Design, Synthesis, and Experimental Validation of Peptide Ligands

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Design, synthesis and experimental validation of peptide ligands targeting Mycobacterium tuberculosis # factors Sneha Vishwanath, Sunaina Banerjee, Anil Kumar Jamithireddy, Narayanaswamy Srinivasan, Balasubramanian Gopal, and Jayanta Chatterjee Biochemistry, Just Accepted Manuscript • DOI: 10.1021/acs.biochem.6b01267 • Publication Date (Web): 29 Mar 2017 Downloaded from http://pubs.acs.org on March 30, 2017

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Design, synthesis and experimental validation of peptide ligands targeting Mycobacterium tuberculosis σ factors Sneha Vishwanath, Sunaina Banerjee, Anil K. Jamithireddy, Narayanaswamy Srinivasan*, Balasubramanian Gopal & Jayanta Chatterjee Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India * Author for correspondence: [email protected] Funding: This research is supported by the Indian Institute of Science – Department of Biotechnology (DBT) partnership program, funding for the structural biology of TB proteins (DBT) as well as by the Mathematical Biology initiative sponsored by the Department of Science & Technology (DST), Government of India and Indo-French collaborative grant (CEFIPRA). We acknowledge funding for infrastructural support from the following programs of the Government of India: DST-FIST, UGC Center for Advanced Study, and the DBT-IISc Partnership Program. NS is a J.C. Bose National Fellow.

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ABBREVIATIONS

σ, Sigma factor; RNAP, RNA polymerase; Mtb, Mycobacterium tuberculosis; CD, Circular dichroism; BLI, Bio-layer interferometry; BSA, Bovine serum albumin; SDS, Sodium dodecyl sulphate; H-bonding, Hydrogen bonding.


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ABSTRACT Transcription in prokaryotes is a multi-step process and is primarily regulated at the initiation stage. Sigma factors are involved in promoter recognition and thus govern prokaryotic gene expression. Mycobacterium tuberculosis sigma factors have been previously suggested as important drug targets through large-scale genome analyses. Here we demonstrate the feasibility of specific targeting of Mtb sigma factors using designed peptides. A peptide library was generated using 3-D structural features corresponding to the interface regions of sigma factors and the RNA polymerase. In-silico optimisation of the peptides, employing structural as well as sequence features, aided specific targeting of sigma factor A and sigma factor B. We synthesised and characterised the best hit peptide from the peptide library along with other control peptides and studied the interaction of these peptides with sigma factor B using bio-layer interferometry. The experimental data validate the design strategy. These studies suggest the feasibility of designing specific peptides via in-silico methods that bind sigma factor B with nano-molar affinity. We note that this strategy can be broadly applied to modulate prokaryotic transcription by designed peptides, thereby providing a tool to study bacterial adaptation as well as hostpathogen interactions in infectious bacteria.


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Sigma factors (σ) of Mycobacterium tuberculosis (Mtb) have been identified as highconfidence drug targets from large-scale analysis of interactome, reactome and genome-scale structural analysis (1). The interaction between σ and DNA-dependent RNA polymerase (RNAP) is fundamental for promoter recognition and initiation of transcription in prokaryotes (2). A σ factor binds RNAP to initiate transcription and dissociates during the elongation phase or at the termination of transcription (2, 3). Thirteen σ (σA-M) from Mtb have been characterised till date (4-6). Of these, σA is the principal transcription factor (group I) involved in regulation of many house-keeping genes (7), σB is referred to as a principal-like transcription factor (group II) (4, 8), σF is classified as sporulation/alternative σ factor (group III) (9) while the rest (σC-E,

G-M

) are

extra-cytoplasmic function σ (ECFs, group IV) (4, 5). Despite similar functional roles, these four groups of σ show high sequence divergence (10). σA consists of four functional domains viz. region 1, region 2, region 3 and region 4. σB has a truncated version of the region 1 while σF lacks region 1 altogether. ECFs have only two regions, viz. region 2 and region 4 (3, 11, 12). Bioinformatics analyses and structural data suggest that the common functional domains among Mtb σ (region 2 and region 4) have high structural similarity despite poor sequence conservation (13). Region 1 has been reported to auto-inhibit binding of σ to DNA in the absence of RNAP but the mechanism of auto-inhibition still remains elusive. This N-terminal polypeptide segment of σA is enriched in negatively charged residues leading to the hypothesis that it occludes DNA binding due to electrostatic repulsion (4). Region 2 and region 4 recognise the -10 and -35 promoters respectively while region 3 recognises the extended -10 promoter (3, 12). Regions 2, 3 and 4 bind exclusively to β, β’ and α-subunit of RNAP (12). Interactions between the RNAP β-


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subunit and region 4 have been reported to be important for the docking of RNAP onto σ and subsequent promoter recognition.

Given the importance of interaction between RNAP and σ for the processivity of the transcription cycle and its implication in the survival of Mtb, interface regions between RNAP and σ are interesting drug targets. Many lines of evidence support protein-protein interface of RNAP and σ as a viable drug target. Few of these are (i) gene expression of σ is sensitive to stoichiometry and affinity of σ and RNAP (14); (ii) σ are recycled after every transcription process (15) and (iii) lethal mutations have been reported in σ which abolish RNAP and σ interactions (5, 13).

