Thrombin inhibitors identified by computer-assisted multiparameter design

Combining flavin photocatalysis with parallel synthesis: a general platform to optimize peptides with non-proteinogenic amino acids

Most peptide drugs contain non-proteinogenic amino acids (NPAAs), born out through extensive structure-activity relationship (SAR) studies using solid-phase peptide synthesis (SPPS). Synthetically laborious and expensive to manufacture, NPAAs also can have poor coupling efficiencies allowing only a small fraction to be sampled by conventional SPPS. To gain general access to NPAA-containing peptides, we developed a first-generation platform that merges contemporary flavin photocatalysis with parallel synthesis to simultaneously make, purify, quantify, and even test up to 96 single-NPAA peptide variants via the unique combination of boronic acids and a dehydroalanine residue in a peptide. We showcase the power of our newly minted platform to introduce NPAAs of diverse chemotypes-aliphatic, aromatic, heteroaromatic-directly into peptides, including 15 entirely new residues, and to evolve a simple proteinogenic peptide into an unnatural inhibitor of thrombin by non-classical peptide SAR.

In Silico Prediction of Hemolytic Toxicity on the Human Erythrocytes for Small Molecules by Machine-Learning and Genetic Algorithm

Hemolytic toxicity of small molecules, as one of the important ADMET end points, can cause the lysis of erythrocytes membrane and leaking of hemoglobin into the blood plasma, which leads to various side effects. Thus, it is very crucial to assess the hemolytic potential of small molecules during the early stage of drug development process. However, so far there is no computational model to predict the human hemolytic toxicity of small molecules. To this end, we manually curate the hemolytic toxicity data set for the small molecules experimentally evaluated on the human erythrocytes, develop the first machine-learning (ML) based models to predict the human hemolytic toxicity of small molecules, harness the genetic algorithm (GA) and ML based model to optimize human hemolytic toxicity based on the molecular fingerprint to derive "optimal virtual fingerprints (OVFs)" with the desired hemolytic/nonhemolytic property, and finally implement a free software for the users to predict/optimize the human hemolytic toxicity with ML and GA in the automatic manner.

Why Some Targets Benefit from beyond Rule of Five Drugs

Beyond rule-of-five (bRo5) compounds are increasingly used in drug discovery. Here we analyze 37 target proteins that have bRo5 drugs or clinical candidates. Targets can benefit from bRo5 drugs if they have "complex" hot spot structure with four or more hots spots, including some strong ones. Complex I targets show positive correlation between binding affinity and molecular weight. These targets are conventionally druggable, but reaching additional hot spots enables improved pharmaceutical properties. Complex II targets, mostly protein kinases, also have strong hot spots but show no correlation between affinity and ligand molecular weight, and the primary motivation for creating larger drugs is to increase selectivity. Each target considered as complex III has some specific reason for requiring bRo5 drugs. Finally, targets with "simple" hot spot structure, i.e., three or fewer weak hot spots, must use larger compounds that interact with surfaces beyond the hot spot region to achieve acceptable affinity.

Combinatorial chemistry by ant colony optimization

Background: Prioritizing building blocks for combinatorial medicinal chemistry represents an optimization task. We present the application of an artificial ant colony algorithm to combinatorial molecular design (Molecular Ant Algorithm [MAntA]). Results: In a retrospective evaluation, the ant algorithm performed favorably compared with other stochastic optimization methods. Application of MAntA to peptide design resulted in new octapeptides exhibiting substantial binding to mouse MHC-I (H-2Kb). In a second study, MAntA generated a new functional factor Xa inhibitor by Ugi-type three-component reaction. Conclusion: This proof-of-concept study validates artificial ant systems as innovative computational tools for efficient building block prioritization in combinatorial chemistry. Focused activity-enriched compound collections are obtained without the need for exhaustive product enumeration.

Molecular evolution of a peptide GPCR ligand driven by artificial neural networks

Peptide ligands of G protein-coupled receptors constitute valuable natural lead structures for the development of highly selective drugs and high-affinity tools to probe ligand-receptor interaction. Currently, pharmacological and metabolic modification of natural peptides involves either an iterative trial-and-error process based on structure-activity relationships or screening of peptide libraries that contain many structural variants of the native molecule. Here, we present a novel neural network architecture for the improvement of metabolic stability without loss of bioactivity. In this approach the peptide sequence determines the topology of the neural network and each cell corresponds one-to-one to a single amino acid of the peptide chain. Using a training set, the learning algorithm calculated weights for each cell. The resulting network calculated the fitness function in a genetic algorithm to explore the virtual space of all possible peptides. The network training was based on gradient descent techniques which rely on the efficient calculation of the gradient by back-propagation. After three consecutive cycles of sequence design by the neural network, peptide synthesis and bioassay this new approach yielded a ligand with 70fold higher metabolic stability compared to the wild type peptide without loss of the subnanomolar activity in the biological assay. Combining specialized neural networks with an exploration of the combinatorial amino acid sequence space by genetic algorithms represents a novel rational strategy for peptide design and optimization.

