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Biomedical subjects

Andrew R Leach

Publications and source records attributed to Andrew R Leach.

6 recordsLinked to original sources

RP3Net: a deep learning model for predicting recombinant protein production in Escherichia coli.

MOTIVATION: Recombinant protein expression can be a limiting step in the production of protein reagents for drug discovery and other biotechnology applications. We introduce RP3Net (Recombinant Protein Production Prediction Network), an AI model of small-scale heterologous soluble protein expression in Escherichia coli. RP3Net utilizes the most recent protein and genomic foundational models. A curated dataset of internal experimental results from AstraZeneca and publicly available data from the Structural Genomics Consortium was used for training, validation and testing of RP3Net. RESULTS: RP3Net achieves an increase in area under the receiver operator curve (AUROC) of 0.15, compared to a baseline model. When experimentally validated on an independent, prospective, manually selected set of 97 constructs, RP3Net outperformed currently available models, with an AUROC of 0.83, delivering accurate predictions in 77% of the cases, and correctly identifying successfully expressing constructs in 92% of cases. AVAILABILITY AND IMPLEMENTATION: The model, along with installation and running instructions, is available under an MIT licence at https://github.com/RP3Net/RP3Net, DOI 10.5281/zenodo.17243498.

Escherichia coli↗

Design and synthesis of orally active pyrrolidin-2-one-based factor Xa inhibitors.

A series of novel, non-basic 3-(6-chloronaphth-2-ylsulfonyl)aminopyrrolidin-2-one-based factor Xa (fXa) inhibitors, incorporating an alanylamide P4 group, was designed and synthesised. Within this series, the N-2-(morpholin-4-yl)-2-oxoethyl derivative 24 was shown to be a potent, selective fXa inhibitor with good anticoagulant activity. Moreover, 24 possessed highly encouraging rat and dog pharmacokinetic profiles with excellent oral bioavailabilities in both species.

Administration, Oral↗

Fragment screening: an introduction.

There are clearly many different philosophies associated with adapting fragment screening into mainstream Drug Discovery Lead Generation strategies. Scientists at Astex, for instance, focus entirely on strategies involving use of X-ray crystallography and NMR. However, AstraZeneca uses a number of different fragment screening strategies. One approach is to screen a 2000 compound fragment set (with close to "lead-like" complexity) at 100 microM in parallel with every HTS such that the data are obtained on the entire screening collection at 10 microM plus the extra samples at 100 microM; this provides valuable compound potency data in a concentration range that is usually unexplored. The fragments are then screen-specific "privileged structures" that can be searched for in the rest of the HTS output and other databases as well as having synthesis follow-up. A typical workflow for a fragment screen within AstraZeneca is shown below (Figure 24) and highlights the desirability (particularly when screening >100 microM) for NMR and X-ray information to validate weak hits and give information on how to optimise them. In this chapter, we have provided an introduction to the theoretical and practical issues associated with the use of fragment methods and lead-likeness. Fragment-based approaches are still in an early stage of development and are just one of many interrelated techniques that are now used to identify novel lead compounds for drug development. Fragment based screening has some advantages, but like every other drug hunting strategy will not be universally applicable. There are in particular some practical challenges associated with fragment screening that relate to the generally lower level of potency that such compounds initially possess. Considerable synthetic effort has to be applied for post-fragment screening to build the sort of potency that would be expected to be found from a traditional HTS. However, if there are no low-hanging fruit in a screening collection to be found by HTS then the use of fragment screening can help find novelty that may lead to a target not being discarded as intractable. As such, the approach offers some significant advantages by providing less complex molecules, which may have better potential for novel drug optimisation and by enabling new chemical space to be more effectively explored. Many literature examples that cover examples of fragment screening approaches are still at the "proof of concept" stage and although delivering inhibitors or ligands, may still prove to be unsuitable when further ADMET and toxicity profiling is done. The next few years should see a maturing of the area, and as our understanding of how the concepts can be best applied, there are likely to be many more examples of attractive, small molecule hits, leads and candidate drugs derived from the approaches described.

Combinatorial Chemistry Techniques↗

Analysis and optimization of structure-based virtual screening protocols. (3). New methods and old problems in scoring function design.

Scoring function research remains a primary focus of current structure-based virtual screening (SVS) technology development. Here, we present an alternative method for scoring function design that attempts to combine crystallographic structural information with data derived from directly within SVS calculations. The technique utilizes a genetic algorithm (GA) to optimize functions based on binding property data derived from multiple virtual screening calculations. These calculations are undertaken on protein data bank (PDB) complex active sites using ligands of known binding mode in conjunction with "noise" compounds. The advantages of such an approach are that the function does not rely on assay data and that it can potentially use the "noise" binding data to recognize the sub-optimal docking interactions inherent in SVS calculations. Initial efforts in technique exploration using DOCK are presented, with comparisons made to existing DOCK scoring functions. An analysis of the problems inherent to scoring function development is also made, including issues in dataset creation and limitations in descriptor utility when viewed from the perspective of docking mode resolution. The future directions such studies might take are also discussed in detail.

Algorithms↗

A comparison of the pharmacophore identification programs: Catalyst, DISCO and GASP.

Three commercially available pharmacophore generation programs, Catalyst/HipHop, DISCO and GASP, were compared on their ability to generate known pharmacophores deduced from protein-ligand complexes extracted from the Protein Data Bank. Five different protein families were included Thrombin, Cyclin Dependent Kinase 2, Dihydrofolate Reductase, HIV Reverse Transcriptase and Thermolysin. Target pharmacophores were defined through visual analysis of the data sets. The pharmacophore models produced were evaluated qualitatively through visual inspection and according to their ability to generate the target pharmacophores. Our results show that GASP and Catalyst outperformed DISCO at reproducing the five target pharmacophores.

Algorithms↗