Electrostatic energyhas a significant contribution to intermolecularinteraction energy, especially in biological systems. Unfortunately,precise quantum mechanics calculations are not feasible for largebiological systems; hence, simpler calculation methods are required.We propose a method called UBDB+EPMM , which shortens computationaltime without losing accuracy. Here, we characterize electrostaticinteractions in selected complexes of IFIT proteins with RNA. IFITproteins are effectors of the innate immune system, and by bindingforeign RNA, they prevent the synthesis of viral proteins in humanhost cells; hence, they block the propagation of viruses. We showthat by using the UBDB+EPMM method it is possible to describe protein-RNAinteractions not only qualitatively but also quantitatively. Lookingat the charge penetration contribution to electrostatic interactions,we find all amino acid residues with strong local interactions. Moreover,we confirm that electrostatic interaction of IFIT5 with pppRNA doesnot depend on the sequence of the RNA. The interfaces of proteins interacting with RNAare enriched for polar and charged amino acids, especially the positivelycharged Arg and Lys, which is in line with the common role of thenegatively charged phosphate backbone in these interactions.2 In addition, the natural 5′ end of RNAbears a triphosphate moiety (pppRNA) which can be modified by cappingwith m7G in the case of eukaryotic mRNA. Some viruses leaveunmodified ends on their genomic RNA or mRNA; for example, the ssRNA(−)genomes of Vesicular Stomatitis or Influenza viruses bear triphosphate groups.3 Dueto RNA capping or processing occurring in the nucleus, unmodifiedpppRNA should normally be absent from the cytosol of human cells andthus can be recognized by cytosolic receptors as pathogen-associatedmolecular patterns (PAMPs) and trigger antiviral response.4RNA binding and recognition are essential for proteins workingon RNA in both transcription and translation as well as RNA degradation.The binding events between proteins and RNA rely largely on electrostaticswhen the molecules approach each other, since these require long-rangeinteractions. It is expected that steric requirements and weaker,more directional interactions such as hydrogen bonds add to the bindingspecificity at a closer range when the forming protein-RNA complexis being fine-tuned. Apart from guiding the spatial orientation ofinteracting molecules, electrostatics remains a major contributionto the binding affinity in general.5 In line with the ability of IFIT5 to distinguishpppRNA from other types of RNA , the ppp group is especiallyimportant for binding affinity and thus conveys selectivity towardpppRNA. Crucial for this interaction is a Mg2+ ion, whichis a common metal used by RNA binding proteins and a key counterionrelevant for RNA folding and interactions.6 The requirement for an unpaired 5′ end comes from the restrictivedimensions of the RNA binding site in IFIT5 which allow only ssRNAwith at least a 4–5 nucleotide unpaired 5′ overhang.IFIT5 was able to cocrystallize with three different artificial homooligonucleotides. In these crystal structures, the nucleobasesform very few hydrogen bonds with IFIT5, based on which it was postulatedthat IFIT5 can bind RNA of any sequence. The role of RNA sequencein binding to IFIT5 has been experimentally addressed to a very limitedextent by RNA binding studies.7 The studiestypically focused more on varying 5′ moieties rather than RNAsequence. Literature reports are not consistent about the selectivityof IFIT proteins and their preferred RNA forms.10 Thus, the common assumption, that IFIT5 binds RNAregardless of the nucleotide sequence, is mostly based on the existenceof the aforementioned crystallographic structures in PDB and meritsfurther quantitative examination. Characterization of interactionsbetween IFIT5 protein and RNA would help us to understand the mechanismand selectivity of recognition of viral RNA. If IFIT5 proteins indeeddo not discriminate against RNA sequence, it would indicate that themechanism of virus detection is universal (sequence-agnostic) in immunesystems, which in turn would suggest that viral 5′ ends mightoccur with any sequence. On the contrary, if IFIT5 displays any preferencesfor particular sequences of pppRNA, it could mean that they take partin the recognition of specific kinds of viral RNA or that the 5′end sequence repertoire of viruses is limited. In this study, we makeuse of the available high-quality crystal structures of IFIT5 withdifferent RNA to address the question of rules regarding the specificityof IFIT5 and more generally of RNA binding proteins.pppRNA recognition is crucial in antiviraldefense: dsRNA is recognizedby the RIG-I receptor which initiates the antiviral signaling cascade,while ssRNA is recognized by the effector protein IFIT5. IFIT5 isunique since it lacks enzymatic activity and reportedly acts by sequesteringpppRNA to prevent viral replication or translation. IFIT5 bindingto pppRNA is demonstrated in detail by three cocrystal structures.11 Furthermore, it is the most robust componentof total energy, because it is the least sensitive to errors in thegeometry of investigated systems. Such errors are often encounteredwhile determining structures of big, biological complexes, for whichit is very difficult to properly define the positions of all atoms.There are affordable computational methods, which enable estimationof the energy of electrostatic interactions in macromolecules; however,most of them rely on simplified methods taken from classical mechanics(force fields). In classical molecular mechanics, electrostatic interactionsare usually approximated by simple Coulomb interactions of atomicpoint charges. To estimate electrostatic energies of interactionsin IFIT-RNA complexes, we used a more sophisticated method calledthe University at Buffalo Pseudoatom DataBank (UBDB)13 plus the Exact Potential Multipole Moments (EPMM) method.14Molecularmodeling plays a crucial role in drug design, understandingmechanisms of biological processes, and specifying structure–functionrelations in proteins. Determination of structure and interactionenergy is essential in the prediction of new drugs or the analysisof protein complexes. Biomacromolecules are too complex systems for precise quantum mechanical computations.At the same time, information about the energy of their interactionsis desired. Electrostatic energy has usually the most significantcontribution to interaction energy and is a key factor in processes of molecular recognition, proteinstabilization, or protein folding. It has been shown that electrostaticenergy is a sufficient approximation of total interaction energy.15 TheUBDB+EPMM method was verified on many occasions for various compounds and compared withquantum chemical results and molecular mechanics. It reproduces wellthe electrostatic energies obtained from quantum chemical calculations,with the RMS difference not larger than 5 kcal/mol, depending mainlyon the type of interacting molecules and the reference method.17 The UBDB+EPMM approach was also tested on larger benchmark sets(S6618 and JSCH-200519) and compared to the force field energies.20 The S66 set represents the most common noncovalent interactionsobserved in biological structures. Results obtained for S66 showedthat the RMSE between the UBDB+EPMM method and the reference B3LYP/aug-cc-pVTZmethod equaled only 1.1 kcal/mol.In the UBDB+EPMM method, the continuousaspherical model of chargedensity is used. Using continuous charge density instead of pointcharges used in many force fields, charge penetration effects aretaken into account. With the UBDB+EPMM method, it is possible to computeelectrostatic energies with similar accuracy as with quantum chemistrymethods, for a wide range of types of interactions  and distances .21 of pseudoatoms to molecular electrondensities obtained from DFT calculations.12 The calculations are done for a couple of thousands of model molecules,and the resulting tens of thousands of pseudoatoms are grouped intoatoms having similar values of electron density parameters and similarchemical topologies. Based on each group, an atom type is defined,and characteristics for those atom type parameters of atomic electrondensity are stored in the databank, in the form of the HCMM parameters.The UBDB was recently restructured, extended, and renamed the MultipolarAtom Types from Theory and Statistical clustering (MATTS) databank.13 The idea of pseudoatom databanks comes fromX-ray crystallography, where the databanks become to be widely usedfor crystal structure refinements replacing the Independent Atom Model(IAM). IAM is a standard model of electron density commonly used incrystallography. In IAM, individual atoms are represented by the sphericallyaveraged electron densities obtained by quantum mechanics methodsfor isolated atoms in the ground state. To obtain more accurate electrondensities, more sophisticated models were designed by quantum crystallography.The HCMM is one such model. In HCMM, atoms are represented with afinite spherical harmonic expansion  of the electrondensity around each atomic center. The electron density of a pseudoatomis defined by core and ρval are free-atom core and valence sphericaldensities normalized toone electron, Rl is theSlater-type radial function, and dlm are density-normalized real spherical harmonic functions.The third term is responsible for aspherical deformations. κand κ′ are expansion-contraction parameters, and Pcore, Pval, and Plm are core, valence, and spherical harmonics populations, respectively.The UBDB is a databank ofaspherical atomic electron densitiesderived by Fourier-space fitting of the Hansen-Coppens Multipole Model(HCMM)Pval, Plm, κ, and κ′transferred from the databank to the studied molecule for which theonly input information is coordinates of atoms. The reconstructedUBDB model provides quantitative information about the electron densityand enables the performing of topological analysis of electron densityon the basis of Bader’s QTAIM theory (Quantum Theory of AtomsIn Molecules).22 In the UBDB approach,one can also compute various atomic and molecular properties suchas charge, dipole and higher moments, molecular electrostatic potential,and electrostatic energy of interactions between molecules.With UBDB, it is possible to reconstruct electron density fromparameters 14 Using the EPMM method, it is possible to take into account the penetrationcontribution to the electrostatic energy. The penetration effect occurswhen two molecules are so close that their electron densities areoverlapping and the computation of electrostatic interaction energyfrom multipole moments is no longer valid. Penetration