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The accurate prediction of protein structures achieved by deep learning (DL) methods is a significant milestone and has deeply impacted structural biology. Shortly after its release, AlphaFold2 has been evaluated for predicting protein-peptide interactions and shown to significantly outperform RoseTTAfold as well as a conventional blind docking method: PIPER-FlexPepDock. Since then, new AlphaFold2 models, trained specifically to predict multimeric assemblies, have been released and a new ab initio folding model OmegaFold has become available. Here, we assess docking success rates for these new DL folding models and compare their performance with our state-of-the-art, focused peptide-docking software AutoDock CrankPep (ADCP). The evaluation is done using the same dataset and performance metric for all methods. We show that, for a set of 99 nonredundant protein-peptide complexes, the new AlphaFold2 model outperforms other Deep Learning approaches and achieves remarkable docking success rates for peptides. While the docking success rate of ADCP is more modest when considering the top-ranking solution only, it samples correct solutions for around 62% of the complexes. Interestingly, different methods succeed on different complexes, and we describe a consensus docking approach using ADCP and AlphaFold2, which achieves a remarkable 60% for the top-ranking results and 66% for the top 5 results for this set of 99 protein-peptide complexes.

In recent years, therapeutic peptides have gained a lot interest as demonstrated by the 60 peptides approved as drugs in major markets and 150+ peptides currently in clinical trials. However, while small molecule docking is routinely used in rational drug design efforts, docking peptides has proven challenging partly because docking scoring functions, developed and calibrated for small molecules, perform poorly for these molecules. Here, we present random forest classifiers trained to discriminate correctly docked peptides. We show that, for a testing set of 47 protein-peptide complexes, structurally dissimilar from the training set and previously used to benchmark AutoDock Vina's ability to dock short peptides, these random forest classifiers improve docking power from ∼25% for AutoDock scoring functions to an average of ∼70%. These results pave the way for peptide-docking success rates comparable to those of small molecule docking. To develop these classifiers, we compiled the ProptPep37_2021 data set, a curated, high-quality set of 322 crystallographic protein-peptides complexes annotated with structural similarity information. The data set also provides a collection of high-quality putative poses with a range of deviations from the crystallographic pose, providing correct and incorrect poses (i.e., decoys) of the peptide for each entry. The ProptPep37_2021 data set as well as the classifiers presented here are freely available.

Under-expression or overexpression of protein kinases has been shown to be associated with unregulated cell signal transduction in cancer cells. Therefore, there is major interest in designing protein kinase inhibitors as anticancer agents. We have previously reported [WR] 5 , a peptide containing alternative arginine (R) and tryptophan (W) residues as a non-competitive c-Src tyrosine kinase inhibitor. A number of larger cyclic peptides containing alternative hydrophobic and positively charged residues [WR] x ( x = 6-9) and hybrid cyclic-linear peptides, [R 6 K]W 6 and [R 5 K]W 7 , containing R and W residues were evaluated for their protein kinase inhibitory potency. Among all the peptides, cyclic peptide [WR] 9 was found to be the most potent tyrosine kinase inhibitor. [WR] 9 showed higher inhibitory activity (IC 50 = 0.21 μM) than [WR] 5 , [WR] 6 , [WR] 7 , and [WR] 8 with IC 50 values of 0.81, 0.57, 0.35, and 0.33 μM, respectively, against c-Src kinase as determined by a radioactive assay using [γ- 33 P]ATP. Consistent with the result above, [WR] 9 inhibited other protein kinases such as Abl kinase activity with an IC 50 value of 0.35 μM, showing 2.2-fold higher inhibition than [WR] 5 (IC 50 = 0.79 μM). [WR] 9 also inhibited PKCa kinase activity with an IC 50 value of 2.86 μM, approximately threefold higher inhibition than [WR] 5 (IC 50 = 8.52 μM). A similar pattern was observed against Braf, c-Src, Cdk2/cyclin A1, and Lck. [WR] 9 exhibited IC 50 values of <0.25 μM against Akt1, Alk, and Btk. These data suggest that [WR] 9 is consistently more potent than other cyclic peptides with a smaller ring size and hybrid cyclic-linear peptides [R 6 K]W 6 and [R 5 K]W 7 against selected protein kinases. Thus, the presence of R and W residues in the ring, ring size, and the number of amino acids in the structure of the cyclic peptide were found to be critical in protein kinase inhibitory potency. We identified three putative binding pockets through automated blind docking of cyclic peptides [WR] (5-9) . The most populated pocket is located between the SH2, SH3, and N-lobe domains on the opposite side of the ATP binding site. The second putative pocket is formed by the same domains and located on the ATP binding site side of the protein. Finally, a third pocket was identified between the SH2 and SH3 domains. These results are consistent with the non-competitive nature of the inhibition displayed by these molecules. Molecular dynamics simulations of the protein-peptide complexes indicate that the presence of either [WR] 5 or [WR] 9 affects the plasticity of the protein and in particular the volume of the ATP binding site pocket in different ways. These results suggest that the second pocket is most likely the site where these peptides bind and offer a plausible rationale for the increased affinity of [WR] 9 .

