As the only component that contacts the ground and rock, the hooves of blue sheep may play a crucial role in their excellent climbing abilities. In this study, we used a combination of techniques, including scanning electron microscopy, infrared spectroscopy and nanoindentation, to characterize the surface morphology, structure, material composition, and mechanical properties of blue sheep hoof and investigate the potential contributions of these properties to the establishment of passive contact stability. Straight and curled microscopic lamellar morphology were found on the hoof surfaces. The cross section of the hoof revealed four layers, and each layer had a unique structure. Finite element analysis was employed to verify that the surface morphology and microstructure effectively contributed to the slip resistance and impact cushioning, respectively. Analyses of the energy and infrared spectra showed that the organic and inorganic substances in different regions of the hoof had similar components but different contents of those components. The hoof was mainly composed of keratin. From the outside to the inside, gradients in both the modulus and hardness were observed. These factors help the hoof alleviate high impact strengths and increase contact stability. These findings further our understanding of the unique mechanism of blue sheep hoof and may help in the development of novel biomimetic materials and mechanical components with enhanced friction and contact stability properties. Pseudois nayaur), a relatively widely distributed mountain ungulate, have cloven hooves and an excellent ability to scamper up precipitous cliffs (even when the slope is ∼90°)  . As the is ∼90°) .Over the past few decades, a large number of studies have been conducted on a variety of topics related to blue sheep, such as determining the effects of human activity on blue sheep , investiAlthough previous studies have greatly improved our understanding of the functions of goat and blue sheep hooves due to the similarities in the hooves of these two mountain ungulates, little is known about the microstructure, mechanical properties, or surface morphology of blue sheep hooves and the potential effects of these parameters related to biological functions such as contact stability, which may provide critical information for the development of novel biomimetic robot feet with enhanced properties such as anti-slip, impact cushioning and impact resistance.The objective of this study was to investigate the microstructure, surface morphology and mechanical properties of a blue sheep hoof using field-emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDS), Fourier transform infrared (FTIR) spectroscopy, nanoindentation and finite element (FE) simulations and to correlate these structural and material properties to the passive contact stability function of the hoof.This study was approved by the Institutional Review Board Committee of Jilin University, Changchun, China (No. 20170925).Blue sheep are under second-class national protection in China, and both the hunting and trading of blue sheep are prohibited. Hooves of a dead blue sheep were donated by a local botanical garden strictly for scientific research. The sheep was 4 years old and had no musculoskeletal disease. The stratum corneum of hooves were fully formed. The left front hoof and left hind hoof of the blue sheep were removed with a scalpel and soaked in a formalin solution for preservation.The surface morphology of the blue sheep hoof was characterized with a stereomicroscope . The lateral surfaces of the front and rear hooves were observed, and the observed areas of these hooves were divided into 15 regions, as shown in The microstructure, material composition and surface morphology of the blue sheep hooves were characterized using FESEM , which is equipped with EDS . Separate samples for surface morphology and for cross-sectional microstructure observation were cut from the hooves. The samples used for surface morphology observations were taken from the blue sheep’s front and rear hooves. The samples cut from the front hoof were taken from regions B, K, D, E, M, and L, and the samples cut from the hind hoof were taken from regions G, N, J, I, O, and P, as shown in The samples were ultrasonically cleaned in distilled water for 20 min, and then the samples were placed in a −86°C ultra-low freezer  for 6 h and dried in a freeze drier  for 24 h. All samples were mounted on aluminum sample holders and coated with 10 nm of gold in a sputter coater . The samples were measured in secondary electron mode at accelerating voltages of 10 and 20 kV. In addition, the material composition of each sample was analyzed by the EDS module of the field-emission scanning electron microscope.The surface distribution of elements in the cross section of the stratum corneum was analyzed with an energy-dispersive spectrometer (X-MAX OXFORD INSTRUMENTS). The six samples were obtained from regions 1–6 see . The YOZ–1, and the scanned wavelength range was 7,800–350 cm–1. The FTIR spectra were acquired in attenuated total reflectance (ATR) mode. Attenuated total reflectance (ATR) mid-infrared (MIR) spectroscopy is a non-invasive technique for functional group analysis  was used to collect the infrared spectra of the stratum corneum from the blue sheep hoof to analyze the main chemical groups present. The highest resolution of the instrument is 0.5 cmanalysis . This teanalysis .Ten samples were taken from regions A–J of the front and rear hooves of the blue sheep, as shown in The tribological properties of the surface of blue sheep hooves at a microscopic level were experimentally determined. The friction coefficients at different positions on the surface of the blue sheep hooves were measured by a Nano Tribometer . During the experiment, the sample is placed at position A, and the friction material is placed at position B see . The motUnlike traditional uniaxial measurements, which provide information only on overall mechanical properties, nanoindentation can provide site-specific data regarding the modulus and hardness of a sample at the nano- or microscale. Therefore, to study the differences in mechanical properties between different layers of the blue sheep’s hoof, nanoindentation tests were conducted on the cross section of each layer from the outside to the inside.One sample was prepared for the nanoindentation test. The sample was taken from region E, as shown in The observed morphology is described in detail in the results section (Surface Morphology). However, to clearly describe the FE simulation, it must be noted that straight and curled lamellar morphologies see  were obsTherefore, two types of models, namely, the morphology model and structural model, and their contrast models were constructed. The surface of the morphology model had both straight and curled lamellar morphological features, whereas the contrast model had a smooth surface see . The strA 3D solid beam was used to simulate the ground with its bottom surface fully fixed. The interaction between the hoof side surface and the ground was defined to have a frictional coefficient of 0.6. The horizontal direction allowed arbitrary separation, sliding and rotation of the contact surfaces . The matStatistically significant differences in hardness and modulus strength among the different layers and friction coefficients of different regions were identified by using one-way analysis of variance (ANOVA). The statistical significance level for the ANOVA was assumed to be 0.05.To describe the cross-sectional structure, a coordinate system is established see . The XOYWhen SEM was performed, each element was synchronously mapped by EDS. As shown in To study the distribution of elements in the stratum corneum from front to back, EDS analysis was carried out on a cross section from the middle of the stratum corneum. The sampling location is shown in –1, and this peak is broadened by the superposition of the −OH stretching vibration absorption peak of groups involved in hydrogen bonding and the stretching vibration absorption peak of −NH. Two absorption bands for the stretching vibrations of C–H exist at ∼2,918 and 2,880 cm–1. There are two absorption peaks at ∼2,961 and 2,874 cm–1, and these correspond to the anti-symmetric CH stretching vibration and the symmetric CH stretching vibration of −CH3 groups. The absorption peak at ∼1,655 cm–1 is the amide I CO stretching vibration, the absorption peak at 1,520–1,541 cm–1 is the amide II CN stretching vibration and NH in-plane deformation vibration, and the absorption peak at ∼1,238 cm–1 is the amide III CN stretching vibration and NH in-plane deformation vibration. The three amide absorption bands in these spectra indicate that the secondary amides in the keratin in these samples are all involved in hydrogen bonding. The absorption peak at ∼1,387 cm–1 is the C–H bending vibration and −CH3 symmetric deformation vibration, and the absorption peak at ∼1456 cm–1 is the CH2 bending vibration and −CH3 deformation vibration.In –1) relative to those of the other three layers, showing a cysteine-rich composition (–1) indicates the oxidation of cystine, which could be due to the influence of the stratum corneum preservation method (position . The pren method . Accordin method .P = 0.005), as shown in The friction coefficients (FCs) of all the samples are shown in The hardness values of layer a, layer b, layer c, and layer d are 0.35 ± 0.14 GPa, 0.26 ± 0.08 GPa, 0.27 ± 0.09 GPa, and 0.33 ± 0.09 GPa, respectively see . The modF = ma, where F = Ft−f. Moreover, the displacement is S = 1/2 at2. Thus,According to Newton’s second law, Ft is the tension applied along the Y-axis, f is the friction between the hoof side surface and the ground, S is the displacement of the smooth model and the bionic model, and t is the simulation time. The displacement, S, is negatively correlated to the frictional force, f.where –1 mm and 3.56 × 10–1 mm, respectively, which confirms that the surface morphology contributes to the anti-slip function and improves the contact stability of the blue sheep hoof.In structural model A, the maximum