Title 統合創薬ソフトウェアNAGARAの開発及び論理的創薬による新規抗プリオン薬の創出( 本文(Fulltext) ) Author(s) 馬, 彪 Report No.(Doctoral Degree) 博士(医科学) 連創博甲第31号 Issue Date 2016-03-25 Type 博士論文 Version ETD URL http://hdl.handle.net/20.500.12099/54542 ※この資料の著作権は、各資料の著者・学協会・出版社等に帰属します。
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⤫ྜ⸆ࢯࣇࢺ࢙࢘ 1$*$5$ ࡢ㛤Ⓨཬࡧ
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XVLQJ1$*$5$
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㤿 ᙭
┠
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ᗎㄽ ⫼ᬒ ◊✲┠ⓗ ⤫ྜ⸆ࢯࣇࢺ࢙࢘ 1$*$5$ ࡢ㛤Ⓨ ᴫせ 㛤Ⓨゝㄒཬࡧࢩࢫࢸ࣒せ௳ 㛤Ⓨゝㄒ ࢩࢫࢸ࣒せ௳ せᶵ⬟࣓ࢽ࣮ࣗ 1$*$5$ ࣓࣓ࣥࢽ࣮ࣗ ࣓ࢽ࣮ࣗ㸯㸸1$*$5$ ࡢ⎔ቃタᐃ ࣓ࢽ࣮ࣗ㸰㸸ᶆⓗศᏊࡢ❧యᵓ㐀ࢆ‽ഛ ࣓ࢽ࣮ࣗ㸱㸸$XWR'RFN9LQD*8, '6 ࣓ࢽ࣮ࣗ㸲㸸$PEHU*8, 0' ࣓ࢽ࣮ࣗ㸳㸸3$,&6*8, 4& ࡑࡢ࣓ࢽ࣮ࣗ㸸3\02/ ࡢ⫼ᬒⰍࡢタᐃࡸṧᇶ㓄ิࢼࣥࣂ࣮ࢆኚ᭦ ㄽ⌮ⓗ⸆ࡼࡿ᪂ࡋ࠸ᢠࣉࣜ࢜ࣥ⸆ࡢฟ ᮦᩱ᪉ἲ LQVLOLFR ࢫࢡ࣮ࣜࢽࣥࢢ LQYLWURH[YLYR ࢫࢡ࣮ࣜࢽࣥࢢ ศᏊືຊᏛࢩ࣑࣮ࣗࣞࢩࣙࣥ 635 ᐃ 105 ᐃ 㔞ᏊᏛィ⟬ ⤖ᯝ LQVLOLFR ࢫࢡ࣮ࣜࢽࣥࢢ LQYLWURH[YLYR ࢫࢡ࣮ࣜࢽࣥࢢ P3U3 ྜ≀ࡢ⤖ྜ⮬⏤࢚ࢿࣝࢠ࣮ࡢィ⟬ 635 ᐃ 105 ᐃ P3U3 ྜ≀ࡢ┦స⏝ゎᯒཬࡧྜ≀ࡢ᭱㐺 ⪃ᐹ ㅰ㎡ ཧ⪃ᩥ⊩
ᗎㄽ
⫼ᬒ
⌧ᅾ▱ࡽࢀ࡚࠸ࡿෆ⛉ⓗᝈࡢከࡃࡣࠊయෆࡢᏛᛂၥ㢟ࡀ࠶ࡿ⌮ゎࡉࢀ࡚ ࠸ࡿࠋၥ㢟ࡢ࠶ࡿᏛᛂࢆไᚚࡍࡿࡣࠊ⸆࡞ࡼࡾయእࡽ㐺ษ࡞ྜ≀ࢆ㏦ ࡾ㎸ࡳࠊᙜヱᏛᛂ㛵ࡍࡿ⏕యศᏊ㸦ࢱࣥࣃࢡ㉁ࠊ᰾㓟ࠊ⬡㉁࡞㸧ࡢᵓ㐀ᡈࡣ ᶵ⬟ࢆไᚚࡍࡿᚲせࡀ࠶ࡿࠋ ࣉࣜ࢜ࣥ>@ࡸࣝࢶࣁ࣐࣮ࡣࢥࣥࣇ࢛࣓࣮ࢩࣙࣥ>@ࡋ࡚▱ࡽࢀ࡚࠾ ࡾࠊᝈ㛵㐃ࢱࣥࣃࢡ㉁ࡢ࣑ࢫࣇ࢛࣮ࣝࢹࣥࢢࡼࡗ࡚ᘬࡁ㉳ࡇࡉࢀࡿࠋࣉࣜ࢜ ࣥࡣࠊࣉࣜ࢜ࣥࢱࣥࣃࢡ㉁ࡀȘࣜࢵࢡࢫࢆከࡃྵࡴṇᖖᆺᵓ㐀ࡽșࢩ࣮ࢺࢆከ ࡃྵࡴ␗ᖖᆺᵓ㐀❧యᵓ㐀ኚࡍࡿࡇࡼࡾᘬࡁ㉳ࡇࡉࢀࡿࡇࡀ▱ࡽࢀ࡚࠸ࡿࠋ ࢥࣥࣇ࢛࣓࣮ࢩࣙࣥࡢᏳᐃᛶక࠸ᙧᡂࡉࢀࡓ࢜ࣜࢦ࣐࣮ᵓ㐀>@ࡣࣉࣜ࢜ࣥࠊ ࣝࢶࣁ࣐࣮>@ࠊࣃ࣮࢟ࣥࢯࣥ>@ࠊ➽ⴎ⦰ᛶഃ⣴◳>@࡞ࡢ⚄⤒ኚᛶᝈ ࡢ࡞ཎᅉ⪃࠼ࡽࢀ࡚࠸ࡿࠋࡇࢀࡽࡢᝈ㛵㐃ࢱࣥࣃࢡ㉁≉␗ⓗ⤖ྜࡋࠊኳ↛ᵓ 㐀ࢆᏳᐃࡉࡏࠊ␗ᖖ࡞จ㞟యᙧᡂࢆᢚไࡍࡿྜ≀ࡋ࡚࣓ࢹ࢝ࣝࢩࣕ࣌ࣟࣥ 㸦0&V㸧>@ࡀ⪃ᐹࡉࢀࠊᐇ㝿㛤Ⓨࡉࢀ࡚࠸ࡿࠋ ࡋࡋࠊࡇࡢࡼ࠺࡞ 0&V ࡢೃ⿵ࡣࠊศᏊ㔞 ⛬ᗘ࡛࠶ࡿࡋ࡚ࡶ⭾࡞ᩘ᪼ࡾ 㸦ಶ㸧ࠊ᭷ᶵྜᡂࡋ࡚ࣂ࢜ࢵࢭࢆ⾜࠺ᐇ㦂࡞ࡼࡾࠊ⥙⨶ⓗࢫࢡ࣮ࣜࢽ ࣥࢢࡋ࡚ᙜヱྜ≀ࢆྠᐃࡍࡿࡇࡣࠊ⌧ᐇⓗ࡛࡞࠸ࠋ ࡑࡢࡓࡵࠊᙜヱྜ≀ࢆྠᐃࡍࡿࡲ࡛ࡢ㛫ࢆ⠇⣙ࡍࡿࡃࠊᶆⓗ࡞ࡿ⏕యศᏊ ᑐࡋ࡚ࢥࣥࣆ࣮ࣗࢱෆ࡛ࢫࢡ࣮ࣜࢽࣥࢢ㸦LQ VLOLFR ࢫࢡ࣮ࣜࢽࣥࢢ㸧ࢆ⾜࠺ࡇ࡛ࠊ ᙜヱྜ≀ࢆㄽ⌮ⓗタィࡍࡿ᪉ἲࡀὀ┠ࡉࢀ࡚࠸ࡿࠋ ࡋࡋࠊ0&V ࡢㄽ⌮ⓗタィࡣᚲࡎࡋࡶᐜ࡛᫆ࡣ࡞ࡃࠊ」㞧࡞ィ⟬ࢆせࡍࡿࠋࡇࢀࡲ࡛ࠊ ࢻࢵ࢟ࣥࢢࢩ࣑࣮ࣗࣞࢩࣙࣥ㸦'6 ࠊศᏊືຊᏛࢩ࣑࣮ࣗࣞࢩࣙࣥ㸦0' ࡸ㔞ᏊᏛィ⟬ 㸦4&㸧ࡘ࠸࡚ࠊከࡃࡢࣉࣟࢢ࣒ࣛ> @ࡀ㛤Ⓨࡉࢀ࡚ࡁࡓࠋࡋࡋࠊࡇࢀࡽࡢࣉࣟࢢ ࣒ࣛࡣࢥ࣐ࣥࢻ࡛ࣛࣥࢥ࣐ࣥࢻࢆධຊࡋ࡚ィ⟬ࢆ⾜࠺ࡓࡵࠊ᧯స᪉ἲࡣ↹㞧࡛࠶ࡾࠊ ◊✲⪅ከࡢ㈇ᢸࡀࡿࡇ࡞ࡿࠋࡇࢀࡽࡢࣉࣟࢢ࣒ࣛࡢ౽ᛶࢆྥୖࡍࡿࡓ ࡵࠊࢢࣛࣇ࣮࢝ࣝࣘࢨ࣮ࣥࢱࣇ࢙࣮ࢫ㸦*8,㸧ࡁࡢ⤫ྜ⸆ᨭࢯࣇࢺ࢙࢘ ࡢ㛤Ⓨࡣᴟࡵ࡚㔜せ࡛࠶ࡿࠋ◊✲┠ⓗ
ᮏ◊✲࡛ࡣࠊ0&V ࢆㄽ⌮ⓗタィࡍࡿ㝿ࠊ◊✲⪅ࡢ㈇ᢸ๐ῶཬࡧ◊✲ຠ⋡ࢆྥୖࡍࡿࡓ ࡵࠊ'6ࠊ0'ࠊ4& ୕ࡘࡢࣔࢹࣝࡢィ⟬ࢆ⤫ྜⓗᐇฟ᮶ࡿ⸆ᨭࢯࣇࢺ࢙࢘ࡢ 㛤Ⓨࢆ┠ᣦࡋࡓࠋ㛤Ⓨࡉࢀࡓࢯࣇࢺ࢙࢘ࡣࠕ1$*$5$ࠖྡࡅࡓࠋ1$*$5$ ࢆࡗ࡚ࠊ ࣮ࣟ࢝ࣝࣃࢯࢥ࡛ࣥィ⟬⏝ࡢࣥࣉࢵࢺࣇࣝࢆ‽ഛࡋࠊࢭࣥࢱ࣮ࡢࢡࣛࢫࢱ࣮ィ ⟬ᶵ࡛つᶍィ⟬ࢆ⾜࠺ࡀྍ⬟࡛࠶ࡿࠋḟࠊᡃࠎࡣ᪂つᢠࣉࣜ࢜ࣥ⸆ࡢ᥈⣴ࢆ㏻ࡌ࡚ࠊ1$*$5$ࡢᛶ⬟ࢆ᳨ドࡋࡓࠋ᪂ࡋ࠸ ᢠࣉࣜ࢜ࣥຠᯝࡀ࠶ࡿྜ≀ࡢ⤖ྜ࣮ࣔࢻࢆண ࡋࠊ᰾☢Ẽඹ㬆㸦105㸧ࢫ࣌ࢡࢺࣝ ᐃࡸ⾲㠃ࣉࣛࢬࣔࣥඹ㬆㸦635㸧 ᐃࢆ⾜࠸ࠊྜ≀ࣉࣜ࢜ࣥ⺮ⓑ㉁ࡢ⤖ྜ㒊 ࡸ⤖ྜᐃᩘࢆồࡵ࡚ẚ㍑᳨ウࡋࡓࠋຠᯝࡀ࠶ࡿྜ≀ࢆ᭱㐺ࡍࡿཧ⪃࡞ࡿ ሗࡣࠊศᏊືຊᏛࢩ࣑࣮ࣗࣞࢩࣙࣥࡸ)02ἲࢆ⏝࠸ࡓ㔞ᏊᏛィ⟬ࡼࡗ࡚ᚓࡿࡇ ࡀฟ᮶ࡓࠋ
⤫ྜ⸆ࢯࣇࢺ࢙࢘ 1$*$5$ ࡢ㛤Ⓨ
ᴫせ
1$*$5$ ࡣࠊ⏕యศᏊ❧యᵓ㐀ࢆྍどࡍࡿࣉࣟࢢ࣒ࣛ 3\02/>@ࡢࣉࣛࢢ࡛ࣥ࠶ ࡿࠋࢻࢵ࢟ࣥࢢࢩ࣑࣮ࣗࣞࢩࣙࣥ㸦$XWR'RFN9LQD>@㸧ࠊศᏊືຊᏛィ⟬㸦$PEHU>@㸧ࠊ 㔞ᏊᏛィ⟬㸦3$,&6> @㸧୕ࡘࡢࣉࣟࢢ࣒ࣛࡢࢢࣛࣇ࣮࢝ࣝࣘࢨ࣮ࣥࢱ࣮ࣇ࢙࣮ ࢫ㸦*8,㸧ࢆᐇࡋࡓࠋ1$*$5$ ࠾࠸࡚ࡣࠊ ࡘࡢ≀⌮ⓗ࡞ࣔࢹࣝࡀ⮬⏤⤌ࡳྜࢃࡏ ࡚ࠊᚲせ࡞⤫ィࣥࢧࣥࣈࣝࢆồࡵࡿࡀྍ⬟࡞ࡗ࡚࠸ࡿࠋ㸦)LJ㸧1$*$5$ ࢆࡗ ࡚ࠊ࣮ࣟ࢝ࣝࣃࢯࢥ࡛ࣥィ⟬⏝ࡢࣥࣉࢵࢺࣇࣝࢆ‽ഛࡋࠊࢭࣥࢱ࣮ࡢࢡࣛࢫࢱ࣮ ィ⟬ᶵ࡛つᶍィ⟬ࢆ⾜࠺ࡀྍ⬟࡛࠶ࡿࠋ㸦)LJ㸧Fig. 1 Overview of NAGARA. NAGARA integrates a preparation procedure and three physical models: NAGARA DS, NAGARA MD, and NAGARA QC. In NAGARA, three physical models can be arbitrarily combined to obtain the desired statistical ensemble.
