Prediction of convection-enhanced drug delivery to the human brain

The treatment for many neurodegenerative diseases of the central nervous system (CNS) involves the delivery of large molecular weight drugs to the brain. The blood brain barrier, however, prevents many therapeutic molecules from entering the CNS. Despite much effort in studying drug dispersion with animal models, accurate drug targeting in humans remains a challenge. This article proposes an engineering approach for the systematic design of targeted drug delivery into the human brain. The proposed method predicts achievable volumes of distribution for therapeutic agents based on first principles transport and chemical kinetics models as well as accurate reconstruction of the brain geometry from patient-specific diffusion tensor magnetic resonance imaging. The predictive capabilities of the methodology will be demonstrated for invasive intraparenchymal drug administration. A systematic procedure to determine the optimal infusion and catheter design parameters to maximize penetration depth and volumes of distribution in the target area will be discussed. The computational results are validated with agarose gel phantom experiments. The methodology integrates interdisciplinary expertise from medical imaging and engineering. This approach will allow physicians and scientists to design and optimize drug administration in a systematic fashion.     

Functional inferences from blind ab initio protein structure predictions

Ab initio protein structure prediction methods have improved dramatically in the past several years. Because these methods require only the sequence of the protein of interest, they are potentially applicable to the open reading frames in the many organisms whose sequences have been and will be determined. Ab initio methods cannot currently produce models of high enough resolution for use in rational drug design, but there is an exciting potential for using the methods for functional annotation of protein sequences on a genomic scale. Here we illustrate how functional insights can be obtained from low-resolution predicted structures using examples from blind ab initio structure predictions from the third and fourth critical assessment of structure prediction (CASP3, CASP4) experiments.     

A modeled structure for amidase-03 from Bacillus anthracis

Homology models of amidase-03 from Bacillus anthracis were constructed using Modeller (9v2). Modeller constructs protein models using an automated approach for comparative protein structure modeling by the satisfaction of spatial restraints. A template structure of Listeria monocytogenes bacteriophage PSA endolysin PlyPSA (PDB ID: 1XOV) was selected from protein databank (PDB) using BLASTp with BLOSUM62 sequence alignment scoring matrix. We generated five models using the Modeller default routine in which initial coordinates are randomized and evaluated by pseudo-energy parameters. The protein models were validated using PROCHECK and energy minimized using the steepest descent method in GROMACS 3.2 (flexible SPC water model in cubic box of size 1 Å instead of rigid SPC model). We used G43a1 force field in GROMACS for energy calculations and the generated structure was subsequently analyzed using the VMD software for stereo-chemistry, atomic clash and misfolding. A detailed analysis of the amidase-03 model structure from Bacillus anthracis will provide insight to the molecular design of suitable inhibitors as drug candidates.     

Functional Inferences from Blind ab Initio Protein Structure Predictions

Ab initio protein structure prediction methods have improved dramatically in the past several years. Because these methods require only the sequence of the protein of interest, they are potentially applicable to the open reading frames in the many organisms whose sequences have been and will be determined. Ab initio methods cannot currently produce models of high enough resolution for use in rational drug design, but there is an exciting potential for using the methods for functional annotation of protein sequences on a genomic scale. Here we illustrate how functional insights can be obtained from low-resolution predicted structures using examples from blind ab initio structure predictions from the third and fourth critical assessment of structure prediction (CASP3, CASP4) experiments.     

Molecular design of new aggrecanases-2 inhibitors

Aggrecanases-2 is a very important potential drug target for the treatment of osteoarthritis. In this study, a series of known aggrecanases-2 inhibitors was analyzed by the technologies of three-dimensional quantitative structure–activity relationships (3D-QSAR) and molecular docking. Two 3D-QSAR models, which based on comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods, were established. Molecular docking was employed to explore the details of the interaction between inhibitors and aggrecanases-2 protein. According to the analyses for these models, several new potential inhibitors with higher activity predicted were designed, and were supported by the simulation of molecular docking. This work propose the fast and effective approach to design and prediction for new potential inhibitors, and the study of the interaction mechanism provide a better understanding for the inhibitors binding into the target protein, which will be useful for the structure-based drug design and modifications.     