von Mises stress is 29.8 MPa, and the minimum value is 0.23 MPa. The stress is mainly concentrated at the ends of layer d (inclined layer), and the maximum stress on the innermost stressed bone is 0.53 MPa see .In structural model B, the maximum stress is 31.7 MPa, and the minimum value is 0.54 MPa. The stress is primarily concentrated at the ends of layer d (inclined layer), and the maximum stress on the innermost stressed bone is 0.62 MPa see .In the unstructured model, the maximum stress is 8.8 MPa, and the minimum value is 0.31 MPa. The stress is predominantly concentrated at the corresponding position of layer c in the biological model, and the maximum stress on the innermost stressed bone is 0.59 MPa see .Previous studies have found that blue sheep have excellent climbing abilities. During climbing, the hoof is the only part to contact the ground and rock. Therefore, the hoof of a blue sheep must offer both impact resistance and slip resistance to ensure stable contact between the hoof and the ground or rock. This study experimentally characterized the microstructure, surface morphology, material composition and mechanical properties of blue sheep hooves to analyze the potential contributions of these properties to establishing passive contact stability. The stereomicroscope results show that regions 1, 4, 7, 8, and 9 of the front and rear hooves have longitudinal stripes. Combined with the measured FCs, these results suggest that the FCs of the areas with stripes are significantly higher than those of the areas without stripes in both the transverse direction and longitudinal direction. Therefore, the stripe morphology on the surface of the stratum corneum of the blue sheep hooves could contribute to the anti-slipping ability of the hoof. Microscopic straight and curled lamellar morphological features were also observed on the surface of the blue sheep’s hooves, and such features had not been reported in previous studies of goat hooves. These lamellar morphologies may affect the interactions between the hooves and rocks. The surface morphology helps increase the friction between the hoof and the rock in a manner similar to the spines on a desert locust’s foot, which increases friction by penetrating the ground or the hollow of the rock. Previous experimental and modeling investigations have shown that a desert locust reduces slippage by adjusting the angle of the spines and using the spines to puncture the concave surface of the ground or rock surface . The expThe microstructures of cross sections of hooves were obtained in the study. The stratum corneum of the sheep’s hooves is a four-layer structure, wherein layer a (the outermost layer) consists of many densely stacked sublayers, layer b consists of crossed fiber bundles, and layers c and d are composed of fiber bundles that are symmetrically distributed. To the best of our knowledge, this structure has not been reported in the stratum corneum of the hooves of other artiodactyls. Stratified structures have been found in equine hooves  and sheeThe infrared spectra showed that the four-layer structure of the sheep’s hooves contained keratin, which has been widely found in the wool, hair, hooves and horns of mammals as well as bird feathers, beaks and reptilian claws. Previous studies have shown that keratin has remarkable mechanical efficiency because of its relatively high fracture toughness and Young’s modulus, and is much lighter than other highly mineralized biological materials, such as bone and teeth . TherefoSimilarly, both blue sheep hooves and goat hooves have hard stratum corneum on the sides and soft tissue in the center of the bottom. But the tratum corneum surface of blue sheep hoof has lamellar and striated morphology, which has not been reported for goat so far. Meantime, layered structures were found in cross section of blue sheep hoof, while previous studies showed the goat hoof has inclined tubule structure inside . Those dNanoindentation measurements of the different layers in the cross section of the hoof show that both the modulus and hardness exhibit a high-low-high-low distribution trend from layer a to layer d. Similarly, the modulus and hardness in sheep hooves, crab claws, snapping shrimp claws and keratinous cattle horns also exhibit gradient variations . Layer aHI) and modulus (EI) of a biological material is a key parameter that serves as a proxy for the ratio between irreversible and reversible deformation in the contact zone during indentation and the material’s yield strength (IE) was determined as follows:A recent study reported that the ratio between the indentation hardness  of the material was obtained by the following equations:Moreover, the yield strength (σA1 is a constant depending on the indenter geometry and the yield criterion (for a Berkovich tip and the von Mises yield criterion, A1≈ 2.75; β = 19.7; IE, from layer a to layer d are 0.078, 0.067, 0.065, and 0.082. All of the obtained IE values are within the range of 0.01–0.1, which is the range previously determined for biological materials (where aterials . The yieTo verify the possible impact resistance function of the layered structure and gradient modulus, FE simulations of the biological model and the comparison model were conducted in this study.The FE simulation results suggested that the hard-soft coupling of structural model A can reduce the stress transmitted to the bone during impact see . TherefoStructural model B can reduce the stress on the internal skeleton and change the distribution of stress (as shown in Based on the analysis above, it can be concluded that the hoof cuticle can passively contribute to anti-slip and cushion of blue sheep during climbing, based on the smart combination of the micro surface morphologies, special structures of cross section and material composition. This may inspire the innovative bionic design of ground-contacting components, such as robot foot for working with irregular terrains, insoles and athletes’ shock-absorbing sneakers, etc. Also it can supply new ideas to develop biomimetic functional materials with enhanced friction, toughness and cushion properties, which can be used in packaging, structure members, functional surfaces, and so on.The study characterized the surface morphology, cross-sectional structure, material composition and mechanical properties of the stratum corneum of blue sheep’s hoof, and the contributions of these parameters to establishing contact stability and the reliability of blue sheep’s hoof were proposed. The main findings and conclusions are as follows:•Three different types of morphologies, including stripe, straight and curved, were observed in the blue sheep’s hooves. These morphologies can improve the friction properties of the contact interface between the hoof surface and the rock or ground. Therefore, these features help enhance the anti-slip performance of the hoof as well as the contact stability during climbing.•The different layers and material compositions in the cross section of the hoof constitute a soft-hard-soft-hard structure, which can effectively attenuate the strength of the contact force, absorb shock energy, and ensure the contact reliability and stability when the hoof collides with the ground or rock.•Based on the above, it can be concluded that the hoof stratum corneum passively contributes to the contact stability of blue sheep during climbing based on the elegant combination of the micro surface morphologies, the unique cross-sectional structures and the material composition. These results may further our understanding of the structure and composition of natural composites and may also inspire the development of new materials or mechanical components with enhanced properties.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.HK and XL were responsible for the experiments and manuscript preparation. JL, WL, and SZ participated in discussions and revisions. ZQ and LR worked as supervisors for all procedures.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


CTD and in that of full‐length wild‐type APOBEC3G.The APOBEC3 (APOBEC3A‐H) enzyme family is part of the human innate immune system that restricts pathogens by scrambling pathogenic single‐stranded (ss) DNA by deamination of cytosines to produce uracil residues. However, APOBEC3‐mediated mutagenesis of viral and cancer DNA promotes its evolution, thus enabling disease progression and the development of drug resistance. Therefore, APOBEC3 inhibition offers a new strategy to complement existing antiviral and anticancer therapies by making such therapies effective for longer periods of time, thereby preventing the emergence of drug resistance. Here, we have synthesised 2′‐deoxynucleoside forms of several known inhibitors of cytidine deaminase (CDA), incorporated them into oligodeoxynucleotides (oligos) in place of 2′‐deoxycytidine in the preferred substrates of APOBEC3A, APOBEC3B, and APOBEC3G, and evaluated their inhibitory potential against these enzymes. An oligo containing a 5‐fluoro‐2′‐deoxyzebularine (5FdZ) motif exhibited an inhibition constant against APOBEC3B 3.5 times better than that of the comparable 2′‐deoxyzebularine‐containing (dZ‐containing) oligo. A similar inhibition trend was observed for wild‐type APOBEC3A. In contrast, use of the 5FdZ motif in an oligo designed for APOBEC3G inhibition resulted in an inhibitor that was less potent than the dZ‐containing oligo both in the case of APOBEC3G Design of ssDNAs as inhibitors of A3 enzymes with the aid of chemically modified nucleosides: Incorporation of 5‐fluoro‐2′‐deoxyzebularine into single‐stranded DNA in place of the target dC residue yielded more powerful inhibition of APOBEC3A and APOBEC3B—but not of full‐length APOBEC3G—than that observed with the non‐fluorinated 2′‐deoxyzebularine. APOBEC3 (A3) enzymes are important components of the innate immune system that protect against pathogens by catalysing the deamination of cytosine residues in the single‐stranded DNA (ssDNA) of the invading viral genome to form uracil residues Scheme  A.1 Thescytidine deaminase (CDA) share a similar structural topology (despite very low sequence identity), together