Fig. 2 The flow of using NAGARA.
㛤Ⓨゝㄒཬࡧࢩࢫࢸ࣒せ௳
ࡇࡢ❶࡛ࡣࠊ1$*$5$ ࡢ㛤Ⓨゝㄒཬࡧᐇ⾜ࡍࡿᚲせ࡞ࢩࢫࢸ࣒せ௳ࢆ⤂ࡍࡿࠋ㛤Ⓨゝㄒ
࡞ࣉࣟࢢ࣑ࣛࣥࢢゝㄒࡣ 3\WKRQ ࢆ⏝ࡋࡓࠊ*8, ࣛࣈ࣮ࣛࣜࡣ 7NLQWHU ࢆ⏝ ࡋࡓࠋࡍ࡚ࡢࢯ࣮ࢫࢥ࣮ࢻࡀ୍ࡘࡢࣇࣝಖᏑࡋ࡚࠸ࡿࡓࡵࠊ⏝⪅ࡀ⡆༢ࢲ ࣮࢘ࣥࣟࢻࡋࠊ3\02/ ࡢࣥࢫࢺ࣮ࣝࡉࢀ࡚࠸ࡿࣇ࢛ࣝࢲࢥࣆ࣮ࡍࡿࡔࡅ࡛ࣥࢫࢺ ࣮࡛ࣝࡁࡿࠋࢩࢫࢸ࣒せ௳
1$*$5$ ࡣ :LQGRZV ୖ 3\02/ ࡢ࣮࣋ࢫ࡛㛤Ⓨࡉࢀࠊୗグࡢ 26 ࡛ࢸࢫࢺࡋࡓࠋ 9 :LQGRZV :LQGRZV :LQGRZV㸶:LQGRZV㸧 9 /LQX[ 8EXQWX 9 0DF26 26; 1$*$5$ ࡢ㒊ࡢᶵ⬟ࢆ⏝ࡍࡿࡓࡵࠊୗグࡢ➨୕ࡢࢯࣇࢺ࢙࢘ィ⟬ࣉࣟࢢ࣒ࣛࡀᚲせ࡛࠶ࡿࠋ 9 $0%(5ศᏊືຊᏛࢩ࣑࣮ࣗࣞࢩࣙࣥཬࡧィ⟬⤖ᯝࡢゎᯒ⏝ࠋ1$*$5$ ࡣ $PEHU $PEHU ࡛ࡢᐇ⾜ࢆ☜ㄆࡋࡓࠋ 9 $XWR'RFN9LQDࢻࢵ࢟ࣥࢢࢩ࣑࣮ࣗࣞࢩࣙࣥࡢᐇࠋ 9 0*/7RROV> @$XWR'RFN9LQD ࡢධຊࣇࣝࡢ‽ഛࠋ 9 2SHQ %DEHO >@ ྜ≀ཬࡧ⏕యศᏊࡢ❧యᵓ㐀ࢹ࣮ࢱࡢࣇ࢛࣮࣐ࢵࢺ㌿ ࡢᚲせࠋ 9 3$,&6)02 ἲࡼࡿ㔞ᏊᏛィ⟬ࡢᐇࠋ 9 3DLFV9LHZ3$,&6 ࡢධຊࣇࣝࡢ‽ഛཬࡧィ⟬⤖ᯝࡢゎᯒࠋ
せᶵ⬟࣓ࢽ࣮ࣗ
ࡇࡢ❶࡛ࡣࠊ1$*$5$ ࡢྛᶵ⬟㛵㐃ࡘࡅࡢ࣓ࢽ࣮ࣗࢆ⤂ࡍࡿࠋ1$*$5$ ࣓࣓ࣥࢽ࣮ࣗ
3\02/ 1$*$5$ ࢆࣥࢫࢺ࣮ࣝࡋࡓࡽࠊ3\02/ ࡢ࣓࣓ࣥࢽ࣮ࣗࠕ1$*$5$ࠖ࠸ ࠺࣓ࢽ࣮ࣗࢆ㏣ຍࡉࢀࡿ㸦)LJ 㸧ࠋࡇࡢ࣓ࢽ࣮ࣗࡽ 1$*$5$ ࡢྛᶵ⬟ࢆ❧ࡕୖࡀࡿ ࡀฟ᮶ࡿࠋFig. 3Screenshot of the NAGARA main menu.
࣓ࢽ࣮ࣗ㸯㸸1$*$5$ ࡢ⎔ቃタᐃ
ࠕ&RQILJXUDWLRQ 6HWWLQJVࠖࡣ 1$*$5$ ࡢసᴗࣇ࢛ࣝࢲࠊィ⟬ࢧ࣮ࣂ࣮ࡢ᥋⥆ ,' ࣃ ࢫ ࣡ ࣮ ࢻ ࠊ ྛ ィ ⟬ ࣉ ࣟ ࢢ ࣛ ࣒ ࡢ ࣥ ࢫ ࢺ ࣮ ࣝ ሙ ᡤ ࡞ ࡢ タ ᐃ ࢆ ⾜ ࠺ ࠋ
Fig. 4 Screenshot of the NAGARA Configuration settings.
࣓ࢽ࣮ࣗ㸰㸸ᶆⓗศᏊࡢ❧యᵓ㐀ࢆ‽ഛ
ࡇࡢ࣓ࢽ࣮ࣗࡽࠊ⏝⪅ࡀᶆⓗ⺮ⓑ㉁ྜ≀ࡢ❧యᵓ㐀ࣇࣝࢆ‽ഛࡍࡿ ࡀ࡛ࡁࡿࠋ⺮ⓑ㉁ࡢ❧యᵓ㐀ࡀ⺮ⓑ㉁ᵓ㐀ࢹ࣮ࢱࣂࣥࢡࡽࢲ࣮࢘ࣥࣟࢻ࡛ࡁࡿࠋ ྛࢹ࣮ࢱ࣮࣋ࢫࡽࢲ࣮࢘ࣥࣟࢻࡋࡓྜ≀ࡢᵓ㐀ࣇࣝࡣ㏻ᖖ୍ࡘࡢ 02/ ᡈ࠸ ࡣ 6') ࣇࣝࡢ୰ᩘ༑ಶࡢྜ≀ࡀグ㍕ࡉࢀ࡚࠸ࡿࠊࡇࡢࡲࡲ࡛ࡣ $XWR'RFN 9LQD ⏝ฟ᮶࡞࠸ࡓࡵࠊ$XWR'RFN 9LQD ࡛⏝ฟ᮶ࡿ 3'%47 ᙧᘧ㌿ࡍࡿᚲせࡀ ࠶ࡿࠋࡇࡢసᴗ࡛ࡣࠊ1$*$5$ ࡣసᴗࣇ࢛ࣝࢲࣜ࢞ࣥࢻࣇࣝࢆಖᏑࡍࡿࢧ࣮ࣈࣇ ࢛ࣝࢲࢆసࡾࠊOLJDQGOLJDQGOLJDQG1 ࡢࡼ࠺࡞ࣇࣝࢆ⏕ᡂࡍࡿࠋFig. 5 Screenshot of the NAGARA Molecular structure preparation.
