The AutoDock suite at 30

The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third‐party developers.     

南亚热带常绿阔叶林林下层植物的生物量及其测定方法的探讨

应用全收割法测定广东省鼎湖山南亚热带常绿阔叶林林下层植物生物量 ,林下植物总生物量为 12 9 58g/m2 ,其中茎、枝、叶、根的生物量占总生物量的比例约为 4 0 % ,9 0 % ,2 2 % ,2 9% .由部分实测数据建立林下植物个体生物量估算模型为W =0 0 0 4 2 ·H1 932 3.应用该模型得到的估算值 ,与收获实测值的相对误差仅为 1 8% ,具有良好的精度 .此外 ,还通过改变取样面积对该模型的适用性进行了探讨 .     

Protein conformational transitions coupled to binding in molecular recognition of unstructured proteins: deciphering the effect of intermolecular interactions on computational structure prediction of the p27Kip1 protein bound to the cyclin A-cyclin-dependent kinase 2 complex

The relationship between folding mechanism coupled to binding and structure prediction of the tertiary complexes is studied for the p27(Kip) (1) protein which has an intrinsically disordered unbound form and undergoes a functional folding transition during complex formation with the phosphorylated cyclin A-cyclin-dependent kinase 2 (Cdk2) binary complex. Hierarchy of p27(Kip1) structural loss determined in our earlier studies from temperature-induced Monte Carlo simulations and subsequent characterization of the transition state ensemble (TSE) for the folding reaction have shown that simultaneous ordering of the p27(Kip1) native intermolecular interface for the beta-hairpin and beta-strand secondary structure elements is critical for nucleating a rapid kinetic transition to the native tertiary complex. In the present study, we investigate the effect of forming specific intermolecular interactions on structure prediction of the p27(Kip1) tertiary complex. By constraining different secondary structure elements of p27(Kip1) in their native bound conformations and conducting multiple simulated annealing simulations, we analyze differences in the success rate of predicting the native structure of p27(Kip1) in the tertiary complex. In accordance with the nucleation-condensation mechanism, we have found that further stabilization of the native intermolecular interface for the beta-hairpin and beta-strand elements of p27(Kip1), that become ordered in the TSE, but are hardly populated in the unbound state, results in a consistent acquisition of the native bound structure. Conversely, the excessive stablization of the local secondary structure elements, which are rarely detected in the TSE, has a detrimental effect on convergence to the native bound structure.     

Fold recognition by combining sequence profiles derived from evolution and from depth-dependent structural alignment of fragments

Recognizing structural similarity without significant sequence identity has proved to be a challenging task. Sequence-based and structure-based methods as well as their combinations have been developed. Here, we propose a fold-recognition method that incorporates structural information without the need of sequence-to-structure threading. This is accomplished by generating sequence profiles from protein structural fragments. The structure-derived sequence profiles allow a simple integration with evolution-derived sequence profiles and secondary-structural information for an optimized alignment by efficient dynamic programming. The resulting method (called SP(3)) is found to make a statistically significant improvement in both sensitivity of fold recognition and accuracy of alignment over the method based on evolution-derived sequence profiles alone (SP) and the method based on evolution-derived sequence profile and secondary structure profile (SP(2)). SP(3) was tested in SALIGN benchmark for alignment accuracy and Lindahl, PROSPECTOR 3.0, and LiveBench 8.0 benchmarks for remote-homology detection and model accuracy. SP(3) is found to be the most sensitive and accurate single-method server in all benchmarks tested where other methods are available for comparison (although its results are statistically indistinguishable from the next best in some cases and the comparison is subjected to the limitation of time-dependent sequence and/or structural library used by different methods.). In LiveBench 8.0, its accuracy rivals some of the consensus methods such as ShotGun-INBGU, Pmodeller3, Pcons4, and ROBETTA. SP(3) fold-recognition server is available on http://theory.med.buffalo.edu.     

Chromatographic retention time prediction for posttranslationally modified peptides

Retention time prediction of peptides in liquid chromatography has proven to be a valuable tool for mass spectrometry-based proteomics, especially in designing more efficient procedures for state-of-the-art targeted workflows. Additionally, accurate retention time predictions can also be used to increase confidence in identifications in shotgun experiments. Despite these obvious benefits, the use of such methods has so far not been extended to (posttranslationally) modified peptides due to the absence of efficient predictors for such peptides. We here therefore describe a new retention time predictor for modified peptides, built on the foundations of our existing Elude algorithm. We evaluated our software by applying it on five types of commonly encountered modifications. Our results show that Elude now yields equally good prediction performances for modified and unmodified peptides, with correlation coefficients between predicted and observed retention times ranging from 0.93 to 0.98 for all the investigated datasets. Furthermore, we show that our predictor handles peptides carrying multiple modifications as well. This latest version of Elude is fully portable to new chromatographic conditions and can readily be applied to other types of posttranslational modifications. Elude is available under the permissive Apache2 open source License at http://per-colator.com or can be run via a web-interface at http://elude.sbc.su.se.     