Of late, there has been a revived interest in peptides as promising candidates for targeting protein-protein interfaces (16). Given characteristics of protein-protein interfaces viz. flatness and large interacting area, peptides have been shown to be better binders at the interface than other kinds of small molecules (17, 18). Another interesting aspect of peptides, which makes peptides advantageous over other kinds of small molecules, is the associated inherent conformational flexibility of peptides. A higher degree of conformational plasticity of peptides allows them to adapt to mutations in the target protein (19). Here, we have designed peptide ligands of σ factors of Mtb which might be useful candidates to abrogate RNAP - σ interaction as well as to study transcription regulation in bacterial systems. Since σA and σB play an important role in virulence (20) and stress response (21), we directed our design strategy to the interface between σA/σB and RNAP. A structure-based approach was adopted to identify peptide fragments from RNAP that can bind to σ at regions critical to recognition of RNAP. An in-silico


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design protocol, encompassing both sequence and structure features, was employed to generate a library of peptide ligands with predicted affinities better than the peptide fragment extracted from Mtb RNAP. One of the best in-silico predicted peptide ligands was further validated invitro; thus supporting the design strategy.

MATERIAL AND METHODS

In-silico structural modelling and optimisation. No experimentally determined structural model of holo- or apo- forms of Mtb RNAP is available in protein databank (PDB) (22). However, structural models were generated for RNAP-σA and RNAP-σB complexes using multiple template homology-based approach. Sequence alignments of queries and templates were generated using MAFFT-E-ins algorithm (23). Structural models were generated using the homology-based modelling tool MODELLER v9.12 (24). Side-chain conformations of the models with the highest DOPE score (25) and best GA341 score (26) were refined using the SCRWL 4.0 library (27) and the structures were energy minimised using GROMACS (28) with conjugate gradient as step integrator to remove any short-contacts, if any. Stereochemical quality of the refined models was ensured using PROCHECK (29) and ProQ (30). If a reliable template was not obtained from PDB, a structural model of the protein was generated using iTasser server (31). An important point to note here is that the structural models of both σA, as well as σB, represent the conformation they adopt when bound to RNAP and further analyses have been done using these conformations. Experimental information on the conformation acquired by σA or σB in unbound form is still elusive thus could not be modelled.


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Interface residues of RNAP and σA/σB were identified from the structural models using the standard van der Waals radii cut-offs, where any two residues are assumed to be interacting if the distance between any two atoms in the residues is at most 0.5Å greater than the summation of the respective van der Waals radii (32). The nature of the interactions between the interacting residues was identified using the in-house protein interaction calculator (PIC) (33). Interface regions of σA/σB were then visually scanned for peptide binding pocket and interface regions of RNAP with σA/σB for contiguous peptide fragments. Both peptide binding pockets and peptide fragments were identified as a cluster of spatially associated residues with a high number of participating interface residues from both chains. The identified binding pockets and the peptide fragments were further characterised for sequence conservation, solvent accessibility and enrichment in hot-spots to identify the best peptide binding pocket and the peptide fragment. The sequence conservation was calculated using CONSURF server (34) using default parameters. It scores each residue in the range of 1-9, with a score of 9 representing high conservation of residue and score of 1 representing poor conservation. NACCESS algorithm (35) was used for the calculation of solvent accessibility. A residue has been defined as solvent exposed if the relative solvent accessibility is greater than 7%. Hot-spots have been identified in-silico using the FOLDX package with FOLDX force field (36). FOLDX force field is an effective empirical energy function including terms for expressing implicit and explicit desolvation, van der Waals forces, hydrogen bonding, coulombic electrostatics, dipole interactions and change in entropy. A residue was identified as a hot-spot for the interaction if ∆∆Gbinding on mutation to alanine is greater than or equivalent to 1 kcal/mol. FOLDX has been successfully used earlier for design and modification of protein-protein interfaces as well for identification of hot-spots (36-39). Identification of hot-spots in both binding pocket and peptide fragment is required to understand


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the contributions of each interface residue to the interaction energy. Residues with high contribution to interaction energy are important sites for manipulation of protein-protein interfaces.

The identified peptide fragment was further optimised in-silico using the FOLDX package (36) to enhance the binding affinities with σA/ σB as well as the stability of peptides. Briefly, each position of the peptide fragment was mutated to every other 19 amino acids using the POSSCAN module of the FOLDX package (36) and ∆∆Gbinding was calculated. If ∆∆Gbinding is negative i.e if ∆Gbinding of the mutant form is greater than the wild-type, the amino acid in the mutant form is considered more favourable with respect to the amino acid in the wild-type form. Each position in the peptide fragment is then enriched with favourable amino acids, obtained by the intersection of the favourable amino acid for σA and σB, and the wild-type amino acid. Thence, using all the permutations and combinations, a peptide library was generated (Figure 1).

The peptide library was then screened for the stability of peptides in-silico. The energy of the wild-type was considered as reference and stability of other peptides was calculated with respect to it using the BUILD module of the FOLDX package (36). The BUILD module was also used to generate the structural model of peptides. While generating the models and calculating the stability, the mutant peptides and the wild-type peptide are assumed to have the same conformation as that observed in the structural model of β-subunit i.e. helical conformation. Peptides with stability worse than the wild-type were removed from the peptide library. Also, peptides with van der Waals clashes more than 0.5 kcal/mol were removed from the library. Peptides with better stability and lower intra van der Waals energy were again screened for


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negative ∆∆Gbinding and inter van der Waals energy using the ANALYSECOMPLEX module of the FOLDX package (36).

The refined library was then scanned in-silico for aggregation propensity and helix propensity. Peptides showing a high propensity for aggregation, calculated using AGGRESCAN algorithm (40), were removed from the library. Peptides are believed to show higher aggregation tendency if the normalised average aggregation propensity values per amino acid (Na4V), as reported by AGGRESCAN (40), is equivalent to or greater than 0. Percentage of helicity for each peptide was predicted using AGADIR algorithm (41-43). AGADIR predicts helicity of peptides based on helix-coil transition theory (44). Percentage helicity was predicted for each peptide at 300K, pH 7 and 0.1M ionic strength. For the calculations, peptides were assumed to be N-terminal acetylated and C-terminal amidated. All the peptides, having percentage helicity lower 1%, were removed from the peptide library. The rationale for this selection was that helicity is likely to be important for interaction as the wild-type is helical in nature.