with, more importantly, structurally homologous zinc‐containing active sites. These active sites each include a crucial Glu residue that functions as a general acid/base in the hydrolysis of cytosine.A3 enzymes and To date, no selective small‐molecule inhibitors of A3A or A3B have been reported. We recently developed the first rationally designed competitive inhibitor of A3 enzymes by incorporating a known inhibitor of CDA—2′‐deoxyzebularine .Ki=0.3 μm versus 2.3 μm, respectively).We propose that the inhibitory potential of ssDNAs can be further improved through the incorporation of potent inhibitors of CDA Herein, we report the first syntheses of the 2′‐deoxy forms of 3‐deazauridine and 5‐fluorozebularine . We also report the incorporation of these nucleosides into ssDNA and their evaluation as A3 inhibitors with the aid of our previously described NMR‐based1 a–c and Hoffer's chlorosugar2–5 a) and its incorporation into DNA, we followed previously described proceduresThe synthesis of modified nucleosides started from heterocycles 2 b) was obtained by use of a silyl modification of the classical Hilbert–Johnson reaction,3 (Supporting Information). Double recrystallisation from EtOH provided 2 b in 50 % yield. Cleavage of the toluoyl protecting groups was accomplished in MeOH/NH4OH to provide nucleoside 3 b,4 b by selective installation of the 4,4′‐dimethoxytrityl (DMT) group on the 5′‐end of the nucleoside followed by benzoyl protection of the 4‐hydroxy group of the nucleobase (81 % yield over three steps from 2 b). Phosphitylation of 4 b was performed under standard conditions with N,N‐diisopropylamino‐2‐cyanoethoxychlorophosphine and Et3N in CH2Cl2, in 84 % yield after silica gel column chromatography.The pure β‐anomer of 3dadU O‐toluoyl‐protected 5FdZ (2 c) in a good yield, although this product was contaminated with the α‐anomer (β/α 9:1). Use of a slow stepwise gradient of acetone (0→20 %) in CH2Cl2 allowed isolation of pure β‐anomer 2 c in 45 % yield. Deprotection was performed with saturated NH3 in MeOH, providing 5FdZ nucleoside 3 c, an analytical sample of which was obtained after preparative TLC on silica gel.Instead, it was necessary to use freshly distilled SnCl1H and 13C NMR spectra for individual forms as reported in the Supporting Information and shown in Figure 1H NMR spectrum and significant shifts in the H6 and C6 signals in the 1H and 13C NMR spectra as a result of a change in hybridisation at C6 suggest the formation of a “cyclic” nucleoside. The 1H,13C HMBC spectrum, which shows three‐bond correlations, was particularly helpful during the assignment . The synthesis of DNA oligos was performed with an automated DNA synthesiser and use of an increased coupling time for phosphoramidites 5 a–c, from 1.5 min for standard phosphoramidites to 5 min.We incorporated the modified nucleosides at the location of dC in the preferred A3 substrate motifs. A3B and A3A prefer the TCA motif .In the cases of oligos containing 3dadU and 3dadZ, cleavage from the solid support and deprotection of phosphates and nucleobases was accomplished in concentrated aqueous NH1H NMR spectroscopy in the presence of enzyme with and without inhibitors.Km values of the substrates with Ki values of inhibitors determined under identical conditions . The enzymes chosen—A3BCTD‐QM‐ΔL3‐AL1swap (hereafter simplified to A3BCTD‐AL1) and GST‐fused A3GCTD—were recombinantly expressed and purified from Escherichia coli. To compare the inhibitory effect of oligonucleotides between A3GCTD and full‐length A3G (flA3G) we used flA3G that was purified from human cells grown planktonicallyTo assess the inhibition of A3 enzymes directly, we used a previously described NMR‐based activity assay in which the DNA substrate deamination is monitored by CTD‐AL1 had no effect on the initial speed of deamination catalysed by A3BCTD‐AL1 .CTD‐AL1, the use of such concentrations would provide a weaker basis than our current strategy for the development of modified 3dadU‐oligos as inhibitors.Oligos containing 3dadZ and 3dadU in place of the target dC component in the preferred TCA‐recognition motif for A3B1 Figure . These oCTD‐AL1 by 5FdZ‐oligo was more powerful than that by dZ‐oligo under identical conditions. Previously, we had confirmed that dZ‐oligo is a competitive inhibitor of this enzyme.Ki) for 5FdZ‐oligo ; this was 3.5 times lower than the Ki of dZ‐oligo [(7.5±1.7) μm]. The overall inhibition effect was improved from 30‐fold (dZ) to nearly 100‐fold (5FdZ) if the apparent inhibition constants (Ki) of dZ‐ and 5FdZ‐containing oligos are compared with the Km of the ssDNA substrate 5′‐ATTT‐C‐ATTT (Km=200 μm). This means that 5FdZ‐containing oligos can potentially be used in cells in the low‐micromolar range to inhibit A3A and A3B. Thanks to the presence of the electron‐withdrawing F, the heterocycle component in 5FdZ is more activated towards the nucleophilic addition of H2O than its counterpart in the case of dZ . This protonation makes C4 in dZ more electrophilic and more susceptible to nucleophilic attack by OH−/H2O coordinated to the Zn2+, which converts dZ into a tetrahedral transition‐state analogue of cytidine deamination (Scheme The fact that 3dadZ does not inhibit A3BCTD) with the wild‐type sequence and full‐length A3G (flA3G)—we decided to test whether inhibition of A3GCTD is a good model for investigation of inhibition of two‐domain enzymes such as flA3G. Our studies were performed with two oligos: an A3G‐preferred CC5FdZ‐oligo in which the dC residue that is first deaminated by A3G was changed to 5FdZ, and the previously reported inhibitor CCdZ‐oligo.CTD, accurately translates to the overall inhibition of flA3G  μm, Figure CTD in comparison with CCdZ‐oligo [Ki=(53±10) μm],CTD‐AL1. Nonetheless, the fact that both CCdZ‐oligo and CC5FdZ‐oligo are inhibitors supports our strategy of targeting the catalytically active C‐terminal domains of A3 enzymes with our DNA‐based inhibitors as a means to inhibit full‐length enzymes.Interestingly, the CC5FdZ‐oligo [50 of 0.16±0.01 μm, compared to the equivalent dZ‐containing oligonucleotide sequence with an IC50 of 0.39±0.03 μm. These data are consistent with the A3BCTD‐AL1 data, because in X‐ray structures A3A and A3BCTD share all the residues surrounding the target cytosine residue.To investigate how a 5FdZ‐containing oligo would inhibit another wild‐type A3 expressed in human cells, we purified A3A from HEK293T and used it to perform our previously published fluorescence‐based activity assay.CTD and A3G should not be completely unexpected, because A3 family members differ strongly in their preferences for deamination of methylated cytosine residues in ssDNA.CTD.The fact that the trend of inhibition by dZ‐ and 5FdZ‐containing oligos is varied for A3A/A3BNevertheless, we have compared active sites of A3A, A3B and A3G to find possible differences in amino acids in proximity to the target cytosine residue. Such differences might explain the preferences of A3 enzymes towards various substrates and inhibitors.CTD‐AL1 complex with ssDNACTD and A3GCTD revealed that not only the zinc‐coordinating residues, but most of the residues in the active site close to the target cytosine moiety, are well conserved between these proteins. However, one residue in the substrate‐binding pocket differs: an isoleucine residue, Ile279/Ile96, in A3BCTD/A3A is a threonine residue (Thr283) in the corresponding position of the A3GCTD sequence. The side chain of Ile279 is ≈4.4 Å distant from the NH2 group of the target cytosine moiety in the inactive, substrate‐binding E255A mutant of A3BCTD (PDB ID: 5TD5). On the other hand, the Thr283 hydroxy group makes hydrogen bonds to a neighbouring Thr residue and to the main chain that forms part of the substrate/inhibitor binding pocket (PDB ID: 3V4J). This interaction might reduce the conformational flexibility needed to accommodate a substituent larger than hydrogen in the 5‐position of cytosine. We note that in the A3BCTD–AL1 structure the bound cytidine residue is tightly supported by Tyr313 (Tyr315 in A3GCTD), and that this in turn is buttressed by a conserved Trp residue (Trp285 in the case of A3GCTD) on the loop that, in the case of A3GCTD, is locked in place by hydrogen bonding to Thr283. Interestingly, mouse CDA (PDB ID: 2FR5) has Ile87 in a similar position in the three‐dimensional structure to that of Ile279 in A3BCTD . Thus, the Ile versus Thr substitution might play a role in the differences seen between 5FdZ and dZ inhibition of A3GCTD and A3BCTD‐AL1. AID, mouse APOBEC1 and mouse APOBEC3 catalyse the deamination both of 5FdC and of 5MedC less efficiently than for dC.CTD and of A3GCTD. The dynamics of the active sites might allow better accommodation of 5FdZ in A3A/A3BCTD than in A3GCTD. In any event, the substrate and inhibitor binding and the deamination mechanism vary subtly between A3s and CDAs. Examples of highly homologous enzymes with significantly different transition states are well‐established.Structural analysis of the A3BCTD, and A3GCTD, as well as on full‐length A3G. On the other hand, the 2′‐deoxyribosyl derivative of 3‐deazauridine, a previously described weak inhibitor of CDA, cannot inhibit A3 upon its incorporation into ssDNA under the conditions tested. Our results indicate that some correlation between CDA and A3 inhibition exists when CDA inhibitors replace the deamination‐susceptible cytidine moiety in the ssDNA sequence. Our results also highlight the importance of protonation of the N3 atom in dZ for its inhibitory behaviour. Noteworthy differences in inhibition profiles among different A3 enzymes observed here point to possibilities of obtaining highly specific A3 inhibitors, thereby supporting our approach to development of oligonucleotide‐based A3 inhibitors with the aid of chemically modified nucleosides, the structures of which can stall enzymatic cytosine deamination.The structures of modified nucleosides dZ and 5FdZ embedded in the otherwise identical DNA sequence determine the inhibitory effects on human A3A, A3BD.A.H. is a co‐founder, shareholder and consultant of ApoGen Biotechnologies, Inc.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.