࣓ࢽ࣮ࣗ㸱㸸$XWR'RFN9LQD*8, '6
ᐇ࡛ࡁࡿࠋ)LJ ࡛♧ࡋࡓࡼ࠺ࠊࢱ࣮ࢤࢵࢺ⺮ⓑ㉁ࡢ❧యᵓ㐀ࠊࣜ࢞ࣥࢻࡢ❧య ᵓ㐀ࡸィ⟬ࢥࣥࢺ࣮ࣟࣝࡍࡿࣃ࣓࣮ࣛࢱࢆ࣮ࣟ࢝ࣝࣃࢯࢥ࡛ࣥタᐃࡍࡿࠋ$XWR'RFN 9LQD ࡢධຊࣇࣝࢆࢭࣥࢱ࣮ࡢࢡࣛࢫࢱ࣮ィ⟬ᶵ㌿㏦ࡋ࡚ࠊࢻࢵ࢟ࣥࢢࢩ࣑ࣗࣞ ࣮ࢩࣙࣥࢆ࣓ࣥࡢィ⟬ᶵ࡛⾜࠺ࠋィ⟬⤖ᯝࡣࣜ࢞ࣥࢻࡈฟຊࣇࣝ᭩ࡁ㎸ ࡲࢀࡿࠋ ࡇࡢᶵ⬟ࡣ 6HHOLJHU ࡽࡀㄝ᫂ࡋࡓࣉࣛࢢࣥఝ࡚࠸ࡿࡀࠊ1$*$5$ ࡣࢡࣛࣥࢺ ̿ࢧ࣮ࣂ࣮ᙧᘧ࡛ࠊࢧ࣮ࣂ࣮ 3\02/ ཬࡧࣉࣛࢢࣥࢆࣥࢫࢺ࣮ࣝࡋ࡞ࡃ࡚ࡶࠊ つᶍ࡞ࢻࢵ࢟ࣥࢢࢩ࣑࣮ࣗࣞࢩࣙࣥࢆᐇฟ᮶ࡿ࠸࠺≉ᚩࢆᣢࡗ࡚࠸ࡿࠋ
Fig. 6 AutoDock Vina GUI
࣓ࢽ࣮ࣗ㸲㸸$PEHU*8, 0'
ࡇࡢ࣓ࢽ࣮ࣗࢆ⤒⏤ࡋ࡚ࠊศᏊືຊᏛࢩ࣑࣮ࣗࣞࢩࣙࣥཬࡧ⺮ⓑ㉁ࣜ࢞ࣥࢻࡢ ⤖ྜ⮬⏤࢚ࢿࣝࢠ࣮ࡢィ⟬ࡀฟ᮶ࡿࠋ)LJ ♧ࡋࡓࡼ࠺ࠊ$PEHU *8, ࡣ 6\VWHP %XLOGLQJ (QHUJ\ 0LQLPL]DWLRQ 0ROHFXODU '\QDPLFV %LQGLQJ )UHH (QHUJ\ &DOFXODWLRQ -RE 0RQLWRU 'DWD $QDO\VLV ࠸࠺ᶵ⬟ࢆྵࡴࠋ2%&ࠊ2%&,,ࠊ+&7 3RLVVRQ㸫%ROW]PDQQ ࡞ࡢ⁐፹ࣔࢹࣝࡶࢧ࣏࣮ࢺࡋ࡚࠸ࡿࠋ
Fig. 7 Amber GUI
࣓ࢽ࣮ࣗ㸳㸸3$,&6*8, 4&
␒┠ࡢ࣓ࢽ࣮ࣗࡣ㔞ᏊᏛᏛィ⟬ࡢࡓࡵ 3$,&6 ࡢධຊࣇࣝࢆ‽ഛࡍࡿ ࡀฟ᮶ࡿࠋ)02 ἲࢆ᥇⏝ࡋࡓ 3$,&6 ࡣ㧗ศᏊ࡛࠶ࡿ⺮ⓑ㉁ྜ≀ࡢ┦స⏝ࢆ㏿ࡸ ィ⟬ฟ᮶ࡿࠋࡇࡢィ⟬࡛ᚓࡓሗࡣࠊྜ≀ࢆ᭱㐺ࡍࡿ㝿ཧ⪃࡞ࡿࠋࡇࢀࡀ 1$*$5$ ࡢ᭱ࡶ≉ᚩⓗ࡞ᶵ⬟࡛࠶ࡿࠋ
Fig. 8 PAICS GUI
ࡑࡢ࣓ࢽ࣮ࣗ㸸3\02/ ࡢ⫼ᬒⰍࡢタᐃࡸṧᇶ㓄ิࢼࣥࣂ࣮ࢆኚ᭦
)LJ ♧ࡍࡼ࠺ࠊ“Set Background Color”࣓ࢽ࣮ࣗࡽࠊ3\02/ ࡢ⾲♧⫼ᬒⰍࢆ⡆
༢ኚ᭦࡛ࡁࡿࠋࡑࡢࠊ$0%(5 ࡛ฟຊࡋࡓ 3'% ࣇࣝࡢ୰ࡢṧᇶ␒ྕࡣ㸯ࡽ୪ࢇ ࡛࠸ࡿࡓࡵࠊィ⟬⤖ᯝࢆゎᯒࡍࡿ㝿ࠊ“Modify Residue Sequence Number”࣓ࢽ࣮ࣗࡼ ࡾࠊPDB ࣇࣝࡢ୰ࡢṧᇶ㓄ิ␒ྕࢆඖࡢ୪ࡧࣜࢭࢵࢺࡍࡿࡀฟ᮶ࡿࠋ
ㄽ⌮ⓗ⸆ࡼࡿ᪂ࡋ࠸ᢠࣉࣜ࢜ࣥ⸆ࡢฟ
ᮦᩱ᪉ἲ
ࡇࡇ࡛ࡣࠊ1$*$5$ ࢆ⏝࠸ࠊㄽ⌮ⓗ⸆ࡼࡿ᪂ࡋ࠸ᢠࣉࣜ࢜ࣥ⸆ࡢฟࢆ⾜ࡗࡓࠋ 㸦)LJ 㸧Fig. 9 Flow of calculation for the discovery of a medical chaperone (MC). In the DS procedure, NAGARA
creates a project folder on the local PC and the remote HPC server; the structure of the receptor and the ligands database are prepared in this folder. The job will automatically run on the remote HPC server. Experimental screening including organic synthesis and bioassay was subsequently performed for selected candidates. In the MD simulation, the initial structure of the receptor and the effective ligands were prepared and the MD
simulation was performed to obtain the best binding mode, ΔΔG, and ΔΔS. In the QC procedure, the PAICS input file was initially prepared using the optimized complex structure. QC fragment molecular orbital (FMO) calculations were then performed, and the interaction between the receptor and ligand were analyzed in detail for further ligand optimization.
LQVLOLFR
ࢫࢡ࣮ࣜࢽࣥࢢ
ᡃࠎࡣ 1$*$5$ ࢆ⏝ࡋ࡚つᶍ࡞ LQVLOLFR ࢫࢡ࣮ࣜࢽࣥࢢࢆ⾜ࡗࡓࠋ࣐࢘ࢫ⏤᮶ ࡢࣉࣜ࢜ࣥࢱࣥࣃࢡ㉁㸦P3U3 ࠊ3'%,' DJ㸧ࢆᶆⓗࢱࣥࣃࢡ㉁ࡋ࡚⺮ⓑ㉁ ᵓ㐀ࢹ࣮ࢱࣂࣥࢡ㸦3'%㸧>@ࡽࢲ࣮࢘ࣥࣟࢻࡋࡓࠋపศᏊྜ≀㸦⣙ ಶ㸧 ᢎㄆ῭⸆㸦⣙ ಶ㸧ࡢ❧యᵓ㐀ࡣࢫࢡ࣮ࣜࢽࣥࢢᑐ㇟ࣜ࢞ࣥࢻࡋ࡚ /LJDQG %R[>@࠸࠺ྜ≀ࢹ࣮ࢱ࣮࣋ࢫࡽධᡭࡋࡓࠋࢻࢵ࢟ࣥࢢࢩ࣑࣮ࣗࣞࢩࣙࣥࢆጞࡵ ࡿ๓ࠊ/LJDQG %R[ ࡽධᡭࡋࡓྜ≀ࡸᢎㄆ῭⸆ࡢ❧యᵓ㐀ࣇࣝࡢࣇ࢛࣮࣐ࢵ ࢺࢆ 1$*$5$ ࡢ͆PROHFXODUVWUXFWXUHSUHSDUDWLRQSOXJLQ͇ࢆ⏝࠸࡚ࠊ$XWR'RFN9LQD ᑓ⏝ࡢ3'%47 ࣇࣝኚࡋࡓࠋᮏ◊✲࡛ࡣࠊP3U3 ࡢࡰయࢆ࢝ࣂ࣮࡛ࡁࡿ༑ศ ࡁ࡞ࢢࣜࢵࢻ࣎ࢵࢡࢫࢆ⏝࠸ࡿࡇࡼࡾࠊP3U3 ศᏊ⾲㠃యࡘ࠸࡚పศᏊ ྜ≀ࡢ⤖ྜ⟠ᡤࢆ᥈⣴ࡋࡓࠋ⏝࠸ࡓࢢࣜࢵࢻ࣎ࢵࢡࢫࡢ୰ᚰࡣ P3U3 ࡢ㔜ᚰ࡛ࠊࡑࡢࢧ ࢬࡣࠊ44 Å × 36 Å × 27 Å ࡛࠶ࡿࠋࢻࢵ࢟ࣥࢢࢩ࣑࣮ࣗࣞࢩࣙࣥࡢࢥࣉࣟࢢ࣒ࣛ $XWR'RFN9LQD ࡢࣃ࣓࣮ࣛࢱࢆ௨ୗࡢ㏻ࡾ࡛タᐃࡋࡓ㸸exhaustiveness = 20ࠊ num_modes = 20ࠊ energy_range = 4ࠋࡇࢀ௨እࡢࣃ࣓࣮ࣛࢱࡣࢹࣇ࢛ࣝࢺ್タᐃࡋࡓࠋLQYLWURH[YLYR
ࢫࢡ࣮ࣜࢽࣥࢢ
