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Small molecules are powerful tools for investigating protein function and can serve as leads for new therapeutics. Most human proteins, however, lack small-molecule ligands, and entire protein classes are considered “undruggable” 1,2. Fragment-based ligand discovery (FBLD) can identify small-molecule probes for proteins that have proven difficult to target using high-throughput screening of complex compound libraries 1,3. Although reversibly binding ligands are commonly pursued, covalent fragments provide an alternative route to small-molecule probes 4–10, including those that can access regions of proteins that are difficult to access through binding affinity alone 5,10,11. In this manuscript, we report a quantitative analysis of cysteine-reactive small-molecule fragments screened against thousands of proteins. Covalent ligands were identified for >700 cysteines found in both druggable proteins and proteins deficient in chemical probes, including transcription factors, adaptor/scaffolding proteins, and uncharacterized proteins. Among the atypical ligand-protein interactions discovered were compounds that react preferentially with pro- (inactive) caspases. We used these ligands to distinguish extrinsic apoptosis pathways in human cell lines versus primary human T-cells, showing that the former is largely mediated by caspase-8 while the latter depends on both caspase-8 and −10. Fragment-based covalent ligand discovery provides a greatly expanded portrait of the ligandable proteome and furnishes compounds that can illuminate protein functions in native biological systems.

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 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 approximately 5 hours.

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 part of the SAMPL4 blind challenge, filtered AutoDock Vina ligand docking predictions and large scale binding energy distribution analysis method binding free energy calculations have been applied to the virtual screening of a focused library of candidate binders to the LEDGF site of the HIV integrase protein. The computational protocol leveraged docking and high level atomistic models to improve enrichment. The enrichment factor of our blind predictions ranked best among all of the computational submissions, and second best overall. This work represents to our knowledge the first example of the application of an all-atom physics-based binding free energy model to large scale virtual screening. A total of 285 parallel Hamiltonian replica exchange molecular dynamics absolute protein-ligand binding free energy simulations were conducted starting from docked poses. The setup of the simulations was fully automated, calculations were distributed on multiple computing resources and were completed in a 6-weeks period. The accuracy of the docked poses and the inclusion of intramolecular strain and entropic losses in the binding free energy estimates were the major factors behind the success of the method. Lack of sufficient time and computing resources to investigate additional protonation states of the ligands was a major cause of mispredictions. The experiment demonstrated the applicability of binding free energy modeling to improve hit rates in challenging virtual screening of focused ligand libraries during lead optimization.

In the present study, Lamarckian genetic algorithm (LGA) based docking (AutoDock 4.0), k-nearest neighbor molecular field analysis (kNN-MFA) based 3D-QSAR, fragment and knowledge based approach were used to generate and screen a virtual library. Three analogues of β-phenylethylidene hydrazine (PEH) were designed as novel GABA-AT inhibitors. The docking interactions of N-[(1E) 2-phenylethylidene]pyridin-2-amine (S 4 ), (2E)-1-phenyl-2-(2-phenylethylidene)hydrazine (S 8 ) and (1E)-1-ethylidene-2-phenylhydrazine (S 21 ) with Lys 329 appears to be in close proximity and explains the high GABA-AT selectivity observed.

Structural biology is rapidly accumulating a wealth of detailed information about protein function, binding sites, RNA, large assemblies and molecular motions. These data are increasingly of interest to a broader community of life scientists, not just structural experts. Visualization is a primary means for accessing and using these data, yet visualization is also a stumbling block that prevents many life scientists from benefiting from three-dimensional structural data. In this review, we focus on key biological questions where visualizing three-dimensional structures can provide insight and describe available methods and tools. Supplementary information The online version of this article (doi:10.1038/nmeth.1427) contains supplementary material, which is available to authorized users.

CD134 is a primary binding receptor for feline immunodeficiency virus (FIV), and with CXCR4 facilitates infection of CD4(+) T cells. Human CD134 fails to support FIV infection. To delineate the regions important for defining virus specificity of CD134, we exchanged domains between human and feline CD134. The binding site for FIV surface glycoprotein (SU) is located in domain 1, in a region distinct from the natural ligand (CD134L)-binding site. Mutagenesis showed that Asp60 and Asp62 are required for interaction with FIV, and modeling studies localized these two residues to the outer edge of domain 1. Substitutions S60D and N62D, in conjunction with H45S, R59G and V64K, imparted both FIV SU binding and receptor function to human CD134. Finally, we demonstrated that soluble CD134 facilitates infection of CD134(-) CXCR4(+) target cells in a manner analogous to CD4 augmentation of HIV infection.

Methods and tools for visualizing biological data have improved considerably over the last decades, but they are still inadequate for some high-throughput data sets. For most users, a key challenge is to benefit from the deluge of data without being overwhelmed by it. This challenge is still largely unfulfilled and will require the development of truly integrated and highly useable tools.

AutoDock 2.4 predicts the bound conformations of a small, flexible ligand to a nonflexible macromolecular target of known structure. The technique combines simulated annealing for conformation searching with a rapid grid-based method of energy evaluation based on the AMBER force field. AutoDock has been optimized in performance without sacrificing accuracy; it incorporates many enhancements and additions, including an intuitive interface. We have developed a set of tools for launching and analyzing many independent docking jobs in parallel on a heterogeneous network of UNIX-based workstations. This paper describes the current release, and the results of a suite of diverse test systems. We also present the results of a systematic investigation into the effects of varying simulated-annealing parameters on molecular docking. We show that even for ligands with a large number of degrees of freedom, root-mean-square deviations of less than 1 A from the crystallographic conformation are obtained for the lowest-energy dockings, although fewer dockings find the crystallographic conformation when there are more degrees of freedom.

Crystallization of the 1:1 molecular complex between the beta-lactamase TEM-1 and the beta-lactamase inhibitory protein BLIP has provided an opportunity to put a stringent test on current protein-docking algorithms. Prior to the successful determination of the structure of the complex, nine laboratory groups were given the refined atomic coordinates of each of the native molecules. Other than the fact that BLIP is an effective inhibitor of a number of beta-lactamase enzymes (KI for TEM-1 approximately 100 pM) no other biochemical or structural data were available to assist the practitioners in their molecular docking. In addition, it was not known whether the molecules underwent conformational changes upon association or whether the inhibition was competitive or non-competitive. All six of the groups that accepted the challenge correctly predicted the general mode of association of BLIP and TEM-1.

Interactive molecular graphics plays an important role in every stage of the drug design process. The technology of molecular graphics has advanced considerably over the past 30 years, both in terms of hardware features for advanced rendering and computation, and in terms of software environments for rapid, flexible and extensible program development. This paper discusses the role of interactive computer graphics in rational drug design, from structure determination, computation and analysis, through prediction and design, to communication and presentation. The emphasis is on the new trends in the technology, and how they can be utilized to facilitate and improve the various stages of the process.

Interacting with 3D-printed molecular models helps students to grasp insightful concepts on the kinetics and thermodynamics of molecular self-assembly, as Arthur J. Olson explains.

To study the nature of antigenic recognition, antibodies have been prepared against a set of peptide sequences representing both highly mobile and well-ordered regions of myohaemerythrin, based on X-ray crystallographic temperature factors. Anti-peptide antibodies against highly mobile regions react strongly with the native protein; anti-peptide antibodies from well-ordered regions do not. Mobility is a major factor in the recognition of the native protein by anti-peptide antibodies; this may be of general significance in protein-protein interactions.