Staphylococcus aureus (S. aureus) is one of the well-known agents causing atopic dermatitis (AD) in susceptible individuals, and Staphylococcus epidermidis (S. epidermidis) produces class I thermostable bacteriocins that can selectively kill S. aureus, suggesting protective roles against AD. There is a large need for developing precise therapies only to target S. aureus and not to harm the beneficial microbiome. On the agar well diffusion assay, live planktonic S. epidermidis showed clear zones of inhibition of S. aureus growth, but heat-killed cells and cell-free supernatants did not show this. These results would lead us to hypothesize that cytoplasmic bacteriocin from S. epidermidis will be a promising agent to inhibit S. aureus growth. Therefore, we have extracted a novel thermolabile cytoplasmic bacteriocin from S. epidermidis using trichloroactic acid (TCA)/acetone precipitation method after cell lysis with a SDS-containing buffer. These bacteriocin selectively exhibited antimicrobial activity against S. aureus and methicillin-resistance Staphylococcus aureus (MRSA), presenting no active actions against S. epidermidis, E. coli, and Salmonella Typhimurium. The extracted cytoplasmic bacteriocin compounds revealed several diffuse bands of approximately 40–70 kDa by SDS-PAGE. These findings suggest that these cytoplasmic bacteriocin compounds would be a great potential means for S. aureus growth inhibition and topical AD treatment. Staphylococcus aureus (S. aureus) with AD during active disease exacerbation has been well documented [S. aureus cell wall product, lipoteichoic acid (LTA), is shown to cause skin barrier damage by inhibiting the expression of epidermal barrier proteins filaggrin and loricrin. Dysbiosis contributes to the pathogenesis of AD by both detrimental effects from S. aureus and by loss of beneficial effects from some members of the microbiome. S. aureus secretes a pore-forming, phenol soluble modulins  which destroys the skin barrier and promotes skin inflammation as a fuel for dermal mast cells. AD patients frequently demonstrate increased levels of IgE and production of IgE specific for staphylococcal superantigens whose levels are positively correlated with AD severity [S. aureus or methicillin-resistant S. aureus (MRSA) induced human mast cell degranulation, but no effect had been found in heat-killed bacteria [Atopic dermatitis (AD) is a chronic inflammatory skin disease that manifests as dry skin and eczematous dermatitis with prominent itch . Despitecumented ,3,4,5. Aseverity ,7. In oubacteria .S. aureus. However, studies imply that topical antibiotic therapy would not be able to reduce bacteria counts and theoretically protect the normal skin microbiome [Current treatments for AD with the usage of topical steroids improve dermatitis due to their strong anti-inflammatory effect. However, skin thinning with long-term use and recurrence of dermatitis after discontinuation are highly associated . In ordecrobiome . Furthercrobiome . ConsequS. epidermidis) are probably in strong competition and may use a variety of strategies to overcome competitors (S. aureus). Bacteria from human microbiota have occasionally been found producing bacteriocins, antimicrobial substances that show an activity against closely related bacteria [S. epidermidis have bactericidal effects towards S. aureus. These antimicrobial peptide-producing coagulase-negative Staphylococcus (CoNS) strains are less frequent in atopic individuals, and the reintroduction of CoNS decreases S. aureus colonization. It has been reported that transplant of S. hominis and S. epidermidis strains secreting antimicrobial peptides (AMPs) is effective in controlling S. aureus overgrowth [S. epidermidis is an opportunistic pathogen holding an emerging risk factor in hospitals worldwide and is often difficult to eradicate due to its virulent strains producing a protective biofilm matrix [S. aureus targeting bacteriocin can become a valuable strategy for preventing S. aureus colonization.Multiple studies are currently focused on the manipulation of the skin microbiome to explore its therapeutic potential. The colonizing bacteria  and heat-stable class I lantibiotics. The low-molecular-weight (< 100 kDa) proteins are not observed in cell-free supernatant (CFS) from S. epidermidis ATCC12228. The majority of the identified proteins (~80%) were predicted to be cytoplasmic in this strain. This indicates that these proteins are expressed but not exported by S. epidermidis ATCC12228 [S. epidermidis ATCC12228 generated a clear zone of growth inhibition S. aureus ATCC 25923, but heat-killed cells and CFS did not show any activities on the agar well diffusion assay. These results revealed that this strain itself could not spontaneously secrete bacteriocins into the CFS at a level of sufficiency directly to inhibit S. aureus.TCC12228 ,17. We aS. epidermidis ATCC12228 by TCA/acetone precipitation method after cell lysis with SDS-containing buffer. We suggest that the application of cytoplasmic bacteriocin compounds in this strain is a safer and more effective therapeutic method than those of live S. epidermidis for the growth inhibition of S. aureus in AD. Herein, we describe the partial purification and characterization of the novel thermolabile cytoplasmic bacteriocin compounds from cell extract of the S. epidermidis strain. These bacteriocin compounds from S. epidermidis are a novel bacteriocin that have potential applications in management of AD.Thus, the aim of this study was to extract cytoplasmic bacteriocin compounds from S. epidermidis (ATCC12228 and NCCP14768) were screened for antimicrobial activity against S. aureus , E. coli and Salmonella Typhimurium by agar well diffusion assay. Both live planktonic S. epidermidis strains showed antimicrobial activity against all S. aureus strains displaying clear zones as a result of inhibition, but heat-killed cells did not show any activities [S. epidermidis strains observed inhibition zones for all S. aureus strains with the treatment of CFS only [S. epidermidis was detected against S. aureus ATCC25923 (11.1 ± 0.1 mm), S. aureus NCCP14780 (11.4 ± 0.2 mm) and MRSA ATCC33591 (10.6 ± 0.1 mm). No inhibition activity was observed in S. epidermidis, E. coli and Salmonella Typhimurium with the combined treatment of both live planktonic strains and CFS [S. epidermidis selectively kill S. aureus, including MRSA.tivities A. None oCFS only B. The in and CFS . The resS. epidermidis ATCC12228 at pH 5.0 (10.8 ± 0.1 mm) or 6.0 (10.4 ± 0.2 mm) present more prominent actions than that of pH 7.4 (8.6 ± 0.1 mm) against S. aureus or MRSA. No antimicrobial activity was observed on the CFS with overnight growth culture of S. epidermidis (The average pH of skin surfaces lies between 5.0 and 6.0. The effect of pH in culture medium on the production of the cytoplasmic bacteriocin compounds has been assessed. The inhibition zone of intra-cytoplasmic protein (IP) from dermidis .S. epidermidis grown on the agar well diffusion assay generated a clear zone of inhibition of S. aureus growth but not in CFS, it is necessary to isolate sufficient amounts of bacteriocin for the antimicrobial activity. Thus, we precipitated CFS by 10% TCA and precipitated proteins were re-suspended with solubilization solution. The precipitated proteins of CFS did not show any antimicrobial activity against all S. aureus on agar well diffusion assay, but the reference strain of Bacillus subtilis showed the antimicrobial activities toward S. aureus (data not shown).As S. epidermidis only have the antimicrobial activity against all S. aureus, but not CFS or precipitate proteins of CFS on the agar well diffusion assay. To answer this question, we performed the extraction of cytoplasmic proteins from dried S. epidermidis by TCA/acetone precipitation method after lysis by SDS-containing buffer.Inquiries came from where live planktonic S. aureus by agar well diffusion method. The production of a clear zone by growth inhibition was noted. The antimicrobial activity of cytoplasmic bacteriocin compounds were found to be stable after heating at 45 °C for 20 min. However, it seemed that it lost its antimicrobial activities on heating to 100 °C for 20 min  proteins from S. epidermidis ATCC 12228 are expressed but not exported into CFS [S. epidermidis by TCA/acetone precipitation method after lysis by SDS-containing buffer.Live planktonic into CFS . TherefoS. aureus by the agar well diffusion method. The production of a clear zone of growth inhibition was noted. The inhibition zone of intra-cytoplasmic protein (IP) from S. epidermidis ATCC 12228 at pH 5.0 or 6.0 was more prominent than pH 7.4 against S. aureus or MRSA [S. aureus growth on the agar well inhibition assay [The extracted cytoplasmic bacteriocin compounds were tested for antimicrobial activity against  or MRSA . The ski or MRSA . The pro or MRSA . The minon assay . Therefoon assay .S. epidermidis strains are thermostable class I lantibiotics [S. epidermidis can selectively kill bacterial pathogens such as S. aureus [S. epidermidis eliminates S. aureus by secreting 27 kDa serine protease (Esp), but non-inhibitory S. epidermidis  did not show the eliminating effect. At present, cytoplasmic protein export mechanisms suggest that some of the cytoplasmic proteins are released from dead cells which then remain attached and decorate the cell walls of bacteria, and the membrane vesicle (MV) type of nonclassical secretion mechanism is likely to be exploited by S. epidermidis, allowing the export of cytoplasmic proteins [S. epidermidis.Based on bacteriocin primary structure, molecular mass, heat stability and molecular organization, bacteriocins are divided into three classes. Class I lantibiotics are thermostable peptides of molecular weight below 5 kDa. Class II non-lantibiotics are thermostable bacteriocins of molecular weight below 10 kDa. Class III includes thermolabile bacteriocins of molecular weight above 30 kDa. The majority of bacteriocin from ibiotics ,6,21,22.. aureus ,24. Iwas. aureus  also havproteins ,26. Our S. epidermidis ATCC12228 strain were shown to selectively inhibit the growth of S. aureus. The result demonstrated that the extracted thermolabile cytoplasmic bacteriocin compound profiles by SDS-PAGE exposed several diffuse bands of approximately 40–70 kDa. Thus, the cytoplasmic bacteriocin compounds have potential topical applications in the management of AD.Putting together the extracted cytoplasmic bacteriocin compounds from the S. aureus ATCC25923 , S. aureus NCCP14780 , MRSA (ATCC33591), and the test strains were S. epidermidis (ATCC12228 and NCCP14768). Strain stocks were stored in freeze medium containing 30% (w/v) glycerol at −80 °C. An overnight starter culture of Staphylococcus was grown for 16 h in Tryptic Soy Broth  at 37 °C with constant agitation (160 rpm) to be used as an inoculum for the growth experiments. To obtain antimicrobial substances, the overnight culture was diluted 1:100 in Luria broth  and incubated at 37 °C with constant agitation (160 rpm) for 4–6 h [The Staphylococcal target strains used in this study were or 4–6 h .Cells were removed by centrifugation at 3500 rpm for 20 min and the supernatants were filtered with a membrane of pore size 0.45 μm, this was then referred to as CFS. The extracellular proteins in the supernatant were precipitated with 10% (w/v) TCA on ice at 4 °Covernight. The precipitates were harvested by centrifugation at 8000 x g for 20 min at 4 °C and washed with ice-cold acetone and dried at 37 °C. The dried protein pellets were stored at -20 °C until further use ,29.After harvesting test strains, cells were washed twice with PBS. The cells were re-suspended in 500 μl of SDS Lysis Buffer . The re-suspension mixture was thoroughly mixed and sonicated on ice  using sonicator . Cell debris was removed by centrifugation at 14,000 rpm  for 30 min. The supernatant containing proteins were precipitated with 10% (w/v) TCA on ice at 4 °C overnight. Finally, the precipitates were collected by microcentrifugation for 30 min at 4 °C and washed three times with ice-cold acetone and dried at 37 °C [S. aureus as the bacterial indicator.The susceptibility of partially purified cytoplasmic bacteriocin compounds to temperature was investigated through the agar well diffusion assay. To determine temperature stability, the purified bacteriocin compound was subjected to 45 °C or 100 °C for 20 minutes. Antimicrobial activity was then determined by carrying out the agar well diffusion assay with LB agar plates (1.5% agar) were overlaid with 4 ml LB soft agar (1%) having 100 µl of Staphylococcal target strain. Wells of 6 mm diameters were bored in agar plates. Precipitated proteins were re-suspended with 150 μl of solubilization solution (25 mM ammonium bicarbonate and 10 mM dithiothreitol). Then, 80 μl of the re-suspended bacteriocin compound from a different condition was transferred into separate wells. The plates were incubated at 37 °C overnight and the growth inhibition zones were measured .S. aureus on the agar well.Two-folded serial dilution of the cytoplasmic bacteriocin compounds was prepared . Then, 80 μl of each bacteriocins was transferred into separate wells. The plates were incubated at 37 °C overnight and the growth inhibition zones were measured. Interpretation of the minimum bactericidal concentration was done as the lowest concentration of the cytoplasmic bacteriocin compounds that did not show any visible inhibition zones of The molecular weight of partially purified bacteriocin compounds was determined by SDS-PAGE. The 30 µL sample was homogenized with 2% SDS and dithiothreitol (DTT) added to a little glycerin to increase the density. The sample was heated in boiling water for 5 min, then loaded onto gel along with molecular weight marker and mixed by using a Mini-Protean 4 cell system , 100 V and 20 mA in the separation gel at pH 8.3. The gel was stained with 50 mL of Coomassie brilliant blue R-250 (0.1 M) for 4 hours. The decolorizing solution was decolorized for 12 h until the electrophoresis band was clear .


