Assessment of fragment docking and scoring with the endothiapepsin model system.

Fragment-based screening has become indispensable in drug discovery. Yet, the weak binding affinities of these small molecules still represent a challenge for the reliable detection of fragment hits. The extent of this issue was illustrated in the literature for the aspartic protease endothiapepsin: When seven biochemical and biophysical in vitro screening methods were applied to screen a library of 361 fragments, very poor overlap was observed between the hit fragments identified by the individual approaches, resulting in high levels of false positive and/or false negative results depending on the mutually compared methods. Here, the reported in vitro findings are juxtaposed with the results from in silico docking and scoring approaches. The docking programs GOLD and Glide were considered with the scoring functions ASP, ChemScore, ChemPLP, GoldScore, DSXCSD, and GlideScore. First, the ranking power and scoring power were assessed for the named scoring functions. Second, the capability of reproducing the crystallized fragment binding modes was tested in a structure-based redocking approach. The redocking success notably depended on the ligand efficiency of the considered fragments. Third, a blinded virtual screening approach was employed to evaluate whether in silico screening can compete with in vitro methods in the enrichment of fragment databases.

Sampling and refinement protocols for template-based macrocycle docking: 2018 D3R Grand Challenge 4.

We describe a new template-based method for docking flexible ligands such as macrocycles to proteins. It combines Monte-Carlo energy minimization on the manifold, a fast manifold search method, with BRIKARD for complex flexible ligand searching, and with the MELD accelerator of Replica-Exchange Molecular Dynamics simulations for atomistic degrees of freedom. Here we test the method in the Drug Design Data Resource blind Grand Challenge competition. This method was among the best performers in the competition, giving sub-angstrom prediction quality for the majority of the targets.

Enhanced ligand sampling for relative protein-ligand binding free energy calculations.

Free energy calculations are used to study how strongly potential drug molecules interact with their target receptors. The accuracy of these calculations depends on the accuracy of the molecular dynamics (MD) force field as well as proper sampling of the major conformations of each molecule. However, proper sampling of ligand conformations can be difficult when there are large barriers separating the major ligand conformations. An example of this is for ligands with an asymmetrically substituted phenyl ring, where the presence of protein loops hinders the proper sampling of the different ring conformations. These ring conformations become more difficult to sample when the size of the functional groups attached to the ring increases. The Adaptive Integration Method (AIM) has been developed, which adaptively changes the alchemical coupling parameter λ during the MD simulation so that conformations sampled at one λ can aid sampling at the other λ values. The Accelerated Adaptive Integration Method (AcclAIM) builds on AIM by lowering potential barriers for specific degrees of freedom at intermediate λ values. However, these methods may not work when there are very large barriers separating the major ligand conformations. In this work, we describe a modification to AIM that improves sampling of the different ring conformations, even when there is a very large barrier between them. This method combines AIM with conformational Monte Carlo sampling, giving improved convergence of ring populations and the resulting free energy. This method, called AIM/MC, is applied to study the relative binding free energy for a pair of ligands that bind to thrombin and a different pair of ligands that bind to aspartyl protease ß-APP cleaving enzyme 1 (BACE1). These protein-ligand binding free energy calculations illustrate the improvements in conformational sampling and the convergence of the free energy compared to both AIM and AcclAIM.

Bioinformatic flowchart and database to investigate the origins and diversity of clan AA peptidases.

