Protein-ligand binding affinity prediction exploiting sequence constituent homology

MOTIVATION: Molecular docking is a commonly used approach for estimating binding conformations and their resultant binding affinities. Machine learning has been successfully deployed to enhance such affinity estimations. Many methods of varying complexity have been developed making use of some or all the spatial and categorical information available in these structures. The evaluation of such methods has mainly been carried out using datasets from PDBbind. Particularly the Comparative Assessment of Scoring Functions (CASF) 2007, 2013, and 2016 datasets with dedicated test sets. This work demonstrates that only a small number of simple descriptors is necessary to efficiently estimate binding affinity for these complexes without the need to know the exact binding conformation of a ligand. RESULTS: The developed approach of using a small number of ligand and protein descriptors in conjunction with gradient boosting trees demonstrates high performance on the CASF datasets. This includes the commonly used benchmark CASF2016 where it appears to perform better than any other approach. This methodology is also useful for datasets where the spatial relationship between the ligand and protein is unknown as demonstrated using a large ChEMBL-derived dataset. AVAILABILITY AND IMPLEMENTATION: Code and data uploaded to https://github.com/abbiAR/PLBAffinity

Beating the Best: Improving on AlphaFold2 at Protein Structure Prediction

The goal of Protein Structure Prediction (PSP) problem is to predict a protein's 3D structure (confirmation) from its amino acid sequence. The problem has been a 'holy grail' of science since the Noble prize-winning work of Anfinsen demonstrated that protein conformation was determined by sequence. A recent and important step towards this goal was the development of AlphaFold2, currently the best PSP method. AlphaFold2 is probably the highest profile application of AI to science. Both AlphaFold2 and RoseTTAFold (another impressive PSP method) have been published and placed in the public domain (code & models). Stacking is a form of ensemble machine learning ML in which multiple baseline models are first learnt, then a meta-model is learnt using the outputs of the baseline level model to form a model that outperforms the base models. Stacking has been successful in many applications. We developed the ARStack PSP method by stacking AlphaFold2 and RoseTTAFold. ARStack significantly outperforms AlphaFold2. We rigorously demonstrate this using two sets of non-homologous proteins, and a test set of protein structures published after that of AlphaFold2 and RoseTTAFold. As more high quality prediction methods are published it is likely that ensemble methods will increasingly outperform any single method.Comment: 12 page

Prognostic factors in patients with complete response of the tumour (ypT0) after neoadjuvant chemoradiotherapy and radical resection of rectal cancer

Background There are only a few studies on the prognosis of patients with complete response of the tumour (ypT0) after neoadjuvant chemoradiotherapy (NCRT) and radical resection of rectal cancer. The aim of the study was to identify prognostic factors with regard to oncological outcome in ypT0 patients after NCRT and radical resection. Methods All ypT0 patients with rectal cancer after NCRT and radical resection between January 2010 and June 2019 were included. Cox univariate and multivariate regression analyses were used to determine the prognostic factors of these patients. Results Seventy-six patients with ypT0 rectal cancer were included. In nine patients (11.8%), lymph node metastasis was identified. Age, gender, elevated carcinoembryonic antigen (CEA) and ypN+ were risk factors associated with a worse 5-year disease-free survival (DFS) rate in univariate analysis (P = 0.08, 0.14, 0.007 and 0.003, respectively). In multivariate analysis, ypN+ and elevated CEA before NCRT were independent risk factors for worse 5-year DFS (P = 0.005 and 0.021, respectively). Elevated CEA before NCRT, post-operative chemotherapy and ypN+ were risk factors associated with worse overall survival in univariate analysis (P = 0.14, 0.002 and 0.17, respectively). However, in multivariate analysis, none of these three factors were independent risk factors for worse overall survival (P = 0.20, 0.34 and 0.06, respectively). Conclusion ypN+ and elevated CEA before NCRT were found to be independent risk factors for an unfavourable DFS in ypT0 patients with complete response of the tumour after neoadjuvant chemoradiotherapy for rectal cancer