Water in nature predominantly freezes with the help of foreign materials through a process known as heterogeneous ice nucleation. Although this effect was exploited more than seven decades ago in Vonnegut’s pioneering cloud seeding experiments, it remains unclear what makes a material a good ice former. Here, we show through a machine learning analysis of nucleation simulations on a database of diverse model substrates that a set of physical descriptors for heterogeneous ice nucleation can be identified. Our results reveal that, beyond Vonnegut’s connection with the lattice match to ice, three new microscopic factors help to predict the ice nucleating ability. These are: local ordering induced in liquid water, density reduction of liquid water near the surface and corrugation of the adsorption energy landscape felt by water. With this we take a step towards quantitative understanding of heterogeneous ice nucleation and the in silico design of materials to control ice formation. Heterogenous ice nucleation is a ubiquitous phenomenon, but predicting the ice nucleation ability of a substrate is challenging. Here the authors develop a machine-learning data-driven approach to predict the ice nucleation ability of substrates, which is based on four descriptors related to physical properties of the interface. However, it is still not understood why some materials are better than others when it comes to promoting ice formation4. Stated differently, robust connections between the physiochemical properties of materials and their potency as ice nucleating agents have yet to be established.There are few, if any, physical processes as ubiquitous as the freezing of water. It is of fundamental importance to the environment, technology, and biology. Generally when water freezes the initial nucleation event takes place at the surface of some foreign material. A remarkably broad variety of materials can act as nucleating agents, ranging from minerals7. Working with the hypothesis that effective ice nucleating agents should offer a template for ice, Vonnegut identified AgI as one such material. Specifically, he noted that the basal faces of AgI and ice Ih have lattice constants that fall within 1.5% of each other. Various studies have now shown that AgI is an effective ice nucleating agent and it is widely used to this day to seed clouds. However, counter-examples exist of materials that have an equally small lattice mismatch with ice and yet are ineffective ice nucleating agents, BaF2 being the most widely studied of these10. Thus, the lattice match alone is not a reliable or robust descriptor for ice nucleation ability. Beyond the lattice match, in the seventies Pruppacher and Klett11 introduced a set of requirements for effective ice nucleating agents. While some of these deal with macroscopic properties from an atmospheric chemistry viewpoint (insolubility and aerosol size requirement) the others  deal with microscopic characteristics. Pruppacher and Klett’s requirements are widely discussed. However, there are again many known exceptions to them and they do not easily lend themselves to quantitative comparisons between materials14.Attempts to understand this issue go back to at least the pioneering experiments of Vonnegut in the late 1940s and early 1950s20 as well as measurements of ice nucleation on materials of atmospheric relevance23, and more27. Computer simulations have also proven to be a powerful tool in providing insights into heterogeneous33 (and homogeneous38) ice nucleation, particularly at the atomic level. There have also been systematic trend studies focused on understanding the connections between nucleation rate or temperature with the lattice constant of the substrate, surface hydrophilicity, hydroxyl group structure and symmetry, and surface flexibility47. Various other influential surface characteristics such as charge-distribution48 or nano-texture49 have also been explored.The last decade or so has been a resurgence of interest in understanding and obtaining well-defined molecular-level information of heterogeneous ice nucleation. From an experimental point of view insights into ice formation on specific substrates have been obtained. These have included measurements that reveal atomic-level structural information on well-defined substrates51, and machine-learning (ML) approaches for the analysis of large data-sets, the prospect of understanding such complex relations is now in reach. This is the approach we take in the current study through the development of predictive ML models trained on molecular dynamics (MD) simulations of heterogeneous ice nucleation and random-structure searches of adsorbed water clusters and overlayers. From this, we identify four key descriptors that when used together can accurately predict the ice nucleating potency of solid substrates. This study deepens our understanding of what makes materials efficient ice nucleating agents and is a step toward a comprehensive and predictive set of descriptors which can be used in future screenings and to guide and interpret experiments.While these studies have significantly deepened understanding of heterogeneous ice nucleation, they have not led to a robust set of descriptors that can predict the ice nucleating (IN) ability for a diverse set of substrates. Even the question of how many different properties of a surface are relevant to ice nucleation is unclear. Building on the earlier work, methodological developments that enable the high throughput computational study of heterogeneous ice nucleation41, the OH-group patterns of ref. 47, graphitic surfaces with modulated water–substrate interaction strength similar to those used in ref. 43, graphene oxide as modeled in ref. 40, and several additional OH-group patterns with different symmetries from those reported in ref. 47. In total we have 900 substrates. On each system we perform cooling ramps and establish the temperature at which the liquid freezes, termed nucleation temperature Tn. Additional information on the simulations is given in the methods and Supplementary Note As a first step to identify the important factors for predicting the IN ability of substrates, we need to have suitable data. We acquire these by performing MD simulations of supercooled water in contact with a large set of structurally diverse model substrates. Our dataset comprises the 400 Lennard–Jones substrates of ref. 50. This has proven to be an extremely powerful model for understanding water and ice, see e.g., refs. 51. Its computational efficiency means that we have been able to screen 900 substrates for their nucleation ability. Such a broad study is barely conceivable with an atomistic model because of the computational cost of performing nucleation simulations with such models53. Thus, using an atomistic model would have severely limited the breadth of our study to only a small subset of substrates or to substrates with a very ice-like geometry such as AgI54. From such a study we would not have sufficient data diversity to identify robust descriptors for Tn. Thus, at present, a coarse-grained model such as mW is most appropriate for a broad screening study such as the current one. However, it is important to emphasize that mW does not capture effects such as polarization, water dissociation or hydrogen bond asymmetry. Understanding how these and other effects influence the conclusions reached in the current study—through simulations with more sophisticated water models—will make interesting work for the future.In this study we have used the mW model to represent water55. The scatterplot shown in Fig. Tn > 225 K) and substrates that are bad with Tn ≤ 225 K. Although there is a slight preponderance of good nucleating substrates in the small lattice mismatch regime, the correlation is clearly not good and there are many substrates which are effective ice nucleating agents and are poorly lattice matched to ice. In terms of the water–surface interaction strength no obvious connection with ice nucleating ability is found in the data. All other simple linear descriptors considered suffered similar problems and none was found to be an accurate descriptor for heterogeneous ice nucleation.As a first step to analyze our data we looked to see if the traditional lattice match descriptor could be used to explain the nucleation temperatures obtained. As an additional potential descriptor we also considered the water–monomer adsorption energy on the surfaces; this is considered a proxy for the hydrophobicity/hyrophilicity of the surfaceTn. After having measured Tn, a large set of physically motivated features  are calculated from separate simulations. We feed these data to a cluster-based feature selection approach. This reduces the number of features used in the model to only a handful, which enables a more in-depth analysis later on. We subsequently check their predictive power over Tn. Last, we employ state-of-the art feature interpretation techniques to gain physical understanding of the selected features. In the following we will call those selected features descriptors to distinguish them from the other calculated quantities. A more detailed overview of the approach is given in the Methods, Supplementary Note Tn from repeated cooling ramps on each system we have a natural uncertainty on Tn of ca. 6 K . This represents a lower bound for the best possible prediction  we can achieve.To help identify descriptors and obtain quantitative insight into heterogeneous ice nucleation we turned to a data-driven methodology. As summarized in Fig. Tn across our entire database. The expected error window can be approximated by ± the RMSE. Given that the range of nucleation temperatures observed in this work spans 70 K, this is mediocre performance at best. Indeed, since the typical experimental range of ice nucleation temperatures explored is about 35 K21, a prediction window of 25 K (±12.5 K) is not sufficiently accurate to make reliable quantitative predictions of the ice nucleating ability of substrates.We have already seen that analysis of the data based on linear descriptors is unsatisfactory. This is confirmed by testing a linear baseline model on our data (an elastic net model which is a version of linear regression). As shown in Fig. Tn. With this accuracy, substrates could be classified into several classes of nucleators to reliably distinguish good from bad ice forming substrates. We are not aware of any previous works that predicted the IN ability across a diversity of many different (out-of-training-set) substrates: The closest previous studies56 focused on more qualitative insight, and did not report quantitative metrics. A representative comparison of predicted and measured Tn for the best ML model is shown in the inset of Fig. R2 value of 0.856 indicates a good correlation between predicted and measured values. A common interpretation of this metric is that the model is able to explain  ~86% of the variance of Tn. We have also used dimensionality reduction to see whether good and bad nucleators are visually separated, which is indeed the case for our best ML model  the local ordering in liquid water; (ii) the local density reduction in the liquid near the surface; and (iii) the corrugation of the adsorption energy landscape felt by water. When these additional descriptors are taken into consideration, the prediction error is reduced by half, compared to just lattice match-based predictions alone. This result is encouraging and suggests that we can find the missing contributions that help to predict whether a substrate is good or bad at ice nucleation. We note that although different feature selection methods can yield slightly different results  and must