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The high levels of sequence diversity and rapid rates of evolution of HIV-1 represent the main challenges for developing effective therapies. However, there are constraints imposed by the three-dimensional protein structure that affect the sequence space accessible to the evolution of HIV-1. Here, we present a strategy for predicting the set of possible amino acid replacements in HIV. Our approach is based on the identification of likely amino acid changes in the context of these structural constraints using environment-specific substitution matrices as well as considering the physical constraints imposed by local structure. Assessment of the power of various published algorithms in predicting the evolution of HIV-1 Gag P17 shows that it is possible to use these methods to make accurate predictions of the sequence diversity. Our own method, SubFit, uses knowledge of local structural constraints; it achieves similar prediction success with the best-performing methods. We also show that erroneous predictions are largely due to infrequently occurring amino acids that will probably have severe fitness costs for the protein. Future improvements; for example, incorporating covariation and immunological constraints will permit more reliable prediction of viral evolution. © 2011 Elsevier Ltd. All rights reserved.

In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements, and how social influence affects estimates. We investigate these questions by taking a meta wisdom of crowds approach. With a distributed team of over 100 student researchers across 17 institutions in the United States and India, we develop a large-scale online experiment to systematically study the wisdom of crowds effect for 1,000 different tasks in 50 subject domains. These tasks involve various types of knowledge (e.g., explicit knowledge, tacit knowledge, and prediction), question formats (e.g., multiple choice and point estimation), and inputs (e.g., text, audio, and video). To examine the effect of social influence, participants are randomly assigned to one of three different experiment conditions in which they see varying degrees of information on the responses of others. In this ongoing project, we are now preparing to recruit participants via Amazon?s Mechanical Turk.