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Figure 1. Pipeline for in-silico optimisation of the peptide fragment to generate a library of peptide ligands of σA and σB. Each position in the peptide fragment was mutated to every other 19 amino acid to identify favourable amino acid for interaction at that position. Each position is then enriched with the amino acids which show better interaction energy with both σA and σB. The peptide library was then screened for various features viz. helicity, stability and aggregation.

Peptide synthesis and characterisation. N-terminal acetylated/N-terminal biotinylated and C-terminal amidated peptides were synthesised by a solid-phase approach using standard Fmoc chemistry in a solid-phase reactor. An extra glycine was added as linker at the N-terminal of the peptides in the case of N-biotinylated peptides. Methionine in the peptides was substituted by isosteric norleucine to avoid oxidation of methionine during peptide synthesis and storage. The synthesised peptides were purified by RP-HPLC-C18 column and the molecular mass was confirmed using MALDI mass spectrometry. The peptides were maintained as a stock solution of 5mM in de-ionised water with 5% DMSO at -20˚C. 10 ACS Paragon Plus Environment

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Circular dichroism was used to study the structural content of the synthesised peptides. CD spectra were acquired at 298K using a 1mm path length quartz cell using JASCO J-715 spectrophotometer in the measurement range of 190-240 nm. The stock solution of the peptides was diluted with distilled water (pH 6.8) to 10 µM for these experiments. The helical content of the peptides was calculated using the following formulae (45): % helix100

[θ]

2.57 -39500 1- n

where n is the number of total peptide bonds and [θ] is the mean residual ellipticity (MRE) at 222 nm.

Protein preparation and characterisation. The gene coding for σB was cloned into pET15b expression vectors (Novagen Inc.). After the plasmid had been transformed into BL21(DE3)* (Novagen Inc.), the cells were grown in Luria broth with antibiotic (100 µg/ml ampicillin) to an A600 of 0.5–0.6 at 310K. The cells were then induced with 0.4mM IPTG. Subsequently, the growth temperature was lowered to 290 K, and the cells were grown for 12–14 h before they were spun down. Cells were lysed by ultra-sonication in lysis buffer (60 mM Phosphate (pH 8.0), 10% glycerol and 250 mM NaCl). The cell-free lysate was incubated with Ni+2-NTA beads, washed with 2M NaCl to remove nucleic acid contamination and eluted by an imidazole gradient in the elution buffer (60 mM Phosphate (pH 8.0), 250mM NaCl). Recombinant protein was further purified by size-exclusion chromatography using a Sephacryl S-200 column (GE Healthcare) after the affinity chromatography step. For analytical gel-filtration experiments, 0.2mg to 0.5mg protein samples were passed through a Superdex 200 10/300 GL column


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equilibrated in 25mM Phosphate (pH 8.0) and 125 mM NaCl at 6 °C. The protein was then concentrated using a membrane-based centrifugal ultra-filtration system (Amicon-Ultra). The purity of the protein was confirmed by SDS-Polyacrylamide gel electrophoresis. The foldedness of the protein was checked using UV spectroscopy and CD spectroscopy.

Bio-layer interferometry binding studies. Interaction of different peptides with σB was studied using bio-layer interferometry (BLI). BLI detects binding between ligand and analyte using the difference in the interference pattern in the absence and presence of the analyte (46). This system monitors the thickness of molecules at the tip of a fibre optic sensor in terms of interference of light. The experiments were conducted using the Octet Red System (Forte bio, Menlo park, California, USA). Biotinylated peptides were immobilised onto kinetics grade Streptavidin biosensors. The peptides, coupled to the biosensors, were titrated with σB at increasing concentration in a buffer with 25mM phosphate, 125mM NaCl (PBS) with 0.1 mg/mL BSA and 0.05% (v/v) TWEEN 20. 0.01% BSA and 0.05% (v/v) TWEEN 20 were used to avoid non-specific interaction of σB with the sensor surface. The analyte was subsequently allowed to dissociate in PBS with 0.1 mg/mL BSA and 0.05% (v/v) TWEEN 20. The ligands were regenerated for reuse by washing the bound analyte (σB) with 1M MgCl2. A loaded biosensor with just interaction buffer was used as a control for non-specific interactions. After the acquisition of the binding profiles, the profiles were analysed using the BIAevaluation 3.0 package. The association and dissociation kinetics were fitted separately and globally to determine the best fit. Binding kinetics was calculated from the kinetic dissociation and kinetic association rate constants.


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RESULTS

Homology modelling of Mtb RNAP-σA and RNAP-σB complexes. Structural models for RNAP-σA and RNAP-σB complexes were generated using a homology-based approach using MODELLER (24) with multiple templates (Table S1). Models could be generated with high query coverage for all the subunits of RNAP viz. α, β, β’ and ω and σB, except σA. The C-terminal of the second α-subunit which interacts with β’-subunit could not be modelled because of the absence of electron density in the crystal structure of the templates. Query coverage of 45.3% could be achieved for σA, with the majority of the density information missing for region 1 of the templates. On assessing the sequence alignment of σA with the templates, poor alignment with many gaps has been observed for region 1.1 and region 1.2, but a good quality sequence alignment has been observed for region 2, 3 and 4 (Supplementary text S1). The quality of the sequence alignments was manually assessed using structural features of the templates represented by JOY 3.2 (47). Subsequently, only regions 2, 3 and 4 of σA were used for further analyses. The region boundaries were obtained from Pfam database (48). Cartoon and surface representations of structural models of RNAP-σA and RNAP-σB have been shown in Figure 2A and 2D respectively.