be regarded as a distribution per feature.We have established that we can build predictive models (with a low number of descriptors) for the IN ability of different substrates. To extract physical insight and guide our interpretation of the black-box decision of the ML model we apply SHapley Additive exPlanations (SHAP)Tn). Whether a point is to the right or left of the graph indicates whether for this substrate the descriptor of that row caused a positive or negative impact on the value of the predicted Tn. We now discuss what we have learned from the SHAP analysis and the four key descriptors that have been identified.In Fig. Tn is the lattice match of the substrate with the basal face of ice, specifically the 2-dimensional in-plane lattice match. If we examine the SHAP values for this descriptor , θ is its rotational orientation relative to the surface normal, Nice is the number of water molecules in the ice crystallite, ri is the position of oxygen atom i at a given r0 and θ and rs are the closest substrate atom to water molecule i. If water molecule i does not have a substrate atom close by (threshold: 3.3 Å), then this water molecule is omitted from the calculation. By choosing different ice crystallites as reference we can probe the match to different faces of ice. Here, we have focused on the basal, prism and secondary prism faces of Ih and the (111) and (001) faces of Ic. The results for the generalized lattice match have been discussed previously and are shown in Fig. To capture also the match to other ice faces we calculated a generalized lattice match Ylm are spherical harmonics and θik and ϕik are the relative orientational angles between the molecule i and k. The sum goes over the Nb(i) neighbors of molecule i . For a given l we then compute the quantity for all possible values of m and store them in a vector l + 1 components. From this we calculate values lql according to:3 is a particularly good descriptor. For lq3, lower values indicate a tetrahedral environment, and as shown in Fig. 3 are predicted as good nucleators while mixed and higher values tend to be associated with poor nucleators.We find that the local order of the liquid is another important descriptor of a good ice nucleating substrate. Local order can be conveniently defined as:3 values appears as the sixth selected descriptor, with higher values being favorable for nucleation. We can interpret this in a practical sense: Since these statistics come from MD trajectories, an increased variability of lq3 indicates a higher fluctuation of ordered structures due to the influence of the substrate. This is consistent with our earlier suggestions that pre-critical fluctuations28 are indicative of the IN ability as well as the type of ice that will form. Thus, the variability of local ordering can be seen as a measure of pre-critical fluctuations, an aspect that could be studied by time-resolved experiments.Further, in addition to the mean, the standard deviation of lq59.We note that we have calculated the various order parameters from MD trajectories at the coexistence temperature for all substrates. This is interesting because the order within the liquid at the coexistence temperature can readily be computed with more sophisticated water and substrate models than those used here and it can also be established experimentally with techniques such as surface X-ray diffractionThe third descriptor appearing in Fig. 34 observed a similar trend with the density, presumably for the same physical reason. In addition, our result shows that the density reduction is also a vital component of good heterogeneous nucleators and could thus potentially be used to control heterogeneous nucleation.We are not aware of previous discussions of this microscopic density principle for heterogeneous ice nucleation, despite its straightforward physical basis. For homogeneous nucleation, Li and coworkersFrom all the substrates considered, we find substrates that both decrease and increase the average number of neighbors in the adjacent liquid. This means that the density descriptor could also be used to screen for both nucleation-promoting and nucleation-inhibiting (these may be overly dense at the interface) surfaces.Eads) play an important role as descriptors. However, unexpectedly it is not absolute values, but rather the diversity of possible adsorption energy values that is important. We computed these descriptors by performing a large set of random-structure searches for diverse ice-like structures on each substrate to characterize their Eads landscape. The statistics that appear as important entropy) and not absolute values (such as mean or median). We interpret this as the corrugation of the adsorption energy landscape being measured, with a less rugged environment  being more beneficial to IN. In Fig. Eads distribution.Finally, we find that adsorption energies . These are expected to differ from bulk near the interface, potentially carrying information about Lattice match of the substrate to a low-index face of ice;3, as well as pre-critical fluctuations (measured as the variability of lq3);Local tetrahedral ordering of the liquid near the surface, ideally measured with lqDensity reduction of the liquid near the surface;Corrugation of the adsorption energy landscape, with a smooth energy landscape preferred to a rugged one.In this work we started by establishing the IN ability of a large number of diverse substrates in contact with water. We then developed a data-driven approach based on machine-learning methods to identify descriptors for heterogeneous ice nucleation and established that the resulting models are indeed predictive. A key conclusion is that no single descriptor has been identified that can reasonably well predict the IN ability. For each of the important descriptors there are exceptions where they are not predictive if used on their own. However, by using the following four microscopic principles in conjunction, we can reliably predict the IN ability of a given substrate. They are:With this we have shown that a ML model can be developed that is capable of learning the important contributions for a quantitative prediction of heterogeneous ice nucleation. This has revealed the particular characteristics beyond the lattice match that help to make a surface a good or bad ice nucleator. While the rediscovery of the importance of lattice match is not new, the fact that it falls out of the ML model serves as a verification of our method. Of the three other descriptors identified, local ordering has received limited prior attention, and the descriptors on the corrugation of the adsorption energy landscape and the density reduction near the surface are new concepts in heterogeneous ice nucleation.Two out of the four descriptors we found are more directly related to intrinsic surface properties (lattice match and adsorption energy landscape) while the others describe interfacial water. Overall, it is more desirable to have descriptors that relate to surface chemistry alone. This goal, however, is much more challenging and for future work we propose to split the process of understanding heterogeneous IN into two steps: (i) relate IN activity to interfacial water properties and then (ii) relate surface chemistry to interfacial water properties.An important question is whether the descriptors identified in this study will apply if ice nucleation is explored with other simulation approaches  or in experiment. The nature of the ML model means that there is unlikely to be a numerical one-to-one correspondence between the descriptors identified in this study and those obtained with other water models. In other words, a covariate shift between descriptor values of different water models is to be expected. However, with this in mind, we have refrained in the current study from reporting specific threshold values for descriptors. Instead, we have reported and discussed broad categories or families of descriptors. The categories of descriptors are all clear and physically motivated quantities that can be probed and tested with other water models and with experiment. In addition, we note that the descriptors were obtained from simulations at the coexistence temperature . Hence, there is no need to study further any system at or close to its actual  freezing point. This drastically simplifies computational and experimental investigations probing the descriptors identified here, since substrates can be compared based on single temperature studies.59 or sum-frequency generation spectroscopy62. The adsorption uniformity could be studied with atomic force microscopy63 or scanning tunneling microscopy64. Our study also calls for time-resolved methods that do not average out structural fluctuations on short timescales, to being able to investigate pre-critical fluctuations. Given our findings we are confident that any such investigation can be done at constant temperature , even when two materials with drastically different IN ability are compared.Before closing, we give some perspective on connections with experiments. There are a variety of experimental techniques already available to study many of the physical properties discussed in this study. The lattice match can already be probed by crystallographic methods. Density and local ordering could be probed by surface X-ray23 but their structure and composition remain elusive.Finally, we note that this study deals with the microscopic influences on IN, i.e., essentially regarding the substrate as infinite and flat (on the order of a few atomic distances). There are other potentially influencing factors such as macroscopic hydrophobicity and large-scale roughness. In this sense, we regard our results as being of primary relevance to the so-called active sites in IN. This viewpoint can connect our work to more macroscopic studies and experiments in which the existence and location of active sites has been demonstratedTo conclude, with this work we have identified a predictive set of microscopic descriptors for the IN ability of substrates and have shown that the quantitative predictions of heterogeneous IN can be made. We expect that our findings will be useful in interpreting future ice nucleation experiments and as a guide for the development of substrates with targeted ice promoting/inhibiting abilities.47 as outlined in the main text. For the MD simulations, the water–water interactions are represented by the coarse-grained mW model50, while the water–substrate interaction varies depending on the substrate type, but mostly employs variations of the water–water interaction as well as Lennard–Jones-like interactions. More simulation details can be found in Supplementary Note We start by giving a brief overview of the workflow developed in this study. For a more detailed description the reader is referred to Supplementary Notes −1) from 273 to 200 K. Cooling ramps are widely used to characterize the IN ability of a given system in simulations47 and experiments24. We define as the nucleation temperature Tn the temperature at which a significant drop in the potential energy (Epot) is seen. This identifies the onset of nucleation as the drop is much more pronounced than the slope caused by the cooling. Tn correlates well with IN ability since a nucleation event at a higher temperature means a larger reduction of the homogeneous nucleation barrier (which increases with higher temperature). See ref. 47 for a comparison of Tn with nucleation rates. With this approach we have a measure to rank the substrates by their IN ability as well as a measure for the uncertainty in the IN ability (the standard deviation of the 5 runs). With 5 runs we strike a balance between precisely