Task-Similarity is a Crucial Factor for Few-Shot Meta-Learning of Structure-Activity Relationships.

Machine learning models support computer-aided molecular design and compound optimization. However, the initial phases of drug discovery often face a scarcity of training data for these models. Meta-learning has emerged as a potentially promising strategy, harnessing the wealth of structure-activity data available for known targets to facilitate efficient few-shot model training for the specific target of interest. In this study, we assessed the effectiveness of two different meta-learning methods, namely model-agnostic meta-learning (MAML) and adaptive deep kernel fitting (ADKF), specifically in the regression setting. We investigated how factors such as dataset size and the similarity of training tasks impact predictability. The results indicate that ADKF significantly outperformed both MAML and a single-task baseline model on the inhibition data. However, the performance of ADKF varied across different test tasks. Our findings suggest that considerable enhancements in performance can be anticipated primarily when the task of interest is similar to the tasks incorporated in the meta-learning process.

Prospective de novo drug design with deep interactome learning.

De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It enables the "zero-shot" construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for protein structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs are chemically synthesized and computationally, biophysically, and biochemically characterized. Potent PPAR partial agonists are identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirms the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules.

Morphing of Amphipathic Helices to Explore the Activity and Selectivity of Membranolytic Antimicrobial Peptides.

Naturally occurring membranolytic antimicrobial peptides (AMPs) are rarely cell-type selective and highly potent at the same time. Template-based peptide design can be used to generate AMPs with improved properties de novo. Following this approach, 18 linear peptides were obtained by computationally morphing the natural AMP Aurein 2.2d2 GLFDIVKKVVGALG into the synthetic model AMP KLLKLLKKLLKLLK. Eleven of the 18 chimeric designs inhibited the growth of Staphylococcus aureus, and six peptides were tested and found to be active against one resistant pathogenic strain or more. One of the peptides was broadly active against bacterial and fungal pathogens without exhibiting toxicity to certain human cell lines. Solution nuclear magnetic resonance and molecular dynamics simulation suggested an oblique-oriented membrane insertion mechanism of this helical de novo peptide. Temperature-resolved circular dichroism spectroscopy pointed to conformational flexibility as an essential feature of cell-type selective AMPs.

In silico design and optimization of selective membranolytic anticancer peptides.

Membranolytic anticancer peptides represent a potential strategy in the fight against cancer. However, our understanding of the underlying structure-activity relationships and the mechanisms driving their cell selectivity is still limited. We developed a computational approach as a step towards the rational design of potent and selective anticancer peptides. This machine learning model distinguishes between peptides with and without anticancer activity. This classifier was experimentally validated by synthesizing and testing a selection of 12 computationally generated peptides. In total, 83% of these predictions were correct. We then utilized an evolutionary molecular design algorithm to improve the peptide selectivity for cancer cells. This simulated molecular evolution process led to a five-fold selectivity increase with regard to human dermal microvascular endothelial cells and more than ten-fold improvement towards human erythrocytes. The results of the present study advocate for the applicability of machine learning models and evolutionary algorithms to design and optimize novel synthetic anticancer peptides with reduced hemolytic liability and increased cell-type selectivity.

De novo design of anticancer peptides by ensemble artificial neural networks.

Membranolytic anticancer peptides (ACPs) are drawing increasing attention as potential future therapeutics against cancer, due to their ability to hinder the development of cellular resistance and their potential to overcome common hurdles of chemotherapy, e.g., side effects and cytotoxicity. In this work, we present an ensemble machine learning model to design potent ACPs. Four counter-propagation artificial neural-networks were trained to identify peptides that kill breast and/or lung cancer cells. For prospective application of the ensemble model, we selected 14 peptides from a total of 1000 de novo designs, for synthesis and testing in vitro on breast cancer (MCF7) and lung cancer (A549) cell lines. Six de novo designs showed anticancer activity in vitro, five of which against both MCF7 and A549 cell lines. The novel active peptides populate uncharted regions of ACP sequence space.