Natural and synthetic inhibitors of kallikrein-related peptidases (KLKs)

Including the true tissue kallikrein KLK1, kallikrein-related peptidases (KLKs) represent a family of fifteen mammalian serine proteases. While the physiological roles of several KLKs have been at least partially elucidated, their activation and regulation remain largely unclear. This obscurity may be related to the fact that a given KLK fulfills many different tasks in diverse fetal and adult tissues, and consequently, the timescale of some of their physiological actions varies significantly. To date, a variety of endogenous inhibitors that target distinct KLKs have been identified. Among them are the attenuating Zn(2+) ions, active site-directed proteinaceous inhibitors, such as serpins and the Kazal-type inhibitors, or the huge, unspecific compartment forming α(2)-macroglobulin. Failure of these inhibitory systems can lead to certain pathophysiological conditions. One of the most prominent examples is the Netherton syndrome, which is caused by dysfunctional domains of the Kazal-type inhibitor LEKTI-1 which fail to appropriately regulate KLKs in the skin. Small synthetic inhibitory compounds and natural polypeptidic exogenous inhibitors have been widely employed to characterize the activity and substrate specificity of KLKs and to further investigate their structures and biophysical properties. Overall, this knowledge leads not only to a better understanding of the physiological tasks of KLKs, but is also a strong fundament for the synthesis of small compound drugs and engineered biomolecules for pharmaceutical approaches. In several types of cancer, KLKs have been found to be overexpressed, which makes them clinically relevant biomarkers for prognosis and monitoring. Thus, down regulation of excessive KLK activity in cancer and in skin diseases by small inhibitor compounds may represent attractive therapeutical approaches.

VitAL: Viterbi algorithm for de novo peptide design

Background

Drug design against proteins to cure various diseases has been studied for several years. Numerous design techniques were discovered for small organic molecules for specific protein targets. The specificity, toxicity and selectivity of small molecules are hard problems to solve. The use of peptide drugs enables a partial solution to the toxicity problem. There has been a wide interest in peptide design, but the design techniques of a specific and selective peptide inhibitor against a protein target have not yet been established.

Methodology/Principal Findings

A novel de novo peptide design approach is developed to block activities of disease related protein targets. No prior training, based on known peptides, is necessary. The method sequentially generates the peptide by docking its residues pair by pair along a chosen path on a protein. The binding site on the protein is determined via the coarse grained Gaussian Network Model. A binding path is determined. The best fitting peptide is constructed by generating all possible peptide pairs at each point along the path and determining the binding energies between these pairs and the specific location on the protein using AutoDock. The Markov based partition function for all possible choices of the peptides along the path is generated by a matrix multiplication scheme. The best fitting peptide for the given surface is obtained by a Hidden Markov model using Viterbi decoding. The suitability of the conformations of the peptides that result upon binding on the surface are included in the algorithm by considering the intrinsic Ramachandran potentials.

Conclusions/Significance

The model is tested on known protein-peptide inhibitor complexes. The present algorithm predicts peptides that have better binding energies than those of the existing ones. Finally, a heptapeptide is designed for a protein that has excellent binding affinity according to AutoDock results.

Novel analytic criteria and effective plate designs for quality control in genome-scale RNAi screens

One of the most fundamental challenges in genome-wide RNA interference (RNAi) screens is to glean biological significance from mounds of data, which relies on the development and adoption of appropriate analytic methods and designs for quality control (QC) and hit selection. Currently, a Z-factor-based QC criterion is widely used to evaluate data quality. However, this criterion cannot take into account the fact that different positive controls may have different effect sizes and leads to inconsistent QC results in experiments with 2 or more positive controls with different effect sizes. In this study, based on a recently proposed parameter, strictly standardized mean difference (SSMD), novel QC criteria are constructed for evaluating data quality in genome-wide RNAi screens. Two good features of these novel criteria are: (1) SSMD has both clear original and probability meanings for evaluating the differentiation between positive and negative controls and hence the SSMD-based QC criteria have a solid probabilistic and statistical basis, and (2) these QC criteria obtain consistent QC results for multiple positive controls with different effect sizes. In addition, I propose multiple plate designs and the guidelines for using them in genome-wide RNAi screens. Finally, I provide strategies for using the SSMD-based QC criteria and effective plate design together to improve data quality. The novel SSMD-based QC criteria, effective plate designs, and related guidelines and strategies may greatly help to obtain high quality of data in genome-wide RNAi screens.

