Rigorous Computational and Experimental Investigations on MDM2/MDMX-Targeted Linear and Macrocyclic Peptides.

There is interest in peptide drug design, especially for targeting intracellular protein-protein interactions. Therefore, the experimental validation of a computational platform for enabling peptide drug design is of interest. Here, we describe our peptide drug design platform (CMDInventus) and demonstrate its use in modeling and predicting the structural and binding aspects of diverse peptides that interact with oncology targets MDM2/MDMX in comparison to both retrospective (pre-prediction) and prospective (post-prediction) data. In the retrospective study, CMDInventus modules (CMDpeptide, CMDboltzmann, CMDescore and CMDyscore) were used to accurately reproduce structural and binding data across multiple MDM2/MDMX data sets. In the prospective study, CMDescore, CMDyscore and CMDboltzmann were used to accurately predict binding affinities for an Ala-scan of the stapled α-helical peptide ATSP-7041. Remarkably, CMDboltzmann was used to accurately predict the results of a novel D-amino acid scan of ATSP-7041. Our investigations rigorously validate CMDInventus and support its utility for enabling peptide drug design.

A combined cheminformatic and bioinformatic approach to address the proteolytic stability challenge in peptide-based drug discovery.

We have created models to predict cleavage sites for several human proteases including caspase-1, caspase-3, caspase-6, caspase-7, cathepsin B, cathepsin D, cathepsin G, cathepsin K, cathepsin L, elastase-2, granzyme A, granzyme B, matrix metallopeptidase-2 (MMP2), MMP7, MMP9, thrombin, and trypsin-1. Rather than representing the sequence pattern around the potential cleavage site through a series of flags with each flag representing one of the 20 standard amino acids, we first represent each amino acid by its calculated properties. For these calculated properties, we use validated cheminformatic descriptors, such as molecular weight, logP, and polar surface area, of the individual amino acids. Finally, the cleavage site-specific descriptors are calculated through various combinations of the individual amino acid descriptors for the residues surrounding the cleavage site. Some of these combinations do not take into account the location of the residue, as long as it is in a prescribed neighborhood of the potential cleavage site, whereas others are sensitive to the precise order of the residues in the sequence. The key advantage of this approach is that it allows one to perform meaningful calculations with nonstandard amino acids for which little or no data exists. Finally, using both docking and molecular dynamics simulations, we examine the potential for and limitations of protease crystal structures to impact the design of proteolytically stable peptides.

Evaluating Free Energies of Binding and Conservation of Crystallographic Waters Using SZMAP.

The SZMAP method computes binding free energies and the corresponding thermodynamic components for water molecules in the binding site of a protein structure [ SZMAP, 1.0.0 ; OpenEye Scientific Software Inc. : Santa Fe, NM, USA , 2011 ]. In this work, the ability of SZMAP to predict water structure and thermodynamic stability is examined for the X-ray crystal structures of a series of protein-ligand complexes. SZMAP results correlate with higher-level replica exchange thermodynamic integration double decoupling calculations of the absolute free energy of bound waters in the test set complexes. In addition, SZMAP calculations show good agreement with experimental data in terms of water conservation (across multiple crystal structures) and B-factors over a subset of the test set. In particular, the SZMAP neutral entropy difference term calculated at crystallographic water positions within each of the complex structures correlates well with whether that crystallographic water is conserved or displaceable. Furthermore, the calculated entropy of the water probe relative to the continuum shows a significant degree of correlation with the B-factors associated with the oxygen atoms of the water molecules. Taken together, these results indicate that SZMAP is capable of quantitatively predicting water positions and their energetics and is potentially a useful tool for determining which waters to attempt to displace, maintain, or build in through water-mediated interactions when evolving a lead series during a drug discovery program.

Fine tuning of the active site modulates specificity in the interaction of O-acetylserine sulfhydrylase isozymes with serine acetyltransferase.

O-acetylserine sulfhydrylase (OASS) catalyzes the synthesis of l-cysteine in the last step of the reductive sulfate assimilation pathway in microorganisms. Its activity is inhibited by the interaction with serine acetyltransferase (SAT), the preceding enzyme in the metabolic pathway. Inhibition is exerted by the insertion of SAT C-terminal peptide into the OASS active site. This action is effective only on the A isozyme, the prevalent form in enteric bacteria under aerobic conditions, but not on the B-isozyme, the form expressed under anaerobic conditions. We have investigated the active site determinants that modulate the interaction specificity by comparing the binding affinity of thirteen pentapeptides, derived from the C-terminal sequences of SAT of the closely related species Haemophilus influenzae and Salmonella typhimurium, towards the corresponding OASS-A, and towards S. typhimurium OASS-B. We have found that subtle changes in protein active sites have profound effects on protein-peptide recognition. Furthermore, affinity is strongly dependent on the pentapeptide sequence, signaling the relevance of P3-P4-P5 for the strength of binding, and P1-P2 mainly for specificity. The presence of an aromatic residue at P3 results in high affinity peptides with K(diss) in the micromolar and submicromolar range, regardless of the species. An acidic residue, like aspartate at P4, further strengthens the interaction and results in the higher affinity ligand of S. typhimurium OASS-A described to date. Since OASS knocked-out bacteria exhibit a significantly decreased fitness, this investigation provides key information for the development of selective OASS inhibitors, potentially useful as novel antibiotic agents.