๓⠇࡛ᐇࡋࡓ LQVLOLFRࢫࢡ࣮ࣜࢽࣥࢢࡼࡿ⤖ྜࢫࢥ㡰㑅ฟࡋࡓྜ≀ࡣ $6,1(;㸦1&㸧 &KHP6FHQH㸦//&㸦1-㸧㸧ࡽ㉎ධࡋࡓࠋᢠࣉࣜ࢜ࣥάᛶศᯒࡣ௨๓ࡢㄽ ᩥ>@࡛⤂ࡋࡓ࢚࢘ࢫࢱࣥࣈࣟࢵࢺἲ㸦:%㸧ࢆ⏝ࡋ࡚⾜ࡗࡓࠋࡇࡇ࡛ࡣࠊே⏤᮶ࡢ ఏᰁᛶᾏ⥥≧⬻㸦76(㸧࢚࣮ࢪ࢙ࣥࢺ㸦)XNXRNDVWUDLQ㸧>@ឤᰁࡋࡓ⣽⬊⣔ࢆ ⏝ࡋࡓࠋ ࣑ࣀ㓟ṧᇶ 㸦P3U3 㸧ࡢ P3U3 ࡣࠊ⭠⳦Ⓨ⌧⣔ࢆ⏝࠸࡚⢭〇ࡋࡓࠋ ࡇࡢࢱࣥࣃࢡ㉁ࡣ௨๓ࡢㄽᩥ>@࡛⤂ࡉࢀࡓࠋ⢭〇ࡉࢀࡓ P3U3 ࡢศᏊ㔞ࡣ ࣐ࢺࣜࢵࢡࢫᨭ࣮ࣞࢨ࣮⬺㞳࢜ࣥἲࢆ⏝࠸ࡓ㣕⾜㛫ᆺ㉁㔞ศᯒィ㸦%UXNHU 'DOWRQLFV%LOOHULFD0$86$㸧࡛ ࡗࡓࠋศᏊືຊᏛࢩ࣑࣮ࣗࣞࢩࣙࣥ
ศᏊືຊᏛ㸦0ROHFXODU'\QDPLFV0'㸧ࡣࠊศᏊࢩ࣑࣮ࣗࣞࢩࣙࣥࡢ୍✀࡛ࠊ≀㉁ ⣔ࢆᵓᡂࡍࡿ⢏Ꮚ㸦ཎᏊࡸศᏊ㸧 ࡘ㸯ࡘᑐࡍࡿ㐠ື᪉⛬ᘧࢆࠊࢥࣥࣆ࣮ࣗࢱࢆࡗ ࡚ᩘ್ⓗゎ࠸࡚ࠊ⨨ࠊ㏿ᗘࠊ࢚ࢿࣝࢠ࣮࡞ࡢ㛫ኚࢆ㏣㊧ࡍࡿ᪉ἲ࡛࠶ࡿࠋ ⏕యศᏊࡢ 0' ࢩ࣑࣮ࣗࣞࢩࣙࣥࢆ⾜࠺㝿ࡣࠊከయၥ㢟ࡢᛶ㉁ᛶ㐺ษ࡞ึᮇᵓ㐀ࢆ⏝ពࡍࡿᚲせࡀ࠶ࡿࠋࡇࡇ࡛ࡣࠊ⺮ⓑ㉁ྜ≀ࡢ」ྜయᵓ㐀ࢆึᮇᵓ㐀ࡋ࡚ࠊ࢚ࢿ ࣝࢠ࣮᭱ᑠ⤖ྜ⮬⏤࢚ࢿࣝࢠ࣮ࡢィ⟬ࢆ 0' ࢩ࣑࣮ࣗࣞࢩ࡛ࣙࣥᐇࡋࡓࠋ๓⠇࡛ ᐇࡋࡓLQYLWURH[YLYRࢫࢡ࣮ࣜࢽࣥࢢ࡛ぢࡘࡗࡓ୕ࡘࡢຠᯝࡀ࠶ࡿྜ≀ Ỉ⣲ཎᏊࢆຍ࠼࡚ࠊࡑࢀࡒࢀࣉࣜ࢜ࣥ⺮ⓑ㉁⤖ྜࡉࡏࡓ」ྜయࢆ⏝ࡋࡓࠋศᏊຊ ሙࡘ࠸࡚ࠊ⺮ⓑ㉁ࡣ $0%(56% ࠸࠺ $0%(5 ຊሙࠊࣜ࢞ࣥࢻࡣ *$)) ࠸࠺ỗ⏝ $0%(5 ຊሙࢆ⏝ࡋࡓࠋ0' ࢩ࣑࣮ࣗࣞࢩࣙࣥࢆᐇࡍࡿ๓ࠊศᏊຊᏛ㸦0ROHFXODU0HFKDQLFV 00㸧ἲࢆ⏝࠸࡚ࠊึᮇᵓ㐀ࡢṍࡳࢆྲྀࡾ㝖ࡃࡓࡵᑐ㇟⣔ࡢయࡢ࢚ࢿࣝࢠ࣮᭱ᑠࢆ⾜ ࡗࡓࠋࡑࡢᚋࠊ⣔యࢆ SVຍ⇕ࡋࠊ ᗘ . Ẽᅽ DWP ࡢ᮲௳࡛ SV ᖹ⾮ ࢆࡉࡏࡓࠋḟࠊ ᗘ .ࠊẼᅽ DWP ࡢ 137 ᮲௳࡛ QV ࡢ 0' ࢩ࣑࣮ࣗࣞࢩࣙ ࣥࢆ⾜ࡗࡓࠋ SV ẖࢩ࣑࣮ࣗࣞࢩࣙࣥࡢ⤖ᯝࢆฟຊࣇࣝグ㘓ࡋࡓࠋ᭱ᚋࠊ 003%6$>@࠸࠺᪉ἲࢆ⏝࠸࡚ࠊ⺮ⓑ㉁ྜ≀ࡢ⤖ྜ⮬⏤࢚ࢿࣝࢠ࣮ࢆィ⟬ࡋ ࡓࠋ
635 ᐃ
⾲㠃ࣉࣛࢬࣔࣥඹ㬆㸦6XUIDFH 3ODVPRQ 5HVRQDQFH 635㸧ࢆ⏝࠸࡚ࣉࣜ࢜ࣥ⺮ⓑ㉁ ྜ ≀ ࡢ ┦ స ⏝ ࢆ ᐃ ࡋ ࡓ ࠋ ᐃ ⨨ ࡣ ࠊ %LDFRUH 7 㸦 *( +HDOWKFDUH %XFNLQJKDPVKLUH8.㸧ࢆ⏝ࡋࡓࠋ࣑ࣥ࢝ࢵࣉࣜࣥࢢ࢟ࢵࢺ㸦*(+HDOWKFDUH㸧ࢆ⏝ ࠸࡚ࠊ&0 ࢭࣥࢧ࣮ࢳࢵࣉ P3U3 ᅛᐃࡋࡓࠋ࠸ࢁ࠸ࢁ࡞⃰ᗘࡢྜ≀ࢆ °C ࡛ ࡢ ȣ/PLQ ࡢὶ㔞࡛ࠊ ศࡢ㛫ࣛࣥࢽࣥࢢࣂࢵࣇ㸦 0 +(3(6 S+ FRQWDLQLQJ01D&OVXUIDFWDQW3DQGGLPHWK\OVXOIR[LGH '062 㸧 ὀධࡋࡓࠋࢥࣥࢺ࣮ࣟࣝࡋ࡚ࣂࢵࣇࡢࡳࡢࢭࣥࢧ࣮ࢳࢵࣉࢆࡗࡓࠋゎ㞳ᐃᩘ 㸦.G㸧ࡣࠊࢭࣥࢧ࣮ࢢ࣒ࣛࡢࣞࢫ࣏ࣥࢫࡢྜ≀⃰ᗘ౫Ꮡᛶࡽィ⟬ࡉࢀࡓࠋ࣮࢝ࣈࣇ ࢵࢸࣥࢢࡢ㝿ࠊ㸸 ࡢ⤖ྜࣔࢹࣝࢆ௬ᐃࡋࠊ.G &㸦5PD[㸫5㸧5 ࢆࡗࡓࠋ.G&5 5PD[ࡑࢀࡒࢀࡣゎ㞳ᐃᩘࠊ ྜ≀⃰ᗘࠊ࠶ࡿྜ≀⃰ᗘᑐᛂࡍࡿ 635 ࣞࢫ࣏ࣥࢫ ᭱ 635 ࣞࢫ࣏ࣥࢫ♧ࡍࠋ5PD[ࡣ⌮ㄽⓗᚓࡽࢀࡿ᭱ 635 ࣞࢫ࣏ࣥࢫࡼࡾᑠࡉ࠸ࠋ105 ᐃ
105 ᐃࡢࡓࡵࠊP0 ࡢᆒ୍1 ࣛ࣋ࣝࡉࢀࡓ P3U3 ̽ ࢆ P0 ࡢ 1D1ࠊࡢ '062 ࡢ '2 ࢆྵࡴ P0 ࡢࢼࢺ࣒࣭ࣜ࢘ࢭࢸ࣮ࢺG ࣂࢵࣇ㸦S+㸧⁐ゎ ࡋࡓࠋྜ≀ࢆΰྜࡋࡓᚋࠊヨᩱ⁐ᾮࡢ S+ ࡣ࢞ࣛࢫ㟁ᴟ S+ ࣓࣮ࢱ࣮ +25,%$.\RWR -DSDQ ࢆࡗ࡚☜ㄆࡋࡓࠋ+̽1+64&㸦KHWHURQXFOHDUVLQJOHTXDQWXPFRKHUHQFH㸧ࢫ ࣌ࢡࢺࣝࡣࠊ°C ࡛ &U\R3UREH %UXNHU%LR6SLQ5KHLQVWHWWHQ*HUPDQ\ ࢆ╔ࡋ ࡓ %UXNHU$YDQFH ศගィࢆ⏝࠸࡚ ᐃࡋࡓࠋࢫ࣌ࢡࢺࣝࡢゎᯒࡣࢯࣇࢺ࢙࢘ࣃ ࢵࢣ࣮ࢪ 72363,1105 %UXNHU %LR6SLQ 5KHLQVWHWWHQ *HUPDQ\ ࣉࣟࢢ࣒ࣛ 6SDUN\ ࢆࡗࡓࠋ㔞ᏊᏛィ⟬
⺮ⓑ㉁ྜ≀ࡢ┦స⏝ࢆゎᯒࡍࡿࡓࡵࠊࣇࣛࢢ࣓ࣥࢺศᏊ㌶㐨ἲ㸦)02 ἲ㸧>@ ࢆ⏝࠸ࡓ㔞ᏊᏛィ⟬ࢆᐇࡋࡓࠋ)02 ἲ࡛ࡣࠊ⣔యࢆẚ㍑ⓗᑠࡉ࡞ࣇࣛࢢ࣓ࣥࢺ ศࡋࠊࣇࣛࢢ࣓ࣥࢺ༢య㸦ࣔࣀ࣐࣮㸧ཬࡧࣇࣛࢢ࣓ࣥࢺ࣌㸦ࢲ࣐࣮㸧ࡢィ⟬ࡢࡳ ࡽࠊศᏊయࡢἼື㛵ᩘࢆ⟬ฟࡍࡿࠋᚑࡗ࡚ࠊᑠࡉ࡞ィ⟬ࢆ」ᩘᅇᐇ⾜ࡍࡿࡔࡅ࡛ࡼ ࡃࠊィ⟬ࢥࢫࢺࢆࡁࡃῶࡽࡍࡇࡀྍ⬟࡞ࡿࠋࡑࡋ࡚ࠊࣔࣀ࣐࣮ࡸࢲ࣐࣮ࡢィ⟬ ࡀ⊂❧ࡋ࡚࠸ࡿࡢ࡛ࠊ㧗࠸୪ิຠ⋡ࡀᐇ⌧ࡉࢀࡿࠋࡲࡓࠊ࢚ࢿࣝࢠ࣮ࡢᐃᘧࡢ୰࡛ࠊ ࣇࣛࢢ࣓ࣥࢺ㛫┦స⏝࢚ࢿࣝࢠ࣮㸦,),(㸧ࡀᐃ⩏ࡉࢀࠊศᏊ㛫┦స⏝ࡢゎᯒ࡞ ࠾࠸࡚᭷ຠ⏝࡛ࡁࡿࡇࡀ▱ࡽࢀ࡚࠸ࡿࠋ ๓⠇࡛ᐇࡋࡓ 0' ࢩ࣑࣮ࣗࣞࢩ࡛ࣙࣥᵓ㐀᭱㐺ࡋࡓ」ྜయᵓ㐀ࢆࡇࡇ࡛⏝ࡋ ࡓࠋࣉࣜ࢜ࣥ⺮ⓑ㉁ࡢ 66 ⤖ྜࡋ࡚࠸ࡿ & & ࢆ㝖࠸ࡓྛ࣑ࣀ㓟ࡣ୍ࡘࡢࣇ ࣛࢢ࣓ࣥࢺࡳ࡞ࡋศࡋࡓࠋྜ≀ࡣ୍ࡘࡢࣇࣛࢢ࣓ࣥࢺࡳ࡞ࡋࠊ᭱ࡶຠᯝࡀ࠶ࡿ ᢎㄆ῭⸆ 7HJREXYLU㸦7*9㸧ࡣ᭦ࡘࡢࣇࣛࢢ࣓ࣥࢺ㸦)UDJ$ )UDJ%㸧ศࡋࡓࠋ ⏕యศᏊࡢᵓ㐀ࡣ⏕⌮ⓗ ᗘ࡛ࡣᗄఱᏛⓗ࡞ኚືࡀ࠶ࡿࡓࡵࠊ୍ᅇࡢィ⟬ࡔࡅ࡛ࡣ ⏕యศᏊ⣔ࡢゎᯒ༑ศ࡛࠶ࡿࠋᅇࡢ◊✲ࡣࠊᡃࠎࡣ␗࡞ࡿึᮇᵓ㐀ࢆࡗ࡚ ᅇࡢ )02 ィ⟬ࢆ⾜ࡗࡓ⤖ᯝࡢᖹᆒࢆゎᯒࡋࡓࠋィ⟬᪉ἲࡣ 03 ἲࠊᇶᗏ㛵ᩘࡣ *> @ ࢆ⏝ࡋࡓࠋ
⤖ᯝ
LQVLOLFR
ࢫࢡ࣮ࣜࢽࣥࢢ
⣙ ಶࡢపศᏊྜ≀⣙ ಶࡢᢎㄆ῭⸆ᑐࡋ࡚ࢻࢵ࢟ࣥࢢࢩ࣑࣮ࣗࣞࢩ ࣙࣥࢆᐇࡋࠊࣉࣜ࢜ࣥ⺮ⓑ㉁ྜ≀ࡢ⤖ྜ࢚ࢿࣝࢠ࣮㸦ࢫࢥ㸧ࢆィ⟬ࡋࡓࠊ⤖ྜ ࢫࢥࡢప࠸㡰࡛ྜ≀ ಶࠊ⸆ ಶࢆ㑅ฟࡋࡓࠋLQYLWURH[YLYR
ࢫࢡ࣮ࣜࢽࣥࢢ
㑅ฟࡉࢀࡓྜ≀ࡸ⸆ࢆ㉎ධࡋࠊ࢙࢘ࢫࢱࣥࣈࣟࢵࢸࣥࢢ㸦:%㸧࡛ྜ≀ࡸ⸆ࡢ ᢠࣉࣜ࢜ࣥຠᯝࢆ᳨ドࡋࡓࠋ)LJ ࡣ *7). ⣽⬊࡛ 3U36Fࢆᢚไࡍࡿຠᯝࡀ࠶ࡿ ྜ≀ %ࠊ* ᢎㄆ῭⸆ 7HJREXYLU㸦7*9㸧ࡢᏛᵓ㐀ࢆ⾲ࡍࠋ:% ࡛ࢥࣥࢺ࣮ࣟࣝࡢ '062 ẚ㍑ࡋ࡚ࠊࡼࡾ 3U36Fࡢᢚไ⋡㧗࠸ࡶࡢࠊࡘࡲࡾࠊ3U36Fࡢቑຍࡀᑡ࡞࠸⸆ᑐ ࡋ࡚ᢠࣉࣜ࢜ࣥຠᯝࡀ࠶ࡿࡳ࡞ࡋࡓ )LJD ࠋ᭱ࡶຠᯝࡀ࠶ࡿࡶࡢࠊ7*9 ࡢ ,&ࡣ P0 ࡛࠶ࡿࠋ
Fig. 10 Chemical structures of effective compounds. The chemical structures of the finally selected effective
compounds—B05, G03, and TGV—are shown.