Integrating experimental and analytic approaches to improve data quality in genome-wide RNAi screens

RNA interference (RNAi) not only plays an important role in drug discovery but can also be developed directly into drugs. RNAi high-throughput screening (HTS) biotechnology allows us to conduct genome-wide RNAi research. A central challenge in genome-wide RNAi research is to integrate both experimental and computational approaches to obtain high quality RNAi HTS assays. Based on our daily practice in RNAi HTS experiments, we propose the implementation of 3 experimental and analytic processes to improve the quality of data from RNAi HTS biotechnology: (1) select effective biological controls; (2) adopt appropriate plate designs to display and/or adjust for systematic errors of measurement; and (3) use effective analytic metrics to assess data quality. The applications in 5 real RNAi HTS experiments demonstrate the effectiveness of integrating these processes to improve data quality. Due to the effectiveness in improving data quality in RNAi HTS experiments, the methods and guidelines contained in the 3 experimental and analytic processes are likely to have broad utility in genome-wide RNAi research.

Evolutionary Pareto-optimization of stably folding peptides

BACKGROUND

As a rule, peptides are more flexible and unstructured than proteins with their substantial stabilizing hydrophobic cores. Nevertheless, a few stably folding peptides have been discovered. This raises the question whether there may be more such peptides that are unknown as yet. These molecules could be helpful in basic research and medicine.

RESULTS

As a method to explore the space of conformationally stable peptides, we have developed an evolutionary algorithm that allows optimization of sequences with respect to several criteria simultaneously, for instance stability, accessibility of arbitrary parts of the peptide, etc. In a proof-of-concept experiment we have perturbed the sequence of the peptide Villin Headpiece, known to be stable in vitro. Starting from the perturbed sequence we applied our algorithm to optimize peptide stability and accessibility of a loop. Unexpectedly, two clusters of sequences were generated in this way that, according to our criteria, should form structures with higher stability than the wild-type. The structures in one of the clusters possess a fold that markedly differs from the native fold of Villin Headpiece. One of the mutants predicted to be stable was selected for synthesis, its molecular 3D-structure was characterized by nuclear magnetic resonance spectroscopy, and its stability was measured by circular dichroism. Predicted structure and stability were in good agreement with experiment. Eight other sequences and structures, including five with a non-native fold are provided as bona fide predictions.

CONCLUSION

The results suggest that much more conformationally stable peptides may exist than are known so far, and that small fold classes could comprise well-separated sub-folds.

Anti-Helicobacter pylori and thrombin inhibitory components from Chinese dragon's blood, Dracaena cochinchinensis

Chemical studies on the constituents of Dracaena cochinchinensis led to the discovery of eight new flavonoid derivatives ( 1- 8) along with 14 known compounds ( 9- 22). The identification and structural elucidation of these isolates were based on spectral analyses. All isolates were tested for antibacterial activities against Helicobacter pylori (ATCC43504) and thrombin inhibitory effects. As a result, new flavonoid derivatives 6 and 7 and (2 S)-4',7-dihydroxy-8-methylflavan ( 11) were found to be most efficacious against H. pylori (ATCC43504) with MIC values of 29.5, 29.5, and 31.3 microM, respectively, and the seven new flavonoid derivatives ( 1- 7) and one known biflavonoid ( 9) were observed to exhibit moderate thrombin inhibitory activity.