AutoDock4 is a widely used program for docking small molecules to macromolecular targets. It describes ligand-receptor interactions using a physics-inspired scoring function that has been proven useful in a variety of drug discovery projects. However, compared to more modern and recent software, AutoDock4 has longer execution times, limiting its applicability to large scale dockings. To address this problem, we describe an OpenCL implementation of AutoDock4, called AutoDock-GPU, that leverages the highly parallel architecture of GPU hardware to reduce docking runtime by up to 350-fold with respect to a single-threaded process. Moreover, we introduce the gradient-based local search method ADADELTA, as well as an improved version of the Solis-Wets random optimizer from AutoDock4. These efficient local search algorithms significantly reduce the number of calls to the scoring function that are needed to produce good results. The improvements reported here, both in terms of docking throughput and search efficiency, facilitate the use of the AutoDock4 scoring function in large scale virtual screening.

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., (© 2020 The Protein Society.)

Background: Activated protein C (APC) is an important homeostatic blood coagulation protease that conveys anticoagulant and cytoprotective activities. Proteolytic inactivation of factors Va and VIIIa facilitated by cofactor protein S is responsible for APC's anticoagulant effects, whereas cytoprotective effects of APC involve primarily the endothelial protein C receptor (EPCR), protease activated receptor (PAR)1 and PAR3., Objective: To date, several binding exosites in the protease domain of APC have been identified that contribute to APC's interaction with its substrates but potential contributions of the C-terminus of the light chain have not been studied in detail., Methods: Site-directed Ala-scanning mutagenesis of six positively charged residues within G142-L155 was used to characterize their contributions to APC's anticoagulant and cytoprotective activities., Results and Conclusions: K151 was involved in protein S dependent-anticoagulant activity of APC with some contribution of K150. 3D structural analysis supported that these two residues were exposed in an extended protein S binding site on one face of APC. Both K150 and K151 were important for PAR1 and PAR3 cleavage by APC, suggesting that this region may also mediate interactions with PARs. Accordingly, APC's cytoprotective activity as determined by endothelial barrier protection was impaired by Ala substitutions of these residues. Thus, both K150 and K151 are involved in APC's anticoagulant and cytoprotective activities. The differential contribution of K150 relative to K151 for protein S-dependent anticoagulant activity and PAR cleavage highlights that binding exosites for protein S binding and for PAR cleavage in the C-terminal region of APC's light chain overlap., (© 2020 International Society on Thrombosis and Haemostasis.)