Fig. 11 Biological and computational characterization of the effective compounds. (a) Anti-prion
efficiencies of the selected compounds relative to the control (DMSO) represented by PrPSc (%) obtained by
WB of PrPSc in GT + FK cells in the presence of compounds. Concentration of each compound was 10 PM.
(b) Binding mode of TGV and mPrP complex after MD simulation. (c) ''G (ڸ) and ''S (ە) obtained by MD simulations for effective and non-effective compounds. ''G and ''S decreased upon decrease in PrPSc
(%), suggesting that an anti-prion activity was achieved by the stabilization of the PrPC conformation and that
P3U3 ྜ≀ࡢ⤖ྜ⮬⏤࢚ࢿࣝࢠ࣮ࡢィ⟬
0' ࢩ࣑࣮ࣗࣞࢩࣙࣥࢆ⏝࠸ࠊຠᯝࡀ࠶ࡿྜ≀ࡸ⸆ᑐࡋ࡚ࠊ⤖ྜ⮬⏤࢚ࢿࣝࢠ ࣮ࢆィ⟬ࡋࡓࠋ'6 ࡛ᚓࡽࢀࡓᵓ㐀ᇶ࡙࠸࡚ 0' ࢩ࣑࣮ࣗࣞࢩࣙࣥࡢึᮇᵓ㐀ࢆ‽ഛ ࡋࠊࡑࢀࡒࢀࡢ」ྜయᵓ㐀ࢆᖹ⾮ࡉࡏ࡚ࡽ⮬⏤࢚ࢿࣝࢠ࣮ィ⟬ࢆ⾜ࡗࡓࠋ)LJE ࡣண ࡋࡓࣉࣜ࢜ࣥ⺮ⓑ㉁ 7*9 ࡢ⤖ྜࣔࢹࣝࢆ♧ࡋࡓࠋ7*9 ࡣࣉࣜ࢜ࣥ⺮ⓑ㉁ࡢ࣍ࢵ ࢺࢫ࣏ࢵࢺ⤖ྜࡍࡿࡇࡀศࡗࡓࠋィ⟬ࡋࡓ࢚ࢿࣝࢠ࣮࣭ࣃ࣓࣮ࣛࢱࢆ 7DEOH㸯 ࣜࢫࢺࢵࣉࡋࡓࠋ)LJF ♧ࡍࡼ࠺ࠊィ⟬ࡉࢀࡓ''* ''6 ್ᑐࡋࠊ3U36F ࡢ┦㛵㛵ಀࢆࣉࣟࢵࢺࡋࡓࠋ''* ࡢῶᑡక࠸ 3U36F ࡀῶᑡࡍࡿഴྥࡀ࠶ࡗࡓࠋࡇ ࡢࡇࡣࠊ⣔ࡢ⮬⏤࢚ࢿࣝࢠ࣮ࡀࣜ࢞ࣥࢻ⤖ྜࡍࡿࡇࡼࡾῶᑡࡍࡿࡇࢆ♧၀ ࡋ࡚࠸ࡿࠋࡇࢀࡣࠊ0& ௬ㄝࢆࢧ࣏࣮ࢺࡋ࡚࠸ࡿࠋࡲࡓࠊ''6 ࡢῶᑡక࠸≉ 3U36F ࢆῶᑡࡉࡏࡿࡇࡀศࡗࡓࠋࡘࡲࡾࠊ3U3&ᵓ㐀ࡢᏳᐃࡣࠊ⤖ྜࢧࢺ࿘㎶࡛ࡢ࣮ࣟ ࢝ࣝ࡞ᦂࡽࡂࡢῶᑡ㛵ಀࡋ࡚࠸ࡿࡇࢆ♧ࡋ࡚࠸ࡿࠋࡇࡢࡼ࠺ࠊ0' ࢩ࣑࣮ࣗࣞࢩ ࣙࣥࡣࠊᢠࣉࣜ࢜ࣥάᛶࡢ࣓࢝ࢽࢬ࣒ࡢ⌮ゎࡁ࡞ᡭࡾࢆᥦ౪ࡍࡿࡇࡀฟ᮶ ࡿࠋ Table 1Calculated parameters and anti-PrPSc effects of compounds B05, G03, and TGV.
Compound DS MD QC In vitro Ex vivo
No. Docking score a ''S
(e.u.) b ''G (kcal/mol) c IFIE (kcal/mol) d K䡀 (PM) PrPSc (%)e B05 −10.2 −69.1 −5.7 −85.2 49.3± 1.0 49.9 G03 −10.0 −64.5 −9.5 −65.6 24.0± 2.8 45.9 TGV −9.9 −79.6 −8.1 −88.2 19.2± 5.9 45.8 TGV(A) −51.9 TGV(B) −36.2
a The binding energy was obtained in the DS.
b The entropy calculated by MD simulation using the normal-mode entropy approximation.
c The binding free energy calculated by MD simulation using the normal-mode entropy approximation. d The interaction energy between the compound and the whole PrP.
e The percentage of PrPSc after treatment by B05, G03, and TGV.
635 ᐃ
ᡃࠎࡣ 635 ࡼࡗ࡚㑅ࡤࢀࡓྜ≀ࡸ⸆ P3U3 ࡢ⤖ྜຊࢆ ᐃࡋࡓࠋ)LJ
♧ࡍࡼ࠺ࠊ%ࠊ* 7*9 ࡢゎ㞳ᐃᩘ㸦Kd㸧ࡣࠊࡑࢀࡒࢀ ࠊࠊ P0 ࡛࠶ࡿ
࡛ࠊ% ࡣẚ㍑ⓗ㧗ࡗࡓࡀࠊࡇࢀࡣ % ࡢ㠀≉␗ⓗ┦స⏝ࡢཎᅉࡶࡋࢀ࡞࠸ࠋᢠ ࣉࣜ࢜ࣥάᛶࡣయⓗᛂࡢࢧࢬࡼࡾࡶࡴࡋࢁ⤖ྜᐃᩘࡼࡃ┦㛵ࡋ࡚࠾ࡾࠊ≉␗ ⓗ࡞┦స⏝ࡣᵓ㐀ኚࢆᢚไࡍࡿࡇ㔜せ࡛࠶ࡿࡇࢆ♧၀ࡋࡓࠋ
Fig. 12 Interaction analyses between compounds and mPrP (90–231) using SPR. SPR responses as a
function of concentration of (a) B05, (b) G03, and (c) TGV were fitted to the theoretical binding isotherm under the assumption of a 1:1 binding model. Kd ± S.D. (μM) and Rmax ± S.D. (r.u.) values for B05, G03, and TGV were estimated to be (49.3 ± 1.0, 320 ± 27), (24.0 ± 2.8, 66.1 ± 2.1), and (19.2 ± 5.9, 21.6 ± 1.7), respectively.
105 ᐃ
ࡉࡽࠊᡃࠎࡣ P3U3 7*9 ࡢ⤖ྜక࠺ࡢᏛࢩࣇࢺࡢኚࢆ ᐃࡋࡓࠋ)LJ D ࡣ P3U3 ̽ 7*9 ࡢ⤖ྜࡢ᭷↓࡛ࡢ +64& ࢫ࣌ࢡࢺࣝࡢẚ㍑ࢆ♧ࡋࡓࠋ +ࠊ7ࠊ7 ࠊ. * ୍⮴ࡋ࡚࠸ࡿ ࡘࡢࣆ࣮ࢡࡣࠊ᫂☜࡞Ꮫࢩࣇࢺ 㸦)LJD㸧ࢆ♧ࡋࡓࠋྛ࣑ࣀ㓟ࡢᏛࢩࣇࢺࡢศᕸࡣ )LJE ࡛♧ࡋ࡚࠸ࡿࠋࡇ ࢀࡽࡢᐇ㦂ⓗ☜ㄆࡋࡓ⤖ྜ㒊ࡣ 0' ࢩ࣑࣮ࣗࣞࢩࣙࣥࡼࡗ࡚ண ࡉࢀࡓ⤖ྜ㒊
ࡼࡃ୍⮴ࡋ࡚࠸ࡿ㸦)LJE㸧ࠋ
Fig. 13 Interaction analysis between TGV and mPrP (90–231) using NMR. (a) Overlay of the 1H–15N
HSQC spectra of mPrP (90–231) in the absence (black contours) and presence (red contours) of TGV. (b) Chemical shift perturbations as a function of residue number. The 1H and 15N chemical shift changes were
calculated using the function 'G [('G1H)2 + 0.17('G15N)2]1/2. Secondary structural elements determined by
NMR are shown by the bars at the top. (c) Mapping of the perturbed residues on the structure of mPrP (PDB entry 1AG2). Perturbed residues with 'G > 0.015 ppm are shown in red and those with 0.015 > 'G > 0.01 ppm are shown in orange.