Protein interaction-targeted drug discovery: evaluating critical issues

Employment of the decision strategies outlined in this general discussion should help to pinpoint mode of activity in drug development and validation. Overall, as a paradigm for drug development, a search for small molecules that can interfere with PPIs would seem to have significant long term potential. At present, the level of structural knowledge in databases is not sufficient to predict in toto the protein binding properties of a modeled drug, but as databases improve, this may become generally feasible. A major point that remains to be determined is how much specificity of protein binding can be incorporated into molecules of generally less than 500 Da. Finally, integration of PPI-targeting strategies with other approaches towards drug design will enhance the number of signaling pathways that can effectively be targeted. These points will be particularly pertinent as technologies permit a systematic identification of encoded protein interactions that govern the proteornic complement of cells.     

Minimalist models for protein folding and design

Protein folding research during the past decade has emphasized the dominant role of native state topology in determining the speed and mechanism of folding for small proteins; this has been illustrated by simulations using minimalist protein models. The advantages of minimalist protein models lie in their ability to rapidly collect meaningful statistics about folding pathways and kinetics, their ease of characterization with coarse-grained order parameters and their concentration on the essential physics of the problem to connect with experimental observables for a target protein. The maturation of experimental protein folding has driven the need for more quantitative protein simulations to better understand the balance between sequence details and fold topology. In the past year, we have seen the emergence of more complex minimalist models, ranging from all-atom Gō potentials to coarse-grained bead models in which Gō interactions are replaced or supplemented by more physically motivated potentials. The reduced computational cost at the coarse-grained level of abstraction will potentially enable both folding studies on a genomic scale and systematic application in protein design.     

A database for Plasmodium falciparum protein models

There is an urgent need for developing alternate strategies to combat Malaria caused by Plasmodium falciparum (P. falciparum) because of growing drug resistance and increased incidents of infection in humans. 3D models of P. falciparum annotated proteins using molecular modeling techniques will enhance our understanding about the mechanism of host parasite interactions for the identification of drug targets and malarial vaccine design. Potential structural templates for P. falciparum annotated proteins were selected from PDB (protein databank) using BLASTP (basic local alignment search tool for proteins). This exercise identified 476 Plasmodium proteins with one or more known structural templates (>or= 40 % identity) for further modeling. The pair-wise sequence alignments generated for protein modeling were manually checked for error. The models were then constructed using MODELLER (a comparative protein modelling program for modelling protein structures) followed by energy minimization in AMBER force field and checked for error using PROCHECK. AVAILABILITY: http://bioinfo.icgeb.res.in/codes/model.html.     

Conformational Transitions upon Ligand Binding: Holo-Structure Prediction from Apo Conformations

Biological function of proteins is frequently associated with the formation of complexes with small-molecule ligands. Experimental structure determination of such complexes at atomic resolution, however, can be time-consuming and costly. Computational methods for structure prediction of protein/ligand complexes, particularly docking, are as yet restricted by their limited consideration of receptor flexibility, rendering them not applicable for predicting protein/ligand complexes if large conformational changes of the receptor upon ligand binding are involved. Accurate receptor models in the ligand-bound state (holo structures), however, are a prerequisite for successful structure-based drug design. Hence, if only an unbound (apo) structure is available distinct from the ligand-bound conformation, structure-based drug design is severely limited. We present a method to predict the structure of protein/ligand complexes based solely on the apo structure, the ligand and the radius of gyration of the holo structure. The method is applied to ten cases in which proteins undergo structural rearrangements of up to 7.1 Å backbone RMSD upon ligand binding. In all cases, receptor models within 1.6 Å backbone RMSD to the target were predicted and close-to-native ligand binding poses were obtained for 8 of 10 cases in the top-ranked complex models. A protocol is presented that is expected to enable structure modeling of protein/ligand complexes and structure-based drug design for cases where crystal structures of ligand-bound conformations are not available.     

Effective handling of induced-fit motion in flexible docking

For structure-based drug design, where various ligand structures need to be docked to a target protein structure, a docking method that can handle conformational flexibility of not only the ligand, but also the protein, is indispensable. We have developed a simple and effective approach for dealing with the local induced-fit motion of the target protein, and implemented it in our docking tool, ADAM. Our approach efficiently combines the following two strategies: a vdW-offset grid in which the protein cavity is enlarged uniformly, and structure optimization allowing the motion of ligand and protein atoms. To examine the effectiveness of our approach, we performed docking validation studies, including redocking in 18 test cases and foreign-docking, in which various ligands from foreign crystal structures of complexes are docked into a target protein structure, in 22 cases (on five target proteins). With the original ADAM, the correct docking modes (RMSD < 2.0 A) were not present among the top 20 models in one case of redocking and four cases of foreign-docking. When the handling of induced-fit motion was implemented, the correct solutions were acquired in all 40 test cases. In foreign-docking on thymidine kinase, the correct docking modes were obtained as the top-ranked solutions for all 10 test ligands by our combinatorial approach, and this appears to be the best result ever reported with any docking tool. The results of docking validation have thus confirmed the effectiveness of our approach, which can provide reliable docking models even in the case of foreign-docking, where conformational change of the target protein cannot be ignored. We expect that this approach will contribute substantially to actual drug design, including virtual screening.     