energy is definedas a difference between the exact electrostatic interaction energycomputed by integration over the continuous distributions of charge(here EPMM) and the electrostatic interaction energy computed frommultipole moments (here MM).The EPMM method applied during the computation of electrostaticenergies from the UBDB electron densities shortens computational timeby combining two different approaches. The EPMM method evaluates theexact Coulomb integral in the inner region (≤4.5 Å) (EP)and combines it with a Buckingham-type multipole moments approximation(MM) for long-range interatomic interactions.26 Here, we will investigate for the first time complexesof proteins with RNA. Moreover, the studied complexes contain magnesiumions, which mediate the protein-RNA interactions. Our study providesa deeper understanding of electrostatics aspects of protein–RNAinteractions and additional insight into the specificity of the antiviralIFIT5 protein toward various features of RNA.The UBDB+EPMM method has been successfullyused in previous researchto analyze interactions in peptides, proteins, and various complexes,e.g., aminoglycosides with RNA.22.154HOR, 4HOS, and 4HOT. For proper calculations,deposited structures were thoroughly checked, and some adjustmentswere done. The 4HOS structure originally contains a sodium ion in the binding site,instead of a magnesium ion. The geometry of the binding site of the 4HOS structure allowsus to place there other metal ligands, e.g., calcium, which was confirmedby the Checkmymetal server.27 The magnesiumion is not the most preferable cation, but it is not forbidden. Forthe purpose of this work, we changed the metal in the binding siteof the 4HOS structureto magnesium. Hydrogen atoms were not present in any deposited structuresof IFIT5-RNA complexes. We used the Molprobity program28 to add hydrogens atoms. Hydrogen atoms werepositioned according to standard neutron distances. All flips suggestedby the program were accepted. In the 4HOR structure, two residues were flipped:Asn346 and Gln462; in the 4HOS structure, four residues were flipped: Gln40, Gln76,Asn346, and His398; and in the 4HOT structure, two residues were flipped:His85 and Gln377. Both in 4HOR and 4HOT structures all histidine residues were neutral, in contrast to 4HOS in which His62 andHis399 were protonated. After adding hydrogens, water molecules havebeen removed except for the three in the coordination sphere of magnesium.Hydrogen atoms for the three water molecules were added geometrically(Table S3) on the basis of analogous smallmolecule structures containing magnesium coordinated by water. Onewas  diaqua-bis(hydrogen diphosphato)-magnesiumdeposited in the Cambridge Structural Database29 under the refcode GEQBIO.30 Thesecond was magnesium bis(dihydrogen phosphate(I)) hexahydrate depositedin the Inorganic Crystal Structure Database31 under the code 2549.32 The whole IFIT5protein consists of 482 amino acid residues. In all the depositedstructures, some residues at the N- or the C-termini of the proteinswere missing. The largest number of missing amino acid residues wasin the complex with the oligoA (4HOT): Met1, Ser2, Glu3, Ile4, Arg5, and Ile482.Also in the complex with the oligoU (4HOS), the RNA chain is shorter than in theother two complexes; only the first three nucleotides are fully described,and the fourth one is incomplete. Therefore, for the proper calculationsand to be able to compare all three complexes, we selected residues7–480 from the protein chain and the first three residues fromthe RNA chain for further analyses. We performed analyses for both,the whole protein and the protein binding site only. The binding sitewas chosen on the basis of the geometrical analysis of all three complexeswith previously added hydrogen atoms. We selected all amino acid residueswithin a 5 Å distance from the first three RNA residues includingthe magnesium ion and water molecules coordinating it. Selected aminoacid residues are listed in the SI (SupplementaryFile SI2). The nucleobase in the second nucleotide (C2) in the RNAfrom the 4HOR structure is disordered over two conformers, the syn and anti conformation of the nucleobase. Both conformerswere taken independently into account, but for expedience, we presentthe detailed electrostatic analysis focusing on the main conformer,which is the syn configuration. The anti conformer was only used for energy comparison with results frommolecular dynamics simulations.Calculations for IFIT5-RNA complexes were based on the followingstructures deposited in the RCSB Protein Data Bank:33 was used to transfer electron density parametersfrom the UBDB databank to investigated structures. For the propertransfer, the LSDB program was used twice for each structure. First,the program was used only to extend the position of hydrogen atomsto neutron distances (with the RADII command for Mg set to 0.01 Åto remove Mg–O bonds from the procedure). Second, the databankwas transferred; during the transfer, the RADII command for Mg wasset at the default value (1.36 Å), and H atoms were not shifted.The extended version of UBDB from the 2012 year was used.12 The extension regarded new atom types neededto describe the magnesium cation and its neighbors. Calculations fornew atoms types were based on the structures deposited in two databases:Cambridge Structural Database29 and InorganicCrystal Structure Database.31 For the calculations,homemade scripts routinely used for building the UBDB databank wereadapted and used. In particular, the multipolar refinement was modified,and the κ′ parameters for Mg were not refined. More detailsabout computing and verifying magnesium-related atom types are givenin the SI. After the UBDB transfer, eachresidue was individually scaled to its formal charge. The exceptionwas made for the first residue of each RNA structure , Glu33 from the protein chain, and the magnesium cationwith three water molecules coordinating it. They were scaled togetheras one fragment to a charge of −3 e. Electrostatic interactionenergies were calculated for each possible pair of an amino acid residueand one of the three first RNA residues using homemade scripts toprepare input files and analyze output files. The magnesium cationand the three water molecules were considered as a part of the firstRNA fragment for electrostatic energy calculations. Two types of methodswere used to compute energies, EPMM and aMM,14 both implemented in the XDPROP module of the XD2016 package.34 From the EPMM-aMM difference, the penetrationcontribution to electrostatic interaction energy (Epen) was calculated.Spherical core and valence electron densities were computed from theatomic Clementi and Roetti35 wave functions.Single-zeta Slater functions for the deformation part were taken fromClementi and Raimondi36 with default valuesof n(l) except for P and S atoms, for which the n equalto 2,4,6,837 and 6,6,6,638 were applied, respectively. Exact integration parameterswere set to iqt = 2, Nrad = 99, and Nang = 590, and the exact potentialzone radius (rcrit1) was set to 5 Å.The LSDB programThe average time oftransferring the UBDB for one protein-RNA complexis around 4 min. The average time of computing electrostatic interactionenergies for one protein with one nucleotide is between 13 to 40 minfor the EPMM method depending on the number of interactions integrated(interatomic distances closer than 5 Å) and 15 s for the MM method.Calculations were performed on a PC with an Intel(R) Core(TM) i7-7700CPU @ 3.60 GHz processor, 32 GB RAM, and Windows10 Pro system.supplementary file “SI_Energies”.Calculated electrostatic interaction energies for all amino acidresidues are deposited in the 39 For RNA maps, cubic grids were generated with the size of a box500 × 500 × 500 Å and voxel size 0.1 Å centeredon the magnesium cation. For the protein maps, the procedure was similarto the difference in the size of the box equal to 250 × 250 ×250 Å and the voxel size 0.5 Å. Figures were prepared inPyMol.40Electrostatic potential mapswere calculated from the UBDB-derivedelectron densities in the XDPROP module of the XD2016 package andvisualized in the MoleCoolQt64 program.2.24HOR structure5 with one additional cytosine residue at the 3′-endof RNA. We considered two conformational states of the second nucleobasewith the torsion angles χ (O4′–C1′–N1–C2)equal to −104° and 77°, corresponding to the conformations anti and syn, named IFIT5-ppp5C(C2anti) and IFIT5-ppp5C(C2syn), respectively. The next system consistedof the 4HOT structure,5 extended with 8 adenines at the 3′-endof the RNA, named further as the IFIT5-ppp12A system. Additionally,two systems without ligands were based on the 4HOQ structure5 with and without the protonation of Asp334. TheMg2+ ions and all the water molecules present in the crystalstructures were preserved. The YASARA v.20.4 program41 was used for modeling the missing protein fragments andadding the hydrogen atoms at pH 7.4, taking into account the hydrogenbonds optimization. The amino acid protonation states were assessedwith PROPKA v 3.0.4.42 All systems weresolvated in a dodecahedral box of water molecules. The salt concentrationwas set to a physiological level of 150 mM NaCl. Each of the modeledsystems contained about 93,000 atoms, except for the IFIT5-ppp12Asystem, which contained more than 180,000 atoms. The CHARMM36m forcefield43 and the TIP3P water model44 were used. Simulations and analyses were performedwith GROMACS 2016.5.45 A cutoff of 12 Åand a 10–12 Å F-switch were applied for the van der Waalsand short-range electrostatic interactions. The Particle Mesh Ewaldmethod46 was used to handle the long-rangeelectrostatic interactions.Fivesystems containing IFIT5 proteins were chosen for simulations. Twosystems with IFIT5-RNA complexes were based on the 47 The velocity-rescale thermostat48 andParrinello–Rahman barostat49 wereused to regulate the temperature and pressure. Short energy minimizationwas done with the steepest descent algorithm. The whole system wasequilibrated in the NVT ensemble for 50 ps with weak positional restraintsapplied on all heavy atoms and in the NPT ensemble in three stagesfor 50, 25, and 25 ps, gradually increasing the temperature to thefinal value of 310.15 K. The production simulation, with 2 fs timestep, was conducted for 1 μs. For each system, 3 independentsimulation runs were performed, which gives the total production simulationtime equal to 15 μs. The analysis of the trajectories was doneusing GROMACS 2016.5 tools and Python 3.6 scripts. Figures were preparedin Chimera.50The LINCS algorithm was used toconstrain the bonds involving hydrogen atoms.anti) andIFIT5-ppp5C(C2syn), were chosen for electrostaticenergy calculations using the UBDB+EPMM method