Trends in Women's Leadership of Oncology Clinical Trials.

It has been widely reported that women are underrepresented in leadership positions within academic medicine. This study aimed to assess trends in women representation as principal investigators (PIs) in oncology clinical trials and to characterize trends in women's leadership in such trials conducted between 1999 and 2019. The gender of 39,240 PIs leading clinical trials was determined using the gender prediction software Genderize.io. In total, 11,516 (27.7%) women served as PIs. Over the past 20 years, an annual increase of 0.65% in women PIs was observed. Analysis by geographic distribution revealed higher women representation among PIs in North America and Europe compared to Asia. Industry-funded trials were associated with lower women PI representation than academic-funded trials (31.4% vs. 18.8%, p<0.001). Also, women PIs were found to be underrepresented in late-phase as compared to early-phase studies (27.9%, 25.7%, 21.6%, and 22.4% in phase I, II, III, and IV, respectively; Cochran-Armitage test for trend, p<0.001). Furthermore, an association was found between the PI's gender and enrolment of female subjects (50% vs. 43% female participants led by women vs men PIs, respectively, p<0.001). Taken together, while the gender gap in women's leadership in oncology trials has been steadily closing, prominent inequalities remain in non-Western countries, advanced study phases, industry-funded trials and appear to be linked to a gender gap in patient accrual. These observations can serve for the development of strategies to increase women's representation and to monitor progress toward gender equality in PIs of cancer clinical trials.

Saturation scanning of ubiquitin variants reveals a common hot spot for binding to USP2 and USP21.

A detailed understanding of the molecular mechanisms whereby ubiquitin (Ub) recognizes enzymes in the Ub proteasome system is crucial for understanding the biological function of Ub. Many structures of Ub complexes have been solved and, in most cases, reveal a large structural epitope on a common face of the Ub molecule. However, owing to the generally weak nature of these interactions, it has been difficult to map in detail the functional contributions of individual Ub side chains to affinity and specificity. Here we took advantage of Ub variants (Ubvs) that bind tightly to particular Ub-specific proteases (USPs) and used phage display and saturation scanning mutagenesis to comprehensively map functional epitopes within the structural epitopes. We found that Ubvs that bind to USP2 or USP21 contain a remarkably similar core functional epitope, or "hot spot," consisting mainly of positions that are conserved as the wild type sequence, but also some positions that prefer mutant sequences. The Ubv core functional epitope contacts residues that are conserved in the human USP family, and thus it is likely important for the interactions of Ub across many family members.

Triathlon for energy functions: who is the winner for design of protein-protein interactions?

Computational prediction of stabilizing mutations into monomeric proteins has become an almost ordinary task. Yet, computational stabilization of protein­protein complexes remains a challenge. Design of protein­protein interactions (PPIs) is impeded by the absence of an energy function that could reliably reproduce all favorable interactions between the binding partners. In this work, we present three energy functions: one function that was trained on monomeric proteins, while the other two were optimized by different techniques to predict side-chain conformations in a dataset of PPIs. The performances of these energy functions are evaluated in three different tasks related to design of PPIs: predicting side-chain conformations in PPIs, recovering native binding-interface sequences, and predicting changes in free energy of binding due to mutations. Our findings show that both functions optimized on side-chain repacking in PPIs are more suitable for PPI design compared to the function trained on monomeric proteins. Yet, no function performs best at all three tasks. Comparison of the three energy functions and their performances revealed that (1) burial of polar atoms should not be penalized significantly in PPI design as in single-protein design and (2) contribution of electrostatic interactions should be increased several-fold when switching from single-protein to PPI design. In addition, the use of a softer van der Waals potential is beneficial in cases when backbone flexibility is important. All things considered, we define an energy function that captures most of the nuances of the binding energetics and hence, should be used in future for design of PPIs.

Optimizing energy functions for protein-protein interface design.

Protein design methods have been originally developed for the design of monomeric proteins. When applied to the more challenging task of protein­protein complex design, these methods yield suboptimal results. In particular, they often fail to recapitulate favorable hydrogen bonds and electrostatic interactions across the interface. In this work, we aim to improve the energy function of the protein design program ORBIT to better account for binding interactions between proteins. By using the advanced machine learning framework of conditional random fields, we optimize the relative importance of all the terms in the energy function, attempting to reproduce the native side-chain conformations in protein­protein interfaces. We evaluate the performance of several optimized energy functions, each describes the van der Waals interactions using a different potential. In comparison with the original energy function, our best energy function (a) incorporates a much "softer" repulsive van der Waals potential, suitable for the discrete rotameric representation of amino acid side chains; (b) does not penalize burial of polar atoms, reflecting the frequent occurrence of polar buried residues in protein­protein interfaces; and (c) significantly up-weights the electrostatic term, attesting to the high importance of these interactions for protein­protein complex formation. Using this energy function considerably improves side chain placement accuracy for interface residues in a large test set of protein­protein complexes. Moreover, the optimized energy function recovers the native sequences of protein­protein interface at a higher rate than the default function and performs substantially better in predicting changes in free energy of binding due to mutations.