knowing Tn and computational efficacy. In hindsight, we see that our best model RMSE is larger than the uncertainty from calculating Tn, suggesting that there is little value of establishing Tn with higher precision.On these systems we perform five cooling ramps each . These features are gathered by post-processing data from either MD or the random-structure-search approach used in ref. 66 including all features. This model type has the advantage that it can easily be trained for a large number of features and comes naturally with a measure for feature importance. The measure is related to the performance of the model (on out-of-sample data points) compared to the performance after randomly permuting the values in that feature. We train the random forest to classify whether the system is a bad or good nucleator (threshold 225 K).To identify the most relevant features out of the  ~3000 considered, we couple two important components. First, we calculate an importance score for each feature. This is extracted from fitting a random forest modeld between two features f1 and f2 for the clustering we choose d = 1 −  MIC, where MIC is the maximum information criterion67, an information-theoretical measure similar to correlation that is capable of capturing non-linear associations, which is also bound to the interval .By construction, we expect many of the features to carry similar, if not identical, information (take for instance the average density in two adjacent layers). Thus, as a second step, we perform hierarchical clustering between the features to deal with correlations among them. As the distance n features, by forming n clusters and selecting from each the feature with the highest importance  as a performance indicator. In the Supplementary Note After selecting relevant features we check whether they are actually able to predict the IN ability of substrates in a satisfactory manner. We do this through a regression of Tn, a model that for instance cannot capture non-linearities might nonetheless yield bad scores. We choose as the estimator model a widely adopted variant of gradient boosting with trees, xgboost68. While it is beyond the scope of this study to benchmark many different ML models, we note that the accuracy assessment was repeated with random forests66 and support vector machines69, yielding worse results but consistent trends . On the other 70% of the data we train the model after performing 5-fold cross-validation to select the best hyperparameters. This split is done prior to the feature selection to avoid the test set influencing the selection process. The hyperparameter search was done utilizing the Bayesian tree-structured Parzen estimator70. As a baseline to compare this with we use the linear elastic net model.We calculate the corresponding model score on a 30% hold-out set, repeated 20 times. The standard deviation of this is reported as the error of the score. As stratification for the splits we divide the Supplementary InformationPeer Review File


Knowing precisely where a spacecraft lands on Mars is important for understanding the regional and local context, setting, and the offset between the inertial and cartographic frames. For the InSight spacecraft, the payload of geophysical and environmental sensors also particularly benefits from knowing exactly where the instruments are located. A ~30 cm/pixel image acquired from orbit after landing clearly resolves the lander and the large circular solar panels. This image was carefully georeferenced to a hierarchically generated and coregistered set of decreasing resolution orthoimages and digital elevation models to the established positive east, planetocentric coordinate system. The lander is located at 4.502384°N, 135.623447°E at an elevation of −2,613.426 m with respect to the geoid in Elysium Planitia. Instrument locations (and the magnetometer orientation) are derived by transforming from Instrument Deployment Arm, spacecraft mechanical, and site frames into the cartographic frame. A viewshed created from 1.5 m above the lander and the high‐resolution orbital digital elevation model shows the lander is on a shallow regional slope down to the east that reveals crater rims on the east horizon ~400 m and 2.4 km away. A slope up to the north limits the horizon to about 50 m away where three rocks and an eolian bedform are visible on the rim of a degraded crater rim. Azimuths to rocks and craters identified in both surface panoramas and high‐resolution orbital images reveal that north in the site frame and the cartographic frame are the same (within 1°). A carefully georeferenced high‐resolution image of the InSight lander shows it is located at 4.5024N, 135.6234E in Elysium Planitia, MarsInstrument locations are derived by transforming from spacecraft and site frames into the cartographic frameA viewshed shows the lander is on a shallow regional slope down to the east and a local slope up to the north We also describe features (rocks and craters) within the panorama from the lander. This work provides the basis for subsequent interpretations of the instrument data that have been returned and will continue to be returned from the InSight lander.This paper provides detailed descriptions of where InSight landed, the local setting and what can be seen from the lander, and the locations of all of the instruments on the spacecraft as well as those deployed on the surface of Mars. We begin with the efforts to image the lander on the surface in high‐resolution orbital images and to place the lander into the cartographic frame on Mars using hierarchically georeferenced base maps. Next, we describe the process used to place the instruments on the surface and the frame transformations between spacecraft mechanical, IDA, site frames, and cartographic frames. We then use these transformations to determine the locations in the cartographic frame on Mars of SEIS, HP23 thermal modeling, and providing a baseline for subsequent eolian change detection  team, the HiRISE team, and the InSight team reviewed the orbits and timing of when possible landing locations would be visible from MRO orbits that passed over the landing ellipse in the time immediately after landing. There were three opportunities to image the landing ellipse in the first few weeks. The first opportunity, 3 days after landing on 29 November 2018, required specifying an exact imaging target location before InSight landed. Spacecraft tracking before the fifth Trajectory Correction Maneuver (TCM‐5) indicated that final maneuver was required because landing was predicted to occur well northeast of the reference landing ellipse, so the center point of the newly predicted landing ellipse that would apply after the TCM‐5 executed was provided as the first imaging location. That location, however, was well east of the final landing location, so the HiRISE image and the colocated Context Camera , 135.62344690°E , at an elevation of −2,613.426 m relative to the local Mars geoid radius from MOLA   prior to entry and within the two‐sigma landing ellipse (1.9σ) Figure . The bac33.13. The workspace where the instruments could be placed was in front of the spacecraft (to the south), next to where the arm is attached to the edge of the lander. The workspace is a crescent shaped area that extends out to roughly 2 m away from the lander and 2 m to either side  was tasked with deciding where to place instruments in the workspace based on the spacecraft tilt, workspace topography, surface characteristics  and instrument placement requirements. The ISSWG was composed of six subgroups: (1) geologists, (2) physical property scientists, (3) arm and deployment engineers, (4) Multi‐mission Image Processing Laboratory (MIPL) personnel, and instrument representatives for (5) SEIS and (6) HPe Figure . Instrume Figure  and HP3 After landing on 26 November 2018, early ICC images showed dust was on the lens, which made initial workspace mapping difficult. After deployment of the arm and acquisition of IDC images of the spacecraft and solar panels, images of the surrounding terrain were acquired. The first IDC mosaic of the workspace acquired with the arm above the lander was available on 10 December 2018, 2 weeks after landing. Stereo images were processed to create orthoimage and DEM mosaics with 2 mm per elevation posting Figures  and 7b . AnalysiA higher resolution workspace stereo mosaic was acquired with the arm below the lander deck and was available on 14 December 2018. Individual frames had a pixel scale of 0.5 mm, and the DEM from these instruments had 1 mm per elevation postings. The preliminary instrument locations met (by a large margin) all instrument deployment requirements in the higher resolution data. The spacecraft testbed with an engineering model lander and arm was sculpted to match the workspace on Mars. The engineering model lander and arm were used to test deployment of the instruments to the selected locations and results showed no problems. The instrument placement locations Figure  were cer3.2Z down perpendicular to the deck, X normal to Z and perpendicular to a line between the two solar arrays, and Y parallel to a line between the two solar arrays in a right handed coordinate frame. The IDA frame is rotated 180° around the +Z axis of the spacecraft mechanical frame, and their axes are otherwise always parallel. In SM frame coordinates, the IDA frame origin is located at . Therefore, the translation of a point in spacecraft SM to IDA frame is defined by the function SM2IDA(argument) = expression (for easier traceability in later equations):The instruments were placed in the workspace in a spacecraft‐centric reference frame, called the IDA frame. To determine their location in the cartographic frame requires a transformation between this frame and others that can be related to their location on Mars. The IDA frame has its origin at the intersection of the IDA azimuth rotation axis (shoulder) and the surface of the lander deck Figure . The IDAZ along the gravity vector, with X north, Y east, and Z down , and is rotated relative to the IDA frame based on postlanding orientation of the vehicle. The site frame thus accounts for the local tilt, expected to be known to <0.6° and orientation of the spacecraft, expected to be known to <0.85° provided by the IMU accelerometers and gyroscopes, respectively. Note the spacecraft is designed to land with +X axis of the IDA frame pointing due south, so the workspace is south of the lander. At touchdown, the IMU indicated the IDA X axis was 0.26° counterclockwise of due south. The IDA frame yaw, pitch, and roll angles  in site frame are 180.495°, −2.684°, and −2.934° and are very close to expected values of 180°, 0°, and 0° when accounting for the lander tilt of 3.975° in an azimuthal direction of 133.408° in site frame measured clockwise from north  or in the IDA frame at an azimuth of 312.913° measured clockwise from the X axis toward +Y in the XY plane. Information needed to transform from the SM and IDA frames to site frame is as follows:The site frame has the same origin as the IDA frame but has −1000−10001. This corresponds to a tilt direction in Site frame  of 133.408°.Note that this IDA frame rotation varies only slightly from the nominal perfectly gravity‐leveled orientation of To convert IDA frame coordinates to site frame, the transformation is defined by the function IDA2Site(argument) = expression:Figure The HiRISE image of the lander resolves the lander deck and the solar panels Figures  and 9. TIn equidistant cylindrical projection using a planetary radius 3,396,190 m, the latitude and longitude values can be converted from degrees to northing and easting in meters by dividing by a unit