Carmenza Martinez

Bio: Carmenza Martinez is an academic researcher from Stony Brook University. The author has contributed to research in topics: Side chain & Protein secondary structure. The author has an hindex of 1, co-authored 1 publications receiving 4140 citations.

ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB

Abstract: Molecular mechanics is powerful for its speed in atomistic simulations, but an accurate force field is required. The Amber ff99SB force field improved protein secondary structure balance and dynamics from earlier force fields like ff99, but weaknesses in side chain rotamer and backbone secondary structure preferences have been identified. Here, we performed a complete refit of all amino acid side chain dihedral parameters, which had been carried over from ff94. The training set of conformations included multidimensional dihedral scans designed to improve transferability of the parameters. Improvement in all amino acids was obtained as compared to ff99SB. Parameters were also generated for alternate protonation states of ionizable side chains. Average errors in relative energies of pairs of conformations were under 1.0 kcal/mol as compared to QM, reduced 35% from ff99SB. We also took the opportunity to make empirical adjustments to the protein backbone dihedral parameters as compared to ff99SB. Multiple sm...

OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins

Abstract: The parametrization and validation of the OPLS3 force field for small molecules and proteins are reported. Enhancements with respect to the previous version (OPLS2.1) include the addition of off-atom charge sites to represent halogen bonding and aryl nitrogen lone pairs as well as a complete refit of peptide dihedral parameters to better model the native structure of proteins. To adequately cover medicinal chemical space, OPLS3 employs over an order of magnitude more reference data and associated parameter types relative to other commonly used small molecule force fields (e.g., MMFF and OPLS_2005). As a consequence, OPLS3 achieves a high level of accuracy across performance benchmarks that assess small molecule conformational propensities and solvation. The newly fitted peptide dihedrals lead to significant improvements in the representation of secondary structure elements in simulated peptides and native structure stability over a number of proteins. Together, the improvements made to both the small mole...

Evolution of the novel coronavirus from the ongoing Wuhan outbreak and modeling of its spike protein for risk of human transmission

Abstract: Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Center for Biosafety Mega-Science, Chinese Academy of Sciences, Shanghai 200031, China; Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200032, China; Key Laboratory of Systems Biomedicine, Ministry of Education, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China; National Engineering Research Center for the Emergence Drugs, Beijing Institute of Pharmacology and Toxicology, Beijing 100850, China; The Joint Program in Infection and Immunity: a. Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou 510623, China; b. Institute Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai 200031, China

Unravelling the Role of O-glycans in Influenza A Virus Infection

Abstract: The initial stage of host cell infection by influenza A viruses (IAV) is mediated through interaction of the viral haemagglutinin (HA) with cell surface glycans. The binding requirement of IAVs for Galβ(1,4)Glc/ GlcNAc (lactose/lactosamine) glycans with a terminal α(2,6)-linked (human receptors) or α(2,3)-linked (avian receptors) N-acetylneuraminic residue commonly found on N-glycans, is well-established. However the role and significance of sialylated Galβ(1,3)GalNAc (core 1) epitopes that are typical O-glycoforms in influenza virus pathogenesis remains poorly detailed. Here we report a multidisciplinary study using NMR spectroscopy, virus neutralization assays and molecular modelling, into the potential for IAV to engage sialyl-Galβ(1,3)GalNAc O-glycoforms for cell attachment. H5 containing virus like particles (VLPs) derived from an H5N1 avian IAV strain show a significant involvement of the O-glycan-specific GalNAc residue, coordinated by a EQTKLY motif conserved in highly pathogenic avian influenza (HPAI) strains. Notably, human pandemic H1N1 influenza viruses shift the preference from 'human-like' α(2,6)-linkages in sialylated Galβ(1,4)Glc/GlcNAc fragments to 'avian-like' α(2,3)-linkages in sialylated Galβ(1,3)GalNAc without involvement of the GalNAc residue. Overall, our study suggests that sialylated Galβ(1,3)GalNAc as O-glycan core 1 glycoforms are involved in the influenza A virus life cycle and play a particularly crucial role during infection of HPAI strains.

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

Abstract: Deep learning is revolutionizing many areas of science and technology, especially image, text, and speech recognition In this paper, we demonstrate how a deep neural network (NN) trained on quantum mechanical (QM) DFT calculations can learn an accurate and transferable potential for organic molecules We introduce ANAKIN-ME (Accurate NeurAl networK engINe for Molecular Energies) or ANI for short ANI is a new method designed with the intent of developing transferable neural network potentials that utilize a highly-modified version of the Behler and Parrinello symmetry functions to build single-atom atomic environment vectors (AEV) as a molecular representation AEVs provide the ability to train neural networks to data that spans both configurational and conformational space, a feat not previously accomplished on this scale We utilized ANI to build a potential called ANI-1, which was trained on a subset of the GDB databases with up to 8 heavy atoms in order to predict total energies for organic molecules containing four atom types: H, C, N, and O To obtain an accelerated but physically relevant sampling of molecular potential surfaces, we also proposed a Normal Mode Sampling (NMS) method for generating molecular conformations Through a series of case studies, we show that ANI-1 is chemically accurate compared to reference DFT calculations on much larger molecular systems (up to 54 atoms) than those included in the training data set

ISOLDE: a physically realistic environment for model building into low-resolution electron-density maps

Abstract: This paper introduces ISOLDE, a new software package designed to provide an intuitive environment for high-fidelity interactive remodelling/refinement of macromolecular models into electron-density maps. ISOLDE combines interactive molecular-dynamics flexible fitting with modern molecular-graphics visualization and established structural biology libraries to provide an immersive interface wherein the model constantly acts to maintain physically realistic conformations as the user interacts with it by directly tugging atoms with a mouse or haptic interface or applying/removing restraints. In addition, common validation tasks are accelerated and visualized in real time. Using the recently described 3.8 A resolution cryo-EM structure of the eukaryotic minichromosome maintenance (MCM) helicase complex as a case study, it is demonstrated how ISOLDE can be used alongside other modern refinement tools to avoid common pitfalls of low-resolution modelling and improve the quality of the final model. A detailed analysis of changes between the initial and final model provides a somewhat sobering insight into the dangers of relying on a small number of validation metrics to judge the quality of a low-resolution model.