- Methodology article
- Open Access
A novel method for evaluation and screening of caspase inhibitory peptides by the amino acid positional fitness score
© Yoshimori et al; licensee BioMed Central Ltd. 2004
- Received: 29 December 2003
- Accepted: 22 May 2004
- Published: 22 May 2004
Since caspases are key executioners of apoptosis in cases of severe diseases including neurodegenerative disorders such as Alzheimer's disease and Huntington's disease, and viral infection diseases such as AIDS and hepatitis, potent and specific inhibitors of caspases have clinical potential. A series of peptide inhibitors has been designed based on cleavage sites of substrate proteins. However, these peptides are not necessarily the most potent to each caspase. Moreover, so far, it has proved to be difficult to design potent and specific peptide inhibitors of each caspase from sequence data of known cleavage sites in substrate proteins. We have attempted to develop a computational screening system for rapid selection of potent and specific peptide inhibitors from a comprehensive peptide library.
We developed a new method for rapid evaluation and screening of peptide inhibitors based on Amino acid Positional Fitness (APF) score. By using this score, all known peptide inhibitors of each caspases-3,-7,-8, and -9 were rapidly selected in their enriched libraries. In this libraries, there were good correlations between predicted binding affinities of the known peptide inhibitors and their experimental Ki values. Furthermore, a novel potent peptide inhibitor, Ac-DNLD-CHO, for caspase-3 was able to be designed by this method. To our knowledge, DNLD is a first reported caspase-3 inhibitory peptide identified by using the computational screening strategy.
Our new method for rapid screening of peptide inhibitors using APF score is an efficient strategy to select potent and specific peptide inhibitors from a comprehensive peptide library. Thus, the APF method has the potential to become a valuable approach for the discovery of the most effective peptide inhibitors. Moreover, it is anticipated that these peptide inhibitors can serve as leads for further drug design and optimization of small molecular inhibitors.
- Peptide Inhibitor
- Energy Evaluation
- Enrich Library
- Docking Parameter
- Predict Binding Affinity
Caspases are cysteine aspartyl proteases that play critical roles during the execution of apoptosis [1–5]. Caspases have been found in organisms ranging from C. elegans to humans. The 14 types of caspase (named caspase-1 to caspase-14) identified so far in mammals are suggested to play distinct roles in apoptosis and inflammation. The main caspase cascades are catalyzed by caspases-8 and -9 (initiator caspases) and caspases-3 and -7 (executioner caspases) [4, 5]. The disregulation of caspase cascade activation is suggested to be involved as a key factor for the development of a variety of diseases, including neurodegenerative disorders and cancers [6–9].
Caspases share similarities in amino acid sequence, structure and substrate specificity, and are characterized by an almost absolute specificity in the active sites for aspartic acid in the P1 position of substrate proteins. Each active site contains a positively charged S1 subsite that binds the negatively charged P1 aspartate on the substrate [10–13]. This S1 binding site is highly conserved; therefore, all caspases cleave solely after aspartate residues. Recognition of at least four amino acids (P1-P4) in the cleavage sites is also a necessary requirement for efficient catalysis. Individual caspases have been shown to have structural differences in the predicted S2-S4 substrate binding sites which vary significantly, resulting in varied substrate specificities for the P2-P4 positions, despite an absolute requirement for aspartate in the P1 position [10–16].
The interactions between caspases and their target substrate proteins are highly flexible, so that many caspase inhibitory peptides, which are derived from natural substrates, are recognized by several members of the caspase family. For instance, the sequence DEVD within poly(ADP-ribose) polymerase (PARP)  is known to be recognized and cleaved by caspase-3, and it has been applied to creating the tetrapeptide inhibitor Ac-DEVD-CHO. However, Ac-DEVD-CHO also shows similar inhibitory activity toward caspases-7, and -8 as well as caspase-3 . Therefore, it is difficult to design selective caspase inhibitory peptides based on the sequence of known cleavage sites.
To identify the sequence with the highest binding affinity for each caspase, the X-X-X-D (X any of the 20 amino acids) peptide sequence motif is utilized for Structure-Based Virtual Screening (SBVS), which involves 203, i.e., 8000, ΔGbind evaluations. In order to show each amino acid position clearly, the X-X-X-D motif is expressed as P4-P3-P2-D. In SBVS using a library consisting of hundreds of thousands small molecules, it is necessary to limit the computing time per compound to at most 1 min. However, even side chains of a peptide with only 4 amino acids have a maximum of 20 rotational bonds. In flexible docking using AutoDock3.0  with default parameters, a computing time of about 20 min per 4-amino acid peptide on a COMPAQ Alphastation DS20E (double 833 MHz processors and 1024 MB of memory) is required. In the case of the design of a 5-amino acid peptide, it is necessary to carry out 3.2 million ΔGbind calculations, so that comprehensive peptide screening is practically difficult. Moreover, the computing time for ΔGbind calculation can be traded off against accuracy. Therefore, the most problematic aspect of ΔGbind calculation is time-consumption for SBVS.