conversion of:0m0m0.956056m in the SM frame. This location has the same latitude and longitude coordinates determined for the center of the spacecraft in the HiRISE image, but the initial elevation relative to MOLA must be calculated. However, unlike site frame, the spacecraft mechanical frame is not horizontal and vertical relative to gravity, so the elevation must first be calculated for a point in the site frame and then transformed via Equations We define the center of the deck of the spacecraft to be at Z value and a known value in a MOLA‐tied HiRISE DEM. The IDA team, using the methods described in section Z axis of the instrument in the corresponding HiRISE DEM is −2,613.454 m. Assuming this location is the same as that determined by the IDA team yields an elevation offset of +0.9957 m, providing an elevation for the site frame origin of −2,612.4583 m.To determine the elevation offset of the site frame origin to MOLA, we take a known site frame 3.3With a horizontal tie established at the deck center between site frame and the Mars global frame, the origin of the site frame can then be calculated by combining the deck center horizontal coordinates, elevation from the site frame's offset value determined in section X, Y values to the site frame origin's horizontal coordinates and subtracting the site frame Z value from the site frame origin vertical coordinate due to the down→up conversion, as defined by the function Site2LL(argument) = expression:Any other coordinates in site frame may now be converted to latitude, longitude, and elevation by adding the site frame Similar to calculating the site frame origin, to determine the latitude, longitude, and elevation (with respect to the MOLA geoid) of other instruments rigidly attached to the lander, we must obtain the full three dimensional coordinate of the spacecraft deck center from the horizontal location of the lander as determined by the georeferenced HiRISE and from summing the elevation offset with the difference in elevation from the spacecraft deck center to the site frame origin:Using these transformations, the frame origin and deck center locations are shown in Table 3.43 support structure (both agreed to within 1–2 cm)  positioned directly above the instrument grapple point was also used. In addition, the workspace orthophotos projected in site frame located on the HiRISE image using the horizontal offset calculated from the center of the lander to the site frame was also used to determine the latitude and longitude of the instruments Figure . Compari3.5X, +Y edge of the deck in the spacecraft mechanical frame , each axis vector is rotated by the SM to IDA to site frame rotations (ignoring the SM to IDA origin translation) as well as a mirroring about the The resulting axes are oriented in the cartographic frame as follows.XYZ location of the magnetometer provided in spacecraft mechanical frame is −23.076, 26.554, and 39.192 in, yielding the location and elevation of the center of the magnetometer shown in Table Y axis) is oriented roughly northwest, with the azimuth from north calculated as arctan(0.8429/0.5357) = 57.56° measured counter‐clockwise from north, or 302.44° measured clockwise from north. The X axis of the magnetometer is orthogonal to the Y axis, toward the southwest with an azimuth of 212.44° measured clockwise from north.The 3.6XYZ provided in spacecraft mechanical frame is as follows: 6.067, 14.276, and 43.663 in, yielding a location shown in Table XYZ provided in spacecraft mechanical frame is as follows: 8.622, 6.942, and 37.544 in, yielding a location on Mars shown in Table The atmospheric pressure sensor (transducer) is located inside the spacecraft with an inlet pressure port near the center of the lander deck Figure . The TraY and −Y directions in the spacecraft mechanical frame  and −Y , these offsets are added to the locations of the TWINS bases in the SM frame to obtain their locations on Mars (Table The Temperature and Winds for InSight (TWINS) masts are located at the edge of the lander deck in the +e Figure . The basrs Table .3.7Y and −Y direction in the SM frame and are used for X‐band direct to Earth radio tracking by RISE. Both antennas point 30° above the deck with one (+Y) pointed 15.5° clockwise from the +Y direction and the other (−Y) pointed 186° clockwise from the +Y direction in the SM frame . A viewshed map of areas within view from a height of 1.5 m at the lander location (the average height of the IDC when acquiring panorama images) was made using the HiRISE DEM of the landing site . Names were assigned in IDC panoramas for features in the middle and far field, in ISSWG mosaics of features in the workspace, and in ICC and IDC images for areas near the lander.The lander is located on the western side of a quasi‐circular depression that has been interpreted to be a degraded ~27 m diameter impact crater  down to the east Figure . The vieWe have identified the azimuths (measured clockwise from north) of features that are in view in both the surface panoramas and the HiRISE image. If the yaw, pitch, and roll of spacecraft and the transformation from the spacecraft mechanical frame to the site frame are known perfectly and there are no errors in georeferencing of the HiRISE orthophoto, then the azimuths measured in the panoramas and on the HiRISE orthophoto of the same features should match. Table 5We will describe features within the IDC panorama, split into eighths (45° with ±5° overlap). We use portions of the evening and afternoon panoramas to highlight features better resolved in those lighting conditions, which are controlled stereo panoramas projected into site frame. We will then describe features in the workspace and area near the lander. We also include an annotated HiRISE image with many of the same features shown Figure . The disThe area to the north‐northeast of the lander  and the rockier terrain of Homestead hollow. There is a slight elevation gain beyond Homestead hollow; Sunset crater is ~14 m away and a smaller crater, Smudge, is a slightly closer. The rim of Homestead hollow here is nothing more than a shallow slope and rocks appear as plentiful inside as they are outside the hollow.The area to the west‐northwest  are those that can also be seen in the far field of the ICC images and closer in the workspace that were characterized by the IDC workspace mosaics and DEMs. The workspace is devoid of rocks larger than a few cm in diameter; however, around the workspace are a number of rocks larger than ~5 cm including Humpback, Pickle, Midnight, Potato, Claw, and Skull Figure . The 5 c6After landing HiRISE imaged the InSight lander on 6 December 2018 based on coordinates provided by RISE from the first day of X‐band radio tracking. This image was registered to a carefully constructed hierarchical orthoimage and topographic base map pyramid composed of 25 cm/pixel HiRISE images, 6 m/pixel CTX images, and 12.5 m/pixel HRSC images all referenced to the IAU/IAG 2000 positive east planetocentric coordinate system based on 463 m/pixel MOLA data. In this coordinate system, InSight is located at 4.50238417°N, 135.62344690°E, at an elevation of −2,613.426 m with respect to the MOLA geoid in the northwest‐central portion of the reference landing ellipse in western Elysium Planitia. The RISE location after 34 sols of tracking is ~220 m to the west, which is a measure of the uncertainty between the cartographic map and inertial coordinates in this part of Mars, which is important for accurately landing spacecraft. The InSight heatshield is located 0.762 km downtrack (to the east), and the backshell/parachute is located 0.553 km to the southeast.IDC stereo image mosaics and DEMs made from them became available on 10 and 14 December 2018. Two mosaics at 2 and 1 mm per elevation posting showed the workspace to be particularly benign with a sandy, granule and pebbly surface, few rocks, and low slopes that met all of the instrument deployment requirements over most of the deployment area. After the locations for the instruments were selected, the testbed was sculpted to match the workspace on Mars. This enabled tests of deployment of the instruments to the selected locations using an engineering model lander and arm. Instrument placement locations were certified and approved by the project on 17 December 2018.Z along the gravity vector, with X north and Y east (right hand rule). The site frame has the same origin as the IDA frame but accounts for the local tilt and orientation of the spacecraft provided by the IMU. Because HiRISE easily resolves the spacecraft and the large circular solar panels, its center can be related to the spacecraft mechanical frame. Offsetting the IDC orthophoto of the workspace in the HiRISE image yields the location of the instruments in the cartographic frame as well as the center of lander deck and the origin of the IDA and site frames. The locations of the SEIS, HP3, magnetometer (including orientation), pressure sensor, temperature sensors, wind sensors, and RISE antennas are all determined by translating between the spacecraft mechanical, IDA, and cartographic frames.On Mars, the instruments were placed in the workspace by the arm using coordinates in the Instrument Deployment Arm frame, whose origin is the shoulder joint of the arm on the lander deck. The IDA frame can be translated into the spacecraft mechanical frame as well as the site frame, which has A viewshed from a height of 1.5 m above the lander location was created from the HiRISE DEM and shows the lander is located on a broad gentle slope down to the east in which crater rims 400 m and 2.4 km away can be distinguished. A slope to the north limits the horizon to ~50 m away formed by a degraded impact crater with three rocks and an eolian bedform. The backshell and parachute are in a slight depression and are not visible from the lander. The heatshield is in an area that should be in view, but at its distance, its maximum height would only be ~1 pixel high. Azimuths to rocks, craters, and eolian bedforms observed in controlled lander panoramas (in site frame) and in carefully georeferenced HiRISE images match within 1°, indicating that the azimuth and tilt recorded by the IMU used to define the site frame are accurate.InSight landed in a quasi‐circular depression, interpreted to be a degraded impact crater called Homestead hollow with a sandy, granule, and pebble rich surface. The western portion of the hollow is rockier (Rocky Field) as is the region beyond the hollow. Rocks in the hollow and at the edge of the hollow appear perched and many are tabular and angular. Individual craters, rocks and eolian bedforms in the controlled lander panoramas have been identified, named, and mapped onto a HiRISE image. Fifteen small craters between 1 and 10 m diameter and 5 and 42 m from the lander have been identified in both InSight and HiRISE images, with their distances, azimuths, and diameters recorded. All craters are shallow sandy depressions. Two secondary craters with distinctive bright ejecta seen in HiRISE images are interpreted to be from the fresh, rayed crater Corinto located ~800 km to the northeast. These secondaries are omnipresent across the landing ellipse. Eolian bedforms are located near the rims of relatively fresh, distant (50 and 400 m diameter) impact craters. Distant shallow, bright quasi‐circular depressions interpreted to be degraded and filled impact craters (hollows) are common toward the east. This detailed information about the location of the InSight lander, its instruments, and setting are important for interpretations of the geophysical and environmental sensors data that have been returned and will continue to be returned from the InSight lander.