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SUMMARY:Hybrid Quantum Mechanics / Molecular Mechanics (QM/MM) Approaches 
 to Biochemistry and Beyond
DTSTART;VALUE=DATE:20260323
DTSTAMP:20260416T081209Z
UID:4a48687be47c101b21ca4223e19444a270bf155bfcf7e3674e89dc45
CATEGORIES:Conferences - Seminars
DESCRIPTION:You can apply to participate and find all the relevant informa
 tion (speakers\, abstracts\, program\,...) on the event website: https://
 www.cecam.org/workshop-details/hybrid-quantum-mechanics-molecular-mechanic
 s-qmmm-approaches-to-biochemistry-and-beyond-1368.\n\nRegistration is requ
 ired to attend the full event\, take part in the social activities and pre
 sent a poster at the poster session (if any).  However\, the EPFL commun
 ity is welcome to attend specific lectures without registration if the
  topic is of interest to their research. Do not hesitate to contact the C
 ECAM Event Manager if you have any question.\n\nDescription\n\n The worl
 dwide acknowledged success of multiscale methods [1-7]\, accompanied by th
 e continuously evolving high performance computing (HPC) architectures [8\
 ,9]\, has boosted the demand for dedicated training courses. A strong poin
 t supporting the above statement is the fact that these approaches have be
 come appealing both in academy and industry. Recent extensions\, going bey
 ond the original purpose that motivated hybrid quantum mechanics / molecul
 ar mechanics (QM/MM) approaches is the rising of the use of QM/MM in combi
 nation with quantum treatment of the nuclei (path integral MD\, PIMD)\, co
 nstruction of still non-existing data bases (DBs) for machine learning (ML
 ) approaches and search algorithms to reduce the phase space of the QM/MM 
 simulations [10-13]. Some of these issues have already been pioneered by u
 s in the last edition of this School\, but explicit requests from the part
 icipants evidenced the need for a deepening of some of these hot topics.\n
     The inclusion of different levels of accuracy in QM/MM algorithms t
 o account for realistically large systems remains a delicate point requiri
 ng a specific and detailed presentation to young students and researchers 
 approaching this field and for which regular university courses are still 
 insufficient or non-existing\, thus calling for continuous formation and u
 pdating oriented to students facing for the first time this field or in se
 arch for a specifically oriented course. On a technical standpoint\, advan
 ces in methods and their implementation in algorithms suited to continuous
 ly evolving HPC architectures toward peta- and exascale-level represent si
 multaneously an unprecedented powerful tool and a continuous challenge [14
 \,15]. Worldwide HPC facilities\, such as in the PRACE initiative and its 
 succeedor EuroHPC\, have played a prominent role also in social and human 
 activities\, for example during the recent pandemic CoViD'19 crisis or gl
 obal environmental problems [16]\, underscoring the impact of this type of
  computational approaches in the general context of human activities.\n 
    The importance of QM/MM approaches\, with particular emphasis in chem
 istry and biochemistry\, was acknowledged in 2013 with the Nobel Prize in 
 Chemistry awarded jointly to M. Karplus\, M. Levitt and A. Warshel. From 
 a historical standpoint\, the first attempt to join quantum and classical 
 molecular mechanics marked in 1976 [17] the official birth of the QM/MM ap
 proach. This seminal work paved the route to the QM/MM that we know nowada
 ys and it has motivated new challenges (now almost paradigms) for exploiti
 ng modern HPC platforms\, leading to a breakthrough in the simulations of 
 realistic bio-systems [18-20].\n    Another important milestone has bee
 n the coupling of QM/MM dynamical simulations to free energy sampling meth
 ods for the exploration of reaction mechanisms [21\,22]. This has boosted 
 the activities in the field of computational biochemistry [23-26]\, making
  de facto experiments in silico (term coined in 1989 by the Mexican mathem
 atician Pedro Miramontes) the natural counterpart of the traditional in vi
 vo and in vitro ones.\n    The wide variety of QM/MM approaches coded i
 n different computer codes\, nowadays available on demand and/or freely do
 wnloadable from open repositories\, make their choice in their approach an
 d implementation\, and often using them poses a major challenge not easy t
 o sort out\, especially for newcomers. Hence\, guidance from experienced p
 ractitioners and developers is of paramount importance to next-generation 
 researchers who are going to continue and take over the work of present ge
 neration researchers. A major risk of the availability of this type of cod
 es open-source and/or freely downloadable is the blind use of these tools 
 as “black boxes”\, often appearing in a transparent way also in publis
 hed work or in presentations at international conferences\, a fact that al
 so the proposers of this School have witnessed.\n    The scope of our S
 chool\, which follows previous editions held since 2011 until 2022 with a 
 two- to three-year periodicity\, is to offer a general and up-to-date insi
 ght into well assessed and forefront QM/MM approaches. Since its original 
 edition\, we have taken particular care to keep this CECAM School dynamica
 l and timely evolving\, including new developments and arising challenges 
 that this specific field experienced over the years. Being both developers
  and experienced practitioners\, we intend to provide\, accounting for the
  feedback of former participants\, an updated overview of methods\, algori
 thms\, and forefront challenges on biomolecular systems. According to our 
 former experience\, the lectures\, exercises and the constant interaction 
 with the participants allows us to provide them a precise know-how on how 
 to set up a QM/MM simulation starting from pristine crystallographic data 
 and going across all the required steps to complete the system\, including
  the addition of missing hydrogen atoms and solvent molecules. The lecture
 s in the previous editions have always been animated by questions and spec
 ific requests from the participants\, accompanied with additional lectures
  and files provided by us via the CECAM web server.\n\nReferences\n\n[1] A
 . Warshel and M. Levitt\, J. Mol. Biol. 103\, 227 (1976). DOI: 10.1016/0
 022-2836(76)90311-9\n[2] M. J. Field\, P. A. Bash and M. Karplus\, J. Comp
 ut. Chem. 11\, 700 (1990). DOI: 10.1002/jcc.540110605\n[3] P. Amara and 
 M. J. Field in “Computational Molecular Biology” Vol. 8\, Ed. by J. Le
 szczynski\, Elsevier\, Amsterdam\, 1999. ISBN: 9780444500304\n[4] M. Boero
 \, "Reactive Simulations for Biochemical Processes"\, in "Atomic-Scale M
 odeling of Nanosystems and Nanostructured Materials"\, Lecture Notes Phys.