P3U3 ྜ≀ࡢ┦స⏝ゎᯒཬࡧྜ≀ࡢ᭱㐺
ᡃࠎࡣ 3$,&6 *8, ࢆ⏝࠸ࡓ㔞ᏊᏛィ⟬ࢆᐇࡋࠊ7*9 P3U3 ࡢ┦స⏝ࢆゎᯒ ࡋࡓࠋゎᯒࡍࡿ㝿ࡣࠊ)LJ ♧ࡋࡓࡼ࠺ࠊP3U3 ࡢྛ࣑ࣀ㓟ṧᇶ 7*9 ࡢ ,),( ࢆィ⟬ࡋࡓࠋࡇࢀࡽࡢṧᇶ 7*9 ࡢ㛫ࡢ⤖ྜ࢚ࢿࣝࢠ࣮ࡣ +DUWUHH̽)RFN ἲ ࡛ィ⟬ࡉࢀࡓࠋ7*9 ࡣ 5ࠊ4ࠊ+ . ᙉࡃ┦స⏝ࡋ࡚࠸ࡿࡇࡀศࡗ ࡓ㸦)LJE㸧ࠋ ྜ≀ࢆ᭱㐺ࡍࡿࡓࡵࠊ)LJ D ୗ㒊࡛♧ࡍࡼ࠺ࠊᡃࠎࡣ 7*9 ࢆࣇࣛࢢ࣓ࣥ ࢺ $ % ศゎࡋࠊྛࣇࣛࢢ࣓ࣥࢺ⺮ⓑ㉁ࡢ┦స⏝ࢆゎᯒࡋࡓࠋࡑࡢ⤖ᯝࡼࡾࠊࣇࣛࢢ࣓ࣥࢺ $ ࡣ % ࡼࡾᙉ࠸┦స⏝ࢆᣢࡘࡇࡀศࡗࡓ㸦)LJD ୖ㒊㸧ࠋࣇࣛࢢ ࣓ࣥࢺ $ ࡣࠊ & ➃ࡢ +㸦+㸧 + + ࡢ㛫ࡢ࣮ࣝࣉ㡿ᇦ㸦4㸧⤖ྜࡋ࡚ ࠸ࡿ㸦)LJF㸧ࠋ୍᪉ࠊࣇࣛࢢ࣓ࣥࢺ % ࡣࠊ+ + ࡢ㛫ࡢ࣮ࣝࣉ㡿ᇦ㸦. 㸧 + 6 ࡢ㛫ࡢ㡿ᇦ㸦5㸧⤖ྜࡋ࡚࠸ࡿ㸦)LJG㸧ࠋᚑࡗ࡚ࠊࣇࣛࢢ࣓ࣥࢺ % ࢆࡢ ࣇࣛࢢ࣓ࣥࢺධࢀ᭰࠼ࡓࡽࠊࡼࡾᙉ࠸┦స⏝ࢆ♧ࡍྍ⬟ᛶࡀ࠶ࡿࠋࡇࡢࡼ࠺ࠊ ྜ≀ࡢࣇࣛࢢ࣓ࣥࢺࢱ࣮ࢤࢵࢺ⺮ⓑ㉁ࡢ┦స⏝ࢆゎᯒࡍࡿࡇࡣࠊྜ≀ࡢ᭱ 㐺ࡍࡿ㝿ࡢᡭࡾࢆᚓࡿࡓࡵᙺ❧ࡘࠋᐇࡣࠊᡃࠎࡣࣇࣛࢢ࣓ࣥࢺ % ࢆ⨨ࡋ ࡓ 7*9 ࡢከᩘࡢ㢮⦕యࢆぢࡘࡅࡓࠋࡇࢀࡽࡢ㢮⦕యࢆ㉎ධࡸ᭷ᶵྜᡂࡋࠊ:%ࠊ0' 4& ࢆ⧞ࡾ㏉ࡍࡇࡼࡗ࡚ࠊ࣮ࣜࢻྜ≀ࢆ᭱㐺ࡍࡿࡇࡀฟ᮶ࡿࠋࡋࡋࠊࡇࡢࣉࣟ ࢭࢫࢆࡋ࡚ࠊ᭱ᚋ᭱㐺ࡉࢀࡓྜ≀ࢆᚓࡿࡇࡣᮏ◊✲ࡢ⠊ᅖࢆ㉸࠼ ࡚࠸ࡿࡓࡵࠊᚋࡢㄢ㢟ࡋࡓࠋ
Fig. 14 Toward further optimization of TGV. (a) (Lower panel) Fragmentation of TGV into two parts: A
and B. (Upper panel) Inter-fragment interaction energy (IFIE) corresponding to each fragment. (b) IFIE for whole TGV and mPrP. (c) IFIE for fragment A and mPrP; fragment A specifically interacts with Q160 and H187. (d) IFIE for fragment B and mPrP; fragment B specifically interacts with R156 and K194.
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ᮏ◊✲࡛ࡣࠊᡃࠎࡣ 0&V ࢆタィࡍࡿࡓࡵࡢㄽ⌮ⓗ⸆ࣉࣛࢵࢺࣇ࢛࣮࣒ࡋ࡚ࠊ ⸆ᨭࢯࣇࢺ࢙࢘ 1$*$5$ ࢆ㛤Ⓨࡋࡓࠋ1$*$5$ ࡣࣜ࢞ࣥࢻࢹ࣮ࢱ࣮࣋ࢫࢫࢡ࣮ࣜࢽࣥ ࢢࡢࡓࡵࡢ '6ࠊೃ⿵ྜ≀ࢱࢤࢵࢺࢱࣥࣃࢡ㉁ࡢ⤖ྜ⮬⏤࢚ࢿࣝࢠ࣮ࢆᚓࡿࡓࡵ ࡢ 0' ࢩ࣑࣮ࣗࣞࢩࣙࣥࠊ࣮ࣜࢻྜ≀ࡢ᭱㐺ࡍࡿࡓࡵࡢ 4& ィ⟬ࢆ୍ࡘࡢࢯࣇࢺ࢘ ࢙ࡲࡵࡓࠋࡇࢀࡽࡢィ⟬ࢆ⧞ࡾ㏉ࡍࡇࡼࡗ࡚ࠊ᭱ࡶຠᯝⓗೃ⿵ྜ≀ࢆྠ ᐃࡍࡿࡇࡀฟ᮶ࡿࠋ1$*$5$ ࢆྵࡴࠊ$0%(5 ௨እࡢ㛵㐃ࣉࣟࢢ࣒ࣛࡣࡍ࡚ࣇ࣮ࣜࢯ ࣇࢺ࢙࡛࢘࠶ࡿࡓࡵࠊ⤒῭ⓗ࡛࠶ࡾࠊከࡃࡢ◊✲⪅ࡓࡕࡢ⏝ࢆᮇᚅ࡛ࡁࡿࠋ ࡑࡋ࡚ࠊᡃࠎࡣ 1$*$5$ ࢆ⏝࠸࡚ࠊᢠࣉࣜ࢜ࣥຠᯝࡀ࠶ࡿྜ≀ࢆ᥈⣴ࡋࠊ7HJREXYLU 㸦7*9㸧ࢆྵࡴ࠸ࡃࡘࡢ᪂つᢠࣉࣜ࢜ࣥྜ≀㸦0&㸧ࢆ≉ᐃࡍࡿࡇࡼࡗ࡚ࠊࡑࡢ ࣃࣇ࢛࣮࣐ࣥࢫࢆ♧ࡋࡓࠋ7*9 ࡣ +&9 ឤᰁᑐࡍࡿೃ⿵⸆ࡋ࡚ᢎㄆࡉࢀ࡚࠸ࡿࡓࡵࠊ ⮫ᗋヨ㦂ࡀẚ㍑ⓗᐜ࡛᫆࠶ࡿࠋ 7*9 ࡣࠊࡶࡶ & ᆺ⫢⅖࢘ࣝࢫ㸦+&9㸧ࡢ 51$ 」〇ࡢ᪂つᢚไࡋ࡚㛤Ⓨࡉࢀ ࡓᙜึࠊ7*9 ࡣ +&9 ࡢ 16% ࣏࣓࣮ࣜࣛࢮᢚไ> @ࡋ࡚⪃࠼ࡽࢀࡓࡀࠊ16% 㓝⣲ά ᛶࢆጉࡆ࡞࠸ࡇࡀᚋ࡛♧ࡉࢀࡓࠋ௦ࢃࡾࡢ࣓࢝ࢽࢬ࣒ࡀࠊࡑࡢᚋᥦࡉࢀ࡚࠸ࡿࠋ +&9 ᑐࡍࡿ 7*9 ࡢ࣓࢝ࢽࢬ࣒ࡀࡲࡔㄝ᫂ࡉࢀ࡚࠸࡞࠸ࡀࠊᢠࣉࣜ࢜ࣥຠᯝࡢሙ ྜࡣࠊ7*9 ࡣ 3U3&⤖ྜࡋ࡚ 3U3&ࡢᵓ㐀ࢆᏳᐃࡉࡏ࡚࠸ࡿࠋᚑࡗ࡚ࠊ7*9 ࡣ 0& ࡛࠶ࡿ⪃࠼ࡽࢀࡿࠋ ㏆ᖺࠊචㄪᩚࠊಙྕఏ㐩ࠊ㖡ࡢ⤖ྜࠊࢩࢼࣉࢫఏ㐩࣏ࢺ࣮ࢩࢫㄏᑟཬࡧ࣏ࢺ ࣮ࢩࢫࡢ่⃭ࡽࡢಖㆤ࡞ࢆྵࡵࡓࣉࣜ࢜ࣥ⺮ⓑ㉁ࡢᵝࠎ࡞⏕⌮ⓗᶵ⬟ࡀ㆟ㄽࡉࢀ ࡚࠸ࡿ[30]ࠋࡇࢀࡽࡢᶵ⬟ࡢヲ⣽ࡣ▱ࡽࢀ࡚࠸࡞࠸ࡶࡋࢀ࡞࠸ࡀࠊࣉࣜ࢜ࣥࡢ≉Ṧ࡞ ❧యᵓ㐀ࡀHCV ឤᰁ㛵ࡍࡿචㄪᩚࣉࣟࢭࢫᙳ㡪ࢆཬࡤࡍࠊ࠶ࡿ࠸ࡣࠊTGV ࡣ ୧᪉ࡶᙳ㡪ࢆཬࡤࡍᮍ▱ࡢ⺮ⓑ㉁┦స⏝ࡍࡿྍ⬟ᛶࡀ࠶ࡿࠋ ᅇࡢ◊✲࠾࠸࡚ࡣࠊ1$*$5$ ࡢࣄࢵࢺ⋡ࡣࠊ௨๓ሗ࿌ࡉࢀࡓ◊✲ྠᵝᩘࣃ ࣮ࢭࣥࢺ⛬ᗘ࡛࠶ࡗࡓࠋࡋࡋࠊ1$*$5$ ࢆ࠺ࡇࡼࡾࠊ0' ࡼࡿ┦స⏝ゎᯒࢆ ྍ⬟࡞ࡾࠊ0& ࡢస⏝࣓࢝ࢽࢬ࣒ࢆ☜ㄆࡍࡿࡇ࡛ࠊ࣮ࣜࢻ᭱㐺ࣉࣟࢭࢫࢆ▷⦰ࡍ ࡿࡀฟ᮶ࡓࠋ⤖ᯝࡋ࡚ࠊ࣑ࢫࣇ࢛࣮ࣝࢹࣥࢢࡢࣉࣟࢭࢫࢆຠ⋡Ⰻࡃᢚไࡍࡿࡀ ฟ᮶ࡓࠋࡉࡽࠊ4& ィ⟬ࡼࡾࠊ᭱㐺ࡢࡓࡵࡢ᭷┈࡞ࣄࣥࢺࡀᚓࡽࢀࡓࠋࡇࢀࡽࡣࠊ 1$*$5$ ࢆ⏝ࡍࡿࡇࡼࡿࡁ࠸࣓ࣜࢵࢺ࡛࠶ࡿ⪃࠼ࡽࢀࡿࠋ 0&V ࢆㄽ⌮ⓗ⸆ἲ࡛タィࡍࡿ㝿ࠊ㏻ᖖࡣ ࡘࡢࢫࢸࢵࣉࡀᚲせ࡛࠶ࡿࠋ㸧ᶆⓗ⺮ ⓑ㉁ࡢ❧యᵓ㐀ࢆỴᐃࡋࠊ㸧0&V ࡢᏛᵓ㐀ࢆ LQVLOLFR ࡛タィࡋࠊ㸧0&V ࢆ᭷ᶵྜ ᡂࡋࠊ㸧ࣂ࢜ࢵࢭ࡛ࡑࡢຠᯝࢆࢸࢫࢺࡍࡿࠋ1$*$5$ ࡣࢫࢸࢵࣉ ࡗ࡚ࠊᚲ せ࡞ࡿࢯࣇࢺ࢙࡛࢘࠶ࡿࠋ
1$*$5$ ࡣࠊ⺮ⓑ㉁ࡢࡼࡾ୍⯡ⓗ࡞ࣟࢫࢸࣜࢵࢡㄪ⠇㐺⏝ࡍࡿࡇࡶ࡛ࡁࡿࠋ⺮ ⓑ㉁㸦ᚲࡎࡋࡶάᛶ㒊࠸࠺ࢃࡅ࡛ࡣ࡞࠸㸧ࡢ࣏ࢣࢵࢺ⤖ྜࡍࡿࡼ࠺ 0&V ࢆタ ィࡍࢀࡤࠊ⺮ⓑ㉁ᶵ⬟ࡢࣟࢫࢸࣜࢵࢡไᚚࡀྍ⬟࡞ࡿࡔࢁ࠺ࠋ
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Logical design of anti-prion agents using NAGARA
Biao Maa, Keiichi Yamaguchia, Mayuko Fukuokaa, Kazuo Kuwata a,b,*
aUnited Graduate School of Drug Discovery and Medical Information Sciences, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan bDepartment of Gene and Development, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan
a r t i c l e i n f o
Article history:
Received 15 December 2015 Accepted 22 December 2015 Available online 24 December 2015 Keywords:
Logical drug design Prion Anti-prion agent Quantum chemistry NAGARA Tegobuvir a b s t r a c t
To accelerate the logical drug design procedure, we created the program“NAGARA,” a plugin for PyMOL, and applied it to the discovery of small compounds called medical chaperones (MCs) that stabilize the cellular form of a prion protein (PrPC). In NAGARA, we constructed a single platform to unify the docking simulation (DS), free energy calculation by molecular dynamics (MD) simulation, and interfragment interaction energy (IFIE) calculation by quantum chemistry (QC) calculation. NAGARA also enables large-scale parallel computing via a convenient graphical user interface. Here, we demonstrated its perfor-mance and its broad applicability from drug discovery to lead optimization with full compatibility with various experimental methods including Western blotting (WB) analysis, surface plasmon resonance (SPR), and nuclear magnetic resonance (NMR) measurements. Combining DS and WB, we discovered anti-prion activities for two compounds and tegobuvir (TGV), a non-nucleoside non-structural protein NS5B polymerase inhibitor showing activity against hepatitis C virus genotype 1. Binding profiles pre-dicted by MD and QC are consistent with those obtained by SPR and NMR. Free energy analyses showed that these compounds stabilize the PrPCconformation by decreasing the conformationalfluctuation of
the PrPC. Because TGV has been already approved as a medicine, its extension to prion diseases is straightforward. Finally, we evaluated the affinities of the fragmented regions of TGV using QC and found a clue for its further optimization. By repeating WB, MD, and QC recursively, we were able to obtain the optimum lead structure.
© 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
Conformational diseases[1]are primarily caused by the mis-folding of disease-related proteins. Although prion diseases[2]are known to be caused by the conformational conversion of a prion protein[3], conformational instabilities resulting in oligomer for-mation are regarded as the major causes of other neurodegenera-tive diseases[4](e.g., Alzheimer’s disease[5], Parkinson’s disease
[6], and amyotrophic lateral sclerosis[7]), certain psychiatric dis-eases (e.g., schizophrenia[8]), diabetes mellitus (i.e., type-II dia-betes)[9], and several cancers involving p53 mutation[10].
Medical chaperones (MCs)[11,12]specifically bind to and sta-bilize the native conformation of relevant proteins and prevent abnormal aggregate formation. They inhibit aggregate formation even if the proteins are intrinsically disordered[13]. MCs can be computationally designed to stabilize the conformations of native
or disordered protein structures. The most convenient and efficient approach to address the complicated computational demands for the logical design of MCs is to modify and unify existing programs into a new program with a comprehensive graphical user interface (GUI).