Alternatively, various pattern-recognition techniques have been used to analyze peptide cleavage data [20, 21]. A statistical technique has been used to calculate binding free energy in the analysis of resistance-evading, peptide mimetic inhibitors of HIV-1 protease . These types of methods are enough to make potent peptide screening tractable. However, many experimental data points are needed to construct a highly precise scoring function.
In the present study, we have developed a new computational screening strategy based on Amino Acid Positional Fitness (APF) score for rapid evaluation of peptides interacting specifically with a target protein molecule. In our system, 1 to 10% of peptides are extracted at random from a comprehensive peptide library. The ΔGbinds between the extracted peptides and the target protein are calculated using AutoDock3.0, and then the data are transferred to obtain the APF score for each amino acid position (for example; P2, P3, and P4 in caspases) using a statistical technique. Consequently, this score is able to evaluate rapidly the ΔGbinds of all peptides in the comprehensive peptide library. We show here the usefulness of this APF method for the screening of potent and specific peptide inhibitors of main caspase family members.
Evaluation of binding modes and affinities of caspase inhibitory peptides by AutoDock3.0
One of goals of this work is the development of a simple and fast scoring function (APF score matrix) for the prediction of binding affinities of peptides against target protein molecules. The APF score matrix is derived from the predicted binding modes and affinities based on a standard docking program, AutoDock3.0. It is, therefore, important to verify the docking results of AutoDock3.0. Initially, we used Root of Mean Squared Deviation (RMSD) value as an indicator of the quality of the predicted binding modes. The RMSD was determined for the atomic positions between the predicted binding mode of peptide and native one as found in the crystal structure. The crystal structures of caspase-3 (1PAU), caspase-7 (1F1J), caspase-8 (1QTN), and caspase-9 (1JXQ) were used to validate the quality of predicted binding modes.
Inhibition of caspases by peptide aldehydes 
Prediction of binding affinities of caspase inhibitory peptides by AutoDock3.0
Corr. Coeff. of ΔGbind and log Ki (R)
To verify the accuracy of the docking results, we analyzed correlation coefficients between ΔGbinds (predicted) and log Ki values (observed). Peptide inhibitors with Ki values of >10,000 nM were set up the value of 10,000 nM. As shown in Table 2, the correlation coefficients were 0.75 (caspase-3), 0.83 (caspase-7), 0.95 (caspase-8), and 0.96 (caspase-9). The results show good correlations between predicted ΔGbind values by AutoDock3.0 and experimental Ki values. These observation imply that our docking parameters and conditions (0.7 million energy evaluations and fixed main chain) are reasonable for predicting binding modes and affinities of caspase inhibitory peptides.
Construction of APF score matrix
Since the APF score matrix of caspase-7 showed a preference for P4: D > E > C, P3: F > E > D, P2: P > I, S > L, the preferred recognition motif for this enzyme is DFPD (Figure 3b). DEVD, known as the optimal recognition sequence, was not shown to be a sequence with the highest APF score from the APF score matrix. However, a good correlation coefficient between APF score and ΔGbind was confirmed (Figure 4b). Although known optimal recognition sequences for caspase-8 and caspase-9 were not shown from the APF score matrix to be sequences with the highest APF score, the correlation coefficients between APF score and ΔGbind in the analysis library were -0.68 and -0.76, respectively (Figure 4c and 4d).
Consequently, it is revealed that the APF score matrices of all caspases have the ability to predict ΔGbind, although known optimal recognition sequences for caspases-7, -8, and -9 were not shown directly.
Prediction of binding affinity of caspase peptide inhibitors
The correlation coefficients between log Ki values and APF scores of caspase-7 and caspase-8 were -0.77 and -0.82, respectively (Figure 5b and 5c). When WEHD was omitted from the correlation analysis of caspase-9, the correlation coefficient was -0.86 (Figure 5d). In comparison, the APF score of LEHD, which is utilized as a selective inhibitor of caspase-9, was 0.44. This score means that LEHD has a high binding affinity for caspase-9. Meanwhile, the APF score of WEHD was 0.59; therefore, it is predicted that WEHD also has a high binding affinity for caspase-9. However, the reported Ki for WEHD is not low (508 nM). APF score of a peptide is the sum of the position-specific scores of the component amino acids, and it is also assumed that each amino acid within a peptide contributes to recognition almost independently. The reason why WEHD does not has low Ki value may be improper interaction of amino acid residues in the peptide.