and the calculationsusing the Coulomb equation with the point charges taken from the CHARMM36mforce field and the TIP3P water model. For each simulation run, wechose a single representative of the biggest cluster (single frameselected from the simulation run that is the closest to the averagestructure). Then, the electrostatic energy calculations were conductedwith the UBDB+EPMM or MM methods, as it was described in supplementary file “SI_Energies”.Two systems, IFIT5-ppp5C(C22.351 vectors in BL21-CodonPlus(DE3)-RILcells. The expression was performed overnight at 25 °C afteradding a 0.5 mM isopropyl-d-1-thiogalactopyranoside solution(IPTG) at OD 0.5–0.6. Bacteria were collected by centrifugationat 4,000 × g, at 4 °C for 20 min. In the case of IFIT5 WTand IFIT5 K150M, cell pellets were resuspended in 50 mM Tris pH 7.5,1 M NaCl, 20 mM imidazole, 10% glycerol, 1 mM MgCl2, and0.5 mM (tris(2-carboxyethyl)phosphine) (TCEP) supplemented with EDTA-freeSigmaFast Protease Inhibitor Cocktail (Merck), DNase I, and lysozyme.After a 1 h incubation, bacteria were sonicated and centrifuged at48,880 × g for 30 min. The supernatant was applied on a HisTrapHP column  equilibrated in the same buffer (withoutsupplementation). IFIT5 proteins were eluted with a gradient of imidazolefrom 20 to 1 M. Fractions with purified proteins were combined anddiluted 10 times with 50 mM Tris pH 7.5, 10% glycerol, and 1 mM MgCl2 to decrease NaCl concentration. Samples were then loadedon a HiTrap Heparin HP column , pre-equilibrated with50 mM Tris pH 7.5, 100 mM NaCl, 10% glycerol, 1 mM MgCl2, and 0.5 mM TCEP buffer. The elution was performed with a gradientof NaCl from 100 mM to 1 M. Finally, IFIT5 proteins were purifiedon a Superdex 200 Increase column in 1x Phosphate-buffered saline(PBS), 5% glycerol, 1 mM MgCl2 and 0.5 mM TCEP buffer.The proteins were frozen in liquid nitrogen and kept at −80°C until use.N-term HisTagIFIT5 WT, IFIT5 K150M, and IFIT5 Q41E/K150M/R253M proteins were expressedfrom pETG10a2 was omitted.Part of the IFIT5 WT and IFIT5 Q41E/K150M/R253Mproteins were purified as described above with some modification.The buffer used for cell resuspension had 0.5 M NaCl concentration,and after HisTrap HP column purification, the sample was diluted 5times. In all purification buffers, MgClCy5 labeled RNA was purchased from Futuresynthesis (Poland) witha 5′-ppp-AAAAAGGAAGGU-Cy5 sequence.52 The RNA concentration was kept constant at 10 nM. The unlabeledprotein was titrated in 1:1 dilutions with the highest concentrationof 5/10 μM IFIT5 WT, 10 μM IFIT5 K150M, and 10/50 μMin the case of IFIT5 Q41E/K150M/R253M. The binding reaction was incubatedat 22 °C for 10 min. The measurement was carried out at 22 °Cwith a 60% IR-laser power and 20/60% LED in the MST buffer .For the wild type and triple mutant, the addition of MgCl2 showed no improvement over its lack ,so all replicates were used for the final calculation. All measurementsof IFIT5 K150M were performed in a buffer with 1 mM MgCl2. The recorded fluorescence in 0.5–1.5 s was normalized tothe fraction bound and analyzed using GraphPad Prism 9.3.0.Microscale thermophoresis (MST) experiments were performed on aMonolith NT.115 device (Nanotemper Technologies).34HOR, 4HOS, and 4HOT. All of them contain as a ligand a shortchain of RNA but with different nucleobases. 4HOR contains cytosine, 4HOS contains uracil,and 4HOT containsadenosine homooligonucleotides. For the comparison and calculations,the first three nucleotides of each structure were investigated. Theywill be denoted as pppCCC, pppUUU, and pppAAA, respectively. In thebinding of RNA to IFIT5, a magnesium cation is involved in general,but in the 4HOS structure, a sodium cation is present in the binding site insteadof magnesium. For the purpose of this study, we replaced sodium withmagnesium, as explained in the In this study, three structuresof complexes of IFIT5 proteinswith RNA were investigated: PDB entries 3.1syn and anti , whereas in the pppUUU chain,the analogous nucleobase is in the anti conformation,and in the pppAAA chain, it is in the syn conformation.All three structures of complexes of IFIT5 withRNA are very similar. The alignment of the protein chains shows nomajor differences 1, and th2O)3pppNNN where N isC, U, or A. We paid a special attention to amino acid residues constitutingthe binding site.In this study, we examined the electrostatic energy of the wholeprotein interacting with the first three residues of the RNA chain.We associated the magnesium cation and water molecules coordinatingit with the RNA; thus in fact, we analyzed interactions of IFIT5 withMg(H2O)3pppN group hasthe formal charge of only −2 e. The charge of the whole analyzedprotein is −2 e for 4HOR and 4HOT and 0 e for 4HOS, but the charge of the binding site is +2 e for all three complexes.Assignment of charges is crucial in electrostaticscalculations.Due to the presence of the triphosphate, the charge of the first residuein the studied RNA chains  has a formal chargeof −4 e, and every next nucleotide has a formal charge of −1e; but when we take the magnesium cation into consideration, thenthe Mg3pppCCC, the C2 residue in the syn conformation was selected for electrostatic interaction analyses.The energiesof electrostatic interactions were calculated for every amino acidresidue with every RNA fragment by both methods: EPMM and MM. TheEPMM is considered a method providing the most accurate electrostaticenergy (Ees). From the EPMM-MM difference, the penetration energy(Epen) was calculated. In the case of IFIT5:Mg(H2O)3pppN1, where N isC, U, or A), whereas this ratio is around 1.25 for the second nucleotideand around 1 for the third one.The charge of investigated RNA chains is always negative. The chargeof the whole protein is −2 or 0 e, but it is so distributedthat the electrostatic energy of binding RNA with the protein is negative,meaning that overall interaction is attractive. The distribution ofthe charge of protein and RNA was confirmed by electrostatic potentialmaps 3. Theto2O)3pppN1 fragment is 2–2.5 timeslarger than for the second (N2) or the third nucleotide (N3) of thesame type. Looking at the binding site only, the ratio is even slightlylarger, 2.7–3.2, favoring the first fragment.Moreover, the first fragment of the RNA has a largercontributionto the binding energy than any next nucleotide. When we analyze interactionswith the whole protein, electrostatic energy for the Mg3pppCCC, Mg(H2O)3pppUUU,and Mg(H2O)3pppAAA are comparedto each other, it is clear there is no significant difference betweenthem, the largest discrepancy being about 10%. Thus, energies of electrostaticinteractions support the conclusion drawn from the structural analysisof IFIT5 cocrystals with three different homooligomers that IFIT5proteins bind pppRNAs with similar strength, irrespective of the sequence.5 The conclusion regards C, U, and A nucleotidesat the first, second, or third position. Nothing is known about interactionswith G at these positions because the crystal structure of IFIT5 complexedwith guanine pppRNA is unknown.Finally, when total Ees energiesfor Mg(H2O)3pppCCC chain. We can observe that, generally, IFIT5 consistsof many charged amino acid residues; for the 4HOR structure, it is68 positively charged and 70 negatively charged amino acids. The aminoacid residues interacting with the highest  electrostaticenergies are in the binding site, but it is worth mentioning thatstill many amino acid residues are interacting with high energiesoutside the binding site.3.4The IFIT5 protein binds RNA viadifferent interactions: long-rangeelectrostatic interactions, hydrogen bonding, short-range van derWaals, stacking, and others. Amino acid residues from protein chainsinteract with different parts of the RNA chains: nucleobases, sugarmoieties, or phosphate bridges. Thanks to our calculations, the detailedanalysis of interactions is possible not only on the basis of thegeometry but also on the basis of computed energies.3.4.12O)3CTP1, i.e., thefirst triphosphate nucleotide (CTP1 = pppC) complexed with the magnesiumcation and three water molecules. The highest attractive electrostaticinteraction energies are observed for interactions of positively chargedamino acid residues, 2O)3CTP1, and it equals −214.6 kcal/mol.Positively charged Lys150 is situated close to the highly negativetriphosphate bridge of the first RNA residue, thus a very strong attractiveinteraction. For this pair, also penetration energy is high and equals−14.2 kcal/mol. A very strong interaction is the result offour hydrogen bonds (HBs), three between the −NH3+ group and phosphate and one between −NH3+ and a water molecule coordinating the magnesium cation,enhanced by charge. The other strongest interactions are as follows:Arg253: Mg(H2O)3CTP1 ,Arg186: Mg(H2O)3CTP1 ,Lys48: Mg(H2O)3CTP1 .For Arg253, we can also see strong penetration due to two HBs. Arg186 has a small penetration contribution  due to one HB, and the Lys48 interaction has no penetrationcontribution at all. On the contrary, negatively charged amino acidstake part in repulsive electrostatic interactions, although repulsiveinteractions are weaker and not so common as interactions with positiveamino acids. The highest repulsive interactions among amino acidsfrom the binding site are observed for Asp334: Mg(H2O)3CTP1  and Asp189: Mg(H2O)3CTP1 . For Asp334, the penetration is 2.7 kcal/mol,and for Asp189, it is equal to zero. For neutral polar amino acids,electrostatic interactions of Gln41  and Tyr250 with Mg(H2O)3CTP1 arenoticeable. The side chain of Gln41 is arranged in a way to pointthe nitrogen atom toward oxygen atoms in triphosphate resulting intwo HBs. Thus, penetration energy for this pair is −6.4 kcal/mol.Tyr250 creates one strong HB between the OH group from the side chainand triphosphate, and penetration energy is −9.4 kcal/mol.The interesting case is Glu33. Despite its negatively charged aminoacid residue, the electrostatic interaction with Mg(H2O)3CTP1 is attractive . It interacts directlywith the magnesium cation, not the nucleobase nor phosphate. Penetrationenergy of Glu33:Mg(H2O)3CTP1 is the highestfor the whole complex and equals −65 kcal/mol. Glu33 createsthree atom–atom contacts, two with the magnesium cation andone with a water molecule. One contact between Glu33 and the magnesiumcation can be classified as a coordination bond.First, we focusedon interactions of amino acids from the binding site of IFIT5 withthe first RNA fragment Mg. Tyr254 creates an HB with theC2 phosphate group; thus, also penetration energy is high  for this pair. Also, Lys257 interacts strongly with C2  and C3  by HBs with phosphate groupsfrom both nucleotides. Glu33:C2 and Glu33:C3 interactions became slightlyrepulsive , and