Docking simulations (DS) and molecular dynamics (MD) simu-lations can be implemented on numerous GUI tools that are developed for this purpose [14e20]. However, these tools were found to be only partially applicable to the previously described general MC design. Hence, here, we unify docking simulation (DS), molecular dynamics (MD) simulations, and quantum chemistry (QC) calculations into a single simulation platform called “NAGARA.” Using NAGARA, we can prepare input files on a local PC and run large-scale calculations on remote high-performance computers (HPCs) such as Linux cluster machines.
Here, we demonstrated the performance of NAGARA through the discovery of novel anti-prion agents. We characterized their binding modes and possible anti-prion mechanisms, discussed the possibility of extending these agents to a medicine for prion dis-eases, and further obtained useful information with respect to lead * Corresponding author. United Graduate School of Drug Discovery and Medical
Information Sciences, Gifu University, 1-1 Yanagido, Gifu 501-1193, Japan. E-mail address:[email protected](K. Kuwata).
Contents lists available atScienceDirect
Biochemical and Biophysical Research Communications
j o u r n a l h o m e p a g e :w w w . e l s e v i e r . c o m / l o c a t e / y b b r c
http://dx.doi.org/10.1016/j.bbrc.2015.12.106
0006-291X/© 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
optimization.
2. Material and methods 2.1. Coding of NAGARA
NAGARA is a plugin for PyMOL[21,22]and integrates three main GUIs: 1) DS, a GUI for DSs and large-scale screening via AutoDock Vina[23]; 2) MD, a GUI for obtaining the stable structure of pro-teineligand complexes and the binding free energy in MD simu-lations via the Amber package[24]; and 3) QC, a PAICS GUI for optimizing the MC molecular structure by QC calculations [25]
(Fig. 1,Supplementary Figs). Further details are in Supplementary Material.
2.2. Preparation of target protein structure and ligand database, and the docking simulation (DS)
We performed in silico large-scale screening of ligands using our program. The target receptor was the 3D structure of mouse prion protein (mPrP) (PDBID:1ag2[26]) downloaded from PDB[27]. The Asinex subset (~360,000) and KEGG DRUG (~7000 approved drugs) subset of Ligand Box[28]were used for ligand screening. Further details are in Supplementary Methods section.
2.3. Molecular dynamics simulation
The structures of the proteineligand complexes were optimized in MD simulations, and the free energies of binding were calcu-lated. Here, we selected complexes with three effective compounds after adding the missing hydrogen atoms, and the ligands were attached to PrP. The AMBER12SB forcefield was used for proteins, and the general AMBER forcefield was used for ligands. The binding free energy was computed using the MMePBSA method[29]and Python script MMPBSA.py[30]. Further details are in Supplemen-tary Methods section.
2.4. Quantum chemistry calculation
Finally, the proteineligand interactions were examined via QC calculations based on the FMO method[31]. We used the atomic coordinates obtained via the earlier process for the FMO calcula-tions in this study. The tegobuvir (TGV) was divided into two fragments (Frag A and Frag B) using the 6-31G basis set[25](see
Fig. 4a). Further details are in Supplementary Methods section.
2.5. Compounds and experimental screening
Compounds were purchased from ASINEX (NC), and Chem-Scene, LLC (NJ). The anti-prion activity assay was performed using Western blotting (WB), as described in our previous paper[32]. We used an immortalized neuronal mouse cell line persistently infec-ted with the human TSE agent (Fukuoka-1 strain)[33].
2.6. Recombinant mPrP (90e231)
Mouse prion protein of amino acid residues 90e231 (mPrP (90e231)) was expressed and purified using an Escherichia coli expression system; this protein has been previously described[34]. The molecular weight of the purified mPrP (90e231) was measured by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (Bruker Daltonics, Billerica, MA, USA).
2.7. Surface plasmon resonance measurements
Interactions between prion proteins and compounds were analyzed using a Biacore T200 system (GE Healthcare, Buck-inghamshire, UK). Experimental details are in Supplementary Methods section.
Fig. 1. Overview of NAGARA and Flow of calculation for the discovery of a medical chaperone (MC). (a) NAGARA integrates a preparation procedure and three physical models: NAGARA DS, NAGARA MD, and NAGARA QC. (b) In NAGARA, three physical models can be arbitrarily combined to obtain the desired statistical ensemble. (c) In the DS procedure, NAGARA creates a project folder on the local PC and the remote HPC server; the structure of the receptor and the ligands database are prepared in this folder. The job will automatically run on the remote HPC server. Experimental screening including organic synthesis and bioassay was subsequently performed for selected candidates. In the MD simulation, the initial structure of the receptor and the effective ligands were prepared and the MD simulation was performed to obtain the best binding mode,DDG, andDDS. In the QC procedure, the PAICS inputfile was initially prepared using the optimized complex structure. QC fragment molecular orbital (FMO) calculations were then performed, and the interaction between the re-ceptor and ligand were analyzed in detail for further ligand optimization.
2.8. NMR measurements
1
He15N heteronuclear single quantum coherence (HSQC) spectra were measured at 25 C on a Bruker Avance600 spec-trometer equipped with a CryoProbe (Bruker BioSpin, Rheinstetten, Germany) and were processed using the TOPSPIN-NMR software package (Bruker BioSpin, Rheinstetten, Germany) and the Sparky program[35]. Resonance frequencies in the spectra were identified using the chemical shifts lists for mPrP (90e232)[36]. The protein structure was generated using PyMOL[21] [22]. Experimental de-tails are in Supplementary Methods section.
3. Results and discussion
As shown in Fig. 1c, the computational flow utilized here is grouped into three categories: stage 1 (DS), stage 2 (MD), and stage 3 (QC). After stage 1 inFig. 1c (DS), we purchased or synthesized 100 compounds and performed the experimental screen using WB.