Precomputed affinity maps are used by AutoDock to efficiently describe rigid biomolecules called receptors in automated docking. These maps greatly speed up the docking process and allow users to experiment with the forcefield. Here, we present AutoGridFR (AGFR): a software tool facilitating the calculation of these maps. We describe a new version of the AutoSite algorithm that improves the description of binding pockets automatically detected on receptors, and an algorithm for adding affinity gradients which help search methods optimize solution using fewer evaluations of the scoring functions. AGFR supports the calculation of maps for various advanced docking techniques such as covalent docking, hydrated docking, and docking with flexible receptor sidechains. Maps are stored in a single file along with metadata supporting data provenance, reproducibility, and facilitating their management. Finally, maps can be calculated from the command line or through a modern graphical user interface which also supports their visualization. © 2019 Wiley Periodicals, Inc., (© 2019 Wiley Periodicals, Inc.)

Motivation: Protein-peptide interactions mediate a wide variety of cellular and biological functions. Methods for predicting these interactions have garnered a lot of interest over the past few years, as witnessed by the rapidly growing number of peptide-based therapeutic molecules currently in clinical trials. The size and flexibility of peptides has shown to be challenging for existing automated docking software programs., Results: Here we present AutoDock CrankPep or ADCP in short, a novel approach to dock flexible peptides into rigid receptors. ADCP folds a peptide in the potential field created by the protein to predict the protein-peptide complex. We show that it outperforms leading peptide docking methods on two protein-peptide datasets commonly used for benchmarking docking methods: LEADS-PEP and peptiDB, comprised of peptides with up to 15 amino acids in length. Beyond these datasets, ADCP reliably docked a set of protein-peptide complexes containing peptides ranging in lengths from 16 to 20 amino acids. The robust performance of ADCP on these longer peptides enables accurate modeling of peptide-mediated protein-protein interactions and interactions with disordered proteins., Availability and Implementation: ADCP is distributed under the LGPL 2.0 open source license and is available at http://adcp.scripps.edu. The source code is available at https://github.com/ccsb-scripps/ADCP., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

While a new therapeutic cyclic peptide is approved nearly every year, docking large macrocycles has remained challenging. Here, we present a new version of our peptide docking software AutoDock CrankPep ( ADCP ), extended to dock peptides cyclized through their backbone and/or side chain disulfide bonds. We show that within the top 10 solutions, ADCP identifies the proper interactions for 71% of a data set of 38 complexes, thus making it a useful tool for rational peptide-based drug design.

Protein S anticoagulant cofactor sensitivity and PAR1 cleavage activity were assayed for 9 recombinant APC mutants.Residues L38, K43, I73, F95, and W115 on one face of the APC light chain define an extended surface containing the protein S binding site., Competing Interests: Conflict-of-interest disclosure: The authors declare no competing financial interests.

Motivation: The identification of ligand-binding sites from a protein structure facilitates computational drug design and optimization, and protein function assignment. We introduce AutoSite: an efficient software tool for identifying ligand-binding sites and predicting pseudo ligand corresponding to each binding site identified. Binding sites are reported as clusters of 3D points called fills in which every point is labelled as hydrophobic or as hydrogen bond donor or acceptor. From these fills AutoSite derives feature points: a set of putative positions of hydrophobic-, and hydrogen-bond forming ligand atoms., Results: We show that AutoSite identifies ligand-binding sites with higher accuracy than other leading methods, and produces fills that better matches the ligand shape and properties, than the fills obtained with a software program with similar capabilities, AutoLigand In addition, we demonstrate that for the Astex Diverse Set, the feature points identify 79% of hydrophobic ligand atoms, and 81% and 62% of the hydrogen acceptor and donor hydrogen ligand atoms interacting with the receptor, and predict 81.2% of water molecules mediating interactions between ligand and receptor. Finally, we illustrate potential uses of the predicted feature points in the context of lead optimization in drug discovery projects., Availability and Implementation: http://adfr.scripps.edu/AutoDockFR/autosite.html CONTACT: sanner@scripps.eduSupplementary information: Supplementary data are available at Bioinformatics online., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Computational docking can be used to predict bound conformations and free energies of binding for small-molecule ligands to macromolecular targets. Docking is widely used for the study of biomolecular interactions and mechanisms, and it is applied to structure-based drug design. The methods are fast enough to allow virtual screening of ligand libraries containing tens of thousands of compounds. This protocol covers the docking and virtual screening methods provided by the AutoDock suite of programs, including a basic docking of a drug molecule with an anticancer target, a virtual screen of this target with a small ligand library, docking with selective receptor flexibility, active site prediction and docking with explicit hydration. The entire protocol will require ∼5 h.