penetration energy,in this case, is also zero.2O)3CTP1. Interactions with C2 Tyr254  and C3 Lys257  have the highest contributionto the penetration energy  and then Lys150 , Arg253 , and Tyr250  for interactions withMg3pppCCCcomplex. We chose different criteria. First, we found all amino acidswhich were within the range of 5 Å from Mg(H2O)3pppNNN molecules ; there were 54 of them. By increasing the radius,we decreased the influence of geometry on our analysis. After that,we calculated electrostatic interaction energies Ees and set a filterto ±5 kcal/mol. It enabled us to distinguish 41 amino acids ofparticular importance. We confirmed that all the interactions foundby Abbas et al. are significant. Moreover, we found other significantinteractions: 17 new interactions for the IFIT5:Mg(H2O)3pppCCC complex. Our third filter was penetrationenergy Epen which helps to identify local interactions. We found threeamino acids with high penetration energy (meaning interacting at aclose distance), which were not found before: Tyr185, Tyr254, andAsp334. Electrostatic interactions for them are respectively nearlyneutral for Tyr185, attractive for Tyr254, and strongly repulsivefor Asp334. Besides, there are also long-range interactions foundwith our method with zero penetration contribution but with importantelectrostatic energies. The largest repulsive interaction found byus among charged amino acids is for Asp189 3pppCCC),the largest new attractive electrostatic interaction is for Arg192, and among neutral amino acids, the strongestinteraction (repulsive) is for Leu149 .Interactions depicted in 3.4.22O)3UTP1) and weaker but still remarkable for U2and U3. For example interactions, Ees energy of Mg(H2O)3UTP1 with Lys150 is −216 kcal/mol, when Lys150:U2 is−31 kcal/mol and Lys150:U3 is −19 kcal/mol. For negativelycharged amino acids, the trend is the same. Contrary to Mg(H2O)3CTP1 (and to Mg(H2O)3ATP1) whichinteractions with Glu33 are attractive, the Glu33:Mg(H2O)3UTP1 interaction is repulsive . It maybe caused by replacing the original sodium cation in the deposited 4HOS structure for magnesium.One distinguishable important unique feature for the pppUUU is a stronginteraction of Gln377:U3 , which is caused bydouble hydrogen bond. For Gln377:C3 and Gln377:A3, this interactionis much weaker, −4 kcal/mol and −3 kcal/mol, respectively,as only one HB is observed.Similar to interactionsof IFIT5 with pppCCC, interactions of IFIT5 with pppUUU are the strongestfor positively charged amino acids situated in the binding site. Theseinteraction are the strongest again for the first RNA fragment . They observed an interaction with Asp334, whichthey omitted for pppCCC and pppAAA complexes. We showed that thisinteraction is important in all three complexes. On the contrary,for pppUUU, they did not choose His374 and Arg192, whereas they listedboth of them for both pppCCC and pppAAA complexes. They claim thatArg192 interacts with the fourth nucleotide, which is missing in theIFIT5:pppUUU structure, but our calculations show that Arg192 hasstrong electrostatic interactions also with the first, second, andthird nucleotide and thus should not be neglected. Again, we showedthat these two interactions His374 and Arg192 are important in allthree complexes.Abbas et al. in their work found23 amino acids interacting with the pppUUU complex (3.4.32O)3pppAAAis that interactions for Tyr185:Mg(H2O)3ATP1and Tyr185:A2 are slightly repulsive ,whereas for Mg(H2O)3pppCCCand Mg(H2O)3pppUUU, analogousinteractions are attractive. The residue Tyr185 interacts with Mg(H2O)3CTP1 and Mg(H2O)3UTP1with the energies equal to −2/–2 kcal/mol with C2 andU2 equal to −6/–7 kcal/mol. This is caused by the differentorientations of the hydrogen atom from the hydroxyl group of the Tyr185side chain in complex with adenine. In IFIT5:Mg(H2O)3pppCCC and IFIT5:Mg(H2O)3pppUUU complexes, this hydrogen atom isdirected toward phosphate in RNA, and in the IFIT5:Mg(H2O)3pppAAA complex, it points in theopposite direction (toward Arg261). Also, interaction with Arg294:A3is twice as weak as Arg294:C3 and Arg294:U3. This is the result ofthe longer distance between the shortest non-hydrogen atoms in Arg294and A3 which is equal to 4.3 Å, whereas for Arg294:C3 and Arg294:U3,it is 2.9 Å. The different values of the interaction energy betweenGln41 and the first RNA fragment are a result of different donor–acceptordistances.Although the thirdcomplex of IFIT5 contains RNA built with a purine: adenine, insteadof previously being built with pyrimidine, the interactions do notdiffer much 6. StrongFigure S3b).They observed the interaction with Asp189 as the interaction withthe fourth nucleotide. We showed that this interaction is importantin all three complexes with all of the first, second, and third RNAfragments.Abbas et al. in their work found 24 amino acids interactingwith the pppAAA molecule  shows that the first four nucleotides are less mobile incomparison with other nucleotides in the RNA chain.The fully atomistic molecular dynamics simulationsof the IFIT5 proteinand its complexes give insight into the dynamics of the complex. First,we aimed to investigate the stability of the RNA nucleotides in thebinding site. For this purpose, we have extended the RNA up to 12adenines to obtain the IFIT5-ppp12A complex. We have chosen this complexbecause the multiadenine RNA chains are commonly seen in biologicalsystems and experimental setups. The analysis of the B-factor valuesfor this complex derived from simulations  conformationswitch to the anti conformation within the first200 ns of the simulation. On the other hand, the simulations startingfrom the IFIT5-ppp5C-(C2anti) system stay in the anti conformation. Nevertheless, the RNA is overall relativelystable in the binding site .Similarly as in the IFIT5-ppp12A complex, here also the positionsof the first four nucleotides are more stable than the other nucleotidesin the complex.The complexof IFIT5 with ppp5C contains two conformations of the second nucleobase, syn), andIFIT5-ppp5C(C2anti) systems, for which we have performedthe simulations, we are able to follow the distances between the centersof mass of chosen amino acids and nucleotides in time. There is aclear correlation between the large in absolute value electrostaticinteraction energies within the binding site shown in Table S4. The most attractive electrostatic interactionswere observed for Lys150 and the first RNA fragment. In the simulations,the average distance between those two residues, Lys150 and CTP1,was very short, around 0.80 nm in all analyzed systems. In addition,the distance was not changing much during the simulation as evidencedby small standard deviations, up to 0.21 nm. The second and thirdstrongest electrostatic attractive interactions were visible betweenthe first RNA fragment and Arg253 and Arg186, which is in agreementwith the short average distances visible in the simulations, rangingfrom 0.63 to 0.97 nm, and small distance variations, ranging from0.05 to 0.12 nm. The residues with the strongest repulsive interactionswith the first RNA fragment were Asp334 and Asp189, for which theaverage distances varied from 1.06 to 1.28 nm, and the standard deviationswere between 0.11 and 0.21 nm.For the IFIT5-ppp12A, IFIT5-ppp5C(C2syn) and IFIT5-ppp5C(C2anti) systems, forwhich we have performed the simulations,we calculated the UBDB+EPMM electrostatic interaction energies toinvestigate the variation of the electrostatic energy in these complexes.To do a fair comparison, as a reference we took energies computedfor the crystal structure with the C2 residue in its anti configuration. The difference between energies for C2syn and C2anti crystal structures is not very large,26 kcal/mol in total in favor for the C2syn, andis located mostly only on a few amino acid residues . In general, mean valuesof electrostatic energies obtained for six structures representingresults from the molecular dynamics simulations are similar to thatcomputed on the basis of the single crystal structure . There were only a few amino acidsfor which the difference between energy from the crystal structureand the mean energy from molecular dynamics was larger than 20 kcal/mol:Glu33, Gln41, Lys48, Lys150, Asp191, Arg192, Lys257, and Gln288. Formost of them, their sample standard deviations were also high; hence,the differences are not so meaningful. Only for three residues withhigh difference in energy, the difference is larger than their threesample standard deviations: Gln41, Lys48, and Gln288. In fact, samplestandard deviations for many amino acid residues were quite high.For the majority of the residues, their standard deviations are atthe level of 10% of their mean interaction energy. Almost 60 of themhad deviations larger than 5 kcal/mol. Among them, there were manyamino acid residues from the binding site which strongly interactwith the RNA. The largest variation in energy was observed for Lys150and Arg186, with sample standard deviations equal to 56 and 51 kcal/mol,respectively. This suggests that the energy for a single frame structuremay not correspond well with the energy of the crystal structure.Only analyses for the whole assembly of structures from moleculardynamics are valid if one wants to contrast them with the crystalstructure. In fact, the crystal structure is also representing a proteinstructure averaged through time (the time of the measurement) andspace .For the IFIT5-ppp5C, though it is still within two sample standard deviations. Obtainedfluctuation in total electrostatic energy indirectly confirms thatthe difference in total energy observed for crystal structures withvarious sequences is not significant, and electrostatic interactionsof RNA to IFIT5 are not sensitive to the RNA sequence.The mean value ofthe electrostatic interaction energy for thewhole protein interacting with the RNA equals −778 kcal/mol,and the sample standard deviation is 110 kcal/mol. The value is smallerthan for the crystal structure 3pppCCC chain with the mean electrostatic energy of interactionsequal to −32 kcal/mol and the sample standard deviation of14 kcal/mol. On the other hand, the electrostatic interaction energyestimated with the point charges derived from the CHARMM36m forcefield and the TIP3P water model was on average above 79 kcal/mol withthe sample standard deviation 23 kcal/mol. Moreover, the energiesof the interactions between the phosphate groups and amino acid residuesare visibly different when estimated by using the UBDB+EPMM methodand the point charges from the force fields (see residues Lys257 andArg260). Even though many contributions to the electrostatic energyare similar for both methods, the UBDB+EPMM method shows larger variabilityin the electrostatic energy for the