Nitration of the tumor suppressor protein p53 at tyrosine 327 promotes p53 oligomerization and activation.

Previous studies demonstrate that nitric oxide (NO) promotes p53 transcriptional activity by a classical DNA damage responsive mechanism involving activation of ATM/ATR and phosphorylation of p53. These studies intentionally used high doses of NO donors to achieve the maximum DNA damage. However, lower concentrations of NO donors also stimulate rapid and unequivocal nuclear retention of p53 but apparently do not require ATM/ATR-dependent p53 phosphorylation or total p53 protein accumulation. To identify possible mechanisms for p53 activation at low NO levels, the role of Tyr nitration in p53 activation was evaluated. Low concentrations of the NO donor, DETA NONOate (<200 microM), exclusively nitrate Tyr327 within the tetramerization domain promoting p53 oligomerization, nuclear accumulation, and increased DNA-binding activity without p53 Ser15 phosphorylation. Molecular modeling indicates that nitration of one Tyr327 stabilizes the dimer by about 2.67 kcal mol(-1). Significant quantitative and qualitative differences in the patterns of p53-target gene modulation by low (50 microM), non-DNA-damaging and high (500 microM), DNA-damaging NO donor concentrations were shown. These results demonstrate a new posttranslational mechanism for modulating p53 transcriptional activity responsive to low NO concentrations and independent of DNA damage signaling.

Web application for studying the free energy of binding and protonation states of protein-ligand complexes based on HINT.

A public web server performing computational titration at the active site in a protein-ligand complex has been implemented. This calculation is based on the Hydropathic interaction noncovalent force field. From 3D coordinate data for the protein, ligand and bridging waters (if available), the server predicts the best combination of protonation states for each ionizable residue and/or ligand functional group as well as the Gibbs free energy of binding for the ionization-optimized protein-ligand complex. The 3D structure for the modified molecules is available as output. In addition, a graph depicting how this energy changes with acidity, i.e., as a function of added protons, can be obtained. This data may prove to be of use in preparing models for virtual screening and molecular docking. A few illustrative examples are presented. In beta secretase (2va7) computational titration flipped the amide groups of Gln12 and Asn37 and protonated a ligand amine yielding an improvement of 6.37 kcal mol(-1) in the protein-ligand binding score. Protonation of Glu139 in mutant HIV-1 reverse transcriptase (2opq) allows a water bridge between the protein and inhibitor that increases the protein-ligand interaction score by 0.16 kcal mol(-1). In human sialidase NEU2 complexed with an isobutyl ether mimetic inhibitor (2f11) computational titration suggested that protonating Glu218, deprotonating Arg237, flipping the amide bond on Tyr334, and optimizing the positions of several other polar protons would increase the protein-ligand interaction score by 0.71 kcal mol(-1).

PeptideNavigator: An interactive tool for exploring large and complex data sets generated during peptide-based drug design projects.

There is growing interest in peptide-based drug design and discovery. Due to their relatively large size, polymeric nature, and chemical complexity, the design of peptide-based drugs presents an interesting "big data" challenge. Here, we describe an interactive computational environment, PeptideNavigator, for naturally exploring the tremendous amount of information generated during a peptide drug design project. The purpose of PeptideNavigator is the presentation of large and complex experimental and computational data sets, particularly 3D data, so as to enable multidisciplinary scientists to make optimal decisions during a peptide drug discovery project. PeptideNavigator provides users with numerous viewing options, such as scatter plots, sequence views, and sequence frequency diagrams. These views allow for the collective visualization and exploration of many peptides and their properties, ultimately enabling the user to focus on a small number of peptides of interest. To drill down into the details of individual peptides, PeptideNavigator provides users with a Ramachandran plot viewer and a fully featured 3D visualization tool. Each view is linked, allowing the user to seamlessly navigate from collective views of large peptide data sets to the details of individual peptides with promising property profiles. Two case studies, based on MHC-1A activating peptides and MDM2 scaffold design, are presented to demonstrate the utility of PeptideNavigator in the context of disparate peptide-design projects.

The Roles of Water in the Protein Matrix: A Largely Untapped Resource for Drug Discovery.