Energetics and stability of transmembrane helix packing: a replica-exchange simulation with a knowledge-based membrane potential

The energetics and stability of the packing of transmembrane helices were investigated by Monte Carlo simulations with the replica-exchange method. The helices were modeled with a united atom representation, and the CHARMM19 force field was employed. Based on known experimental structures of membrane proteins, an implicit knowledge-based potential was developed to describe the helix-membrane interactions at the residue level, whose validity was tested through prediction of the orientations when single helices were inserted into a membrane. Two systems were studied in this article, namely the glycophorin A dimer, and helices A and B of Bacteriorhodopsin. For the glycophorin A dimer, the most stable structure (0.5 A away from the experimental structure) is mainly stabilized by the favorable helix-helix interactions, and has the most population regardless of the helix-membrane interaction. However, for helices A and B of Bacteriorhodopsin, it was found that the packing determined by helix-helix interactions is nonspecific, and a native-like structure (0.2 A from the experimental one) can be identified from several structural analogs as the most stable one only after applying the membrane potential. Our results suggest that the contribution from the helix-membrane interaction could be critical in the correct packing of transmembrane helices in the membrane.     

Conformational dynamics of nonsynonymous variants at protein interfaces reveals disease association

Recent studies have shown that the protein interface sites between individual monomeric units in biological assemblies are enriched in disease‐associated non‐synonymous single nucleotide variants (nsSNVs). To elucidate the mechanistic underpinning of this observation, we investigated the conformational dynamic properties of protein interface sites through a site‐specific structural dynamic flexibility metric (dfi) for 333 multimeric protein assemblies. dfi measures the dynamic resilience of a single residue to perturbations that occurred in the rest of the protein structure and identifies sites contributing the most to functionally critical dynamics. Analysis of dfi profiles of over a thousand positions harboring variation revealed that amino acid residues at interfaces have lower average dfi (31%) than those present at non‐interfaces (50%), which means that protein interfaces have less dynamic flexibility. Interestingly, interface sites with disease‐associated nsSNVs have significantly lower average dfi (23%) as compared to those of neutral nsSNVs (42%), which directly relates structural dynamics to functional importance. We found that less conserved interface positions show much lower dfi for disease nsSNVs as compared to neutral nsSNVs. In this case, dfi is better as compared to the accessible surface area metric, which is based on the static protein structure. Overall, our proteome‐wide conformational dynamic analysis indicates that certain interface sites play a critical role in functionally related dynamics (i.e., those with low dfi values), therefore mutations at those sites are more likely to be associated with disease. Proteins 2015; 83:428–435. © 2014 Wiley Periodicals, Inc.     

Helix-coil transition of PrP106-126: molecular dynamic study.

A set of 34 molecular dynamic (MD) simulations totaling 305 ns of simulation time of the prion protein-derived peptide PrP106-126 was performed with both explicit and implicit solvent models. The objective of these simulations is to investigate the relative stability of the alpha-helical conformation of the peptide and the mechanism for conversion from the helix to a random-coil structure. At neutral pH, the wild-type peptide was found to lose its initial helical structure very fast, within a few nanoseconds (ns) from the beginning of the simulations. The helix breaks up in the middle and then unwinds to the termini. The spontaneous transition into the random coil structure is governed by the hydrophobic interaction between His(111) and Val(122). The A117V mutation, which is linked to GSS disease, was found to destabilize the helix conformation of the peptide significantly, leading to a complete loss of helicity approximately 1 ns faster than in the wild-type. Furthermore, the A117V mutant exhibits a different mechanism for helix-coil conversion, wherein the helix begins to break up at the C-terminus and then gradually to unwind towards the N-terminus. In most simulations, the mutation was found to speed up the conversion through an additional hydrophobic interaction between Met(112) and the mutated residue Val(117), an interaction that did not exist in the wild-type peptide. Finally, the beta-sheet conformation of the wild-type peptide was found to be less stable at acidic pH due to a destabilization of the His(111)-Val(122), since at acidic pH this histidine is protonated and is unlikely to participate in hydrophobic interaction.     

Protein structure prediction assisted with sparse NMR data in CASP13

CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and ¹⁵N-¹H residual dipolar coupling data, typical of that obtained for ¹⁵N,¹³C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.