Biology subjects

Geng, C.

Publications and source records attributed to Geng, C..

23 records · Page 2Linked to original sources

bioRxiv2019

An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45.

Our information-driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modelling process, and to the robustness of its scoring function. We participated in CAPRI both as server and as manual predictors.\n\nIn CAPRI rounds 38-45, we have used various strategies depending on the information at hand. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template-based refinement / CA-CA restraint-guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse-grained force field in HADDOCK. Overall in the current rounds, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable models) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces.\n\nAn analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models.

bioinformatics↗

bioRxiv2019

PALM-Seq: simultaneous sequencing of cell-free long RNA and small RNA

Cell-free RNA, including both long RNA and small RNA, has been considered important for its biological functions and potential clinical usage, but the major challenge is to effectively sequence them at the same time. Here we present PolyAdenylation Ligation Mediated-Seq (PALM-Seq), an integrated sequencing method for cell-free long and small RNA. Through terminal modification and addition of 3 polyadenylation and 5 adaptor, we could get mRNA, long non-coding RNA, microRNA, tRNA, piRNA and other RNAs in a single library. With target RNA depletion, all these RNAs could be sequenced with relatively low depth. Using PALM-Seq, we identified pregnant-related mRNAs, long non-coding RNAs and microRNAs in female plasma. We also applied PALM-Seq to sequence RNA from amniotic fluids, leukocytes and placentas, and could find RNA signatures associated with specific sample type. PALM-Seq provides an integrated, cost-effective and simple method to characterize the landscape of cell-free RNA, and can stimulate further progress in cell-free RNA study and usage.

molecular biology↗

bioRxiv2019

Compartmentalized Biosynthesis of Mycophenolic Acid

Mycophenolic acid (MPA) from filamentous fungi is the first natural product antibiotic in human history and a first-line immunosuppressive drug for organ transplantations and autoimmune diseases. However, its biosynthetic mechanisms have remained a long-standing mystery. Here, we elucidate the MPA biosynthetic pathway that features both compartmentalized enzymatic steps and unique cooperation between biosynthetic and {beta}-oxidation catabolism machineries based on targeted gene inactivation, feeding experiments in heterologous expression hosts, enzyme functional characterization and kinetic analysis, and microscopic observation of protein subcellular localization. Besides identification of the oxygenase MpaB as the long-sought key enzyme responsible for the oxidative cleavage of sesquiterpene side chain, we reveal the intriguing pattern of compartmentalization for the MPA biosynthetic enzymes, including the cytosolic polyketide synthase MpaC and O-methyltransferase MpaG, the Golgi apparatus-associated prenyltransferase MpaA, the endoplasmic reticulum-bound oxygenase MpaB and P450-hydrolase fusion enzyme MpaDE, and the peroxisomal acyl-CoA hydrolase MpaH. The whole pathway is elegantly co-mediated by these compartmentalized enzymes, together with the peroxisomal {beta}-oxidation machinery. Beyond characterizing the remaining outstanding steps of the MPA biosynthetic pathway, our study highlights the importance of considering subcellular contexts and the broader cellular metabolism in natural product biosynthesis.\n\nSignificance StatementHere we elucidate the full biosynthetic pathway of the fungal natural product mycophenolic acid (MPA), which represents an unsolved mystery for decades. Besides the intriguing enzymatic mechanisms, we reveal that the MPA biosynthetic enzymes are elegantly compartmentalized; and the subcellular localization of the acyl-CoA hydrolase MpaH in peroxisomes is required for the unique cooperation between biosynthetic and {beta}-oxidation catabolism machineries. This work highlights the importance of a cell biology perspective for understanding the unexplored organelle-associated essential catalytic mechanisms in natural product biosynthesis of fungi and other higher organisms. The insights provided by our work will also benefit future efforts for both industrial strain improvement and novel drug development.

biochemistry↗

bioRxiv2018

iScore: A novel graph kernel-based function for scoring protein-protein docking models

Protein complexes play a central role in many aspects of biological function. Knowledge of the three-dimensional (3D) structures of protein complexes is critical for gaining insights into the structural basis of interactions and their roles in the biomolecular pathways that orchestrate key cellular processes. Because of the expense and effort associated with experimental determination of 3D structures of protein complexes, computational docking has evolved as a valuable tool to predict the 3D structures of biomolecular complexes. Despite recent progress, reliably distinguishing near-native docking conformations from a large number of candidate conformations, the so-called scoring problem, remains a major challenge. Here we present iScore, a novel approach to scoring docked conformations that combines HADDOCK energy terms with a score obtained using a graph representation of the protein-protein interfaces and a measure of evolutionary conservation. It achieves a scoring performance competitive with, or superior to that of the state-of-the-art scoring functions on independent data sets consisting docking software-specific data sets and the CAPRI score set built from a wide variety of docking approaches. iScore ranks among the top scoring approaches on the CAPRI score set (13 targets) when compared with the 37 scoring groups in CAPRI. The results demonstrate the utility of combining evolutionary and topological, and physicochemical information for scoring docked conformations. This work represents the first successful demonstration of graph kernel to protein interfaces for effective discrimination of near-native and non-native conformations of protein complexes. It paves the way for the further development of computational methods for predicting the structure of protein complexes.

bioinformatics↗

bioRxiv2018

iSEE: Interface Structure, Evolution and Energy-based machine learning predictor of binding affinity changes upon mutations

Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning-based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of interface Structure, Evolution and Energy-based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41 kcal mol-1 on a diverse training dataset consisting of 1102 mutations in 57 protein-protein complexes. It competes with existing state-of-the-art methods on two blind test datasets. Predictions for a new dataset of 540 mutations in 58 protein complexes from the recently published SKEMPI 2.0 database reveals that none of the current methods perform well (PCC<0.4), although their combination does improve the predictions. Feature analysis for iSEE underlines the significance of evolutionary conservations for quantitative prediction of mutation effects.

bioinformatics↗