BACKGROUND: Clan AA of aspartic peptidases relates the family of pepsin monomers evolutionarily with all dimeric peptidases encoded by eukaryotic LTR retroelements. Recent findings describing various pools of single-domain nonviral host peptidases, in prokaryotes and eukaryotes, indicate that the diversity of clan AA is larger than previously thought. The ensuing approach to investigate this enzyme group is by studying its phylogeny. However, clan AA is a difficult case to study due to the low similarity and different rates of evolution. This work is an ongoing attempt to investigate the different clan AA families to understand the cause of their diversity. RESULTS: In this paper, we describe in-progress database and bioinformatic flowchart designed to characterize the clan AA protein domain based on all possible protein families through ancestral reconstructions, sequence logos, and hidden markov models (HMMs). The flowchart includes the characterization of a major consensus sequence based on 6 amino acid patterns with correspondence with Andreeva's model, the structural template describing the clan AA peptidase fold. The set of tools is work in progress we have organized in a database within the GyDB project, referred to as Clan AA Reference Database http://gydb.uv.es/gydb/phylogeny.php?tree=caard. CONCLUSION: The pre-existing classification combined with the evolutionary history of LTR retroelements permits a consistent taxonomical collection of sequence logos and HMMs. This set is useful for gene annotation but also a reference to evaluate the diversity of, and the relationships among, the different families. Comparisons among HMMs suggest a common ancestor for all dimeric clan AA peptidases that is halfway between single-domain nonviral peptidases and those coded by Ty3/Gypsy LTR retroelements. Sequence logos reveal how all clan AA families follow similar protein domain architecture related to the peptidase fold. In particular, each family nucleates a particular consensus motif in the sequence position related to the flap. The different motifs constitute a network where an alanine-asparagine-like variable motif predominates, instead of the canonical flap of the HIV-1 peptidase and closer relatives.

Conformation of a coarse-grained protein chain (an aspartic acid protease) model in effective solvent by a bond-fluctuating Monte Carlo simulation.

In a coarse-grained description of a protein chain, all of the 20 amino acid residues can be broadly divided into three groups: Hydrophobic (H) , polar (P) , and electrostatic (E) . A protein can be described by nodes tethered in a chain with a node representing an amino acid group. Aspartic acid protease consists of 99 residues in a well-defined sequence of H , P , and E nodes tethered together by fluctuating bonds. The protein chain is placed on a cubic lattice where empty lattice sites constitute an effective solvent medium. The amino groups (nodes) interact with the solvent (S) sites with appropriate attractive (PS) and repulsive (HS) interactions with the solvent and execute their stochastic movement with the Metropolis algorithm. Variations of the root mean square displacements of the center of mass and that of its center node of the protease chain and its gyration radius with the time steps are examined for different solvent strength. The structure of the protease swells on increasing the solvent interaction strength which tends to enhance the relaxation time to reach the diffusive behavior of the chain. Equilibrium radius of gyration increases linearly on increasing the solvent strength: A slow rate of increase in weak solvent regime is followed by a faster swelling in stronger solvent. Variation of the gyration radius with the time steps suggests that the protein chain moves via contraction and expansion in a somewhat quasiperiodic pattern particularly in strong solvent.

Design and development of BACE-1 inhibitors.

In early 1999, beta-amyloid cleaving enzyme-1 (BACE-1) was identified as the protease responsible for the critical first step in the processing of beta-amyloid precursor protein that ultimately leads to the production of Abeta peptides in the brain. Accumulation of these peptides has been implicated in the pathology of Alzheimer's disease (AD). An inhibitor of BACE-1 would therefore have therapeutic potential to slow or halt the progression of this debilitating and ultimately fatal disease. This review provides a perspective on the recent developments in the design of BACE-1 inhibitors. An overview of early research is also included, with particular emphasis on a comprehensive survey of the patent literature.

A heterogeneous enzymatic assay for quantification of Plasmepsin II activity and the evaluation of its inhibitors.

The emergence and worldwide spreading of Plasmodium falciparum strains that shown to be resistant to traditional drugs is considered a very serious health problem, given the high mortality and morbidity rate of Malaria. In the search for new drugs against this parasite, Hb hydrolyzing enzymes, such as Plasmepsin II (Plm II), have been classified as very promising targets for therapeutic attacks. In this work, it is developed a cheap and high-throughput heterogeneous enzymatic assay for measuring Plasmepsin II activity in order to use it as a tool in the discovery of new inhibitors of this enzyme. In this assay, Plasmepsin II acts upon a solid-phase bound synthetic peptide (DU2) whose sequence comprises the cleavage site F(33)-L(34) present in Hb alpha-chain. The peptide surface density is quantified by means of a classical ELISA-based procedure. In order to estimate the kinetic constants of the system and to quantify both, enzymatic and inhibitory activity, it was used a model for the kinetics of enzyme quasi-saturable systems previously developed by our group, that fitted very well to the experimental data. It was used Pepstatin as a model inhibitor of Plasmepsin II and the resulting dose-response relation agreed with the expected behavior for the Pepstatin-Plasmepsin II pair under the employed experimental conditions.