Identification and characterisation of peptide fragment and binding pocket. Using the structural models, interface residues of RNAP and σA/σB were identified using distance criteria (32). Both σA and σB have an extensive interaction interface with β and β’ (Figure 2B and 2E). Interface regions are mainly enriched with hydrophobic interaction and H-bonding. On manual analysis of the interface regions between σA/ σB and β and σA/ σB and β’, a binding pocket in


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region 4 of σ and a contiguous 16 amino acids long helical peptide fragment (the β flap-tip helix) from the β-subunit (Figure 2C and 2F) were identified. The interface region between the binding pocket and the peptide fragment is enriched with hydrophobic interactions with very few other types of interactions (Figure S1).

An analysis of drug-resistance conferring mutations in RNAP reveals no drug resistanceconferring mutations in the regions of our interest in this enzyme (Tuberculosis Drug Resistance Mutation Database (49)). While it is likely that this absence of drug-resistance mutations in the region of interest is correlated with the fact that this region has not been a target for therapeutic intervention, it also suggests a lack of allosteric interactions involving this region of a sigma factor.

This finding suggests that specific targeting of the pocket of region 4 is a viable

proposition.

Figure 2. Structural model of RNAP with (A) σA and (D) σB. The α-subunits are coloured light green, the β-subunit is coloured light pink, the β’-subunit is coloured yellow, the ω-subunit is


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coloured grey and the σ-subunit is coloured light blue. Both σA and σB form extensive interface with the β- and β’- subunits. The interface residues are represented as spheres for (B) σA and (E) σB. Interacting residues of β-subunit with σA/σB are represented as red spheres. The blue spheres represent the interface residues of the β’-subunit with σA/σB. The interface residues of σA/σB with the β-subunit are represented by teal spheres and the interface residues of σA/σB with the β’subunit are represented as purple spheres. On analysing this extensive interfacial area, a binding pocket in both the σA and σB and a peptide fragment from the β-subunit (the β flap-tip helix) have been identified. Surface and cartoon representation of the pocket and the peptide fragment (encircled in black) are shown for (C) σA and (F) σB. Template details are summarised in Table S1.

Not all the pockets on the protein surface will be equally druggable because of various restraints. Often druggable pockets are solvent exposed and enriched with hot-spots and evolutionarily conserved residues (50). To assess the suitability of the binding pocket as a target and the peptide fragment as a ligand, we characterised both the peptide fragment and the binding pocket in-silico. Hot-spots were identified using the FOLDX force field (36). Residues were defined as hot-spots if the ∆∆Gbinding ≥ 1 kcal/mol upon mutation to alanine. 41% of the interface residues were identified as hot-spots in both σA and σB and 38% of residues in the peptide fragment have been identified as hot-spots (Figure 3A and 3D). Enrichment of both the binding pocket and the peptide fragment with the hot-spots stresses the importance of these regions in the docking of the σ and β-subunit. The binding pocket has also been observed to be solvent exposed (Figure 3B and 3E), thus implying the accessibility of the pocket to peptide ligands. The sequence conservation of the residues was scored using the CONSURF algorithm (34). Both the


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binding pockets are enriched with highly conserved residues (Figure 3C and 3F). The high conservation of residues signifies the functional importance of the binding pocket.

Figure 3. Hot-spots residues (coloured red) are mapped to both the binding pocket (coloured light blue) and the peptide fragment (coloured light pink). The binding pocket of σA is shown in (A) and σB is shown in (D). Both the binding pockets are enriched with hot-spot residues as well as the peptide fragment. The relative solvent accessibility (RSA) area of binding pockets of σA and σB is shown respectively in (B) and (E). A residue is defined as solvent exposed if RSA ≥ 7%. A major proportion of the binding pockets (~ 75%) is solvent exposed. The conservation score of each residue is mapped onto the binding pocket of (C) σA and (F) σB. The colour code corresponding to the conservation score is shown at the bottom of the panel. A score of 1 corresponds to poor conservation while a score of 9 corresponds to high conservation. Both the binding pockets are enriched with the conserved residues. Enrichment of the pockets with hotspots and conserved residues make the pockets a viable drug target.


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Generation of peptide library through in-silico optimisation of peptide fragment. After we established the suitability of the pocket as a target, we optimised the peptide fragment insilico to enhance the interaction between the binding pocket and the peptide fragment as well as the stability of the peptide fragment. To achieve this, we mutated each of the 16 residues to all other 19 residue types using the FOLDX package (36). This allowed us to identify amino acids which enhance the interaction between the binding pocket and the peptide fragment as compared to that observed in the structural model. Amino acids which increase the binding affinity (∆∆Gmut < 0) are represented as cooler shades in the heat-map shown in Figure 4A and 4B. As expected, residues at the termini show the maximum number of favourable amino acid substitutions while the central residues, which are highly conserved as well, show the least number of favourable substitutions (Figure 4A and 4B). For each residue position, an intersection of amino acids, preferred for both σA and σB, is considered for further analysis. Using all the permutations and combinations of preferred amino acids at each position, a library of 552,960 peptide ligands was generated. Stability of 552,960 peptide ligands was then determined and compared with the peptide fragment from the β flap-tip helix of the RNAP using the FOLDX package (36). Only 1771 peptide ligands showed stability equivalent to or better than the β flaptip helix peptide fragment (Figure 4C). Many peptides had unfavourable energy because of van der Waals clashes between non-bonded atoms in the peptides. It has to be noted that many peptides, which have been removed from the library on account of unfavourable interaction, may have favourable interaction on accounting for backbone motion. For this study, we have not taken into consideration the backbone motion while designing the peptides. All 1771 showed favourable interaction with σA (Figure S2) while a few of these peptide ligands showed unfavourable interaction with σB (Figure 4D) and were subsequently removed from the library.


