Computational methods are increasingly gaining importance as an aid in identifying active sites. Mostly these methods tend to have structural information that supplement sequence conservation based analyses. Development of tools that compute electrostatic potentials has further improved our ability to better characterize the active site residues in proteins. We have described a computational methodology for detecting active sites based on structural and electrostatic conformity - C ata L ytic A ctive S ite P rediction (CLASP). In our pipelined model, physical 3D signature of any particular enzymatic function as defined by its active sites is used to obtain spatially congruent matches. While previous work has revealed that catalytic residues have large pKa deviations from standard values, we show that for a given enzymatic activity, electrostatic potential difference (PD) between analogous residue pairs in an active site taken from different proteins of the same family are similar. False positives in spatially congruent matches are further pruned by PD analysis where cognate pairs with large deviations are rejected. We first present the results of active site prediction by CLASP for two enzymatic activities - β-lactamases and serine proteases, two of the most extensively investigated enzymes. The results of CLASP analysis on motifs extracted from Catalytic Site Atlas (CSA) are also presented in order to demonstrate its ability to accurately classify any protein, putative or otherwise, with known structure. The source code and database is made available at www.sanchak.com/clasp/. Subsequently, we probed alkaline phosphatases (AP), one of the well known promiscuous enzymes, for additional activities. Such a search has led us to predict a hitherto unknown function of shrimp alkaline phosphatase (SAP), where the protein acts as a protease. Finally, we present experimental evidence of the prediction by CLASP by showing that SAP indeed has protease activity in vitro.

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Exclusion of UV (280-380 nm) radiation from the solar spectrum can be an important tool to assess the impact of ambient UV radiation on plant growth and performance of crop plants. The effect of exclusion of UV-B and UV-A from solar radiation on the growth and photosynthetic components in soybean (Glycine max) leaves were investigated. Exclusion of solar UV-B and UV-B/A radiation, enhanced the fresh weight, dry weight, leaf area as well as induced a dramatic increase in plant height, which reflected a net increase in biomass. Dry weight increase per unit leaf area was quite significant upon both UV-B and UV-B/A exclusion from the solar spectrum. However, no changes in chlorophyll a and b contents were observed by exclusion of solar UV radiation but the content of carotenoids was significantly (34-46%) lowered. Analysis of chlorophyll (Chl) fluorescence transient parameters of leaf segments suggested no change in the F v/F m value due to UV-B or UV-B/A exclusion. Only a small reduction in photo-oxidized signal I (P700+)/unit Chl was noted. Interestingly the total soluble protein content per unit leaf area increased by 18% in UV-B/A and 40% in UV-B excluded samples, suggesting a unique upregulation of biosynthesis and accumulation of biomass. Solar UV radiation thus seems to primarily affect the photomorphogenic regulatory system that leads to an enhanced growth of leaves and an enhanced rate of net photosynthesis in soybean, a crop plant of economic importance. The presence of ultra-violet components in sunlight seems to arrest carbon sequestration in plants.

Error-prone PCR, DNA shuffling, and saturation mutagenesis are techniques used by protein engineers to mimic the natural "evolutionary walk" that conjures new enzymes. Rational design is often critical in efforts to accelerate this "random walk" into a "resolute sprint." Previous work by our group established a computational method for detecting active sites (CLASP) based on spatial and electrostatic properties of catalytic residues, and a method to quantify promiscuous activities in a wide range of proteins (PROMISE). Here, we describe a rational design flow (DECAAF) based on the PROMISE methodology to choose a protein which, when subjected to minimal mutations, is most likely to mirror the scaffold of a desired enzymatic function. Modeling the diversity in catalytic sites and providing precise user control to guide the search is a key goal of our implementation. The flow details have been worked out in a real-life example to select a plant protein to substitute for human neutrophil elastase in a chimeric antimicrobial enzyme designed to bolster the innate immune defense system in plants.