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Comparison of sequence profiles. Strategies for structural predictions using sequence information.

ABSTRACT: Distant homologies between proteins are often discovered only after three-dimensional structures of both proteins are solved. The sequence divergence for such proteins can be so large that simple comparison of their sequences fails to identify any similarity. New generation of sensitive alignment tools use averaged sequences of entire homologous families (profiles) to detect such homologies. Several algorithms, including the newest generation of BLAST algorithms and BASIC, an algorithm used in our group to assign fold predictions for proteins from several genomes, are compared to each other on the large set of structurally similar proteins with little sequence similarity. Proteins in the benchmark are classified according to the level of their similarity, which allows us to demonstrate that most of the improvement of the new algorithms is achieved for proteins with strong functional similarities, with almost no progress in recognizing distant fold similarities. It is also shown that details of profile calculation strongly influence its sensitivity in recognizing distant homologies. The most important choice is how to include information from diverging members of the family, avoiding generating false predictions, while accounting for entire sequence divergence within a family. PSI-BLAST takes a conservative approach, deriving a profile from core members of the family, providing a solid improvement without almost any false predictions. BASIC strives for better sensitivity by increasing the weight of divergent family members and paying the price in lower reliability. A new FFAS algorithm introduced here uses a new procedure for profile generation that takes into account all the relations within the family and matches BASIC sensitivity with PSI-BLAST like reliability.

SUBMITTER: Rychlewski L

PROVIDER: S-EPMC2144550 | biostudies-other | 2000 Feb

REPOSITORIES: biostudies-other

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Comparison of sequence profiles. Strategies for structural predictions using sequence information.

Rychlewski L L Jaroszewski L L Li W W Godzik A A

Protein science : a publication of the Protein Society 20000201 2

Distant homologies between proteins are often discovered only after three-dimensional structures of both proteins are solved. The sequence divergence for such proteins can be so large that simple comparison of their sequences fails to identify any similarity. New generation of sensitive alignment tools use averaged sequences of entire homologous families (profiles) to detect such homologies. Several algorithms, including the newest generation of BLAST algorithms and BASIC, an algorithm used in o ...[more]

PMID: 10716175

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Project description:The envelope glycoproteins (Envs) of HIV-1 continuously evolve in the host by random mutations and recombination events. The resulting diversity of Env variants circulating in the population and their continuing diversification process limit the efficacy of AIDS vaccines. We examined the historic changes in Env sequence and structural features (measured by integrity of epitopes on the Env trimer) in a geographically defined population in the United States. As expected, many Env features were relatively conserved during the 1980s. From this state, some features diversified whereas others remained conserved across the years. We sought to identify "clues" to predict the observed historic diversification patterns. Comparison of viruses that cocirculate in patients at any given time revealed that each feature of Env (sequence or structural) exists at a defined level of variance. The in-host variance of each feature is highly conserved among individuals but can vary between different HIV-1 clades. We designate this property "volatility" and apply it to model evolution of features as a linear diffusion process that progresses with increasing genetic distance. Volatilities of different features are highly correlated with their divergence in longitudinally monitored patients. Volatilities of features also correlate highly with their population-level diversification. Using volatility indices measured from a small number of patient samples, we accurately predict the population diversity that developed for each feature over the course of 30 years. Amino acid variants that evolved at key antigenic sites are also predicted well. Therefore, small "fluctuations" in feature values measured in isolated patient samples accurately describe their potential for population-level diversification. These tools will likely contribute to the design of population-targeted AIDS vaccines by effectively capturing the diversity of currently circulating strains and addressing properties of variants expected to appear in the future.

Dataset Information

Comparison of sequence profiles. Strategies for structural predictions using sequence information.

Publications

Comparison of sequence profiles. Strategies for structural predictions using sequence information.

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