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While generating the library of peptide ligands, the backbone scaffold of the peptide was kept same as the β flap-tip helix but the helix scaffold may not hold good for all the peptide ligands as a consequence of the nature of the amino acid sequences. Helicity of all the 1766 peptide ligands was predicted using AGADIR (41-43). All the 1766 peptides showed helix propensity greater than 4% and hence were retained in the library (Figure S3A). Since a reliable peptide ligand should not show aggregation in solution, aggregation propensity of 1766 peptides was predicted using AGGRESCAN (40). Fifty peptides showed high aggregation propensity with Na4V score ≥ 0 (Figure S3B) and hence were removed from the peptide library reducing the library to 1716 entries (Figure S3C). It has to be noted that the absolute value of the AGGRESCAN score does not affect any binding property of peptides; it only suggests the probability of peptides to aggregate.

Figure 4. Heat-maps representing energy substitution matrix of the β flap-tip helix peptide fragment from (A) β-subunit and σA complex and (B) β-subunit and σB complex. The X-axis 18 ACS Paragon Plus Environment

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represents the sequence of β flap-tip helix peptide fragment and the Y-axis represents the 20 amino acids. Hotter the colour in the substitution matrix, the substitution is energetically unfavourable while cooler the colour, the substitution is energetically favourable. Favourable amino acids (∆∆Gmut < 0, boxed in the colour bar) were combined in all permutations and combinations, resulting in 552,960 peptide ligands. (C) Distribution of the relative stability of the peptide ligands. Only a small fraction of the peptides (1771 peptide ligands) has better stability than β flap-tip helix peptide fragment (∆∆Gstability < 0). (D) The interaction energy profile of peptides with σB. A small fraction of peptides showed unfavourable interaction with σB, which were subsequently removed from the dataset. The X-axis in (C) and (D) represents the peptide sequences in the library. We performed classical molecular dynamics at three temperatures (300K, 400K and 500K) to validate the prediction of AGADIR (Supplementary text S2). For simulations, only the central helical region (residue no 3–14) was considered for the peptides. This choice also enabled us to validate the cut-off of 1%, employed earlier. The central helical region of three peptides (including the wild-type fragment) from the library with AGADIR score ranging from 1.91 to 4.96 were selected for the simulation. From the simulation, it was observed that the average number of hydrogen bonds at 300K equals the AGADIR score and as the temperature increases i.e. at 400K and 500K, though the number of hydrogen bonds decreases but never collapses to zero for a long time (Fig. S4). These simulations suggest that AGADIR scores provide a reliable estimate of helicity and also the choice of cut-off 1% is justified. To assess the potency of the in-silico designed peptides as σ binder, we chose a candidate peptide for in-vitro studies. It was selected on the basis of the highest favourable interaction energy, better helicity than the β flap-tip helix and low aggregation propensity (Table 1). The 19 ACS Paragon Plus Environment

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chosen peptide ligand will henceforth be mentioned as MT4 and the β flap-tip helix peptide fragment as MT2. To understand the influence of helicity on the strength of interaction, we designed another peptide apart from the peptides represented in the peptide library and refer it as MT1. MT1 is essentially same as the wild-type but the third amino acid from the N-terminal was mutated to Pro to reduce the helicity (Table 1). To understand the specificity of interaction of the peptides with σB, two types of control peptides could be synthesised; (i) peptide generated by randomising sequence of MT4 or (ii) peptide generated by mutating important pharmacophore with unfavourable amino acids. We chose to mutate all the important pharmacophores to unfavourable amino acid. Unfavourable amino acids were identified using the same protocol as is used for predicting the favourable amino acids (Figure S5). Henceforth, this control peptide will be referred as MT6.

Table 1. Properties of peptides using in-silico and in-vitro prediction. Peptide

Sequence

name

∆∆Gbinding

Agadir score Helicity (%)a

Aggrescan

(kcal/mol)

(%)

score

MT1

ELPPEERLLRAIFGEK

-0.23

7.16

11.8

-24.1

MT2

ELTPEERLLRAIFGEK

0

26.54

18.2

-23.0

MT4

ELTPEERMLRLIFGRK

-1.17

34.74

27.7

-7.4

MT6

ELTPRESDDRHDSGRD

15

0.22

-----

-94.3

a

Determined using circular dichroism

In-vitro characterisation of peptides. Peptides of purity >95% (Figure S6) was synthesised using solid phase synthesis with Fmoc chemistry. The sequence and the corresponding mass of the synthesised peptides were confirmed using MALDI (Table S2). To validate the helical 20 ACS Paragon Plus Environment

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content prediction and our design strategy, we studied the secondary structural content of the peptides using far-UV circular dichroism (CD). Helicity calculated from the CD spectra corroborated well with the predicted helicity from AGADIR (Table 1, Figure 5A). Since we aimed to perform kinetic studies of peptides and σB using Streptavidin chemistry, we synthesised biotinylated peptides with a glycine as a linker. CD profiles of biotinylated peptides were comparable to acetylated peptides with minuscule differences (Figure S7).