Fig. 2abc shows the chemical structures of the selected compounds B05, G03, and tegobuvir (TGV), which effectively suppressed the PrPScin GTþ FK cells; their anti-prion activities as represented by PrPSc(%) relative to those of DMSO obtained by WB are shown in
Fig. 2d. The IC50of the most effective compound, TGV, was
esti-mated to be 1.7mM according to WB[34](data not shown). At stage 2 (MD) inFig. 1c, we calculatedDDG upon binding. We constructed the initial complex structures for MD on the basis of DS binding modes and started the free energy calculation after system equilibration for each complex. The binding mode of TGV and mPrP predicted by MD is shown inFig. 2f; it involves the hot spot in a prion protein [34]. The calculated energy parameters DDG and TDDS are less than5.0 and 19 kcal/mol, respectively, as listed in
Table 1. InFig. 2e, the calculatedDDG andDDS values for repre-sentative compounds including those of less effective ones are plotted as a function of PrPSc(%). TheDDG tended to decrease with decreasing PrPSc(%), suggesting that the free energy of the system is efficiently decreased upon ligand binding, supporting the MC hypothesis. The DDS, in particular, substantially decreased with
decreasing PrPSc(%), as shown inFig. 2e, indicating that the stabi-lization of the PrPCconformation is associated with the decrease in the localfluctuation around the binding sites. Thus MD simulation offers a clue for understanding the anti-prion mechanism.
We subsequently measured the affinity of the selected com-pounds to mPrP by SPR. As shown inFig. 3abc, the dissociation constants (Kd) of B05, G03, and TGV were estimated to be 49, 24,
and 19mM, respectively; these results are approximately consistent with the PrPSc(%) (Fig. 2d). Although the SPR response was rela-tively high in B05, this high response may be attributed to the non-specific interaction of B05. Anti-prion activities were well corre-lated with the binding constants rather than the size of overall responses, suggesting that the specific interaction is the key to suppressing the conversion reaction.
Moreover, we measured the chemical shift perturbation profile of a prion protein upon binding of the TGV.Fig. 3d shows the su-perposition of the HSQC spectra of mPrP (90e231) with and without TGV. Five peaks corresponding to H187, T188, T192, K194, and G195 exhibited clear chemical shifts (Fig. 3d). The distribution of the chemical shift perturbation is plotted as a function of residue number in Fig. 3e and mapped on the 3D structure of a prion protein inFig. 3f. These experimentally determined binding sites correspond well with those predicted by MD simulation (Fig. 2f).
At stage 3 (QC) inFig. 1c, we evaluated the local interaction energy by IFIE using the PAICS GUI. In the interaction analysis, we selected amino acid residues located within 3.0 Å of TGV and plotted their IFIE as a function of residue number, as shown in
Fig. 4b. These residues are located around the hot spots[34]of the PrPC, which constitute the critical region of pathogenic conversion to prions. The interaction energies between these selected residues and both compounds were calculated at the HartreeeFock level. Interestingly, TGV strongly interacts with R156, Q160, H187, and K194, as shown inFig. 4b.
For optimization of the lead compound, we divided TGV into fragments A and B, as shown in the lower part ofFig. 4a. We then evaluated the affinity of each fragment by calculating its IFIE independently, and we observed that fragment A has a higher
Fig. 2. Chemical structures and biological and computational characterization of the effective compounds. (a, b, c) The chemical structures of thefinally selected effective com-poundsdB05, G03, and TGVdare shown. (d) Anti-prion efficiencies of the selected compounds relative to the control (DMSO) represented by PrPSc(%) obtained by WB of PrPScin
GTþ FK cells in the presence of compounds. Concentration of each compound was 10mM. (e)DDG (:) andDDS (C) obtained by MD simulations for effective and non-effective compounds.DDG andDDS decreased upon decrease in PrPSc(%), suggesting that an anti-prion activity was achieved by the stabilization of the PrPCconformation and that
conformational stabilization was associated with the decrease in the conformationalfluctuation. (f) Binding mode of TGV and mPrP complex after MD simulation. B. Ma et al. / Biochemical and Biophysical Research Communications 469 (2016) 930e935
affinity, as shown in the upper part ofFig. 4a. Fragment A mainly binds with the C-terminal of helix 2 (c.f. H187) and the loop region between helices 1 and 2 (c.f. Q160), as shown inFig. 4c, whereas fragment B interacts with the loop regions between helices 2 and 3 (c.f. K194) and between S2 and helix 1 (c.f. R156), as shown in
Fig. 4d. Therefore, it may be advantageous to replace fragment B with another fragment with higher affinity. Thus, inter-fragment interaction analysis of ligand fragments would be helpful to obtain a clue for further optimization. In fact, we found numerous derivatives of TGV with modification of fragment B (data not shown). Either by purchasing or synthesizing the derivatives, we can conduct stage 2 inFig. 1c. By repeating WB, MD, and QC, we can actually optimize the lead compound, i.e., TGV. However, completing this process andfinally obtaining the fully optimized compounds is beyond the scope of this study.
TGV was initially developed as a novel imidazole inhibitor of RNA replication of hepatitis C virus (HCV). Although TGV was initially considered as a HCV NS5B polymerase inhibitor[37], it was later demonstrated to not inhibit the NS5B enzymatic activity; an alternative mechanism was subsequently proposed[38]. Although
the details of the working mechanism of TGV on HCV has not yet been completely elucidated, in the case of its anti-prion effect, TGV binds to the PrPCand stabilizes the PrPCconformation. Hence, TGV is considered to be an MC[12].
Recently, various physiological functions of prion protein have been observed, including immunoregulation, signal transduction, copper binding, synaptic transmission, and induction of apoptosis or protection against apoptotic stimuli[39]. Although the details of these functions might be unknown, specific perturbation of prion conformation would affect the immunoregulation process in rela-tion to HCV infecrela-tion. Alternatively, TGV might interact with some unknown proteins that affect both the prion and HCV infection.
The hit rate of NAGARA in this study was on the order of several percent, similar to that previously reported[32]. However, using NAGARA, interaction analysis by MD promoted the lead selection process by confirming the action mechanism of the MC, i.e., by suppressing the conformationalfluctuation of the target protein, thereby inhibiting the mis-folding process. Furthermore, QC cal-culations offered useful hints for further optimization. These as-pects are strong advantages of NAGARA.
Table 1
Calculated parameters and anti-PrPSceffects of compounds B05, G03, and TGV.
Compound no. DSa MD QC In vitro Ex vivo
Docking score DDS (e.u.)b DDG (kcal/mol)c IFIE (kcal/mol)d K
d(mM) PrPSc(%)e B05 10.2 69.1 5.6 85.2 49.3± 1.0 49.9 G03 10.0 64.5 9.5 65.6 24.0± 2.8 45.9 TGV 9.9 79.6 8.1 88.2 19.2± 5.9 45.8 TGV(A) 52.0 TGV(B) 36.2
a The binding energy was obtained in the DS.
b The entropy calculated by MD simulation using the normal-mode entropy approximation.
c The binding free energy calculated by MD simulation using the normal-mode entropy approximation. d The interaction energy between the compound and the whole PrP.
e The percentage of PrPScafter treatment by B05, G03, and TGV.
Fig. 3. Interaction analyses between compounds and mPrP (90e231) using SPR and NMR. SPR responses as a function of concentration of (a) B05, (b) G03, and (c) TGV were fitted to the theoretical binding isotherm under the assumption of a 1:1 binding model. KD± S.D. (mM) and Rmax± S.D. (r.u.) values for B05, G03, and TGV were estimated to be (49.3 ± 1.0,
320± 27), (24.0 ± 2.8, 66.1 ± 2.1), and (19.2 ± 5.9, 21.6 ± 1.7), respectively. (d) Overlay of the1He15N HSQC spectra of mPrP (90e231) in the absence (black contours) and presence
(red contours) of TGV. (e) Chemical shift perturbations as a function of residue number. The1H and15N chemical shift changes were calculated using the functionDd¼ [(Dd 1H) 2þ 0.17(Dd
15N)2]1/2. Secondary structural elements determined by NMR are shown by the bars at the top. (f) Mapping of the perturbed residues on the structure of mPrP (PDB entry
1AG2). Perturbed residues withDd> 0.015 ppm are shown in red and those with 0.015 >Dd> 0.01 ppm are shown in orange.
MCs are usually developed by the four-step logical drug design procedure as follows: 1) structural determination of the target protein, 2) in silico design of the MC’s chemical structure, 3) organic synthesis of the MC, and 4) effectiveness testing by bioassay. Steps 1, 2, 3, and 4 confirm the “localizability” of the hot spot [34], “regulability” of the conformation, “synthesizability” of the computationally designed MC structure, and “specificity” of its biological function [11], respectively. Localizability can be esti-mated by careful examination of the folding process or conforma-tionalfluctuation at atomic resolution, whereas the regulability by an MC can be evaluated, for instance, by computing the changes in the stability (DDG) of the conformation upon MC binding to the target protein using MD simulations and IFIE using QC calculations. In contrast, synthesizability and specificity can be only proved by empirical methods (organic synthesis and bioassay experiments, respectively). In this regard, NAGARA is an indispensable measure for step 2.
In summary, using NAGARA, we performed database screening by DS and experimental screening, interaction analysis for further lead selection using MD, and lead optimization by QC. By repeating this procedure recursively, we were able to identify the most effective drug candidate.
NAGARA can also be applied to the more general allosteric regulation of proteins. An MC can be designed to bind within the pocket of the protein (which is not necessarily an active site), inducing an allosteric effect on the protein’s function.
4. Conclusion
We developed a plugin program, NAGARA, as a logical drug design platform for MCs. NAGARA integrates DS for ligand database searching, MD simulations for obtaining DDG upon binding of candidate compounds, and QC calculations for further optimiza-tion. Such integration is achieved by exploiting large-scale parallel
computing with GUIs based on PyMOL. Here, we successfully applied NAGARA to the discovery of MCs targeting prion diseases and demonstrated its performance by identifying several novel anti-prion compounds including TGV. TGV could be used in clinical trials as it has already been approved as a medicine targeting HCV infection. Moreover, NAGARA enables further optimization of TGV with respect of anti-prion activity.
Acknowledgments
We thank Ms. Tomomi Saeki and Ms. Miki Horii for experi-mental assistance. This work was supported by grants from the Ministry of Health, Labour and Welfare of Japan [Research on Measures for Intractable Diseases (Prion Disease and Slow Virus Infections (12944816) and Development of Low Molecular Weight Medical Chaperone Therapeutics for Prion Diseases (12945725)] and from the Ministry of Education, Culture, Sports, Science and Technology of Japan [Grantsein Aid for Scientific Research, and X-ray Free Electron Laser (XFEL) Program (12000201)]
Appendix A. Supplementary data
Supplementary data related to this article can be found athttp:// dx.doi.org/10.1016/j.bbrc.2015.12.106.
Transparency document
Transparency document related to this article can be found online athttp://dx.doi.org/10.1016/j.bbrc.2015.12.106
References
[1] V. Castillo, S. Ventura, Amyloidogenic regions and interaction surfaces overlap in globular proteins related to conformational diseases, PLoS Comput. Biol. 5
Fig. 4. Toward further optimization of TGV. (a) (Lower panel) Fragmentation of TGV into two parts: A and B. (Upper panel) Inter-fragment interaction energy (IFIE) corresponding to each fragment. (b) IFIE for whole TGV and mPrP. (c) IFIE for fragment A and mPrP; fragment A specifically interacts with Q160 and H187. (d) IFIE for fragment B and mPrP; fragment B specifically interacts with R156 and K194.
B. Ma et al. / Biochemical and Biophysical Research Communications 469 (2016) 930e935 934