Structure of a novel thrombin inhibitor with an uncharged D-amino acid as P1 residue

Thrombin, the ultimate proteinase of the coagulation cascade, is an attractive target for the treatment of a variety of cardiovascular diseases. Previously, a series of novel thrombin inhibitors, discovered by employing a powerful and new computer-assisted multiparameter optimization process (CADDIS), have been synthesized. We have now crystallized the complex of human alpha-thrombin with the most potent of these inhibitors, 8-5 (K(i)=3 nM), and have determined its 2.3A X-ray crystal structure. The Fourier map displayed clear electron density for the inhibitor. The central part of the inhibitor binds in an improved melagatran-like mode, while the structure identifies a d-tyrosine as P1 residue which forms a charged hydrogen bond with Asp 189 of thrombin. This is the first crystal structure of a thrombin-inhibitor complex, where an uncharged inhibitor residue makes hydrogen bonds within the thrombin S1 pocket. Additionally, novel favourable intermolecular hydrogen bonds of the inhibitor with the thrombin backbone become possible due to the d-configuration of the P1 residue. Two flanking voluminous side chains increase the strength of the subjacent hydrogen bonding system by shielding it from the bulk solvent.

Tick anti-hemostatics: targets for future vaccines and therapeutics

For ticks, a significant obstacle in obtaining a blood meal is counteracting the hemostatic system of the host. To this end, ticks have developed a broad array of anti-hemostatics, which is reflected in the presence of structurally related tick proteins with different functions. Disruption of blood flow which blocks successful tick feeding makes anti-hemostatics attractive targets for anti-tick vaccines. Moreover, the limited number of drugs currently available for a range of important cardio-vascular diseases makes ticks a potential source of novel therapeutics. This review aims to summarize the key features of tick anti-hemostatics, their structures, mode of action and possible future application as vaccines and novel therapeutic agents.

A pair of new statistical parameters for quality control in RNA interference high-throughput screening assays

RNA interference (RNAi) high-throughput screening (HTS) enables massive parallel gene silencing and is increasingly being used to reveal novel connections between genes and disease-relevant phenotypes. The application of genome-scale RNAi relies on the development of high-quality RNAi HTS assays. To obtain high-quality HTS assays, there is a strong need for an easily interpretable and theoretically based quality control (QC) metric. Signal-to-noise ratio (S/N), signal-to-background ratio (S/B), and Z-factor have been adopted as QC metrics in HTS assays. In this paper, I proposed a pair of new parameters, strictly standardized mean difference (SSMD) and coefficient of variability in difference (CVD), as QC metrics in RNAi HTS assays. Compared to S/B and S/N, SSMD and CVD capture the variabilities in both compared populations. Compared to Z-factor, SSMD and CVD have a clear probability interpretation and a solid statistical basis. Accordingly, the cutoff criteria of using SSMD or CVD as a QC metric in HTS assays are fully theoretically based. In addition, I discuss the relationship between the SSMD-based criterion and the popular Z-factor-based criterion and elucidate why p-value from t-test of testing mean difference fails to serve as a QC metric.

Murine Thrombin Lacks Na+ Activation but Retains High Catalytic Activity

Human thrombin utilizes Na+ as a driving force for the cleavage of substrates mediating its procoagulant, prothrombotic, and signaling functions. Murine thrombin has Asp-222 in the Na+ binding site of the human enzyme replaced by Lys. The charge reversal substitution abrogates Na+ activation, which is partially restored with the K222D mutation, and ensures high activity even in the absence of Na+. This property makes the murine enzyme more resistant to the effect of mutations that destabilize Na+ binding and shift thrombin to its anticoagulant slow form. Compared with the human enzyme, murine thrombin cleaves fibrinogen and protein C with similar k(cat)/K(m) values but activates PAR1 and PAR4 with k(cat)/K(m) values 4- and 26-fold higher, respectively. The significantly higher specificity constant toward PAR4 accounts for the dominant role of this receptor in platelet activation in the mouse. Murine thrombin can also cleave substrates carrying Phe at P1, which potentially broadens the repertoire of molecular targets available to the enzyme in vivo.

Application of Fragment Screening and Fragment Linking to the Discovery of Novel Thrombin Inhibitors

The screening of fragments is an alternative approach to high-throughput screening for the identification of leads for therapeutic targets. Fragment hits have been discovered using X-ray crystallographic screening of protein crystals of the serine protease enzyme thrombin. The fragment library was designed to avoid any well-precedented, strongly basic functionality. Screening hits included a novel ligand (3), which binds exclusively to the S2-S4 pocket, in addition to smaller fragments which bind to the S1 pocket. The structure of these protein-ligand complexes are presented. A chemistry strategy to link two such fragments together and to synthesize larger drug-sized compounds resulted in the efficient identification of hybrid inhibitors with nanomolar potency (e.g., 7, IC50 = 3.7 nM). These potent ligands occupy the same area of the active site as previously described peptidic inhibitors, while having very different chemical architecture.