The value of thoroughly understanding the thermodynamics specific to a drug discovery/design study is well known. Over the past decade, the crucial roles of water molecules in protein structure, function, and dynamics have also become increasingly appreciated. This Perspective explores water in the biological environment by adopting its point of view in such phenomena. The prevailing thermodynamic models of the past, where water was seen largely in terms of an entropic gain after its displacement by a ligand, are now known to be much too simplistic. We adopt a set of terminology that describes water molecules as being "hot" and "cold", which we have defined as being easy and difficult to displace, respectively. The basis of these designations, which involve both enthalpic and entropic water contributions, are explored in several classes of biomolecules and structural motifs. The hallmarks for characterizing water molecules are examined, and computational tools for evaluating water-centric thermodynamics are reviewed. This Perspective's summary features guidelines for exploiting water molecules in drug discovery.

Rational, computer-enabled peptide drug design: principles, methods, applications and future directions.

Peptides provide promising templates for developing drugs to occupy a middle space between small molecules and antibodies and for targeting 'undruggable' intracellular protein-protein interactions. Importantly, rational or in cerebro design, especially when coupled with validated in silico tools, can be used to efficiently explore chemical space and identify islands of 'drug-like' peptides to satisfy diverse drug discovery program objectives. Here, we consider the underlying principles of and recent advances in rational, computer-enabled peptide drug design. In particular, we consider the impact of basic physicochemical properties, potency and ADME/Tox opportunities and challenges, and recently developed computational tools for enabling rational peptide drug design. Key principles and practices are spotlighted by recent case studies. We close with a hypothetical future case study.

Factors influencing protein tyrosine nitration--structure-based predictive models.

Models for exploring tyrosine nitration in proteins have been created based on 3D structural features of 20 proteins for which high-resolution X-ray crystallographic or NMR data are available and for which nitration of 35 total tyrosines has been experimentally proven under oxidative stress. Factors suggested in previous work to enhance nitration were examined with quantitative structural descriptors. The role of neighboring acidic and basic residues is complex: for the majority of tyrosines that are nitrated the distance to the heteroatom of the closest charged side chain corresponds to the distance needed for suspected nitrating species to form hydrogen bond bridges between the tyrosine and that charged amino acid. This suggests that such bridges play a very important role in tyrosine nitration. Nitration is generally hindered for tyrosines that are buried and for those tyrosines for which there is insufficient space for the nitro group. For in vitro nitration, closed environments with nearby heteroatoms or unsaturated centers that can stabilize radicals are somewhat favored. Four quantitative structure-based models, depending on the conditions of nitration, have been developed for predicting site-specific tyrosine nitration. The best model, relevant for both in vitro and in vivo cases, predicts 30 of 35 tyrosine nitrations (positive predictive value) and has a sensitivity of 60/71 (11 false positives).

Design of O-acetylserine sulfhydrylase inhibitors by mimicking nature.

The inhibition of cysteine biosynthesis in prokaryotes and protozoa has been proposed to be relevant for the development of antibiotics. Haemophilus influenzae O-acetylserine sulfhydrylase (OASS), catalyzing l-cysteine formation, is inhibited by the insertion of the C-terminal pentapeptide (MNLNI) of serine acetyltransferase into the active site. Four-hundred MNXXI pentapeptides were generated in silico, docked into OASS active site using GOLD, and scored with HINT. The terminal P5 Ile accounts for about 50% of the binding energy. Glu or Asp at position P4 and, to a lesser extent, at position P3 also significantly contribute to the binding interaction. The predicted affinity of 14 selected pentapeptides correlated well with the experimentally determined dissociation constants. The X-ray structure of three high affinity pentapeptide-OASS complexes were compared with the docked poses. These results, combined with a GRID analysis of the active site, allowed us to define a pharmacophoric scaffold for the design of peptidomimetic inhibitors.

Computational Insight Concerning Catalytic Decision Points of the Transition Metal Catalyzed [2 + 2 + 1] Cyclocarbonylation Reaction of Allenes.

Rhodium and molybdenum catalyzed allenic [2 + 2 + 1] cycloaddition reactions give 4-alkylidene and α-alkylidene cylopentenones, respectively. The selective reaction of one double bond of the allene over another is controlled by the transition metal and not the substrate structure. Calculations were performed to explain this unique control element using the B3LYP functional as implemented in Gaussian 03. The 6-31G(d) basis set was applied to all elements except rhodium, which is described with the LANL2 effective core potential and the LANL2DZ basis set. The product-determining step for both reaction pathways is oxidative addition of the metal to the alkynyl allene to form the corresponding metallocycles B and B'. The transition state calculations strongly suggest that geometry constraints imposed by the metal in the transition state are the key controlling factor of the double bond selectivity. The transition state structure of rhodium-catalyzed oxidative addition has a distorted square planar geometry that affords a lower transition state energy when coordinated to the distal double bond of the allene. In turn, the distorted trigonal bipyramidal geometry of molybdenum in the transition state structure imposes conformational constraints upon binding to the distal double on the allene and thus leads to the energetically preferred complexation and reaction with the proximal double bond.