:739-747). The program successfully locates homologues with high Z-score and low root-mean-score deviation within the top 30-50 predictions in the overwhelming majority of cases.

Medical target prediction from genome sequence: combining different sequence analysis algorithms with expert knowledge and input from artificial intelligence approaches

By exploiting the rapid increase in available sequence data, the definition of medically relevant protein targets has been improved by a combination of: (i) differential genome analysis (target list): and (ii) analysis of individual proteins (target analysis). Fast sequence comparisons, data mining, and genetic algorithms further promote these procedures. Mycobacterium tuberculosis proteins were chosen as applied examples.

Review: protein secondary structure prediction continues to rise

Methods predicting protein secondary structure improved substantially in the 1990s through the use of evolutionary information taken from the divergence of proteins in the same structural family. Recently, the evolutionary information resulting from improved searches and larger databases has again boosted prediction accuracy by more than four percentage points to its current height of around 76% of all residues predicted correctly in one of the three states, helix, strand, and other. The past year also brought successful new concepts to the field. These new methods may be particularly interesting in light of the improvements achieved through simple combining of existing methods. Divergent evolutionary profiles contain enough information not only to substantially improve prediction accuracy, but also to correctly predict long stretches of identical residues observed in alternative secondary structure states depending on nonlocal conditions. An example is a method automatically identifying structural switches and thus finding a remarkable connection between predicted secondary structure and aspects of function. Secondary structure predictions are increasingly becoming the work horse for numerous methods aimed at predicting protein structure and function. Is the recent increase in accuracy significant enough to make predictions even more useful? Because the recent improvement yields a better prediction of segments, and in particular of beta strands, I believe the answer is affirmative. What is the limit of prediction accuracy? We shall see.

Fold recognition and accurate query-template alignment by a combination of PSI-BLAST and threading

A homology-based structure prediction method ideally gives both a correct fold assignment and an accurate query-template alignment. In this article we show that the combination of two existing methods, PSI-BLAST and threading, leads to significant enhancement in the success rate of fold recognition. The combined approach, termed COBLATH, also yields much higher alignment accuracy than found in previous studies. It consists of two-way searches both by PSI-BLAST and by threading. In the PSI-BLAST portion, a query is used to search for hits in a library of potential templates and, conversely, each potential template is used to search for hits in a library of queries. In the threading portion, the scoring function is the sum of a sequence profile and a 6x6 substitution matrix between predicted query and known template secondary structure and solvent exposure. "Two-way" in threading means that the query's sequence profile is used to match the sequences of all potential templates and the sequence profiles of all potential templates are used to match the query's sequence. When tested on a set of 533 nonhomologous proteins, COBLATH was able to assign folds for 390 (73%). Among these 390 queries, 265 (68%) had root-mean-square deviations (RMSDs) of less than 8 A between predicted and actual structures. Such high success rate and accuracy make COBLATH an ideal tool for structural genomics.

Combination of threading potentials and sequence profiles improves fold recognition

Using a benchmark set of structurally similar proteins, we conduct a series of threading experiments intended to identify a scoring function with an optimal combination of contact-potential and sequence-profile terms. The benchmark set is selected to include many medium-difficulty fold recognition targets, where sequence similarity is undetectable by BLAST but structural similarity is extensive. The contact potential is based on the log-odds of non-local contacts involving different amino acid pairs, in native as opposed to randomly compacted structures. The sequence profile term is that used in PSI-BLAST. We find that combination of these terms significantly improves the success rate of fold recognition over use of either term alone, with respect to both recognition sensitivity and the accuracy of threading models. Improvement is greatest for targets between 10 % and 20 % sequence identity and 60 % to 80 % superimposable residues, where the number of models crossing critical accuracy and significance thresholds more than doubles. We suggest that these improvements account for the successful performance of the combined scoring function at CASP3. We discuss possible explanations as to why sequence-profile and contact-potential terms appear complementary.