short-range interactions, givingbetter balanced and richer information than the simple point chargemethod.54 It is also very well visiblefor the change in balance in electrostatic interactions of negativelycharged, positively charged, and neutral amino acid residues. Thenet energy of electrostatic interactions for charged residues interactingwith the Mg(H2O)3pppC1 fragmentsof the RNA is ca. 10% larger in absolute value for the UBDB+EPMM methodthan for point charges, while for interactions with the C2 and C3fragments, the net energies are almost the same . However, the presenceof RNA changes the global movement of the subdomains in IFIT5 aroundthe pivot subdomains. This movement can be effectively measured usinga pseudodihedral angle consisting of the centers of mass of subdomainI, subdomain II, pivot, and subdomain III. The analysis has shownthat this pseudodihedral angle in IFIT5 without ligands stays within60–70 degrees, whereas in the presence of RNA it shifts toaround 80 degrees . The localfluctuations shown in Figures S4, S6, and S10 suggest that the presence of RNA is stabilizing the protein. Inthe complexes, also increased mobility of the flap containing residuesArg192 and Glu193 is observed. We noticed that this flap frequentlyremains closed in the presence of RNA, whereas it is open and lessmobile in IFIT5 without the ligands.We have also investigated the influence of the presenceof RNAon the dynamics of the IFIT5 protein. For this purpose, we have performedthe simulations of the ligand-free IFIT5 in two protonation statesof Asp334, as the p3.65 estimated it as250–500 nM by the EMSA method. The following year Kumar etal.8 using the primer extension methodcalculated it as 372 (±21) nM. More recent studies by Miedziaket al.7 showed that the binding affinityof IFIT5 with pppRNA is equal to 42.7 (±1.6) or 113 (±12)nM depending on the presence of the magnesium cation, whereas bindingto GpppRNA or m7GpppRNA is weak  with different RNAs by various biophysical methods, and thedata are shown in We decided to compare the experimentalresults for our mutantsto the previous experiments, as IFIT5 mutants have been proposed andinvestigated since the structure of IFIT5 was described.5 and Katibah et al.9 showed only a weak influence on the binding of RNA to IFIT5. However,for T37A and Q41E, Katibah et al. showed a weak influence on binding,whereas for both T37V and Q41E, Abbas et al. showed a strong influenceon binding strength. Our calculations show that E33 does not havea significant value of electrostatic interaction energy; however,the penetration contribution is significant. T37 interacts electrostaticallyweakly, whereas Q41 interacts strongly, and both have a noticeablepenetration contribution. Moreover, the nucleotide with adenine atthe first positions has a slightly larger penetration contributionto the interaction than with cytidine and uracil. Unfortunately, inall biophysics experiments, RNA used contained A or G as the firstnucleotide and not C or U, so we could not compare our theoreticalcalculations directly with them.We can distinguish two kinds of protein regions on which authorsfocused. First, it is the pocket where the 5′-end of RNA isbound, mainly amino acids interacting with phosphate or the triphosphategroup or the magnesium cation. Here, e.g., E33A, T37V/A, and Q41Emutants were constructed. The results obtained for those residuesare debatable. For E33A substitution, both Abbas et al.2O)3N1 and with the sum of the three fragmentsof RNA, while R253 was the second and R186 was the third. K150 andR253 belong also to the residues for which the highest penetrationcontribution to the energy is observed. Also, our MST experiment confirmsthat the K150M mutant of IFIT5 has a strong influence on binding affinityto RNA, and the triple mutant Q41E/K150M/R253M influences it evenmuch stronger.The second group of amino acidsis positively charged amino acids,lysines and arginines, situated along the RNA binding site. They weremutated to neutral amino acids alanine or methionine, e.g., K150M,R186A, and R253M. Results of all research groups agree with the statementthat mutations of many lysine and arginine residues have a strongimpact on the binding affinity of IFIT5; however, for the R186 residue,the Katibah et al. group shows that although R186H strongly compromisedIFIT5 binding to 5′-p WNV30 RNA and cellular RNA, this substitutiondid not affect IFIT5 binding to 5′-ppp WNV30 RNA by EMSA. Ourcalculations confirm that these residues are important (includingR186), and we showed high electrostatic interaction energies for lysineor arginine residues interacting with the first RNA fragment. K150was the first strongest attractively interacting amino acid both withMg. Moreover,we found strong long-distance interactions which have not been distinguishedon the basis of geometry only in the article of Abbas et al. Lookingat the penetration contribution, it was possible to find all aminoacids with strong local interactions. With our calculations, we candefine the binding site without using the distance criteria. However,using distance criteria (5 Å) helps us to limit amino acids withlong-distance interactions to those belonging to the binding site,creating a better-defined binding site. Combining three filters usedby us , we were able to find the well-defined bindingsite of investigated complexes.Even though in investigatedcomplexes the geometries of some atomsdiffer slightly causing small differences in interaction energies,the total electrostatic interaction energy of the three complexesis comparable. Moreover, contributions of particular amino acid residuesto the electrostatic interactions with the RNA in the three complexesare comparable. Thus, we confirmed that the electrostatic energy ofIFIT5 interaction with pppRNA does not depend on the sequence of theRNA.


As local recurrence remains a challenge and the advantages of the simultaneous integrated boost (SIB) technique have been validated in photon radiotherapy, we applied the SIB technique to CIRT. The aim was to investigate the metabolomic changes of the CIRT with concurrent androgen deprivation therapy (ADT) in localized prostate cancer (PCa) and the unique metabolic effect of the SIB technique.68Ga-PSMA PET/CT and mpMRI images. Urine samples collected before and after CIRT were analyzed by the Q-TOF UPLC-MS/MS system. R platform and MetDNA were used for peak detection and identification. Statistical analysis and metabolic pathway analysis were performed on Metaboanalyst.This study enrolled 24 pathologically confirmed PCa patients. All patients went through CIRT with concurrent ADT. The gross target volume (GTV) boost was defined as positive lesions on both The metabolite profiles were significantly altered after CIRT. The most significantly altered metabolic pathway is PSMA participated alanine, aspartate and glutamate metabolism. Metabolites in this pathway showed a trend to be better suppressed in the SIB group. A total of 11 identified metabolites were significantly discriminative between two groups and all of them were better down-regulated in the SIB group. Meanwhile, among these metabolites, three metabolites in DNA damage and repair related purine metabolism were down-regulated to a greater extent in the SIB group.Metabolic dysfunction was one of the typical characteristics of PCa. CIRT with ADT showed a powerful inhibition of PCa metabolism, especially in PSMA participated metabolic pathway. The SIB CIRT showed even better performance on down-regulation of most metabolism than uniform-dose-distribution CIRT. Meanwhile, the SIB CIRT also showed its unique superiority to inhibit purine metabolism. PSMA PET/CT guided SIB CIRT showed its potentials to further benefit PCa patients. Prostate cancer (PCa) ranked as the fourth common malignancy worldwide  poses maFor definitive localized PCa irradiation, the clinical target volume (CTV) is mainly defined as the entire prostate and part of the seminal vesicle with a uniform dose delivery. Nonetheless, the local recurrence remains a challenge and is still under investigation, for several studies , 13 haveTo our knowledge, PCa has typical and unique metabolic profiles, with dysregulation in polyamines, tricarboxylic acid (TCA) cycle, amino acids, and fatty acids metabolism , and somAs the most downstream of omics cascade, metabolomics provides a comprehensive description of individual phenotypes. Previous studies have investigated the metabolomic changes after several therapeutic strategies: neoadjuvant chemotherapy with androgen deprivation therapy (ADT) , radical68Ga-PSMA PET/CT. Patient characteristics are summarized in From July 2020 to May 2021, 24 patients pathologically confirmed of staged II to III  prostate adenocarcinoma were enrolled in this study. One patient dropped due to acute toxicity, and finally, 23 patients were included in this analysis. The flow chart of this study is shown in 68Ga-PSMA PET/CT and mpMRI images.The treatment included two phases. CTV1 was defined as the entire prostate gland and inferior 1-1.5cm of seminal vesicles for intermediate-risk patients, and the entire prostate gland and inferior 2-2.5cm of seminal vesicles for high- and very high-risk patients. The shrink-down strategy was then used to spare bladder and rectum, while CTV2 was defined as the entire prostate gland. The delineation of the CTVs was consistent in both groups. The delineation of the CTVs complied with the ESTRO ACROP consensus guideline and was consistent in both groups . The GTV68Ga-PSMA PET/CT imaging (68Ga-PSMA-PET defined GTV was twice the volume (median 4.9 mL vs 2.8 mL) than mpMRI (The determination of the gross target volume (GTV) boost was critical work in CIRT. Normally, multiparametric magnetic resonance imaging (mpMRI) is recommended by the European Association of Urology and the European Society of Urogenital Radiology , the National Comprehensive Cancer Network  for PCa diagnosis. mpMRI is prominent in high soft-tissue contrast and is not only able to portray detailed distinction between the prostate and periprostatic structures but also clearly visualize intraprostatic lesions, which is proved to benefit the accuracy in radiotherapy planning. PSMA PET/CT is another advanced technique for PCa detection. Over 90% of intraprostatic tumor lesions are positive in  imaging . 