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Ten years after the idea of hydrophobic cluster analysis (HCA) was conceived and first published, theoretical and practical experience has shown this unconventional method of protein sequence analysis to be particularly efficient and sensitive, especially with families of sequences sharing low levels of sequence identity. This extreme sensitivity has made it possible to predict the functions of genes whose sequence similarities are hardly if at all detectable by current one-dimensional (1D) methods alone, and offers a new way to explore the enormous amount of data generated by genome sequencing. HCA also provides original tools to understand fundamental features of protein stability and folding. Since the last review of HCA published in 1990 [1], significant improvements have been made and several new facets have been addressed. Here we wish to update and summarize this information.

Motivation: When routinely analysing protein sequences, detailed analysis of database search results made with BLAST and FASTA becomes exceedingly time consuming and tedious work, as the resultant file may contain a list of hundreds of potential homologies. The interpretation of these results is usually carried out with a text editor which is not a convenient tool for this analysis. In addition, the format of data within BLAST and FASTA output files makes them difficult to read., Results: To facilitate and accelerate this analysis, we present for the first time, two easy-to-use programs designed for interactive analysis of full BLAST and FASTA output files containing protein sequence alignments. The programs, Visual BLAST and Visual FASTA, run under Microsoft Windows 95 or NT systems. They are based on the same intuitive graphical user interface (GUI) with extensive viewing, searching, editing, printing and multithreading capabilities. These programs improve the browsing of BLAST/FASTA results by offering a more convenient presentation of these results. They also implement on a computer several analytical tools which automate a manual methodology used for detailed analysis of BLAST and FASTA outputs. These tools include a pairwise sequence alignment viewer, a Hydrophobic Cluster Analysis plot alignment viewer and a tool displaying a graphical map of all database sequences aligned with the query sequence. In addition. Visual Blast includes tools for multiple sequence alignment analysis (with an amino acid patterns search engine), and Visual FASTA provides a GUI to the FASTA program.

Loops are regions of non-repetitive conformation connecting regular secondary structures. They are both the most difficult and error prone regions of a protein to solve by X-ray crystallography and the hardest regions to model using comparative procedures. Although a loop can sometimes be modelled from a homologue, very often it must be selected from outside the family. The loop prediction procedure, SLoop, attempts to identify the conformational class of the loop rather than to select a specific loop from a set of fragments extracted from known structures or generated ab initio. Templates are constructed for each of the 161 loop conformational classes that have been identified from the clustering of the structures of some 2024 loops of one to eight residues in length. A class template describes both sequence preferences and relative disposition of bounding secondary structures. During comparative modelling, the conformation of a loop can be predicted by identifying a loop class with which its sequence and disposition of bounding secondary structures are compatible. The procedure is tested on an unrelated non-redundant set of 1785 loops under stringent and lax evaluation schemes. Optimal sequence score cut-offs are identified such that the prediction rate is equal to the percentage of loops assigned to acceptable classes. Under the stringent evaluation, at the optimal sequence score cut-off, a conformation is predicted for 50% of loops of which 47% are correct, while under the lax evaluation a conformation is predicted for 63% of loops of which 54% are correct. Sequence score is shown to be a good indicator of the probability of a prediction being correct. Loop length also has a strong affect on prediction outcomes. Considering only loops of two to five residues in length, under the stringent evaluation 62% of loops are predicted with 52% of these predictions being correct while under the lax evaluation predictions are provided for 75% of loops of which 57% are correct.

Loops are regions of nonrepetitive conformation connecting regular secondary structures. We identified 2,024 loops of one to eight residues in length, with acceptable main-chain bond lengths and peptide bond angles, from a database of 223 protein and protein-domain structures. Each loop is characterized by its sequence, main-chain conformation, and relative disposition of its bounding secondary structures as described by the separation between the tips of their axes and the angle between them. Loops, grouped according to their length and type of their bounding secondary structures, were superposed and clustered into 161 conformational classes, corresponding to 63% of all loops. Of these, 109 (51% of the loops) were populated by at least four nonhomologous loops or four loops sharing a low sequence identity. Another 52 classes, including 12% of the loops, were populated by at least three loops of low sequence similarity from three or fewer nonhomologous groups. Loop class suprafamilies resulting from variations in the termini of secondary structures are discussed in this article. Most previously described loop conformations were found among the classes. New classes included a 2:4 type IV hairpin, a helix-capping loop, and a loop that mediates dinucleotide-binding. The relative disposition of bounding secondary structures varies among loop classes, with some classes such as beta-hairpins being very restrictive. For each class, sequence preferences as key residues were identified; those most frequently at these conserved positions than in proteins were Gly, Asp, Pro, Phe, and Cys. Most of these residues are involved in stabilizing loop conformation, often through a positive phi conformation or secondary structure capping. Identification of helix-capping residues and beta-breakers among the highly conserved positions supported our decision to group loops according to their bounding secondary structures. Several of the identified loop classes were associated with specific functions, and all of the member loops had the same function; key residues were conserved for this purpose, as is the case for the parvalbumin-like calcium-binding loops. A significant number, but not all, of the member loops of other loop classes had the same function, as is the case for the helix-turn-helix DNA-binding loops. This article provides a systematic and coherent conformational classification of loops, covering a broad range of lengths and all four combinations of bounding secondary structure types, and supplies a useful basis for modelling of loop conformations where the bounding secondary structures are known or reliably predicted.

Loops are regions of non-repetitive conformation connecting regular secondary structures. They are both the most difficult and error prone regions of a protein to solve by X-ray crystallography and the hardest regions to model using knowledge-based procedures. While the core of a protein can be straight forwardly modelled from the structurally conserved regions of homologues of known structure, loops must be modelled from a selected homologue or from a loop chosen from outside the family. Here we present a loop prediction procedure that attempts to identify the conformational class of the loop rather than to select a specific loop from a database of fragments. The structures of some 2083 loops of one to eight residues in length were extracted from a database of 225 protein and protein domain structures. For each loop, the relative disposition of its bounding secondary structures is described by the separation between the tips of their axes, the angle and dihedral angle between their axes. From the clustering of the loops according to the root mean square deviation of their spatial fit, a total of 162 loop conformational classes, including 79% of loops, were identified. One-hundred and eight of these, involving 66% of the loops, were populated by at least four non-homologous loops or four loops sharing a low sequence identity. Another 54 classes, including 13% of the loops, were populated by at least three loops of low sequence similarity from three or fewer non-homologous groups. Most of the previously described loop conformations were found among the populated classes. For each class a template was constructed containing both sequence preferences and the relative disposition of bounding secondary structures among member loops. During comparative modelling, the conformation of a loop can be predicted by identifying a loop class with which its sequence and disposition of bounding secondary structures are compatible.