Binding studies. Binding kinetics between the peptides and σB were studied using bio-layer interferometry (BLI). The interaction between the analyte and the ligand is reported in terms of the difference in the interference pattern. We studied the interaction between σB and MT1, MT2, MT4 and MT6 by immobilising the peptides on a Streptavidin bio-sensor tip. The binding profile of MT4 is shown in Figure 5B and the binding profile of MT1 and MT2 is shown in Figure S8A and S8C respectively. Both the designed peptides (MT1 and MT4) and the β flaptip helix peptide fragment (MT2) binds to σB with nM affinity (Table 2). The control peptide with pharmacophores mutated to unfavourable amino acids (MT6) shows a drastic decrease in the interaction (µM affinity) (Figure S8C) signifying the importance of the pharmacophore in the interaction as well as the specificity of interaction. Moreover, to check non-specific interaction of the peptides (MT1, MT2 and MT4) due to the presence of N-terminal His tag, the peptides were titrated with protein polynucleotide phosphorylase (PNPase) from S.epidermis (Figure S8D). PNPase also happens to share fold of one of the domains (SCOP fold a.4) with region 4 of σ factors. The peptides were observed not to interact with the protein even at a concentration of 1µM PNPase, thus ruling out any non-specific interaction. To further evaluate the specificity for a σ factor target with other closely related σ factors, we examined the


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interaction of MT4 with region 4 of another sigma factor from Mtb - σD (Figure S9). σD4 shows a low micro-molar affinity towards MT4, thus establishing the specificity of MT4 towards σB.

Table 2. Kinetic parameters of the interaction of σB with the peptides. Peptide

ka (1/Ms)

SE(ka)

kd (1/s)

SE(kd)

KD ± SD (nM)

MT1

8400

113

1.17E-03

6.43E-06

139 ± 5.9

MT2

8560

141

0.98E-03

6.81E-06

115 ± 1.4

MT4

20800

251

1.06E-03

4.3E-06

51 ± 6.5

MT4b

13600

250

1.46E-03

7.1E-06

108 ± 7.2

MT6

687.1

11.7

8.05E-03

7.7E-05

11700 ± 5800

b

Corresponds to titration with σB – DNA complex

The designed peptide (MT4) shows 2 fold better binding to σB than MT2 (Figure 5D). Interestingly, binding kinetics of MT1 and MT2 is comparable. Since MT1 and MT2 have essentially the same pharmacophores but differ only in the helix content, the similarity between the binding kinetics of MT1 and MT2 gave rise to two propositions: (i) high helicity is not a necessary condition for binding of peptides to σB or (ii) helicity of peptides increases upon binding to σB. To delineate the two propositions, we compared the available crystal structures of the β flap-tip helix of apo- and holo-RNAP from E.coli, T.aquaticus and T.thermophilus. Comparison shows increase in helicity of the β flap-tip helix in holo-RNAP as compared to the apo-forms (Figure S10), thus supporting the second proposition over the first.


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Figure 5. (A) Circular dichroism plots of acetylated peptide. MT4 shows higher helicity than MT2, as predicted in-silico. Mutation of third amino acid of MT2 to Pro decreases helicity drastically, as observed from the profile of MT1. (B) Binding kinetic profile of MT4 with σB. (C) Binding kinetic profile of MT4 with 1.12 σB – promoter DNA complex. (D) A comparison of the binding kinetics of MT4 with σB, MT4 with the σB- promoter DNA complex, MT2 with σB, MT1 with σB, MT6 with σB and MT4 with blank buffer (1X PBS with 0.1mg/mL BSA and 0.05% (v/v) TWEEN 20). σB is titrated at 1 µM. We further studied the influence of promoter DNA on the interaction of σB with peptides. Earlier studies have shown that σB forms a high-affinity complex with the DNA promoter invitro (unpublished data). For the study with σB-DNA complex, we focused only on MT4. The bio-sensor loaded with MT4 was titrated with 1:1.2 complex of the σB- promoter DNA. The binding profile is shown in Figure 5C. The binding kinetics alters on the introduction of promoter DNA, with the dissociation rate constant increasing and the association rate constant


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decreasing, thus increasing the dissociation constant by 2 fold (Table 2). Since the promoter DNA and peptide bind at opposite faces of region 4 (Figure S11A), we believe that this decrease is due to conformational arrest of σB in presence of DNA which is consistent with normal mode analysis (NMA) on in-silico generated structural model of σB-promoter DNA and σB (Text S3 and Figure S11C). NMA shows that the conformational flexibility of region 4 decreases on DNA binding. From the study of the interaction of MT4 with σB-promoter DNA, we conclude that σB undergoes some local conformational change on peptide binding.

DISCUSSION The interaction between σ and RNAP is fundamental for proper promoter recognition and processivity of the transcription process. Any disruption of this interaction is critical for the survivability of the organism. With the ultimate aim of disrupting RNAP and σ interaction in Mtb, we have generated a library of peptide binders of σB, which binds σB with high affinity and can potentially abrogate the interaction between RNAP and σB. Sequence and structural scaffold of these peptide binders have been derived from a helical peptide fragment (β flap-tip helix) identified from the in-silico generated structural model of RNAP-σB complex. The cognate binding pocket (region 4) of the peptide fragment has also been identified from the in-silico generated structural model of RNAP-σB complex. Interaction of region 4 and the β flap-tip helix has been identified as important for docking of RNAP onto σ (51), with many natural protein inhibitors or protein regulators abrogating the interaction between β flap-tip helix and region 4 to dissociate RNAP-σ complex. For example, cognate anti-sigma factors of extra-cytoplasmic function σ factors often bind in the same part of region 4 to prevent the formation of RNAP-σ complex (52-54). A recent report also suggests that Thermus thermophilus phage P23-45 gp39


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Biochemistry

protein binds to the RNAP β flap domain and region 4 to hijack the transcription mechanism of the host (55). The transcription termination regulator NusA protein is also known to bind β flaptip helix for initiating transcriptional pausing for transcription termination and subsequent dissociation of σ (56). All these observations strongly favour targeting the interaction between region 4 and β flap-tip helix for abrogating RNAP- σ and as well supporting our selection of region 4 as a target.

