Drug-target residence times can impact drug efficacy and safety, and are therefore increasingly being considered during lead optimization. For this purpose, computational methods to predict residence times, τ, for drug-like compounds and to derive structure-kinetic relationships are desirable. A challenge for approaches based on molecular dynamics (MD) simulation is the fact that drug residence times are typically orders of magnitude longer than computationally feasible simulation times. Therefore, enhanced sampling methods are required. We recently reported one such approach: the τRAMD procedure for estimating relative residence times by performing a large number of random acceleration MD (RAMD) simulations in which ligand dissociation occurs in times of about a nanosecond due to the application of an additional randomly oriented force to the ligand. The length of the RAMD simulations is used to deduce τ. The RAMD simulations also provide information on ligand egress pathways and dissociation mechanisms. Here, we describe a machine learning approach to systematically analyze protein-ligand binding contacts in the RAMD trajectories in order to derive regression models for estimating τ and to decipher the molecular features leading to longer τ values. We demonstrate that the regression models built on the protein-ligand interaction fingerprints of the dissociation trajectories result in robust estimates of τ for a set of 94 drug-like inhibitors of heat shock protein 90 (HSP90), even for the compounds for which the length of the RAMD trajectories does not provide a good estimation of τ. Thus, we find that machine learning helps to overcome inaccuracies in the modeling of protein-ligand complexes due to incomplete sampling or force field deficiencies. Moreover, the approach facilitates the identification of features important for residence time. In particular, we observed that interactions of the ligand with the sidechain of F138, which is located on the border between the ATP binding pocket and a hydrophobic transient sub-pocket, play a key role in slowing compound dissociation. We expect that the combination of the τRAMD simulation procedure with machine learning analysis will be generally applicable as an aid to target-based lead optimization.

Aneuploidy, chromosomal instability, somatic copy-number alterations, and whole-genome doubling (WGD) play key roles in cancer evolution and provide information for the complex task of phylogenetic inference. We present MEDICC2, a method for inferring evolutionary trees and WGD using haplotype-specific somatic copy-number alterations from single-cell or bulk data. MEDICC2 eschews simplifications such as the infinite sites assumption, allowing multiple mutations and parallel evolution, and does not treat adjacent loci as independent, allowing overlapping copy-number events. Using simulations and multiple data types from 2780 tumors, we use MEDICC2 to demonstrate accurate inference of phylogenies, clonal and subclonal WGD, and ancestral copy-number states.

Environmental carcinogenic exposures are major contributors to global disease burden yet how they promote cancer is unclear. Over 70 years ago, the concept of tumour promoting agents driving latent clones to expand was rst proposed. In support of this model, recent evidence suggests that human tissue contains a patchwork of mutant clones, some of which harbour oncogenic mutations, and many environmental carcinogens lack a clear mutational signature. We hypothesised that the environmental carcinogen, <2.5μm particulate matter (PM2.5), might promote lung cancer promotion through nonmutagenic mechanisms by acting on pre-existing mutant clones within normal tissues in patients with lung cancer who have never smoked, a disease with a high frequency of EGFR activating mutations. We analysed PM2.5 levels and cancer incidence reported by UK Biobank, Public Health England, Taiwan Chang Gung Memorial Hospital (CGMH) and Korean Samsung Medical Centre (SMC) from a total of 463,679 individuals between 2006-2018. We report associations between PM2.5 levels and the incidence of several cancers, including EGFR mutant lung cancer. We nd that pollution on a background of EGFR mutant lung epithelium promotes a progenitor-like cell state and demonstrate that PM accelerates lung cancer progression in EGFR and Kras mutant mouse lung cancer models. Through parallel exposure studies in mouse and human participants, we nd evidence that in ammatory mediators, such as interleukin-1 , may act upon EGFR mutant clones to drive expansion of progenitor cells. Ultradeep mutational pro ling of histologically normal lung tissue from 247 individuals across 3 clinical cohorts revealed oncogenic EGFR and KRAS driver mutations in 18% and 33% of normal tissue samples, respectively. These results support a tumour-promoting role for PM acting on latent mutant clones in normal lung tissue and add to evidence providing an urgent mandate to address air pollution in urban areas.