Near-Optimal Motion Planning Algorithms Via A Topological and Geometric Perspective

Motion planning is a fundamental problem in robotics, which involves finding a path for an autonomous system, such as a robot, from a given source to a destination while avoiding collisions with obstacles. The properties of the planning space heavily influence the performance of existing motion planning algorithms, which can pose significant challenges in handling complex regions, such as narrow passages or cluttered environments, even for simple objects. The problem of motion planning becomes deterministic if the details of the space are fully known, which is often difficult to achieve in constantly changing environments. Sampling-based algorithms are widely used among motion planning paradigms because they capture the topology of space into a roadmap. These planners have successfully solved high-dimensional planning problems with a probabilistic-complete guarantee, i.e., it guarantees to find a path if one exists as the number of vertices goes to infinity. Despite their progress, these methods have failed to optimize the sub-region information of the environment for reuse by other planners. This results in re-planning overhead at each execution, affecting the performance complexity for computation time and memory space usage. In this research, we address the problem by focusing on the theoretical foundation of the algorithmic approach that leverages the strengths of sampling-based motion planners and the Topological Data Analysis methods to extract intricate properties of the environment. The work contributes a novel algorithm to overcome the performance shortcomings of existing motion planners by capturing and preserving the essential topological and geometric features to generate a homotopy-equivalent roadmap of the environment. This roadmap provides a mathematically rich representation of the environment, including an approximate measure of the collision-free space. In addition, the roadmap graph vertices sampled close to the obstacles exhibit advantages when navigating through narrow passages and cluttered environments, making obstacle-avoidance path planning significantly more efficient. The application of the proposed algorithms solves motion planning problems, such as sub-optimal planning, diverse path planning, and fault-tolerant planning, by demonstrating the improvement in computational performance and path quality. Furthermore, we explore the potential of these algorithms in solving computational biology problems, particularly in finding optimal binding positions for protein-ligand or protein-protein interactions. Overall, our work contributes a new way to classify routes in higher dimensional space and shows promising results for high-dimensional robots, such as articulated linkage robots. The findings of this research provide a comprehensive solution to motion planning problems and offer a new perspective on solving computational biology problems

Uniform Sampling Framework for Sampling Based Motion Planning and Its Applications to Robotics and Protein Ligand Binding

Sampling-based motion planning aims to find a valid path from a start to a goal by sampling in the planning space. Planning on surfaces is an important problem in many research problems, including traditional robotics and computational biology. It is also a difficult research question to plan on surfaces as the surface is only a small subspace of the entire planning space. For example, robots are currently widely used for product assembly. Contact between the robot manipulator and the product are required to assemble each piece precisely. The configurations in which the robot fingers are in contact with the object form a surface in the planning space. However, these configurations are only a small proportion of all possible robot configurations. Several sampling-based motion planners aim to bias sampling to specific surfaces, such as Cobst surfaces, as needed for tasks requiring contact, or along the medial axis, which maximizes clearance. While some of these methods work well in practice, none of them are able to provide any information regarding the distribution of the samples they generate. It would be interesting and useful to know, for example, that a particular surface has been sampled uniformly so that one could argue regarding the probability of finding a path on that surface. Unfortunately, despite great interest for nearly two decades, it has remained an open problem to develop a method for sampling on such surfaces that can provide any information regarding the distribution of the resulting samples. Our research focuses on solving this open problem and introduces a framework that is guaranteed to uniformly sample any surface in Cspace. Instead of explicitly constructing the target surfaces, which is generally intractable, our uniform sampling framework only requires detecting intersections between a line segment and the target surface, which can often be done efficiently. Intuitively, since we uniformly distribute the line segments, the intersections between the segments and the surfaces will also be uniformly distributed. We present two particular instances of the framework: Uniform Obstacle-based PRM (UOBPRM) that uniformly samples Cobst surfaces, and Uniform Medial-Axis PRM (UMAPRM) that uniformly samples the Cspace medial axis. We provide a theoretical analysis for this framework that establishes uniformity and probabilistic completeness and also the probability of sampling in narrow passages. We show applications of this uniform sampling framework in robotics (both UOBPRM and UMAPRM) and in biology (UOBPRM). We are able to solve some difficult motion planning problems more efficiently than other sampling methods, including PRM, OBPRM, Gaussian PRM, Bridge Test PRM, and MAPRM. Moreover, we show that UOBPRM and UMAPRM have similar computational overhead as other approaches. UOBPRM is used to study the ligand binding affinity ranking problem in computational biology. Our experimental results show that UOBPRM is a potential technique to rank ligand binding affinity which can be further applied as a cost-saving tool for pharmaceutical companies to narrow the search for drug candidates