A new algorithm for the alignment of multiple protein structures using Monte Carlo optimization.

We have developed a new algorithm for the alignment of multiple protein structures based on a Monte Carlo optimization technique. The algorithm uses pair-wise structural alignments as a starting point. Four different types of moves were designed to generate random changes in the alignment. A distance-based score is calculated for each trial move and moves are accepted or rejected based on the improvement in the alignment score until the alignment is converged. Initial tests on 66 protein structural families show promising results, the score increases by 69% on average. The increase in score is accompanied by an increase (12%) in the number of residue positions incorporated into the alignment. Two specific families, protein kinases and aspartic proteinases were tested and compared against curated alignments from HOMSTRAD and manual alignments. This algorithm has improved the overall number of aligned residues while preserving key catalytic residues. Further refinement of the method and its application to generate multiple alignments for all protein families in the PDB, is currently in progress.

A mean field model of ligand-protein interactions: implications for the structural assessment of human immunodeficiency virus type 1 protease complexes and receptor-specific binding.

We propose a general mean field model of ligand-protein interactions to determine the thermodynamic equilibrium of a system at finite temperature. The method is employed in structural assessments of two human immuno-deficiency virus type 1 protease complexes where the gross effects of protein flexibility are incorporated by utilizing a data base of crystal structures. Analysis of the energy spectra for these complexes has revealed that structural and thermo-dynamic aspects of molecular recognition can be rationalized on the basis of the extent of frustration in the binding energy landscape. In particular, the relationship between receptor-specific binding of these ligands to human immunodeficiency virus type 1 protease and a minimal frustration principle is analyzed.

Parameterization studies for the SAM and HMMER methods of hidden Markov model generation.

Multiple sequence alignment of distantly related viral proteins remains a challenge to all currently available alignment methods. The hidden Markov model approach offers a new, flexible method for the generation of multiple sequence alignments. The results of studies attempting to infer appropriate parameter constraints for the generation of de novo HMMs for globin, kinase, aspartic acid protease, and ribonuclease H sequences by both the SAM and HMMER methods are described.

Prediction of human immunodeficiency virus protease cleavage sites in proteins.

Knowledge of the polyprotein cleavage sites by HIV protease will refine our understanding of its specificity and the information thus acquired is useful for designing specific and efficient HIV protease inhibitors. The pace in searching for the proper inhibitors of HIV protease will be greatly expedited if one can find an accurate and rapid method for predicting the cleavage sites in proteins by HIV protease. Various prediction models or algorithms have been developed during the past 5 years. This Review is devoted to addressing the following problems: (1) Why is it important to predict the cleavability of a peptide by HIV protease? (2) What progresses have been made in developing the prediction methods, and what merits and weakness does each of these methods carry? The attention is focused on the state-of-the-art, which is featured by a discriminant function algorithm developed very recently as well as an improved database (the program and database are available upon request) established according to new experimental results.

Distance-constrained molecular docking by simulated annealing.

An optimized method based on the principle of simulated annealing is presented for determining the relative position and orientation of interacting molecules. The spatial relationships of these molecules are described by intermolecular distance constraints between specific pairs of atoms, such as found in hydrogen bonds or from experimentally determined data. The method makes use of a random walk through six rotational and translational degrees of freedom where the constituent molecules are treated as rigid bodies. Van der Waals repulsions are used only to define a lower bound on distances between constrained atom pairs within the docking procedure. A cost function comprised of purely geometric constraints is optimized via simulated annealing, in order to search for the best orientation and position of the two molecules. Our docking procedure is applied to eight serine proteinase complexes from the Brookhaven Protein Data Bank. For each simulation 100 computations were performed. A typical docking computation requires only a few seconds of CPU time on a VAXserver 3500. The influence of the number of constraints on the final docked positions was studied. The sensitivity of the docking procedure to a ligand structure which is not well defined is also addressed. Possible applications of this method include using approximate distances incorporating complete energy functions.