Automated docking of drug-like molecules into receptors is an essential tool in structure-based drug design. While modeling receptor flexibility is important for correctly predicting ligand binding, it still remains challenging. This work focuses on an approach in which receptor flexibility is modeled by explicitly specifying a set of receptor side-chains a-priori. The challenges of this approach include the: 1) exponential growth of the search space, demanding more efficient search methods; and 2) increased number of false positives, calling for scoring functions tailored for flexible receptor docking. We present AutoDockFR-AutoDock for Flexible Receptors (ADFR), a new docking engine based on the AutoDock4 scoring function, which addresses the aforementioned challenges with a new Genetic Algorithm (GA) and customized scoring function. We validate ADFR using the Astex Diverse Set, demonstrating an increase in efficiency and reliability of its GA over the one implemented in AutoDock4. We demonstrate greatly increased success rates when cross-docking ligands into apo receptors that require side-chain conformational changes for ligand binding. These cross-docking experiments are based on two datasets: 1) SEQ17 -a receptor diversity set containing 17 pairs of apo-holo structures; and 2) CDK2 -a ligand diversity set composed of one CDK2 apo structure and 52 known bound inhibitors. We show that, when cross-docking ligands into the apo conformation of the receptors with up to 14 flexible side-chains, ADFR reports more correctly cross-docked ligands than AutoDock Vina on both datasets with solutions found for 70.6% vs. 35.3% systems on SEQ17, and 76.9% vs. 61.5% on CDK2. ADFR also outperforms AutoDock Vina in number of top ranking solutions on both datasets. Furthermore, we show that correctly docked CDK2 complexes re-create on average 79.8% of all pairwise atomic interactions between the ligand and moving receptor atoms in the holo complexes. Finally, we show that down-weighting the receptor internal energy improves the ranking of correctly docked poses and that runtime for AutoDockFR scales linearly when side-chain flexibility is added.

cellPACK assembles computational models of the biological mesoscale, an intermediate scale (10-100 nm) between molecular and cellular biology scales. cellPACK's modular architecture unites existing and novel packing algorithms to generate, visualize and analyze comprehensive three-dimensional models of complex biological environments that integrate data from multiple experimental systems biology and structural biology sources. cellPACK is available as open-source code, with tools for validation of models and with 'recipes' and models for five biological systems: blood plasma, cytoplasm, synaptic vesicles, HIV and a mycoplasma cell. We have applied cellPACK to model distributions of HIV envelope protein to test several hypotheses for consistency with experimental observations. Biologists, educators and outreach specialists can interact with cellPACK models, develop new recipes and perform packing experiments through scripting and graphical user interfaces at http://cellPACK.org/.

As knowledge of individual biological processes grows, it becomes increasingly useful to frame new findings within their larger biological contexts in order to generate new systems-scale hypotheses. This report highlights two major iterations of a whole virus model of HIV-1, generated with the cellPACK software. cellPACK integrates structural and systems biology data with packing algorithms to assemble comprehensive 3D models of cell-scale structures in molecular detail. This report describes the biological data, modeling parameters and cellPACK methods used to specify and construct editable models for HIV-1. Anticipating that cellPACK interfaces under development will enable researchers from diverse backgrounds to critique and improve the biological models, we discuss how cellPACK can be used as a framework to unify different types of data across all scales of biology.