The sequence of the peptide fragment was further optimised to enhance the selectivity of the peptide binder towards σB and σA, as the natural peptide fragment interacts with all the σ in the cell of the organism. An energetic-based approach was taken to enhance the selectivity of the peptide binders toward σB as well as σA. The peptides with an enhanced affinity towards both σB and σA were further filtered for aggregation propensity, as the peptide fragment is enriched with hydrophobic residues. The peptide library was also screened for the propensity for helix, as the β flap-tip helix is helical in nature. We assumed that maintenance of helicity is important for the interaction but the later in-vitro binding studies showed it to be not. For testing our in-silico design strategy, further in-vitro binding studies were performed on the top hit (MT4) from the peptide library and the β flap-tip helix peptide fragment (MT2). In-vitro characterisation of peptides corroborated with the in-silico prediction. Both MT4 and MT2 showed nano-molar affinity to σB in accordance to the in-silico calculation. But MT4 showed two-fold better binding than MT2, thus validating our design strategy. Surprisingly, studies with the control peptide (MT1) showed helicity of the peptides in unbound form is not an important feature for binding of these peptides to σB. Binding studies with another control peptide (MT6), where all the insilico identified important pharmacophores were mutated to unfavourable amino acids, showed


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the importance of these pharmacophores for the interaction and reliability of force fields used for the energetic calculation. Binding affinity between MT6 and σB was observed in µM range. To assess whether binding of DNA to σB will abrogate binding of peptides to σB, binding studies were carried out for DNA-σB complex and MT4. Although promoter DNA and the peptide fragment bind to the opposite faces of region 4, altered kinetics were observed on the introduction of promoter DNA. Comparison of binding kinetics of MT4 to σB in the absence and presence of DNA suggests conformational changes in σB on peptide binding since DNA binding to σB is known to arrest conformation of σB.

Corroboration between the in-silico and in-vitro observations validated our design strategy, which can be extended to other σ factors and other protein-protein complexes. The designed peptide binders of σB reported here can be further explored as chemical tools to study transcription regulation, as transcription is known to be sensitive to the expression level of different σ (14). Nevertheless, further studies on the cell permeability and proteolytic stability of the peptides and the ability of the peptides to abrogate RNAP-σ interaction are required for further exploration of the peptide ligands as lead molecules for targeting Mtb. Proteolytic stability and cell permeability of peptides can be achieved by including conformational constraints in peptides by including appropriate staples or cyclisation of the peptide or by incorporation of unnatural amino acids (including D-amino acids) (57). Put together, this study provides an evaluation of the specific targeting of a prokaryotic transcription initiation factor.


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Biochemistry

ACKNOWLEDGEMENT SV thanks, Ishita for her help in peptide synthesis and Rishi Raj for providing PNPase protein. SV thanks all the lab members, Madhulika and Mayuri for useful discussion and inputs during the course of the project.

ASSOCIATED CONTENT Supplementary tables (Table S1 and Table S2), supplementary text (S1, S2 and S3) and supplementary figures (S1 – S12) is available as a supplementary file. Table S1 lists all the information about the templates used for homology modelling. Table S2 lists the mass of the peptides observed in MALDI and the theoretical mass. Text S1 shows joy alignment for the σA and the templates. Text S2 describes the methodology for molecular dynamics studies of peptides. Text S3 describe the methodology for in-silico modelling of promoter DNA and normal mode analysis. Figure S1 describes the nature of the interaction between the peptide fragment and the binding pocket. Figure S2 describes the interaction energy profile of the refined peptide library with σA. Figure S3 shows the AGADIR score, AGGRESCAN score distribution of the peptide library and the web-logo representation of the peptide library. Figure S4 shows the distribution of the hydrogen bonds in peptides, as observed in simulation. Figure S5 illustrates in-silico designing pipeline of the control peptide (MT6). Figure S6 shows the HPLC chromatogram of purified peptides. Figure S7 shows the CD profile of biotinylated peptides. Figure S8 shows the binding profile of MT1, MT2, MT6 and PNPase. Figure S9 shows the SDS-PAGE gel, UV spectrum, CD spectrum and binding profile of σD4. Figure S10 shows the comparison of the β flap-tip helix in the apo and holo form. Figure S11 shows the


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DNA promoter and peptide binding site of σ, modelling of the σB-DNA complex and NMA of σB-DNA complex. Figure S12 shows the SDS-PAGE gel, UV spectrum and CD spectrum of σB.

AUTHOR CONTRIBUTION NS and SV designed the in-silico design strategy, performed computational analysis and analysed the computational data. JC designed the strategy for the chemical synthesis of peptides. SV synthesised the peptides. BG designed protein purification and binding studies. SV, SB and AKJ performed protein purification, peptide characterisation and binding studies. SB and AKJ contributed equally to experimental studies. All the authors analysed and interpreted the binding studies data and other data. SV, NS, JC and BG wrote the manuscript. All the authors reviewed and approved the manuscript.

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Geszvain, K., Gruber, T. M., Mooney, R. A., Gross, C. A., and Landick, R. (2004) A hydrophobic patch on the flap-tip helix of E.coli RNA polymerase mediates sigma(70) region 4 function, J Mol Biol 343, 569-587.

