Discovery of BBO-8520, a first-in-class direct and covalent dual inhibitor of GTP-bound (ON) and GDP-bound (OFF) KRASG12C.

Approved inhibitors of KRASG12C prevent oncogenic activation by sequestering the inactive, GDP-bound (OFF) form rather than directly binding and inhibiting the active, GTP-bound (ON) form. This approach provides no direct target coverage of the active protein. Expectedly, adaptive resistance to KRASG12C (OFF)-only inhibitors is observed in association with increased expression and activity of KRASG12C(ON).

Comprehensive structure-function analysis reveals gain- and loss-of-function mechanisms impacting oncogenic KRAS activity.

To dissect variant-function relationships in the KRAS oncoprotein, we performed deep mutational scanning (DMS) screens for both wild-type and KRAS mutant alleles. We defined the spectrum of oncogenic potential for nearly all possible variants, identifying several novel transforming alleles and elucidating a model to describe the frequency of mutations in human cancer as a function of transforming potential, mutational probability, and tissue-specific mutational signatures. Biochemical and structural analyses of variants identified in a KRAS second-site suppressor DMS screen revealed that attenuation of oncogenic KRAS can be mediated by protein instability and conformational rigidity, resulting in reduced binding affinity to effector proteins, such as RAF and PI3-kinases, or reduced SOS-mediated nucleotide exchange activity.

The prominent pervasive oncogenic role and tissue specific permissiveness of RAS gene mutations.

In cancer research, RAS biology has been focused on only a handful of tumor types. While RAS genes have long been suspected as common contributors to a wide spectrum of cancer types, robust evidence is required to firmly establish their critical oncogenic significance. We present a data mining study using DepMap genome-wide CRISPR screening data, which provide substantial evidence to support the prominent pervasive oncogenic role and tissue-specific permissiveness of RAS gene mutations.

Generating Protein Structures for Pathway Discovery Using Deep Learning.

Resolving the intricate details of biological phenomena at the molecular level is fundamentally limited by both length- and time scales that can be probed experimentally. Molecular dynamics (MD) simulations at various scales are powerful tools frequently employed to offer valuable biological insights beyond experimental resolution. However, while it is relatively simple to observe long-lived, stable configurations of, for example, proteins, at the required spatial resolution, simulating the more interesting rare transitions between such states often takes orders of magnitude longer than what is feasible even on the largest supercomputers available today.

ABodyBuilder3: improved and scalable antibody structure predictions.

Summary: In this article, we introduce ABodyBuilder3, an improved and scalable antibody structure prediction model based on ABodyBuilder2. We achieve a new state-of-the-art accuracy in the modelling of CDR loops by leveraging language model embeddings, and show how predicted structures can be further improved through careful relaxation strategies. Finally, we incorporate a predicted Local Distance Difference Test into the model output to allow for a more accurate estimation of uncertainties.