Construction of enriched library
Since the sequences of DEVD, DEPD, DELD, and DGPD, which are cleavage sites for caspase-3, also satisfy the condition of being in the top 5% of the library, it is expected that these sequences have a high binding affinity for caspase-3 (DEVD is already known as an inhibitory peptide, Ac-DEVD-CHO etc.). Meanwhile, the sequences of WEHD and YVAD have high Ki values, 1960 nM and >10,000 nM, respectively. These sequences are located at position 6506 (WEHD) and 7322 (YVAD) in the library. The sequences of DEVD, AEVD, and IETD have low Ki values for caspase-8, 0.92 nM, 1.6 nM, and 1.05 nM, respectively. The library of caspase-8 also reflects the high binding affinities of these sequences, positions of 125, 228, and 328, respectively.
We, therefore, believe that peptides with high affinities for each caspase can be efficiently screened from the enriched library as compared with comprehensive peptide libraries.
Design of a novel caspase-3 inhibitory peptide
Ten Predicted potent peptide inhibitors of caspase-3
Inhibitory effect of DNLD on caspase-3 and its binding mode
In the present study, we have developed a new in silico strategy based on APF score for rapid evaluation and screening of binding peptide to a target protein molecule. In the Structure-Based Virtual Screening, the computational calculation time of ΔGbind is an important factor as well as the computational accuracy. On a common day workstation, it is difficult to predict an appropriate binding mode and binding free energy for a peptide, which has 20 to 30 rotational bonds, against a target protein within a few minutes. Meanwhile, methods for constructing scoring functions from experimental cleavage data are rapid enough to predict binding affinities. However, many experimental data points are required to construct a precise scoring function.
The APF score developed here allows rapid prediction of peptide binding affinities. The statistical basis of APF score is derived from the calculated ΔGbinds between randomly extracted peptides and a target caspase using AutoDock3.0. Therefore, our system does not require experimental data for constructing a scoring function. It should be noted that the binding affinities of caspase inhibitory peptides derived rapidly from the APF score correlate well with their reported inhibitory kinetics, and that all peptides with Ki values less than 2 nM were condensed in the top 5% of the sorted comprehensive peptide libraries.
To prove the utility of this method, an attractive candidate for a new caspase-3 inhibitor in the enriched library was selected by the APF score. Ac-DNLD-CHO, whose sequence is so far not known in any caspase substrate, and has a low ΔGbind, was chosen and its inhibitory activity was measured. As expected, Ac-DNLD-CHO had similar potent inhibitory activity to a well-known inhibitor Ac-DEVD-CHO. Although the system needs further validation, it is likely that by using the APF method, peptides with high affinities can be efficiently selected from the enriched library as compared with random screening.
At present, small peptides have been suggested to have clinical potential, because in specific protein-protein interactions occurred in many signalling pathways, small critical domains on the molecules are revealed to be involved in their biological activities. Furthermore, many peptide ligands exist in living organisms. Thus, new computational methods for the identification of active peptide will be powerful tools for the development of new pharmaceuticals. The APF method developed here is an attractive strategy that could discover novel therapeutic agents.
Ac-DNLD-CHO was synthesized by Peptide Institute, Inc. Ac-DEVD-CHO was purchased from Peptide Institute, Inc. Ac-DEVD-pNA and recombinant human caspase-3 were from CALBIOCHEM.
Caspase-3 Inhibition assay
To determine inhibitory potency of Ac-DNLD-CHO against caspase-3, an in vitro caspase activity assay was performed. The activity of caspase-3 was measured using Ac-DEVD-pNA as a substrate. One unit of active recombinant human caspase-3 was incubated with peptide inhibitor at indicated concentrations in assay buffer (100 mM NaCl, 50 mM HEPES, 10 mM DTT, 1 mM EDTA, 10% glycerol, and 0.1% CHAPS, pH 7.4) at 25°C for 30 min with shaking in a 96-well assay plate (CORNING). Then, 100 μM of Ac-DEVD-pNA was added to each well and absorbance at 405 nm was measured at 25°C using a 96-well plate reader Wallace 1420 ARVOsx (PerkinElmer) at 1 min intervals for 60 min.