Increasingly complex research has made it more difficult to prepare data for publication, education, and outreach. Many scientists must also wade through black-box code to interface computational algorithms from diverse sources to supplement their bench work. To reduce these barriers we have developed an open-source plug-in, embedded Python Molecular Viewer (ePMV), that runs molecular modeling software directly inside of professional 3D animation applications (hosts) to provide simultaneous access to the capabilities of these newly connected systems. Uniting host and scientific algorithms into a single interface allows users from varied backgrounds to assemble professional quality visuals and to perform computational experiments with relative ease. By enabling easy exchange of algorithms, ePMV can facilitate interdisciplinary research, smooth communication between broadly diverse specialties, and provide a common platform to frame and visualize the increasingly detailed intersection(s) of cellular and molecular biology., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

We describe the testing and release of AutoDock4 and the accompanying graphical user interface AutoDockTools. AutoDock4 incorporates limited flexibility in the receptor. Several tests are reported here, including a redocking experiment with 188 diverse ligand-protein complexes and a cross-docking experiment using flexible sidechains in 87 HIV protease complexes. We also report its utility in analysis of covalently bound ligands, using both a grid-based docking method and a modification of the flexible sidechain technique., ((c) 2009 Wiley Periodicals, Inc.)

PPARgamma agonist DIM-Ph-4-CF(3), a template for RXRalpha agonist (E)-3-[5-di(1-methyl-1H-indol-3-yl)methyl-2-thienyl] acrylic acid: DIM-Ph-CF(3) is reported to inhibit cancer growth independent of PPARgamma and to interact with NR4A1. As both receptors dimerize with RXR, and natural PPARgamma ligands activate RXR, DIM-Ph-4-CF(3) was investigated as an RXR ligand. It displaces 9-cis-retinoic acid from RXRalpha but does not activate RXRalpha. Structure-based direct design led to an RXRalpha agonist.1-Di(1H-indol-3-yl)methyl-4-trifluoromethylbenzene (DIM-Ph-4-CF(3)) is reported to inhibit cancer cell growth and to act as a transcriptional agonist of peroxisome proliferator-activated receptor gamma (PPARgamma) and nuclear receptor 4A subfamily member 1 (NR4A1). In addition, DIM-Ph-4-CF(3) exerts anticancer effects independent of these receptors because PPARgamma antagonists do not block its inhibition of cell growth, and the small pocket in the NR4A1 crystal structure suggests no ligand can bind. Because PPARgamma and NR4A1 heterodimerize with retinoid X receptor (RXR), and several PPARgamma ligands transcriptionally activate RXR, DIM-Ph-4-CF(3) was investigated as an RXR ligand. DIM-Ph-4-CF(3) displaces 9-cis-retinoic acid from RXRalpha but does not transactivate RXRalpha. Structure-based design using DIM-Ph-4-CF(3) as a template led to the RXRalpha transcriptional agonist (E)-3-[5-di(1-methyl-1H-indol-3-yl)methyl-2-thienyl]acrylic acid. Its docked pose in the RXRalpha ligand binding domain suggests that binding is stabilized by interactions of its carboxylate group with arginine 316, its indoles with cysteines 269 and 432, and its 1-methyl groups with hydrophobic residues lining the binding pocket. As is expected of a selective activator of RXRalpha, but not of RARs and PPARgamma, this RXRalpha agonist, unlike DIM-Ph-4-CF(3), does not appreciably decrease cancer cell growth or induce apoptosis at pharmacologically relevant concentrations.

