Fast, accurate, and reliable molecular docking with QuickVina 2

Abstract Motivation: The need for efficient molecular docking tools for high-throughput screening is growing alongside the rapid growth of drug-fragment databases. AutoDock Vina (‘Vina’) is a widely used docking tool with parallelization for speed. QuickVina (‘QVina 1’) then further enhanced the speed via a heuristics, requiring high exhaustiveness. With low exhaustiveness, its accuracy was compromised. We present in this article the latest version of QuickVina (‘QVina 2’) that inherits both the speed of QVina 1 and the reliability of the original Vina. Results: We tested the efficacy of QVina 2 on the core set of PDBbind 2014. With the default exhaustiveness level of Vina (i.e. 8), a maximum of 20.49-fold and an average of 2.30-fold acceleration with a correlation coefficient of 0.967 for the first mode and 0.911 for the sum of all modes were attained over the original Vina. A tendency for higher acceleration with increased number of rotatable bonds as the design variables was observed. On the accuracy, Vina wins over QVina 2 on 30% of the data with average energy difference of only 0.58 kcal/mol. On the same dataset, GOLD produced RMSD smaller than 2 Å on 56.9% of the data while QVina 2 attained 63.1%. Availability and implementation: The C++ source code of QVina 2 is available at (www.qvina.org). Contact:  [email protected] Supplementary information:  Supplementary data are available at Bioinformatics online.</jats:p

Accurately accelerating drug design workflow

In a world where pathogenic bacteria, viruses, as well as cancer develop resistance to drugs on faster pace than discovering new ones, researchers bear the heaviest weight to design new drugs to overcome such phenomenon of drug resistance. Drug design is one of the most challenging tasks in computational and structural biology, which aims at developing new drugs or enhancing currently known drugs against certain diseases based on the knowledge of a biological target. This thesis is about accelerating drug design work flow, through accurate acceleration of molecular docking tools, proper selection of candidates for Multiple Receptors Conformation (MRC) docking, and molecular dynamics (MD) simulation. In this work, I first developed QuickVina 2, a fast, accurate, and reliable molecular docking tool that depends on the powerful scoring function of AutoDock Vina and accelerated search of QuickVina. QuickVina 2 was tested against the 195 protein-ligand complexes of the core set of PDBbind 2014, using default exhaustiveness level of 8. It successfully attained up to 20.49-fold acceleration over Vina with tendency for higher acceleration when the number of dimensions/variables increases. Meanwhile, 70% of its predicted modes were equal to or better than original Vina in terms of binding energy. The remaining 30% had average Energy difference only 0.58 Kcal/mol. The Pearson’s correlation coefficient (r) between AutoDock Vina’s and QuickVina 2’s binding energy was 0.967 for the first predicted mode and 0.911 for the sum of all predicted modes. QuickVina 2 was found to be more accurate than GOLD 5.2 and is only slightly less accurate than Dock 6.6. QuickVina 2 was employed to propose drug fragments for Dengue Virus Non-Structure Protein 5 (DENV-NS5), and the result was compared to AutoDock Vina result as a measure of double confirmation. Both QuickVina 2 and AutoDock Vina detected the same 13 fragments with slight differences in their estimated binding energies while QuickVina 2 detected three additional fragments. Two of the fragments were subjected to MD simulations for in silico validation. The simulation results suggest that the proposed ligands are plausible and could be considered for further computational and experimental validation, as well as lead optimization. The work also involved refining the selection criteria of receptor conformation candidates that undergo MRC docking, in order to ensure diversity and increase sensitivity (decrease false negative rate) of detection. QuickVina 2 was taken then to another dimension by enabling it to search wide search spaces, after introducing inter-process spatio-temporal integration between the searching threads to communicate their collective wisdom. That work resulted in the release of QuickVina-W, a tool suitable for Blind Docking. QuickVina-W explores four folds the number of points that Vina explores, in a more efficient way. It proved to be faster than QuickVina 2 (with average and maximum normalized overall time accelerations of 3.60 and 34.33 folds in relation to Vina versus 1.98 and 18.02 respectively), yet better than AutoDock Vina in terms of binding energy (78% of predictions with binding energy better than or equal to Vina) and RMSD (Root Mean Square Distance) to experimental data (with success rate of 72% by QuickVina-W versus 63% by Vina). It was based on the observation that the Average Sum of Proximity relative Frequencies (ASoF) of searching threads is ever increasing with search progression, and on the theory that allowing a searching thread to communicate with other nearby threads to make use of their wisdom, would increase the speed and sensitivity of that searching thread, in a way relevant to the increasing ASoF. This work monitored the ASoF and proved its direct relation to decision taking increased speed and accuracy which are reflected in turn on the search process.Doctor of Philosoph