Preparation of caspase/inhibitory peptide complex structure
In this study, we evaluated the binding mode and affinity of caspase inhibitory peptides by AutoDock3.0, and then constructed the APF score matrices of caspases-3, -7, -8, and -9. The coordinates of caspases-3, -7, -8, and -9 were obtained from the Protein Data Bank (codes 1PAU, 1F1J, 1QTN, and 1JXQ) . Since the caspase-9 inhibitor in the crystallographic structure is not a 4-amino acid peptide, LEHD, known to be an optimal sequence , was docked against caspase-9 using AutoDock3.0 , and the predicted complex structure was used for subsequent analysis. The crystallographic structures including water were deleted, and energy minimization using the TINKER software package  was used and energy minimization using the Amber94 forcefield was performed. During the minimization, the RMSD gradient limits were 0.01.
Construction of APF Score matrix
The feature of our computational screening strategy is construction of the Amino acid Positional Fitness (APF) score matrix for a target protein. This score matrix allow us to predict binding affinities between a target protein and peptides rapidly.
Our system comprises four steps as follows (Figure 7).
Step 1. Construction of peptide library
To identify the P2-P4 preferences of each caspase, we constructed an analysis library with aspartic acid fixed at P1 and randomized amino acids at P2-P4. In order to analyze the P4-P3-P2-D motif comprehensively, it is necessary to analyze 8000 combinations. In this system, 400 peptides equivalent to 5% of all combinations were extracted at random, and an analysis library consisting of 360 peptides and a prediction library consisting of 40 peptides were prepared. To generate initial conformation for peptides in the analysis and prediction libraries, the main chain coordinates were used for the inhibitory peptides in the crystallographic structures, and the side chains were added. Moreover, in order to exclude VDW contacts of side chains, the TINKER software package  was used and energy minimization using the Amber94 forcefield was performed. During the minimization, the main chain atoms were fixed and RMSD gradient limits were 0.01. Kollman atom charges were assigned and the rotational bonds of the peptides were defined with AutoTors .
Step 2. Calculation of binding free energy using flexible ligand docking
The ΔGbinds of 400 randomly extracted peptides with a caspase were calculated.
In this study, the main chains of the peptides were fixed and the side chains were made flexible. During the docking, the number of energy evaluations was 0.7 million and other parameters were default values. The predicted binding mode of each peptide with caspase was inspected to determine whether the binding mode was appropriate. In order to arrange a functional group such as CHO on the caspase active site, a suitable binding mode of peptide is important. In generally, good docking solutions have an RMSD of 2.0 Å or less , and 3 peptides with Ki value of >10,000 nM dose not have an RMSD of 2.0 Å or less in Table 2. Therefore, peptides with 2Å or more RMSD compared with the peptide inhibitor in the crystallographic structure were defined as not binding to caspases.
Step 3. Construction of APF Score matrix
APF score matrix based on the frequency of appearance of 20 amino acids at each of positions P4, P3, and P2 were generated in the analysis library. The APF score for amino acid i at position j is calculated as follows:
cij denotes the number of times amino acid i appears at position j among peptides in the analysis library. nall is the number of peptides in the analysis library. c'ij denotes the number of times amino acids i appears at position j among peptides below the threshold value (ΔGth) of binding free energy and the RMSD 2 Å in an analysis library, and nbinder is the number of peptides below the threshold value (ΔGth) and RMSD 2 Å. The range of position j is from 1 to 3, and corresponding to P2 to P4. The range of amino acid i is from 1 to 20, and corresponding to individual amino acids.
By using APF score matrix, it becomes possible to calculate the APF score, which shows the binding affinity of a peptide and a caspase. The APF score of a peptide is given by Equation .
A peptide with the P4-P3-P2-D motif is represented by a 20 × 3 matrix (cij) of 0s and 1s, where cij = 1 if amino acid i is at position j .
Correlation analysis of APF score and ΔGbind of peptides consisting in the analysis library was carried out. If a high correlation is observed between APF score and ΔGbind, it becomes possible to predict ΔGbind using APF score. A threshold value (ΔGth) was set up the value that makes the correlation coefficient between APF score and ΔGbind maximal.
Next, correlation analysis between APF score and ΔGbind of peptides involved in a prediction library was carried out. Here, the APF score was calculated by using APF score matrix generated from the analysis library, and the prediction capability was validated.
Step 4. Construction of an enriched library by APF score
It is assumed that potent peptide inhibitors are distributed randomly in the comprehensive peptide library. The APF scores of all peptides in the comprehensive peptide library were calculated using APF score matrix generated from the analysis library. Since a high APF score reflects high binding affinity, peptides in the comprehensive peptide library were sorted in descending order according to their APF score. This operation takes the high affinity peptides, which are distributed throughout the library, condenses them at a higher rank in the library. The enriched library was defined as the top 5% of the sorted comprehensive peptide library.
We are grateful to Dr. J. W. Ponder for providing the Tinker program and Dr. A. J. Olson for providing the AutoDock3.0 program.
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