Structural Investigation of Binding Events in Proteins

Understanding the biophysical properties that describe protein binding events has allowed for the advancement of drug discovery through structure-based drug design and in silico methodology. The accuracy of these in silico methods depends entirely on the parameters that we determine for them. Many of these parameters are derived from the structural information we have obtained as a community and therein resides the importance of integrity of the quality of this structural data. First, the curation and contents of the Binding MOAD database are extensively described. This database serves as a repository of 25,759 high-quality, ligand-bound X-ray protein crystal structures complemented by 9138 hand-curated binding affinity data for as many of those ligands as appropriate. The newly implemented extended binding site feature is presented, establishing more robust definitions of ligand binding sites than those provided by other databases. Finally, the contents of Binding MOAD are compared to similar databases, establishing the value of our dataset and which purposes it best serves. Second, a robust dataset of 305 unique protein sequences with at least two ligand-bound and two ligand-free structures for each unique protein is cultivated from Binding MOAD and the PDB. Protein flexibility is assessed using C-alpha RMSD for backbone motion and chi-1 angles to quantify side-chain motions. We establish that there is no statistically significant difference between the available conformational space for the backbones or the side chains of unbound proteins when compared to their bound structures. Examining the change in occupied conformational space upon ligand binding reveals a statistically significant increase in backbone conformational space of miniscule magnitude, but a significant increase of side-chain conformational space. To quantify the conformational space available to the side chains, flexibility profiles are established for each amino acid. We found no correlation between backbone and side-chain flexibility. Parallels are then made to common practices in flexible docking techniques. Six binding-site prediction algorithms are then benchmarked on a derivation of the previously established dataset of 305 proteins. We assessed the performance of ligand-bound vs ligand-free structures with these methods and concluded that five of the six methods showed no preference for either structure type. The remaining method, Fpocket, showed decreased performance for ligand-free structures. There was a staggering amount of inconsistency in performance with the methods; different structures of the exact same protein could achieve wildly different rates of success with the same method. The performance of individual structures for all six methods indicated that success and failure rates were seemingly random. Finally, we establish no correlation between the performance of the same structures with different methods, or the performance of the structures with structure resolution, Cruickshank DPI, or number of unresolved residues in their binding sites. Last, we examine the chemical and physical properties of protein-protein interactions (PPIs) with regard to their geometric location in the interface. First, we found that the relative elevation changes of the protein interface landscapes demonstrate that these interfaces are not as flat as previously described. Second, the hollows of druggable PPI interfaces are more sharply shaped and nonpolar in nature, and the protrusions of these druggable PPI interfaces are very polar in character. Last, no correlations exist between the binding affinity describing the subunits of a PPI and other physical and chemical parameters that we measured.PHDMedicinal ChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145943/1/jordanjc_1.pd

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 352)

This bibliography lists 147 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during July 1991. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

The role of serotonergic and dopaminergic mechanisms and their interaction in Levodopa-induced dyskinesias

Long–term levodopa treatment in Parkinson’s disease (PD) is commonly associated with troublesome levodopa–induced dyskinesias (LIDs). Striatal serotonergic terminals amid the degenerating dopaminergic ones are proposed to play an important role in LIDs by taking up exogenous levodopa and releasing dopamine in an unregulated fashion. However, to date, the underlying mechanisms of LIDs are not fully understood. By using single photon emission computed tomography (SPECT) with 123I–Ioflupane and positron emission tomography (PET) with 11C–DASB and 11C–PE2I, the clinical studies conducted for this thesis aimed (a) to estimate the role of striatal dopamine transporter (DAT) availability in early PD as a prognostic marker for LIDs, (b) to explore whether striatal DAT availability changes over time are related to the appearance of LIDs, (c) to estimate the role of striatal serotonin-to-dopamine transporter (SERT–to–DAT) binding ratios to LIDs, and (d) to look for a relation between the changes in striatal SERT, DAT and SERT–to–DAT binding ratios over time and the appearance of LIDs. The main findings are as follows: (a) in early PD, striatal DAT availability alone does not predict the appearance of future LIDs, (b) at later stages, the occurrence of LIDs may be dependent on the magnitude of DAT decline in the putamen, (c) the SERT–to–DAT binding ratio in the putamen is increased in PD patients as compared to controls, and within PD, it is higher in patients with LIDs as compared to nondyskinetic patients, (d) as PD continues to progress, putaminal serotonergic terminals remain relatively unchanged in comparison to the dopaminergic ones and the aforementioned imbalance (as reflected by the binding ratio) increases over time. These findings provide fundamental insight in the pathophysiology of LIDs and have direct implications for further research towards novel therapeutics in PD dyskinesia.Open Acces