Identification of Chemokine Ligands by Biochemical Fragmentation and Simulated Peptide Evolution.

Short linear peptides can overcome certain limitations of small molecules for targeting protein-protein interactions (PPIs). Herein, the interaction between the human chemokine CCL19 with chemokine receptor CCR7 was investigated to obtain receptor-derived CCL19-binding peptides. After identifying a linear binding site of CCR7, five hexapeptides binding to CCL19 in the low micromolar to nanomolar range were designed, guided by pharmacophore and lipophilicity screening of computationally generated peptide libraries. The results corroborate the applicability of the computational approach and the chosen selection criteria to obtain short linear peptides mimicking a protein-protein interaction site.

Simulated Molecular Evolution for Anticancer Peptide Design.

A computational technique based on a simulated molecular evolution protocol was employed for anticancer peptide (ACP) design. Starting from known ACPs, innovative bioactive peptides were automatically generated in computer-assisted design-synthesize-test cycles. This design algorithm offers a viable strategy for the generation of novel peptide sequences, without requiring a priori structure-activity knowledge. Sequence morphing and activity improvement were achieved through iterative amino acid variation and selection. Results show that not only the interaction of ACPs with the target membrane is important for their anticancer activity, but also the degree of peptide dimerization, which was corroborated by temperature profiling and electrospray mass spectrometry.

Lipophilicity prediction of peptides and peptide derivatives by consensus machine learning.

Lipophilicity prediction is routinely applied to small molecules and presents a working alternative to experimental log P or log D determination. For compounds outside the domain of classical medicinal chemistry these predictions lack accuracy, advocating the development of bespoke in silico approaches. Peptides and their derivatives and mimetics fill the structural gap between small synthetic drugs and genetically engineered macromolecules. Here, we present a data-driven machine learning method for peptide log D 7.4 prediction. A model for estimating the lipophilicity of short linear peptides consisting of natural amino acids was developed. In a prospective test, we obtained accurate predictions for a set of newly synthesized linear tri- to hexapeptides. Further model development focused on more complex peptide mimetics from the AstraZeneca compound collection. The results obtained demonstrate the applicability of the new prediction model to peptides and peptide derivatives in a log D 7.4 range of approximately -3 to 5, with superior accuracy to established lipophilicity models for small molecules.

Quantification of hydrolyzed peptides and proteins by amino acid fluorescence.

Reliable quantification of peptides and proteins is essential for drug discovery. We report the successful development and validation of an accurate and broadly applicable high performance liquid chromatography hyphenated to fluorescence detector procedure for the quantitative determination of the aromatic amino acids tyrosine, phenylalanine, and tryptophan, without relying on derivatization chemistry. Using ion-pair chromatography, fluorescent amino acids were clearly separated within 10 minutes. The hydrolysis of peptides was performed under acidic and heated conditions to yield the monomeric building blocks. Various protecting agents were tested to ensure tryptophan stability. The presented analytical method accurately (>95%) quantifies all fluorescent residues. The power of the method was confirmed by correct quantification of protein reference standard to 98.6% over all fluorescence traces. The method allowed us to identify pre-analytical differences between the nominal and actual concentrations of 12 peptide solutions. Salt formation, weighing errors, and other pre-analytical pitfalls resulted in noteworthy differences of up to 85% between the indicated and actual concentration of peptide solutions, subsequently leading to false positive or negative interpretation of activity data. Finally, only one solution is needed to perform quantification as well as UV-purity tests and can further be used as stock solution for activity testing.

Designing Anticancer Peptides by Constructive Machine Learning.

