Chemical Science

Allosteric and cryptic binding sites provide unique opportunities in drug discovery for achieving selectivity, reducing toxicity and unlocking challenging therapeutical targets. Yet, cryptic pockets are particularly elusive in that they are difficult to capture with experimental methodologies, and therefore often remain undetected. Computational approaches for flexible cryptic site detection traditionally involve extensive unbiased molecular dynamics simulations, or more efficient but computationally still highly demanding biased MD alternatives that make use of enhanced sampling along low-frequency motions of the protein structure. Here we utilize a new variant of the recently introduced and orders of magnitude faster, MD-free COVIB algorithm to identify cryptic pockets efficiently in two challenging targets, the KRAS oncoprotein and the N-terminal domain of the STAT5B transcription factor. Using experimental and AI-modelled structures, respectively, we identify numerous cryptic sites, some of which have not yet been reported. The sites are prioritized and validated with our pharmacophore optimized photoaffinity-tagged fragment library via MS-based peptide mapping and NMR spectroscopy. Besides identifying new, targetable regions on the surface of these important oncotargets, we have established a protocol combining COVIB-based cryptic pocket detection and photoaffinity-based experimental peptide mapping as a robust approach for exploring unique and tractable cryptic pockets.

Designing photochemical properties in proteins is a frontier challenge in biomolecular design, which, when realised, will open new avenues in catalysis and biomolecular electronics. Here we demonstrate an integrated computational design framework, supported by ultrafast spectroscopy, combining protein design, quantum mechanical/molecular mechanics molecular dynamics (QM/MM MD) and electronic structure calculations, to control flavin excited states in de novo proteins. Iterative design yields FC-4D2, a flavocytochrome with covalently bound flavin and non-covalently bound heme B and Fl-4D2, where the heme-binding site is removed. Both proteins are highly thermostable, and access all physiologically relevant flavin redox and protonation states, while high-resolution X-ray crystallography confirms the near-[A]ngstrom-level accuracy in design. Incorporating QM/MM MD and linear-response time-dependent density functional theory (LR-TDDFT) into the design workflow enables engineering of a flavin-tryptophan charge-transfer variant, Fl-4D2CT, that modifies the flavin absorption spectrum, substantially alters its redox properties and suppresses fluorescence. Ultrafast studies reveal how protein design reshapes the excited state relaxation timescale and mechanism. Our study establishes a modular strategy for programming flavin photochemistry in designed proteins.

Antibiotic resistance driven by {beta}-lactamases has become a major threat to public health. Here, we propose a computational protocol for rapid estimation of {beta}-lactamase activity towards carbapenem antibiotics from sequence alone. Starting from AI-predicted acylenzyme complexes, only approximate transition state ensembles are sampled to compute catalytic electric fields, which strongly correlate with experimental activation barriers (R2 = 0.8). Compared with full quantum mechanics/molecular mechanics (QM/MM) reaction simulations, the protocol substantially reduces the amount of configurational sampling and thus computational cost. Our protocol enables efficient identification of carbapenem breakdown efficiency, further highlighting the important role of electric fields in enzyme catalysis.

Transition state (TS) generation is typically evaluated by geometric similarity, yet low root-mean-square deviation (RMSD) does not establish first-order saddle-point character or reaction-path connectivity. Here we introduce Kairos-GFM, a deterministic equivariant flow-matching model that combines a two-dimensional reaction graph with the Kabsch-aligned mean of three-dimensional reactant and product geometries. Using 881 Transition1x reactions whose reference TSs passed strict saddle-point and bidirectional intrinsic reaction coordinate validation, Kairos-GFM generates one candidate in 0.0118 s with a median RMSD of 0.14 [A]. A hierarchical physical-validation framework--spanning reaction-center geometry, raw single-point energy, DFT saddle-point optimization, harmonic frequency analysis, and IRC endpoint matching reveals that low RMSD and saddle-point convergence do not guarantee the intended reaction channel. Kairos-GFM recovers the target channel in 75.94% of reactions, outperforming diffusion-based baselines but remaining below transport-based methods. These results position Kairos-GFM as a rapid initial-geometry generator for quantum-chemical refinement and provide a transferable validation standard that distinguishes geometric similarity from physical fidelity.

Aromatic side chains carry the optical and redox chemistry of peptides, and lengthening one is widely expected to extend its conjugation and narrow its HOMO-LUMO gap. We test that expectation with plane-wave density functional theory on 22 isolated neutral peptides NH2-(X)n-COOH (X = Phe, Tyr, Trp, Cys; n = 1 to 5, and n = 10 for Cys and Trp). The gap does narrow, at the same rate for all three aromatic residues, i.e., -0.17 to -0.18 eV per residue, even though benzene, phenol and indole are very different chromophores. Residue-resolved projections reveal why: nothing delocalises. Every aromatic frontier orbital is 97 to 100 % confined to a single residue and the HOMO participation number saturates at one ring. Each residue instead keeps its own local gap: for Trp it is invariant to within 0.03 eV over n = 2 to 10, while over that same span the global gap falls by 1.19 eV. The global gap is therefore an extreme-value functional of the residue site energies, gap(n) =<g_f>-{Delta}(n), whose alignment penalty accounts for the entire effect: the polar backbone generates a monotonic internal electrostatic ramp that leaves the two gap edges at opposite ends of the chain, 32 [A] apart. We set out the resulting design rules for peptide-based bio-organic semiconductors.

Proteolysis-targeting chimeras (PROTACs) are large, flexible molecules that frequently extend beyond conventional drug-like chemical space, including Lipinski's rule-of-five boundaries. Their membrane permeability has often been linked to molecular chameleonicity and the ability to adopt compact conformations with reduced polar surface exposure; however, the underlying conformational determinants remain unclear. Here, we investigated 20 PROTACs spanning distinct passive-permeability classes using enhanced-sampling molecular dynamics (ES-MD). All permeability classes accessed compact states with low radius of gyration (Rg) and polar surface area (PSA), indicating that compact-state accessibility alone does not distinguish permeable from non-permeable PROTACs. We therefore introduce maximum ring size (MRS), defined as the size of the largest intramolecular hydrogen-bonded ring, as a metric for identifying compact conformations stabilized by long-range intramolecular hydrogen bonding. Like the other descriptors examined, MRS neither separates nor ranks the permeability classes, but helps locate folded states in the large ensemble. Representative compact conformations from selected highly permeable, moderately permeable, and non-permeable compounds were subsequently examined by all-atom unbiased MD in water and chloroform. The highly permeable PROTAC held a closed-flat ensemble in both environments, but in two structurally distinct arrangements, whereas the other two redistributed across heterogeneous ensembles. Passive diffusion therefore appears to depend on the population, stability, and environment-dependent persistence of permeable-like states rather than on compactness alone.

Cyclic peptides can engage targets beyond the reach of small molecules, but their discovery typically relies on libraries reused across targets, diversification around a known interaction motif, or computational methods limited to a handful of non-canonical building blocks. Here, we present nCycle-Forge, a simulated annealing framework that designs cyclic peptide binders by iteratively substituting building blocks and scoring each proposal by cofolding with the target. We designed, synthesized, and screened 1,472 cyclic peptides against the active-site pocket of Thrombin and the shallow p53-binding surface of MDM2 from the target sequence alone and without fixing binding mo-tifs. The library yielded chemically diverse hits against both targets in a single round without experimental optimization, with hit rates comparable to or up to 24-fold higher than in larger experimental libraries. Purified designs reached sub- to low-micromolar potency, with the best Thrombin inhibitor at Ki 0.14 M and the best MDM2 binder at EC50 2.4 M. Surface plasmon resonance confirmed direct binding, competition with or-thosteric inhibitors at both sites, and weaker binding under conditions expected to linearize the designs. nCycle-Forge requires no building block-specific parameterization, conformer generation, or retraining, and provides a target-focused route to functional cyclic peptides beyond the canonical amino acid alphabet.

Peptide offers a powerful modality for targeting protein-protein interfaces, yet their computational design remains challenging because sequence identity, backbone geometry and side-chain packing must be optimized simultaneously within a three-dimensional pocket constrained environment. Here we present PeTFlow, a pocket-conditioned all-atom peptide co-design framework that rationalizes this constrained design problem through discrete side-chain state tokenization. In PeTFlow, each residue is represented as a chemically compatible state token, enabling unified modeling of peptide sequence, conformation and all-atom interface geometry. PeTFlow achieved superior generation quality on peptide-design benchmarks and can further be applied to therapeutically relevant peptide drug design.

Generative protein design has made sequence and structure programmable, yet its chemical alphabet remains largely confined to the canonical twenty. Here, we establish CheMoDesign, an experimentally validated generative framework for designing proteins de novo around user-specified chemistry. CheMoDesign weakens pair-representation conditioning in a pretrained all-atom structure-prediction model while preserving chemical anchors, then refines generated backbones through sequence-conditioned denoising. By treating post-translational modifications, synthetic residues, and chemical modifications as user-defined building blocks, the framework generated proteins with chemistry-dependent recognition and reactivity in single-round experimental campaigns. Sulfotyrosine and nitrotyrosine enabled modification-dependent recognition of thrombin and HER2, respectively. Genetically encoded noncanonical residues enabled bioorthogonal affinity switching and light- or proximity-triggered covalent target capture. Post-expression chemical modifications yielded a light-activated covalent PD-L1 binder and an isoform-selective CA IX inhibitor (KD = 45 nM) designed to engage catalytic Zn2+;. CheMoDesign makes chemical mechanism a programmable dimension of de novo protein design.

Transition-metal ion Forster resonance energy transfer (tmFRET) measures short-range distances and conformational changes in proteins, but its distance sensitivity depends on the donor-acceptor pair. Here, we establish the metal-chelating noncanonical amino acid bipyridylalanine (bpyA) as a flexible, genetically encoded acceptor platform whose Forster distance (R0) can be tuned without changing protein labeling. Cu2+, Cu2+ complexed with nitrilotriacetic acid (NTA), and Fe2+ form distinct acceptors at the same bpyA site, allowing distance sensitivity to be adjusted by changing the solution conditions. We incorporated bpyA at two sites in maltose binding protein (MBP) and labeled nearby engineered cysteines with the donor fluorophore BODIPY. The resulting [Cu(bpyA)]2+, [CuNTA(bpyA)]2+, and [Fe(bpyA)]2+ acceptors provided R0 values ranging from 13.9 to 21.2 [A]. Analysis of time-resolved tmFRET data recovered donor-acceptor distance distributions and maltose-dependent changes consistent with structural predictions. At the 237/295 site pair, both copper and iron acceptors resolved the maltose-dependent decrease in distance. At the 309/322 site pair, [Cu(bpyA)]2+ did not appreciably resolve the distance increase, whereas adding NTA shifted R0 toward shorter distances and enabled detection of an increase from 12.1 to 14.4 [A]. These results demonstrate that a single bpyA-labeled protein preparation supports multiple tmFRET acceptors, providing a cysteine-independent acceptor-labeling strategy with adjustable distance sensitivity.

Fluorescent proteins (FPs) are the workhorse of modern-day cellular imaging and biosensing. However, rationally predicting and engineering the photophysical properties of FPs remain challenging, as these are governed by complex three-dimensional microenvironments and post-translational modifications. Here, we present a novel surface-mutation-guided engineering strategy combined with iterative rounds of rational mutagenesis to improve key photophysical properties of red fluorescent proteins (RFPs). This approach generated two bright, monomeric RFP variants from the mFruits lineage, mCranberry and mCranberry2, with quantum yields of 85% and 80%, respectively. Strategic incorporation of charged amino acid residues on the protein surface enhanced fluorescence quantum yield without compromising photostability. Comprehensive photophysical characterization and live-cell microscopy demonstrate enhanced performance of these variants and establish their utility as robust probes for fluorescence imaging. However, the exact mechanism of such improvements is subject to further investigation. Collectively, this work introduces a strategy for optimizing fluorescence properties and provides a framework that may be broadly applicable to other pigment-protein systems such as genetically encoded metal-ion indicators and photosynthetic light-harvesting complexes.

Lysine-directed covalent liganding offers a promising strategy to expand the covalently ligandable proteome beyond cysteine. However, broadly applicable and selective lysine-reactive scaffolds remain limited. Here, we designed SCL-alkyne, a simplified azaphilone probe derived from the natural product sclerotiorin (SCL), for the global profiling of lysine reactivity in bacteria. SCL-alkyne displayed high intrinsic selectivity for lysine in vitro and in live Escherichia coli cells. In situ proteome profiling revealed that 84% of engaged sites belong to lysine residues and the protein N-terminus. Reactivity mapping provided an inventory of highly reactive sites showing that only a limited subset of reactive lysines is located in functional regions of proteins. Analysis of proteins enriched via the SCL-alkyne probe unraveled bacterial chemotaxis as a major target pathway which was confirmed by an impaired chemotactic response upon treatment of bacteria with SCL-alkyne. Competitive profiling of three structurally related azaphilone natural products identified 60 protein targets belonging to pathways related to iron-sulfur cluster assembly and antioxidant function. This study not only highlights the diverse modes-of-action of natural product azaphilones, but also establishes its core scaffold as effective lysine-reactive chemotype for identifying ligandable lysines.

The incidence of infections caused by multidrug-resistant bacterial agents has increased at an alarming rate worldwide. With the aim of identifying novel antimicrobial peptides (AMPs), we screened bacteria isolated from various environmental samples. Our hypothesis was that the sequence space of natural AMPs belonging to known AMP classes is far from saturation. We used a classical pipeline of bacterial culturing, overlay assays, extraction and fractionation to purify AMPs for identification. Using LC/MS analysis, the structure of AMPs isolated from a strain of Paenibacillus was narrowed down and a new subclass of polymyxins was uncovered. These harbor three aliphatic residues within the cyclic C-terminal, and in certain cases Ser replaces Thr at position A2. Four discrete polymyxins fitting the new class, but not identical to the natural polymyxins were synthesized and tested against 29 human pathogenic bacteria. Each displayed an antibacterial spectrum different from that of colistin, with the best candidate surpassing it in potency against ten bacterial strains, but underperforming against three others. These results demonstrate that current screening of natural bacterial isolates can still permit the design of novel antimicrobial peptide variants without the substantial threat of diminishing returns.

Constrained macrocyclic peptides can engage protein surfaces that resist both small molecules and biologics but producing them at library scale still depends on chemical synthesis and macrocyclisation workflows that are slow, specialised and hard to parallelise. We describe a recombinant pipeline for disulphide-stapled cyclic peptides, compatible with 96 well plates, that lowers this barrier. Peptide-encoding sequences are introduced by extension PCR and in vivo assembly (IVA) cloning at about $2 per variant, expressed as small ubiquitin-like modifier (SUMO) fusions secreted to the oxidising periplasm, where the intramolecular disulphide forms spontaneously, and purified by immobilised metal-affinity chromatography (IMAC) with analytical size-exclusion chromatography (SEC). The route from a list of designs to characterised material takes about one week, much of it unattended. Applied to a 60-design library across nine protein targets, the pipeline gave purified products for all 60 designs, at a median total soluble yield of approximately 135 micrograms per 6 mL culture (range 9 to 261) that varied by target. Ellman's assay across 38 designs showed disulphide-formation failure to be sequence-driven and uncorrelated with yield, detecting a failure mode invisible to chromatography. Surface plasmon resonance confirmed target binding and showed that the recombinant SUMO fusion constructs retained affinities comparable to their corresponding peptide-only forms. Complementary to general-purpose recombinant platforms, the pipeline enables higher-throughput disulphide macrocycle screening and is amenable to partially or fully automated workflows.

The enzymatic oxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA) is a key step in the sustainable production of polyethylene furanoate (PEF), yet the final oxidation of 5-formyl-2-furancarboxylic acid (FFCA) to FDCA remains the principal bottleneck for industrial application. Although hydroxymethylfurfural oxidases (HMFOs) can catalyze this transformation, naturally occurring enzymes with high FFCA-oxidizing activity remain largely unexplored. Here, we present an iterative computational bioprospecting campaign to identify natural HMFOs with improved activity toward this rate-limiting reaction. Across four successive rounds, the computational workflow evolved from conservative homology-based screening to large-scale sequence mining assisted by machine learning, with each iteration redesigned according to the principal limitations identified through molecular simulations and experimental characterization. This adaptive strategy led to the discovery of several natural HMFOs with substantially improved FFCA oxidation activity, including candidates whose performance under the tested conditions approached that of the engineered benchmark 8BxHMFO. Beyond the identification of promising biocatalysts, the study demonstrates the value of continuous integration of computational prediction and experimental feedback for refining enzyme discovery campaigns, and provides practical insights into balancing catalytic prioritization, sequence diversity, developability, and computational scalability in future simulation-guided bioprospecting efforts.

The dopamine D2 receptor is a principal target of antipsychotic and antiparkinsonian drugs, yet well-characterized fluorescent ligands for the D2 receptor remain scarce compared with its relative, the D3 receptor. Here we show that fluorescent ligands developed for the D3 subtype can be repurposed as tools for studying the D2 receptor pharmacology. Using a machine-learning-assisted, live-cell fluorescence microscopy assay, we screened twelve fluorescent probes and identified eight ligands that bound specifically to the D2 receptor at 10 nM. Four ligands exhibited classical saturable binding, yielding Kd in the nanomolar range, whereas the remaining probes displayed unsaturated, time-dependent binding profiles. Kinetic analyses revealed substantial differences in association and dissociation behavior, highlighting the importance of time-resolved measurements. Competition binding with dopamine and haloperidol yielded affinity estimates consistent with literature values. These results expand the toolkit of validated D2 receptor fluorescent probes and support cross-receptor repurposing as a strategy for fluorescent ligand development.

Advances in machine learning (ML) have enabled the de novo design of synthetic proteins and peptides tailored for specific structural or functional properties. As ML becomes a central tool in forward design, we suggest that its value extends beyond optimizing known objectives. It can also lead to what we term here in this work as silicon exaptation, revealing unexpected functional properties that were never part of the original design goals. This reflects a principle common in biology, that repurposes traits where features evolved for one role can later enable unforeseen functions. In this context, these accidental findings are not constrained by preconceptions but arise from latent function emergence and the algorithm's gradual drift, a new process that can co-evolves with human curiosity, intuition, and interpretive reasoning, which can be used to recognize unexpected patterns and transform them into new discoveries. In this study, we discovered a set of peptides through silicon exaptation, in which the ML pipeline was originally optimized for structural characteristics unrelated to any antimicrobial functionalities. The peptides were originally designed to adopt defined secondary structures in response to environmental stimuli, with no antimicrobial properties intended or included in the training objectives. We systematically evaluated the antimicrobial potential of these peptides using broth microdilution assays and membrane integrity tests, identifying several candidates with potent and selective antibacterial activity without detectable cytotoxicity toward mammalian cells.

Catalyst-controlled stereodivergent synthesis of acyclic molecules bearing multiple stereogenic centers remains a longstanding challenge in asymmetric catalysis, particularly through intermolecular radical C-C coupling. Here, we report the directed evolution of pyridoxal 5'-phosphate (PLP)-dependent biocatalysts for the stereodivergent synthesis of non canonical amino acids bearing either skipped , {gamma}-stereochemical dyads or contiguous , {beta}, {gamma}-stereochemical triads. Starting from a thermophilic tryptophan synthase {beta}-subunit, protein engineering generated complementary enzyme variants that selectively construct three of the four possible , {gamma}-stereoisomers through biocatalyst-controlled reversal of both remote {gamma}-stereochemistry and proximal -stereochemistry. These engineered enzymes catalyzed intermolecular radical C-C bond formation with a broad range of secondary alkyltrifluoroborates, affording , {gamma}-disubstituted amino acids in excellent yields and stereoselectivities. Furthermore, directed evolution enabled efficient {beta}-dehydroxylative radical coupling of the non-native threonine-derived aminoacrylate intermediate, providing amino acids bearing contiguous , {beta}, {gamma}-stereochemical triads with up to 96:4:<0.1:<0.1 d.r. and >99:1 e.r.. To further demonstrate the synthetic utility of this platform, the stereochemically enriched amino acids were transformed into valuable {gamma}-chiral amines through engineered PLP dependent tryptophan decarboxylases, establishing a dual PLP enzyme based biocatalytic cascade for the enantiodivergent synthesis of privileged amine scaffolds. Molecular dynamics simulations reveal that enzyme controlled {gamma}-stereoselectivity originates from active-site remodeling that modulates {pi}-{pi} interactions and steric contacts governing the facial selectivity of the radical addition step. This work expands the synthetic capabilities of radical biocatalysis by enabling catalyst-controlled stereodivergent assembly of complex acyclic stereochemical arrays and establishes engineered PLP enzymes as versatile catalysts for the synthesis of stereochemically complex amino acids and chiral amines.

Intrinsically disordered proteins, topologically complex protein surfaces, and biomolecular condensates regulate essential cellular processes but remain difficult to drug with conventional small molecules. Here, we present PepSpace, a physics-driven directed-evolution platform coupling residue-resolution coarse-grained molecular dynamics with a multi-objective genetic algorithm. PepSpace designs peptide binders by rewarding target engagement while penalizing peptide self-association and off-target binding. Evaluated alongside deep-learning generative models across multiple targets, PepSpace-designed peptides achieve predicted interaction strengths up to two orders of magnitude greater than machine-learning designs, while reproducing experimentally observed binding hierarchies and revealing distinct binding modes. For PIEZO1, PepSpace designs 25-mer peptides that preferentially engage a defined 30-residue intracellular beam epitope while suppressing interactions with the remainder 2,547-residues of the protein. For CHERP biomolecular condensates, dual-action peptides weaken native intermolecular contacts and destabilize the condensed phase. PepSpace provides a physically grounded framework for de novo peptide design against dynamic protein targets and biomolecular condensates, with explicit control over specificity, solubility, and collective interactions.

De novo-designed enzymes have recently achieved high catalytic activity and stereoselectivity while demonstrating exceptional thermostability in entirely novel protein scaffolds. Among these, -helical barrel protein scaffolds are attractive structures for biocatalysis due to their structural simplicity, high thermostability, and rationalizable sequence patterning. However, enabling major structural reengineering of these scaffolds while maintaining the structure, stability and catalytic activity while also improving soluble protein production remain major challenges and pose the fundamental question how engineerable a de novo backbone-sequence pair is. Here, we combine deep learning based and classic computational protein design to modify and optimize de novo -helical barrel biocatalysts. Using the previously reported six-helical barrel 6H5L as a model scaffold, AlphaFold2-guided RosettaRemodel enabled the design of a truncated variant, whose crystal structure closely matches the design model. Additional sequence-redesign using ProteinMPNN generated a variant with a tenfold increase of soluble protein yield in Escherichia coli. Biochemical, biophysical, and structural analyses showed that both variants retained the overall barrel architecture, high thermal stability, and catalytic activity for both purified protein and whole-cell systems. Detailed kinetic analysis on the variants showed both variation in kcat and Km, reflecting changes in catalytic turnover and substrate binding. Together, these approaches provide new insights and possibilities for the further engineering of functional de novo -helical barrels, their ability to withstand dramatically large sequence changes and their broader application in biocatalysis and biotechnology.