In this work, we validate and analyze the results of previously published cross docking experiments and classify failed dockings based on the conformational changes observed in the receptors. We show that a majority of failed experiments (i.e. 25 out of 33, involving four different receptors: cAPK, CDK2, Ricin and HIVp) are due to conformational changes in side chains near the active site. For these cases, we identify the side chains to be made flexible during docking calculation by superimposing receptors and analyzing steric overlap between various ligands and receptor side chains. We demonstrate that allowing these side chains to assume rotameric conformations enables the successful cross docking of 19 complexes (ligand all atom RMSD < 2.0 A) using our docking software FLIPDock. The number of side receptor side chains interacting with a ligand can vary according to the ligand's size and shape. Hence, when starting from a complex with a particular ligand one might have to extend the region of potential interacting side chains beyond the ones interacting with the known ligand. We discuss distance-based methods for selecting additional side chains in the neighborhood of the known active site. We show that while using the molecular surface to grow the neighborhood is more efficient than Euclidian-distance selection, the number of side chains selected by these methods often remains too large and additional methods for reducing their count are needed. Despite these difficulties, using geometric constraints obtained from the network of bonded and non-bonded interactions to rank residues and allowing the top ranked side chains to be flexible during docking makes 22 out of 25 complexes successful.

Conformational changes of biological macromolecules when binding with ligands have long been observed and remain a challenge for automated docking methods. Here we present a novel protein-ligand docking software called FLIPDock (Flexible LIgand-Protein Docking) allowing the automated docking of flexible ligand molecules into active sites of flexible receptor molecules. In FLIPDock, conformational spaces of molecules are encoded using a data structure that we have developed recently called the Flexibility Tree (FT). While the FT can represent fully flexible ligands, it was initially designed as a hierarchical and multiresolution data structure for the selective encoding of conformational subspaces of large biological macromolecules. These conformational subspaces can be built to span a range of conformations important for the biological activity of a protein. A variety of motions can be combined, ranging from domains moving as rigid bodies or backbone atoms undergoing normal mode-based deformations, to side chains assuming rotameric conformations. In addition, these conformational subspaces are parameterized by a small number of variables which can be searched during the docking process, thus effectively modeling the conformational changes in a flexible receptor. FLIPDock searches the variables using genetic algorithm-based search techniques and evaluates putative docking complexes with a scoring function based on the AutoDock3.05 force-field. In this paper, we describe the concepts behind FLIPDock and the overall architecture of the program. We demonstrate FLIPDock's ability to solve docking problems in which the assumption of a rigid receptor previously prevented the successful docking of known ligands. In particular, we repeat an earlier cross docking experiment and demonstrate an increased success rate of 93.5%, compared to original 72% success rate achieved by AutoDock over the 400 cross-docking calculations. We also demonstrate FLIPDock's ability to handle conformational changes involving backbone motion by docking balanol to an adenosine-binding pocket of protein kinase A.

The interactive visualization of large biological assemblies poses a number of challenging problems, including the development of multiresolution representations and new interaction methods for navigating and analyzing these complex systems. An additional challenge is the development of flexible software environments that will facilitate the integration and interoperation of computational models and techniques from a wide variety of scientific disciplines. In this paper, we present a component-based software development strategy centered on the high-level, object-oriented, interpretive programming language: Python. We present several software components, discuss their integration, and describe some of their features that are relevant to the visualization of large molecular assemblies. Several examples are given to illustrate the interoperation of these software components and the integration of structural data from a variety of experimental sources. These examples illustrate how combining visual programming with component-based software development facilitates the rapid prototyping of novel visualization tools.

The interaction of the alphaLbeta2 integrin with its cellular ligand the intercellular adhesion molecule-1 (ICAM-1) is critical for the tight binding interaction between most leukocytes and the vascular endothelium before transendothelial migration to the sites of inflammation. In this article we have modeled the alphaL subunit I-domain in its active form, which was computationally docked with the D1 domain of the ICAM-1 to probe potential protein-protein interactions. The experimentally observed key interaction between the carboxylate of Glu 34 in the ICAM-1 D1 domain and the metal ion-dependent adhesion site (MIDAS) in the open alphaL I-domain was consistently reproduced by our calculations. The calculations reveal the nature of the alphaLbeta2/ICAM-1 interaction and suggest an explanation for the increased ligand-binding affinity in the "open" versus the "closed" conformation of the alphaL I-domain. A mechanism for substrate selectivity among alphaL, alphaM, and alpha2 I-domains is suggested whereby the orientation of the loops within the I-domain is critical in mediating the interaction of the Glu 34 carboxylate of ICAM-1 D1 with the MIDAS., (Copyright 2002 Wiley-Liss, Inc.)