Structure- and Ligand-Based Design of Novel Antimicrobial Agents

The use of computer based techniques in the design of novel therapeutic agents is a rapidly emerging field. Although the drug-design techniques utilized by Computational Medicinal Chemists vary greatly, they can roughly be classified into structure-based and ligand-based approaches. Structure-based methods utilize a solved structure of the design target, protein or DNA, usually obtained by X-ray or NMR methods to design or improve compounds with activity against the target. Ligand-based methods use active compounds with known affinity for a target that may yet be unresolved. These methods include Pharmacophore-based searching for novel active compounds or Quantitative Structure-Activity Relationship (QSAR) studies. The research presented here utilized both structure and ligand-based methods against two bacterial targets: Bacillus anthracis and Mycobacterium tuberculosis. The first part of this thesis details our efforts to design novel inhibitors of the enzyme dihydropteroate synthase from B. anthracis using crystal structures with known inhibitors bound. The second part describes a QSAR study that was performed using a series of novel nitrofuranyl compounds with known, whole-cell, inhibitory activity against M. tuberculosis. Dihydropteroate synthase (DHPS) catalyzes the addition of p-amino benzoic acid (pABA) to dihydropterin pyrophosphate (DHPP) to form pteroic acid as a key step in bacterial folate biosynthesis. It is the traditional target of the sulfonamide class of antibiotics. Unfortunately, bacterial resistance and adverse effects have limited the clinical utility of the sulfonamide antibiotics. Although six bacterial crystal structures are available, the flexible loop regions that enclose pABA during binding and contain key sulfonamide resistance sites have yet to be visualized in their functional conformation. To gain a new understanding of the structural basis of sulfonamide resistance, the molecular mechanism of DHPS action, and to generate a screening structure for high-throughput virtual screening, molecular dynamics simulations were applied to model the conformations of the unresolved loops in the active site. Several series of molecular dynamics simulations were designed and performed utilizing enzyme substrates and inhibitors, a transition state analog, and a pterin-sulfamethoxazole adduct. The positions of key mutation sites conserved across several bacterial species were closely monitored during these analyses. These residues were shown to interact closely with the sulfonamide binding site. The simulations helped us gain new understanding of the positions of the flexible loops during inhibitor binding that has allowed the development of a DHPS structural model that could be used for high-through put virtual screening (HTVS). Additionally, insights gained on the location and possible function of key mutation sites on the flexible loops will facilitate the design of new, potent inhibitors of DHPS that can bypass resistance mutations that render sulfonamides inactive. Prior to performing high-throughput virtual screening, the docking and scoring functions to be used were validated using established techniques against the B. anthracis DHPS target. In this validation study, five commonly used docking programs, FlexX, Surflex, Glide, GOLD, and DOCK, as well as nine scoring functions, were evaluated for their utility in virtual screening against the novel pterin binding site. Their performance in ligand docking and virtual screening against this target was examined by their ability to reproduce a known inhibitor conformation and to correctly detect known active compounds seeded into three separate decoy sets. Enrichment was demonstrated by calculated enrichment factors at 1% and Receiver Operating Characteristic (ROC) curves. The effectiveness of post-docking relaxation prior to rescoring and consensus scoring were also evaluated. Of the docking and scoring functions evaluated, Surflex with SurflexScore and Glide with GlideScore performed best overall for virtual screening against the DHPS target. The next phase of the DHPS structure-based drug design project involved high-throughput virtual screening against the DHPS structural model previously developed and docking methodology validated against this target. Two general virtual screening methods were employed. First, large, virtual libraries were pre-filtered by 3D pharmacophore and modified Rule-of-Three fragment constraints. Nearly 5 million compounds from the ZINC databases were screened generating 3,104 unique, fragment-like hits that were subsequently docked and ranked by score. Second, fragment docking without pharmacophore filtering was performed on almost 285,000 fragment-like compounds obtained from databases of commercial vendors. Hits from both virtual screens with high predicted affinity for the pterin binding pocket, as determined by docking score, were selected for in vitro testing. Activity and structure-activity relationship of the active fragment compounds have been developed. Several compounds with micromolar activity were identified and taken to crystallographic trials. Finally, in our ligand-based research into M. tuberculosis active agents, a series of nitrofuranylamide and related aromatic compounds displaying potent activity was investigated utilizing 3-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) techniques. Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods were used to produce 3D-QSAR models that correlated the Minimum Inhibitory Concentration (MIC) values against M. tuberculosis with the molecular structures of the active compounds. A training set of 95 active compounds was used to develop the models, which were then evaluated by a series of internal and external cross-validation techniques. A test set of 15 compounds was used for the external validation. Different alignment and ionization rules were investigated as well as the effect of global molecular descriptors including lipophilicity (cLogP, LogD), Polar Surface Area (PSA), and steric bulk (CMR), on model predictivity. Models with greater than 70% predictive ability, as determined by external validation and high internal validity (cross validated r2 \u3e .5) were developed. Incorporation of lipophilicity descriptors into the models had negligible effects on model predictivity. The models developed will be used to predict the activity of proposed new structures and advance the development of next generation nitrofuranyl and related nitroaromatic anti-tuberculosis agents

