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	<h1>SDM</h1>
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		<h3>SDM: a server for predicting effects of mutations on protein stability</h3>
		<a href="https://scholar.google.co.uk/citations?user=vi-VM9AAAAAJ&hl=en">Arun P. Pandurangan</a>,
                <a>Bernardo Ochoa-Montaño</a>,
		<a href="http://scholar.google.co.uk/citations?user=7KrAVc0AAAAJ&hl=en">David B. Ascher</a>, 
		<a href="http://www.bioc.cam.ac.uk/uto/blundell">Tom L. Blundell</a> <br/>
		
		<h5>Nucleic Acids Research, v. 45, Issue W1, Pages W229–W235, 2017
		<a href="https://academic.oup.com/nar/article/45/W1/W229/3835311/SDM-a-server-for-predicting-effects-of-mutations">
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		<a href="https://academic.oup.com/nar/article-pdf/45/W1/W229/18137659/gkx439.pdf">
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		<h4>Abstract</h4>

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		Studying the effects of mutation on protein stability and function is important in understanding its role in disease. Site Directed Mutator (SDM) is a computational method that analyses the variation of amino acid replacements occuring at specific structural environment that are tolerated within the family of homologous proteins of known 3-D strucures and convert them into substitution probability tables. These tables are uses as a quantative measure for predicting the protein stability upon mutation. Here we report the improved and updated SDM web server.
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		<!--<b>Results:</b> We show that mCSM performs as well as or better than other methods that are used widely. mCSM signatures were 
		successfully used in very different tasks demonstrating that the impact of a mutation can be correlated with the 
		atomic-distance patterns surrounding an amino acid residue. Finally, we showed that mCSM can predict stability 
		changes of a wide range of mutations occurring in the tumour suppressor protein p53, demonstrating the applicability of 
		the proposed method in a challenging disease scenario.-->
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