Enzymes with buried active sites utilize molecular tunnels to exchange substrates, products, and solvent molecules with the surface. These transport mechanisms are crucial for protein function and influence various properties. As proteins are inherently dynamic, their tunnels also vary structurally. Understanding these dynamics is essential for elucidating structure-function relationships, drug discovery, and bioengineering. Caver Web 2.0 is a user-friendly web server that retains all Caver Web 1.0 functionalities while introducing key improvements: (i) generation of dynamic ensembles via automated molecular dynamics with YASARA, (ii) analysis of dynamic tunnels with CAVER 3.0, (iii) prediction of ligand trajectories in multiple snapshots with CaverDock 1.2, and (iv) customizable ligand libraries for virtual screening. Users can assess protein flexibility, identify and characterize tunnels, and predict ligand trajectories and energy profiles in both static and dynamic structures. Additionally, the platform supports virtual screening with FDA/EMA-approved drugs and user-defined datasets. Caver Web 2.0 is a versatile tool for biological research, protein engineering, and drug discovery, aiding the identification of strong inhibitors or new substrates to bind to the active sites or tunnels, and supporting drug repurposing efforts. The server is freely accessible at https://loschmidt.chemi.muni.cz/caverweb.

• MeSH
• internet MeSH
• katalytická doména MeSH
• konformace proteinů MeSH
• ligandy MeSH
• objevování léků MeSH
• proteiny * chemie   metabolismus   MeSH
• simulace molekulární dynamiky MeSH
• software * MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• ligandy MeSH
• proteiny * MeSH

Enzymes play a crucial role in sustainable industrial applications, with their optimization posing a formidable challenge due to the intricate interplay among residues. Computational methodologies predominantly rely on evolutionary insights of homologous sequences. However, deciphering the evolutionary variability and complex dependencies among residues presents substantial hurdles. Here, we present a new machine-learning method based on variational autoencoders and evolutionary sampling strategy to address those limitations. We customized our method to generate novel sequences of model enzymes, haloalkane dehalogenases. Three design-build-test cycles improved the solubility of variants from 11% to 75%. Thorough experimental validation including the microfluidic device MicroPEX resulted in 20 multiple-point variants. Nine of them, sharing as little as 67% sequence similarity with the template, showed a melting temperature increase of up to 9 °C and an average improvement of 3 °C. The most stable variant demonstrated a 3.5-fold increase in activity compared to the template. High-quality experimental data collected with 20 variants represent a valuable data set for the critical validation of novel protein design approaches. Python scripts, jupyter notebooks, and data sets are available on GitHub (https://github.com/loschmidt/vae-dehalogenases), and interactive calculations will be possible via https://loschmidt.chemi.muni.cz/fireprotasr/.

• Publikační typ
• časopisecké články MeSH

Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid in the discovery and annotation of promising enzymes, as well as in suggesting beneficial mutations for improving known targets. The field of machine learning for protein engineering is gathering steam, driven by recent success stories and notable progress in other areas. It already encompasses ambitious tasks such as understanding and predicting protein structure and function, catalytic efficiency, enantioselectivity, protein dynamics, stability, solubility, aggregation, and more. Nonetheless, the field is still evolving, with many challenges to overcome and questions to address. In this Perspective, we provide an overview of ongoing trends in this domain, highlight recent case studies, and examine the current limitations of machine learning-based methods. We emphasize the crucial importance of thorough experimental validation of emerging models before their use for rational protein design. We present our opinions on the fundamental problems and outline the potential directions for future research.

• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

The importance of the quantitative description of protein unfolding and aggregation for the rational design of stability or understanding the molecular basis of protein misfolding diseases is well established. Protein thermostability is typically assessed by calorimetric or spectroscopic techniques that monitor different complementary signals during unfolding. The CalFitter webserver has already proved integral to deriving invaluable energy parameters by global data analysis. Here, we introduce CalFitter 2.0, which newly incorporates singular value decomposition (SVD) of multi-wavelength spectral datasets into the global fitting pipeline. Processed time- or temperature-evolved SVD components can now be fitted together with other experimental data types. Moreover, deconvoluted basis spectra provide spectral fingerprints of relevant macrostates populated during unfolding, which greatly enriches the information gains of the CalFitter output. The SVD analysis is fully automated in a highly interactive module, providing access to the results to users without any prior knowledge of the underlying mathematics. Additionally, a novel data uploading wizard has been implemented to facilitate rapid and easy uploading of multiple datasets. Together, the newly introduced changes significantly improve the user experience, making this software a unique, robust, and interactive platform for the analysis of protein thermal denaturation data. The webserver is freely accessible at https://loschmidt.chemi.muni.cz/calfitter.

• MeSH
• denaturace proteinů MeSH
• proteiny * chemie   MeSH
• rozbalení proteinů * MeSH
• software MeSH
• teplota MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• proteiny * MeSH

The transplantation of loops between structurally related proteins is a compelling method to improve the activity, specificity and stability of enzymes. However, despite the interest of loop regions in protein engineering, the available methods of loop-based rational protein design are scarce. One particular difficulty related to loop engineering is the unique dynamism that enables them to exert allosteric control over the catalytic function of enzymes. Thus, when engaging in a transplantation effort, such dynamics in the context of protein structure need consideration. A second practical challenge is identifying successful excision points for the transplantation or grafting. Here, we present LoopGrafter (https://loschmidt.chemi.muni.cz/loopgrafter/), a web server that specifically guides in the loop grafting process between structurally related proteins. The server provides a step-by-step interactive procedure in which the user can successively identify loops in the two input proteins, calculate their geometries, assess their similarities and dynamics, and select a number of loops to be transplanted. All possible different chimeric proteins derived from any existing recombination point are calculated, and 3D models for each of them are constructed and energetically evaluated. The obtained results can be interactively visualized in a user-friendly graphical interface and downloaded for detailed structural analyses.

• MeSH
• internet MeSH
• molekulární modely MeSH
• proteinové inženýrství MeSH
• proteiny * genetika   chemie   MeSH
• software * MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• proteiny * MeSH