68Ga-PSan mpMRI . Using Pan mpMRI  applied The CIRT was delivered by the Siemens IONTRIS particle therapy system. Syngo  with the local effect model (LEM) I was used for the treatment planning. The patients received the following irradiation doses: 55.2 GyE in 12 fractions (4.6 GyE per daily fraction), 65.6-72 GyE in 16 fractions (4.1-4.5 GyE per daily fraction). Patients with 55.2 GyE irradiation were included in the overall analysis and excluded in the comparison of the SIB group and the uniform-dose-distribution group (non-SIB group).For the SIB group and the non-SIB group, the prescription dose of the entire prostate gland was 65.6 GyE in 16 fractions and delivered within two phases. The total dose of 49.2GyE in 12 fractions was prescribed to CTV1 in the first phase and the prescription dose of 16.4GyE in 4 fractions was delivered to CTV2 in the second phase. For the SIB group, a total dose of 72 GyE in 16 fractions to GTV was simultaneously boosted, and the prescription dose to GTV was consistent. 95% of the prescribed dose should cover 100% of the CTVs and 90% of the isodose line should cover 100% of the PTVs. Bladder and rectum preparation were performed before each irradiation, and both in-room CT and 2D PORT film were performed for position matching. The standard for position error should be meet the following criteria: X, Y, and Z directions error should be less than 0.3cm, and the rotation error should be less than 1° as well. A 3mm margin outside GTV was also set up to make sure that the positive lesion could be fully covered with a 95% isodose line in case there was any position error. The total ADT duration was based on different risk groups. Patients underwent neoadjuvant ADT 2-3 months before CIRT, followed by concurrent ADT with CIRT, and adjuvant ADT thereafter. There was no significant difference (p=0.4251) between the two groups in the duration of ADT before CIRT.Urine samples were gathered within the 4 hours before receiving the first fraction and 4 hours after finishing the last fraction and then stored in a 4°C fridge immediately. Contaminated bacteria were removed by 0.22 μm membrane filters. 800 μL chilled methanol/acetonitrile  mixed solution was added to each 200 μL thawed sample. The mixture was centrifuged and the supernatant was transferred into a new microcentrifuge tube, and then evaporated to dryness. Re-dissolved the dry substance into a 100 μL chilled mixture of acetonitrile/water , and then transferred the solution into sample vials. Quality control (QC) samples consisted of the mixture of equal amounts (50 μL) of each sample.The urine samples were analyzed by the high-throughput ultrasensitive liquid chromatography, linked to tandem mass spectrometry (AB SCIEX ExionLCY system combined with 500R QTOF). The ACQUITY UPLC BEH Amide 1.7 μm (2.1 mm × 100 mm) column was used to separate the urinary metabolites. Mobile phase A: a mixture of 20 mmol ammonium hydroxide and 20 mmol ammonium formate to 1 L water and mixing thoroughly. Mobile phase B: acetonitrile. The flow program was set as follows: 0 min, 95% B; 14 min, 65% B; 16 min, 40% B; 18.1 min, 95% B; 23 min, 95% B. Electrospray ionization mode was used in the analysis. Auto-calibration was performed every five analysis. Four blanks and six QC samples were injected at the start for column conditioning, and the QC samples were analyzed every ten injections. Auto calibration was performed every five analyses.https://www.metaboanalyst.ca/MetaboAnalyst/ModuleView.xhtml) for statistical and metabolic pathway analysis. Concentrations of all metabolites were expressed as peak area and normalized according to the creatinine levels.The raw data was got from a Q-TOF UPLC -MS/MS, and peak detection and dataset alignment were performed by the R platform. MetDNA was used to identify the metabolites. The generated peak tables were uploaded onto the Metaboanalyst  analysis and Wilcoxon signed-rank test was used to identify statistically significantly different metabolites  and the heat maps were performed to show the differences of metabolite profiles. Wilcoxon signed-rank test was used for statistical analysis, and the significance level was defined as p<0.05. The significant separation shift between compared groups was tested using the unsupervised principal component analysis (PCA). Supervised multivariate analysis by partial least-squares discriminant analysis (PLS-DA) was performed to achieve maximum separation among the groups. The pathway analysis was performed between pre-CIRT samples and post-CIRT samples, and the significance level was defined as FDR<0.05, Impact>0.2. Box plots showed the mean value with the standard deviation. To compare the difference between the non-SIB group and the SIB group, the following statistical methods were applied. The volcano plot was used to identify statistically significantly different metabolites . Mann Whitney test and Pearson χ68Ga-PSMA PET/CT and mpMRI visibly positive lesion. We tried to compare the metabolic effect of these two CIRT strategies. All patients’ urine samples were collected at the beginning and the end of the whole course and then monitored by high-throughput Q-TOF UPLC-MS/MS. Finally, a total of 6468 peaks were monitored in patients’ urine samples.Urine samples from 23 patients were enrolled in the analysis. Among all patients, 13 patients received a 65.6 GyE uniform-dose-distribution CIRT and 7 patients received 65.6 GyE CIRT with a 72 GyE SIB to To explore CIRT induced metabolic alternation, we took pre-CIRT and post-CIRT urine samples from all patients into metabolomic analysis. The PCA and the PLS-DA analysis showed a clear separation between pre-CIRT and post-CIRT samples, which meant that the metabolite profiles were significantly altered after CIRT , shown in Post-CIRT values were normalized by the division of the corresponding pre-CIRT values to compare the differences of metabolic effect between these two strategies. The heatmap of normalized post-CIRT values showed that the SIB group had overall lower metabolites level than the non-SIB group, indicating the SIB CIRT technique had better inhibition on most metabolism than uniform-dose-distribution CIRT.  family, which involves in regulating the alanine, aspartate and glutamate metabolism pathway by cleaving the terminal poly glutamates connected with N-Acetyl-L-aspartate. The direct substrates of PSMA, such as N-acetylaspartylglutamylglutamate and N-acetyl-L-aspartate were significantly down-regulated or showed a trend to be down-regulated. Other substrates in this pathway like L-aspartate, L-asparagine, N-(L-arginino)succinate, and beta-citryl-L-glutamate were also significantly down-regulated. There were no significant differences in other metabolic pathways according to the criteria of FDR<0.05 and impact>0.2. Both groups showed a down-regulation of metabolites in this PSMA participated metabolic pathway, which indicated both schemes could nicely inhibit the metabolism of PSMA positive cells. It is worth noting that most metabolites in this pathway showed a trend to be suppressed to a lower level in the SIB group, which possibly meant that the SIB CIRT to the PSMA positive focal lesion may be more effective to inhibit the metabolism of PCa cells. This pathway was reported to be up-regulated in PCa tissues , and its18F-choline-PET/CT-directed SIB group had a trend to improved clinical results than the controlled group. The 5-year biochemical tumor control was 92% vs. 85% in the SIB vs. conventional groups (p=0.17). The 5-year OS was 100% and 88%, respectively (p=0.06) (111In Capromab pendetide (ProstaScint) scan) SIB with patients who did not receive SIB. The 5- and 10-year biochemical control rates for patients who received the SIB were 94% and 85%, respectively, while for patients who did not receive SIB were 86% and 61% (p=0.02).Several clinical trials have worked on the efficacy and safety of the SIB technique using photon irradiation. Results of FLAME randomized phase III Trial  indicate(p=0.06) . Meanwhi(p=0.06) . compareAll these clinical results concluded that the SIB technique was more effective and pretty necessary for localized PCa, yet studies seldom looked into the underlying mechanism. Up till now, the functional imaging-guided SIB technique has not yet been applied to CIRT according to our knowledge, and there have been few studies dug into the underlying mechanism. This is the first study using metabolomics to explore the distinction of the two CIRT strategies: SIB CIRT and the uniform-dose-distribution CIRT from the perspective of metabolism. Back to the results in this study, patients who received the SIB CIRT technique showed a greater extent of reduction after CIRT. The metabolite profiles clearly and intuitively showed that the SIB CIRT technique was more powerful to inhibit most metabolism of PCa than uniform-dose-distribution CIRT, which may be an explanation for better outcome of the SIB group in previous clinical trials –46.de novo purine synthesis impairs DNA reparation , the Natural Science Foundation of Shanghai (21ZR1481800).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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Y. pestis lcrV gene) were tiled to produce overlapping crRNA sets. We tested for possible effects that included crRNA/target sequence, size and secondary structures, and the commercial source of DNA oligomers used to generate crRNAs. Detection of a 981 nt target RNA was initially successful with 271 out of 296 tested guide RNAs, and that was improved to 287 out of 296 (97%) after protocol optimizations. For this specific example, we determined that crRNA efficacy did not strongly depend on the target region or crRNA physical properties, but was dependent on the source of DNA oligomers used for RNA preparation. Our high-throughput methods for screening crRNAs has general applicability to the optimization of Cas12 and Cas13 guide RNA designs.All CRISPR/CAS systems utilize CRISPR guide RNAs (crRNAs), the design of which depend on the type of CAS protein, genetic target and the environment/matrix. While machine learning approaches have recently been developed to optimize some crRNA designs, candidate crRNAs must still be screened for efficacy under relevant conditions. Here, we demonstrate a high-throughput method to screen hundreds of candidate crRNAs for activation of Cas13a collateral RNA cleavage. Entire regions of a model gene transcript ( Improved methods of predicting amino acid structure and function have allowed investigators to continue identifying CRISPR-Cas systems from new organisms, therefore growing the field and increasing potential for biotechnological innovation. Some examples include applications in microbial engineering3, agricultural engineering5, novel analyte detection6, disease-mediation7, stem cell development8 and viral detection9. CRISPR-Cas systems generally fall into two classes based on the number of required Cas components for full function, wherein Class 2  consists of single protein systems that are most useful for biotechnological development due to their relative simplicity10. There is considerable