Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Evan Komp
Iterative computational and rational design generates diverse PET hydrolases for plastic depolymerization
Abstract Evolutionary relationships among homologous enzymes can be leveraged to introduce large numbers of amino acid substitutions for improved functional properties. Here, we combine evolution-informed design (EVdesign) with rational design to engineer improved poly(ethylene terephthalate) (PET) hydrolases (PETases).
Machine Learning-Guided Identification of PET Hydrolases from Natural Diversity
Putative Thermo- and Acid Tolerant PET Hydrolases Identified Using Bioinformatics and Iterative Machine Learning We conducted a PET hydrolase homolog screen consisting of three rounds (candidate ID per round: DP for Round 1, TEP for Round 2, and ESM for Round 3, vide infra), each augmented by machine learning on literature data or on previous round data ( Figure 1 A). A summary of the PET hydrolase data we used for this search is summarized in Table 1 .
A learnable transition from low temperature to high temperature proteins with neural machine translation
Abstract This work presents Neural Optimization for Melting-temperature Enabled by Leveraging Translation (NOMELT), a novel approach for designing and ranking high-temperature stable proteins using neural machine translation. The model, trained on over 4 million protein homologous pairs from organisms adapted to different temperatures, demonstrates promising capability in targeting thermal stability.
Author Correction: Homologous Pairs of Low and High Temperature Originating Proteins Spanning the Known Prokaryotic Universe - Scientific Data
Correction to: Scientific Data https://doi.org/10.1038/s41597-023-02553-w, published online 07 October 2023 In this article the author name Humood N. Alanzi was incorrectly written as Humood H. Alanzi. In addition, the author name Amin Mosallanejad was incorrectly written as Amin Mossallanejad. The original article has been corrected.
Homologous Pairs of Low and High Temperature Originating Proteins Spanning the Known Prokaryotic Universe - Scientific Data
Abstract Stability of proteins at high temperature has been a topic of interest for many years, as this attribute is favourable for applications ranging from therapeutics to industrial chemical manufacturing. Our current understanding and methods for designing high-temperature stability into target proteins are inadequate.
Homologous Pairs of Low and High Temperature Originating Proteins Spanning the Known Prokaryotic Universe
Abstract Stability of proteins at high temperature has been a topic of interest for many years, as this attribute is favourable for applications ranging from therapeutics to industrial chemical manufacturing. Our current understanding and methods for designing high-temperature stability into target proteins are inadequate.
Low-cost prediction of molecular and transition state partition functions via machine learning
Low-cost prediction of molecular and transition state partition functions via machine learning Abstract We have generated an open-source dataset of over 30000 organic chemistry gas phase partition functions. With this data, a machine learning deep neural network estimator was trained to predict partition functions of unknown organic chemistry gas phase transition states. This estimator only relies on reactant and product geometries and partition functions.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Evan
Contact Evan, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack