One chiral fingerprint to find them all

Molecular fingerprints are indispensable tools in cheminformatics. However, stereochemistry is generally not considered, which is problematic for large molecules which are almost all chiral. Herein we report MAP4C, a chiral version of our previously reported fingerprint MAP4, which lists MinHashes computed from character strings containing the SMILES of all pairs of circular substructures up to a diameter of four bonds and the shortest topological distance between their central atoms. MAP4C includes the Cahn-Ingold-Prelog (CIP) annotation (R, S, r or s) whenever the chiral atom is the center of a circular substructure, a question mark for undefined stereocenters, and double bond cis-trans information if specified. MAP4C performs slightly better than the achiral MAP4, ECFP and AP fingerprints in non-stereoselective virtual screening benchmarks. Furthermore, MAP4C distinguishes between stereoisomers in chiral molecules from small molecule drugs to large natural products and peptides comprising thousands of diastereomers, with a degree of distinction smaller than between structural isomers and proportional to the number of chirality changes. Due to its excellent performance across diverse molecular classes and its ability to handle stereochemistry, MAP4C is recommended as a generally applicable chiral molecular fingerprint. SCIENTIFIC CONTRIBUTION: The ability of our chiral fingerprint MAP4C to handle stereoisomers from small molecules to large natural products and peptides is unprecedented and opens the way for cheminformatics to include stereochemistry as an important molecular parameter across all fields of molecular design.

Overcoming Protein Orientation Mismatch Enables Efficient Nanoscale Light-Driven ATP Production

Adenosine triphosphate (ATP)-producing modules energized by light-driven proton pumps are powerful tools for the bottom-up assembly of artificial cell-like systems. However, the maximum efficiency of such modules is prohibited by the random orientation of the proton pumps during the reconstitution process into lipid-surrounded nanocontainers. Here, we overcome this limitation using a versatile approach to uniformly orient the light-driven proton pump proteorhodopsin (pR) in liposomes. pR is post-translationally either covalently or noncovalently coupled to a membrane-impermeable protein domain guiding orientation during insertion into preformed liposomes. In the second scenario, we developed a novel bifunctional linker, trisNTA-SpyTag, that allows for the reversible connection of any SpyCatcher-containing protein and a HisTag-carrying protein. The desired protein orientations are verified by monitoring vectorial proton pumping and membrane potential generation. In conjunction with ATP synthase, highly efficient ATP production is energized by the inwardly pumping population. In comparison to other light-driven ATP-producing modules, the uniform orientation allows for maximal rates at economical protein concentrations. The presented technology is highly customizable and not limited to light-driven proton pumps but applicable to many membrane proteins and offers a general approach to overcome orientation mismatch during membrane reconstitution, requiring little to no genetic modification of the protein of interest.

Highly Potent Cationic Chitosan Derivatives Coupled to Antimicrobial Peptide Dendrimers to Combat Pseudomonas aeruginosa

The burden of bacterial wound infections has considerably increased due to antibiotic resistance to most of the currently available antimicrobial drugs. Herein, for the first time, a chemical coupling of two cationic N-aryl (pyridyl and aminocinnamyl) chitosan derivatives to antimicrobial peptide dendrimers (AMPDs) of different generations (first, second, and third) via thioether-haloacetyl reaction is reported. The new chitosan-AMPD conjugates show high selectivity by killing Pseudomonas aeruginosa and very low toxicity toward mammalian cells, as well as extremely low hemolysis to red blood cells. Electron microscopy reveals that the new chitosan derivatives coupled to AMPD destroy both the inner and outer membranes of Gram-negative P. aeruginosa. Moreover, chitosan-AMPD conjugates show synergetic effects within extremely low concentrations. The new chitosan-AMPD conjugates can be used as potent antimicrobial therapeutic agents, to eradicate pathogens such as those present in acute and chronic infected wounds.

Antimicrobial Peptide-Peptoid Hybrids with and without Membrane Disruption

Among synthetic analogues of antimicrobial peptides (AMPs) under investigation to address antimicrobial resistance, peptoids (N-alkylated oligoglycines) have been reported to act both by membrane disruption and on intracellular targets. Here we gradually introduced peptoid units into the membrane-disruptive undecapeptide KKLLKLLKLLL to test a possible transition toward intracellular targeting. We found that selected hybrids containing up to five peptoid units retained the parent AMP's α-helical folding, membrane disruption, and antimicrobial effects against Gram-negative bacteria including multidrug-resistant (MDR) strains of Pseudomonas aeruginosa and Klebsiella pneumoniae while showing reduced hemolysis and cell toxicities. Furthermore, some hybrids containing as few as three peptoid units as well as the full peptoid lost folding, membrane disruption, hemolysis, and cytotoxicity but displayed strong antibacterial activity under dilute medium conditions typical for proline-rich antimicrobial peptides (PrAMPs), pointing to intracellular targeting. These findings parallel previous reports that partially helical amphiphilic peptoids are privileged oligomers for antibiotic development.

Expanding Bioactive Fragment Space with the Generated Database GDB-13s

Identifying innovative fragments for drug design can help medicinal chemistry address new targets and overcome the limitations of the classical molecular series. By deconstructing molecules into ring fragments (RFs, consisting of ring atoms plus ring-adjacent atoms) and acyclic fragments (AFs, consisting of only acyclic atoms), we find that public databases of molecules (i.e., ZINC and PubChem) and natural products (i.e., COCONUT) contain mostly RFs and AFs of up to 13 atoms. We also find that many RFs and AFs are enriched in bioactive vs inactive compounds from ChEMBL. We then analyze the generated database GDB-13s, which enumerates 99 million possible molecules of up to 13 atoms, for RFs and AFs resembling ChEMBL bioactive RFs and AFs. This analysis reveals a large number of novel RFs and AFs that are structurally simple, have favorable synthetic accessibility scores, and represent opportunities for synthetic chemistry to contribute to drug innovation in the context of fragment-based drug discovery.

Alchemical analysis of FDA approved drugs

Chemical space maps help visualize similarities within molecular sets. However, there are many different molecular similarity measures resulting in a confusing number of possible comparisons. To overcome this limitation, we exploit the fact that tools designed for reaction informatics also work for alchemical processes that do not obey Lavoisier's principle, such as the transmutation of lead into gold. We start by using the differential reaction fingerprint (DRFP) to create tree-maps (TMAPs) representing the chemical space of pairs of drugs selected as being similar according to various molecular fingerprints. We then use the Transformer-based RXNMapper model to understand structural relationships between drugs, and its confidence score to distinguish between pairs related by chemically feasible transformations and pairs related by alchemical transmutations. This analysis reveals a diversity of structural similarity relationships that are otherwise difficult to analyze simultaneously. We exemplify this approach by visualizing FDA-approved drugs, EGFR inhibitors, and polymyxin B analogs.

Organic Catalytic Activity as a Method for Agnostic Life Detection

An ideal life detection instrument would have high sensitivity but be insensitive to abiotic processes and would be capable of detecting life with alternate molecular structures. In this study, we propose that catalytic activity can be the basis of a nearly ideal life detection instrument. There are several advantages to catalysis as an agnostic life detection method. Demonstrating catalysis does not necessarily require culturing/growing the alien life and in fact may persist even in dead biomass for some time, and the amplification by catalysis is large even by minute amounts of catalysts and, hence, can be readily detected against abiotic background rates. In specific, we propose a hydrolytic catalysis detection instrument that could detect activity in samples of extraterrestrial organic material from unknown life. The instrument uses chromogenic assay-based detection of various hydrolytic catalytic activities, which are matched to corresponding artificial substrates having the same, chromogenic (preferably fluorescent) upon release, group; D- and L-enantiomers of these substrates can be used to also answer the question whether unknown life is chiral. Since catalysis is a time-proportional product-concentration amplification process, hydrolytic catalytic activity can be measured on a sample of even a minute size, and with instruments based on, for example, optofluidic chip technology.

Multistep retrosynthesis combining a disconnection aware triple transformer loop with a route penalty score guided tree search

Computer-aided synthesis planning (CASP) aims to automatically learn organic reactivity from literature and perform retrosynthesis of unseen molecules. CASP systems must learn reactions sufficiently precisely to propose realistic disconnections, while avoiding overfitting to leave room for diverse options, and explore possible routes such as to allow short synthetic sequences to emerge. Herein we report an open-source CASP tool proposing original solutions to both challenges. First, we use a triple transformer loop (TTL) predicting starting materials (T1), reagents (T2), and products (T3) to explore various disconnection sites defined by combining systematic, template-based, and transformer-based tagging procedures. Second, we integrate TTL into a multistep tree search algorithm (TTLA) prioritizing sequences using a route penalty score (RPScore) considering the number of steps, their confidence score, and the simplicity of all intermediates along the route. Our approach favours short synthetic routes to commercial starting materials, as exemplified by retrosynthetic analyses of recently approved drugs.

To Fold or Not to Fold: Diastereomeric Optimization of an α-Helical Antimicrobial Peptide

Membrane disruptive α-helical antimicrobial peptides (AMPs) offer an opportunity to address multidrug resistance; however, most AMPs are toxic and unstable in serum. These limitations can be partly overcome by introducing D-residues, which often confers protease resistance and reduces toxicity without affecting antibacterial activity, presumably due to lowered α-helicity. Here, we investigated 31 diastereomers of the α-helical AMP KKLLKLLKLLL. Three diastereomers containing two, three, and four D-residues showed increased antibacterial effects, comparable hemolysis, reduced toxicity against HEK293 cells, and excellent serum stability, while another diastereomer with four D-residues additionally displayed lower hemolysis. X-ray crystallography confirmed that high or low α-helicity as measured by circular dichroism indicated α-helical or disordered structures independently of the number of chirality switched residues. In contrast to previous reports, α-helicity across diastereomers correlated with both antibacterial activity and hemolysis and revealed a complex relationship between stereochemistry, activity, and toxicity, highlighting the potential of diastereomers for property optimization.

Structure, sequon recognition and mechanism of tryptophan C-mannosyltransferase

C-linked glycosylation is essential for the trafficking, folding and function of secretory and transmembrane proteins involved in cellular communication processes. The tryptophan C-mannosyltransferase (CMT) enzymes that install the modification attach a mannose to the first tryptophan of WxxW/C sequons in nascent polypeptide chains by an unknown mechanism. Here, we report cryogenic-electron microscopy structures of Caenorhabditis elegans CMT in four key states: apo, acceptor peptide-bound, donor-substrate analog-bound and as a trapped ternary complex with both peptide and a donor-substrate mimic bound. The structures indicate how the C-mannosylation sequon is recognized by this CMT and its paralogs, and how sequon binding triggers conformational activation of the donor substrate: a process relevant to all glycosyltransferase C superfamily enzymes. Our structural data further indicate that the CMTs adopt an unprecedented electrophilic aromatic substitution mechanism to enable the C-glycosylation of proteins. These results afford opportunities for understanding human disease and therapeutic targeting of specific CMT paralogs.

Dipropylamine for 9-Fluorenylmethyloxycarbonyl (Fmoc) Deprotection with Reduced Aspartimide Formation in Solid-Phase Peptide Synthesis

Herein, we report dipropylamine (DPA) as a fluorenylmethyloxycarbonyl (Fmoc) deprotection reagent to strongly reduce aspartimide formation compared to piperidine (PPR) in high-temperature (60 °C) solid-phase peptide synthesis (SPPS). In contrast to PPR, DPA is readily available, inexpensive, low toxicity, and nonstench. DPA also provides good yields in SPPS of non-aspartimide-prone peptides and peptide dendrimers.

Molecular Framework Analysis of the Generated Database GDB-13s

The generated databases (GDBs) list billions of possible molecules from systematic enumeration following simple rules of chemical stability and synthetic feasibility. To assess the originality of GDB molecules, we compared their Bemis and Murcko molecular frameworks (MFs) with those in public databases. MFs result from molecules by converting all atoms to carbons, all bonds to single bonds, and removing terminal atoms iteratively until none remain. We compared GDB-13s (99,394,177 molecules up to 13 atoms containing simplified functional groups, 22,130 MFs) with ZINC (885,905,524 screening compounds, 1,016,597 MFs), PubChem50 (100,852,694 molecules up to 50 atoms, 1,530,189 MFs), and COCONUT (401,624 natural products, 42,734 MFs). While MFs in public databases mostly contained linker bonds and six-membered rings, GDB-13s MFs had diverse ring sizes and ring systems without linker bonds. Most GDB-13s MFs were exclusive to this database, and many were relatively simple, representing attractive targets for synthetic chemistry aiming at innovative molecules.

Molecular Similarity for Drug Discovery, Target Prediction and Chemical Space Visualization

Similar drug molecules often have similar properties and activities. Therefore, quantifying molecular similarity is central to drug discovery and optimization. Here I review computational methods using molecular similarity measures developed in my group within the interdisciplinary network NCCR TransCure investigating the physiology, structural biology and pharmacology of ion channels and membrane transporters. We designed a 3D molecular shape and pharmacophore comparison algorithm to optimize weak and unselective inhibitors by scaffold hopping and discovered potent and selective inhibitors of the ion channels TRPV6 and TRPM4, of endocannabinoid membrane transport, and of the divalent metal transporters DMT1 and ZIP8. We predicted off-target effects by combining molecular similarity searches from different molecular fingerprints against target annotated compounds from the ChEMBL database. Finally, we created interactive chemical space maps reflecting molecular similarities to facilitate the selection of screening compounds and the analysis of screening results. These different tools are available online at https://gdb.unibe.ch/tools/.

Physiological and Molecular Function of the Sodium/Hydrogen Exchanger NHA2 (SLC9B2)

NHA2, also known as SLC9B2, is an orphan intracellular Na+/H+ exchanger (NHE) that has been associated with arterial hypertension and diabetes mellitus in humans. The objective of this NCCR TransCure project was to define the physiological and molecular function of NHA2, to develop a high resolution kinetic transport assay for NHA2 and to identify specific and potent compounds targeting NHA2. In this review, we summarize the results of this highly interdisciplinary and interfaculty effort, led by the groups of Proffs. Jean-Louis Reymond, Christoph von Ballmoos and Daniel Fuster.

Vitaport: A Learning and Artistic Path for Public Outreach on Transporter Biology, Health and Drug Discovery

In occasion of its conclusion, the National Center of Competence in Research (NCCR) TransCure launched a temporary learning and artistic path in the city of Bern named 'Vitaport - Was unser Körper transportiert'. The path explained how nutrients are transported through our body and how molecules find their way to the right organ to achieve their effect there. NCCR TransCure researchers, together with students of the Bern School of Design, developed ceramic objects, texts and information graphics that took the public on a multidisciplinary journey of discovery through the human body. In this article, we report about aims, development, challenges and outcome of this ambitious science outreach project in which we could experience a rewarding and successful collaboration between scientists and artists.

An intrinsically disordered antimicrobial peptide dendrimer from stereorandomized virtual screening

Membrane-disruptive amphiphilic antimicrobial peptides behave as intrinsically disordered proteins by being unordered in water and becoming α-helical in contact with biological membranes. We recently discovered that synthesizing the α-helical antimicrobial peptide dendrimer L-T25 ((KL)₈(KKL)₄(KLL)₂ KKLL) using racemic amino acids to form stereorandomized sr-T25, an analytically pure mixture of all possible diastereoisomers of L-T25, preserved antibacterial activity but abolished hemolysis and cytotoxicity, pointing to an intrinsically disordered antibacterial conformation and an α-helical cytotoxic conformation. In this study, to identify non-toxic intrinsically disordered homochiral antimicrobial peptide dendrimers (AMPDs), we surveyed sixty-three sr-analogs of sr-T25 selected by virtual screening. One of the analogs, sr-X18 ((KL)₈(KLK)₄(KLL)₂ KLLL), lost antibacterial activity as L-enantiomer and became hemolytic due to α-helical folding. By contrast, the L- and D-enantiomers of sr-X22 ((KL)₈(KL)₄(KKLL)₂ KLKK) were equally antibacterial, non-hemolytic, and non-toxic, implying an intrinsically disordered bioactive conformation. Screening stereorandomized libraries may be generally useful to identify or optimize intrinsically disordered bioactive peptides.

Molecular basis for glycan recognition and reaction priming of eukaryotic oligosaccharyltransferase

Oligosaccharyltransferase (OST) is the central enzyme of N-linked protein glycosylation. It catalyzes the transfer of a pre-assembled glycan, GlcNAc₂Man₉Glc₃, from a dolichyl-pyrophosphate donor to acceptor sites in secretory proteins in the lumen of the endoplasmic reticulum. Precise recognition of the fully assembled glycan by OST is essential for the subsequent quality control steps of glycoprotein biosynthesis. However, the molecular basis of the OST-donor glycan interaction is unknown. Here we present cryo-EM structures of S. cerevisiae OST in distinct functional states. Our findings reveal that the terminal glucoses (Glc₃) of a chemo-enzymatically generated donor glycan analog bind to a pocket formed by the non-catalytic subunits WBP1 and OST2. We further find that binding either donor or acceptor substrate leads to distinct primed states of OST, where subsequent binding of the other substrate triggers conformational changes required for catalysis. This alternate priming allows OST to efficiently process closely spaced N-glycosylation sites.

Medium-Chain Lipid Conjugation Facilitates Cell-Permeability and Bioactivity

The majority of bioactive molecules act on membrane proteins or intracellular targets and therefore needs to partition into or cross biological membranes. Natural products often exhibit lipid modifications to facilitate critical molecule-membrane interactions, and in many cases their bioactivity is markedly reduced upon removal of a lipid group. However, despite its importance in nature, lipid-conjugation of small molecules is not commonly used in chemical biology and medicinal chemistry, and the effect of such conjugation has not been systematically studied. To understand the composition of lipids found in natural products, we carried out a chemoinformatic characterization of the "natural product lipidome". According to this analysis, lipidated natural products predominantly contain saturated medium-chain lipids (MCLs), which are significantly shorter than the long-chain lipids (LCLs) found in membranes and lipidated proteins. To study the usefulness of such modifications in probe design, we systematically explored the effect of lipid conjugation on five different small molecule chemotypes and find that permeability, cellular retention, subcellular localization, and bioactivity can be significantly modulated depending on the type of lipid tail used. We demonstrate that MCL conjugation can render molecules cell-permeable and modulate their bioactivity. With all explored chemotypes, MCL-conjugates consistently exhibited superior uptake or bioactivity compared to LCL-conjugates and either comparable or superior uptake or bioactivity to short-chain lipid (SCL)-conjugates. Together, our findings suggest that conjugation of small molecules with MCLs could be a powerful strategy for the design of probes and drugs.

Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anti-Cancer Peptides

Most antimicrobial peptides (AMPs) and anticancer peptides (ACPs) fold into membrane disruptive cationic amphiphilic α‐helices, many of which are however also unpredictably hemolytic and toxic. Here we exploited the ability of recurrent neural networks (RNN) to distinguish active from inactive and non‐hemolytic from hemolytic AMPs and ACPs to discover new non‐hemolytic ACPs. Our discovery pipeline involved: 1) sequence generation using either a generative RNN or a genetic algorithm, 2) RNN classification for activity and hemolysis, 3) selection for sequence novelty, helicity and amphiphilicity, and 4) synthesis and testing. Experimental evaluation of thirty‐three peptides resulted in eleven active ACPs, four of which were non‐hemolytic, with properties resembling those of the natural ACP lasioglossin III. These experiments show the first example of direct machine learning guided discovery of non‐hemolytic ACPs.

Automatic Extraction of Reaction Templates for Synthesis Prediction

Several tools for the computational planning of synthetic routes have been developed over the last 60 years. Traditionally these have been built on manually or automatically extracted reaction rules or templates obtained from a deep knowledge of organic chemistry in the case of the former, and reaction databases for the latter. Herein we give an introductory overview to the process of automatically extracting reaction templates, starting from methods for reaction centre identification, through to their use in computer aided synthesis planning and the de novo design of compounds.

Reaction classification and yield prediction using the differential reaction fingerprint DRFP

Predicting the nature and outcome of reactions using computational methods is a crucial tool to accelerate chemical research. The recent application of deep learning-based learned fingerprints to reaction classification and reaction yield prediction has shown an impressive increase in performance compared to previous methods such as DFT- and structure-based fingerprints. However, learned fingerprints require large training data sets, are inherently biased, and are based on complex deep learning architectures. Here we present the differential reaction fingerprint DRFP. The DRFP algorithm takes a reaction SMILES as an input and creates a binary fingerprint based on the symmetric difference of two sets containing the circular molecular n-grams generated from the molecules listed left and right from the reaction arrow, respectively, without the need for distinguishing between reactants and reagents. We show that DRFP performs better than DFT-based fingerprints in reaction yield prediction and other structure-based fingerprints in reaction classification, reaching the performance of state-of-the-art learned fingerprints in both tasks while being data-independent.

Differential Reaction Fingerprint DRFP is a chemical reaction fingerprint enabling simple machine learning models running on standard hardware to reach DFT- and deep learning-based accuracies in reaction yield prediction and reaction classification.

Discovery of novel drug-like antitubercular hits targeting the MEP pathway enzyme DXPS by strategic application of ligand-based virtual screening

In the present manuscript, we describe how we successfully used ligand-based virtual screening (LBVS) to identify two small-molecule, drug-like hit classes with excellent ADMET profiles against the difficult to address microbial enzyme 1-deoxy-d-xylulose-5-phosphate synthase (DXPS). In the fight against antimicrobial resistance (AMR), it has become increasingly important to address novel targets such as DXPS, the first enzyme of the 2-C-methyl-d-erythritol-4-phosphate (MEP) pathway, which affords the universal isoprenoid precursors. This pathway is absent in humans but essential for pathogens such as Mycobacterium tuberculosis, making it a rich source of drug targets for the development of novel anti-infectives. Standard computer-aided drug-design tools, frequently applied in other areas of drug development, often fail for targets with large, hydrophilic binding sites such as DXPS. Therefore, we introduce the concept of pseudo-inhibitors, combining the benefits of pseudo-ligands (defining a pharmacophore) and pseudo-receptors (defining anchor points in the binding site), for providing the basis to perform a LBVS against M. tuberculosis DXPS. Starting from a diverse set of reference ligands showing weak inhibition of the orthologue from Deinococcus radiodurans DXPS, we identified three structurally unrelated classes with promising in vitro (against M. tuberculosis DXPS) and whole-cell activity including extensively drug-resistant strains of M. tuberculosis. The hits were validated to be specific inhibitors of DXPS and to have a unique mechanism of inhibition. Furthermore, two of the hits have a balanced profile in terms of metabolic and plasma stability and display a low frequency of resistance development, making them ideal starting points for hit-to-lead optimization of antibiotics with an unprecedented mode of action.

We identified two drug-like antitubercular hits with submicromolar inhibition constants against the target 1-deoxy-d-xylulose-5-phosphate synthase (DXPS) with a new mode of action and promising activity against drug-resistant tuberculosis.

An Immunomodulatory Peptide Dendrimer Inspired from Glatiramer Acetate

Glatiramer acetate (GA) is a random polypeptide drug used to treat multiple sclerosis (MS), a chronic autoimmune disease. With the aim of identifying a precisely defined alternative to GA, we synthesized a library of peptide dendrimers with an amino acid composition similar to GA. We then challenged the dendrimers to trigger the release of the anti‐inflammatory cytokine interleukin‐1 receptor antagonist (IL‐1Ra) from human monocytes, which is one of the effects of GA on immune cells. Several of the largest dendrimers tested were as active as GA. Detailed profiling of the best hit showed that this dendrimer induces the differentiation of monocytes towards an M2 (anti‐inflammatory) state as GA does, however with a distinct immune marker profile. Our peptide dendrimer might serve as starting point to develop a well‐defined immunomodulatory analog of GA.

A mixed chirality α-helix in a stapled bicyclic and a linear antimicrobial peptide revealed by X-ray crystallography

The peptide α-helix is right-handed when containing amino acids with l-chirality, and left-handed with d-chirality, however mixed chirality peptides generally do not form α-helices unless a helix inducer such as the non-natural residue amino-isobutyric acid is used. Herein we report the first X-ray crystal structures of mixed chirality α-helices in short peptides comprising only natural residues as the example of a stapled bicyclic and a linear membrane disruptive amphiphilic antimicrobial peptide (AMP) containing seven l- and four d-residues, as complexes of fucosylated analogs with the bacterial lectin LecB. The mixed chirality α-helices are superimposable onto the homochiral α-helices and form under similar conditions as shown by CD spectra and MD simulations but non-hemolytic and resistant to proteolysis. The observation of a mixed chirality α-helix with only natural residues in the protein environment of LecB suggests a vast unexplored territory of α-helical mixed chirality sequences and their possible use for optimizing bioactive α-helical peptides.

We report the first X-ray crystal structures of mixed chirality α-helices comprising only natural residues as the example of bicyclic and linear membrane disruptive amphiphilic antimicrobial peptides containing seven l- and four d-residues.

Classifying natural products from plants, fungi or bacteria using the COCONUT database and machine learning

Natural products (NPs) represent one of the most important resources for discovering new drugs. Here we asked whether NP origin can be assigned from their molecular structure in a subset of 60,171 NPs in the recently reported Collection of Open Natural Products (COCONUT) database assigned to plants, fungi, or bacteria. Visualizing this subset in an interactive tree-map (TMAP) calculated using MAP4 (MinHashed atom pair fingerprint) clustered NPs according to their assigned origin ( https://tm.gdb.tools/map4/coconut_tmap/ ), and a support vector machine (SVM) trained with MAP4 correctly assigned the origin for 94% of plant, 89% of fungal, and 89% of bacterial NPs in this subset. An online tool based on an SVM trained with the entire subset correctly assigned the origin of further NPs with similar performance ( https://np-svm-map4.gdb.tools/ ). Origin information might be useful when searching for biosynthetic genes of NPs isolated from plants but produced by endophytic microorganisms.

Machine learning designs non-hemolytic antimicrobial peptides

Machine learning (ML) consists of the recognition of patterns from training data and offers the opportunity to exploit large structure-activity databases for drug design. In the area of peptide drugs, ML is mostly being tested to design antimicrobial peptides (AMPs), a class of biomolecules potentially useful to fight multidrug-resistant bacteria. ML models have successfully identified membrane disruptive amphiphilic AMPs, however mostly without addressing the associated toxicity to human red blood cells. Here we trained recurrent neural networks (RNN) with data from DBAASP (Database of Antimicrobial Activity and Structure of Peptides) to design short non-hemolytic AMPs. Synthesis and testing of 28 generated peptides, each at least 5 mutations away from training data, allowed us to identify eight new non-hemolytic AMPs against Pseudomonas aeruginosa, Acinetobacter baumannii, and methicillin-resistant Staphylococcus aureus (MRSA). These results show that machine learning (ML) can be used to design new non-hemolytic AMPs.

Predicting enzymatic reactions with a molecular transformer

The use of enzymes for organic synthesis allows for simplified, more economical and selective synthetic routes not accessible to conventional reagents. However, predicting whether a particular molecule might undergo a specific enzyme transformation is very difficult. Here we used multi-task transfer learning to train the molecular transformer, a sequence-to-sequence machine learning model, with one million reactions from the US Patent Office (USPTO) database combined with 32 181 enzymatic transformations annotated with a text description of the enzyme. The resulting enzymatic transformer model predicts the structure and stereochemistry of enzyme-catalyzed reaction products with remarkable accuracy. One of the key novelties is that we combined the reaction SMILES language of only 405 atomic tokens with thousands of human language tokens describing the enzymes, such that our enzymatic transformer not only learned to interpret SMILES, but also the natural language as used by human experts to describe enzymes and their mutations.

Peptide Dendrimers: From Enzyme Models to Antimicrobials and Transfection Reagents

Aiming at studying cooperativity effects between amino acids in easily accessible protein models, we have explored the chemistry of peptide dendrimers, which we obtain as pure products by solid-phase peptide synthesis using a branching diamino acid such as lysine at every second or third position in a peptide sequence, followed by reverse-phase HPLC purification. This article reviews discoveries driven by combinatorial library synthesis and screening, including enantioselective esterase and aldolase enzyme models, cobalamin binding and peroxidase dendrimers, glycopeptide dendrimer biofilm inhibitors and their X-ray crystal structures as complexes with lectins, antimicrobial peptide dendrimers active against multidrug resistant Gram-negative bacteria, and transfection reagents for siRNA and CRISPR-Cas9 plasmid DNA. Latest developments include cheminformatics and artificial intelligence for exploring the peptide chemical space, and the principle of stereorandomization to understand the role of peptide chirality in activity.

Antimicrobial Peptide Dendrimers and Quorum-Sensing Inhibitors in Formulating Next-Generation Anti-Infection Cell Therapy Dressings for Burns

Multidrug resistance infections are the main cause of failure in the pro-regenerative cell-mediated therapy of burn wounds. The collagen-based matrices for delivery of cells could be potential substrates to support bacterial growth and subsequent lysis of the collagen leading to a cell therapy loss. In this article, we report the development of a new generation of cell therapy formulations with the capacity to resist infections through the bactericidal effect of antimicrobial peptide dendrimers and the anti-virulence effect of anti-quorum sensing MvfR (PqsR) system compounds, which are incorporated into their formulation. Anti-quorum sensing compounds limit the pathogenicity and antibiotic tolerance of pathogenic bacteria involved in the burn wound infections, by inhibiting their virulence pathways. For the first time, we report a biological cell therapy dressing incorporating live progenitor cells, antimicrobial peptide dendrimers, and anti-MvfR compounds, which exhibit bactericidal and anti-virulence properties without compromising the viability of the progenitor cells.

The antibacterial activity of peptide dendrimers and polymyxin B increases sharply above pH 7.4

pH-activity profiling reveals that antimicrobial peptide dendrimers (AMPDs) kill Klebsiella pneumoniae and Methicillin-resistant Staphylococcus aureus (MRSA) at pH = 8.0, against which they are inactive at pH = 7.4, due to stronger electrostatic binding to bacterial cells at higher pH. A similar effect occurs with polymyxin B and might be general for polycationic antimicrobials.

Substrate specificities and reaction kinetics of the yeast oligosaccharyltransferase isoforms

Oligosaccharyltransferase (OST) catalyzes the central step in N-linked protein glycosylation, the transfer of a preassembled oligosaccharide from its lipid carrier onto asparagine residues of secretory proteins. The prototypic hetero-octameric OST complex from the yeast Saccharomyces cerevisiae exists as two isoforms that contain either Ost3p or Ost6p, both noncatalytic subunits. These two OST complexes have different protein substrate specificities in vivo. However, their detailed biochemical mechanisms and the basis for their different specificities are not clear. The two OST complexes were purified from genetically engineered strains expressing only one isoform. The kinetic properties and substrate specificities were characterized using a quantitative in vitro glycosylation assay with short peptides and different synthetic lipid-linked oligosaccharide (LLO) substrates. We found that the peptide sequence close to the glycosylation sequon affected peptide affinity and turnover rate. The length of the lipid moiety affected LLO affinity, while the lipid double-bond stereochemistry had a greater influence on LLO turnover rates. The two OST complexes had similar affinities for both the peptide and LLO substrates but showed significantly different turnover rates. These data provide the basis for a functional analysis of the Ost3p and Ost6p subunits.