Constructive (generative) machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules. This study presents the experimental application of such a deep machine learning model to design membranolytic anticancer peptides (ACPs) de novo. A recurrent neural network with long short-term memory cells was trained on α-helical cationic amphipathic peptide sequences and then fine-tuned with 26 known ACPs by transfer learning. This optimized model was used to generate unique and novel amino acid sequences. Twelve of the peptides were synthesized and tested for their activity on MCF7 human breast adenocarcinoma cells and selectivity against human erythrocytes. Ten of these peptides were active against cancer cells. Six of the active peptides killed MCF7 cancer cells without affecting human erythrocytes with at least threefold selectivity. These results advocate constructive machine learning for the automated design of peptides with desired biological activities.

Recurrent Neural Network Model for Constructive Peptide Design.

We present a generative long short-term memory (LSTM) recurrent neural network (RNN) for combinatorial de novo peptide design. RNN models capture patterns in sequential data and generate new data instances from the learned context. Amino acid sequences represent a suitable input for these machine-learning models. Generative models trained on peptide sequences could therefore facilitate the design of bespoke peptide libraries. We trained RNNs with LSTM units on pattern recognition of helical antimicrobial peptides and used the resulting model for de novo sequence generation. Of these sequences, 82% were predicted to be active antimicrobial peptides compared to 65% of randomly sampled sequences with the same amino acid distribution as the training set. The generated sequences also lie closer to the training data than manually designed amphipathic helices. The results of this study showcase the ability of LSTM RNNs to construct new amino acid sequences within the applicability domain of the model and motivate their prospective application to peptide and protein design without the need for the exhaustive enumeration of sequence libraries.

The N-terminal extension of the P. falciparum GBP130 signal peptide is irrelevant for signal sequence function.

The malaria parasite P. falciparum exports a large number of proteins to its host cell, the mature human erythrocyte. Although the function of the majority of these proteins is not well understood, many exported proteins appear to play a role in modification of the erythrocyte following invasion. Protein export to the erythrocyte is a secretory process that begins with entry to the endoplasmic reticulum. For most exported proteins, this step is mediated by hydrophobic signal peptides found towards the N-terminal end of proteins. The signal peptides present on P. falciparum exported proteins often differ in length from those found in other systems, and generally contain a highly extended N-terminal region. Here we have investigated the function of these extended N-terminal regions, using the exported parasite protein GBP130 as a model. Surprisingly, several deletions of the extended N-terminal regions of the GBP130 signal peptide have no effect on the ability of the signal peptide to direct a fluorescent reporter to the secretory pathway. Addition of the same N-terminal extension to a canonical signal peptide does not affect transport of either soluble or membrane proteins to their correct respective subcellular localisations. Finally, we show that extended signal peptides are able to complement canonical signal peptides in driving protein traffic to the apicoplast of the parasite, and are also functional in a mammalian cell system. Our study is the first detailed analysis of an extended P. falciparum signal peptide and suggests that N-terminal extensions of exported Plasmodium falciparum proteins are not required for entry to the secretory system, and are likely to be involved in other, so far unknown, processes.

Rational Design of Membrane-Pore-Forming Peptides.

Specific interactions of peptides with lipid membranes are essential for cellular communication and constitute a central aspect of the innate host defense against pathogens. A computational method for generating innovative membrane-pore-forming peptides inspired by natural templates is presented. Peptide representation in terms of sequence- and topology-dependent hydrophobic moments is introduced. This design concept proves to be appropriate for the de novo generation of first-in-class membrane-active peptides with the anticipated mode of action. The designed peptides outperform the natural template in terms of their antibacterial activity. They form a kinked helical structure and self-assemble in the membrane by an entropy-driven mechanism to form dynamically growing pores that are dependent on the lipid composition. The results of this study demonstrate the unique potential of natural template-based peptide design for chemical biology and medicinal chemistry.

Peptide-Membrane Interaction between Targeting and Lysis.