Drug-resistant strains are rapidly selected during AIDS therapy because of the high rate of mutation in HIV. In this report, we present an evolutionary simulation method for analysis of viral mutation and its use for optimization of HIV-1 protease drugs to improve their robustness in the face of resistance mutation. We first present an analysis of the range of resistant mutants that produce viable viruses by using a volume-based viral fitness model. Then, we analyze how this range of mutant proteases allows development of resistance to an optimal inhibitor previously designed by computational coevolution techniques. Finally, we evaluate the resistance patterns of commercially available drugs, and we discuss how resistance might be overcome by optimizing the size of specific side-chains of these inhibitors., (Copyright 2002 Wiley-Liss, Inc.)

Protein motion and heterogeneity of structural waters are approximated in ligand-docking simulations, using an ensemble of protein structures. Four methods of combining multiple target structures within a single grid-based lookup table of interaction energies are tested. The method is evaluated using complexes of 21 peptidomimetic inhibitors with human immunodeficiency virus type 1 (HIV-1) protease. Several of these structures show motion of an arginine residue, which is essential for binding of large inhibitors. A structural water is also present in 20 of the structures, but it must be absent in the remaining one for proper binding. Mean and minimum methods perform poorly, but two weighted average methods permit consistent and accurate ligand docking, using a single grid representation of the target protein structures., (Copyright 2001 Wiley-Liss, Inc.)

One of the challenges in biocomputing is to enable the efficient use of a wide variety of fast-evolving computational methods to simulate, analyze, and understand the complex properties and interactions of molecular systems. Our laboratory investigates several areas including molecular visualization, protein-ligand docking, protein-protein docking, molecular surfaces, and the derivation of phenomenological potentials. In this paper we present an approach based on the Python programming language to achieve a high level of integration between these different computational methods and our primary visualization system AVS. This approach removes many limitations of AVS while increasing dramatically the inter-operability of our computational tools. Several examples are shown to illustrate how this approach enables a high level of integration and inter-operability between different tools, while retaining modularity and avoiding the creation of a large monolithic package that is difficult to extend and maintain.

We present two new sets of energy functions for protein structure recognition, given the primary sequence of amino acids along the polypeptide chain. The first set of potentials is based on the positions of alpha- and the second on positions of beta- and alpha-carbon atoms of amino acid residues. The potentials are derived using a theory of Boltzmann-like statistics of protein structure. The energy terms incorporate both long-range interactions between residues remote along a chain and short-range interactions between near neighbors. Distance dependence is approximated by a piecewise constant function defined on intervals of equal size. The size of the interval is optimized to preserve as much detail as possible without introducing excessive error due to limited statistics. A database of 214 non-homologous proteins was used both for the derivation of the potentials, and for the 'threading' test originally suggested by Hendlich et al. (1990) J. Mol. Biol., 216, 167-180. Special care is taken to avoid systematic error in this test. For threading, we used 100 non-homologous protein chains of 60-205 residues. The energy of each of the native structures was compared with the energy of 43,000 to 19,000 alternative structures generated by threading. Of these 100 native structures, 92 have the lowest energy with alpha-carbon-based potentials and, even more, 98 of these 100 structures, have the lowest energy with the beta- and alpha-carbon based potentials.