Molecular simulations on proteins of biomedical interest : A. Ligand-protein hydration B. Cytochrome P450 2D6 and 2C9 C. Myelin associated glycoprotein (MAG)

TOPIC 1: Water molecules mediating polar interactions in ligand–protein complexes contribute to both binding affinity and specificity. To account for such water molecules in computer-aided drug discovery, we performed an extensive search in the Cambridge Structural Database (CSD) to identify the geometrical criteria defining interactions of water molecules with ligand and protein. In addition, ab initio calculations were used to derive the propensity of ligand hydration. Based on these information we developed an algorithm (AcquaAlta) to reproduce water molecules bridging polar interactions between ligand and protein moieties. This approach was validated using 20 crystal structures and yielded a match of 76% between experimental and calculated water positions. The solvation algorithm was then applied to the docking of oligopeptides to the periplasmic oligopeptide binding protein A (OppA), supported by a pharmacophore-based alignment tool. TOPIC 2: Drug metabolism, toxicity, and interaction profile are major issues in the drug discovery and lead optimization processes. The Cytochromes P450 (CYPs) 2D6 and 2C9 are enzymes involved in the oxidative metabolism of a majority of the marketed drugs. By identifying the binding mode using pharmacophore pre-alignement and automated flexible docking, and quantifying the binding affinity by multi-dimensional QSAR, we validated a model family of 56 compounds (46 training, 10 test) and 85 (68 training, 17 test) for CYP2D6 and CYP2C9, respectively. The correlation with the experimental data (cross- validated r2 = 0.811 for CYP2D6 and 0.687 for CYP2C9) suggests that our approach is suited for predicting the binding affinity of compounds towards the CYP2D6 and CYP2C9. The models were challenged by Y-scrambling, and by testing an external dataset of binding compounds (15 compounds for CYP2D6 and 40 for CYP2C9) and not binding compounds (64 compounds for CYP2D6 and 56 for CYP2C9). TOPIC 3: After injury, neurites from mammalian adult central nervous systems are inhibited to regenerate by inhibitory proteins such as the myelin-associated glycoprotein (MAG). The block of MAG with potent glycomimetic antagonists could be a fruitful approach to enhance axon regeneration. Libraries of MAG antagonists were derived and synthesized starting from the (general) sialic acid moiety. The binding data were rationalized by docking studies, molecular dynamics simulations and free energy perturbations on a homology model of MAG. The pharmacokinetic profile (i.e. stability in cerebrospinal fluid, logD, and blood-brain barrier permeation) of these compounds has been thoroughly investigated to evaluate the drug-likeness of the identified antagonists