Proposing drug fragments for dengue virus NS5 protein

Dengue fever is a febrile illness caused by Dengue Virus, which belongs to the Flaviviridae family. Among its proteome, the nonstructural protein 5 (NS5) is the biggest and most conserved. It has a primer-independent RNA-dependent RNA polymerase (RdRp) domain at its C-Terminus. Zou et al. studied the biological relevance of the two conserved cavities (named A and B) within the NS5 proteins of dengue virus (DENV) and West Nile Virus (WNV) using mutagenesis and revertant analysis and found four mutations located at cavity B having effects on viral replication. They recommended Cavity B, but not Cavity A as a potential target for drugs against flavivirus RdRp. In this study, we virtually screened the MayBridge drug fragments dataset for potential small molecule binders of cavity B using both AutoDock Vina, the standard docking tool, and QuickVina 2, our previously developed tool. We selected 16 fragments that appeared in the top 100 docking results of each of the representative structures of NS5. Visual inspection suggests that they have reasonable binding poses. The 16 predicted fragments are plausible drug candidates and should be considered for further validation, optimization, and linking to come up with a suitable inhibitor of dengue virus

Protein-Ligand Blind Docking Using QuickVina-W With Inter-Process Spatio-Temporal Integration

“Virtual Screening” is a common step of in silico drug design, where researchers screen a large library of small molecules (ligands) for interesting hits, in a process known as “Docking”. However, docking is a computationally intensive and time-consuming process, usually restricted to small size binding sites (pockets) and small number of interacting residues. When the target site is not known (blind docking), researchers split the docking box into multiple boxes, or repeat the search several times using different seeds, and then merge the results manually. Otherwise, the search time becomes impractically long. In this research, we studied the relation between the search progression and Average Sum of Proximity relative Frequencies (ASoF) of searching threads, which is closely related to the search speed and accuracy. A new inter-process spatio-temporal integration method is employed in Quick Vina 2, resulting in a new docking tool, QuickVina-W, a suitable tool for “blind docking”, (not limited in search space size or number of residues). QuickVina-W is faster than Quick Vina 2, yet better than AutoDock Vina. It should allow researchers to screen huge ligand libraries virtually, in practically short time and with high accuracy without the need to define a target pocket beforehand.MOE (Min. of Education, S’pore)Published versio

Versatile clinical movement analysis using statistical parametric mapping in MovementRx

Clinical gait analysis is an important biomechanics field that is often influenced by subjectivity in time-varying analysis leading to type I and II errors. Statistical Parametric Mapping can operate on all time-varying joint dynamics simultaneously, thereby overcoming subjectivity errors. We present MovementRx, the first gait analysis modelling application that correctly models the deviations of joints kinematics and kinetics both in 3 and 1 degrees of freedom; presented with easy-to-understand color maps for clinicians with limited statistical training. MovementRx is a python-based versatile GUI-enabled movement analysis decision support system, that provides a holistic view of all lower limb joints fundamental to the kinematic/kinetic chain related to functional gait. The user can cascade the view from single 3D multivariate result down to specific single joint individual 1D scalar movement component in a simple, coherent, objective, and visually intuitive manner. We highlight MovementRx benefit by presenting a case-study of a right knee osteoarthritis (OA) patient with otherwise undetected postintervention contralateral OA predisposition. MovementRx detected elevated frontal plane moments of the patient's unaffected knee. The patient also revealed a surprising adverse compensation to the contralateral limb.Agency for Science, Technology and Research (A*STAR)Nanyang Technological UniversityPublished versionRehabilitation Research Institute of Singapore (RRIS) is funded by tripartite funding: Agency for Science, Technology and Research (A-STAR), National Health Group (NHG), and Nanyang Technological University (NTU Singapore). Tis work is part of the Ability data project in RRIS

Identification of Secondary Biomechanical Abnormalities in the Lower Limb Joints after Chronic Transtibial Amputation: A Proof-of-Concept Study Using SPM1D Analysis

SPM is a statistical method of analysis of time-varying human movement gait signal, depending on the random field theory (RFT). MovementRx is our inhouse-developed decision-support system that depends on SPM1D Python implementation of the SPM (spm1d.org). We present the potential application of MovementRx in the prediction of increased joint forces with the possibility to predispose to osteoarthritis in a sample of post-surgical Transtibial Amputation (TTA) patients who were ambulant in the community. We captured the three-dimensional movement profile of 12 males with TTA and studied them using MovementRx, employing the SPM1D Python library to quantify the deviation(s) they have from our corresponding reference data, using &ldquo;Hotelling 2&rdquo; and &ldquo;T test 2&rdquo; statistics for the 3D movement vectors of the 3 main lower limb joints (hip, knee, and ankle) and their nine respective components (3 joints &times; 3 dimensions), respectively. MovementRx results visually demonstrated a clear distinction in the biomechanical recordings between TTA patients and a reference set of normal people (ABILITY data project), and variability within the TTA patients&rsquo; group enabled identification of those with an increased risk of developing osteoarthritis in the future. We conclude that MovementRx is a potential tool to detect increased specific joint forces with the ability to identify TTA survivors who may be at risk for osteoarthritis