Certain cationic peptides interact with biological membranes. These often-complex interactions can result in peptide targeting to the membrane, or in membrane permeation, rupture, and cell lysis. We investigated the relationship between the structural features of membrane-active peptides and these effects, to better understand these processes. To this end, we employed a computational method for morphing a membranolytic antimicrobial peptide into a nonmembranolytic mitochondrial targeting peptide by "directed simulated evolution." The results obtained demonstrate that superficially subtle sequence modifications can strongly affect the peptides' membranolytic and membrane-targeting abilities. Spectroscopic and computational analyses suggest that N- and C-terminal structural flexibility plays a crucial role in determining the mode of peptide-membrane interaction.

modlAMP: Python for antimicrobial peptides.

SUMMARY: We have implemented the lecular esign aboratory's nti icrobial eptides package ( ), a Python-based software package for the design, classification and visual representation of peptide data. modlAMP offers functions for molecular descriptor calculation and the retrieval of amino acid sequences from public or local sequence databases, and provides instant access to precompiled datasets for machine learning. The package also contains methods for the analysis and representation of circular dichroism spectra. AVAILABILITY AND IMPLEMENTATION: The modlAMP Python package is available under the BSD license from URL http://doi.org/10.5905/ethz-1007-72 or via pip from the Python Package Index (PyPI). CONTACT: gisbert.schneider@pharma.ethz.ch. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hybrid Network Model for "Deep Learning" of Chemical Data: Application to Antimicrobial Peptides.

We present a "deep" network architecture for chemical data analysis and classification together with a prospective proof-of-concept application. The model features a self-organizing map (SOM) as the input layer of a feedforward neural network. The SOM converts molecular descriptors to a two-dimensional image for further processing. We implemented lateral neuron inhibition for contrast enhancement. The model achieved improved classification accuracy and predictive robustness compared to feedforward network classifiers lacking the SOM layer. By nonlinear dimensionality reduction the networks extracted meaningful chemical features from the data and outperformed linear principal component analysis (PCA). The learning machine was trained on the sequence-length independent recognition of antibacterial peptides and correctly predicted the killing activity of a synthetic test peptide against Staphylococcus aureus in an in vitro experiment.

Scoring of de novo Designed Chemical Entities by Macromolecular Target Prediction.

Computational de novo molecular design and macromolecular target prediction have become routine in applied cheminformatics. In this study, we have generated populations of drug template-derived designs using ligand-based building block assembly, and predicted their potential targets. The results of our analysis show that the reaction-based de novo design generated new chemical entities with similar properties and pharmacophores as that of the template drugs as well as up to 44 % of the de novo compounds receiving the correct target predictions. Keeping in mind the probabilistic nature of the methods, such a combination of fast and meaningful computational structure generation by reaction-based design and product scoring by target class prediction may be appropriate for prospective application in medicinal chemistry.

Sparse Neural Network Models of Antimicrobial Peptide-Activity Relationships.

We present an adaptive neural network model for chemical data classification. The method uses an evolutionary algorithm for optimizing the network structure by seeking sparsely connected architectures. The number of hidden layers, the number of neurons in each layer and their connectivity are free variables of the system. We used the method for predicting antimicrobial peptide activity from the amino acid sequence. Visualization of the evolved sparse network structures suggested a high charge density and a low aggregation potential in solution as beneficial for antimicrobial activity. However, different training data sets and peptide representations resulted in greatly varying network structures. Overall, the sparse network models turned out to be less accurate than fully-connected networks. In a prospective application, we synthesized and tested 10 de novo generated peptides that were predicted to either possess antimicrobial activity, or to be inactive. Two of the predicted antibacterial peptides showed cosiderable bacteriostatic effects against both Staphylococcus aureus and Escherichia coli. None of the predicted inactive peptides possessed antibacterial properties. Molecular dynamics simulations of selected peptide structures in water and TFE suggest a pronounced peptide helicity in a hydrophobic environment. The results of this study underscore the applicability of neural networks for guiding the computer-assisted design of new peptides with desired properties.

Deep Learning in Drug Discovery.

Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks.