Small Motifs Research Articles

Synthesis of 3,3-Disubstituted Thietane Dioxides.

4-Membered heterocycles have been increasingly exploited in medicinal chemistry and, as small polar motifs, often show important influence on activity and physicochemical properties. Thietane dioxides similarly offer potential in both agricultural and pharmaceutical applications but are notably understudied. Here we report a divergent approach to 3,3-disubstituted thietane dioxide derivatives by forming carbocations on the 4-membered ring with catalytic Lewis or Brønsted acids. Benzylic tertiary alcohols of the thietane dioxides are coupled directly with arenes, thiols, and alcohols.

Substrate specificity of a branch of aromatic dioxygenases determined by three distinct motifs

The inversion of substrate size specificity is an evolutionary roadblock for proteins. The Duf4243 dioxygenases GedK and BTG13 are known to catalyze the aromatic cleavage of bulky tricyclic hydroquinone. In this study, we discover a Duf4243 dioxygenase PaD that favors small monocyclic hydroquinones from the penicillic-acid biosynthetic pathway. Sequence alignments between PaD and GedK and BTG13 suggest PaD has three additional motifs, namely motifs 1-3, distributed at different positions in the protein sequence. X-ray crystal structures of PaD with the substrate at high resolution show motifs 1-3 determine three loops (loops 1-3). Most intriguing, loops 1-3 stack together at the top of the pocket, creating a lid-like tertiary structure with a narrow channel and a clearly constricted opening. This drastically changes the substrate specificity by determining the entry and binding of much smaller substrates. Further genome mining suggests Duf4243 dioxygenases with motifs 1-3 belong to an evolutionary branch that is extensively involved in the biosynthesis of natural products and has the ability to degrade diverse monocyclic hydroquinone pollutants. This study showcases how natural enzymes alter the substrate specificity fundamentally by incorporating new small motifs, with a fixed overall scaffold-architecture. It will also offer a theoretical foundation for the engineering of substrate specificity in enzymes and act as a guide for the identification of aromatic dioxygenases with distinct substrate specificities.

Structural Basis for the Interaction Between Yeast Chromatin Assembly Factor 1 and Proliferating Cell Nuclear Antigen

Proliferating cell nuclear antigen (PCNA), the homotrimeric eukaryotic sliding clamp protein, recruits and coordinates the activities of a multitude of proteins that function on DNA at the replication fork. Chromatin assembly factor 1 (CAF-1), one such protein, is a histone chaperone that deposits histone proteins onto DNA immediately following replication. The interaction between CAF-1 and PCNA is essential for proper nucleosome assembly at silenced genomic regions. Most proteins that bind PCNA contain a PCNA-interacting peptide (PIP) motif, a conserved motif containing only eight amino acids. Precisely how PCNA is able to discriminate between binding partners at the replication fork using only these small motifs remains unclear. Yeast CAF-1 contains a PIP motif on its largest subunit, Cac1. We solved the crystal structure of the PIP motif of CAF-1 bound to PCNA using a new strategy to produce stoichiometric quantities of one PIP motif bound to each monomer of PCNA. The PIP motif of CAF-1 binds to the hydrophobic pocket on the front face of PCNA in a similar manner to most known PIP-PCNA interactions. However, several amino acids immediately flanking either side of the PIP motif bind the IDCL or C-terminus of PCNA, as observed for only a couple other known PIP-PCNA interactions. Furthermore, mutational analysis suggests positively charged amino acids in these flanking regions are responsible for the low micromolar affinity of CAF-1 for PCNA, whereas the presence of a negative charge upstream of the PIP prevents a more robust interaction with PCNA. These results provide additional evidence that positive charges within PIP-flanking regions of PCNA-interacting proteins are crucial for specificity and affinity of their recruitment to PCNA at the replication fork.

Structure-based mapping of the histone-binding pocket of KDM4D using functionalized tetrazole and pyridine core compounds

KDM4 histone demethylases became an exciting target for inhibitor development as the evidence linking them directly to tumorigenesis mounts. In this study, we set out to better understand the binding cavity using an X-ray crystallographic approach to provide a detailed landscape of possible interactions within the under-investigated region of KDM4. Our design strategy was based on utilizing known KDM binding motifs, such as nicotinic acid and tetrazolylhydrazides, as core motifs that we decided to enrich with flexible tails to map the distal histone binding site. The resulting X-ray structures of the novel compounds bound to KDM4D, a representative of the KDM4 family, revealed the interaction pattern with distal residues in the histone-binding site. The most prominent protein rearrangement detected upon ligand binding is the loop movement that blocks the accessibility to the histone binding site. Apart from providing new sites that potential inhibitors can target, the novel compounds may prove helpful in exploring the capacity of ligands to bind in sites distal to the cofactor-binding site of other KDMs or 2-oxoglutarate (2OG)-dependent oxygenases. The case study proves that combining a strong small binding motif with flexible tails to probe the binding pocket will facilitate lead discovery in classical drug-discovery campaigns, given the ease of accessing X-ray quality crystals.

SLiMAn 2.0: meaningful navigation through peptide-protein interaction networks.

Among the myriad of protein-protein interactions occurring in living organisms, a substantial amount involves small linear motifs (SLiMs) recognized by structured domains. However, predictions of SLiM-based networks are tedious, due to the abundance of such motifs and a high portion of false positive hits. For this reason, a webserver SLiMAn (Short Linear Motif Analysis) was developed to focus the search on the most relevant SLiMs. Using SLiMAn, one can navigate into a given (meta-)interactome and tune a variety of parameters associated to each type of SLiMs in attempt to identify functional ELM motifs and their recognition domains. The IntAct and BioGRID databases bring experimental information, while IUPred and AlphaFold provide boundaries of folded and disordered regions. Post-translational modifications listed in PhosphoSite+ are highlighted. Links to PubMed accelerate scrutiny into the literature, to support (or not) putative pairings. Dedicated visualization features are also incorporated, such as Cytoscape for macromolecular networks and BINANA for intermolecular contacts within structural models generated by SCWRL 3.0. The use of SLiMAn 2.0 is illustrated on a simple example. It is freely available at https://sliman2.cbs.cnrs.fr.

Late-stage meta-C-H alkylation of pharmaceuticals to modulate biological properties and expedite molecular optimisation in a single step.

Catalysed C-H activation has emerged as a transformative platform for molecular synthesis and provides new opportunities in drug discovery by late-stage functionalisation (LSF) of complex molecules. Notably, small aliphatic motifs have gained significant interest in medicinal chemistry for their beneficial properties and applications as sp3-rich functional group bioisosteres. In this context, we disclose a versatile strategy with broad applicability for the ruthenium-catalysed late-stage meta-C(sp2)-H alkylation of pharmaceuticals. This general protocol leverages numerous directing groups inherently part of bioactive scaffolds to selectivity install a variety of medicinally relevant bifunctional alkyl units within drug compounds. Our strategy enables the direct modification of unprotected lead structures to quickly generate an array of pharmaceutically useful analogues without resorting to de novo syntheses. Moreover, productive late-stage modulation of key biological characteristics of drug candidates upon remote C-H alkylation proves viable, highlighting the major benefits of our approach to offer in drug development programmes.

From interaction networks to interfaces, scanning intrinsically disordered regions using AlphaFold2

The revolution brought about by AlphaFold2 opens promising perspectives to unravel the complexity of protein-protein interaction networks. The analysis of interaction networks obtained from proteomics experiments does not systematically provide the delimitations of the interaction regions. This is of particular concern in the case of interactions mediated by intrinsically disordered regions, in which the interaction site is generally small. Using a dataset of protein-peptide complexes involving intrinsically disordered regions that are non-redundant with the structures used in AlphaFold2 training, we show that when using the full sequences of the proteins, AlphaFold2-Multimer only achieves 40% success rate in identifying the correct site and structure of the interface. By delineating the interaction region into fragments of decreasing size and combining different strategies for integrating evolutionary information, we manage to raise this success rate up to 90%. We obtain similar success rates using a much larger dataset of protein complexes taken from the ELM database. Beyond the correct identification of the interaction site, our study also explores specificity issues. We show the advantages and limitations of using the AlphaFold2 confidence score to discriminate between alternative binding partners, a task that can be particularly challenging in the case of small interaction motifs.

E. James Milner-White (1945-2023).

This is a short appreciation of the contributions made by E. James Milner-White to the field of protein structure, in particular his description of small hydrogen-bonded motifs.

Vimo - Visual Analysis of Neuronal Connectivity Motifs.

Recent advances in high-resolution connectomics provide researchers with access to accurate petascale reconstructions of neuronal circuits and brain networks for the first time. Neuroscientists are analyzing these networks to better understand information processing in the brain. In particular, scientists are interested in identifying specific small network motifs, i.e., repeating subgraphs of the larger brain network that are believed to be neuronal building blocks. Although such motifs are typically small (e.g., 2 - 6 neurons), the vast data sizes and intricate data complexity present significant challenges to the search and analysis process. To analyze these motifs, it is crucial to review instances of a motif in the brain network and then map the graph structure to detailed 3D reconstructions of the involved neurons and synapses. We present Vimo, an interactive visual approach to analyze neuronal motifs and motif chains in large brain networks. Experts can sketch network motifs intuitively in a visual interface and specify structural properties of the involved neurons and synapses to query large connectomics datasets. Motif instances (MIs) can be explored in high-resolution 3D renderings. To simplify the analysis of MIs, we designed a continuous focus&context metaphor inspired by visual abstractions. This allows users to transition from a highly-detailed rendering of the anatomical structure to views that emphasize the underlying motif structure and synaptic connectivity. Furthermore, Vimo supports the identification of motif chains where a motif is used repeatedly (e.g., 2 - 4 times) to form a larger network structure. We evaluate Vimo in a user study and an in-depth case study with seven domain experts on motifs in a large connectome of the fruit fly, including more than 21,000 neurons and 20 million synapses. We find that Vimo enables hypothesis generation and confirmation through fast analysis iterations and connectivity highlighting.

Composing recurrent spiking neural networks using locally-recurrent motifs and risk-mitigating architectural optimization.

In neural circuits, recurrent connectivity plays a crucial role in network function and stability. However, existing recurrent spiking neural networks (RSNNs) are often constructed by random connections without optimization. While RSNNs can produce rich dynamics that are critical for memory formation and learning, systemic architectural optimization of RSNNs is still an open challenge. We aim to enable systematic design of large RSNNs via a new scalable RSNN architecture and automated architectural optimization. We compose RSNNs based on a layer architecture called Sparsely-Connected Recurrent Motif Layer (SC-ML) that consists of multiple small recurrent motifs wired together by sparse lateral connections. The small size of the motifs and sparse inter-motif connectivity leads to an RSNN architecture scalable to large network sizes. We further propose a method called Hybrid Risk-Mitigating Architectural Search (HRMAS) to systematically optimize the topology of the proposed recurrent motifs and SC-ML layer architecture. HRMAS is an alternating two-step optimization process by which we mitigate the risk of network instability and performance degradation caused by architectural change by introducing a novel biologically-inspired "self-repairing" mechanism through intrinsic plasticity. The intrinsic plasticity is introduced to the second step of each HRMAS iteration and acts as unsupervised fast self-adaptation to structural and synaptic weight modifications introduced by the first step during the RSNN architectural "evolution." We demonstrate that the proposed automatic architecture optimization leads to significant performance gains over existing manually designed RSNNs: we achieve 96.44% on TI46-Alpha, 94.66% on N-TIDIGITS, 90.28% on DVS-Gesture, and 98.72% on N-MNIST. To the best of the authors' knowledge, this is the first work to perform systematic architecture optimization on RSNNs.

Keyboard fantasies by Georg Philip Telemann in the context of stylistic trends of the time

The relevance of the study. It consists of considering the keyboard fantasies TWV 33 by Georg Philipp Telemann in the context of the author's creative searches, as well as of the stylistic trends in German music of the first half of the eighteenth century. The main objective of the study is to identify the main features of Georg Philipp Telemann's compositional style on the example of his 36 fantasies for keyboard. The methodology includes biographical (to highlight the main stages of Telemann's life and work), historical (to find out the evolutionary processes in the culture of the first half of the eighteenth century), genre-stylistic and structural-functional (for a comprehensive analysis of musical works), comparative (to compare Telemann's fantasies with each other and with examples of the genre in the works of his predecessors and contemporaries). 
 Results and conclusions. The cycle of keyboard fantasies TWV 33 by Georg Philipp Telemann is considered as an example of the embodiment of a new stylistic trend in German music of the first half of the eighteenth century. The reflection in these works of the influence of the "mixed taste" trend prevailing at that time is emphasized. The main stages of the formation and development of Telemann's creative guidelines are traced and the factors that directly contributed to the formation of his compositional style are identified. 36 fantasies included in the collection and differentiated by the author into "Italian" and "French" are analyzed. The main differences in their compositional organization and stylistic manner are revealed. On the basis of a detailed analysis of Fantasy No. 28 from the third part, the conclusion is drawn that the composer has a perfect command of the French and Italian styles of writing and their organic combination within one work. The author also points to the reflection in Telemann's keyboard fantasies of the "gallant style" that was formed in the music of the first half of the eighteenth century as opposed to the counterpoint and polyphonic style of the late Baroque. The features of the gallant style in Telemann's cycle TWV 33 are manifested in the elegance of melodic lines richly decorated with melismatics, the involvement of song themes, the dominance of homophonic composition, transparent, mostly two-voice texture, and the syntactic structure of themes by combining a number of small contrasting motifs. It is emphasized that the eighteenth-century understanding of the gallant style as a synonym for the "free style" resonates with the main vector of the evolution of the fantasy genre — from the centuries-old tradition of creating plays of this type within the strict framework of counterpoint writing to the natural manifestations of creative imagination.

Streamlined construction of robust heteroprotein complexes by self-induced in-cell disulfide pairing

Biomolecules and their functional subdomains are essential building blocks in the creation of multifunctional nanocomplexes. Methyl-binding domain protein 2 (MBD2) and p66α stand out as small α-helical motifs with an ability to self-assemble into a heterodimeric coiled-coil, making them promising building units. Yet, their practical use is hindered by rapid dissociation upon dilution. In this study, novel fusion tags, MBD2 and p66α variants, were developed to covalently link during co-expression in E. coli SHuffle. Through strategic placement of cysteine at each α-helix's terminus, intracellular crosslinking occurred with high specificity and yield, facilitated by preserved α-helical interactions. This instant disulfide bonding in the oxidative cytoplasm of E. coli SHuffle efficiently overcame the need for inefficient in vitro oxidation and protein extraction prone to creating non-specific adducts and suboptimal bioprocesses. In contrast to their wild-type counterparts, the GFP-mCherry protein complex cross-linked by the fusion tags maintained the heterodimeric state even under extensive dilution. The fusion tags, when combined with the E. coli SHuffle system, allowed for the streamlined co-expression of a stable protein complex through self-induced intracellular cysteine coupling. The approach demonstrated herein holds great promise for producing multifunctional and robust heteroprotein complexes.

Prediction and design of protease enzyme specificity using a structure-aware graph convolutional network

Site-specific proteolysis by the enzymatic cleavage of small linear sequence motifs is a key posttranslational modification involved in physiology and disease. The ability to robustly and rapidly predict protease-substrate specificity would also enable targeted proteolytic cleavage by designed proteases. Current methods for predicting protease specificity are limited to sequence pattern recognition in experimentally derived cleavage data obtained for libraries of potential substrates and generated separately for each protease variant. We reasoned that a more semantically rich and robust model of protease specificity could be developed by incorporating the energetics of molecular interactions between protease and substrates into machine learning workflows. We present Protein Graph Convolutional Network (PGCN), which develops a physically grounded, structure-based molecular interaction graph representation that describes molecular topology and interaction energetics to predict enzyme specificity. We show that PGCN accurately predicts the specificity landscapes of several variants of two model proteases. Node and edge ablation tests identified key graph elements for specificity prediction, some of which are consistent with known biochemical constraints for protease:substrate recognition. We used a pretrained PGCN model to guide the design of protease libraries for cleaving two noncanonical substrates, and found good agreement with experimental cleavage results. Importantly, the model can accurately assess designs featuring diversity at positions not present in the training data. The described methodology should enable the structure-based prediction of specificity landscapes of a wide variety of proteases and the construction of tailor-made protease editors for site-selectively and irreversibly modifying chosen target proteins.

Direct Competition of ATCUN Peptides with Human Serum Albumin for Copper(II) Ions Determined by LC-ICP MS.

Copper is an indispensable biometal, primarily serving as a redox-competent cofactor in numerous proteins. Apart from preformed copper-binding sites within the protein structures, small peptide motifs exist called ATCUN, which are composed of an N-terminal tripeptide XZH, able to bind Cu(II) ions in exchangeable form. These motifs are common for serum albumin, but they are also present in a wide range of proteins and peptides. These proteins and peptides can be involved in copper metabolism, and copper ions can affect their biological role. The distribution of copper between the ATCUN peptides, including truncated amyloid-β (Aβ) peptides Aβ4-42 and Aβ11-42, which may be involved in Alzheimer's disease pathogenesis, is mainly determined by their concentrations and relative Cu(II)-binding affinities. The Cu(II)-binding affinity (log Kd) of several ATCUN peptides, determined by different methods and authors, varies by more than three orders of magnitude. This variation may be attributed to the chemical properties of peptides but can also be influenced by the differences in methods and experimental conditions used for the determination of Kd. In the current study, we performed direct competition experiments between selected ATCUN peptides and HSA by using an LC-ICP MS-based approach. We demonstrated that ATCUN and truncated Aβ peptides Aβ4-16 and Aβ11-15 bind Cu(II) ions with an affinity similar to that for HSA. Our results demonstrate that ATCUN motifs cannot compete with excess HSA for the binding of Cu(II) ions in the blood and cerebrospinal fluid.

Assessing Global-Local Secondary Structure Fingerprints to Classify RNA Sequences With Deep Learning.

RNA elements that are transcribed but not translated into proteins are called non-coding RNAs (ncRNAs). They play wide-ranging roles in biological processes and disorders. Just like proteins, their structure is often intimately linked to their function. Many examples have been documented where structure is conserved across taxa despite sequence divergence. Thus, structure is often used to identify function. Specifically, the secondary structure is predicted and ncRNAs with similar structures are assumed to have same or similar functions. However, a strand of RNA can fold into multiple possible structures, and some strands even fold differently in vivo and in vitro. Furthermore, ncRNAs often function as RNA-protein complexes, which can affect structure. Because of these, we hypothesized using one structure per sequence may discard information, possibly resulting in poorer classification accuracy. Therefore, we propose using secondary structure fingerprints, comprising two categories: a higher-level representation derived from RNA-As-Graphs (RAG), and free energy fingerprints based on a curated repertoire of small structural motifs. The fingerprints take into account the difference between global and local structural matches. We also evaluated our deep learning architecture with k-mers. By combining our global-local fingerprints with 6-mer, we achieved an accuracy, precision, and recall of 91.04%, 91.10%, and 91.00%.

DNA framework-engineered chimeras platform enables selectively targeted protein degradation

A challenge in developing proteolysis targeting chimeras (PROTACs) is the establishment of a universal platform applicable in multiple scenarios for precise degradation of proteins of interest (POIs). Inspired by the addressability, programmability, and rigidity of DNA frameworks, we develop covalent DNA framework-based PROTACs (DbTACs), which can be synthesized in high-throughput via facile bioorthogonal chemistry and self-assembly. DNA tetrahedra are employed as templates and the spatial position of each atom is defined. Thus, by precisely locating ligands of POI and E3 ligase on the templates, ligand spacings can be controllably manipulated from 8 Å to 57 Å. We show that DbTACs with the optimal linker length between ligands achieve higher degradation rates and enhanced binding affinity. Bispecific DbTACs (bis-DbTACs) with trivalent ligand assembly enable multi-target depletion while maintaining highly selective degradation of protein subtypes. When employing various types of warheads (small molecules, antibodies, and DNA motifs), DbTACs exhibit robust efficacy in degrading diverse targets, including protein kinases and transcription factors located in different cellular compartments. Overall, utilizing modular DNA frameworks to conjugate substrates offers a universal platform that not only provides insight into general degrader design principles but also presents a promising strategy for guiding drug discovery.

Repression motif in HSF1 regulated by phosphorylation

The heat shock factor 1 (HSF1) is a transcription factor that itself is a sensor for stress and integrates various intrinsic or environmental stress sensing pathways. Thus HSF1 orchestrates the heat shock response (HSR) by translating these pathways into a distinct transcriptional program that aids the cells to cope with and adapt to proteotoxic stress. Although heavily researched the regulation of HSF1 activation is still not completely understood. A conserved reaction to stress is the hyperphosphorylation of the otherwise confined constitutive phosphorylated HSF1. Therefore, this stress specific phosphorylation is believed to be involved in the regulatory mechanism and hence, was and is focus of many studies, ascribing various effects to single phosphorylation sites. To gain additional insight into effects of phosphorylation, HSF1 carrying amino acid substitutions on up to 18 amino acids were tested for their transactivation potential on an HSR reporter plasmid. A pattern of eleven phosphor-mimicking and diminishing amino acid substitutions on well-known phosphorylation sites of HSF1 were introduced to produce transcriptional active [11 M(+)] or repressed [11 M(−)] phenotypes. It could be confirmed that heat activates HSF1 regardless of phosphorylation. Distinct cellular stress, obtained by chemical HSR inducers or mimicked by a constitutively active HSF1, showed clear differences in the activation potential of HSF1-11 M(+) and 11 M(−). Further refinement to the single amino acid level identified the S303/307 double-phosphorylation motif, wherein phosphorylation of S303 was sole responsible for the repressing effect. The effect could be reproduced in different cell lines and is not entirely based on degradation. A small repression motif could be dissociated from the HSF1 context, which is still capable of repressing the background transcription of a specifically designed reporter plasmid. Taken together these results indicate, that besides already described mechanisms of pS303/307 mediated repression of HSF1 activation, an additional mechanism repressing the transcriptional output of the entire HSE containing promoter is mediated by this small repressive motif.

More than just pattern recognition: Prediction of uncommon protein structure features by AI methods

The CASP14 experiment demonstrated the extraordinary structure modeling capabilities of artificial intelligence (AI) methods. That result has ignited a fierce debate about what these methods are actually doing. One of the criticisms has been that the AI does not have any sense of the underlying physics but is merely performing pattern recognition. Here, we address that issue by analyzing the extent to which the methods identify rare structural motifs. The rationale underlying the approach is that a pattern recognition machine tends to choose the more frequently occurring motifs, whereas some sense of subtle energetic factors is required to choose infrequently occurring ones. To reduce the possibility of bias from related experimental structures and to minimize the effect of experimental errors, we examined only CASP14 target protein crystal structures determined to a resolution limit better than 2 Å, which lacked significant amino acid sequence homology to proteins of known structure. In those experimental structures and in the corresponding models, we track cis peptides, π-helices, 310-helices, and other small 3D motifs that occur in the PDB database at a frequency of lower than 1% of total amino acid residues. The best-performing AI method, AlphaFold2, captured these uncommon structural elements exquisitely well. All discrepancies appeared to be a consequence of crystal environment effects. We propose that the neural network learned a protein structure potential of mean force, enabling it to correctly identify situations where unusual structural features represent the lowest local free energy because of subtle influences from the atomic environment.

Distinct Non-conservative Behavior of Dissolved Organic Matter after Mixing Solimões/Negro and Amazon/Tapajós River Waters.

Positive and negative electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry and 1H NMR revealed major compositional and structural changes of dissolved organic matter (DOM) after mixing two sets of river waters in Amazon confluences: the Solimões and Negro Rivers (S + N) and the Amazon and Tapajós Rivers (A + T). We also studied the effects of water mixing ratios and incubation time on the composition and structure of DOM molecules. NMR spectra demonstrated large-scale structural transformations in the case of S + N mixing, with gain of pure and functionalized aliphatic units and loss of all other structures after 1d incubation. A + T mixing resulted in comparatively minor structural alterations, with a major gain of small aliphatic biomolecular binding motifs. Remarkably, structural alterations from mixing to 1d incubation were in essence reversed from 1d to 5d incubation for both S + N and A + T mixing experiments. Heterotrophic bacterial production (HBP) in endmembers S, N, and S + N mixtures remained near 0.03 μgC L-1 h-1, whereas HBP in A, T, and A + T were about five times higher. High rates of dark carbon fixation took place at S + N mixing in particular. In-depth biogeochemical characterization revealed major distinctions between DOM biogeochemical changes and temporal evolution at these key confluence sites within the Amazon basin.

Evolutionary Stability of Small Molecular Regulatory Networks That Exhibit Near-Perfect Adaptation.

Large-scale protein regulatory networks, such as signal transduction systems, contain small-scale modules ('motifs') that carry out specific dynamical functions. Systematic characterization of the properties of small network motifs is therefore of great interest to molecular systems biologists. We simulate a generic model of three-node motifs in search of near-perfect adaptation, the property that a system responds transiently to a change in an environmental signal and then returns near-perfectly to its pre-signal state (even in the continued presence of the signal). Using an evolutionary algorithm, we search the parameter space of these generic motifs for network topologies that score well on a pre-defined measure of near-perfect adaptation. We find many high-scoring parameter sets across a variety of three-node topologies. Of all possibilities, the highest scoring topologies contain incoherent feed-forward loops (IFFLs), and these topologies are evolutionarily stable in the sense that, under 'macro-mutations' that alter the topology of a network, the IFFL motif is consistently maintained. Topologies that rely on negative feedback loops with buffering (NFLBs) are also high-scoring; however, they are not evolutionarily stable in the sense that, under macro-mutations, they tend to evolve an IFFL motif and may-or may not-lose the NFLB motif.