Extraction of organic chemistry grammar from unsupervised learning of chemical reactions

Humans use different domain languages to represent, explore, and communicate scientific concepts. During the last few hundred years, chemists compiled the language of chemical synthesis inferring a series of "reaction rules" from knowing how atoms rearrange during a chemical transformation, a process called atom-mapping. Atom-mapping is a laborious experimental task and, when tackled with computational methods, requires continuous annotation of chemical reactions and the extension of logically consistent directives. Here, we demonstrate that Transformer Neural Networks learn atom-mapping information between products and reactants without supervision or human labeling. Using the Transformer attention weights, we build a chemically agnostic, attention-guided reaction mapper and extract coherent chemical grammar from unannotated sets of reactions. Our method shows remarkable performance in terms of accuracy and speed, even for strongly imbalanced and chemically complex reactions with nontrivial atom-mapping. It provides the missing link between data-driven and rule-based approaches for numerous chemical reaction tasks.

Stereorandomization as a Method to Probe Peptide Bioactivity

Solid-phase peptide synthesis (SPPS) is usually performed with optically pure building blocks to prepare peptides as single enantiomers. Herein we report that SPPS using racemic amino acids provides stereorandomized (sr) peptides, containing up to billions of different stereoisomers, as well-defined single HPLC peaks, single mass products with high yield, which can be used to investigate peptide bioactivity. To exemplify our method, we show that stereorandomization abolishes the membrane-disruptive effect of α-helical amphiphilic antimicrobial peptides but preserves their antibiofilm effect, implying different mechanisms involving folded versus disordered conformations. For antimicrobial peptide dendrimers by contrast, stereorandomization preserves antibacterial, membrane-disruptive, and antibiofilm effects but reduces hemolysis and cytotoxicity, thereby increasing their therapeutic index. Finally, we identify partially stereorandomized analogues of the last resort cyclic peptide antibiotic polymyxin B with preserved antibacterial activity but lacking membrane-disruptive and lipopolysaccharide-neutralizing activity, pointing to the existence of additional targets.

Retrosynthetic accessibility score (RAscore) - rapid machine learned synthesizability classification from AI driven retrosynthetic planning

Computer aided synthesis planning (CASP) is part of a suite of artificial intelligence (AI) based tools that are able to propose synthesis routes to a wide range of compounds. However, at present they are too slow to be used to screen the synthetic feasibility of millions of generated or enumerated compounds before identification of potential bioactivity by virtual screening (VS) workflows. Herein we report a machine learning (ML) based method capable of classifying whether a synthetic route can be identified for a particular compound or not by the CASP tool AiZynthFinder. The resulting ML models return a retrosynthetic accessibility score (RAscore) of any molecule of interest, and computes at least 4500 times faster than retrosynthetic analysis performed by the underlying CASP tool. The RAscore should be useful for pre-screening millions of virtual molecules from enumerated databases or generative models for synthetic accessibility and produce higher quality databases for virtual screening of biological activity.

Retrosynthetic accessibility score (RAscore) - rapid machine learned synthesizability classification from AI driven retrosynthetic planning

Computer aided synthesis planning (CASP) is part of a suite of artificial intelligence (AI) based tools that are able to propose synthesis routes to a wide range of compounds. However, at present they are too slow to be used to screen the synthetic feasibility of millions of generated or enumerated compounds before identification of potential bioactivity by virtual screening (VS) workflows. Herein we report a machine learning (ML) based method capable of classifying whether a synthetic route can be identified for a particular compound or not by the CASP tool AiZynthFinder. The resulting ML models return a retrosynthetic accessibility score (RAscore) of any molecule of interest, and computes at least 4500 times faster than retrosynthetic analysis performed by the underlying CASP tool. The RAscore should be useful for pre-screening millions of virtual molecules from enumerated databases or generative models for synthetic accessibility and produce higher quality databases for virtual screening of biological activity.

The retrosynthetic accessibility score (RAscore) is based on AI driven retrosynthetic planning, and is useful for rapid scoring of synthetic feasability and pre-screening of large datasets of virtual/generated molecules.

Artificial intelligence and automation in computer aided synthesis planning

In this perspective we deal with questions pertaining to the development of synthesis planning technologies over the course of recent years.

What can reaction databases teach us about Buchwald-Hartwig cross-couplings?

Despite the widespread and increasing usage of Pd-catalyzed C-N cross couplings, finding good conditions for these reactions can be challenging. Practitioners mostly rely on few methodology studies or anecdotal experience. This is surprising, since the advent of data-driven experimentation and the large amount of knowledge in databases allow for data-driven insight. In this work, we address this by analyzing more than 62 000 Buchwald-Hartwig couplings gathered from CAS, Reaxys and the USPTO. Our meta-analysis of the reaction performance generates data-driven cheatsheets for reaction condition recommendation. It also provides an interactive tool to find rarer ligands with optimal performance regarding user-selected substrate properties. With this we give practitioners promising starting points. Furthermore, we study bias and diversity in the literature and summarize the current state of the reaction data, including its pitfalls. Hence, this work will also be useful for future data-driven developments such as the optimization of reaction conditions via machine learning.

What can reaction databases teach us about Buchwald-Hartwig cross-couplings?

