<?xml version="1.0" encoding="utf-8"?>
<rss version="2.0" xmlns:media="http://search.yahoo.com/mrss/">
    <channel>
                    <title>Heidelberg Institute for Theoretical Studies in the news</title>
            <link>https://phys.org/</link>
            <language>en-us</language>
            <description>Latest news from Heidelberg Institute for Theoretical Studies</description>

                            <item>
                    <title>Flabby and flexible: How machine learning helps to build new proteins</title>
                    <description>The natural protein universe is vast, and yet, going beyond and designing new proteins not observed in nature can yield new functions and can solve problems in medicine or materials science. The past few years have marked the golden age of de novo protein design: machine learning methods have led to an unprecedented level of modeling accuracy. This progress enables researchers to design protein structures with specific functional properties never observed before. This is of particular interest for biotechnological applications, therapeutics development and sustainability problems, such as plastic degradation.</description>
                    <link>https://phys.org/news/2025-07-flabby-flexible-machine-proteins.html</link>
                    <category>Biotechnology</category>                    <pubDate>Wed, 23 Jul 2025 13:00:01 EDT</pubDate>
                    <guid isPermaLink="false">news672494244</guid>
                                            <media:thumbnail url="https://scx1.b-cdn.net/csz/news/tmb/2025/flabby-and-flexible-ho.jpg" width="90" height="90" />
                                    </item>
                        </channel>
</rss>