Biology subjects

Kuhn, L. A.

Publications and source records attributed to Kuhn, L. A..

4 recordsLinked to original sources

bioRxiv2022

Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot Spots

With machine learning now transforming the sciences, successful prediction of biological structure or activity is mainly limited by the extent and quality of data available for training, the astute choice of features for prediction, and thorough assessment of the robustness of prediction on a variety of new cases. Here we address these issues while developing and sharing protocols to build a robust dataset and rigorously compare several predictive classifiers using the opensource Python machine learning library, scikit-learn. We show how to evaluate whether enough data has been used for training and whether the classifier has been overfit to training data. The most telling experiment is 500-fold repartitioning of the training and test sets, followed by prediction, which gives a good indication of whether a classifier performs consistently well on different datasets. An intuitive method is used to quantify which features are most important for correct prediction. The resulting well-trained classifier, hotspotter, can robustly predict the small subset of amino acid residues on the surface of a protein that are energetically most important for binding a protein partner: the interaction hot spots. Hotspotter has been trained and tested here on a curated dataset assembled from 1,046 non-redundant alanine scanning mutation sites with experimentally measured change in binding free energy values from 97 different protein complexes; this dataset is available to download. The accessible surface area of the wild-type residue at a given site and its degree of evolutionary conservation proved the most important features to identify hot spots. A variant classifier was trained and validated for proteins where only the amino acid sequence is available, augmented by secondary structure assignment. This version of hotspotter requiring fewer features is almost as robust as the structure-based classifier. Application to the ACE2 receptor, which mediates COVID-19 virus entry into human cells, identified the critical hot spot triad of ACE2 residues at the center of the small interface with the CoV-2 spike protein. Hotspotter results can be used to guide the strategic design of protein interfaces and ligands and also to identify likely interfacial residues for protein:protein docking.

molecular biology↗

bioRxiv2018

SpeciFlex: A Protocol for Mining Binding Site Conformational Selectivity in Structure-Based Inhibitor Discovery

Selectivity for a target site is challenging when the site is conserved in homologous proteins. A novel protocol is presented for attaining selectivity by taking advantage of conformational population differences between homologs. Conformational ensembles of the targeted protein and the homolog are compared to identify pockets significantly populated in the target, but rarely or never sampled in the homolog. SLIDE screening then identifies molecules that fit the unique pocket and also interact well with an adjacent substrate pocket. The SpeciFlex protocol, demonstrated for a pair of pyrophosphokinases, yields ligand candidates with good interactions in both the substrate and unique pockets of the target Yersinia pestis protein, while selecting against interactions with the homologous site in Escherichia coli.

bioinformatics↗

bioRxiv2018

Protein-ligand interfaces are polarized: Discovery of a strong trend for intermolecular hydrogen bonds to favor donors on the protein side with implications for predicting and designing ligand complexes

Understanding how proteins encode ligand specificity is fascinating and similar in importance to deciphering the genetic code. For protein-ligand recognition, the combination of an almost infinite variety of interfacial shapes and patterns of chemical groups makes the problem especially challenging. Here we analyze data across non-homologous proteins in complex with small biological ligands to address observations made in our inhibitor discovery projects: that proteins favor donating H-bonds to ligands and avoid using groups with both H-bond donor and acceptor capacity. The resulting clear and significant chemical group matching preferences elucidate the code for protein-native ligand binding, similar to the dominant patterns found in nucleic acid base-pairing. On average, 90% of the keto and carboxylate oxygens occurring in the biological ligands formed direct H-bonds to the protein. A two-fold preference was found for protein atoms to act as H-bond donors and ligand atoms to act as acceptors, and 76% of all intermolecular H-bonds involved an amine donor. Together, the tight chemical and geometric constraints associated with satisfying donor groups generate a hydrogen-bonding lock that can be matched only by ligands bearing the right acceptor-rich key. Measuring an index of H-bond preference based on the observed chemical trends proved sufficient to predict other protein-ligand complexes and can be used to guide molecular design. The resulting Hbind and Protein Recognition Index software packages are being made available for rigorously defining intermolecular H-bonds and measuring the extent to which H-bonding patterns in a given complex match the preference key.\n\nAbbreviations

bioinformatics↗

bioRxiv2018

Enabling the hypothesis-driven prioritization of ligand candidates in big databases: Screenlamp and its application to GPCR inhibitor discovery for invasive species control

While the advantage of screening vast databases of molecules to cover greater molecular diversity is often mentioned, in reality, only a few studies have been published demonstrating inhibitor discovery by screening more than a million compounds for features that mimic a known three-dimensional ligand. Two factors contribute: the general difficulty of discovering potent inhibitors, and the lack of free, user-friendly software to incorporate project-specific knowledge and user hypotheses into 3D ligand-based screening. The Screenlamp modular toolkit presented here was developed with these needs in mind. We show Screenlamps ability to screen more than 12 million commercially available molecules and identify potent in vivo inhibitors of a G protein-coupled bile acid receptor within the first year of a discovery project. This pheromone receptor governs sea lamprey reproductive behavior, and to our knowledge, this project is the first to establish the efficacy of computational screening in discovering lead compounds for aquatic invasive species control. Significant enhancement in activity came from selecting compounds based on one of the hypotheses: that matching two distal oxygen groups in the three-dimensional structure of the pheromone is crucial for activity. Six of the 15 most active compounds met these criteria. A second hypothesis - that presence of an alkyl sulfate side chain results in high activity - identified another 6 compounds in the top 10, demonstrating the significant benefits of hypothesis-driven screening.

bioinformatics↗