functional variability within this class, including DNA-identifying Cas9 that is able to hydrolyze specific DNA sequences and initiate directed genome editing, along with other proteins that utilize the RNA-identifying Cas12 to cleave DNA reporters9 or Cas13 to cleave RNA reporters11 that have been used as highly sensitive methods for nucleic acid detection.The field of nucleic acid detection has exploded in recent years following the discovery of Clustered Regularly Interspersed Short Palindromic Repeat (CRISPR)-Cas systems14 and review16. One particular application of interest involves coupling target amplification with Cas13a RNase activity that offers reproducible single molecule detection of nucleic acids with striking sequence fidelity11. The sensitivity of this assay, termed SHERLOCK, has generated immense interest for its potential in many areas of diagnostics9, sparked the generation of multiple companies, and become the focus of research programs for its transition to field applications. Such interest has additionally brought attention to increasing the accessibility of CRISPR-based detection, as there is an apparent need for universal and simplified crispr RNA (crRNA) design rules. Many investigators have dedicated work towards studying the required RNA structure and protein interactions with various Cas systems18 (and reviewed in19), but a universal ruleset for guide RNA design seems to be fleeting. There are many examples of design strategies proven successful in a variety of settings as discussed above, however each crRNA or CRISPR-array design is dependent on its associated Cas protein, and each of these harbor slight variations in sequence fidelity and length intrinsic to their unique properties. Therefore, all CRISPR applications require some degree of optimization, prompting some investigators to develop algorithms20 and methods22 to relieve these bottlenecks in the interest of accessibility. Extensive trial and error, however, is still common for the understanding and optimization of many applications.The recent global focus on genetic target detection has been met with the design of multiple systems to streamline the application of CRISPR-Cas to this end, and this topic has been the focus of extensive recent study9, and subsequent studies have demonstrated the application of these principles to differentially identify the lcrV gene from three Yersinia spp. with high sequence similarity23. This gene encodes a Y. pestis virulence factor that has evaded antigen-based detection methods. Furthermore, the investigators optimized shortcuts in the SHERLOCK protocol that greatly reduced bench time without sacrificing detection sensitivity to allow for rapid assessment of hundreds of tiled crRNAs. Tiled crRNAs in that study, however, yielded variable Cas13a activation results despite homology to adjacent target RNA sequences. We suspected that some target RNA sequences might be inaccessible for Cas13a activation due to inherent secondary structure or sequence bias, but it remained unclear how to predict such difficult intrinsic nucleic acid criteria when designing crRNAs. As such, we sought to supplement the demonstrated workflow and shed light on the mechanisms crRNA efficacy.Recent investigations have shown the SHERLOCK assay to be a successful viral detection strategyY. pestis lcrV gene that represented regions previously observed to have variable Cas13a detection efficacies. Our intention was to survey sequence regions on a nucleotide by nucleotide basis that corresponded to consistent and inconsistent Cas13a detection to assess why some crRNAs fail to detect a target RNA when others do not. Considering the success of the streamlined crRNA production and test protocols without the loss of SHERLOCK detection sensitivity23, we decided to simply assay Cas13a activation to represent the end of the SHERLOCK assay using a high-throughput format via acoustic liquid handler. The results of this effort include thousands of Cas13a activation assays on multiple lcrV-derived targets in a number of experimental formats that allowed us to identify characteristics that aid or deter from successful design or implementation of nucleic acid detection strategies. As a result we noted the initial success of the majority of our crRNA (> 91%) regardless of target region or nucleic acid physical properties. We furthermore found that this success could be improved to 97% through altering RNA preparations, target RNA length, and most importantly by changing the commercial source of DNA oligomers used for crRNA preparation. These findings build on previous efforts in the field to underscore the accessibility of CRISPR-Cas protocols for modern biotech applications, and demonstrate the potential scalability of multiplex assays through the use of liquid handling technology and optimized batch reagent preparation protocols implemented without compromising the specificity and sensitivity for which CRISPR-Cas is known.Here, we tested hundreds of candidate crRNAs for activity bias with Cas13a. Sets of hundreds of crRNAs were tested targeting discrete sequence ranges of the lcrV gene from Y. pestis strain CO9223, where the investigators showed activity from 4 pairs of crRNA with either consistent (cr5/cr6 and cr10/cr11) or inconsistent (cr15/cr16 and cr20/cr21) Cas13a activation. Similarly, each crRNA maintained an architecture consisting of a 39 nt direct repeat for Leptotrichia wadei Cas13a (LwaCas13a)24 and a 28 nt spacer region targeting the lcrV gene. For our purposes, we expanded the regions targeted by the four pairs of previously characterized crRNAs into four regions ranging 102–108 nt in length, within which the spacer sequences of 74 crRNAs were tiled per nucleotide  or Integrated DNA Technologies  and listed in Supplemental Table 23.The crRNA molecules were obtained by conducting in vitro transcription (IVT) of DNA oligonucleotides as previously describedlcrV gene from Yersinia pestis strain CO9225 were amplified using primers listed in Supplemental Table lcrV fragments included T7 promotor sequences for its incorporation into the amplicons. Two separate fragments of lcrV were amplified: the full length version (981 nt) using the primers T7lcrVF and YpestislcrVR and the short version  containing the target sequences for the first group of crRNAs using the primers RPA56F and RPA56R. Transcription reactions were set up using 2 μL of unpurified DNA amplicon preparation, 2 μL of T7 RNA polymerase, 10 μL of 2 × NTP buffer and 16 μL of nuclease-free ddH2O  and incubated at 37 °C for 2 h, after which 5 μL Turbo DNAse  and 15 μL of nuclease-free ddH2O were added  and incubated further 30 min at 37 °C to remove the template DNA. The transcript preparations were cleaned up using using RNA Clean and Concentrator 25 kit  according to manufacturer’s instructions. RNA concentration was determined using a Qubit fluorometer and RNA BR (broad range) Assay kit  and diluted to 50 ng/μL (full length target) or 12 ng/μL (short target) for use in Cas13a activity assays.Target RNA sequences were prepared also using the same HiScribe in vitro transcription system as used for crRNA synthesis. Fragments of the Both crRNA synthesis and Cas13a activity assays were conducted using a high throughput workflow in 384 well plates with fluid transfer handled by an Echo 525 acoustic liquid handler  using the Plate Reformat software provided by the manufacturer.g to bring all the liquid to the bottom of the wells and remove air bubbles. The plate was sealed using MicroAmp Clear Adhesive Film sealer  and incubated for 2 h at 37 °C. After incubation, the plates with transcribed crRNAs were stored in − 80 °C. For long term storage MicroAmp sealers were replaced with Adhesive PCR Sealing Foil .In order to generate crRNAs in this manner, 64 crRNA transcription reactions were set up using reagent volumes as described above for an individual reaction. This included 62 test crRNAs, a positive control crRNA (set 0056 crRNA 7) and a negative control (crRNA template oligo replaced with TE buffer) as shown in Fig. L. wadei11 which was synthesized and purified by GenScript Biotech . The enzyme was stored and diluted in buffer . Each nuclease activity assay was performed in 20 μL reaction that included 1 μL of 1 μM Cas13a, 1 μL of 2 μM RNase alert v.2 , 17.2 μL of nuclease assay buffer , 0.4 μL of crRNA (from unpurified transcription reaction) and 0.4 μL of target RNA (50 ng/μL for full length target or 12 ng/μL for short target). For each crRNA a total of six reactions were set up, with three target negative reactions and three target positive. First, master mix containing all reaction components except for crRNA and target RNA were distributed using the Echo from 6 well Echo qualified Reservoir plate to a 384 well assay plate . A total volume of 19.2 μL of the master mix was transferred to each well. Next, 0.4 μL crRNAs from previously prepared 384 well microplate were transferred using the Echo to the wells containing the master mix in such a way that each crRNA was added to 6 subsequent wells in the reaction plate . Finally, 0.4 μL of the target RNAs (previously placed in the area of the crRNA plate not occupied by transcribed crRNAs) were added to three of the wells for each crRNA . The Cas13a reaction plates were spun briefly in a centrifuge at approximately 1500 × g to bring the liquid to the bottom of the wells and remove air bubbles. Immediately after spinning, the reaction plates were sealed using the MicroAmp sealers. The plates were incubated in a Biotek Synergy Neo2 plate reader  at 37 °C (unless otherwise noted) and fluorescence was read from the bottom of the wells every 5 min for 2 h using excitation at 490 nm, emission at 520 nm and gain set at 100.To determine the efficacy of each crRNA, Cas13a nuclease activity assays were conducted using Cas13a enzyme from lcrV target RNA of 981 nt or the 237 nt truncated version as noted in each experiment. Experiments involving variations in temperature were assembled by hand to the same reaction concentrations as above, but were incubated in the Bio-Tek plate reader for fluorescence monitoring at the specified reaction temperatures of 37 °C, 42 °C, 47 °C, 52 °C or 57 °C. Other experiments involving temperature variations included the combination and pre-incubation of full-length lcrV target RNA and crRNA 0056–07 at 60 °C for 15 min before the addition of Cas13a and buffer components, followed by fluorescence monitoring of the reaction carried out at 37 °C.All Cas13a assays took place following this protocol using either the full length 26. Analysis of variance was carried out where appropriate using rstatix27, and principal component analyses were computed with factoextra28.All