Despite the widespread and increasing usage of Pd-catalyzed C–N cross couplings, finding good conditions for these reactions can be challenging. Practitioners mostly rely on few methodology studies or anecdotal experience. This is surprising, since the advent of data-driven experimentation and the large amount of knowledge in databases allow for data-driven insight. In this work, we address this by analyzing more than 62 000 Buchwald–Hartwig couplings gathered from CAS, Reaxys and the USPTO. Our meta-analysis of the reaction performance generates data-driven cheatsheets for reaction condition recommendation. It also provides an interactive tool to find rarer ligands with optimal performance regarding user-selected substrate properties. With this we give practitioners promising starting points. Furthermore, we study bias and diversity in the literature and summarize the current state of the reaction data, including its pitfalls. Hence, this work will also be useful for future data-driven developments such as the optimization of reaction conditions via machine learning.

An analysis of the entire literature on Pd-catalyzed C–N couplings enables data-driven insight and provides recommendations for reaction conditions.

Synergistic Effect of Propidium Iodide and Small Molecule Antibiotics with the Antimicrobial Peptide Dendrimer G3KL against Gram-Negative Bacteria

There is an urgent need to develop new antibiotics against multidrug-resistant bacteria. Many antimicrobial peptides (AMPs) are active against such bacteria and often act by destabilizing membranes, a mechanism that can also be used to permeabilize bacteria to other antibiotics, resulting in synergistic effects. We recently showed that G3KL, an AMP with a multibranched dendritic topology of the peptide chain, permeabilizes the inner and outer membranes of Gram-negative bacteria including multidrug-resistant strains, leading to efficient bacterial killing. Here, we show that permeabilization of the outer and inner membranes of Pseudomonas aeruginosa by G3KL, initially detected using the DNA-binding fluorogenic dye propidium iodide (PI), also leads to a synergistic effect between G3KL and PI in this bacterium. We also identify a synergistic effect between G3KL and six different antibiotics against the Gram-negative Klebsiella pneumoniae, against which G3KL is inactive.

AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning

We present the open-source AiZynthFinder software that can be readily used in retrosynthetic planning. The algorithm is based on a Monte Carlo tree search that recursively breaks down a molecule to purchasable precursors. The tree search is guided by an artificial neural network policy that suggests possible precursors by utilizing a library of known reaction templates. The software is fast and can typically find a solution in less than 10 s and perform a complete search in less than 1 min. Moreover, the development of the code was guided by a range of software engineering principles such as automatic testing, system design and continuous integration leading to robust software with high maintainability. Finally, the software is well documented to make it suitable for beginners. The software is available at http://www.github.com/MolecularAI/aizynthfinder.

Inactivation-mimicking block of the epithelial calcium channel TRPV6

Epithelial calcium channel TRPV6 plays vital roles in calcium homeostasis, and its dysregulation is implicated in multifactorial diseases, including cancers. Here, we study the molecular mechanism of selective nanomolar-affinity TRPV6 inhibition by (4-phenylcyclohexyl)piperazine derivatives (PCHPDs). We use x-ray crystallography and cryo-electron microscopy to solve the inhibitor-bound structures of TRPV6 and identify two types of inhibitor binding sites in the transmembrane region: (i) modulatory sites between the S1-S4 and pore domains normally occupied by lipids and (ii) the main site in the ion channel pore. Our structural data combined with mutagenesis, functional and computational approaches suggest that PCHPDs plug the open pore of TRPV6 and convert the channel into a nonconducting state, mimicking the action of calmodulin, which causes inactivation of TRPV6 channels under physiological conditions. This mechanism of inhibition explains the high selectivity and potency of PCHPDs and opens up unexplored avenues for the design of future-generation biomimetic drugs.

Assigning the Origin of Microbial Natural Products by Chemical Space Map and Machine Learning

Microbial natural products (NPs) are an important source of drugs, however, their structural diversity remains poorly understood. Here we used our recently reported MinHashed Atom Pair fingerprint with diameter of four bonds (MAP4), a fingerprint suitable for molecules across very different sizes, to analyze the Natural Products Atlas (NPAtlas), a database of 25,523 NPs of bacterial or fungal origin. To visualize NPAtlas by MAP4 similarity, we used the dimensionality reduction method tree map (TMAP). The resulting interactive map organizes molecules by physico-chemical properties and compound families such as peptides and glycosides. Remarkably, the map separates bacterial and fungal NPs from one another, revealing that these two compound families are intrinsically different despite their related biosynthetic pathways. We used these differences to train a machine learning model capable of distinguishing between NPs of bacterial or fungal origin.

Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates

Organic synthesis methodology enables the synthesis of complex molecules and materials used in all fields of science and technology and represents a vast body of accumulated knowledge optimally suited for deep learning. While most organic reactions involve distinct functional groups and can readily be learned by deep learning models and chemists alike, regio- and stereoselective transformations are more challenging because their outcome also depends on functional group surroundings. Here, we challenge the Molecular Transformer model to predict reactions on carbohydrates where regio- and stereoselectivity are notoriously difficult to predict. We show that transfer learning of the general patent reaction model with a small set of carbohydrate reactions produces a specialized model returning predictions for carbohydrate reactions with remarkable accuracy. We validate these predictions experimentally with the synthesis of a lipid-linked oligosaccharide involving regioselective protections and stereoselective glycosylations. The transfer learning approach should be applicable to any reaction class of interest.