Inverse folding models have proven to be highly effective zero-shot predictors of protein stability. Despite this success, the link between the amino acid preferences of an inverse folding model and the free-energy considerations underlying thermodynamic stability remains incompletely understood. A better understanding would be of interest not only from a theoretical perspective, but also potentially provide the basis for stronger zero-shot stability prediction. In this paper, we take steps to clarify the free-energy foundations of inverse folding models. Our derivation reveals the standard practice of likelihood ratios as a simplistic approximation and suggests several paths towards better estimates of the relative stability. We empirically assess these approaches and demonstrate that considerable gains in zero-shot performance can be achieved with fairly simple means.

Reliable prediction of protein variant effects is crucial for both protein optimization and for advancing biological understanding. For practical use in protein engineering, it is important that we can also provide reliable uncertainty estimates for our predictions, and while prediction accuracy has seen much progress in recent years, uncertainty metrics are rarely reported. We here provide a Gaussian process regression model, Kermut, with a novel composite kernel for modeling mutation similarity, which obtains state-of-the-art performance for supervised protein variant effect prediction while also offering estimates of uncertainty through its posterior. An analysis of the quality of the uncertainty estimates demonstrates that our model provides meaningful levels of overall calibration, but that instance-specific uncertainty calibration remains more challenging.

MOTIVATION: Cytochromes P450 are the most important class of drug metabolizing enzymes. Prediction of drug metabolism is important in development of new drugs, to understand and reduce adverse drug reactions and to reduce animal testing. RESULTS: SMARTCyp 3.0 is an updated version of our previous web server for prediction of site-of-metabolism for Cytochrome P450-mediated metabolism, now in Python 3 with increased structural coverage and new features. The SMARTCyp program is a first principle-based method using density functional theory determined activation energies for more than 250 molecules to identify the most likely site-of-metabolism. New features include a similarity measure between the query molecule and the model fragment, a new graphical interface and additional parameters expanding the structural coverage of the SMARTCyp program. AVAILABILITY AND IMPLEMENTATION: The SMARTCyp server is freely available for use on the web at smartcyp.sund.ku.dk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The SMARTCyp server is the first web application for site of metabolism prediction of cytochrome P450-mediated drug metabolism. AVAILABILITY: The SMARTCyp server is freely available for use on the web at www.farma.ku.dk/smartcyp where the SMARTCyp Java program and source code is also available for download. CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.