Recently we introduced the Reduced Surface as an efficient tool to built molecular surfaces. We describe here how this geometric construct can be used to efficiently reconstruct the solvent excluded surface of a protein for which the coordinates of a subset of atoms are changing. We show that, the complexity of that operation is not dependent upon the size of the molecule and is in O[tlog(t)] where t is the maximum of the number of probes and atoms involved in the reconstruction of the surface. The algorithms described here have been implemented and tested on several proteins. The triangulation of the solvent excluded surface of proteins in which a side chain was changing conformation could be updated at rates ranging from 7 to 22 frames per second. We also applied this method to compute the surface area fluctuation of the FIV protease undergoing a constrained molecular dynamics simulation (16 mobile residues). Rate of 6 frames per second were obtained in this case.

We present two new sets of energy functions for protein structure recognition. The first set of potentials is based on the positions of alpha- and the second on positions of beta-carbon atoms of amino acid residues. The potentials are derived using a theory of Boltzmann-like statistics of protein structure by Finkelstein et al. The energy terms incorporate both long-range interactions between residues remote along a chain and short-range interactions between near neighbors. Distance-dependence is approximated by a piecewise constant function defined on intervals of equal size. The size of this interval is optimized. A database of 222 non-homologous proteins was used both for the derivation of the potentials, and for the "threading" test originally suggested by Hendlich et al. For threading, we used 102 non-homologous protein chains of 60 to 200 residues. The energy of each of the native structures was compared with the energy of 45 to 20 thousand alternative structures generated by threading. Of these 102 native structures 94 have the lowest energy with alpha-carbon-based potentials, and even more, 100 of these 102 structures, have the lowest energy with the beta-carbon-based potentials.

Lattice models of proteins can approximate off-lattice structure to arbitrary precision with RMS (root mean squared) deviations roughly equal to half the lattice spacing (Rykunov et al., Proteins 22:100-109, 1995; Reva et al., J. Comp. Biol., 1996). However, even small distortions in the positions of chain links lead to significant errors in lattice-based energy calculations (Reva et al., J. Comp. Chem., 1996). These errors arise mainly from rigid interactions (such as steric repulsion) which change their energies considerably at a range which is much smaller than the usual accuracy of lattice modeling (> 1.0 A). To reduce this error, we suggest a procedure of adjusting energy functions to a given lattice. The general approach is illustrated with energy calculations based on pairwise potentials by Kolinski et al. (J. Chem. Phys. 98:1-14, 1993). At all the lattice spacings, from 0.5-3.8 A, the lattice-adjusted potentials improve the accuracy of lattice-based energy calculations and increase the correlations between off-lattice and lattice energies.

Because of their wide use in molecular modeling, methods to compute molecular surfaces have received a lot of interest in recent years. However, most of the proposed algorithms compute the analytical representation of only the solvent-accessible surface. There are a few programs that compute the analytical representation of the solvent-excluded surface, but they often have problems handling singular cases of self-intersecting surfaces and tend to fail on large molecules (more than 10,000 atoms). We describe here a program called MSMS, which is shown to be fast and reliable in computing molecular surfaces. It relies on the use of the reduced surface that is briefly defined here and from which the solvent-accessible and solvent-excluded surfaces are computed. The four algorithms composing MSMS are described and their complexity is analyzed. Special attention is given to the handling of self-intersecting parts of the solvent-excluded surface called singularities. The program has been compared with Connolly's program PQMS [M.L. Connolly (1993) Journal of Molecular Graphics, Vol. 11, pp. 139-141] on a set of 709 molecules taken from the Brookhaven Data Base. MSMS was able to compute topologically correct surfaces for each molecule in the set. Moreover, the actual time spent to compute surfaces is in agreement with the theoretical complexity of the program, which is shown to be O[n log(n)] for n atoms. On a Hewlett-Packard 9000/735 workstation, MSMS takes 0.73 s to produce a triangulated solvent-excluded surface for crambin (1 crn, 46 residues, 327 atoms, 4772 triangles), 4.6 s for thermolysin (3tln, 316 residues, 2437 atoms, 26462 triangles), and 104.53 s for glutamine synthetase (2gls, 5676 residues, 43632 atoms, 476665 triangles).