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Shukla, J., Gupta, R., Thakur, K. G., Gokhale, R., and Gopal, B. (2014) Structural basis for the redox sensitivity of the Mycobacterium tuberculosis SigK-RskA sigma-anti-sigma complex, Acta Crystallogr D Biol Crystallogr 70, 1026-1036.

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Sorenson, M. K., Ray, S. S., and Darst, S. A. (2004) Crystal structure of the flagellar sigma/anti-sigma complex sigma(28)/FlgM reveals an intact sigma factor in an inactive conformation, Mol Cell 14, 127-138.

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Campbell, E. A., Tupy, J. L., Gruber, T. M., Wang, S., Sharp, M. M., Gross, C. A., and Darst, S. A. (2003) Crystal structure of Escherichia coli sigmaE with the cytoplasmic domain of its anti-sigma RseA, Mol Cell 11, 1067-1078.

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Vorherr, T. (2015) Modifying peptides to enhance permeability, Future Med Chem 7, 1009-1021.


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Biochemistry

For Table of Contents use only

Design, synthesis and experimental validation of peptide ligands targeting Mycobacterium tuberculosis σ factors

Sneha Vishwanath, Sunaina Banerjee, Anil K. Jamithireddy, Narayanaswamy Srinivasan*, Balasubramanian Gopal & Jayanta Chatterjee. Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560012, India


Biochemistry

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Pipeline for in-silico optimisation of the peptide fragment to generate library of peptide ligands of σA and σB. Each position in the peptide fragment was mutated to every other 19 amino acid to identify favourable amino acid for interaction at that position. Each position is then enriched with the amino acids which show better interaction energy with both σA and σB. The peptide library was then screened for various features viz. helicity, stability and aggregation. permutations and combinations, 41x23mm (300 x 300 DPI)

ACS Paragon Plus Environment

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Biochemistry

Figure 2| Structural model of RNAP with (A) σA and (D) σB. The α-subunit is coloured green, β-subunit is coloured light pink, β’-subunit is coloured yellow, ω-subunit is coloured grey and the σ-subunit is coloured light blue. Both σA and σB form an extensive interface with the β and β’ subunits. The interface residues are represented as spheres for (B) σA and (E) σB. Interacting residues of β-subunit with σA/σB are represented as red spheres. The blue spheres represent the interface residues of the β’-subunit with σA/σB. The interface residues of σA/σB with β-subunit are represented by teal spheres and the interface residues of σA/σB with β’-subunit are represented as purple spheres. On analysing this extensive interfacial area, a binding pocket in both σA and σB and a peptide fragment from β-subunit (the β flap-tip helix) have been identified. Surface and cartoon representation of the pocket and the peptide fragment (encircled in black) are shown for (C) σA and (F) σB. Template details are summarised in Table S1. Cartoon and surface representa 47x28mm (300 x 300 DPI)


Biochemistry

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Hot-spots residues (coloured red) are mapped to both the binding pocket (coloured light blue) and the peptide fragment (coloured light pink). The binding pocket of σA is shown in (A) and σB is shown in (D). Both the binding pockets are enriched with hot-spot residues as well as the peptide fragment. The relative solvent accessibility (RSA) area of binding pockets of σA and σB is shown respectively in (B) and (E). A residue is defined as solvent exposed if RSA ≥ 7%. A major proportion of the binding pockets (~ 75%) are solvent exposed. The conservation score of each residue is mapped onto the binding pocket of (C) σA and (F) σB. The color code corresponding to the conservation score is shown at the bottom of the panel. Score of 1 corresponds to poor conservation while a score of 9 corresponds to high conservation. Both the binding pockets are enriched with the conserved residues. Enrichment of the pockets with hot-spots and conserved residues make the pockets a viable drug target. residues in the peptide fragme 47x29mm (300 x 300 DPI)


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Biochemistry

Heat-maps representing energy substitution matrix of the β flap-tip helix peptide fragment from (A) βsubunit and σA complex and (B) β-subunit and σB complex. The X-axis represents the sequence of β flap-tip helix peptide fragment and the Y-axis represents the 20 amino acids. Hotter the colour in the substitution matrix, the substitution is energetically unfavourable while cooler the colour the substitution is energetically favourable. Favourable amino acids (∆∆Gmut < 0, boxed in the colour bar) were combined in all permutations and combinations, resulting in 552,960 peptide ligands. (C) Distribution of the relative stability of the peptide ligands. Only a small fraction of the peptides (1771 peptide ligands) has better stability than β flap-tip helix peptide fragment (∆∆Gstability < 0). (D) The interaction energy profile of peptides with σB. A small fraction of peptides showed unfavourable interaction with σB, which were subsequently removed from the dataset. The X-axis in (C) and (D) represents the peptide sequences. show the least number of favou 47x28mm (300 x 300 DPI)


Biochemistry

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(A) Circular dichroism plots of acetylated peptide. MT4 shows higher helicity than MT2, as predicted in-silico. Mutation of third amino acid of MT2 to Pro decreases helicity drastically, as observed from profile of MT1. (B) Binding kinetic profile of MT4 with σB. (C) Binding kinetic profile of MT4 with 1.12 σB – promoter DNA complex. (D) Comparison plot of binding kinetics of MT4 with σB, MT4 with σB- promoter DNA complex, MT2 with σB, MT1 with σB, MT6 with σB and MT4 with blank buffer (1X PBS with 0.1mg/mL BSA and 0.05% (v/v) TWEEN 20). σB is titrated at 1 µM. CD spectra corroborated well w 47x31mm (300 x 300 DPI)


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Design, Synthesis, and Experimental Validation of Peptide Ligands

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