reactions, control or experimental, were carried out in triplicate. The performance of the crRNA was calculated by subtracting the sum of averages of fluorescence measured for template negative samples over the course of the experiment (25 measurements) from sum of averages for template positive samples. Each sum was normalized to that of an internal control  that was run on each plate. Data was visualized using R package ggplot217, we sought to obtain a deeper understanding of the requisite criteria for Cas13a activation. We therefore generated several sets of crRNAs and screened their resultant Cas13a activation potential to this end. Given the recently highlighted shortcomings of peptide-based identification methods against the lcrV gene from Y. pestis23, we used this gene as a template for the design of 296 crRNAs focusing on four distinct regions 29 that itself is a source of technical variability, we elected to fix target RNA concentrations throughout experiments without RPA to focus on the Cas13a activation potential of each crRNA. This simplification allowed us to observe activity against the 981 nt target RNA in a high throughput format using the LabCyte Echo acoustic liquid handler.The use of Cas13a to recognize difficult pathogens provides an accessible method with high potential for easy adaptation for emerging targets. Successful adaptation, however, depends on the identification of viable crRNAs. While crRNA design criteria exist for various Cas protein activitiesThe raw data from each Cas13a activation reaction appeared as fluorescence intensity traces over the 2 h reaction time at 37 °C , however, also produced unexpected results such as small numbers of inactive crRNAs within groups of active crRNAs. These results were more easily visualized when transforming the data into summary plots and normalizing them to our internal controls Fig. B. Certai18, and target homopolymer preferences30. However, our experiments did not involve CRISPR arrays that may have resulted in crRNA misfolding. Additionally, there was a poor likelihood of inactive crRNA secondary structures occurring between adjacently tiled crRNAs which only had a 3% change to nucleotide composition, especially considering that 60% of each crRNA sequence was conserved in all designs (as predicted by T-coffee). We therefore concluded that previous reports of crRNA structure negatively affecting Cas activation were unlikely to explain our observations of adjacent crRNAs in a series with different Cas13a activation capabilities.When analyzing the fluorescence intensity and the production times of the active, inactive and poorly active subsets, it became apparent that the poorly active subset presented a problem. Cas13a activation should be easily observed considering the standardized reaction components and the high rate of Cas13a hydrolysis, and the resulting quick production of peak fluorescence intensity was observed for most of the effective crRNAs. In fact, these assays were designed such that the only variable affecting fluorescence accumulation would be the concentration of activated Cas13a. This activity is not guaranteed, as previous studies have shown Cas enzymes to be poorly activated due to factors such as misfolded pre-crRNA produced in CRISPR arrays, complementarity of the spacer and target sequences below threshold levelsWe hypothesized that the poor Cas13a activation we observed could arise from a number of sources. One was the RNase contamination of the crRNA that would result in significantly less Cas13a-activation capabilities. Another possible source could have been due to design flaws reducing the complementarity between the crRNAs and target mRNA. A third source of poor Cas13a activation could be the degree of structural heterogeneity of the 981 nt target RNA, wherein multiple complex secondary structures exist in equilibrium that affect the concentration of target RNA with accessible sequences for nucleoprotein complex formation. We therefore designed experiments to further investigate each of these potential sources for inconsistent Cas13a activation.Our first hypothesis to explain poor Cas13a activation was that the quality of specific crRNAs was poor due to low concentrations arising from nuclease contamination. Indeed, RNase contamination is a ubiquitous danger to all RNA experiments. Fortunately, the indicator compound used for our assays was developed as a detection agent for RNase contamination, and it was therefore easy to observe whether our crRNA was contaminated by checking the fluorescence traces of negative control reactions lacking target RNA  could easily contribute to varying accessibility of sequence regions, thereby affecting the ability for consistent crRNA binding that ultimately leads to reduced Cas13a activation.Nucleic acid structure has been known to convey interesting complexity and activity to specific sequences for a long time37 therefore appeared to provide the nucleoprotein complex with stability against the subsequent temperature changes. Considering our relative abundance of reagents in the reaction (~ 20-fold more Cas13a and ~ 300-fold more crRNA than target RNA), all of the Cas13a should ideally be ready to seek the target RNA, which was set as the limiting reagent for the reaction. Therefore these reaction conditions showed no evidence of changing target RNA binding.We decided to alter the structure of our target RNA to test whether there were any structural effects on Cas13a activation. As any denaturant meant for the target RNA would similarly affect the crRNA and protein, we sought the use of heat as a mild and accessible option to incrementally alter nucleic acid secondary structure. Keeping in mind that protein stability could not be compromised, we utilized incremental increases in reaction temperature with the expectation that they might provide structural changes to present previously inaccessible regions of the target RNA. Considering that the crRNAs in question appeared inactive only in narrow sequence windows with respect to the target RNA, we believed any existing secondary structure affecting Cas13a activation would not require extensive denaturation for its disruption. Four crRNAs were selected for these assays that were complementary to sequences within the 1011 region, three of which had been classified “inactive” , and one that was “active” (crRNA 1101-07). Reactions were prepared by hand at room temperature and then incubated for the Cas13a assay at the specified temperatures Fig. . While t38, our results suggested that temperature changes would not be able to rescue the inability to activate Cas13a by these crRNAs. Interestingly, our results also showed the resilience of the Cas13a assay to suboptimal reaction conditions, wherein Cas13a activity could still be observed after dramatic changes in both reaction temperature as well as reaction preparation.We reasoned that if the incremental increases in reaction temperature were not enough to nudge the potentially inhibitory target RNA structure into an accessible one, then perhaps it may be accomplished by pre-heating RNA before combining with Cas13a. We therefore pre-heated both target RNA and crRNA together at 60 °C for 15 min before the addition of Cas13a and incubation at 37 °C. Again, we observed no changes in Cas13a activation. Similar to previous temperature experiments, the observed fluorescence intensities were lower than reactions carried out at 37 °C, but Cas13a activation was concordant with other reactions at this temperature. Considering that all structural changes and molecular associations took place in a cooling solution (37 °C at the lowest) with no incubation time before analysis, we were intrigued to find that the RNA folding, protein conformational change, and complex association steps were still successful. While previous investigators have shown small temperature changes are enough to significantly alter RNA structural equilibrium between ON and OFF states related to downstream activityY. pestis lrcV gene, and previous experiments had indicated that if target RNA structure was altering the Cas13a activation potential of similar crRNAs, then that structure was not easily disrupted with heat. We therefore shortened our target RNA to a 237 nt section within the 0056 region of the lcrV gene (Fcigure 6A). As discussed above, this set contained 8 crRNAs that continued to display poor Cas13a activation after repeating assays with freshly prepared RNA. We therefore repeated crRNA preparation and carried out assays against the new shortened target  represented 15 crRNAs that previously were either “poorly active” or “inactive”. We used these oligos to perform batch IVT using the same methods as before and observed large differences in Cas13a activity . While algorithm development for crRNA performance prediction can indeed be useful, we believe the true value of a design tool to be the target identification capabilities of broad-targeting crRNAs across sequence diversity with minimal experimental input. With this application in mind, the above-mentioned tools in addition to unpublished ones such the ADAPT algorithm20 show promise in minimizing the number of crRNAs required to span the phylogenetic diversity of emerging bacterial and viral species. Building from previous structural and activity studies on various Cas proteins, the guidelines for mature crRNA design employed in this study are streamlined. Our experiments furthermore appear to indicate that finding the “perfect” target sequence for crRNA design is relatively unimportant, allowing design algorithms that can focus on capturing phylogenetic diversity. While we tested neither a variety of targets nor an extensive set of crRNAs, we did note that the systematic production of 296 crRNAs varying only within the 28 nt spacer region of a 67 nt shell and targeting 4 distinct regions of a single 981 nt RNA resulted in successful Cas13a activation in > 91% of experiments without optimization  less than half a day. Compared to the production times of antibodies and their efficacy shortfalls, our data reinforce the power of modern nucleic acid identification methods that, coinciding with the numerous examples of associated sequence discrimination capabilities in the last decade, indicate the value of a concerted direction and organization for the continued implementation of wide-scoped diagnostic tools.The feasibility of generating large amounts of reproducible data with minimal user input further improves each day. Here, we have shown that by employing Acoustic Liquid Handling technology we continued to increase workflow efficiency of Cas13a assays building on previous workSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.