Toward a computational tool predicting the stereochemical outcome of asymmetric reactions: development of the molecular mechanics-based program ACE and application to asymmetric epoxidation reactions

The development and application of ACE, a program that predicts the stereochemical outcome of asymmetric reactions is presented. As major implementations, ACE includes a genetic algorithm to carry out an efficient global conformational search combined with a conjugate gradient minimization routine for local optimization and a corner flap algorithm to search ring conformations. Further improvements have been made that enable ACE to generate Boltzmann populations of conformations, to investigate highly asynchronous reactions, to compute fluctuating partial atomic charges and solvation energy and to automatically construct reactants and products from libraries of catalysts and substrates. Validation on previously investigated reactions (asymmetric Diels Alder cycloadditions and organocatalyzed aldol reactions) followed by application to a number of alkene epoxidation reactions and a comparative study of DFT-derived and ACE-derived predictions demonstrate the accuracy and usefulness of ACE in the context of asymmetric catalyst design.     

CABS‐NMR—De novo tool for rapid global fold determination from chemical shifts,residual dipolar couplings and sparse methyl‐methyl noes

Recent development of nuclear magnetic resonance (NMR) techniques provided new types of structural restraints that can be successfully used in fast and low‐cost global protein fold determination. Here, we present CABS‐NMR, an efficient protein modeling tool, which takes advantage of such structural restraints. The restraints are converted from original NMR data to fit the coarse grained protein representation of the C‐Alpha‐Beta‐Side‐group (CABS) algorithm. CABS is a Monte Carlo search algorithm that uses a knowledge‐based force field. Its versatile structure enables a variety of protein‐modeling protocols, including purely de novo folding, folding guided by restraints derived from template structures or, structure assembly based on experimental data. In particular, CABS‐NMR uses the distance and angular restraints set derived from various NMR experiments. This new modeling technique was successfully tested in structure determination of 10 globular proteins of size up to 216 residues, for which sparse NMR data were available. Additional detailed analysis was performed for a S100A1 protein. Namely, we successfully predicted Nuclear Overhauser Effect signals on the basis of low‐energy structures obtained from chemical shifts by CABS‐NMR. It has been observed that utility of chemical shifts and other types of experimental data (i.e. residual dipolar couplings and methyl‐methyl Nuclear Overhauser Effect signals) in the presented modeling pipeline depends mainly on size of a protein and complexity of its topology. In this work, we have provided tools for either post‐experiment processing of various kinds of NMR data or fast and low‐cost structural analysis in the still challenging field of new fold predictions. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2011     

cgRNASP-CN: a minimal coarse-grained representation-based statistical potential for RNA 3D structure evaluation

Knowledge of RNA 3-dimensional (3D) structures is critical to understand the important biological functions of RNAs, and various models have been developed to predict RNA 3D structures in silico. However, there is still lack of a reliable and efficient statistical potential for RNA 3D structure evaluation. For this purpose, we developed a statistical potential based on a minimal coarse-grained representation and residue separation, where every nucleotide is represented by C4' atom for backbone and N1 (or N9) atom for base. In analogy to the newly developed all-atom rsRNASP, cgRNASP-CN is composed of short-ranged and long-ranged potentials, and the short-ranged one was involved more subtly. The examination indicates that the performance of cgRNASP-CN is close to that of the all-atom rsRNASP and is superior to other top all-atom traditional statistical potentials and scoring functions trained from neural networks, for two realistic test datasets including the RNA-Puzzles dataset. Very importantly, cgRNASP-CN is about 100 times more efficient than existing all-atom statistical potentials/scoring functions including rsRNASP. cgRNASP-CN is available at website: https://github.com/Tan-group/cgRNASP-CN.     

基于轻量化卷积神经网络的光伏电池片缺陷检测方法研究

光伏电池片中的缺陷会影响整个光伏系统使用寿命及发电效率。针对现有电池片自动检测中尺寸弱小缺陷漏检率高的问题,建立了一种特征增强型轻量化卷积神经网络模型。针对性地设计了特征增强提取模块,提高了弱边界的提取能力,同时根据多尺度识别原理,增加了小目标预测层,实现了多尺度特征预测。在实验测试中,该模型平均精度均值（mAP）达到87.55%,比传统模型提高了6.78个百分点,同时检测速度达到40帧/s,满足精准性与实时性的检测要求。     

Theoretical and Experimental Exploration of the Energy Landscape of the Quasi‐Binary Cesium Chloride/Lithium Chloride System

As a case study, the energy landscape of the cesium chloride/lithium chloride system was investigated by combining theoretical and experimental methods. Global optimization for many compositions of this quasi‐binary system gave candidates for possible modifications that constitute promising targets for subsequent syntheses based on solid‐state reactions. Owing to the synergetic and complementary nature of the computational and experimental approaches, a substantially better efficiency of exploration was achieved. Several new phases were found in this system, for the compositions CsLiCl₂ and CsLi₂Cl₃, and their thermodynamic ranking with respect to the already‐known phases was clarified. In particular, the new CsLiCl₂ modification was shown to be the low‐temperature phase, whilst the already‐known modification for this composition corresponded to a high‐temperature phase. Based on these results, an improved cesium chloride/lithium chloride phase diagram was derived, and this approach points the way to more rational and more efficient solid‐state synthesis.     

SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles

Accurate prediction of protein secondary structure is essential for accurate sequence alignment, three-dimensional structure modeling, and function prediction. The accuracy of ab initio secondary structure prediction from sequence, however, has only increased from around 77 to 80% over the past decade. Here, we developed a multistep neural-network algorithm by coupling secondary structure prediction with prediction of solvent accessibility and backbone torsion angles in an iterative manner. Our method called SPINE X was applied to a dataset of 2640 proteins (25% sequence identity cutoff) previously built for the first version of SPINE and achieved a 82.0% accuracy based on 10-fold cross validation (Q(3)). Surpassing 81% accuracy by SPINE X is further confirmed by employing an independently built test dataset of 1833 protein chains, a recently built dataset of 1975 proteins and 117 CASP 9 targets (critical assessment of structure prediction techniques) with an accuracy of 81.3%, 82.3% and 81.8%, respectively. The prediction accuracy is further improved to 83.8% for the dataset of 2640 proteins if the DSSP assignment used above is replaced by a more consistent consensus secondary structure assignment method. Comparison to the popular PSIPRED and CASP-winning structure-prediction techniques is made. SPINE X predicts number of helices and sheets correctly for 21.0% of 1833 proteins, compared to 17.6% by PSIPRED. It further shows that SPINE X consistently makes more accurate prediction in helical residues (6%) without over prediction while PSIPRED makes more accurate prediction in coil residues (3-5%) and over predicts them by 7%. SPINE X Server and its training/test datasets are available at http://sparks.informatics.iupui.edu/     

Potential of mean force of water–proton bath and molecular dynamic simulation of proteins at constant pH

An advanced implicit solvent model of water–proton bath for protein simulations at constant pH is presented. The implicit water–proton bath model approximates the potential of mean force of a protein in water solvent in a presence of hydrogen ions. Accurate and fast computational implementation of the implicit water–proton bath model is developed using the continuum electrostatic Poisson equation model for calculation of ionization equilibrium and the corrected MSR6 generalized Born model for calculation of the electrostatic atom–atom interactions and forces. Molecular dynamics (MD) method for protein simulation in the potential of mean force of water–proton bath is developed and tested on three proteins. The model allows to run MD simulations of proteins at constant pH, to calculate pH‐dependent properties and free energies of protein conformations. The obtained results indicate that the developed implicit model of water–proton bath provides an efficient way to study thermodynamics of biomolecular systems as a function of pH, pH‐dependent ionization‐conformation coupling, and proton transfer events. © 2012 Wiley Periodicals, Inc.     

蛋白质相互作用预测、设计与调控

蛋白质相互作用是生命活动在分子水平上的基本事件. 蛋白质相互作用的三维图像可以给出关键生命活动过程的分子细节. 了解蛋白质相互作用的原理有助于揭示生命活动的机制, 并在此基础上开展有重要价值的蛋白质设计. 本文对于蛋白质相互作用预测、设计和调控研究的近期进展进行了总结归纳, 介绍了作者实验室在相关领域的研究进展, 并对今后的研究方向进行了展望. 主要包括: (1) 蛋白质相互作用网络、蛋白质相互作用机制和蛋白质复合物结构计算分析; (2) 基于序列、结合位点以及复合物结构的蛋白质相互作用预测; (3)蛋白质相互作用设计方法; (4) 利用化学分子调控蛋白质相互作用的方法; (5) 针对蛋白质相互作用的蛋白质药物设计方法.     

蛋白质-RNA相互作用界面预测与设计

蛋白质-RNA之间的相互作用是蛋白质在细胞里面行使功能的重要方式之一. 结构生物学家利用实验手段可以得到蛋白质-RNA复合物的三维结构, 通过原子水平的晶体结构来解释蛋白质与RNA的识别过程. 但实验取得蛋白质-RNA的复合物结构非常困难, 耗钱、耗时, 同时受限于其相互作用强度. 因而利用理论的方法对蛋白质-RNA相互作用界面进行预测与设计在生物医学研究中十分重要. 本文主要综述了近期蛋白质-RNA相互作用界面预测与设计方面的进展, 包括以下几个方面: (1) 蛋白质-RNA分子对接算法以及对接前后存在的构象变化的处理; (2) 蛋白质-RNA 识别机制的研究; (3) 基于蛋白质-RNA 相互作用界面的分子设计. 蛋白质-RNA分子对接算法逐步完善将有助于我们对大量未知功能的蛋白质与RNA进行功能注释, 而基于生物大分子相互作用界面的分子设计将在药物设计领域中有广阔的应用前景.