The B‐cell lymphoma 2 (BCL2) proteins are a class of apoptosis regulators that control the release of apoptogenic factors from mitochondria. Under normal physiological conditions, apoptosis is inhibited through the actions of anti‐apoptotic (repressor) BCL2 proteins that bind semi‐indiscriminately to the helical BH3 domains of pro‐apoptotic (effector) BCL2 proteins. In this work, we developed a series of BH3 domain mimetics by grafting residues from the effector BCL2 protein Bax onto the α‐helix of scyllatoxin (ScTx). These so‐called “ScTx‐Bax” constructs were then used to gain insight into the physicochemical nature of repressor/effector BCL2 interactions. Specifically, we utilized competitive binding and isothermal titration calorimetry (ITC) to investigate the inhibitory potential and binding thermodynamics of ScTx‐Bax structural variants that target the repressor protein Bcl‐2 (proper) in vitro. Our data show that ScTx‐Bax mimetics compete with isolated Bax BH3 domain peptides for Bcl‐2 with IC₅₀ values in the mid‐nanomolar range and that greater flexibility within the ScTx‐Bax BH3 domain correlates with more effective inhibition. Furthermore, ITC experiments revealed that unstructured ScTx‐Bax variants target Bcl‐2 with greater entropic, but lower enthalpic, efficiencies than structured ScTx‐Bax peptides. These results suggest that entropic contributions to binding Bcl‐2 are more favorable for flexible BH3 domains; however, this enhancement is counterbalanced by a moderate enthalpic penalty. Overall, this study improves understanding of how structural properties of effector BH3 domains influence the promiscuous binding patterns of BCL2 proteins and expands the utility of ScTx‐based BH3 domain mimetics as molecular tools to study discrete recognition elements that facilitate repressor/effector BCL2 interactions.

Several influential studies published around the turn of the millennium reported that advancing a lead compound to a drug tended to involve certain predictable changes in molecular properties, most notably marked increases in molecular weight (MW) and in lipophilicity. To assess how lead optimization practices have changed over the past 20 years, we examined oral drugs approved during 2020-2023 and their corresponding lead compounds with respect to their molecular properties. As well as comparing property averages, we analyzed the changes introduced in each individual drug discovery program to directly assess the trends that characterize the modern lead-to-drug process. We found that changes in MW from lead to drug are substantially smaller than in the older studies, with a median mass change corresponding to addition of just a single heavy atom, and cLogP is now slightly more likely to decrease than to become greater. Thus, the notion that lead optimization tends to involve making the compound ‘bigger and greasier’ no longer applies. Rather, approaches have been found to refine compound potency and selectivity, and to eliminate ADME liabilities, without adding substantial mass or overly relying on the hydrophobic effect as a driver of binding. One such tactic may be avoiding addition of aromatic groups during lead optimization, coupled with a tendency to favor heterocycles over aromatic carbocycles. Overall, our results imply that it may no longer be useful to think of ‘lead-like’ and ‘drug-like’ compounds as occupying distinct property space.

Machine learning (ML) is rapidly gaining traction in many areas of experimental molecular science for elucidating relationships and patterns in large or complex data sets. Historically, ML was largely the preserve of those with specialized training in fields such as statistics or cheminformatics. Increasingly, however, ML methodologies are becoming part of the standard toolkit for experimental scientists across a range of disciplines. Lowering the barrier of entry to these ML techniques, for scientists without a significant background in computer science or statistics, is important to broadening access to these powerful methods. Here we provide detailed, step by step tutorials for performing four ML methods that are particularly useful for applications in biochemistry, cell biology, and drug discovery: hierarchical clustering, Principal Component Analysis (PCA), Partial Least-Squares Discriminant Analysis (PLSDA), and Partial Least-Squares Regression (PLSR). The protocols are written for the widely used software MATLAB, but no prior experience with MATLAB is required to use them. We include an explanation of each step, pitched at a level to be understood by investigators without any prior experience with ML, MATLAB, or any kind of coding. We also highlight the scientific issues pertaining to selecting and scaling the data to be analyzed, and describe controls to test the validity of the results obtained. Throughout, we emphasize the relationship between the scientific question and how to choose data and methods that will allow it to be addressed in a meaningful way. Our aim is to provide a basic introduction that will equip experimental chemical biologists and other chemical and biomedical scientists with the knowledge required to use ML to aid in the design of experiments, the formulation and data-driven testing of hypotheses, and the analysis of experimental data. Basic Protocol 1: Clustering Basic Protocol 2: Principal Component Analysis (PCA) Basic Protocol 3: Partial Least Squares Discriminant Analysis (PLSDA) Basic Protocol 4: Partial Least Squares Regression (PLSR)