  795\, p. 81-98\, Springer\, Berlin Heidelberg 2010. DOI: 10.1007/978-3-
 642-04650-6_3\n[5] L. Raich\, A. Nin-Hill\, A. Ardèvol and C. Rovira\, Me
 thod. Enzymol. 577\, 159-183 (2016). DOI: 10.1016/bs.mie.2016.05.015\n[6
 ] C. M. Clemente\, L. Capece and M. A. Martì\, J. Chem. Inform. Modeling
  63\, 2609 (2023). DOI: 10.1021/acs.jcim.2c01522\n[7] G. A. Bramley\, O.
  T. Beynon\, P. V. Stishenko and A. J. Logsdail\, Phys. Chem. Chem. Phys.
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 ie\, J. Phys. Chem. B 125\, 689 (2021). DOI: 10.1021/acs.jpcb.0c09898\n[
 9] V. Bolnykh\, U. Rothlisberger and P. Carloni\, Isr. J. Chem. 60\, 694 
 (2020). DOI: 10.1002/ijch.202000022\n[10] L. Bösselt\, M. Thürlemann an
 d S. Riniker\, J. Chem. Theory Comput. 17\, 2641 (2021). DOI: 10.1021/ac
 s.jctc.0c01112\n[11] T. J. Giese\, J. Zeng\, S. Ekesan and D. M. York\, J.
  Chem. Theory Comput. 18\, 4304 (2022). DOI: 10.1021/acs.jctc.2c00151\n[
 12] M. Shoji\, T. Murakawa\, S. Nakanishi\, M. Boero\, Y. Shigeta\, H. Hay
 ashi\, K. Tanizawa and T. Okajima\, Chem. Sci. 13\, 10923 (2022). DOI: 1
 0.1039/D2SC01356H\n[13] M. Shoji\, Y. Kitazawa\, A. Sato\, N. Watanabe\, M
 . Boero\, Y. Shigeta and M. Umemura\, J. Phys. Chem. Lett. 14\, 3243 (202
 3). DOI: 10.1021/acs.jpclett.2c03862\n[14] P. Sherwood\, in "Modern Meth
 ods and Algorithms of Quantum Chemistry" NIC Ser. Vol. 1\, John von Neuma
 nn Institute of Computing\, Juelich 2000\, pp. 257-277. ISBN 3-00-005834-6
 \n[15] V. Gavini et alia\, Modell. Simul. Materials Sci. Eng. 31\, 0633
 01 (2023). DOI: 10.1088/1361-651X/acdf06\n[16] F. Pietrucci\, M. Boero an
 d W. Andreoni\, Chem. Sci. 12\, 2979 (2021). DOI: 10.1039/D0SC06204A\n[1
 7] A. Warshel and M. Levitt\, J. Mol. Biol. 103\, 227 (1976). DOI: 10.10
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 Ed. 48\, 1198 (2009). DOI: 10.1002/anie.200802019\n[19] M. W. van der Ka
 mp and A. J. Mulholland\, Biochem. 52\, 2708 (2013). DOI: 10.1021/bi4002
 15w\n[20] A. de Ruiter and C Oostenbrink\, Curr. Op. Struct. Biol. 61\, 2
 07 (2020). DOI: 10.1016/j.sbi.2020.01.016\n[21] F.L. Gervasio\, M. Boero 
 and M. Parrinello\, Angew. Chem. Int. Ed. 45\, 5606 (2006). DOI: 10.1002
 /anie.200602106\n[22] M. Boero\, T. Ikeda\, E. Ito and K. Terakura\, J. Am
 . Chem. Soc. 128\, 16798 (2006). DOI: 10.1021/ja064117k\n[23] E. Brunk a
 nd Röthlisberger\, Chem. Rev. 115\, 6217-6263 (2015). DOI: 10.1021/cr50
 0628b\n[24] A. Ardèvol and C. Rovira\, J. Am. Chem. Soc. 137\, 7528-7547
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LOCATION:BCH 2103 https://plan.epfl.ch/?room==BCH%202103
STATUS:CONFIRMED
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