Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach

The function of a protein that has no sequence homolog of known function is difficult to assign on the basis of sequence similarity. The same problem may arise for homologous proteins of different functions if one is newly discovered and the other is the only known protein of similar sequence. It is desirable to explore methods that are not based on sequence similarity. One approach is to assign functional family of a protein to provide useful hint about its function. Several groups have employed a statistical learning method, support vector machines (SVMs), for predicting protein functional family directly from sequence irrespective of sequence similarity. These studies showed that SVM prediction accuracy is at a level useful for functional family assignment. But its capability for assignment of distantly related proteins and homologous proteins of different functions has not been critically and adequately assessed. Here SVM is tested for functional family assignment of two groups of enzymes. One consists of 50 enzymes that have no homolog of known function from PSI-BLAST search of protein databases. The other contains eight pairs of homologous enzymes of different families. SVM correctly assigns 72% of the enzymes in the first group and 62% of the enzyme pairs in the second group, suggesting that it is potentially useful for facilitating functional study of novel proteins. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi

Predicting functional family of novel enzymes irrespective of sequence similarity: a statistical learning approach

The function of a protein that has no sequence homolog of known function is difficult to assign on the basis of sequence similarity. The same problem may arise for homologous proteins of different functions if one is newly discovered and the other is the only known protein of similar sequence. It is desirable to explore methods that are not based on sequence similarity. One approach is to assign functional family of a protein to provide useful hint about its function. Several groups have employed a statistical learning method, support vector machines (SVMs), for predicting protein functional family directly from sequence irrespective of sequence similarity. These studies showed that SVM prediction accuracy is at a level useful for functional family assignment. But its capability for assignment of distantly related proteins and homologous proteins of different functions has not been critically and adequately assessed. Here SVM is tested for functional family assignment of two groups of enzymes. One consists of 50 enzymes that have no homolog of known function from PSI-BLAST search of protein databases. The other contains eight pairs of homologous enzymes of different families. SVM correctly assigns 72% of the enzymes in the first group and 62% of the enzyme pairs in the second group, suggesting that it is potentially useful for facilitating functional study of novel proteins. A web version of our software, SVMProt, is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi

Struggling with the Creative Class

This article develops a critique of the recently popularized concepts of the ‘creative class’ and ‘creative cities’. The geographic reach and policy salience of these discourses is explained not in terms of their intrinsic merits, which can be challenged on a number of grounds, but as a function of the profoundly neoliberalized urban landscapes across which they have been traveling. For all their performative display of liberal cultural innovation, creativity strategies barely disrupt extant urban‐policy orthodoxies, based on interlocal competition, place marketing, property‐ and market‐led development, gentrification and normalized socio‐spatial inequality. More than this, these increasingly prevalent strategies extend and recodify entrenched tendencies in neoliberal urban politics, seductively repackaging them in the soft‐focus terms of cultural policy. This has the effect of elevating creativity to the status of a new urban imperative — defining new sites, validating new strategies, placing new subjects and establishing new stakes in the realm of competitive interurban relations

Why do companies not produce sustainability reports?

Sustainability reporting emerged on the corporate scene nearly 30 years ago as a key mechanism through which business organisations would manage a transition to a new business landscape dominated by greater concern and consciousness about sustainability. While it has become something of a feature on the corporate agenda in some parts of the world, the majority of business organisations do not undertake this type of reporting. This paper explores why 23 of Australia\u27s top 200 companies do not undertake sustainability reporting. The study is situated in the context of a considerable literature that promised numerous benefits to be derived from this type of reporting. The paper uncovers various social and organisational factors that raise some new questions about legitimacy theory, corporate accountability and the spread and uptake of this organisational practice