Discovery and development of novel inhibitors for the kinase Pim-1 and G-Protein Coupled Receptor Smoothened

Investigation of the cause of disease is no easy business. This is particularly so when one reflects upon the lessons taught us in antiquity. Prior to the beginning of the last century, diagnosis and treatment of diseases such as cancers was so bereft of hope that there was little physicians could offer in the way of comfort, let alone treatment. One of the major insights from investigations into cancers this century has been that those involved in research leading to treatments are not dealing with a singular malady but multiple families of diseases with different mechanisms and modes of action. Therefore, despite the end game being similar in cancers, that of uncontrolled growth and replication leading to cellular dysfunction, different diseases require different approaches in targeting them. This leads us to a particular broad treatment approach, that of drug design. A drug is, in the classical sense, a small molecule that, upon introduction into the body, interacts with biochemical targets to induce a wider biological effect, ideally with both an intended target and intended effect. The conceptual basis underpinning this `lock-and-key' paradigm was elucidated over a century ago and the primary occupation of those involved in biochemical research has been to determine as much information as possible about both of these protein locks and drug keys. And, as inferred from the paradigm, molecular shape is all-important in determining and controlling action against the most important locks with the most potent and specific keys. The two most important target classes in drug discovery for some time have been protein kinases and G Protein-Coupled Receptors (GPCRs). Both classes of proteins are large families that perform very different tasks within the body. Kinases activate and inactive many cellular processes by catalysing the transfer of a phosphate group from Adenosine Tri-Phosphate (ATP) to other targets. GPCRs perform the job of interacting with chemical signals and communicating them into a biological response. Dysfunction in both types of proteins in certain cells can lead to a loss of biological control and, ultimately, a cancer. Both of kinases and GPCRs have entirely different chemical structures so structural knowledge therefore becomes crucial in any approach targeting cells where dysfunction has occurred. Thus, for this thesis, a member from each class was investigated using a combination of structural approaches. From the kinase class, the kinase Proviral Integration site for MuLV (Pim-1) and from the GPCR class, the cell membrane-bound Smoothened receptor (SMO). The kinase \pimone\ was the target of various approaches in \autoref{chap:three}. Although a heavily studied target from the mid-2000's, there is a paucity of inhibitors targeting residues more remote from structural characteristics that define kinases. Further limiting extension possibilities is that \pimone\ is constitutively active so no inhibitors targeting an inactive state are possible. An initial project (\pone) used the known binding properties of small molecules, or, `fragments' to elucidate structural and dynamic information useful for targeting \pimone. This was followed by three projects, all with the goal of inhibitor discovery, all with different foci. In \ptwo, fragment binding modes from \pone\ provided the basis for the extension and development of drug-like inhibitors with a focus on synthetic feasibility. In contrast, inhibitors were found in \pthree\ via a large-scale public dataset of purchasable molecules that possess drug-like properties. Finally, \pfour\ took the truncated form of a particularly attractive fragment from \pone\ that was crystallised with \pimone, verified its binding mode and then generated extensions with, again, a focus on synthetic feasibility. The GPCR \smo\ has fewer molecular studies and much about its structural behaviour remains unknown. As the most `druggable' protein in the Hedgehog pathway, structural studies have primarily focussed on stabilising its inactive state to prevent signal transduction. Allied to this is that there are generally few inhibitors for \smo\ and the drugs for cancers related to its dysfunction are vulnerable to mutations that significantly reduce their effectiveness or abrogate it entirely. The elucidation of structural information in therefore of high priority. An initial study attempting to identify an unknown molecule from prior experiments led to insights regarding binding characteristics of specific moieties. This was particularly important to understand not just where favourable moieties bind but also sections of the \smo\ binding pocket with unfavourable binding. In both subsequent virtual screens performed in Chapter 4, the primary aim was to find new drug-like inhibitors of \smo\ using large public datasets of commercially-available molecules. The initial screen retrieved relatively few inhibitors so the binding pocket was modified to find a structural state more amenable to small molecule binding. These modifications led to a significant number of new, chemically novel inhibitors for \smo, some structural information useful for future inhibitors and the elucidation of structure-activity relationships useful for inhibitor design. This underpins the idea that structural information is of critical importance in the discovery and design of molecular inhibitors