3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Multilevel Framework for Analysis of Protein Folding Involving Disulfide Bond Formation

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          In this study, a three-layered multicenter ONIOM approach is implemented to characterize the naive folding pathway of bovine pancreatic trypsin inhibitor (BPTI). Each layer represents a distinct level of theory, where the initial layer, encompassing the entire protein, is modeled by a general all-atom force-field GFN-FF. An intermediate electronic structure layer consisting of three multicenter fragments is introduced with the state-of-the-art semiempirical tight-binding method GFN2- xTB. Higher accuracy, specifically addressing the breaking and formation of the three disulfide bonds, is achieved at the innermost layer using the composite DFT method r 2SCAN-3c. Our analysis sheds light on the structural stability of BPTI, particularly the significance of interlinking disulfide bonds. The accuracy and efficiency of the multicenter QM/SQM/MM approach are benchmarked using the oxidative formation of cystine. For the folding pathway of BPTI, relative stabilities are investigated through the calculation of free energy contributions for selected intermediates, focusing on the impact of the disulfide bond. Our results highlight the intricate trade-off between accuracy and computational cost, demonstrating that the multicenter ONIOM approach provides a well-balanced and comprehensive solution to describe electronic structure effects in biomolecular systems. We conclude that multiscale energy landscape exploration provides a robust methodology for the study of intriguing biological targets.

          Related collections

          Most cited references77

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy.

            Gaussian basis sets of quadruple zeta valence quality for Rb-Rn are presented, as well as bases of split valence and triple zeta valence quality for H-Rn. The latter were obtained by (partly) modifying bases developed previously. A large set of more than 300 molecules representing (nearly) all elements-except lanthanides-in their common oxidation states was used to assess the quality of the bases all across the periodic table. Quantities investigated were atomization energies, dipole moments and structure parameters for Hartree-Fock, density functional theory and correlated methods, for which we had chosen Møller-Plesset perturbation theory as an example. Finally recommendations are given which type of basis set is used best for a certain level of theory and a desired quality of results.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              A climbing image nudged elastic band method for finding saddle points and minimum energy paths

                Bookmark

                Author and article information

                Journal
                J Phys Chem B
                J Phys Chem B
                jp
                jpcbfk
                The Journal of Physical Chemistry. B
                American Chemical Society
                1520-6106
                1520-5207
                21 March 2024
                04 April 2024
                : 128
                : 13
                : 3145-3156
                Affiliations
                Yusuf Hamied Department of Chemistry, University of Cambridge , Lensfield Road, Cambridge CB2 1EW, U.K.
                Author notes
                Author information
                https://orcid.org/0000-0002-7751-980X
                https://orcid.org/0000-0002-3555-6645
                https://orcid.org/0000-0002-8495-9504
                Article
                10.1021/acs.jpcb.4c00104
                11000224
                38512062
                9ef6d371-aea8-4231-bafa-96aac858a4f1
                © 2024 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 05 January 2024
                : 06 March 2024
                : 06 March 2024
                Funding
                Funded by: Alexander von Humboldt-Stiftung, doi 10.13039/100005156;
                Award ID: NA
                Funded by: Engineering and Physical Sciences Research Council, doi 10.13039/501100000266;
                Award ID: EP/W524633/1
                Categories
                Article
                Custom metadata
                jp4c00104
                jp4c00104

                Physical chemistry
                Physical chemistry

                Comments

                Comment on this article