The role of multiscale computational approaches for rational design of conventional and nanoparticle oral drug delivery systems

Multiscale computational modeling of drug delivery systems (DDS) is poised to provide predictive capabilities for the rational design of targeted drug delivery systems, including multi-functional nanoparticles. Realistic, mechanistic models can provide a framework for understanding the fundamental physico-chemical interactions between drug, delivery system, and patient. Multiscale computational modeling, however, is in its infancy even for conventional drug delivery. The wide range of emerging nanotechnology systems for targeted delivery further increases the need for reliable in silico predictions. This review will present existing computational approaches at different scales in the design of traditional oral drug delivery systems. Subsequently, a multiscale framework for integrating continuum, stochastic, and computational chemistry models will be proposed and a case study will be presented for conventional DDS. The extension of this framework to emerging nanotechnology delivery systems will be discussed along with future directions. While oral delivery is the focus of the review, the outlined computational approaches can be applied to other drug delivery systems as well.     

Systems biology in drug discovery

The hope of the rapid translation of 'genes to drugs' has foundered on the reality that disease biology is complex, and that drug development must be driven by insights into biological responses. Systems biology aims to describe and to understand the operation of complex biological systems and ultimately to develop predictive models of human disease. Although meaningful molecular level models of human cell and tissue function are a distant goal, systems biology efforts are already influencing drug discovery. Large-scale gene, protein and metabolite measurements ('omics') dramatically accelerate hypothesis generation and testing in disease models. Computer simulations integrating knowledge of organ and system-level responses help prioritize targets and design clinical trials. Automation of complex primary human cell-based assay systems designed to capture emergent properties can now integrate a broad range of disease-relevant human biology into the drug discovery process, informing target and compound validation, lead optimization, and clinical indication selection. These systems biology approaches promise to improve decision making in pharmaceutical development.     

Amyotrophic lateral sclerosis--the tools of the trait

The aim of this review is to analyze how our knowledge on the etiology, pathology, and treatment of amyotrophic lateral sclerosis (ALS) has profited from the application of biotechnology tools for the identification of disease markers, the development of animal disease models, and the design of innovative therapeutics. In humans, ALS-specific clinical, genetic or protein biomarkers, or panels of biomarkers stemming from genomics and proteomics analyses can be critical for early diagnosis, monitoring of disease progression, drug validation in clinical trials, and identification of therapeutic targets for subsequent drug development. At the same time, animal models representing a number of human superoxide dismutase 1 mutations, intermediate-filament disorganization or axonal-transport defects have been invaluable in unraveling aspects of the pathophysiology of the disease; in each case, these only represent a small proportion of all ALS patients. Preclinical and clinical trials, although at present heavily concentrating on pharmacological approaches, are embracing the emerging alternative strategies of stem-cell and gene therapy. In combination with a further subcategorization of patients and the development of corresponding model systems for functional analyses, they will significantly influence the already changing face of ALS therapy.     

FAMCS： Finding All Maximal Common Substructures in Proteins

Finding the common substructures shared by two proteins is considered as one of the central issues in computational biology because of its usefulness in understanding the structure-function relationship and application in drug and vaccine design. In this paper, we propose a novel algorithm called FAMCS （Finding All Maximal Common Substructures） for the common substructure identification problem. Our method works initially at the protein secondary structural element （SSE） level and starts with the identification of all structurally similar SSE pairs. These SSE pairs are then merged into sets using a modified Apriori algorithm, which will test the similarity of various sets of SSE pairs incrementally until all the maximal sets of SSE pairs that deemed to be similar are found. The maximal common substructures of the two proteins will be formed from these maximal sets. A refinement algorithm is also proposed to fine tune the alignment from the SSE level to the residue level. Comparison of FAMCS with other methods on various proteins shows that FAMCS can address all four requirements and infer interesting biological discoveries.